diff options
276 files changed, 18323 insertions, 2781 deletions
diff --git a/.travis.yml b/.travis.yml index 76a83e64..94ef2e2d 100644 --- a/.travis.yml +++ b/.travis.yml @@ -6,6 +6,7 @@ addons: packages: - gcc - libgit2-dev + - libmpfr-dev cache: packages repos: CRAN: https://cloud.r-project.org @@ -15,7 +16,7 @@ r_packages: - covr - vdiffr r_github_packages: - - saemixdevelopment/saemixextension@master + - jranke/saemixextension@warp_combined script: - R CMD build . - R CMD check --no-tests mkin_*.tar.gz diff --git a/DESCRIPTION b/DESCRIPTION index f5b7f070..4689cb2a 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,8 +1,8 @@ Package: mkin Type: Package Title: Kinetic Evaluation of Chemical Degradation Data -Version: 1.0.4 -Date: 2021-04-20 +Version: 1.1.0 +Date: 2021-06-23 Authors@R: c( person("Johannes", "Ranke", role = c("aut", "cre", "cph"), email = "jranke@uni-bremen.de", @@ -18,8 +18,8 @@ Description: Calculation routines based on the FOCUS Kinetics Report (2006, note that no warranty is implied for correctness of results or fitness for a particular purpose. Depends: R (>= 2.15.1), parallel -Imports: stats, graphics, methods, deSolve, R6, inline (>= 0.3.17), numDeriv, - lmtest, pkgbuild, nlme (>= 3.1-151), purrr +Imports: stats, graphics, methods, deSolve, R6, inline (>= 0.3.19), numDeriv, + dplyr, lmtest, pkgbuild, nlme (>= 3.1-151), purrr, saemix, nlmixr Suggests: knitr, rbenchmark, tikzDevice, testthat, rmarkdown, covr, vdiffr, benchmarkme, tibble, stats4 License: GPL diff --git a/GNUmakefile b/GNUmakefile index a6abacac..9056a0ec 100644 --- a/GNUmakefile +++ b/GNUmakefile @@ -109,7 +109,7 @@ pd: roxygen "$(RBIN)/Rscript" -e "pkgdown::build_site(run_dont_run = TRUE, lazy = TRUE)" git add -A -pd_release: roxygen +pd_all: roxygen "$(RBIN)/Rscript" -e "pkgdown::build_site(run_dont_run = TRUE)" git add -A @@ -16,6 +16,7 @@ S3method(mixed,mmkin) S3method(mkinpredict,mkinfit) S3method(mkinpredict,mkinmod) S3method(nlme,mmkin) +S3method(nlmixr,mmkin) S3method(nobs,mkinfit) S3method(parms,mkinfit) S3method(parms,mmkin) @@ -30,11 +31,18 @@ S3method(print,mkinmod) S3method(print,mmkin) S3method(print,nafta) S3method(print,nlme.mmkin) +S3method(print,nlmixr.mmkin) +S3method(print,saem.mmkin) S3method(print,summary.mkinfit) S3method(print,summary.nlme.mmkin) +S3method(print,summary.nlmixr.mmkin) +S3method(print,summary.saem.mmkin) S3method(residuals,mkinfit) +S3method(saem,mmkin) S3method(summary,mkinfit) S3method(summary,nlme.mmkin) +S3method(summary,nlmixr.mmkin) +S3method(summary,saem.mmkin) S3method(update,mkinfit) S3method(update,mmkin) S3method(update,nlme.mmkin) @@ -54,6 +62,7 @@ export(f_time_norm_focus) export(get_deg_func) export(ilr) export(invilr) +export(invtffm0) export(loftest) export(logistic.solution) export(lrtest) @@ -82,19 +91,29 @@ export(nafta) export(nlme) export(nlme_data) export(nlme_function) +export(nlmixr) +export(nlmixr_data) +export(nlmixr_model) export(parms) export(plot_err) export(plot_res) export(plot_sep) +export(saem) +export(saemix_data) +export(saemix_model) export(sigma_twocomp) +export(tffm0) export(transform_odeparms) import(deSolve) import(graphics) import(nlme) importFrom(R6,R6Class) +importFrom(dplyr,"%>%") importFrom(grDevices,dev.cur) importFrom(lmtest,lrtest) importFrom(methods,signature) +importFrom(nlmixr,nlmixr) +importFrom(nlmixr,tableControl) importFrom(parallel,detectCores) importFrom(parallel,mclapply) importFrom(parallel,parLapply) @@ -105,6 +124,7 @@ importFrom(stats,aggregate) importFrom(stats,as.formula) importFrom(stats,coef) importFrom(stats,coefficients) +importFrom(stats,confint) importFrom(stats,cov2cor) importFrom(stats,dist) importFrom(stats,dnorm) @@ -124,7 +144,10 @@ importFrom(stats,qnorm) importFrom(stats,qt) importFrom(stats,residuals) importFrom(stats,rnorm) +importFrom(stats,sd) importFrom(stats,shapiro.test) importFrom(stats,update) +importFrom(stats,vcov) importFrom(utils,getFromNamespace) +importFrom(utils,packageVersion) importFrom(utils,write.table) @@ -1,3 +1,15 @@ +# mkin 1.0.5 (unreleased) + +## Mixed-effects models + +- Introduce an interface to nlmixr, supporting estimation methods 'saem' and 'focei': S3 method 'nlmixr.mmkin' using the helper functions 'nlmixr_model' and 'nlmixr_data' to set up nlmixr models for mmkin row objects, with summary and plot methods. + +- Reintroduce the interface to current development versions (not on CRAN) of saemix, in particular the generic function 'saem' with a generator 'saem.mmkin', currently using 'saemix_model' and 'saemix_data', summary and plot methods + +- 'mean_degparms': New argument 'test_log_parms' that makes the function only consider log-transformed parameters where the untransformed parameters pass the t-test for a certain confidence level. This can be used to obtain more plausible starting parameters for the different mixed-effects model backends + +- 'plot.mixed.mmkin': Gains arguments 'test_log_parms' and 'conf.level' + # mkin 1.0.4 (2021-04-20) - All plotting functions setting graphical parameters: Use on.exit() for resetting graphical parameters diff --git a/R/dimethenamid_2018.R b/R/dimethenamid_2018.R index 189da618..770649e2 100644 --- a/R/dimethenamid_2018.R +++ b/R/dimethenamid_2018.R @@ -15,7 +15,58 @@ #' @source 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 -#' \url{http://registerofquestions.efsa.europa.eu/roqFrontend/outputLoader?output=ON-5211} +#' \url{https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716} #' @examples #' print(dimethenamid_2018) +#' dmta_ds <- lapply(1:8, 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[["Borstel"]] <- rbind(dmta_ds[["Borstel 1"]], dmta_ds[["Borstel 2"]]) +#' dmta_ds[["Borstel 1"]] <- NULL +#' dmta_ds[["Borstel 2"]] <- NULL +#' dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]]) +#' dmta_ds[["Elliot 1"]] <- NULL +#' dmta_ds[["Elliot 2"]] <- NULL +#' \dontrun{ +#' dfop_sfo3_plus <- mkinmod( +#' DMTA = mkinsub("DFOP", c("M23", "M27", "M31")), +#' M23 = mkinsub("SFO"), +#' M27 = mkinsub("SFO"), +#' M31 = mkinsub("SFO", "M27", sink = FALSE), +#' quiet = TRUE +#' ) +#' f_dmta_mkin_tc <- mmkin( +#' list("DFOP-SFO3+" = dfop_sfo3_plus), +#' dmta_ds, quiet = TRUE, error_model = "tc") +#' nlmixr_model(f_dmta_mkin_tc) +#' # The focei fit takes about four minutes on my system +#' system.time( +#' f_dmta_nlmixr_focei <- nlmixr(f_dmta_mkin_tc, est = "focei", +#' control = nlmixr::foceiControl(print = 500)) +#' ) +#' summary(f_dmta_nlmixr_focei) +#' plot(f_dmta_nlmixr_focei) +#' # Using saemix takes about 18 minutes +#' system.time( +#' f_dmta_saemix <- saem(f_dmta_mkin_tc, test_log_parms = TRUE) +#' ) +#' +#' # nlmixr with est = "saem" is pretty fast with default iteration numbers, most +#' # of the time (about 2.5 minutes) is spent for calculating the log likelihood at the end +#' # The likelihood calculated for the nlmixr fit is much lower than that found by saemix +#' # Also, the trace plot and the plot of the individual predictions is not +#' # convincing for the parent. It seems we are fitting an overparameterised +#' # model, so the result we get strongly depends on starting parameters and control settings. +#' system.time( +#' f_dmta_nlmixr_saem <- nlmixr(f_dmta_mkin_tc, est = "saem", +#' control = nlmixr::saemControl(print = 500, logLik = TRUE, nmc = 9)) +#' ) +#' traceplot(f_dmta_nlmixr_saem$nm) +#' summary(f_dmta_nlmixr_saem) +#' plot(f_dmta_nlmixr_saem) +#' } "dimethenamid_2018" diff --git a/R/endpoints.R b/R/endpoints.R index b5872e68..6bf52f07 100644 --- a/R/endpoints.R +++ b/R/endpoints.R @@ -10,8 +10,8 @@ #' Additional DT50 values are calculated from the FOMC DT90 and k1 and k2 from #' HS and DFOP, as well as from Eigenvalues b1 and b2 of any SFORB models #' -#' @param fit An object of class [mkinfit] or [nlme.mmkin] -#' or another object that has list components +#' @param fit An object of class [mkinfit], [nlme.mmkin], [saem.mmkin] or +#' [nlmixr.mmkin]. Or another object that has list components #' mkinmod containing an [mkinmod] degradation model, and two numeric vectors, #' bparms.optim and bparms.fixed, that contain parameter values #' for that model. @@ -20,8 +20,8 @@ #' 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 -#' @note The function is used internally by [summary.mkinfit] -#' and [summary.nlme.mmkin] +#' @note The function is used internally by [summary.mkinfit], +#' [summary.nlme.mmkin] and [summary.saem.mmkin]. #' @author Johannes Ranke #' @examples #' diff --git a/R/mean_degparms.R b/R/mean_degparms.R new file mode 100644 index 00000000..ec20c068 --- /dev/null +++ b/R/mean_degparms.R @@ -0,0 +1,62 @@ +#' Calculate mean degradation parameters for an mmkin row object +#' +#' @return 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. +#' @param object An mmkin row object containing several fits of the same model to different datasets +#' @param random Should a list with fixed and random effects be returned? +#' @param test_log_parms 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. +#' @param conf.level Possibility to adjust the required confidence level +#' for parameter that are tested if requested by 'test_log_parms'. +#' @export +mean_degparms <- function(object, random = FALSE, test_log_parms = FALSE, conf.level = 0.6) +{ + if (nrow(object) > 1) stop("Only row objects allowed") + parm_mat_trans <- sapply(object, parms, transformed = TRUE) + + if (test_log_parms) { + parm_mat_dim <- dim(parm_mat_trans) + parm_mat_dimnames <- dimnames(parm_mat_trans) + + log_parm_trans_names <- grep("^log_", rownames(parm_mat_trans), value = TRUE) + log_parm_names <- gsub("^log_", "", log_parm_trans_names) + + t_test_back_OK <- matrix( + sapply(object, function(o) { + suppressWarnings(summary(o)$bpar[log_parm_names, "Pr(>t)"] < (1 - conf.level)) + }), nrow = length(log_parm_names)) + rownames(t_test_back_OK) <- log_parm_trans_names + + parm_mat_trans_OK <- parm_mat_trans + for (trans_parm in log_parm_trans_names) { + parm_mat_trans_OK[trans_parm, ] <- ifelse(t_test_back_OK[trans_parm, ], + parm_mat_trans[trans_parm, ], NA) + } + } else { + parm_mat_trans_OK <- parm_mat_trans + } + + mean_degparm_names <- setdiff(rownames(parm_mat_trans), names(object[[1]]$errparms)) + degparm_mat_trans <- parm_mat_trans[mean_degparm_names, , drop = FALSE] + degparm_mat_trans_OK <- parm_mat_trans_OK[mean_degparm_names, , drop = FALSE] + + fixed <- apply(degparm_mat_trans_OK, 1, mean, na.rm = TRUE) + if (random) { + random <- t(apply(degparm_mat_trans[mean_degparm_names, , drop = FALSE], 2, function(column) column - fixed)) + # If we only have one parameter, apply returns a vector so we get a single row + if (nrow(degparm_mat_trans) == 1) random <- t(random) + rownames(random) <- levels(nlme_data(object)$ds) + + # For nlmixr we can specify starting values for standard deviations eta, and + # we ignore uncertain parameters if test_log_parms is FALSE + eta <- apply(degparm_mat_trans_OK, 1, stats::sd, na.rm = TRUE) + + return(list(fixed = fixed, random = list(ds = random), eta = eta)) + } else { + return(fixed) + } +} + diff --git a/R/mixed.mmkin.R b/R/mixed.mmkin.R index 7aa5edd5..682a9a34 100644 --- a/R/mixed.mmkin.R +++ b/R/mixed.mmkin.R @@ -3,6 +3,8 @@ #' @param object An [mmkin] row object #' @param method The method to be used #' @param \dots Currently not used +#' @return An object of class 'mixed.mmkin' which has the observed data in a +#' single dataframe which is convenient for plotting #' @examples #' sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) #' n_biphasic <- 8 @@ -54,7 +56,6 @@ mixed.mmkin <- function(object, method = c("none"), ...) { if (nrow(object) > 1) stop("Only row objects allowed") method <- match.arg(method) - if (method == "default") method = c("naive", "nlme") ds_names <- colnames(object) res <- list(mmkin = object, mkinmod = object[[1]]$mkinmod) diff --git a/R/mkinsub.R b/R/mkinsub.R index 886f712c..93af3f16 100644 --- a/R/mkinsub.R +++ b/R/mkinsub.R @@ -1,8 +1,3 @@ -#' Function to set up a kinetic submodel for one state variable -#' -#' This is a convenience function to set up the lists used as arguments for -#' \code{\link{mkinmod}}. -#' #' @rdname mkinmod #' @param submodel Character vector of length one to specify the submodel type. #' See \code{\link{mkinmod}} for the list of allowed submodel names. @@ -36,7 +36,7 @@ #' nlme_f <- nlme_function(f) #' # These assignments are necessary for these objects to be #' # visible to nlme and augPred when evaluation is done by -#' # pkgdown to generated the html docs. +#' # pkgdown to generate the html docs. #' assign("nlme_f", nlme_f, globalenv()) #' assign("grouped_data", grouped_data, globalenv()) #' @@ -125,30 +125,6 @@ nlme_function <- function(object) { } #' @rdname nlme -#' @return If random is FALSE (default), a named vector containing mean values -#' of the fitted degradation model parameters. If random is TRUE, a list with -#' fixed and random effects, in the format required by the start argument of -#' nlme for the case of a single grouping variable ds. -#' @param random Should a list with fixed and random effects be returned? -#' @export -mean_degparms <- function(object, random = FALSE) { - if (nrow(object) > 1) stop("Only row objects allowed") - parm_mat_trans <- sapply(object, parms, transformed = TRUE) - mean_degparm_names <- setdiff(rownames(parm_mat_trans), names(object[[1]]$errparms)) - degparm_mat_trans <- parm_mat_trans[mean_degparm_names, , drop = FALSE] - fixed <- apply(degparm_mat_trans, 1, mean) - if (random) { - random <- t(apply(degparm_mat_trans[mean_degparm_names, , drop = FALSE], 2, function(column) column - fixed)) - # If we only have one parameter, apply returns a vector so we get a single row - if (nrow(degparm_mat_trans) == 1) random <- t(random) - rownames(random) <- levels(nlme_data(object)$ds) - return(list(fixed = fixed, random = list(ds = random))) - } else { - return(fixed) - } -} - -#' @rdname nlme #' @importFrom purrr map_dfr #' @return A \code{\link{groupedData}} object #' @export diff --git a/R/nlme.mmkin.R b/R/nlme.mmkin.R index ff1f2fff..7049a9a1 100644 --- a/R/nlme.mmkin.R +++ b/R/nlme.mmkin.R @@ -24,7 +24,7 @@ get_deg_func <- function() { #' This functions sets up a nonlinear mixed effects model for an mmkin row #' object. An mmkin row object is essentially a list of mkinfit objects that #' have been obtained by fitting the same model to a list of datasets. -#' +#' #' Note that the convergence of the nlme algorithms depends on the quality #' of the data. In degradation kinetics, we often only have few datasets #' (e.g. data for few soils) and complicated degradation models, which may @@ -34,10 +34,9 @@ get_deg_func <- function() { #' @param data Ignored, data are taken from the mmkin model #' @param fixed Ignored, all degradation parameters fitted in the #' mmkin model are used as fixed parameters -#' @param random If not specified, correlated random effects are set up -#' for all optimised degradation model parameters using the log-Cholesky -#' parameterization [nlme::pdLogChol] that is also the default of -#' the generic [nlme] method. +#' @param random If not specified, no correlations between random effects are +#' set up for the optimised degradation model parameters. This is +#' achieved by using the [nlme::pdDiag] method. #' @param groups See the documentation of nlme #' @param start If not specified, mean values of the fitted degradation #' parameters taken from the mmkin object are used @@ -135,7 +134,7 @@ nlme.mmkin <- function(model, data = "auto", function(el) eval(parse(text = paste(el, 1, sep = "~")))), random = pdDiag(fixed), groups, - start = mean_degparms(model, random = TRUE), + start = mean_degparms(model, random = TRUE, test_log_parms = TRUE), correlation = NULL, weights = NULL, subset, method = c("ML", "REML"), na.action = na.fail, naPattern, diff --git a/R/nlmixr.R b/R/nlmixr.R new file mode 100644 index 00000000..9c364c4f --- /dev/null +++ b/R/nlmixr.R @@ -0,0 +1,539 @@ +utils::globalVariables(c("predicted", "std", "ID", "TIME", "CMT", "DV", "IPRED", "IRES", "IWRES")) + +#' @export +nlmixr::nlmixr + +#' Fit nonlinear mixed models using nlmixr +#' +#' This function uses [nlmixr::nlmixr()] as a backend for fitting nonlinear mixed +#' effects models created from [mmkin] row objects using the Stochastic Approximation +#' Expectation Maximisation algorithm (SAEM). +#' +#' An mmkin row object is essentially a list of mkinfit objects that have been +#' obtained by fitting the same model to a list of datasets using [mkinfit]. +#' +#' @importFrom nlmixr nlmixr tableControl +#' @importFrom dplyr %>% +#' @param object An [mmkin] row object containing several fits of the same +#' [mkinmod] model to different datasets +#' @param data Not used, the data are extracted from the mmkin row object +#' @param est Estimation method passed to [nlmixr::nlmixr] +#' @param degparms_start Parameter values given as a named numeric vector will +#' be used to override the starting values obtained from the 'mmkin' object. +#' @param eta_start Standard deviations on the transformed scale given as a +#' named numeric vector will be used to override the starting values obtained +#' from the 'mmkin' object. +#' @param test_log_parms If TRUE, an attempt is made to use more robust starting +#' values for population parameters fitted as log parameters in mkin (like +#' rate constants) by only considering rate constants that pass the t-test +#' when calculating mean degradation parameters using [mean_degparms]. +#' @param conf.level Possibility to adjust the required confidence level +#' for parameter that are tested if requested by 'test_log_parms'. +#' @param data Not used, as the data are extracted from the mmkin row object +#' @param table Passed to [nlmixr::nlmixr] +#' @param error_model Possibility to override the error model which is being +#' set based on the error model used in the mmkin row object. +#' @param control Passed to [nlmixr::nlmixr] +#' @param \dots Passed to [nlmixr_model] +#' @param save Passed to [nlmixr::nlmixr] +#' @param envir Passed to [nlmixr::nlmixr] +#' @return An S3 object of class 'nlmixr.mmkin', containing the fitted +#' [nlmixr::nlmixr] object as a list component named 'nm'. The +#' object also inherits from 'mixed.mmkin'. +#' @seealso [summary.nlmixr.mmkin] [plot.mixed.mmkin] +#' @examples +#' \dontrun{ +#' ds <- lapply(experimental_data_for_UBA_2019[6:10], +#' function(x) subset(x$data[c("name", "time", "value")])) +#' names(ds) <- paste("Dataset", 6:10) +#' +#' f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP", "HS"), ds, quiet = TRUE, cores = 1) +#' f_mmkin_parent_tc <- mmkin(c("SFO", "FOMC", "DFOP"), ds, error_model = "tc", +#' cores = 1, quiet = TRUE) +#' +#' f_nlmixr_sfo_saem <- nlmixr(f_mmkin_parent["SFO", ], est = "saem") +#' f_nlmixr_sfo_focei <- nlmixr(f_mmkin_parent["SFO", ], est = "focei") +#' +#' f_nlmixr_fomc_saem <- nlmixr(f_mmkin_parent["FOMC", ], est = "saem") +#' f_nlmixr_fomc_focei <- nlmixr(f_mmkin_parent["FOMC", ], est = "focei") +#' +#' f_nlmixr_dfop_saem <- nlmixr(f_mmkin_parent["DFOP", ], est = "saem") +#' f_nlmixr_dfop_focei <- nlmixr(f_mmkin_parent["DFOP", ], est = "focei") +#' +#' f_nlmixr_hs_saem <- nlmixr(f_mmkin_parent["HS", ], est = "saem") +#' f_nlmixr_hs_focei <- nlmixr(f_mmkin_parent["HS", ], est = "focei") +#' +#' f_nlmixr_fomc_saem_tc <- nlmixr(f_mmkin_parent_tc["FOMC", ], est = "saem") +#' f_nlmixr_fomc_focei_tc <- nlmixr(f_mmkin_parent_tc["FOMC", ], est = "focei") +#' +#' AIC( +#' f_nlmixr_sfo_saem$nm, f_nlmixr_sfo_focei$nm, +#' f_nlmixr_fomc_saem$nm, f_nlmixr_fomc_focei$nm, +#' f_nlmixr_dfop_saem$nm, f_nlmixr_dfop_focei$nm, +#' f_nlmixr_hs_saem$nm, f_nlmixr_hs_focei$nm, +#' f_nlmixr_fomc_saem_tc$nm, f_nlmixr_fomc_focei_tc$nm) +#' +#' AIC(nlme(f_mmkin_parent["FOMC", ])) +#' AIC(nlme(f_mmkin_parent["HS", ])) +#' +#' # nlme is comparable to nlmixr with focei, saem finds a better +#' # solution, the two-component error model does not improve it +#' plot(f_nlmixr_fomc_saem) +#' +#' sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), +#' A1 = mkinsub("SFO")) +#' fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"), +#' A1 = mkinsub("SFO")) +#' dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"), +#' A1 = mkinsub("SFO")) +#' +#' f_mmkin_const <- mmkin(list( +#' "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo), +#' ds, quiet = TRUE, error_model = "const") +#' f_mmkin_obs <- mmkin(list( +#' "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo), +#' ds, quiet = TRUE, error_model = "obs") +#' f_mmkin_tc <- mmkin(list( +#' "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo), +#' ds, quiet = TRUE, error_model = "tc") +#' +#' # A single constant variance is currently only possible with est = 'focei' in nlmixr +#' f_nlmixr_sfo_sfo_focei_const <- nlmixr(f_mmkin_const["SFO-SFO", ], est = "focei") +#' f_nlmixr_fomc_sfo_focei_const <- nlmixr(f_mmkin_const["FOMC-SFO", ], est = "focei") +#' f_nlmixr_dfop_sfo_focei_const <- nlmixr(f_mmkin_const["DFOP-SFO", ], est = "focei") +#' +#' # Variance by variable is supported by 'saem' and 'focei' +#' f_nlmixr_fomc_sfo_saem_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "saem") +#' f_nlmixr_fomc_sfo_focei_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "focei") +#' f_nlmixr_dfop_sfo_saem_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "saem") +#' f_nlmixr_dfop_sfo_focei_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "focei") +#' +#' # Identical two-component error for all variables is only possible with +#' # est = 'focei' in nlmixr +#' f_nlmixr_fomc_sfo_focei_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei") +#' f_nlmixr_dfop_sfo_focei_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei") +#' +#' # Two-component error by variable is possible with both estimation methods +#' # Variance by variable is supported by 'saem' and 'focei' +#' f_nlmixr_fomc_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "saem", +#' error_model = "obs_tc") +#' f_nlmixr_fomc_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei", +#' error_model = "obs_tc") +#' f_nlmixr_dfop_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "saem", +#' error_model = "obs_tc") +#' f_nlmixr_dfop_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei", +#' error_model = "obs_tc") +#' +#' AIC( +#' f_nlmixr_sfo_sfo_focei_const$nm, +#' f_nlmixr_fomc_sfo_focei_const$nm, +#' f_nlmixr_dfop_sfo_focei_const$nm, +#' f_nlmixr_fomc_sfo_saem_obs$nm, +#' f_nlmixr_fomc_sfo_focei_obs$nm, +#' f_nlmixr_dfop_sfo_saem_obs$nm, +#' f_nlmixr_dfop_sfo_focei_obs$nm, +#' f_nlmixr_fomc_sfo_focei_tc$nm, +#' f_nlmixr_dfop_sfo_focei_tc$nm, +#' f_nlmixr_fomc_sfo_saem_obs_tc$nm, +#' f_nlmixr_fomc_sfo_focei_obs_tc$nm, +#' f_nlmixr_dfop_sfo_saem_obs_tc$nm, +#' f_nlmixr_dfop_sfo_focei_obs_tc$nm +#' ) +#' # Currently, FOMC-SFO with two-component error by variable fitted by focei gives the +#' # lowest AIC +#' plot(f_nlmixr_fomc_sfo_focei_obs_tc) +#' summary(f_nlmixr_fomc_sfo_focei_obs_tc) +#' } +#' @export +nlmixr.mmkin <- function(object, data = NULL, + est = NULL, control = list(), + table = tableControl(), + error_model = object[[1]]$err_mod, + test_log_parms = TRUE, + conf.level = 0.6, + degparms_start = "auto", + eta_start = "auto", + ..., + save = NULL, + envir = parent.frame() +) +{ + m_nlmixr <- nlmixr_model(object, est = est, + error_model = error_model, add_attributes = TRUE, + test_log_parms = test_log_parms, conf.level = conf.level, + degparms_start = degparms_start, eta_start = eta_start + ) + d_nlmixr <- nlmixr_data(object) + + mean_dp_start <- attr(m_nlmixr, "mean_dp_start") + mean_ep_start <- attr(m_nlmixr, "mean_ep_start") + + attributes(m_nlmixr) <- NULL + + fit_time <- system.time({ + f_nlmixr <- nlmixr(m_nlmixr, d_nlmixr, est = est, control = control) + }) + + if (is.null(f_nlmixr$CMT)) { + nlmixr_df <- as.data.frame(f_nlmixr[c("ID", "TIME", "DV", "IPRED", "IRES", "IWRES")]) + nlmixr_df$CMT <- as.character(object[[1]]$data$variable[1]) + } else { + nlmixr_df <- as.data.frame(f_nlmixr[c("ID", "TIME", "DV", "CMT", "IPRED", "IRES", "IWRES")]) + } + + return_data <- nlmixr_df %>% + dplyr::transmute(ds = ID, name = CMT, time = TIME, value = DV, + predicted = IPRED, residual = IRES, + std = IRES/IWRES, standardized = IWRES) %>% + dplyr::arrange(ds, name, time) + + bparms_optim <- backtransform_odeparms(f_nlmixr$theta, + object[[1]]$mkinmod, + object[[1]]$transform_rates, + object[[1]]$transform_fractions) + + result <- list( + mkinmod = object[[1]]$mkinmod, + mmkin = object, + transform_rates = object[[1]]$transform_rates, + transform_fractions = object[[1]]$transform_fractions, + nm = f_nlmixr, + est = est, + time = fit_time, + mean_dp_start = mean_dp_start, + mean_ep_start = mean_ep_start, + bparms.optim = bparms_optim, + bparms.fixed = object[[1]]$bparms.fixed, + data = return_data, + err_mod = error_model, + date.fit = date(), + nlmixrversion = as.character(utils::packageVersion("nlmixr")), + mkinversion = as.character(utils::packageVersion("mkin")), + Rversion = paste(R.version$major, R.version$minor, sep=".") + ) + + class(result) <- c("nlmixr.mmkin", "mixed.mmkin") + return(result) +} + +#' @export +#' @rdname nlmixr.mmkin +#' @param x An nlmixr.mmkin object to print +#' @param digits Number of digits to use for printing +print.nlmixr.mmkin <- function(x, digits = max(3, getOption("digits") - 3), ...) { + cat("Kinetic nonlinear mixed-effects model fit by", x$est, "using nlmixr") + cat("\nStructural model:\n") + diffs <- x$mmkin[[1]]$mkinmod$diffs + nice_diffs <- gsub("^(d.*) =", "\\1/dt =", diffs) + writeLines(strwrap(nice_diffs, exdent = 11)) + cat("\nData:\n") + cat(nrow(x$data), "observations of", + length(unique(x$data$name)), "variable(s) grouped in", + length(unique(x$data$ds)), "datasets\n") + + cat("\nLikelihood:\n") + print(data.frame( + AIC = AIC(x$nm), + BIC = BIC(x$nm), + logLik = logLik(x$nm), + row.names = " "), digits = digits) + + cat("\nFitted parameters:\n") + print(x$nm$parFixed, digits = digits) + + invisible(x) +} + +#' @rdname nlmixr.mmkin +#' @param add_attributes Should the starting values used for degradation model +#' parameters and their distribution and for the error model parameters +#' be returned as attributes? +#' @return An function defining a model suitable for fitting with [nlmixr::nlmixr]. +#' @export +nlmixr_model <- function(object, + est = c("saem", "focei"), + degparms_start = "auto", + eta_start = "auto", + test_log_parms = TRUE, conf.level = 0.6, + error_model = object[[1]]$err_mod, add_attributes = FALSE) +{ + if (nrow(object) > 1) stop("Only row objects allowed") + est = match.arg(est) + + mkin_model <- object[[1]]$mkinmod + obs_vars <- names(mkin_model$spec) + + if (error_model == object[[1]]$err_mod) { + + if (length(object[[1]]$mkinmod$spec) > 1 & est == "saem") { + if (error_model == "const") { + message( + "Constant variance for more than one variable is not supported for est = 'saem'\n", + "Changing the error model to 'obs' (variance by observed variable)") + error_model <- "obs" + } + if (error_model =="tc") { + message( + "With est = 'saem', a different error model is required for each observed variable", + "Changing the error model to 'obs_tc' (Two-component error for each observed variable)") + error_model <- "obs_tc" + } + } + } + + degparms_mmkin <- mean_degparms(object, + test_log_parms = test_log_parms, + conf.level = conf.level, random = TRUE) + + degparms_optim <- degparms_mmkin$fixed + + degparms_optim_ilr_names <- grep("^f_.*_ilr", names(degparms_optim), value = TRUE) + obs_vars_ilr <- unique(gsub("f_(.*)_ilr.*$", "\\1", degparms_optim_ilr_names)) + degparms_optim_noilr <- degparms_optim[setdiff(names(degparms_optim), + degparms_optim_ilr_names)] + + degparms_optim_back <- backtransform_odeparms(degparms_optim, + object[[1]]$mkinmod, + object[[1]]$transform_rates, + object[[1]]$transform_fractions) + + if (degparms_start[1] == "auto") { + degparms_start <- degparms_optim_noilr + for (obs_var_ilr in obs_vars_ilr) { + ff_names <- grep(paste0("^f_", obs_var_ilr, "_"), + names(degparms_optim_back), value = TRUE) + f_tffm0 <- tffm0(degparms_optim_back[ff_names]) + f_tffm0_qlogis <- qlogis(f_tffm0) + names(f_tffm0_qlogis) <- paste0("f_", obs_var_ilr, + "_tffm0_", 1:length(f_tffm0), "_qlogis") + degparms_start <- c(degparms_start, f_tffm0_qlogis) + } + } + + if (eta_start[1] == "auto") { + eta_start <- degparms_mmkin$eta[setdiff(names(degparms_optim), + degparms_optim_ilr_names)] + for (obs_var_ilr in obs_vars_ilr) { + ff_n <- length(grep(paste0("^f_", obs_var_ilr, "_"), + names(degparms_optim_back), value = TRUE)) + eta_start_ff <- rep(0.3, ff_n) + names(eta_start_ff) <- paste0("f_", obs_var_ilr, + "_tffm0_", 1:ff_n, "_qlogis") + eta_start <- c(eta_start, eta_start_ff) + } + } + + + degparms_fixed <- object[[1]]$bparms.fixed + + odeini_optim_parm_names <- grep('_0$', names(degparms_optim), value = TRUE) + odeini_fixed_parm_names <- grep('_0$', names(degparms_fixed), value = TRUE) + + odeparms_fixed_names <- setdiff(names(degparms_fixed), odeini_fixed_parm_names) + odeparms_fixed <- degparms_fixed[odeparms_fixed_names] + + odeini_fixed <- degparms_fixed[odeini_fixed_parm_names] + names(odeini_fixed) <- gsub('_0$', '', odeini_fixed_parm_names) + + # Definition of the model function + f <- function(){} + + ini_block <- "ini({" + + # Initial values for all degradation parameters + for (parm_name in names(degparms_start)) { + # As initials for state variables are not transformed, + # we need to modify the name here as we want to + # use the original name in the model block + ini_block <- paste0( + ini_block, + parm_name, " = ", + as.character(degparms_start[parm_name]), + "\n", + "eta.", parm_name, " ~ ", + as.character(eta_start[parm_name]), + "\n" + ) + } + + # Error model parameters + error_model_mkin <- object[[1]]$err_mod + + errparm_names_mkin <- names(object[[1]]$errparms) + errparms_mkin <- sapply(errparm_names_mkin, function(parm_name) { + mean(sapply(object, function(x) x$errparms[parm_name])) + }) + + sigma_tc_mkin <- errparms_ini <- errparms_mkin[1] + + mean(unlist(sapply(object, function(x) x$data$observed)), na.rm = TRUE) * + errparms_mkin[2] + + if (error_model == "const") { + if (error_model_mkin == "tc") { + errparms_ini <- sigma_tc_mkin + } else { + errparms_ini <- mean(errparms_mkin) + } + names(errparms_ini) <- "sigma" + } + + if (error_model == "obs") { + errparms_ini <- switch(error_model_mkin, + const = rep(errparms_mkin["sigma"], length(obs_vars)), + obs = errparms_mkin, + tc = sigma_tc_mkin) + names(errparms_ini) <- paste0("sigma_", obs_vars) + } + + if (error_model == "tc") { + if (error_model_mkin != "tc") { + stop("Not supported") + } else { + errparms_ini <- errparms_mkin + } + } + + if (error_model == "obs_tc") { + if (error_model_mkin != "tc") { + stop("Not supported") + } else { + errparms_ini <- rep(errparms_mkin, length(obs_vars)) + names(errparms_ini) <- paste0( + rep(names(errparms_mkin), length(obs_vars)), + "_", + rep(obs_vars, each = 2)) + } + } + + for (parm_name in names(errparms_ini)) { + ini_block <- paste0( + ini_block, + parm_name, " = ", + as.character(errparms_ini[parm_name]), + "\n" + ) + } + + ini_block <- paste0(ini_block, "})") + + body(f)[2] <- parse(text = ini_block) + + model_block <- "model({" + + # Population initial values for the ODE state variables + for (parm_name in odeini_optim_parm_names) { + model_block <- paste0( + model_block, + parm_name, "_model = ", + parm_name, " + eta.", parm_name, "\n", + gsub("(.*)_0", "\\1(0)", parm_name), " = ", parm_name, "_model\n") + } + + # Population initial values for log rate constants + for (parm_name in grep("^log_", names(degparms_start), value = TRUE)) { + model_block <- paste0( + model_block, + gsub("^log_", "", parm_name), " = ", + "exp(", parm_name, " + eta.", parm_name, ")\n") + } + + # Population initial values for logit transformed parameters + for (parm_name in grep("_qlogis$", names(degparms_start), value = TRUE)) { + model_block <- paste0( + model_block, + gsub("_qlogis$", "", parm_name), " = ", + "expit(", parm_name, " + eta.", parm_name, ")\n") + } + + # Calculate formation fractions from tffm0 transformed values + for (obs_var_ilr in obs_vars_ilr) { + ff_names <- grep(paste0("^f_", obs_var_ilr, "_"), + names(degparms_optim_back), value = TRUE) + pattern <- paste0("^f_", obs_var_ilr, "_to_(.*)$") + target_vars <- gsub(pattern, "\\1", + grep(paste0("^f_", obs_var_ilr, "_to_"), names(degparms_optim_back), value = TRUE)) + for (i in 1:length(target_vars)) { + ff_name <- ff_names[i] + ff_line <- paste0(ff_name, " = f_", obs_var_ilr, "_tffm0_", i) + if (i > 1) { + for (j in (i - 1):1) { + ff_line <- paste0(ff_line, " * (1 - f_", obs_var_ilr, "_tffm0_", j , ")") + } + } + model_block <- paste0( + model_block, + ff_line, + "\n" + ) + } + } + + # Differential equations + model_block <- paste0( + model_block, + paste( + gsub("d_(.*) =", "d/dt(\\1) =", mkin_model$diffs), + collapse = "\n"), + "\n" + ) + + # Error model + if (error_model == "const") { + model_block <- paste0(model_block, + paste(paste0(obs_vars, " ~ add(sigma)"), collapse = "\n")) + } + if (error_model == "obs") { + model_block <- paste0(model_block, + paste(paste0(obs_vars, " ~ add(sigma_", obs_vars, ")"), collapse = "\n"), + "\n") + } + if (error_model == "tc") { + model_block <- paste0(model_block, + paste(paste0(obs_vars, " ~ add(sigma_low) + prop(rsd_high)"), collapse = "\n"), + "\n") + } + if (error_model == "obs_tc") { + model_block <- paste0(model_block, + paste( + paste0(obs_vars, " ~ add(sigma_low_", obs_vars, ") + ", + "prop(rsd_high_", obs_vars, ")"), collapse = "\n"), + "\n") + } + + model_block <- paste0(model_block, "})") + + body(f)[3] <- parse(text = model_block) + + if (add_attributes) { + attr(f, "mean_dp_start") <- degparms_optim + attr(f, "eta_start") <- degparms_mmkin$eta + attr(f, "mean_ep_start") <- errparms_ini + } + + return(f) +} + +#' @rdname nlmixr.mmkin +#' @return An dataframe suitable for use with [nlmixr::nlmixr] +#' @export +nlmixr_data <- function(object, ...) { + if (nrow(object) > 1) stop("Only row objects allowed") + d <- lapply(object, function(x) x$data) + compartment_map <- 1:length(object[[1]]$mkinmod$spec) + names(compartment_map) <- names(object[[1]]$mkinmod$spec) + ds_names <- colnames(object) + + ds_list <- lapply(object, function(x) x$data[c("time", "variable", "observed")]) + names(ds_list) <- ds_names + ds_nlmixr <- purrr::map_dfr(ds_list, function(x) x, .id = "ds") + ds_nlmixr$variable <- as.character(ds_nlmixr$variable) + ds_nlmixr_renamed <- data.frame( + ID = ds_nlmixr$ds, + TIME = ds_nlmixr$time, + AMT = 0, EVID = 0, + CMT = ds_nlmixr$variable, + DV = ds_nlmixr$observed, + stringsAsFactors = FALSE) + + return(ds_nlmixr_renamed) +} diff --git a/R/plot.mixed.mmkin.R b/R/plot.mixed.mmkin.R index 4c1f1531..1ac62b07 100644 --- a/R/plot.mixed.mmkin.R +++ b/R/plot.mixed.mmkin.R @@ -2,7 +2,7 @@ utils::globalVariables("ds") #' Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object #' -#' @param x An object of class [mixed.mmkin], [nlme.mmkin] +#' @param x An object of class [mixed.mmkin], [saem.mmkin] or [nlme.mmkin] #' @param i A numeric index to select datasets for which to plot the individual predictions, #' in case plots get too large #' @inheritParams plot.mkinfit @@ -10,6 +10,10 @@ utils::globalVariables("ds") #' `resplot = "time"`. #' @param pred_over Named list of alternative predictions as obtained #' from [mkinpredict] with a compatible [mkinmod]. +#' @param test_log_parms Passed to [mean_degparms] in the case of an +#' [mixed.mmkin] object +#' @param conf.level Passed to [mean_degparms] in the case of an +#' [mixed.mmkin] object #' @param rel.height.legend The relative height of the legend shown on top #' @param rel.height.bottom The relative height of the bottom plot row #' @param ymax Vector of maximum y axis values @@ -36,9 +40,23 @@ utils::globalVariables("ds") #' #' # For this fit we need to increase pnlsMaxiter, and we increase the #' # tolerance in order to speed up the fit for this example evaluation +#' # It still takes 20 seconds to run #' f_nlme <- nlme(f, control = list(pnlsMaxIter = 120, tolerance = 1e-3)) #' plot(f_nlme) #' +#' f_saem <- saem(f, transformations = "saemix") +#' plot(f_saem) +#' +#' f_obs <- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE, error_model = "obs") +#' f_nlmix <- nlmix(f_obs) +#' plot(f_nlmix) +#' +#' # We can overlay the two variants if we generate predictions +#' pred_nlme <- mkinpredict(dfop_sfo, +#' f_nlme$bparms.optim[-1], +#' c(parent = f_nlme$bparms.optim[[1]], A1 = 0), +#' seq(0, 180, by = 0.2)) +#' plot(f_saem, pred_over = list(nlme = pred_nlme)) #' } #' @export plot.mixed.mmkin <- function(x, @@ -49,6 +67,8 @@ plot.mixed.mmkin <- function(x, xlim = range(x$data$time), resplot = c("predicted", "time"), pred_over = NULL, + test_log_parms = FALSE, + conf.level = 0.6, ymax = "auto", maxabs = "auto", ncol.legend = ifelse(length(i) <= 3, length(i) + 1, ifelse(length(i) <= 8, 3, 4)), nrow.legend = ceiling((length(i) + 1) / ncol.legend), @@ -67,7 +87,7 @@ plot.mixed.mmkin <- function(x, backtransform = TRUE if (identical(class(x), "mixed.mmkin")) { - degparms_pop <- mean_degparms(x$mmkin) + degparms_pop <- mean_degparms(x$mmkin, test_log_parms = test_log_parms, conf.level = conf.level) degparms_tmp <- parms(x$mmkin, transformed = TRUE) degparms_i <- as.data.frame(t(degparms_tmp[setdiff(rownames(degparms_tmp), names(fit_1$errparms)), ])) @@ -82,6 +102,30 @@ plot.mixed.mmkin <- function(x, type = ifelse(standardized, "pearson", "response")) } + if (inherits(x, "saem.mmkin")) { + if (x$transformations == "saemix") backtransform = FALSE + degparms_i <- saemix::psi(x$so) + rownames(degparms_i) <- ds_names + degparms_i_names <- setdiff(x$so@results@name.fixed, names(fit_1$errparms)) + colnames(degparms_i) <- degparms_i_names + residual_type = ifelse(standardized, "standardized", "residual") + residuals <- x$data[[residual_type]] + degparms_pop <- x$so@results@fixed.effects + names(degparms_pop) <- degparms_i_names + } + + if (inherits(x, "nlmixr.mmkin")) { + eta_i <- random.effects(x$nm)[-1] + names(eta_i) <- gsub("^eta.", "", names(eta_i)) + degparms_i <- eta_i + degparms_pop <- x$nm$theta + for (parm_name in names(degparms_i)) { + degparms_i[parm_name] <- eta_i[parm_name] + degparms_pop[parm_name] + } + residual_type = ifelse(standardized, "standardized", "residual") + residuals <- x$data[[residual_type]] + } + degparms_fixed <- fit_1$fixed$value names(degparms_fixed) <- rownames(fit_1$fixed) degparms_all <- cbind(as.matrix(degparms_i), diff --git a/R/saem.R b/R/saem.R new file mode 100644 index 00000000..9db2c04a --- /dev/null +++ b/R/saem.R @@ -0,0 +1,543 @@ +utils::globalVariables(c("predicted", "std")) + +#' Fit nonlinear mixed models with SAEM +#' +#' This function uses [saemix::saemix()] as a backend for fitting nonlinear mixed +#' effects models created from [mmkin] row objects using the Stochastic Approximation +#' Expectation Maximisation algorithm (SAEM). +#' +#' An mmkin row object is essentially a list of mkinfit objects that have been +#' obtained by fitting the same model to a list of datasets using [mkinfit]. +#' +#' Starting values for the fixed effects (population mean parameters, argument +#' psi0 of [saemix::saemixModel()] are the mean values of the parameters found +#' using [mmkin]. +#' +#' @importFrom utils packageVersion +#' @param object An [mmkin] row object containing several fits of the same +#' [mkinmod] model to different datasets +#' @param verbose Should we print information about created objects of +#' type [saemix::SaemixModel] and [saemix::SaemixData]? +#' @param transformations Per default, all parameter transformations are done +#' in mkin. If this argument is set to 'saemix', parameter transformations +#' are done in 'saemix' for the supported cases. Currently this is only +#' supported in cases where the initial concentration of the parent is not fixed, +#' SFO or DFOP is used for the parent and there is either no metabolite or one. +#' @param degparms_start Parameter values given as a named numeric vector will +#' be used to override the starting values obtained from the 'mmkin' object. +#' @param test_log_parms If TRUE, an attempt is made to use more robust starting +#' values for population parameters fitted as log parameters in mkin (like +#' rate constants) by only considering rate constants that pass the t-test +#' when calculating mean degradation parameters using [mean_degparms]. +#' @param conf.level Possibility to adjust the required confidence level +#' for parameter that are tested if requested by 'test_log_parms'. +#' @param solution_type Possibility to specify the solution type in case the +#' automatic choice is not desired +#' @param fail_with_errors Should a failure to compute standard errors +#' from the inverse of the Fisher Information Matrix be a failure? +#' @param quiet Should we suppress the messages saemix prints at the beginning +#' and the end of the optimisation process? +#' @param nbiter.saemix Convenience option to increase the number of +#' iterations +#' @param control Passed to [saemix::saemix]. +#' @param \dots Further parameters passed to [saemix::saemixModel]. +#' @return An S3 object of class 'saem.mmkin', containing the fitted +#' [saemix::SaemixObject] as a list component named 'so'. The +#' object also inherits from 'mixed.mmkin'. +#' @seealso [summary.saem.mmkin] [plot.mixed.mmkin] +#' @examples +#' \dontrun{ +#' ds <- lapply(experimental_data_for_UBA_2019[6:10], +#' function(x) subset(x$data[c("name", "time", "value")])) +#' names(ds) <- paste("Dataset", 6:10) +#' f_mmkin_parent_p0_fixed <- mmkin("FOMC", ds, +#' state.ini = c(parent = 100), fixed_initials = "parent", quiet = TRUE) +#' f_saem_p0_fixed <- saem(f_mmkin_parent_p0_fixed) +#' +#' f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE) +#' f_saem_sfo <- saem(f_mmkin_parent["SFO", ]) +#' f_saem_fomc <- saem(f_mmkin_parent["FOMC", ]) +#' f_saem_dfop <- saem(f_mmkin_parent["DFOP", ]) +#' +#' # The returned saem.mmkin object contains an SaemixObject, therefore we can use +#' # functions from saemix +#' library(saemix) +#' compare.saemix(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so) +#' plot(f_saem_fomc$so, plot.type = "convergence") +#' plot(f_saem_fomc$so, plot.type = "individual.fit") +#' plot(f_saem_fomc$so, plot.type = "npde") +#' plot(f_saem_fomc$so, plot.type = "vpc") +#' +#' f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc") +#' f_saem_fomc_tc <- saem(f_mmkin_parent_tc["FOMC", ]) +#' compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so) +#' +#' sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), +#' A1 = mkinsub("SFO")) +#' fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"), +#' A1 = mkinsub("SFO")) +#' dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"), +#' A1 = mkinsub("SFO")) +#' # The following fit uses analytical solutions for SFO-SFO and DFOP-SFO, +#' # and compiled ODEs for FOMC that are much slower +#' f_mmkin <- mmkin(list( +#' "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo), +#' ds, quiet = TRUE) +#' # saem fits of SFO-SFO and DFOP-SFO to these data take about five seconds +#' # each on this system, as we use analytical solutions written for saemix. +#' # When using the analytical solutions written for mkin this took around +#' # four minutes +#' f_saem_sfo_sfo <- saem(f_mmkin["SFO-SFO", ]) +#' f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ]) +#' # We can use print, plot and summary methods to check the results +#' print(f_saem_dfop_sfo) +#' plot(f_saem_dfop_sfo) +#' summary(f_saem_dfop_sfo, data = TRUE) +#' +#' # The following takes about 6 minutes +#' #f_saem_dfop_sfo_deSolve <- saem(f_mmkin["DFOP-SFO", ], solution_type = "deSolve", +#' # control = list(nbiter.saemix = c(200, 80), nbdisplay = 10)) +#' +#' #saemix::compare.saemix(list( +#' # f_saem_dfop_sfo$so, +#' # f_saem_dfop_sfo_deSolve$so)) +#' +#' # If the model supports it, we can also use eigenvalue based solutions, which +#' # take a similar amount of time +#' #f_saem_sfo_sfo_eigen <- saem(f_mmkin["SFO-SFO", ], solution_type = "eigen", +#' # control = list(nbiter.saemix = c(200, 80), nbdisplay = 10)) +#' } +#' @export +saem <- function(object, ...) UseMethod("saem") + +#' @rdname saem +#' @export +saem.mmkin <- function(object, + transformations = c("mkin", "saemix"), + degparms_start = numeric(), + test_log_parms = TRUE, + conf.level = 0.6, + solution_type = "auto", + nbiter.saemix = c(300, 100), + control = list(displayProgress = FALSE, print = FALSE, + nbiter.saemix = nbiter.saemix, + save = FALSE, save.graphs = FALSE), + fail_with_errors = TRUE, + verbose = FALSE, quiet = FALSE, ...) +{ + transformations <- match.arg(transformations) + m_saemix <- saemix_model(object, verbose = verbose, + degparms_start = degparms_start, + test_log_parms = test_log_parms, conf.level = conf.level, + solution_type = solution_type, + transformations = transformations, ...) + d_saemix <- saemix_data(object, verbose = verbose) + + fit_time <- system.time({ + utils::capture.output(f_saemix <- saemix::saemix(m_saemix, d_saemix, control), split = !quiet) + FIM_failed <- NULL + if (any(is.na(f_saemix@results@se.fixed))) FIM_failed <- c(FIM_failed, "fixed effects") + if (any(is.na(c(f_saemix@results@se.omega, f_saemix@results@se.respar)))) { + FIM_failed <- c(FIM_failed, "random effects and residual error parameters") + } + if (!is.null(FIM_failed) & fail_with_errors) { + stop("Could not invert FIM for ", paste(FIM_failed, collapse = " and ")) + } + }) + + transparms_optim <- f_saemix@results@fixed.effects + names(transparms_optim) <- f_saemix@results@name.fixed + + if (transformations == "mkin") { + bparms_optim <- backtransform_odeparms(transparms_optim, + object[[1]]$mkinmod, + object[[1]]$transform_rates, + object[[1]]$transform_fractions) + } else { + bparms_optim <- transparms_optim + } + + return_data <- nlme_data(object) + + return_data$predicted <- f_saemix@model@model( + psi = saemix::psi(f_saemix), + id = as.numeric(return_data$ds), + xidep = return_data[c("time", "name")]) + + return_data <- transform(return_data, + residual = value - predicted, + std = sigma_twocomp(predicted, + f_saemix@results@respar[1], f_saemix@results@respar[2])) + return_data <- transform(return_data, + standardized = residual / std) + + result <- list( + mkinmod = object[[1]]$mkinmod, + mmkin = object, + solution_type = object[[1]]$solution_type, + transformations = transformations, + transform_rates = object[[1]]$transform_rates, + transform_fractions = object[[1]]$transform_fractions, + so = f_saemix, + time = fit_time, + mean_dp_start = attr(m_saemix, "mean_dp_start"), + bparms.optim = bparms_optim, + bparms.fixed = object[[1]]$bparms.fixed, + data = return_data, + err_mod = object[[1]]$err_mod, + date.fit = date(), + saemixversion = as.character(utils::packageVersion("saemix")), + mkinversion = as.character(utils::packageVersion("mkin")), + Rversion = paste(R.version$major, R.version$minor, sep=".") + ) + + class(result) <- c("saem.mmkin", "mixed.mmkin") + return(result) +} + +#' @export +#' @rdname saem +#' @param x An saem.mmkin object to print +#' @param digits Number of digits to use for printing +print.saem.mmkin <- function(x, digits = max(3, getOption("digits") - 3), ...) { + cat( "Kinetic nonlinear mixed-effects model fit by SAEM" ) + cat("\nStructural model:\n") + diffs <- x$mmkin[[1]]$mkinmod$diffs + nice_diffs <- gsub("^(d.*) =", "\\1/dt =", diffs) + writeLines(strwrap(nice_diffs, exdent = 11)) + cat("\nData:\n") + cat(nrow(x$data), "observations of", + length(unique(x$data$name)), "variable(s) grouped in", + length(unique(x$data$ds)), "datasets\n") + + cat("\nLikelihood computed by importance sampling\n") + print(data.frame( + AIC = AIC(x$so, type = "is"), + BIC = BIC(x$so, type = "is"), + logLik = logLik(x$so, type = "is"), + row.names = " "), digits = digits) + + cat("\nFitted parameters:\n") + conf.int <- x$so@results@conf.int[c("estimate", "lower", "upper")] + rownames(conf.int) <- x$so@results@conf.int[["name"]] + print(conf.int, digits = digits) + + invisible(x) +} + +#' @rdname saem +#' @return An [saemix::SaemixModel] object. +#' @export +saemix_model <- function(object, solution_type = "auto", transformations = c("mkin", "saemix"), + degparms_start = numeric(), test_log_parms = FALSE, verbose = FALSE, ...) +{ + if (packageVersion("saemix") < "3.1.9000") { + stop("To use the interface to saemix, you need to install a development version\n", + "preferably https://github.com/jranke/saemixextension@warp_combined") + } + + if (nrow(object) > 1) stop("Only row objects allowed") + + mkin_model <- object[[1]]$mkinmod + + degparms_optim <- mean_degparms(object, test_log_parms = test_log_parms) + if (transformations == "saemix") { + degparms_optim <- backtransform_odeparms(degparms_optim, + object[[1]]$mkinmod, + object[[1]]$transform_rates, + object[[1]]$transform_fractions) + } + degparms_fixed <- object[[1]]$bparms.fixed + + # Transformations are done in the degradation function + transform.par = rep(0, length(degparms_optim)) + + odeini_optim_parm_names <- grep('_0$', names(degparms_optim), value = TRUE) + odeini_fixed_parm_names <- grep('_0$', names(degparms_fixed), value = TRUE) + + odeparms_fixed_names <- setdiff(names(degparms_fixed), odeini_fixed_parm_names) + odeparms_fixed <- degparms_fixed[odeparms_fixed_names] + + odeini_fixed <- degparms_fixed[odeini_fixed_parm_names] + names(odeini_fixed) <- gsub('_0$', '', odeini_fixed_parm_names) + + model_function <- FALSE + + # Model functions with analytical solutions + # Fixed parameters, use_of_ff = "min" and turning off sinks currently not supported here + # In general, we need to consider exactly how the parameters in mkinfit were specified, + # as the parameters are currently mapped by position in these solutions + sinks <- sapply(mkin_model$spec, function(x) x$sink) + if (length(odeparms_fixed) == 0 & mkin_model$use_of_ff == "max" & all(sinks)) { + # Parent only + if (length(mkin_model$spec) == 1) { + parent_type <- mkin_model$spec[[1]]$type + if (length(odeini_fixed) == 1) { + if (parent_type == "SFO") { + stop("saemix needs at least two parameters to work on.") + } + if (parent_type == "FOMC") { + model_function <- function(psi, id, xidep) { + odeini_fixed / (xidep[, "time"]/exp(psi[id, 2]) + 1)^exp(psi[id, 1]) + } + } + if (parent_type == "DFOP") { + model_function <- function(psi, id, xidep) { + g <- plogis(psi[id, 3]) + t <- xidep[, "time"] + odeini_fixed * (g * exp(- exp(psi[id, 1]) * t) + + (1 - g) * exp(- exp(psi[id, 2]) * t)) + } + } + if (parent_type == "HS") { + model_function <- function(psi, id, xidep) { + tb <- exp(psi[id, 3]) + t <- xidep[, "time"] + k1 = exp(psi[id, 1]) + odeini_fixed * ifelse(t <= tb, + exp(- k1 * t), + exp(- k1 * tb) * exp(- exp(psi[id, 2]) * (t - tb))) + } + } + } else { + if (parent_type == "SFO") { + if (transformations == "mkin") { + model_function <- function(psi, id, xidep) { + psi[id, 1] * exp( - exp(psi[id, 2]) * xidep[, "time"]) + } + } else { + model_function <- function(psi, id, xidep) { + psi[id, 1] * exp( - psi[id, 2] * xidep[, "time"]) + } + transform.par = c(0, 1) + } + } + if (parent_type == "FOMC") { + model_function <- function(psi, id, xidep) { + psi[id, 1] / (xidep[, "time"]/exp(psi[id, 3]) + 1)^exp(psi[id, 2]) + } + } + if (parent_type == "DFOP") { + if (transformations == "mkin") { + model_function <- function(psi, id, xidep) { + g <- plogis(psi[id, 4]) + t <- xidep[, "time"] + psi[id, 1] * (g * exp(- exp(psi[id, 2]) * t) + + (1 - g) * exp(- exp(psi[id, 3]) * t)) + } + } else { + model_function <- function(psi, id, xidep) { + g <- psi[id, 4] + t <- xidep[, "time"] + psi[id, 1] * (g * exp(- psi[id, 2] * t) + + (1 - g) * exp(- psi[id, 3] * t)) + } + transform.par = c(0, 1, 1, 3) + } + } + if (parent_type == "HS") { + model_function <- function(psi, id, xidep) { + tb <- exp(psi[id, 4]) + t <- xidep[, "time"] + k1 = exp(psi[id, 2]) + psi[id, 1] * ifelse(t <= tb, + exp(- k1 * t), + exp(- k1 * tb) * exp(- exp(psi[id, 3]) * (t - tb))) + } + } + } + } + + # Parent with one metabolite + # Parameter names used in the model functions are as in + # https://nbviewer.jupyter.org/urls/jrwb.de/nb/Symbolic%20ODE%20solutions%20for%20mkin.ipynb + types <- unname(sapply(mkin_model$spec, function(x) x$type)) + if (length(mkin_model$spec) == 2 &! "SFORB" %in% types ) { + # Initial value for the metabolite (n20) must be fixed + if (names(odeini_fixed) == names(mkin_model$spec)[2]) { + n20 <- odeini_fixed + parent_name <- names(mkin_model$spec)[1] + if (identical(types, c("SFO", "SFO"))) { + if (transformations == "mkin") { + model_function <- function(psi, id, xidep) { + t <- xidep[, "time"] + n10 <- psi[id, 1] + k1 <- exp(psi[id, 2]) + k2 <- exp(psi[id, 3]) + f12 <- plogis(psi[id, 4]) + ifelse(xidep[, "name"] == parent_name, + n10 * exp(- k1 * t), + (((k2 - k1) * n20 - f12 * k1 * n10) * exp(- k2 * t)) / (k2 - k1) + + (f12 * k1 * n10 * exp(- k1 * t)) / (k2 - k1) + ) + } + } else { + model_function <- function(psi, id, xidep) { + t <- xidep[, "time"] + n10 <- psi[id, 1] + k1 <- psi[id, 2] + k2 <- psi[id, 3] + f12 <- psi[id, 4] + ifelse(xidep[, "name"] == parent_name, + n10 * exp(- k1 * t), + (((k2 - k1) * n20 - f12 * k1 * n10) * exp(- k2 * t)) / (k2 - k1) + + (f12 * k1 * n10 * exp(- k1 * t)) / (k2 - k1) + ) + } + transform.par = c(0, 1, 1, 3) + } + } + if (identical(types, c("DFOP", "SFO"))) { + if (transformations == "mkin") { + model_function <- function(psi, id, xidep) { + t <- xidep[, "time"] + n10 <- psi[id, 1] + k2 <- exp(psi[id, 2]) + f12 <- plogis(psi[id, 3]) + l1 <- exp(psi[id, 4]) + l2 <- exp(psi[id, 5]) + g <- plogis(psi[id, 6]) + ifelse(xidep[, "name"] == parent_name, + n10 * (g * exp(- l1 * t) + (1 - g) * exp(- l2 * t)), + ((f12 * g - f12) * l2 * n10 * exp(- l2 * t)) / (l2 - k2) - + (f12 * g * l1 * n10 * exp(- l1 * t)) / (l1 - k2) + + ((((l1 - k2) * l2 - k2 * l1 + k2^2) * n20 + + ((f12 * l1 + (f12 * g - f12) * k2) * l2 - + f12 * g * k2 * l1) * n10) * exp( - k2 * t)) / + ((l1 - k2) * l2 - k2 * l1 + k2^2) + ) + } + } else { + model_function <- function(psi, id, xidep) { + t <- xidep[, "time"] + n10 <- psi[id, 1] + k2 <- psi[id, 2] + f12 <- psi[id, 3] + l1 <- psi[id, 4] + l2 <- psi[id, 5] + g <- psi[id, 6] + ifelse(xidep[, "name"] == parent_name, + n10 * (g * exp(- l1 * t) + (1 - g) * exp(- l2 * t)), + ((f12 * g - f12) * l2 * n10 * exp(- l2 * t)) / (l2 - k2) - + (f12 * g * l1 * n10 * exp(- l1 * t)) / (l1 - k2) + + ((((l1 - k2) * l2 - k2 * l1 + k2^2) * n20 + + ((f12 * l1 + (f12 * g - f12) * k2) * l2 - + f12 * g * k2 * l1) * n10) * exp( - k2 * t)) / + ((l1 - k2) * l2 - k2 * l1 + k2^2) + ) + } + transform.par = c(0, 1, 3, 1, 1, 3) + } + } + } + } + } + + if (is.function(model_function) & solution_type == "auto") { + solution_type = "analytical saemix" + } else { + + if (solution_type == "auto") + solution_type <- object[[1]]$solution_type + + model_function <- function(psi, id, xidep) { + + uid <- unique(id) + + res_list <- lapply(uid, function(i) { + + transparms_optim <- as.numeric(psi[i, ]) # psi[i, ] is a dataframe when called in saemix.predict + names(transparms_optim) <- names(degparms_optim) + + odeini_optim <- transparms_optim[odeini_optim_parm_names] + names(odeini_optim) <- gsub('_0$', '', odeini_optim_parm_names) + + odeini <- c(odeini_optim, odeini_fixed)[names(mkin_model$diffs)] + + ode_transparms_optim_names <- setdiff(names(transparms_optim), odeini_optim_parm_names) + odeparms_optim <- backtransform_odeparms(transparms_optim[ode_transparms_optim_names], mkin_model, + transform_rates = object[[1]]$transform_rates, + transform_fractions = object[[1]]$transform_fractions) + odeparms <- c(odeparms_optim, odeparms_fixed) + + xidep_i <- subset(xidep, id == i) + + if (solution_type == "analytical") { + out_values <- mkin_model$deg_func(xidep_i, odeini, odeparms) + } else { + + i_time <- xidep_i$time + i_name <- xidep_i$name + + out_wide <- mkinpredict(mkin_model, + odeparms = odeparms, odeini = odeini, + solution_type = solution_type, + outtimes = sort(unique(i_time)), + na_stop = FALSE + ) + + out_index <- cbind(as.character(i_time), as.character(i_name)) + out_values <- out_wide[out_index] + } + return(out_values) + }) + res <- unlist(res_list) + return(res) + } + } + + error.model <- switch(object[[1]]$err_mod, + const = "constant", + tc = "combined", + obs = "constant") + + if (object[[1]]$err_mod == "obs") { + warning("The error model 'obs' (variance by variable) can currently not be transferred to an saemix model") + } + + error.init <- switch(object[[1]]$err_mod, + const = c(a = mean(sapply(object, function(x) x$errparms)), b = 1), + tc = c(a = mean(sapply(object, function(x) x$errparms[1])), + b = mean(sapply(object, function(x) x$errparms[2]))), + obs = c(a = mean(sapply(object, function(x) x$errparms)), b = 1)) + + degparms_psi0 <- degparms_optim + degparms_psi0[names(degparms_start)] <- degparms_start + psi0_matrix <- matrix(degparms_psi0, nrow = 1) + colnames(psi0_matrix) <- names(degparms_psi0) + + res <- saemix::saemixModel(model_function, + psi0 = psi0_matrix, + "Mixed model generated from mmkin object", + transform.par = transform.par, + error.model = error.model, + verbose = verbose + ) + attr(res, "mean_dp_start") <- degparms_optim + return(res) +} + +#' @rdname saem +#' @return An [saemix::SaemixData] object. +#' @export +saemix_data <- function(object, verbose = FALSE, ...) { + if (nrow(object) > 1) stop("Only row objects allowed") + ds_names <- colnames(object) + + ds_list <- lapply(object, function(x) x$data[c("time", "variable", "observed")]) + names(ds_list) <- ds_names + ds_saemix_all <- purrr::map_dfr(ds_list, function(x) x, .id = "ds") + ds_saemix <- data.frame(ds = ds_saemix_all$ds, + name = as.character(ds_saemix_all$variable), + time = ds_saemix_all$time, + value = ds_saemix_all$observed, + stringsAsFactors = FALSE) + + res <- saemix::saemixData(ds_saemix, + name.group = "ds", + name.predictors = c("time", "name"), + name.response = "value", + verbose = verbose, + ...) + return(res) +} diff --git a/R/summary.nlmixr.mmkin.R b/R/summary.nlmixr.mmkin.R new file mode 100644 index 00000000..a023f319 --- /dev/null +++ b/R/summary.nlmixr.mmkin.R @@ -0,0 +1,250 @@ +#' Summary method for class "nlmixr.mmkin" +#' +#' Lists model equations, initial parameter values, optimised parameters +#' for fixed effects (population), random effects (deviations from the +#' population mean) and residual error model, as well as the resulting +#' endpoints such as formation fractions and DT50 values. Optionally +#' (default is FALSE), the data are listed in full. +#' +#' @importFrom stats confint sd +#' @param object an object of class [nlmixr.mmkin] +#' @param x an object of class [summary.nlmixr.mmkin] +#' @param data logical, indicating whether the full data should be included in +#' the summary. +#' @param verbose Should the summary be verbose? +#' @param distimes logical, indicating whether DT50 and DT90 values should be +#' included. +#' @param digits Number of digits to use for printing +#' @param \dots optional arguments passed to methods like \code{print}. +#' @return The summary function returns a list obtained in the fit, with at +#' least the following additional components +#' \item{nlmixrversion, mkinversion, Rversion}{The nlmixr, mkin and R versions used} +#' \item{date.fit, date.summary}{The dates where the fit and the summary were +#' produced} +#' \item{diffs}{The differential equations used in the degradation model} +#' \item{use_of_ff}{Was maximum or minimum use made of formation fractions} +#' \item{data}{The data} +#' \item{confint_back}{Backtransformed parameters, with confidence intervals if available} +#' \item{ff}{The estimated formation fractions derived from the fitted +#' model.} +#' \item{distimes}{The DT50 and DT90 values for each observed variable.} +#' \item{SFORB}{If applicable, eigenvalues of SFORB components of the model.} +#' The print method is called for its side effect, i.e. printing the summary. +#' @importFrom stats predict vcov +#' @author Johannes Ranke for the mkin specific parts +#' nlmixr authors for the parts inherited from nlmixr. +#' @examples +#' # Generate five datasets following DFOP-SFO kinetics +#' sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) +#' dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "m1"), +#' m1 = mkinsub("SFO"), quiet = TRUE) +#' set.seed(1234) +#' k1_in <- rlnorm(5, log(0.1), 0.3) +#' k2_in <- rlnorm(5, log(0.02), 0.3) +#' g_in <- plogis(rnorm(5, qlogis(0.5), 0.3)) +#' f_parent_to_m1_in <- plogis(rnorm(5, qlogis(0.3), 0.3)) +#' k_m1_in <- rlnorm(5, log(0.02), 0.3) +#' +#' pred_dfop_sfo <- function(k1, k2, g, f_parent_to_m1, k_m1) { +#' mkinpredict(dfop_sfo, +#' c(k1 = k1, k2 = k2, g = g, f_parent_to_m1 = f_parent_to_m1, k_m1 = k_m1), +#' c(parent = 100, m1 = 0), +#' sampling_times) +#' } +#' +#' ds_mean_dfop_sfo <- lapply(1:5, function(i) { +#' mkinpredict(dfop_sfo, +#' c(k1 = k1_in[i], k2 = k2_in[i], g = g_in[i], +#' f_parent_to_m1 = f_parent_to_m1_in[i], k_m1 = k_m1_in[i]), +#' c(parent = 100, m1 = 0), +#' sampling_times) +#' }) +#' names(ds_mean_dfop_sfo) <- paste("ds", 1:5) +#' +#' ds_syn_dfop_sfo <- lapply(ds_mean_dfop_sfo, function(ds) { +#' add_err(ds, +#' sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2), +#' n = 1)[[1]] +#' }) +#' +#' \dontrun{ +#' # Evaluate using mmkin and nlmixr +#' f_mmkin_dfop_sfo <- mmkin(list(dfop_sfo), ds_syn_dfop_sfo, +#' quiet = TRUE, error_model = "tc", cores = 5) +#' f_saemix_dfop_sfo <- mkin::saem(f_mmkin_dfop_sfo) +#' f_nlme_dfop_sfo <- mkin::nlme(f_mmkin_dfop_sfo) +#' f_nlmixr_dfop_sfo_saem <- nlmixr(f_mmkin_dfop_sfo, est = "saem") +#' # The following takes a very long time but gives +#' f_nlmixr_dfop_sfo_focei <- nlmixr(f_mmkin_dfop_sfo, est = "focei") +#' AIC(f_nlmixr_dfop_sfo_saem$nm, f_nlmixr_dfop_sfo_focei$nm) +#' summary(f_nlmixr_dfop_sfo_sfo, data = TRUE) +#' } +#' +#' @export +summary.nlmixr.mmkin <- function(object, data = FALSE, verbose = FALSE, distimes = TRUE, ...) { + + mod_vars <- names(object$mkinmod$diffs) + + conf.int <- confint(object$nm) + dpnames <- setdiff(rownames(conf.int), names(object$mean_ep_start)) + ndp <- length(dpnames) + + confint_trans <- as.matrix(conf.int[dpnames, c(1, 3, 4)]) + colnames(confint_trans) <- c("est.", "lower", "upper") + + bp <- backtransform_odeparms(confint_trans[, "est."], object$mkinmod, + object$transform_rates, object$transform_fractions) + bpnames <- names(bp) + + # Transform boundaries of CI for one parameter at a time, + # with the exception of sets of formation fractions (single fractions are OK). + f_names_skip <- character(0) + for (box in mod_vars) { # Figure out sets of fractions to skip + f_names <- grep(paste("^f", box, sep = "_"), dpnames, value = TRUE) + n_paths <- length(f_names) + if (n_paths > 1) f_names_skip <- c(f_names_skip, f_names) + } + + confint_back <- matrix(NA, nrow = length(bp), ncol = 3, + dimnames = list(bpnames, colnames(confint_trans))) + confint_back[, "est."] <- bp + + for (pname in dpnames) { + if (!pname %in% f_names_skip) { + par.lower <- confint_trans[pname, "lower"] + par.upper <- confint_trans[pname, "upper"] + names(par.lower) <- names(par.upper) <- pname + bpl <- backtransform_odeparms(par.lower, object$mkinmod, + object$transform_rates, + object$transform_fractions) + bpu <- backtransform_odeparms(par.upper, object$mkinmod, + object$transform_rates, + object$transform_fractions) + confint_back[names(bpl), "lower"] <- bpl + confint_back[names(bpu), "upper"] <- bpu + } + } + + # Correlation of fixed effects (inspired by summary.nlme) + varFix <- vcov(object$nm) + stdFix <- sqrt(diag(varFix)) + object$corFixed <- array( + t(varFix/stdFix)/stdFix, + dim(varFix), + list(dpnames, dpnames)) + + object$confint_trans <- confint_trans + object$confint_back <- confint_back + + object$date.summary = date() + object$use_of_ff = object$mkinmod$use_of_ff + + object$diffs <- object$mkinmod$diffs + object$print_data <- data # boolean: Should we print the data? + + names(object$data)[4] <- "observed" # rename value to observed + + object$verbose <- verbose + + object$fixed <- object$mmkin_orig[[1]]$fixed + object$AIC = AIC(object$nm) + object$BIC = BIC(object$nm) + object$logLik = logLik(object$nm) + + ep <- endpoints(object) + if (length(ep$ff) != 0) + object$ff <- ep$ff + if (distimes) object$distimes <- ep$distimes + if (length(ep$SFORB) != 0) object$SFORB <- ep$SFORB + class(object) <- c("summary.nlmixr.mmkin") + return(object) +} + +#' @rdname summary.nlmixr.mmkin +#' @export +print.summary.nlmixr.mmkin <- function(x, digits = max(3, getOption("digits") - 3), verbose = x$verbose, ...) { + cat("nlmixr version used for fitting: ", x$nlmixrversion, "\n") + cat("mkin version used for pre-fitting: ", x$mkinversion, "\n") + cat("R version used for fitting: ", x$Rversion, "\n") + + cat("Date of fit: ", x$date.fit, "\n") + cat("Date of summary:", x$date.summary, "\n") + + cat("\nEquations:\n") + nice_diffs <- gsub("^(d.*) =", "\\1/dt =", x[["diffs"]]) + writeLines(strwrap(nice_diffs, exdent = 11)) + + cat("\nData:\n") + cat(nrow(x$data), "observations of", + length(unique(x$data$name)), "variable(s) grouped in", + length(unique(x$data$ds)), "datasets\n") + + cat("\nDegradation model predictions using RxODE\n") + + cat("\nFitted in", x$time[["elapsed"]], "s\n") + + cat("\nVariance model: ") + cat(switch(x$err_mod, + const = "Constant variance", + obs = "Variance unique to each observed variable", + tc = "Two-component variance function", + obs_tc = "Two-component variance unique to each observed variable"), "\n") + + cat("\nMean of starting values for individual parameters:\n") + print(x$mean_dp_start, digits = digits) + + cat("\nMean of starting values for error model parameters:\n") + print(x$mean_ep_start, digits = digits) + + cat("\nFixed degradation parameter values:\n") + if(length(x$fixed$value) == 0) cat("None\n") + else print(x$fixed, digits = digits) + + cat("\nResults:\n\n") + cat("Likelihood calculated by", nlmixr::getOfvType(x$nm), " \n") + print(data.frame(AIC = x$AIC, BIC = x$BIC, logLik = x$logLik, + row.names = " "), digits = digits) + + cat("\nOptimised parameters:\n") + print(x$confint_trans, digits = digits) + + if (nrow(x$confint_trans) > 1) { + corr <- x$corFixed + class(corr) <- "correlation" + print(corr, title = "\nCorrelation:", ...) + } + + cat("\nRandom effects (omega):\n") + print(x$nm$omega, digits = digits) + + cat("\nVariance model:\n") + print(x$nm$sigma, digits = digits) + + cat("\nBacktransformed parameters:\n") + print(x$confint_back, digits = digits) + + printSFORB <- !is.null(x$SFORB) + if(printSFORB){ + cat("\nEstimated Eigenvalues of SFORB model(s):\n") + print(x$SFORB, digits = digits,...) + } + + printff <- !is.null(x$ff) + if(printff){ + cat("\nResulting formation fractions:\n") + print(data.frame(ff = x$ff), digits = digits,...) + } + + printdistimes <- !is.null(x$distimes) + if(printdistimes){ + cat("\nEstimated disappearance times:\n") + print(x$distimes, digits = digits,...) + } + + if (x$print_data){ + cat("\nData:\n") + print(format(x$data, digits = digits, ...), row.names = FALSE) + } + + invisible(x) +} diff --git a/R/summary.saem.mmkin.R b/R/summary.saem.mmkin.R new file mode 100644 index 00000000..e92c561c --- /dev/null +++ b/R/summary.saem.mmkin.R @@ -0,0 +1,268 @@ +#' Summary method for class "saem.mmkin" +#' +#' Lists model equations, initial parameter values, optimised parameters +#' for fixed effects (population), random effects (deviations from the +#' population mean) and residual error model, as well as the resulting +#' endpoints such as formation fractions and DT50 values. Optionally +#' (default is FALSE), the data are listed in full. +#' +#' @param object an object of class [saem.mmkin] +#' @param x an object of class [summary.saem.mmkin] +#' @param data logical, indicating whether the full data should be included in +#' the summary. +#' @param verbose Should the summary be verbose? +#' @param distimes logical, indicating whether DT50 and DT90 values should be +#' included. +#' @param digits Number of digits to use for printing +#' @param \dots optional arguments passed to methods like \code{print}. +#' @return The summary function returns a list based on the [saemix::SaemixObject] +#' obtained in the fit, with at least the following additional components +#' \item{saemixversion, mkinversion, Rversion}{The saemix, mkin and R versions used} +#' \item{date.fit, date.summary}{The dates where the fit and the summary were +#' produced} +#' \item{diffs}{The differential equations used in the degradation model} +#' \item{use_of_ff}{Was maximum or minimum use made of formation fractions} +#' \item{data}{The data} +#' \item{confint_trans}{Transformed parameters as used in the optimisation, with confidence intervals} +#' \item{confint_back}{Backtransformed parameters, with confidence intervals if available} +#' \item{confint_errmod}{Error model parameters with confidence intervals} +#' \item{ff}{The estimated formation fractions derived from the fitted +#' model.} +#' \item{distimes}{The DT50 and DT90 values for each observed variable.} +#' \item{SFORB}{If applicable, eigenvalues of SFORB components of the model.} +#' The print method is called for its side effect, i.e. printing the summary. +#' @importFrom stats predict vcov +#' @author Johannes Ranke for the mkin specific parts +#' saemix authors for the parts inherited from saemix. +#' @examples +#' # Generate five datasets following DFOP-SFO kinetics +#' sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) +#' dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "m1"), +#' m1 = mkinsub("SFO"), quiet = TRUE) +#' set.seed(1234) +#' k1_in <- rlnorm(5, log(0.1), 0.3) +#' k2_in <- rlnorm(5, log(0.02), 0.3) +#' g_in <- plogis(rnorm(5, qlogis(0.5), 0.3)) +#' f_parent_to_m1_in <- plogis(rnorm(5, qlogis(0.3), 0.3)) +#' k_m1_in <- rlnorm(5, log(0.02), 0.3) +#' +#' pred_dfop_sfo <- function(k1, k2, g, f_parent_to_m1, k_m1) { +#' mkinpredict(dfop_sfo, +#' c(k1 = k1, k2 = k2, g = g, f_parent_to_m1 = f_parent_to_m1, k_m1 = k_m1), +#' c(parent = 100, m1 = 0), +#' sampling_times) +#' } +#' +#' ds_mean_dfop_sfo <- lapply(1:5, function(i) { +#' mkinpredict(dfop_sfo, +#' c(k1 = k1_in[i], k2 = k2_in[i], g = g_in[i], +#' f_parent_to_m1 = f_parent_to_m1_in[i], k_m1 = k_m1_in[i]), +#' c(parent = 100, m1 = 0), +#' sampling_times) +#' }) +#' names(ds_mean_dfop_sfo) <- paste("ds", 1:5) +#' +#' ds_syn_dfop_sfo <- lapply(ds_mean_dfop_sfo, function(ds) { +#' add_err(ds, +#' sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2), +#' n = 1)[[1]] +#' }) +#' +#' \dontrun{ +#' # Evaluate using mmkin and saem +#' f_mmkin_dfop_sfo <- mmkin(list(dfop_sfo), ds_syn_dfop_sfo, +#' quiet = TRUE, error_model = "tc", cores = 5) +#' f_saem_dfop_sfo <- saem(f_mmkin_dfop_sfo) +#' summary(f_saem_dfop_sfo, data = TRUE) +#' } +#' +#' @export +summary.saem.mmkin <- function(object, data = FALSE, verbose = FALSE, distimes = TRUE, ...) { + + mod_vars <- names(object$mkinmod$diffs) + + pnames <- names(object$mean_dp_start) + np <- length(pnames) + + conf.int <- object$so@results@conf.int + rownames(conf.int) <- conf.int$name + confint_trans <- as.matrix(conf.int[pnames, c("estimate", "lower", "upper")]) + colnames(confint_trans)[1] <- "est." + + # In case objects were produced by earlier versions of saem + if (is.null(object$transformations)) object$transformations <- "mkin" + + if (object$transformations == "mkin") { + bp <- backtransform_odeparms(confint_trans[, "est."], object$mkinmod, + object$transform_rates, object$transform_fractions) + bpnames <- names(bp) + + # Transform boundaries of CI for one parameter at a time, + # with the exception of sets of formation fractions (single fractions are OK). + f_names_skip <- character(0) + for (box in mod_vars) { # Figure out sets of fractions to skip + f_names <- grep(paste("^f", box, sep = "_"), pnames, value = TRUE) + n_paths <- length(f_names) + if (n_paths > 1) f_names_skip <- c(f_names_skip, f_names) + } + + confint_back <- matrix(NA, nrow = length(bp), ncol = 3, + dimnames = list(bpnames, colnames(confint_trans))) + confint_back[, "est."] <- bp + + for (pname in pnames) { + if (!pname %in% f_names_skip) { + par.lower <- confint_trans[pname, "lower"] + par.upper <- confint_trans[pname, "upper"] + names(par.lower) <- names(par.upper) <- pname + bpl <- backtransform_odeparms(par.lower, object$mkinmod, + object$transform_rates, + object$transform_fractions) + bpu <- backtransform_odeparms(par.upper, object$mkinmod, + object$transform_rates, + object$transform_fractions) + confint_back[names(bpl), "lower"] <- bpl + confint_back[names(bpu), "upper"] <- bpu + } + } + } else { + confint_back <- confint_trans + } + + # Correlation of fixed effects (inspired by summary.nlme) + varFix <- vcov(object$so)[1:np, 1:np] + stdFix <- sqrt(diag(varFix)) + object$corFixed <- array( + t(varFix/stdFix)/stdFix, + dim(varFix), + list(pnames, pnames)) + + # Random effects + rnames <- paste0("SD.", pnames) + confint_ranef <- as.matrix(conf.int[rnames, c("estimate", "lower", "upper")]) + colnames(confint_ranef)[1] <- "est." + + # Error model + enames <- if (object$err_mod == "const") "a.1" else c("a.1", "b.1") + confint_errmod <- as.matrix(conf.int[enames, c("estimate", "lower", "upper")]) + colnames(confint_errmod)[1] <- "est." + + + object$confint_trans <- confint_trans + object$confint_ranef <- confint_ranef + object$confint_errmod <- confint_errmod + object$confint_back <- confint_back + + object$date.summary = date() + object$use_of_ff = object$mkinmod$use_of_ff + object$error_model_algorithm = object$mmkin_orig[[1]]$error_model_algorithm + err_mod = object$mmkin_orig[[1]]$err_mod + + object$diffs <- object$mkinmod$diffs + object$print_data <- data # boolean: Should we print the data? + so_pred <- object$so@results@predictions + + names(object$data)[4] <- "observed" # rename value to observed + + object$verbose <- verbose + + object$fixed <- object$mmkin_orig[[1]]$fixed + object$AIC = AIC(object$so) + object$BIC = BIC(object$so) + object$logLik = logLik(object$so, method = "is") + + ep <- endpoints(object) + if (length(ep$ff) != 0) + object$ff <- ep$ff + if (distimes) object$distimes <- ep$distimes + if (length(ep$SFORB) != 0) object$SFORB <- ep$SFORB + class(object) <- c("summary.saem.mmkin") + return(object) +} + +#' @rdname summary.saem.mmkin +#' @export +print.summary.saem.mmkin <- function(x, digits = max(3, getOption("digits") - 3), verbose = x$verbose, ...) { + cat("saemix version used for fitting: ", x$saemixversion, "\n") + cat("mkin version used for pre-fitting: ", x$mkinversion, "\n") + cat("R version used for fitting: ", x$Rversion, "\n") + + cat("Date of fit: ", x$date.fit, "\n") + cat("Date of summary:", x$date.summary, "\n") + + cat("\nEquations:\n") + nice_diffs <- gsub("^(d.*) =", "\\1/dt =", x[["diffs"]]) + writeLines(strwrap(nice_diffs, exdent = 11)) + + cat("\nData:\n") + cat(nrow(x$data), "observations of", + length(unique(x$data$name)), "variable(s) grouped in", + length(unique(x$data$ds)), "datasets\n") + + cat("\nModel predictions using solution type", x$solution_type, "\n") + + cat("\nFitted in", x$time[["elapsed"]], "s using", paste(x$so@options$nbiter.saemix, collapse = ", "), "iterations\n") + + cat("\nVariance model: ") + cat(switch(x$err_mod, + const = "Constant variance", + obs = "Variance unique to each observed variable", + tc = "Two-component variance function"), "\n") + + cat("\nMean of starting values for individual parameters:\n") + print(x$mean_dp_start, digits = digits) + + cat("\nFixed degradation parameter values:\n") + if(length(x$fixed$value) == 0) cat("None\n") + else print(x$fixed, digits = digits) + + cat("\nResults:\n\n") + cat("Likelihood computed by importance sampling\n") + print(data.frame(AIC = x$AIC, BIC = x$BIC, logLik = x$logLik, + row.names = " "), digits = digits) + + cat("\nOptimised parameters:\n") + print(x$confint_trans, digits = digits) + + if (nrow(x$confint_trans) > 1) { + corr <- x$corFixed + class(corr) <- "correlation" + print(corr, title = "\nCorrelation:", ...) + } + + cat("\nRandom effects:\n") + print(x$confint_ranef, digits = digits) + + cat("\nVariance model:\n") + print(x$confint_errmod, digits = digits) + + if (x$transformations == "mkin") { + cat("\nBacktransformed parameters:\n") + print(x$confint_back, digits = digits) + } + + printSFORB <- !is.null(x$SFORB) + if(printSFORB){ + cat("\nEstimated Eigenvalues of SFORB model(s):\n") + print(x$SFORB, digits = digits,...) + } + + printff <- !is.null(x$ff) + if(printff){ + cat("\nResulting formation fractions:\n") + print(data.frame(ff = x$ff), digits = digits,...) + } + + printdistimes <- !is.null(x$distimes) + if(printdistimes){ + cat("\nEstimated disappearance times:\n") + print(x$distimes, digits = digits,...) + } + + if (x$print_data){ + cat("\nData:\n") + print(format(x$data, digits = digits, ...), row.names = FALSE) + } + + invisible(x) +} diff --git a/R/tffm0.R b/R/tffm0.R new file mode 100644 index 00000000..bb5f4cf5 --- /dev/null +++ b/R/tffm0.R @@ -0,0 +1,48 @@ +#' Transform formation fractions as in the first published mkin version +#' +#' The transformed fractions can be restricted between 0 and 1 in model +#' optimisations. Therefore this transformation was used originally in mkin. It +#' was later replaced by the [ilr] transformation because the ilr transformed +#' fractions can assumed to follow normal distribution. As the ilr +#' transformation is not available in [RxODE] and can therefore not be used in +#' the nlmixr modelling language, this transformation is currently used for +#' translating mkin models with formation fractions to more than one target +#' compartment for fitting with nlmixr in [nlmixr_model]. However, +#' this implementation cannot be used there, as it is not accessible +#' from RxODE. +#' +#' @param ff Vector of untransformed formation fractions. The sum +#' must be smaller or equal to one +#' @param ff_trans Vector of transformed formation fractions that can be +#' restricted to the interval from 0 to 1 +#' @return A vector of the transformed formation fractions +#' @export +#' @examples +#' ff_example <- c( +#' 0.10983681, 0.09035905, 0.08399383 +#' ) +#' ff_example_trans <- tffm0(ff_example) +#' invtffm0(ff_example_trans) +tffm0 <- function(ff) { + n <- length(ff) + res <- numeric(n) + f_remaining <- 1 + for (i in 1:n) { + res[i] <- ff[i]/f_remaining + f_remaining <- f_remaining - ff[i] + } + return(res) +} +#' @rdname tffm0 +#' @export +#' @return A vector of backtransformed formation fractions for natural use in degradation models +invtffm0 <- function(ff_trans) { + n <- length(ff_trans) + res <- numeric(n) + f_remaining <- 1 + for (i in 1:n) { + res[i] <- ff_trans[i] * f_remaining + f_remaining <- f_remaining - res[i] + } + return(res) +} diff --git a/R/transform_odeparms.R b/R/transform_odeparms.R index 4fe4e5c2..174e7c2d 100644 --- a/R/transform_odeparms.R +++ b/R/transform_odeparms.R @@ -229,13 +229,18 @@ backtransform_odeparms <- function(transparms, mkinmod, if (length(trans_f) > 0) { if(transform_fractions) { if (any(grepl("qlogis", names(trans_f)))) { - parms[f_names] <- plogis(trans_f) + f_tmp <- plogis(trans_f) + if (any(grepl("_tffm0_.*_qlogis$", names(f_tmp)))) { + parms[f_names] <- invtffm0(f_tmp) + } else { + parms[f_names] <- f_tmp + } } else { - f <- invilr(trans_f) + f_tmp <- invilr(trans_f) if (spec[[box]]$sink) { - parms[f_names] <- f[1:length(f)-1] + parms[f_names] <- f_tmp[1:length(f_tmp)-1] } else { - parms[f_names] <- f + parms[f_names] <- f_tmp } } } else { diff --git a/_pkgdown.yml b/_pkgdown.yml index aaade416..f0a95468 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -1,8 +1,8 @@ url: https://pkgdown.jrwb.de/mkin development: - mode: release - version_label: default + mode: devel + version_label: info template: bootswatch: spacelab @@ -42,10 +42,15 @@ reference: desc: Create and work with nonlinear mixed effects models contents: - nlme.mmkin + - saem.mmkin + - nlmixr.mmkin - plot.mixed.mmkin - summary.nlme.mmkin + - summary.nlmixr.mmkin + - summary.saem.mmkin - nlme_function - get_deg_func + - saemix_model - mixed - title: Datasets and known results contents: @@ -82,12 +87,14 @@ reference: - mkinpredict - transform_odeparms - ilr + - tffm0 - logLik.mkinfit - residuals.mkinfit - nobs.mkinfit - mkinresplot - mkinparplot - mkinerrplot + - mean_degparms - create_deg_func - title: Analytical solutions desc: Parent only model solutions @@ -6,5 +6,5 @@ * creating vignettes ... OK * checking for LF line-endings in source and make files and shell scripts * checking for empty or unneeded directories -* building ‘mkin_1.0.4.tar.gz’ +* building ‘mkin_1.1.0.tar.gz’ @@ -1,16 +1,14 @@ * using log directory ‘/home/jranke/git/mkin/mkin.Rcheck’ -* using R version 4.0.5 (2021-03-31) +* using R version 4.1.0 (2021-05-18) * 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 ‘1.0.4’ +* this is package ‘mkin’ version ‘1.0.5’ * package encoding: UTF-8 -* checking CRAN incoming feasibility ... NOTE +* checking CRAN incoming feasibility ... Note_to_CRAN_maintainers Maintainer: ‘Johannes Ranke <jranke@uni-bremen.de>’ - -The Date field is over a month old. * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK @@ -43,9 +41,7 @@ The Date field is over a month old. * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK -* checking R code for possible problems ... NOTE -mmkin: possible error in Sys.info("sysname"): unused argument - ("sysname") +* checking R code for possible problems ... OK * checking Rd files ... OK * checking Rd metadata ... OK * checking Rd line widths ... OK @@ -57,6 +53,7 @@ mmkin: possible error in Sys.info("sysname"): unused argument * checking for unstated dependencies in examples ... OK * checking contents of ‘data’ directory ... OK * checking data for non-ASCII characters ... OK +* checking LazyData ... OK * checking data for ASCII and uncompressed saves ... OK * checking installed files from ‘inst/doc’ ... OK * checking files in ‘vignettes’ ... OK @@ -71,9 +68,5 @@ mmkin: possible error in Sys.info("sysname"): unused argument * checking for detritus in the temp directory ... OK * DONE -Status: 2 NOTEs -See - ‘/home/jranke/git/mkin/mkin.Rcheck/00check.log’ -for details. - +Status: OK diff --git a/docs/dev/404.html b/docs/dev/404.html index bea38406..98c0b1e0 100644 --- a/docs/dev/404.html +++ b/docs/dev/404.html @@ -71,7 +71,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="https://pkgdown.jrwb.de/mkin/index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> </span> </div> diff --git a/docs/dev/articles/FOCUS_D.html b/docs/dev/articles/FOCUS_D.html index dd86f677..a35a255a 100644 --- a/docs/dev/articles/FOCUS_D.html +++ b/docs/dev/articles/FOCUS_D.html @@ -32,7 +32,7 @@ </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -81,7 +81,7 @@ <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -95,14 +95,13 @@ - </header><link href="FOCUS_D_files/anchor-sections-1.0/anchor-sections.css" rel="stylesheet"> -<script src="FOCUS_D_files/anchor-sections-1.0/anchor-sections.js"></script><div class="row"> + </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"> <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 class="date">2020-11-30</h4> + <h4 class="date">Last change 31 January 2019 (rebuilt 2021-02-15)</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> <div class="hidden name"><code>FOCUS_D.rmd</code></div> @@ -187,10 +186,10 @@ <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: 0.9.50.4 +<pre><code>## mkin version used for fitting: 1.0.3.9000 ## R version used for fitting: 4.0.3 -## Date of fit: Mon Nov 30 16:01:50 2020 -## Date of summary: Mon Nov 30 16:01:50 2020 +## 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 @@ -241,11 +240,11 @@ ## ## 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 -1.171e-06 -## log_k_parent 5.174e-01 1.000e+00 -3.263e-01 -5.426e-01 -8.481e-07 -## log_k_m1 -1.688e-01 -3.263e-01 1.000e+00 7.478e-01 8.209e-07 +## 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 -1.171e-06 -8.481e-07 8.209e-07 1.305e-06 1.000e+00 +## 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/docs/dev/articles/FOCUS_D_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/dev/articles/FOCUS_D_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 00000000..ca349fd6 --- /dev/null +++ b/docs/dev/articles/FOCUS_D_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/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 8ddca415..abf26715 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/header-attrs-2.6/header-attrs.js b/docs/dev/articles/FOCUS_D_files/header-attrs-2.6/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/dev/articles/FOCUS_D_files/header-attrs-2.6/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/dev/articles/FOCUS_L.html b/docs/dev/articles/FOCUS_L.html index 2695db5e..547ec630 100644 --- a/docs/dev/articles/FOCUS_L.html +++ b/docs/dev/articles/FOCUS_L.html @@ -32,7 +32,7 @@ </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -81,7 +81,7 @@ <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -95,14 +95,13 @@ - </header><link href="FOCUS_L_files/anchor-sections-1.0/anchor-sections.css" rel="stylesheet"> -<script src="FOCUS_L_files/anchor-sections-1.0/anchor-sections.js"></script><div class="row"> + </header><script src="FOCUS_L_files/header-attrs-2.6/header-attrs.js"></script><script src="FOCUS_L_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 Laboratory Data L1 to L3</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2020-11-30</h4> + <h4 class="date">Last change 17 November 2016 (rebuilt 2021-02-15)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/master/vignettes/FOCUS_L.rmd"><code>vignettes/FOCUS_L.rmd</code></a></small> <div class="hidden name"><code>FOCUS_L.rmd</code></div> @@ -128,10 +127,10 @@ <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r"> <code class="sourceCode R"><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 class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html">summary</a></span><span class="op">(</span><span class="va">m.L1.SFO</span><span class="op">)</span></code></pre></div> -<pre><code>## mkin version used for fitting: 0.9.50.4 +<pre><code>## mkin version used for fitting: 1.0.3.9000 ## R version used for fitting: 4.0.3 -## Date of fit: Mon Nov 30 16:01:52 2020 -## Date of summary: Mon Nov 30 16:01:52 2020 +## Date of fit: Mon Feb 15 17:13:39 2021 +## Date of summary: Mon Feb 15 17:13:39 2021 ## ## Equations: ## d_parent/dt = - k_parent * parent @@ -234,17 +233,17 @@ <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: 0.9.50.4 +<pre><code>## mkin version used for fitting: 1.0.3.9000 ## R version used for fitting: 4.0.3 -## Date of fit: Mon Nov 30 16:01:53 2020 -## Date of summary: Mon Nov 30 16:01:53 2020 +## Date of fit: Mon Feb 15 17:13:40 2021 +## Date of summary: Mon Feb 15 17:13:40 2021 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 380 model solutions performed in 0.087 s +## Fitted using 369 model solutions performed in 0.084 s ## ## Error model: Constant variance ## @@ -272,22 +271,22 @@ ## ## Results: ## -## AIC BIC logLik -## 95.88778 99.44927 -43.94389 +## AIC BIC logLik +## 95.88781 99.44929 -43.9439 ## ## Optimised, transformed parameters with symmetric confidence intervals: ## Estimate Std. Error Lower Upper ## parent_0 92.47 1.2820 89.720 95.220 -## log_alpha 16.92 NaN NaN NaN -## log_beta 19.26 NaN NaN NaN -## sigma 2.78 0.4501 1.814 3.745 +## log_alpha 13.78 NaN NaN NaN +## log_beta 16.13 NaN NaN NaN +## sigma 2.78 0.4598 1.794 3.766 ## ## Parameter correlation: -## parent_0 log_alpha log_beta sigma -## parent_0 1.000000 NaN NaN 0.002218 -## log_alpha NaN 1 NaN NaN -## log_beta NaN NaN 1 NaN -## sigma 0.002218 NaN NaN 1.000000 +## parent_0 log_alpha log_beta sigma +## parent_0 1.0000000 NaN NaN 0.0001671 +## log_alpha NaN 1 NaN NaN +## log_beta NaN NaN 1 NaN +## sigma 0.0001671 NaN NaN 1.0000000 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -295,9 +294,9 @@ ## for estimators of untransformed parameters. ## Estimate t value Pr(>t) Lower Upper ## parent_0 9.247e+01 NA NA 89.720 95.220 -## alpha 2.223e+07 NA NA NA NA -## beta 2.325e+08 NA NA NA NA -## sigma 2.780e+00 NA NA 1.814 3.745 +## alpha 9.658e+05 NA NA NA NA +## beta 1.010e+07 NA NA NA NA +## sigma 2.780e+00 NA NA 1.794 3.766 ## ## FOCUS Chi2 error levels in percent: ## err.min n.optim df @@ -346,10 +345,10 @@ <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 class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html">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></code></pre></div> -<pre><code>## mkin version used for fitting: 0.9.50.4 +<pre><code>## mkin version used for fitting: 1.0.3.9000 ## R version used for fitting: 4.0.3 -## Date of fit: Mon Nov 30 16:01:54 2020 -## Date of summary: Mon Nov 30 16:01:54 2020 +## Date of fit: Mon Feb 15 17:13:40 2021 +## Date of summary: Mon Feb 15 17:13:40 2021 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent @@ -391,10 +390,10 @@ ## ## Parameter correlation: ## parent_0 log_alpha log_beta sigma -## parent_0 1.000e+00 -1.151e-01 -2.085e-01 -7.436e-09 -## log_alpha -1.151e-01 1.000e+00 9.741e-01 -1.617e-07 -## log_beta -2.085e-01 9.741e-01 1.000e+00 -1.386e-07 -## sigma -7.436e-09 -1.617e-07 -1.386e-07 1.000e+00 +## parent_0 1.000e+00 -1.151e-01 -2.085e-01 -7.828e-09 +## log_alpha -1.151e-01 1.000e+00 9.741e-01 -1.602e-07 +## log_beta -2.085e-01 9.741e-01 1.000e+00 -1.372e-07 +## sigma -7.828e-09 -1.602e-07 -1.372e-07 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -427,10 +426,10 @@ <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 class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html">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></code></pre></div> -<pre><code>## mkin version used for fitting: 0.9.50.4 +<pre><code>## mkin version used for fitting: 1.0.3.9000 ## R version used for fitting: 4.0.3 -## Date of fit: Mon Nov 30 16:01:54 2020 -## Date of summary: Mon Nov 30 16:01:54 2020 +## Date of fit: Mon Feb 15 17:13:41 2021 +## Date of summary: Mon Feb 15 17:13:41 2021 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -439,7 +438,7 @@ ## ## Model predictions using solution type analytical ## -## Fitted using 581 model solutions performed in 0.136 s +## Fitted using 581 model solutions performed in 0.134 s ## ## Error model: Constant variance ## @@ -470,18 +469,18 @@ ## Optimised, transformed parameters with symmetric confidence intervals: ## Estimate Std. Error Lower Upper ## parent_0 93.950 9.998e-01 91.5900 96.3100 -## log_k1 3.117 1.929e+03 -4558.0000 4564.0000 +## log_k1 3.112 1.842e+03 -4353.0000 4359.0000 ## log_k2 -1.088 6.285e-02 -1.2370 -0.9394 ## g_qlogis -0.399 9.946e-02 -0.6342 -0.1638 ## sigma 1.414 2.886e-01 0.7314 2.0960 ## ## Parameter correlation: -## parent_0 log_k1 log_k2 g_qlogis sigma -## parent_0 1.000e+00 6.459e-07 9.147e-11 2.665e-01 8.413e-11 -## log_k1 6.459e-07 1.000e+00 1.061e-04 -2.087e-04 -9.802e-06 -## log_k2 9.147e-11 1.061e-04 1.000e+00 -7.903e-01 -2.429e-09 -## g_qlogis 2.665e-01 -2.087e-04 -7.903e-01 1.000e+00 4.049e-09 -## sigma 8.413e-11 -9.802e-06 -2.429e-09 4.049e-09 1.000e+00 +## parent_0 log_k1 log_k2 g_qlogis sigma +## parent_0 1.000e+00 6.783e-07 -3.390e-10 2.665e-01 -2.967e-10 +## log_k1 6.783e-07 1.000e+00 1.116e-04 -2.196e-04 -1.031e-05 +## log_k2 -3.390e-10 1.116e-04 1.000e+00 -7.903e-01 2.917e-09 +## g_qlogis 2.665e-01 -2.196e-04 -7.903e-01 1.000e+00 -4.408e-09 +## sigma -2.967e-10 -1.031e-05 2.917e-09 -4.408e-09 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -489,7 +488,7 @@ ## for estimators of untransformed parameters. ## Estimate t value Pr(>t) Lower Upper ## parent_0 93.9500 9.397e+01 2.036e-12 91.5900 96.3100 -## k1 22.5800 5.303e-04 4.998e-01 0.0000 Inf +## k1 22.4800 5.553e-04 4.998e-01 0.0000 Inf ## k2 0.3369 1.591e+01 4.697e-07 0.2904 0.3909 ## g 0.4016 1.680e+01 3.238e-07 0.3466 0.4591 ## sigma 1.4140 4.899e+00 8.776e-04 0.7314 2.0960 @@ -501,7 +500,7 @@ ## ## Estimated disappearance times: ## DT50 DT90 DT50back DT50_k1 DT50_k2 -## parent 0.5335 5.311 1.599 0.0307 2.058</code></pre> +## 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. However, the failure to calculate the covariance matrix indicates that the parameter estimates correlate excessively. Therefore, the FOMC model may be preferred for this dataset.</p> </div> </div> @@ -533,10 +532,10 @@ <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 class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html">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></code></pre></div> -<pre><code>## mkin version used for fitting: 0.9.50.4 +<pre><code>## mkin version used for fitting: 1.0.3.9000 ## R version used for fitting: 4.0.3 -## Date of fit: Mon Nov 30 16:01:55 2020 -## Date of summary: Mon Nov 30 16:01:55 2020 +## Date of fit: Mon Feb 15 17:13:41 2021 +## Date of summary: Mon Feb 15 17:13:42 2021 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -545,7 +544,7 @@ ## ## Model predictions using solution type analytical ## -## Fitted using 376 model solutions performed in 0.081 s +## Fitted using 376 model solutions performed in 0.082 s ## ## Error model: Constant variance ## @@ -583,11 +582,11 @@ ## ## Parameter correlation: ## parent_0 log_k1 log_k2 g_qlogis sigma -## parent_0 1.000e+00 1.732e-01 2.282e-02 4.009e-01 -9.671e-08 -## log_k1 1.732e-01 1.000e+00 4.945e-01 -5.809e-01 7.148e-07 +## parent_0 1.000e+00 1.732e-01 2.282e-02 4.009e-01 -9.664e-08 +## log_k1 1.732e-01 1.000e+00 4.945e-01 -5.809e-01 7.147e-07 ## log_k2 2.282e-02 4.945e-01 1.000e+00 -6.812e-01 1.022e-06 -## g_qlogis 4.009e-01 -5.809e-01 -6.812e-01 1.000e+00 -7.929e-07 -## sigma -9.671e-08 7.148e-07 1.022e-06 -7.929e-07 1.000e+00 +## g_qlogis 4.009e-01 -5.809e-01 -6.812e-01 1.000e+00 -7.926e-07 +## sigma -9.664e-08 7.147e-07 1.022e-06 -7.926e-07 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -646,10 +645,10 @@ <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 class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html">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></code></pre></div> -<pre><code>## mkin version used for fitting: 0.9.50.4 +<pre><code>## mkin version used for fitting: 1.0.3.9000 ## R version used for fitting: 4.0.3 -## Date of fit: Mon Nov 30 16:01:55 2020 -## Date of summary: Mon Nov 30 16:01:55 2020 +## Date of fit: Mon Feb 15 17:13:42 2021 +## Date of summary: Mon Feb 15 17:13:42 2021 ## ## Equations: ## d_parent/dt = - k_parent * parent @@ -711,17 +710,17 @@ ## parent 106 352</code></pre> <div class="sourceCode" id="cb31"><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">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></code></pre></div> -<pre><code>## mkin version used for fitting: 0.9.50.4 +<pre><code>## mkin version used for fitting: 1.0.3.9000 ## R version used for fitting: 4.0.3 -## Date of fit: Mon Nov 30 16:01:55 2020 -## Date of summary: Mon Nov 30 16:01:56 2020 +## Date of fit: Mon Feb 15 17:13:42 2021 +## Date of summary: Mon Feb 15 17:13:42 2021 ## ## 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 0.046 s ## ## Error model: Constant variance ## @@ -756,10 +755,10 @@ ## ## Parameter correlation: ## parent_0 log_alpha log_beta sigma -## parent_0 1.000e+00 -4.696e-01 -5.543e-01 -2.456e-07 -## log_alpha -4.696e-01 1.000e+00 9.889e-01 2.169e-08 -## log_beta -5.543e-01 9.889e-01 1.000e+00 4.910e-08 -## sigma -2.456e-07 2.169e-08 4.910e-08 1.000e+00 +## parent_0 1.000e+00 -4.696e-01 -5.543e-01 -2.468e-07 +## log_alpha -4.696e-01 1.000e+00 9.889e-01 2.478e-08 +## log_beta -5.543e-01 9.889e-01 1.000e+00 5.211e-08 +## sigma -2.468e-07 2.478e-08 5.211e-08 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -783,7 +782,7 @@ <div id="references" class="section level1 unnumbered"> <h1 class="hasAnchor"> <a href="#references" class="anchor"></a>References</h1> -<div id="refs" class="references"> +<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> diff --git a/docs/dev/articles/FOCUS_L_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/dev/articles/FOCUS_L_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 00000000..ca349fd6 --- /dev/null +++ b/docs/dev/articles/FOCUS_L_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"); 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We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/dev/articles/index.html b/docs/dev/articles/index.html index 6daa6960..3896120a 100644 --- a/docs/dev/articles/index.html +++ b/docs/dev/articles/index.html @@ -71,7 +71,7 @@ </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> </span> </div> diff --git a/docs/dev/articles/mkin.html b/docs/dev/articles/mkin.html index 2de8b9f0..fed85a33 100644 --- a/docs/dev/articles/mkin.html +++ b/docs/dev/articles/mkin.html @@ -32,7 +32,7 @@ </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -81,7 +81,7 @@ <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -95,14 +95,13 @@ - </header><link href="mkin_files/anchor-sections-1.0/anchor-sections.css" rel="stylesheet"> -<script src="mkin_files/anchor-sections-1.0/anchor-sections.js"></script><div class="row"> + </header><script src="mkin_files/header-attrs-2.6/header-attrs.js"></script><script src="mkin_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>Introduction to mkin</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2020-11-30</h4> + <h4 class="date">Last change 15 February 2021 (rebuilt 2021-02-15)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/master/vignettes/mkin.rmd"><code>vignettes/mkin.rmd</code></a></small> <div class="hidden name"><code>mkin.rmd</code></div> @@ -150,28 +149,34 @@ <div id="background" class="section level1"> <h1 class="hasAnchor"> <a href="#background" class="anchor"></a>Background</h1> -<p>Many approaches are possible regarding the evaluation of chemical degradation data.</p> -<p>The <code>mkin</code> package <span class="citation">(Ranke 2019)</span> implements the approach 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> for simple decline data series, data series with transformation products, commonly termed metabolites, and for data series for more than one compartment. It is also possible to include back reactions, so equilibrium reactions and equilibrium partitioning can be specified, although this oftentimes leads to an overparameterisation of the model.</p> +<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 code was first uploaded to the BerliOS 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 occasionally 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 relative standard deviation that has to be assumed for the residuals, such that the <span class="math inline">\(\chi^2\)</span> goodness-of-fit test as defined by the FOCUS kinetics workgroup would pass using an significance level <span class="math inline">\(\alpha\)</span> of 0.05. This relative error, expressed as a percentage, is often termed <span class="math inline">\(\chi^2\)</span> error level or similar.</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, however, has become somehow obsolete, as the use of compiled code described below gives even smaller execution times.</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 class="hasAnchor"> <a href="#derived-software-tools" class="anchor"></a>Derived software tools</h2> -<p>Soon after the publication of <code>mkin</code>, two derived tools were published, namely KinGUII (available from 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 3.3 of CAKE release in March 2016 uses a basic scheme for up to six metabolites in a flexible arrangement, but does not support back-reactions (non-instantaneous equilibria) or biphasic kinetics for metabolites.</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">Ranke, Wöltjen, and Meinecke (2018)</span>.</p> </div> -<div id="recent-developments" class="section level2"> -<h2 class="hasAnchor"> -<a href="#recent-developments" class="anchor"></a>Recent developments</h2> -<p>Currently (July 2019), the main features available in <code>mkin</code> which are not present in KinGUII or CAKE, are the speed increase by using compiled code when a compiler is present, parallel model fitting on multicore machines using the <code>mmkin</code> function, and the estimation of parameter confidence intervals based on transformed parameters.</p> -<p>In addition, the possibility to use two alternative error models to constant variance have been integrated. The variance by variable error model introduced by <span class="citation">Gao et al. (2011)</span> has been available via an iteratively reweighted least squares (IRLS) procedure since mkin <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 replaced 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.</p> </div> +<div id="unique-features" class="section level1"> +<h1 class="hasAnchor"> +<a href="#unique-features" class="anchor"></a>Unique features</h1> +<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> +</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> </div> <div id="internal-parameter-transformations" class="section level1"> <h1 class="hasAnchor"> @@ -179,13 +184,13 @@ <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.</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> <div id="confidence-intervals-based-on-transformed-parameters" class="section level2"> <h2 class="hasAnchor"> <a href="#confidence-intervals-based-on-transformed-parameters" class="anchor"></a>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 only one formation fraction is to be estimated, directly corresponding to one component of the ilr transformed parameter.</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"> @@ -200,36 +205,39 @@ <h1 class="hasAnchor"> <a href="#references" class="anchor"></a>References</h1> <!-- vim: set foldmethod=syntax: --> -<div id="refs" class="references"> +<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> <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" class="uri">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> +<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> <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" class="uri">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> +<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> <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> +<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> <div id="ref-pkg:mkin"> -<p>Ranke, J. 2019. <em>‘mkin‘: Kinetic Evaluation of Chemical Degradation Data</em>. <a href="https://CRAN.R-project.org/package=mkin" class="uri">https://CRAN.R-project.org/package=mkin</a>.</p> +<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> <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> </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="uri">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="http://chem.uft.uni-bremen.de/ranke/posters/piacenza_2015.pdf">http://chem.uft.uni-bremen.de/ranke/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">https://doi.org/10.3390/environments6120124</a>.</p> </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" class="uri">https://doi.org/10.1186/s12302-018-0145-1</a>.</p> +<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> <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> <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://www.jstatsoft.org/v33/i03/" class="uri">https://www.jstatsoft.org/v33/i03/</a>.</p> +<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://www.jstatsoft.org/v33/i03/">https://www.jstatsoft.org/v33/i03/</a>.</p> </div> </div> </div> diff --git a/docs/dev/articles/mkin_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/dev/articles/mkin_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 00000000..ca349fd6 --- /dev/null +++ b/docs/dev/articles/mkin_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/dev/articles/mkin_files/figure-html/unnamed-chunk-2-1.png b/docs/dev/articles/mkin_files/figure-html/unnamed-chunk-2-1.png Binary files differindex 9b82cd62..bf38fdd7 100644 --- a/docs/dev/articles/mkin_files/figure-html/unnamed-chunk-2-1.png +++ b/docs/dev/articles/mkin_files/figure-html/unnamed-chunk-2-1.png diff --git a/docs/dev/articles/mkin_files/header-attrs-2.6/header-attrs.js b/docs/dev/articles/mkin_files/header-attrs-2.6/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/dev/articles/mkin_files/header-attrs-2.6/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/dev/articles/twa.html b/docs/dev/articles/twa.html index 42ed6fa5..30eeb5a6 100644 --- a/docs/dev/articles/twa.html +++ b/docs/dev/articles/twa.html @@ -32,7 +32,7 @@ </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -81,7 +81,7 @@ <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -95,14 +95,13 @@ - </header><link href="twa_files/anchor-sections-1.0/anchor-sections.css" rel="stylesheet"> -<script src="twa_files/anchor-sections-1.0/anchor-sections.js"></script><div class="row"> + </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"> <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 class="date">2020-11-30</h4> + <h4 class="date">Last change 18 September 2019 (rebuilt 2021-02-15)</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> <div class="hidden name"><code>twa.rmd</code></div> @@ -143,9 +142,9 @@ \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> -<div id="refs" class="references"> +<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" class="uri">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">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> </div> </div> </div> diff --git a/docs/dev/articles/twa_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/dev/articles/twa_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 00000000..ca349fd6 --- /dev/null +++ b/docs/dev/articles/twa_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/dev/articles/twa_files/header-attrs-2.6/header-attrs.js b/docs/dev/articles/twa_files/header-attrs-2.6/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/dev/articles/twa_files/header-attrs-2.6/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/dev/articles/web_only/FOCUS_Z.html b/docs/dev/articles/web_only/FOCUS_Z.html index 15c41eb7..694b33ca 100644 --- a/docs/dev/articles/web_only/FOCUS_Z.html +++ b/docs/dev/articles/web_only/FOCUS_Z.html @@ -32,7 +32,7 @@ </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -81,7 +81,7 @@ <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -95,14 +95,13 @@ - </header><link href="FOCUS_Z_files/anchor-sections-1.0/anchor-sections.css" rel="stylesheet"> -<script src="FOCUS_Z_files/anchor-sections-1.0/anchor-sections.js"></script><div class="row"> + </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"> <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 class="date">2020-11-30</h4> + <h4 class="date">Last change 16 January 2018 (rebuilt 2021-02-15)</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> <div class="hidden name"><code>FOCUS_Z.rmd</code></div> @@ -239,12 +238,12 @@ <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 96.839001 1.994273 48.5585 4.0276e-42 92.827060 100.850943 -## k_Z0 2.215367 0.118456 18.7021 1.0410e-23 1.989432 2.466960 -## k_Z1 0.478310 0.028258 16.9265 6.2430e-22 0.424712 0.538673 -## k_Z2 0.451628 0.042139 10.7176 1.6313e-14 0.374337 0.544877 -## k_Z3 0.058692 0.015245 3.8498 1.7806e-04 0.034806 0.098972 -## f_Z2_to_Z3 0.471498 0.058350 8.0805 9.6614e-11 0.357741 0.588294 +## 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> <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> @@ -255,9 +254,9 @@ ## $distimes ## DT50 DT90 ## Z0 0.31288 1.0394 -## Z1 1.44916 4.8140 +## Z1 1.44917 4.8141 ## Z2 1.53478 5.0984 -## Z3 11.80983 39.2314</code></pre> +## 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> </div> <div id="using-the-sforb-model" class="section level1"> @@ -353,13 +352,13 @@ ## ## $SFORB ## Z0_b1 Z0_b2 Z3_b1 Z3_b2 -## 2.4471371 0.0075126 0.0800070 0.0000000 +## 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 92.265 NA NA +## 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 5.4525 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> </div> @@ -367,9 +366,9 @@ <h1 class="hasAnchor"> <a href="#references" class="anchor"></a>References</h1> <!-- vim: set foldmethod=syntax: --> -<div id="refs" class="references"> +<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" class="uri">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">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> </div> </div> </div> diff --git a/docs/dev/articles/web_only/FOCUS_Z_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/dev/articles/web_only/FOCUS_Z_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 00000000..ca349fd6 --- /dev/null +++ b/docs/dev/articles/web_only/FOCUS_Z_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"); 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We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/dev/articles/web_only/NAFTA_examples.html b/docs/dev/articles/web_only/NAFTA_examples.html index fca15672..b9784415 100644 --- a/docs/dev/articles/web_only/NAFTA_examples.html +++ b/docs/dev/articles/web_only/NAFTA_examples.html @@ -32,7 +32,7 @@ </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -81,7 +81,7 @@ <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -95,14 +95,13 @@ - </header><link href="NAFTA_examples_files/anchor-sections-1.0/anchor-sections.css" rel="stylesheet"> -<script src="NAFTA_examples_files/anchor-sections-1.0/anchor-sections.js"></script><div class="row"> + </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"> <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 class="date">2020-11-30</h4> + <h4 class="date">26 February 2019 (rebuilt 2021-02-15)</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> <div class="hidden name"><code>NAFTA_examples.rmd</code></div> @@ -158,7 +157,7 @@ ## 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 2.42e-12 5.00e-01 0.0000 Inf +## 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 ## @@ -167,7 +166,7 @@ ## 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 5.22e+11 2.86e+11 +## DFOP 55.5 5.59e+11 3.07e+11 ## ## Representative half-life: ## [1] 321.51</code></pre> @@ -209,7 +208,7 @@ ## 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 1.10e-11 5.00e-01 0.0000 Inf +## 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 ## @@ -218,7 +217,7 @@ ## 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 1.03e+11 6.29e+10 +## DFOP 83.6 1.32e+11 8.04e+10 ## ## Representative half-life: ## [1] 215.87</code></pre> @@ -260,7 +259,7 @@ ## 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 3.60e-11 5.00e-01 0.0000 Inf +## 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 ## @@ -269,7 +268,7 @@ ## 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 9.07e+09 1.93e+10 +## DFOP 34.1 1.01e+10 2.15e+10 ## ## Representative half-life: ## [1] 53.17</code></pre> @@ -311,7 +310,7 @@ ## 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 2.89e-10 5.00e-01 0.0000 Inf +## 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 ## @@ -320,7 +319,7 @@ ## 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 4.75e+09 2.40e+09 +## DFOP 96.4 3.77e+09 1.91e+09 ## ## Representative half-life: ## [1] 454.55</code></pre> @@ -422,7 +421,7 @@ ## 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 9.03e-13 5.00e-01 0.000 Inf +## 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 ## @@ -431,7 +430,7 @@ ## 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 7.67e+11 +## DFOP 10.5 1.38e+12 7.69e+11 ## ## Representative half-life: ## [1] 101.43</code></pre> @@ -443,15 +442,16 @@ <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> -<div class="sourceCode" id="cb43"><pre class="downlit sourceCode r"> +<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> <p><img src="NAFTA_examples_files/figure-html/p9b-1.png" width="700"></p> -<div class="sourceCode" id="cb44"><pre class="downlit sourceCode r"> +<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 @@ -475,12 +475,12 @@ ## sigma 1.288 1.76e-04 0.7456 1.830 ## ## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 94.7123 NA 93.1355 96.2891 -## k1 0.0389 NA 0.0266 0.0569 -## k2 0.0389 NA 0.0255 0.0592 -## g 0.5256 NA NA NA -## sigma 1.5957 NA 0.9135 2.2779 +## Estimate Pr(>t) Lower Upper +## parent_0 94.7123 1.61e-16 93.1355 96.2891 +## k1 0.0389 1.08e-04 0.0266 0.0569 +## k2 0.0389 2.23e-04 0.0255 0.0592 +## g 0.5256 NaN NA NA +## sigma 1.5957 2.50e-04 0.9135 2.2779 ## ## ## DTx values: @@ -496,15 +496,18 @@ <div id="example-on-page-10" class="section level2"> <h2 class="hasAnchor"> <a href="#example-on-page-10" class="anchor"></a>Example on page 10</h2> -<div class="sourceCode" id="cb46"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb47"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">p10</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p10"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code>## Warning in sqrt(diag(covar_notrans)): NaNs produced</code></pre> +<pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> +<pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> +<pre><code>## Warning in cov2cor(ans$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> -<div class="sourceCode" id="cb50"><pre class="downlit sourceCode r"> +<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> <p><img src="NAFTA_examples_files/figure-html/p10-1.png" width="700"></p> -<div class="sourceCode" id="cb51"><pre class="downlit sourceCode r"> +<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 @@ -530,9 +533,9 @@ ## $DFOP ## Estimate Pr(>t) Lower Upper ## parent_0 101.7315 1.41e-09 91.6534 111.8097 -## k1 0.0495 5.63e-03 0.0240 0.1020 -## k2 0.0495 1.93e-03 0.0272 0.0903 -## g 0.4487 NaN 0.0000 1.0000 +## 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 ## ## @@ -553,14 +556,14 @@ <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="sourceCode" id="cb53"><pre class="downlit sourceCode r"> +<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> -<div class="sourceCode" id="cb56"><pre class="downlit sourceCode r"> +<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> <p><img src="NAFTA_examples_files/figure-html/p11-1.png" width="700"></p> -<div class="sourceCode" id="cb57"><pre class="downlit sourceCode r"> +<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 @@ -596,10 +599,10 @@ ## 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 6.97e+11 +## DFOP 3.07e+11 1.93e+12 6.98e+11 ## ## Representative half-life: -## [1] 41148171</code></pre> +## [1] 41148170</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> @@ -610,19 +613,21 @@ <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="sourceCode" id="cb59"><pre class="downlit sourceCode r"> +<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 - -## 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> -<div class="sourceCode" id="cb63"><pre class="downlit sourceCode r"> +<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> <p><img src="NAFTA_examples_files/figure-html/p12a-1.png" width="700"></p> -<div class="sourceCode" id="cb64"><pre class="downlit sourceCode r"> +<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 @@ -646,12 +651,12 @@ ## sigma 3.965 NA NA NA ## ## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 100.521 2.74e-10 NA NA -## k1 0.124 2.53e-05 NA NA -## k2 0.124 2.52e-02 NA NA -## g 0.793 5.00e-01 NA NA -## sigma 7.048 2.50e-04 NA NA +## Estimate Pr(>t) Lower Upper +## parent_0 100.521 2.74e-10 92.2366 108.805 +## k1 0.124 2.53e-05 0.0908 0.170 +## k2 0.124 2.52e-02 0.0456 0.339 +## g 0.793 NaN NA NA +## sigma 7.048 2.50e-04 4.0349 10.061 ## ## ## DTx values: @@ -666,20 +671,18 @@ <div id="example-on-page-12-lower-panel" class="section level2"> <h2 class="hasAnchor"> <a href="#example-on-page-12-lower-panel" class="anchor"></a>Example on page 12, lower panel</h2> -<div class="sourceCode" id="cb66"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb73"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">p12b</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p12b"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> <pre><code>## Warning in qt(alpha/2, rdf): NaNs produced</code></pre> <pre><code>## Warning in qt(1 - alpha/2, rdf): NaNs produced</code></pre> -<pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> -<pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is -## doubtful</code></pre> +<pre><code>## 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> -<div class="sourceCode" id="cb74"><pre class="downlit sourceCode r"> +<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> <p><img src="NAFTA_examples_files/figure-html/p12b-1.png" width="700"></p> -<div class="sourceCode" id="cb75"><pre class="downlit sourceCode r"> +<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 @@ -704,11 +707,11 @@ ## ## $DFOP ## Estimate Pr(>t) Lower Upper -## parent_0 97.6840 NA NaN NaN -## k1 0.0589 NA NA NA -## k2 0.0589 NA NA NA -## g 0.6473 NA NA NA -## sigma 3.4323 NA NaN NaN +## parent_0 97.6840 NaN NaN NaN +## k1 0.0589 NaN NA NA +## k2 0.0589 NaN NA NA +## g 0.6473 NaN NA NA +## sigma 3.4323 NaN NaN NaN ## ## ## DTx values: @@ -723,18 +726,14 @@ <div id="example-on-page-13" class="section level2"> <h2 class="hasAnchor"> <a href="#example-on-page-13" class="anchor"></a>Example on page 13</h2> -<div class="sourceCode" id="cb77"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb83"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">p13</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p13"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></code></pre></div> -<pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> -<pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> -<pre><code>## Warning in cov2cor(ans$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> -<div class="sourceCode" id="cb83"><pre class="downlit sourceCode r"> +<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> <p><img src="NAFTA_examples_files/figure-html/p13-1.png" width="700"></p> -<div class="sourceCode" id="cb84"><pre class="downlit sourceCode r"> +<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 @@ -760,9 +759,9 @@ ## $DFOP ## Estimate Pr(>t) Lower Upper ## parent_0 92.73500 NA 8.95e+01 95.92118 -## k1 0.00258 NA 4.25e-04 0.01569 -## k2 0.00258 NA 1.76e-03 0.00379 -## g 0.16452 NA NA NA +## 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 ## ## @@ -779,7 +778,7 @@ <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="sourceCode" id="cb86"><pre class="downlit sourceCode r"> +<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> @@ -787,10 +786,10 @@ ## 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> -<div class="sourceCode" id="cb92"><pre class="downlit sourceCode r"> +<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> <p><img src="NAFTA_examples_files/figure-html/p14-1.png" width="700"></p> -<div class="sourceCode" id="cb93"><pre class="downlit sourceCode r"> +<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 @@ -817,7 +816,7 @@ ## 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 5.33e-12 5.00e-01 0.00000 Inf +## 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 ## @@ -826,7 +825,7 @@ ## 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 3.48e+10 3.37e+11 1.30e+11 +## DFOP 3.05e+10 2.95e+11 1.14e+11 ## ## Representative half-life: ## [1] 6697.44</code></pre> @@ -835,14 +834,14 @@ <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="sourceCode" id="cb95"><pre class="downlit sourceCode r"> +<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> -<div class="sourceCode" id="cb98"><pre class="downlit sourceCode r"> +<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> <p><img src="NAFTA_examples_files/figure-html/p15a-1.png" width="700"></p> -<div class="sourceCode" id="cb99"><pre class="downlit sourceCode r"> +<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 @@ -866,12 +865,12 @@ ## sigma 3.105 1.78e-04 1.795 4.416 ## ## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 97.96751 NA 94.21913 101.7159 -## k1 0.00952 NA 0.00221 0.0411 -## k2 0.00952 NA 0.00626 0.0145 -## g 0.21241 NA 0.00000 1.0000 -## sigma 4.18778 NA 2.39747 5.9781 +## 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: @@ -882,7 +881,7 @@ ## ## Representative half-life: ## [1] 41.33</code></pre> -<div class="sourceCode" id="cb101"><pre class="downlit sourceCode r"> +<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> @@ -890,10 +889,10 @@ ## 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> -<div class="sourceCode" id="cb107"><pre class="downlit sourceCode r"> +<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> <p><img src="NAFTA_examples_files/figure-html/p15b-1.png" width="700"></p> -<div class="sourceCode" id="cb108"><pre class="downlit sourceCode r"> +<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 @@ -911,7 +910,7 @@ ## ## $IORE ## Estimate Pr(>t) Lower Upper -## parent_0 99.83 1.81e-16 97.51348 102.14 +## 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 @@ -938,16 +937,16 @@ <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="sourceCode" id="cb110"><pre class="downlit sourceCode r"> +<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> -<div class="sourceCode" id="cb115"><pre class="downlit sourceCode r"> +<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> <p><img src="NAFTA_examples_files/figure-html/p16-1.png" width="700"></p> -<div class="sourceCode" id="cb116"><pre class="downlit sourceCode r"> +<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 @@ -998,7 +997,7 @@ <div id="references" class="section level1 unnumbered"> <h1 class="hasAnchor"> <a href="#references" class="anchor"></a>References</h1> -<div id="refs" class="references"> +<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> </div> diff --git a/docs/dev/articles/web_only/NAFTA_examples_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/dev/articles/web_only/NAFTA_examples_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 00000000..ca349fd6 --- /dev/null +++ b/docs/dev/articles/web_only/NAFTA_examples_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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We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/dev/articles/web_only/benchmarks.html b/docs/dev/articles/web_only/benchmarks.html index 8e157c0f..a6d52649 100644 --- a/docs/dev/articles/web_only/benchmarks.html +++ b/docs/dev/articles/web_only/benchmarks.html @@ -32,7 +32,7 @@ </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -81,7 +81,7 @@ <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -95,14 +95,13 @@ - </header><link href="benchmarks_files/anchor-sections-1.0/anchor-sections.css" rel="stylesheet"> -<script src="benchmarks_files/anchor-sections-1.0/anchor-sections.js"></script><div class="row"> + </header><script src="benchmarks_files/header-attrs-2.6/header-attrs.js"></script><script src="benchmarks_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>Benchmark timings for mkin</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2020-11-30</h4> + <h4 class="date">Last change 13 May 2020 (rebuilt 2021-02-15)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/master/vignettes/web_only/benchmarks.rmd"><code>vignettes/web_only/benchmarks.rmd</code></a></small> <div class="hidden name"><code>benchmarks.rmd</code></div> @@ -137,17 +136,11 @@ 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 class="va">t3</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="fu">mmkin_bench</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="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">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 class="va">t4</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="fu">mmkin_bench</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="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">list</a></span><span class="op">(</span><span class="va">FOCUS_D</span><span class="op">)</span>, - error_model <span class="op">=</span> <span class="st">"tc"</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></code></pre></div> -<pre><code>## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...): Optimisation did not converge: -## iteration limit reached without convergence (10) - -## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...): Optimisation did not converge: -## iteration limit reached without convergence (10)</code></pre> -<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">t5</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="fu">mmkin_bench</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="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">list</a></span><span class="op">(</span><span class="va">FOCUS_D</span><span class="op">)</span>, + error_model <span class="op">=</span> <span class="st">"tc"</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 class="va">t5</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="fu">mmkin_bench</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="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">list</a></span><span class="op">(</span><span class="va">FOCUS_D</span><span class="op">)</span>, error_model <span class="op">=</span> <span class="st">"obs"</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></code></pre></div> <p>Two metabolites, synthetic data:</p> -<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">m_synth_SFO_lin</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>, @@ -174,7 +167,7 @@ error_model <span class="op">=</span> <span class="st">"obs"</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 class="va">t11</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="fu">mmkin_bench</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="va">m_synth_DFOP_par</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="va">DFOP_par_c</span><span class="op">)</span>, error_model <span class="op">=</span> <span class="st">"obs"</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></code></pre></div> -<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">mkin_benchmarks</span><span class="op">[</span><span class="va">system_string</span>, <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span><span class="op">(</span><span class="st">"t"</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">11</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">c</a></span><span class="op">(</span><span class="va">t1</span>, <span class="va">t2</span>, <span class="va">t3</span>, <span class="va">t4</span>, <span class="va">t5</span>, <span class="va">t6</span>, <span class="va">t7</span>, <span class="va">t8</span>, <span class="va">t9</span>, <span class="va">t10</span>, <span class="va">t11</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/save.html">save</a></span><span class="op">(</span><span class="va">mkin_benchmarks</span>, file <span class="op">=</span> <span class="st">"~/git/mkin/vignettes/web_only/mkin_benchmarks.rda"</span><span class="op">)</span></code></pre></div> @@ -235,6 +228,16 @@ <td align="right">1.786</td> <td align="right">3.729</td> </tr> +<tr class="odd"> +<td align="left">1.0.3</td> +<td align="right">1.722</td> +<td align="right">3.419</td> +</tr> +<tr class="even"> +<td align="left">1.0.3.9000</td> +<td align="right">2.770</td> +<td align="right">3.458</td> +</tr> </tbody> </table> </div> @@ -298,13 +301,25 @@ <td align="right">7.251</td> <td align="right">2.810</td> </tr> +<tr class="odd"> +<td align="left">1.0.3</td> +<td align="right">1.402</td> +<td align="right">6.343</td> +<td align="right">2.802</td> +</tr> +<tr class="even"> +<td align="left">1.0.3.9000</td> +<td align="right">1.405</td> +<td align="right">6.417</td> +<td align="right">2.824</td> +</tr> </tbody> </table> </div> <div id="two-metabolites" class="section level3"> <h3 class="hasAnchor"> <a href="#two-metabolites" class="anchor"></a>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 class="table"> <thead><tr class="header"> <th align="left">mkin version</th> @@ -388,6 +403,24 @@ <td align="right">1.958</td> <td align="right">3.105</td> </tr> +<tr class="odd"> +<td align="left">1.0.3</td> +<td align="right">0.771</td> +<td align="right">1.251</td> +<td align="right">1.464</td> +<td align="right">3.074</td> +<td align="right">1.940</td> +<td align="right">2.831</td> +</tr> +<tr class="even"> +<td align="left">1.0.3.9000</td> +<td align="right">0.772</td> +<td align="right">1.263</td> +<td align="right">1.483</td> +<td align="right">3.101</td> +<td align="right">1.958</td> +<td align="right">2.843</td> +</tr> </tbody> </table> </div> diff --git a/docs/dev/articles/web_only/benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/dev/articles/web_only/benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 00000000..ca349fd6 --- /dev/null +++ b/docs/dev/articles/web_only/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/dev/articles/web_only/benchmarks_files/header-attrs-2.6/header-attrs.js b/docs/dev/articles/web_only/benchmarks_files/header-attrs-2.6/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/dev/articles/web_only/benchmarks_files/header-attrs-2.6/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/dev/articles/web_only/compiled_models.html b/docs/dev/articles/web_only/compiled_models.html index a444f4bc..49a579fc 100644 --- a/docs/dev/articles/web_only/compiled_models.html +++ b/docs/dev/articles/web_only/compiled_models.html @@ -32,7 +32,7 @@ </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -81,7 +81,7 @@ <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -95,14 +95,13 @@ - </header><link href="compiled_models_files/anchor-sections-1.0/anchor-sections.css" rel="stylesheet"> -<script src="compiled_models_files/anchor-sections-1.0/anchor-sections.js"></script><div class="row"> + </header><script src="compiled_models_files/header-attrs-2.6/header-attrs.js"></script><script src="compiled_models_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>Performance benefit by using compiled model definitions in mkin</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2020-11-30</h4> + <h4 class="date">2021-02-15</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/master/vignettes/web_only/compiled_models.rmd"><code>vignettes/web_only/compiled_models.rmd</code></a></small> <div class="hidden name"><code>compiled_models.rmd</code></div> @@ -160,10 +159,10 @@ <span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="st">"R package rbenchmark is not available"</span><span class="op">)</span> <span class="op">}</span></code></pre></div> <pre><code>## test replications relative elapsed -## 4 analytical 1 1.000 0.187 -## 3 deSolve, compiled 1 1.807 0.338 -## 2 Eigenvalue based 1 2.032 0.380 -## 1 deSolve, not compiled 1 43.048 8.050</code></pre> +## 4 analytical 1 1.000 0.182 +## 3 deSolve, compiled 1 1.824 0.332 +## 2 Eigenvalue based 1 2.082 0.379 +## 1 deSolve, not compiled 1 46.181 8.405</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"> @@ -190,13 +189,13 @@ <span class="op">}</span></code></pre></div> <pre><code>## Temporary DLL for differentials generated and loaded</code></pre> <pre><code>## test replications relative elapsed -## 2 deSolve, compiled 1 1.000 0.483 -## 1 deSolve, not compiled 1 29.969 14.475</code></pre> -<p>Here we get a performance benefit of a factor of 30 using the version of the differential equation model compiled from C code!</p> -<p>This vignette was built with mkin 0.9.50.4 on</p> +## 2 deSolve, compiled 1 1.000 0.541 +## 1 deSolve, not compiled 1 29.091 15.738</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 1.0.3.9000 on</p> <pre><code>## R version 4.0.3 (2020-10-10) ## Platform: x86_64-pc-linux-gnu (64-bit) -## Running under: Debian GNU/Linux 10 (buster)</code></pre> +## Running under: Debian GNU/Linux bullseye/sid</code></pre> <pre><code>## CPU model: AMD Ryzen 7 1700 Eight-Core Processor</code></pre> </div> </div> diff --git a/docs/dev/articles/web_only/compiled_models_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/dev/articles/web_only/compiled_models_files/accessible-code-block-0.0.1/empty-anchor.js new file mode 100644 index 00000000..ca349fd6 --- /dev/null +++ b/docs/dev/articles/web_only/compiled_models_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/dev/articles/web_only/compiled_models_files/header-attrs-2.6/header-attrs.js b/docs/dev/articles/web_only/compiled_models_files/header-attrs-2.6/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/dev/articles/web_only/compiled_models_files/header-attrs-2.6/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/dev/authors.html b/docs/dev/authors.html index d592b39f..4208dc24 100644 --- a/docs/dev/authors.html +++ b/docs/dev/authors.html @@ -71,7 +71,7 @@ </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> </span> </div> @@ -147,15 +147,15 @@ </li> <li> <p><strong>Katrin Lindenberger</strong>. Contributor. - </p> + <br /><small>contributed to mkinresplot()</small></p> </li> <li> <p><strong>René Lehmann</strong>. Contributor. - </p> + <br /><small>ilr() and invilr()</small></p> </li> <li> <p><strong>Eurofins Regulatory AG</strong>. Copyright holder. - </p> + <br /><small>copyright for some of the contributions of JR 2012-2014</small></p> </li> </ul> diff --git a/docs/dev/index.html b/docs/dev/index.html index a4399963..6e3fa6e1 100644 --- a/docs/dev/index.html +++ b/docs/dev/index.html @@ -38,7 +38,7 @@ </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> </span> </div> @@ -133,23 +133,40 @@ <div id="features" class="section level2"> <h2 class="hasAnchor"> <a href="#features" class="anchor"></a>Features</h2> +<div id="general" class="section level3"> +<h3 class="hasAnchor"> +<a href="#general" class="anchor"></a>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>As of version 0.9-39, fitting of several models to several datasets, optionally in parallel, is supported, see for example <a href="https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html"><code>plot.mmkin</code></a>.</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>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>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. This has the side effect that no constraints are needed in the optimisation. Thanks to René Lehmann for the nice cooperation on this, especially the isometric log-ratio transformation that is now used for the formation fractions.</li> -<li>A side effect of this is that 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 can not occur in a single experiment with a single defined radiolabel position.</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>When a metabolite decline phase is not described well by SFO kinetics, SFORB kinetics can be used for the metabolite.</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.</li> <li>The ‘variance by variable’ error model which is often fitted using Iteratively Reweighted Least Squares (IRLS) should now be specified as <code>error_model = "obs"</code>.</li> -<li>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>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> method.</li> </ul> </div> +<div id="unique-in-mkin" class="section level3"> +<h3 class="hasAnchor"> +<a href="#unique-in-mkin" class="anchor"></a>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> method. 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).</li> +</ul> +</div> +<div id="performance" class="section level3"> +<h3 class="hasAnchor"> +<a href="#performance" class="anchor"></a>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> +</ul> +</div> +</div> <div id="gui" class="section level2"> <h2 class="hasAnchor"> <a href="#gui" class="anchor"></a>GUI</h2> @@ -158,7 +175,7 @@ <div id="news" class="section level2"> <h2 class="hasAnchor"> <a href="#news" class="anchor"></a>News</h2> -<p>There is a ChangeLog, for the latest <a href="https://cran.r-project.org/package=mkin/news/news.html">CRAN release</a> and one for the <a href="https://github.com/jranke/mkin/blob/master/NEWS.md">github master 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/master/NEWS.md">the main branch</a>.</p> </div> <div id="credits-and-historical-remarks" class="section level2"> <h2 class="hasAnchor"> @@ -171,6 +188,17 @@ <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://www.tessella.com/showcase/computer-assisted-kinetic-evaluation">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> +<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> +</ul> </div> <div id="references" class="section level2"> <h2 class="hasAnchor"> diff --git a/docs/dev/news/index.html b/docs/dev/news/index.html index c3597efe..234ba02f 100644 --- a/docs/dev/news/index.html +++ b/docs/dev/news/index.html @@ -71,7 +71,7 @@ </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> </span> </div> @@ -141,13 +141,60 @@ <small>Source: <a href='https://github.com/jranke/mkin/blob/master/NEWS.md'><code>NEWS.md</code></a></small> </div> - <div id="mkin-0-9-50-4-unreleased" class="section level1"> -<h1 class="page-header" data-toc-text="0.9.50.4"> -<a href="#mkin-0-9-50-4-unreleased" class="anchor"></a>mkin 0.9.50.4 (unreleased)<small> Unreleased </small> -</h1> -<div id="general-new-features" class="section level2"> + <div id="mkin-105-unreleased" class="section level1"> +<h1 class="page-header" data-toc-text="1.0.5"> +<a href="#mkin-105-unreleased" class="anchor"></a>mkin 1.0.5 (unreleased)</h1> +<div id="mixed-effects-models" class="section level2"> <h2 class="hasAnchor"> -<a href="#general-new-features" class="anchor"></a>General new features</h2> +<a href="#mixed-effects-models" class="anchor"></a>Mixed-effects models</h2> +<ul> +<li><p>Introduce an interface to nlmixr, supporting estimation methods ‘saem’ and ‘focei’: S3 method ‘nlmixr.mmkin’ using the helper functions ‘nlmixr_model’ and ‘nlmixr_data’ to set up nlmixr models for mmkin row objects, with summary and plot methods.</p></li> +<li><p>Reintroduce the interface to current development versions (not on CRAN) of saemix, in particular the generic function ‘saem’ with a generator ‘saem.mmkin’, currently using ‘saemix_model’ and ‘saemix_data’, summary and plot methods</p></li> +<li><p>‘mean_degparms’: New argument ‘test_log_parms’ that makes the function only consider log-transformed parameters where the untransformed parameters pass the t-test for a certain confidence level. This can be used to obtain more plausible starting parameters for the different mixed-effects model backends</p></li> +<li><p>‘plot.mixed.mmkin’: Gains arguments ‘test_log_parms’ and ‘conf.level’</p></li> +</ul> +</div> +</div> + <div id="mkin-104-2021-04-20" class="section level1"> +<h1 class="page-header" data-toc-text="1.0.4"> +<a href="#mkin-104-2021-04-20" class="anchor"></a>mkin 1.0.4 (2021-04-20)</h1> +<ul> +<li><p>All plotting functions setting graphical parameters: Use on.exit() for resetting graphical parameters</p></li> +<li><p>‘plot.mkinfit’: Use xlab and xlim for the residual plot if show_residuals is TRUE</p></li> +<li><p>‘mmkin’: Use cores = 1 per default on Windows to make it easier for first time users</p></li> +</ul> +</div> + <div id="mkin-103-2021-02-15" class="section level1"> +<h1 class="page-header" data-toc-text="1.0.3"> +<a href="#mkin-103-2021-02-15" class="anchor"></a>mkin 1.0.3 (2021-02-15)</h1> +<ul> +<li>Review and update README, the ‘Introduction to mkin’ vignette and some of the help pages</li> +</ul> +</div> + <div id="mkin-102-unreleased" class="section level1"> +<h1 class="page-header" data-toc-text="1.0.2"> +<a href="#mkin-102-unreleased" class="anchor"></a>mkin 1.0.2 (Unreleased)</h1> +<ul> +<li>‘mkinfit’: Keep model names stored in ‘mkinmod’ objects, avoiding their loss in ‘gmkin’</li> +</ul> +</div> + <div id="mkin-101-2021-02-10" class="section level1"> +<h1 class="page-header" data-toc-text="1.0.1"> +<a href="#mkin-101-2021-02-10" class="anchor"></a>mkin 1.0.1 (2021-02-10)</h1> +<ul> +<li><p>‘confint.mmkin’, ‘nlme.mmkin’, ‘transform_odeparms’: Fix example code in dontrun sections that failed with current defaults</p></li> +<li><p>‘logLik.mkinfit’: Improve example code to avoid warnings and show convenient syntax</p></li> +<li><p>‘mkinresplot’: Re-add Katrin Lindenberger as coauthor who was accidentally removed long ago</p></li> +<li><p>Remove tests relying on non-convergence of the FOMC fit to the FOCUS A dataset as this is platform dependent (revealed by the new additional tests on CRAN, thanks!)</p></li> +<li><p>Increase test tolerance for some parameter comparisons that also proved to be platform dependent</p></li> +</ul> +</div> + <div id="mkin-100-2021-02-03" class="section level1"> +<h1 class="page-header" data-toc-text="1.0.0"> +<a href="#mkin-100-2021-02-03" class="anchor"></a>mkin 1.0.0 (2021-02-03)</h1> +<div id="general" class="section level2"> +<h2 class="hasAnchor"> +<a href="#general" class="anchor"></a>General</h2> <ul> <li><p>‘mkinmod’ models gain arguments ‘name’ and ‘dll_dir’ which, in conjunction with a current version of the ‘inline’ package, make it possible to still use the DLL used for fast ODE solutions with ‘deSolve’ after saving and restoring the ‘mkinmod’ object.</p></li> <li><p>‘mkindsg’ R6 class for groups of ‘mkinds’ datasets with metadata</p></li> @@ -156,26 +203,23 @@ <li><p>‘focus_soil_moisture’ FOCUS default soil moisture data</p></li> <li><p>‘update’ method for ‘mmkin’ objects</p></li> <li><p>‘transform_odeparms’, ‘backtransform_odeparms’: Use logit transformation for solitary fractions like the g parameter of the DFOP model, or formation fractions for a pathway to only one target variable</p></li> +<li><p>‘plot.mmkin’: Add a ylab argument, making it possible to customize the y axis label of the panels on the left without affecting the residual plots. Reduce legend size and vertical distance between panels</p></li> +<li><p>‘plot.mkinfit’: Change default ylab from “Observed” to “Residue”. Pass xlab to residual plot if show_residuals is TRUE.</p></li> </ul> </div> -<div id="mixed-effects-models" class="section level2"> +<div id="mixed-effects-models-1" class="section level2"> <h2 class="hasAnchor"> -<a href="#mixed-effects-models" class="anchor"></a>Mixed-effects models</h2> +<a href="#mixed-effects-models-1" class="anchor"></a>Mixed-effects models</h2> <ul> <li><p>‘mixed.mmkin’ New container for mmkin objects for plotting with the ‘plot.mixed.mmkin’ method</p></li> -<li><p>‘plot.mixed.mmkin’ method used for ‘nlme.mmkin’ and ‘saem.mmkin’, both inheriting from ‘mixed.mmkin’ (currently virtual)</p></li> +<li><p>‘plot.mixed.mmkin’ method used for ‘nlme.mmkin’ inheriting from ‘mixed.mmkin’ (currently virtual)</p></li> <li><p>‘plot’, ‘summary’ and ‘print’ methods for ‘nlme.mmkin’ objects</p></li> -<li><p>Add the current development version of the saemix package as a second, optional backend for mixed-effects models</p></li> -<li><p>DESCRIPTION: Additional_repositories entry pointing to my drat repository on github for a suitable saemix version</p></li> -<li><p>‘saemix_model’, ‘saemix_data’: Helper functions to fit nonlinear mixed-effects models for mmkin row objects.</p></li> -<li><p>‘saem’ generic function to fit saemix models using ‘saemix_model’ and ‘saemix_data’, with a generator ‘saem.mmkin’, summary and plot methods</p></li> </ul> </div> </div> - <div id="mkin-0-9-50-3-2020-10-08" class="section level1"> + <div id="mkin-09503-2020-10-08" class="section level1"> <h1 class="page-header" data-toc-text="0.9.50.3"> -<a href="#mkin-0-9-50-3-2020-10-08" class="anchor"></a>mkin 0.9.50.3 (2020-10-08)<small> 2020-10-08 </small> -</h1> +<a href="#mkin-09503-2020-10-08" class="anchor"></a>mkin 0.9.50.3 (2020-10-08)</h1> <ul> <li><p>‘parms’: Add a method for mmkin objects</p></li> <li><p>‘mmkin’ and ‘confint(method = ’profile’): Use all cores detected by parallel::detectCores() per default</p></li> @@ -188,37 +232,33 @@ <li><p>‘endpoints’: Back-calculate DT50 value from DT90 also for the biphasic models DFOP, HS and SFORB</p></li> </ul> </div> - <div id="mkin-0-9-50-2-2020-05-12" class="section level1"> + <div id="mkin-09502-2020-05-12" class="section level1"> <h1 class="page-header" data-toc-text="0.9.50.2"> -<a href="#mkin-0-9-50-2-2020-05-12" class="anchor"></a>mkin 0.9.50.2 (2020-05-12)<small> 2020-05-12 </small> -</h1> +<a href="#mkin-09502-2020-05-12" class="anchor"></a>mkin 0.9.50.2 (2020-05-12)</h1> <ul> <li><p>Increase tolerance for a platform specific test results on the Solaris test machine on CRAN</p></li> <li><p>Updates and corrections (using the spelling package) to the documentation</p></li> </ul> </div> - <div id="mkin-0-9-50-1-2020-05-11" class="section level1"> + <div id="mkin-09501-2020-05-11" class="section level1"> <h1 class="page-header" data-toc-text="0.9.50.1"> -<a href="#mkin-0-9-50-1-2020-05-11" class="anchor"></a>mkin 0.9.50.1 (2020-05-11)<small> 2020-05-11 </small> -</h1> +<a href="#mkin-09501-2020-05-11" class="anchor"></a>mkin 0.9.50.1 (2020-05-11)</h1> <ul> <li><p>Support SFORB with formation fractions</p></li> <li><p>‘mkinmod’: Make ‘use_of_ff’ = “max” the default</p></li> <li><p>Improve performance by a) avoiding expensive calls in the cost function like merge() and data.frame(), and b) by implementing analytical solutions for SFO-SFO and DFOP-SFO</p></li> </ul> </div> - <div id="mkin-0-9-49-11-2020-04-20" class="section level1"> + <div id="mkin-094911-2020-04-20" class="section level1"> <h1 class="page-header" data-toc-text="0.9.49.11"> -<a href="#mkin-0-9-49-11-2020-04-20" class="anchor"></a>mkin 0.9.49.11 (2020-04-20)<small> 2020-04-20 </small> -</h1> +<a href="#mkin-094911-2020-04-20" class="anchor"></a>mkin 0.9.49.11 (2020-04-20)</h1> <ul> <li>Increase a test tolerance to make it pass on all CRAN check machines</li> </ul> </div> - <div id="mkin-0-9-49-10-2020-04-18" class="section level1"> + <div id="mkin-094910-2020-04-18" class="section level1"> <h1 class="page-header" data-toc-text="0.9.49.10"> -<a href="#mkin-0-9-49-10-2020-04-18" class="anchor"></a>mkin 0.9.49.10 (2020-04-18)<small> 2020-04-18 </small> -</h1> +<a href="#mkin-094910-2020-04-18" class="anchor"></a>mkin 0.9.49.10 (2020-04-18)</h1> <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> <li><p>‘mean_degparms, nlme_data, nlme_function’: Three new functions to facilitate building nlme models from mmkin row objects</p></li> @@ -228,19 +268,17 @@ <li><p>‘summary.mkinfit’: Add AIC, BIC and log likelihood to the summary</p></li> </ul> </div> - <div id="mkin-0-9-49-9-2020-03-31" class="section level1"> + <div id="mkin-09499-2020-03-31" class="section level1"> <h1 class="page-header" data-toc-text="0.9.49.9"> -<a href="#mkin-0-9-49-9-2020-03-31" class="anchor"></a>mkin 0.9.49.9 (2020-03-31)<small> 2020-03-31 </small> -</h1> +<a href="#mkin-09499-2020-03-31" class="anchor"></a>mkin 0.9.49.9 (2020-03-31)</h1> <ul> <li><p>‘mkinmod’: Use pkgbuild::has_compiler instead of Sys.which(‘gcc’), as the latter will often fail even if Rtools are installed</p></li> <li><p>‘mkinds’: Use roxygen for documenting fields and methods of this R6 class</p></li> </ul> </div> - <div id="mkin-0-9-49-8-2020-01-09" class="section level1"> + <div id="mkin-09498-2020-01-09" class="section level1"> <h1 class="page-header" data-toc-text="0.9.49.8"> -<a href="#mkin-0-9-49-8-2020-01-09" class="anchor"></a>mkin 0.9.49.8 (2020-01-09)<small> 2020-01-09 </small> -</h1> +<a href="#mkin-09498-2020-01-09" class="anchor"></a>mkin 0.9.49.8 (2020-01-09)</h1> <ul> <li><p>‘aw’: Generic function for calculating Akaike weights, methods for mkinfit objects and mmkin columns</p></li> <li><p>‘loftest’: Add a lack-of-fit test</p></li> @@ -249,19 +287,17 @@ <li><p>‘confint.mkinfit’: Make the quadratic approximation the default, as the likelihood profiling takes a lot of time, especially if the fit has more than three parameters</p></li> </ul> </div> - <div id="mkin-0-9-49-7-2019-11-01" class="section level1"> + <div id="mkin-09497-2019-11-01" class="section level1"> <h1 class="page-header" data-toc-text="0.9.49.7"> -<a href="#mkin-0-9-49-7-2019-11-01" class="anchor"></a>mkin 0.9.49.7 (2019-11-01)<small> 2019-11-02 </small> -</h1> +<a href="#mkin-09497-2019-11-01" class="anchor"></a>mkin 0.9.49.7 (2019-11-01)</h1> <ul> <li><p>Fix a bug introduced in 0.9.49.6 that occurred if the direct optimisation yielded a higher likelihood than the three-step optimisation in the d_3 algorithm, which caused the fitted parameters of the three-step optimisation to be returned instead of the parameters of the direct optimisation</p></li> <li><p>Add a ‘nobs’ method for mkinfit objects, enabling the default ‘BIC’ method from the stats package. Also, add a ‘BIC’ method for mmkin column objects.</p></li> </ul> </div> - <div id="mkin-0-9-49-6-2019-10-31" class="section level1"> + <div id="mkin-09496-2019-10-31" class="section level1"> <h1 class="page-header" data-toc-text="0.9.49.6"> -<a href="#mkin-0-9-49-6-2019-10-31" class="anchor"></a>mkin 0.9.49.6 (2019-10-31)<small> 2019-10-31 </small> -</h1> +<a href="#mkin-09496-2019-10-31" class="anchor"></a>mkin 0.9.49.6 (2019-10-31)</h1> <ul> <li><p>Implement a likelihood ratio test as a method for ‘lrtest’ from the lmtest package</p></li> <li><p>Add an ‘update’ method for mkinfit objects which remembers fitted parameters if appropriate</p></li> @@ -278,10 +314,9 @@ <li><p>Support summarizing ‘mkinfit’ objects generated with versions < 0.9.49.5</p></li> </ul> </div> - <div id="mkin-0-9-49-5-2019-07-04" class="section level1"> + <div id="mkin-09495-2019-07-04" class="section level1"> <h1 class="page-header" data-toc-text="0.9.49.5"> -<a href="#mkin-0-9-49-5-2019-07-04" class="anchor"></a>mkin 0.9.49.5 (2019-07-04)<small> 2019-07-04 </small> -</h1> +<a href="#mkin-09495-2019-07-04" class="anchor"></a>mkin 0.9.49.5 (2019-07-04)</h1> <ul> <li><p>Several algorithms for minimization of the negative log-likelihood for non-constant error models (two-component and variance by variable). In the case the error model is constant variance, least squares is used as this is more stable. The default algorithm ‘d_3’ tries direct minimization and a three-step procedure, and returns the model with the highest likelihood.</p></li> <li><p>The argument ‘reweight.method’ to mkinfit and mmkin is now obsolete, use ‘error_model’ and ‘error_model_algorithm’ instead</p></li> @@ -297,10 +332,9 @@ <li><p>Add example datasets obtained from risk assessment reports published by the European Food Safety Agency.</p></li> </ul> </div> - <div id="mkin-0-9-48-1-2019-03-04" class="section level1"> + <div id="mkin-09481-2019-03-04" class="section level1"> <h1 class="page-header" data-toc-text="0.9.48.1"> -<a href="#mkin-0-9-48-1-2019-03-04" class="anchor"></a>mkin 0.9.48.1 (2019-03-04)<small> 2019-03-04 </small> -</h1> +<a href="#mkin-09481-2019-03-04" class="anchor"></a>mkin 0.9.48.1 (2019-03-04)</h1> <ul> <li><p>Add the function ‘logLik.mkinfit’ which makes it possible to calculate an AIC for mkinfit objects</p></li> <li><p>Add the function ‘AIC.mmkin’ to make it easy to compare columns of mmkin objects</p></li> @@ -314,38 +348,34 @@ <li><p>‘nafta’: Add evaluations according to the NAFTA guidance</p></li> </ul> </div> - <div id="mkin-0-9-47-5-2018-09-14" class="section level1"> + <div id="mkin-09475-2018-09-14" class="section level1"> <h1 class="page-header" data-toc-text="0.9.47.5"> -<a href="#mkin-0-9-47-5-2018-09-14" class="anchor"></a>mkin 0.9.47.5 (2018-09-14)<small> 2018-09-14 </small> -</h1> +<a href="#mkin-09475-2018-09-14" class="anchor"></a>mkin 0.9.47.5 (2018-09-14)</h1> <ul> <li><p>Make the two-component error model stop in cases where it is inadequate to avoid nls crashes on windows</p></li> <li><p>Move two vignettes to a location where they will not be built on CRAN (to avoid more NOTES from long execution times)</p></li> <li><p>Exclude more example code from testing on CRAN to avoid NOTES from long execution times</p></li> </ul> </div> - <div id="mkin-0-9-47-3" class="section level1"> + <div id="mkin-09473" class="section level1"> <h1 class="page-header" data-toc-text="0.9.47.3"> -<a href="#mkin-0-9-47-3" class="anchor"></a>mkin 0.9.47.3<small> Unreleased </small> -</h1> +<a href="#mkin-09473" class="anchor"></a>mkin 0.9.47.3</h1> <ul> <li><p>‘mkinfit’: Improve fitting the error model for reweight.method = ‘tc’. Add ‘manual’ to possible arguments for ‘weight’</p></li> <li><p>Test that FOCUS_2006_C can be evaluated with DFOP and reweight.method = ‘tc’</p></li> </ul> </div> - <div id="mkin-0-9-47-2-2018-07-19" class="section level1"> + <div id="mkin-09472-2018-07-19" class="section level1"> <h1 class="page-header" data-toc-text="0.9.47.2"> -<a href="#mkin-0-9-47-2-2018-07-19" class="anchor"></a>mkin 0.9.47.2 (2018-07-19)<small> 2018-07-19 </small> -</h1> +<a href="#mkin-09472-2018-07-19" class="anchor"></a>mkin 0.9.47.2 (2018-07-19)</h1> <ul> <li><p>‘sigma_twocomp’: Rename ‘sigma_rl’ to ‘sigma_twocomp’ as the Rocke and Lorenzato model assumes lognormal distribution for large y. Correct references to the Rocke and Lorenzato model accordingly.</p></li> <li><p>‘mkinfit’: Use 1.1 as starting value for N parameter of IORE models to obtain convergence in more difficult cases. Show parameter names when ‘trace_parms’ is ‘TRUE’.</p></li> </ul> </div> - <div id="mkin-0-9-47-1-2018-02-06" class="section level1"> + <div id="mkin-09471-2018-02-06" class="section level1"> <h1 class="page-header" data-toc-text="0.9.47.1"> -<a href="#mkin-0-9-47-1-2018-02-06" class="anchor"></a>mkin 0.9.47.1 (2018-02-06)<small> 2018-02-06 </small> -</h1> +<a href="#mkin-09471-2018-02-06" class="anchor"></a>mkin 0.9.47.1 (2018-02-06)</h1> <ul> <li><p>Skip some tests on CRAN and winbuilder to avoid timeouts</p></li> <li><p>‘test_data_from_UBA_2014’: Added this list of datasets containing experimental data used in the expertise from 2014</p></li> @@ -355,46 +385,41 @@ <li><p>‘summary.mkinfit’: Show versions of mkin and R used for fitting (not the ones used for the summary) if the fit was generated with mkin >= 0.9.47.1</p></li> </ul> </div> - <div id="mkin-0-9-46-3-2017-11-16" class="section level1"> + <div id="mkin-09463-2017-11-16" class="section level1"> <h1 class="page-header" data-toc-text="0.9.46.3"> -<a href="#mkin-0-9-46-3-2017-11-16" class="anchor"></a>mkin 0.9.46.3 (2017-11-16)<small> 2017-11-16 </small> -</h1> +<a href="#mkin-09463-2017-11-16" class="anchor"></a>mkin 0.9.46.3 (2017-11-16)</h1> <ul> <li><p><code>README.md</code>, <code>vignettes/mkin.Rmd</code>: URLs were updated</p></li> <li><p><code>synthetic_data_for_UBA</code>: Add the code used to generate the data in the interest of reproducibility</p></li> </ul> </div> - <div id="mkin-0-9-46-2-2017-10-10" class="section level1"> + <div id="mkin-09462-2017-10-10" class="section level1"> <h1 class="page-header" data-toc-text="0.9.46.2"> -<a href="#mkin-0-9-46-2-2017-10-10" class="anchor"></a>mkin 0.9.46.2 (2017-10-10)<small> 2017-10-10 </small> -</h1> +<a href="#mkin-09462-2017-10-10" class="anchor"></a>mkin 0.9.46.2 (2017-10-10)</h1> <ul> <li><p>Converted the vignette FOCUS_Z from tex/pdf to markdown/html</p></li> <li><p><code>DESCRIPTION</code>: Add ORCID</p></li> </ul> </div> - <div id="mkin-0-9-46-1-2017-09-14" class="section level1"> + <div id="mkin-09461-2017-09-14" class="section level1"> <h1 class="page-header" data-toc-text="0.9.46.1"> -<a href="#mkin-0-9-46-1-2017-09-14" class="anchor"></a>mkin 0.9.46.1 (2017-09-14)<small> 2017-09-14 </small> -</h1> +<a href="#mkin-09461-2017-09-14" class="anchor"></a>mkin 0.9.46.1 (2017-09-14)</h1> <ul> <li><p><code>plot.mkinfit</code>: Fix scaling of residual plots for the case of separate plots for each observed variable</p></li> <li><p><code>plot.mkinfit</code>: Use all data points of the fitted curve for y axis scaling for the case of separate plots for each observed variable</p></li> <li><p>Documentation updates</p></li> </ul> </div> - <div id="mkin-0-9-46-2017-07-24" class="section level1"> + <div id="mkin-0946-2017-07-24" class="section level1"> <h1 class="page-header" data-toc-text="0.9.46"> -<a href="#mkin-0-9-46-2017-07-24" class="anchor"></a>mkin 0.9.46 (2017-07-24)<small> 2017-07-29 </small> -</h1> +<a href="#mkin-0946-2017-07-24" class="anchor"></a>mkin 0.9.46 (2017-07-24)</h1> <ul> <li>Remove <code>test_FOMC_ill-defined.R</code> as it is too platform dependent</li> </ul> </div> - <div id="mkin-0-9-45-2-2017-07-24" class="section level1"> + <div id="mkin-09452-2017-07-24" class="section level1"> <h1 class="page-header" data-toc-text="0.9.45.2"> -<a href="#mkin-0-9-45-2-2017-07-24" class="anchor"></a>mkin 0.9.45.2 (2017-07-24)<small> 2017-07-22 </small> -</h1> +<a href="#mkin-09452-2017-07-24" class="anchor"></a>mkin 0.9.45.2 (2017-07-24)</h1> <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> <li><p>Update URLs in documentation</p></li> @@ -402,10 +427,9 @@ <li><p>Switch from <code>microbenchmark</code> to <code>rbenchmark</code> as the former is not supported on all platforms</p></li> </ul> </div> - <div id="mkin-0-9-45-1-2016-12-20" class="section level1"> + <div id="mkin-09451-2016-12-20" class="section level1"> <h1 class="page-header" data-toc-text="0.9.45.1"> -<a href="#mkin-0-9-45-1-2016-12-20" class="anchor"></a>mkin 0.9.45.1 (2016-12-20)<small> Unreleased </small> -</h1> +<a href="#mkin-09451-2016-12-20" class="anchor"></a>mkin 0.9.45.1 (2016-12-20)</h1> <div id="new-features" class="section level2"> <h2 class="hasAnchor"> <a href="#new-features" class="anchor"></a>New features</h2> @@ -414,10 +438,9 @@ </ul> </div> </div> - <div id="mkin-0-9-45-2016-12-08" class="section level1"> + <div id="mkin-0945-2016-12-08" class="section level1"> <h1 class="page-header" data-toc-text="0.9.45"> -<a href="#mkin-0-9-45-2016-12-08" class="anchor"></a>mkin 0.9.45 (2016-12-08)<small> 2016-12-08 </small> -</h1> +<a href="#mkin-0945-2016-12-08" class="anchor"></a>mkin 0.9.45 (2016-12-08)</h1> <div id="minor-changes" class="section level2"> <h2 class="hasAnchor"> <a href="#minor-changes" class="anchor"></a>Minor changes</h2> @@ -428,10 +451,9 @@ </ul> </div> </div> - <div id="mkin-0-9-44-2016-06-29" class="section level1"> + <div id="mkin-0944-2016-06-29" class="section level1"> <h1 class="page-header" data-toc-text="0.9.44"> -<a href="#mkin-0-9-44-2016-06-29" class="anchor"></a>mkin 0.9.44 (2016-06-29)<small> 2016-06-29 </small> -</h1> +<a href="#mkin-0944-2016-06-29" class="anchor"></a>mkin 0.9.44 (2016-06-29)</h1> <div id="bug-fixes" class="section level2"> <h2 class="hasAnchor"> <a href="#bug-fixes" class="anchor"></a>Bug fixes</h2> @@ -440,10 +462,9 @@ </ul> </div> </div> - <div id="mkin-0-9-43-2016-06-28" class="section level1"> + <div id="mkin-0943-2016-06-28" class="section level1"> <h1 class="page-header" data-toc-text="0.9.43"> -<a href="#mkin-0-9-43-2016-06-28" class="anchor"></a>mkin 0.9.43 (2016-06-28)<small> 2016-06-28 </small> -</h1> +<a href="#mkin-0943-2016-06-28" class="anchor"></a>mkin 0.9.43 (2016-06-28)</h1> <div id="major-changes" class="section level2"> <h2 class="hasAnchor"> <a href="#major-changes" class="anchor"></a>Major changes</h2> @@ -479,10 +500,9 @@ </ul> </div> </div> - <div id="mkin-0-9-42-2016-03-25" class="section level1"> + <div id="mkin-0942-2016-03-25" class="section level1"> <h1 class="page-header" data-toc-text="0.9.42"> -<a href="#mkin-0-9-42-2016-03-25" class="anchor"></a>mkin 0.9.42 (2016-03-25)<small> 2016-03-25 </small> -</h1> +<a href="#mkin-0942-2016-03-25" class="anchor"></a>mkin 0.9.42 (2016-03-25)</h1> <div id="major-changes-1" class="section level2"> <h2 class="hasAnchor"> <a href="#major-changes-1" class="anchor"></a>Major changes</h2> @@ -500,10 +520,9 @@ </ul> </div> </div> - <div id="mkin-0-9-41-2015-11-09" class="section level1"> + <div id="mkin-09-41-2015-11-09" class="section level1"> <h1 class="page-header" data-toc-text="0.9-41"> -<a href="#mkin-0-9-41-2015-11-09" class="anchor"></a>mkin 0.9-41 (2015-11-09)<small> 2015-11-09 </small> -</h1> +<a href="#mkin-09-41-2015-11-09" class="anchor"></a>mkin 0.9-41 (2015-11-09)</h1> <div id="minor-changes-3" class="section level2"> <h2 class="hasAnchor"> <a href="#minor-changes-3" class="anchor"></a>Minor changes</h2> @@ -523,10 +542,9 @@ </ul> </div> </div> - <div id="mkin-0-9-40-2015-07-21" class="section level1"> + <div id="mkin-09-40-2015-07-21" class="section level1"> <h1 class="page-header" data-toc-text="0.9-40"> -<a href="#mkin-0-9-40-2015-07-21" class="anchor"></a>mkin 0.9-40 (2015-07-21)<small> 2015-07-21 </small> -</h1> +<a href="#mkin-09-40-2015-07-21" class="anchor"></a>mkin 0.9-40 (2015-07-21)</h1> <div id="bug-fixes-3" class="section level2"> <h2 class="hasAnchor"> <a href="#bug-fixes-3" class="anchor"></a>Bug fixes</h2> @@ -544,10 +562,9 @@ </ul> </div> </div> - <div id="mkin-0-9-39-2015-06-26" class="section level1"> + <div id="mkin-09-39-2015-06-26" class="section level1"> <h1 class="page-header" data-toc-text="0.9-39"> -<a href="#mkin-0-9-39-2015-06-26" class="anchor"></a>mkin 0.9-39 (2015-06-26)<small> 2015-06-26 </small> -</h1> +<a href="#mkin-09-39-2015-06-26" class="anchor"></a>mkin 0.9-39 (2015-06-26)</h1> <div id="major-changes-2" class="section level2"> <h2 class="hasAnchor"> <a href="#major-changes-2" class="anchor"></a>Major changes</h2> @@ -565,10 +582,9 @@ </ul> </div> </div> - <div id="mkin-0-9-38-2015-06-24" class="section level1"> + <div id="mkin-09-38-2015-06-24" class="section level1"> <h1 class="page-header" data-toc-text="0.9-38"> -<a href="#mkin-0-9-38-2015-06-24" class="anchor"></a>mkin 0.9-38 (2015-06-24)<small> 2015-06-23 </small> -</h1> +<a href="#mkin-09-38-2015-06-24" class="anchor"></a>mkin 0.9-38 (2015-06-24)</h1> <div id="minor-changes-4" class="section level2"> <h2 class="hasAnchor"> <a href="#minor-changes-4" class="anchor"></a>Minor changes</h2> @@ -586,10 +602,9 @@ </ul> </div> </div> - <div id="mkin-0-9-36-2015-06-21" class="section level1"> + <div id="mkin-09-36-2015-06-21" class="section level1"> <h1 class="page-header" data-toc-text="0.9-36"> -<a href="#mkin-0-9-36-2015-06-21" class="anchor"></a>mkin 0.9-36 (2015-06-21)<small> 2015-06-21 </small> -</h1> +<a href="#mkin-09-36-2015-06-21" class="anchor"></a>mkin 0.9-36 (2015-06-21)</h1> <div id="major-changes-3" class="section level2"> <h2 class="hasAnchor"> <a href="#major-changes-3" class="anchor"></a>Major changes</h2> @@ -608,10 +623,9 @@ </ul> </div> </div> - <div id="mkin-0-9-35-2015-05-15" class="section level1"> + <div id="mkin-09-35-2015-05-15" class="section level1"> <h1 class="page-header" data-toc-text="0.9-35"> -<a href="#mkin-0-9-35-2015-05-15" class="anchor"></a>mkin 0.9-35 (2015-05-15)<small> 2015-05-15 </small> -</h1> +<a href="#mkin-09-35-2015-05-15" class="anchor"></a>mkin 0.9-35 (2015-05-15)</h1> <div id="major-changes-4" class="section level2"> <h2 class="hasAnchor"> <a href="#major-changes-4" class="anchor"></a>Major changes</h2> @@ -640,10 +654,9 @@ </ul> </div> </div> - <div id="mkin-0-9-34-2014-11-22" class="section level1"> + <div id="mkin-09-34-2014-11-22" class="section level1"> <h1 class="page-header" data-toc-text="0.9-34"> -<a href="#mkin-0-9-34-2014-11-22" class="anchor"></a>mkin 0.9-34 (2014-11-22)<small> 2014-11-22 </small> -</h1> +<a href="#mkin-09-34-2014-11-22" class="anchor"></a>mkin 0.9-34 (2014-11-22)</h1> <div id="new-features-2" class="section level2"> <h2 class="hasAnchor"> <a href="#new-features-2" class="anchor"></a>New features</h2> @@ -662,10 +675,9 @@ </ul> </div> </div> - <div id="mkin-0-9-33-2014-10-22" class="section level1"> + <div id="mkin-09-33-2014-10-22" class="section level1"> <h1 class="page-header" data-toc-text="0.9-33"> -<a href="#mkin-0-9-33-2014-10-22" class="anchor"></a>mkin 0.9-33 (2014-10-22)<small> 2014-10-12 </small> -</h1> +<a href="#mkin-09-33-2014-10-22" class="anchor"></a>mkin 0.9-33 (2014-10-22)</h1> <div id="new-features-3" class="section level2"> <h2 class="hasAnchor"> <a href="#new-features-3" class="anchor"></a>New features</h2> @@ -695,17 +707,16 @@ </ul> </div> </div> - <div id="mkin-0-9-32-2014-07-24" class="section level1"> + <div id="mkin-09-32-2014-07-24" class="section level1"> <h1 class="page-header" data-toc-text="0.9-32"> -<a href="#mkin-0-9-32-2014-07-24" class="anchor"></a>mkin 0.9-32 (2014-07-24)<small> 2014-07-24 </small> -</h1> +<a href="#mkin-09-32-2014-07-24" class="anchor"></a>mkin 0.9-32 (2014-07-24)</h1> <div id="new-features-4" class="section level2"> <h2 class="hasAnchor"> <a href="#new-features-4" class="anchor"></a>New features</h2> <ul> <li><p>The number of degrees of freedom is difficult to define in the case of ilr transformation of formation fractions. Now for each source compartment the number of ilr parameters (=number of optimised parameters) is divided by the number of pathways to metabolites (=number of affected data series) which leads to fractional degrees of freedom in some cases.</p></li> <li><p>The default for the initial value for the first state value is now taken from the mean of the observations at time zero, if available.</p></li> -<li><p>The kinetic model can alternatively be specified with a shorthand name for parent only degradation models, e.g. <code>SFO</code>, or <code>DFOP</code>.</p></li> +<li><p>The kinetic model can alternatively be specified with a shorthand name for parent only degradation models, e.g. <code>SFO</code>, or <code>DFOP</code>.</p></li> <li><p>Optimisation method, number of model evaluations and time elapsed during optimisation are given in the summary of mkinfit objects.</p></li> <li><p>The maximum number of iterations in the optimisation algorithm can be specified using the argument <code>maxit.modFit</code> to the mkinfit function.</p></li> <li><p>mkinfit gives a warning when the fit does not converge (does not apply to SANN method). This warning is included in the summary.</p></li> @@ -732,10 +743,9 @@ </ul> </div> </div> - <div id="mkin-0-9-31-2014-07-14" class="section level1"> + <div id="mkin-09-31-2014-07-14" class="section level1"> <h1 class="page-header" data-toc-text="0.9-31"> -<a href="#mkin-0-9-31-2014-07-14" class="anchor"></a>mkin 0.9-31 (2014-07-14)<small> 2014-07-14 </small> -</h1> +<a href="#mkin-09-31-2014-07-14" class="anchor"></a>mkin 0.9-31 (2014-07-14)</h1> <div id="bug-fixes-9" class="section level2"> <h2 class="hasAnchor"> <a href="#bug-fixes-9" class="anchor"></a>Bug fixes</h2> @@ -744,10 +754,9 @@ </ul> </div> </div> - <div id="mkin-0-9-30-2014-07-11" class="section level1"> + <div id="mkin-09-30-2014-07-11" class="section level1"> <h1 class="page-header" data-toc-text="0.9-30"> -<a href="#mkin-0-9-30-2014-07-11" class="anchor"></a>mkin 0.9-30 (2014-07-11)<small> 2014-07-11 </small> -</h1> +<a href="#mkin-09-30-2014-07-11" class="anchor"></a>mkin 0.9-30 (2014-07-11)</h1> <div id="new-features-5" class="section level2"> <h2 class="hasAnchor"> <a href="#new-features-5" class="anchor"></a>New features</h2> @@ -759,7 +768,7 @@ <h2 class="hasAnchor"> <a href="#major-changes-5" class="anchor"></a>Major changes</h2> <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> +<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> <li><p>The order of some of the information blocks in <code>print.summary.mkinfit.R()</code> has been ordered in a more logical way.</p></li> </ul> </div> @@ -776,30 +785,27 @@ </ul> </div> </div> - <div id="mkin-0-9-29-2014-06-27" class="section level1"> + <div id="mkin-09-29-2014-06-27" class="section level1"> <h1 class="page-header" data-toc-text="0.9-29"> -<a href="#mkin-0-9-29-2014-06-27" class="anchor"></a>mkin 0.9-29 (2014-06-27)<small> 2014-06-27 </small> -</h1> +<a href="#mkin-09-29-2014-06-27" class="anchor"></a>mkin 0.9-29 (2014-06-27)</h1> <ul> <li><p>R/mkinresplot.R: Make it possible to specify <code>xlim</code></p></li> <li><p>R/geometric_mean.R, man/geometric_mean.Rd: Add geometric mean function</p></li> <li><p>R/endpoints.R, man/endpoints.Rd: Calculate additional (pseudo)-DT50 values for FOMC, DFOP, HS and SFORB. Avoid calculation of formation fractions from rate constants when they are directly fitted</p></li> </ul> </div> - <div id="mkin-0-9-28-2014-05-20" class="section level1"> + <div id="mkin-09-28-2014-05-20" class="section level1"> <h1 class="page-header" data-toc-text="0.9-28"> -<a href="#mkin-0-9-28-2014-05-20" class="anchor"></a>mkin 0.9-28 (2014-05-20)<small> 2014-05-20 </small> -</h1> +<a href="#mkin-09-28-2014-05-20" class="anchor"></a>mkin 0.9-28 (2014-05-20)</h1> <ul> <li><p>Do not backtransform confidence intervals for formation fractions if more than one compound is formed, as such parameters only define the pathways as a set</p></li> <li><p>Add historical remarks and some background to the main package vignette</p></li> <li><p>Correct ‘isotropic’ into ‘isometric’ for the ilr transformation</p></li> </ul> </div> - <div id="mkin-0-9-27-2014-05-10" class="section level1"> + <div id="mkin-09-27-2014-05-10" class="section level1"> <h1 class="page-header" data-toc-text="0.9-27"> -<a href="#mkin-0-9-27-2014-05-10" class="anchor"></a>mkin 0.9-27 (2014-05-10)<small> 2014-05-10 </small> -</h1> +<a href="#mkin-09-27-2014-05-10" class="anchor"></a>mkin 0.9-27 (2014-05-10)</h1> <ul> <li><p>Fork the GUI into a separate package <a href="https://github.com/jranke/gmkin">gmkin</a></p></li> <li><p>DESCRIPTION, NAMESPACE, TODO: Adapt and add copyright information</p></li> @@ -820,20 +826,18 @@ <li><p>Add gmkin workspace datasets FOCUS_2006_gmkin and FOCUS_2006_Z_gmkin</p></li> </ul> </div> - <div id="mkin-0-9-24-2013-11-06" class="section level1"> + <div id="mkin-09-24-2013-11-06" class="section level1"> <h1 class="page-header" data-toc-text="0.9-24"> -<a href="#mkin-0-9-24-2013-11-06" class="anchor"></a>mkin 0.9-24 (2013-11-06)<small> 2013-11-06 </small> -</h1> +<a href="#mkin-09-24-2013-11-06" class="anchor"></a>mkin 0.9-24 (2013-11-06)</h1> <ul> <li><p>Bugfix re-enabling the fixing of any combination of initial values for state variables</p></li> <li><p>Default values for kinetic rate constants are not all 0.1 any more but are “salted” with a small increment to avoid numeric artefacts with the eigenvalue based solutions</p></li> <li><p>Backtransform fixed ODE parameters for the summary</p></li> </ul> </div> - <div id="mkin-0-9-22-2013-10-26" class="section level1"> + <div id="mkin-09-22-2013-10-26" class="section level1"> <h1 class="page-header" data-toc-text="0.9-22"> -<a href="#mkin-0-9-22-2013-10-26" class="anchor"></a>mkin 0.9-22 (2013-10-26)<small> 2013-10-26 </small> -</h1> +<a href="#mkin-09-22-2013-10-26" class="anchor"></a>mkin 0.9-22 (2013-10-26)</h1> <ul> <li><p>Get rid of the optimisation step in <code>mkinerrmin</code> - this was unnecessary. Thanks to KinGUII for the inspiration - actually this is equation 6-2 in FOCUS kinetics p. 91 that I had overlooked originally</p></li> <li><p>Fix <code>plot.mkinfit</code> as it passed graphical arguments like main to the solver</p></li> diff --git a/docs/dev/pkgdown.yml b/docs/dev/pkgdown.yml index 2963e810..b2c50e79 100644 --- a/docs/dev/pkgdown.yml +++ b/docs/dev/pkgdown.yml @@ -1,4 +1,4 @@ -pandoc: 2.2.1 +pandoc: 2.9.2.1 pkgdown: 1.6.1 pkgdown_sha: ~ articles: @@ -10,7 +10,7 @@ articles: web_only/NAFTA_examples: NAFTA_examples.html web_only/benchmarks: benchmarks.html web_only/compiled_models: compiled_models.html -last_built: 2021-01-25T13:41Z +last_built: 2021-06-17T12:41Z 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 b332257e..8c791755 100644 --- a/docs/dev/reference/AIC.mmkin.html +++ b/docs/dev/reference/AIC.mmkin.html @@ -73,7 +73,7 @@ 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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -122,7 +122,7 @@ same dataset." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -187,8 +187,7 @@ dataframe if there are several fits in the column).</p> <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> -</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='co'># We get a warning because the FOMC model does not converge for the</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> @@ -199,15 +198,15 @@ dataframe if there are several fits in the column).</p> <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 57.28211 +#> 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 49.28211 +#> 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 57.59987 +#> 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> diff --git a/docs/dev/reference/CAKE_export.html b/docs/dev/reference/CAKE_export.html index d3f45bf0..e187772f 100644 --- a/docs/dev/reference/CAKE_export.html +++ b/docs/dev/reference/CAKE_export.html @@ -73,7 +73,7 @@ specified as well." /> </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -122,7 +122,7 @@ specified as well." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> diff --git a/docs/dev/reference/D24_2014.html b/docs/dev/reference/D24_2014.html index 2498c58e..9ecb6330 100644 --- a/docs/dev/reference/D24_2014.html +++ b/docs/dev/reference/D24_2014.html @@ -77,7 +77,7 @@ constrained by data protection regulations." /> </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> diff --git a/docs/dev/reference/DFOP.solution.html b/docs/dev/reference/DFOP.solution.html index 22b28732..3ee660f2 100644 --- a/docs/dev/reference/DFOP.solution.html +++ b/docs/dev/reference/DFOP.solution.html @@ -73,7 +73,7 @@ two exponential decline functions." /> </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -122,7 +122,7 @@ two exponential decline functions." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> diff --git a/docs/dev/reference/Extract.mmkin.html b/docs/dev/reference/Extract.mmkin.html index 0c02355f..8381337a 100644 --- a/docs/dev/reference/Extract.mmkin.html +++ b/docs/dev/reference/Extract.mmkin.html @@ -72,7 +72,7 @@ </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -121,7 +121,7 @@ <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -220,7 +220,7 @@ either a list of mkinfit objects or a single mkinfit object.</p></td> <span class='op'>)</span> </div><div class='output co'>#> $par #> parent_0 log_alpha log_beta sigma -#> 99.666193 2.549849 5.050586 1.890202 +#> 99.666192 2.549850 5.050586 1.890202 #> #> $objective #> [1] 28.58291 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 16d12378..a188430d 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 @@ -76,7 +76,7 @@ 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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -125,7 +125,7 @@ in this fit." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> 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 6b8a6119..0bee1c16 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 @@ -76,7 +76,7 @@ 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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -125,7 +125,7 @@ in this fit." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> 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 076a66c6..460fdf0d 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 @@ -76,7 +76,7 @@ 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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -125,7 +125,7 @@ in this fit." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> 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 08f1d416..c1a5fdff 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 @@ -76,7 +76,7 @@ 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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -125,7 +125,7 @@ in this fit." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> diff --git a/docs/dev/reference/FOCUS_2006_datasets.html b/docs/dev/reference/FOCUS_2006_datasets.html index df4651f8..fb3a8f17 100644 --- a/docs/dev/reference/FOCUS_2006_datasets.html +++ b/docs/dev/reference/FOCUS_2006_datasets.html @@ -72,7 +72,7 @@ </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -121,7 +121,7 @@ <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> diff --git a/docs/dev/reference/FOMC.solution.html b/docs/dev/reference/FOMC.solution.html index ed5c4d21..1a5124e0 100644 --- a/docs/dev/reference/FOMC.solution.html +++ b/docs/dev/reference/FOMC.solution.html @@ -73,7 +73,7 @@ a decreasing rate constant." /> </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -122,7 +122,7 @@ a decreasing rate constant." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> diff --git a/docs/dev/reference/HS.solution.html b/docs/dev/reference/HS.solution.html index 8cf5c7f9..1b79e8b6 100644 --- a/docs/dev/reference/HS.solution.html +++ b/docs/dev/reference/HS.solution.html @@ -73,7 +73,7 @@ between them." /> </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -122,7 +122,7 @@ between them." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> diff --git a/docs/dev/reference/IORE.solution.html b/docs/dev/reference/IORE.solution.html index 29d615dc..bc17319e 100644 --- a/docs/dev/reference/IORE.solution.html +++ b/docs/dev/reference/IORE.solution.html @@ -73,7 +73,7 @@ a concentration dependent rate constant." /> </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -122,7 +122,7 @@ a concentration dependent rate constant." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> diff --git a/docs/dev/reference/NAFTA_SOP_2015-1.png b/docs/dev/reference/NAFTA_SOP_2015-1.png Binary files differindex 4d823d77..4f0d7833 100644 --- a/docs/dev/reference/NAFTA_SOP_2015-1.png +++ b/docs/dev/reference/NAFTA_SOP_2015-1.png diff --git a/docs/dev/reference/NAFTA_SOP_2015.html b/docs/dev/reference/NAFTA_SOP_2015.html index 4243faba..fb65fec8 100644 --- a/docs/dev/reference/NAFTA_SOP_2015.html +++ b/docs/dev/reference/NAFTA_SOP_2015.html @@ -72,7 +72,7 @@ </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -121,7 +121,7 @@ <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> diff --git a/docs/dev/reference/NAFTA_SOP_Attachment-1.png b/docs/dev/reference/NAFTA_SOP_Attachment-1.png Binary files differindex 6eb10cde..9417685e 100644 --- a/docs/dev/reference/NAFTA_SOP_Attachment-1.png +++ b/docs/dev/reference/NAFTA_SOP_Attachment-1.png diff --git a/docs/dev/reference/NAFTA_SOP_Attachment.html b/docs/dev/reference/NAFTA_SOP_Attachment.html index de984984..311a7c61 100644 --- a/docs/dev/reference/NAFTA_SOP_Attachment.html +++ b/docs/dev/reference/NAFTA_SOP_Attachment.html @@ -72,7 +72,7 @@ </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -121,7 +121,7 @@ <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -193,7 +193,7 @@ #> 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 2.42e-12 5.00e-01 0.0000 Inf +#> 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 #> @@ -202,7 +202,7 @@ #> 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 5.22e+11 2.86e+11 +#> 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> diff --git a/docs/dev/reference/Rplot001.png b/docs/dev/reference/Rplot001.png Binary files differindex bca41e2c..17a35806 100644 --- a/docs/dev/reference/Rplot001.png +++ b/docs/dev/reference/Rplot001.png diff --git a/docs/dev/reference/Rplot002.png b/docs/dev/reference/Rplot002.png Binary files differindex 9b97a634..32c64fcd 100644 --- a/docs/dev/reference/Rplot002.png +++ b/docs/dev/reference/Rplot002.png diff --git a/docs/dev/reference/Rplot003.png b/docs/dev/reference/Rplot003.png Binary files differindex ff6bc722..5726488c 100644 --- a/docs/dev/reference/Rplot003.png +++ b/docs/dev/reference/Rplot003.png diff --git a/docs/dev/reference/Rplot004.png b/docs/dev/reference/Rplot004.png Binary files differindex 98dd019e..c279f831 100644 --- a/docs/dev/reference/Rplot004.png +++ b/docs/dev/reference/Rplot004.png diff --git a/docs/dev/reference/Rplot005.png b/docs/dev/reference/Rplot005.png Binary files differindex 5e675828..55aa7eec 100644 --- a/docs/dev/reference/Rplot005.png +++ b/docs/dev/reference/Rplot005.png diff --git a/docs/dev/reference/Rplot006.png b/docs/dev/reference/Rplot006.png Binary files differindex da52f580..4c728f4e 100644 --- a/docs/dev/reference/Rplot006.png +++ b/docs/dev/reference/Rplot006.png diff --git a/docs/dev/reference/Rplot007.png b/docs/dev/reference/Rplot007.png Binary files differindex fce3b6ee..10b7455a 100644 --- a/docs/dev/reference/Rplot007.png +++ b/docs/dev/reference/Rplot007.png diff --git a/docs/dev/reference/SFO.solution.html b/docs/dev/reference/SFO.solution.html index b3e7ef9a..43c434c6 100644 --- a/docs/dev/reference/SFO.solution.html +++ b/docs/dev/reference/SFO.solution.html @@ -72,7 +72,7 @@ </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -121,7 +121,7 @@ <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> diff --git a/docs/dev/reference/SFORB.solution.html b/docs/dev/reference/SFORB.solution.html index 9310212f..807fbe5c 100644 --- a/docs/dev/reference/SFORB.solution.html +++ b/docs/dev/reference/SFORB.solution.html @@ -76,7 +76,7 @@ and no substance in the bound fraction." /> </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -125,7 +125,7 @@ and no substance in the bound fraction." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> diff --git a/docs/dev/reference/add_err-1.png b/docs/dev/reference/add_err-1.png Binary files differindex d2ce797f..9ba106db 100644 --- a/docs/dev/reference/add_err-1.png +++ b/docs/dev/reference/add_err-1.png diff --git a/docs/dev/reference/add_err-2.png b/docs/dev/reference/add_err-2.png Binary files differindex ac220c9e..3088c40e 100644 --- a/docs/dev/reference/add_err-2.png +++ b/docs/dev/reference/add_err-2.png diff --git a/docs/dev/reference/add_err-3.png b/docs/dev/reference/add_err-3.png Binary files differindex 40465b71..493a761a 100644 --- a/docs/dev/reference/add_err-3.png +++ b/docs/dev/reference/add_err-3.png diff --git a/docs/dev/reference/add_err.html b/docs/dev/reference/add_err.html index 6ea30515..b94cef29 100644 --- a/docs/dev/reference/add_err.html +++ b/docs/dev/reference/add_err.html @@ -74,7 +74,7 @@ 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-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -123,7 +123,7 @@ may depend on the predicted value and is specified as a standard deviation." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> diff --git a/docs/dev/reference/aw.html b/docs/dev/reference/aw.html index ef808dd8..3b06f2a6 100644 --- a/docs/dev/reference/aw.html +++ b/docs/dev/reference/aw.html @@ -74,7 +74,7 @@ by Burnham and Anderson (2004)." /> </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -123,7 +123,7 @@ by Burnham and Anderson (2004)." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> diff --git a/docs/dev/reference/confint.mkinfit.html b/docs/dev/reference/confint.mkinfit.html index 515a7c9e..2237a539 100644 --- a/docs/dev/reference/confint.mkinfit.html +++ b/docs/dev/reference/confint.mkinfit.html @@ -79,7 +79,7 @@ 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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -128,7 +128,7 @@ method of Venzon and Moolgavkar (1988)." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -273,68 +273,69 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, <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='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>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</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 -#> 3.900 0.929 3.548 </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> +#> 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 from about 50</span> -<span class='co'># seconds to about 12 seconds if we use at least four cores</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'>#> <span class='warning'>Warning: scheduled cores 3, 2, 1 encountered errors in user code, all values of the jobs will be affected</span></div><div class='output co'>#> <span class='error'>Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 0.009 0.034 0.257</span></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 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</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> +</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 96.403833585 102.79311650 -#> 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</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> +</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 96.403833589 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='co'># Against the expectation based on Bates and Watts (1988), the confidence</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 TRUE TRUE -#> k_m1 FALSE FALSE -#> f_parent_to_m1 TRUE FALSE -#> sigma TRUE 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 0.000960 0.000900 -#> k_m1 0.030700 0.029100 -#> f_parent_to_m1 0.004690 0.002780 -#> 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 0.004610 0.002370 -#> k_m1 0.014700 0.002530 -#> f_parent_to_m1 0.004700 0.002350 -#> sigma 0.055000 0.032700</div><div class='input'> +</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> @@ -348,14 +349,14 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, #> 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 96.403833585 102.79311650 +#> 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 96.403833589 1.027931e+02 +#> 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 @@ -373,15 +374,15 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, #> 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 0.0005408689 0.0002217234 +#> parent_0 0.0005408690 0.0002217233 #> k_parent 0.0009598532 0.0009001864 -#> k_m1 0.0307283044 0.0290588365 -#> f_parent_to_m1 0.0046881768 0.0027780063 +#> 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 0.0002217233 -#> k_parent 0.0046102155 0.0023732281 -#> k_m1 0.0146740688 0.0025291817 +#> 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> @@ -395,18 +396,18 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, 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 94.596126334 106.19944007 -#> k_M1 0.037605408 0.04490759 -#> k_M2 0.008568739 0.01087675 -#> f_parent_to_M1 0.021463787 0.62023881 -#> f_parent_to_M2 0.015166531 0.37975349 -#> k1 0.273897467 0.33388084 -#> k2 0.018614555 0.02250379 -#> g 0.671943606 0.73583278 -#> sigma_low 0.251283766 0.83992113 -#> rsd_high 0.040411014 0.07662005</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> +#> 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 94.59613 106.1994</div><div class='input'><span class='co'># }</span> +#> parent_0 94.59604 106.1995</div><div class='input'><span class='co'># }</span> </div></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> diff --git a/docs/dev/reference/create_deg_func.html b/docs/dev/reference/create_deg_func.html index 4945d157..65a682bb 100644 --- a/docs/dev/reference/create_deg_func.html +++ b/docs/dev/reference/create_deg_func.html @@ -72,7 +72,7 @@ </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -121,7 +121,7 @@ <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -181,8 +181,8 @@ 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>, replications <span class='op'>=</span> <span class='fl'>2</span><span class='op'>)</span> </div><div class='output co'>#> <span class='message'>Loading required package: rbenchmark</span></div><div class='output co'>#> test replications elapsed relative user.self sys.self user.child -#> 1 analytical 2 0.396 1.000 0.395 0 0 -#> 2 deSolve 2 0.694 1.753 0.693 0 0 +#> 1 analytical 2 0.396 1.00 0.396 0 0 +#> 2 deSolve 2 0.709 1.79 0.707 0 0 #> sys.child #> 1 0 #> 2 0</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> @@ -193,8 +193,8 @@ 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>, replications <span class='op'>=</span> <span class='fl'>2</span><span class='op'>)</span> </div><div class='output co'>#> test replications elapsed relative user.self sys.self user.child -#> 1 analytical 2 0.838 1.000 0.838 0.001 0 -#> 2 deSolve 2 1.573 1.877 1.572 0.000 0 +#> 1 analytical 2 0.844 1.000 0.844 0 0 +#> 2 deSolve 2 1.624 1.924 1.624 0 0 #> sys.child #> 1 0 #> 2 0</div><div class='input'><span class='co'># }</span> diff --git a/docs/dev/reference/dimethenamid_2018-1.png b/docs/dev/reference/dimethenamid_2018-1.png Binary files differnew file mode 100644 index 00000000..52b8a2be --- /dev/null +++ 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 21dea623..e255765e 100644 --- a/docs/dev/reference/dimethenamid_2018.html +++ b/docs/dev/reference/dimethenamid_2018.html @@ -77,7 +77,7 @@ constrained by data protection regulations." /> </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> </span> </div> @@ -203,7 +203,291 @@ specific pieces of information in the comments.</p> #> Elliot 2 0.75 33.37 23 #> Flaach 0.40 NA 20 #> BBA 2.2 0.40 NA 20 -#> BBA 2.3 0.40 NA 20</div></pre> +#> BBA 2.3 0.40 NA 20</div><div class='input'><span class='va'>dmta_ds</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='fl'>1</span><span class='op'>:</span><span class='fl'>8</span>, <span class='kw'>function</span><span class='op'>(</span><span class='va'>i</span><span class='op'>)</span> <span class='op'>{</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'>$</span><span class='va'>data</span> + <span class='va'>ds_i</span><span class='op'>[</span><span class='va'>ds_i</span><span class='op'>$</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='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'>$</span><span class='va'>f_time_norm</span><span class='op'>[</span><span class='va'>i</span><span class='op'>]</span> + <span class='va'>ds_i</span> +<span class='op'>}</span><span class='op'>)</span> +<span class='fu'><a href='https://rdrr.io/r/base/names.html'>names</a></span><span class='op'>(</span><span class='va'>dmta_ds</span><span class='op'>)</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>sapply</a></span><span class='op'>(</span><span class='va'>dimethenamid_2018</span><span class='op'>$</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 class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Borstel"</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'>rbind</a></span><span class='op'>(</span><span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Borstel 1"</span><span class='op'>]</span><span class='op'>]</span>, <span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Borstel 2"</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'>"Borstel 1"</span><span class='op'>]</span><span class='op'>]</span> <span class='op'><-</span> <span class='cn'>NULL</span> +<span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Borstel 2"</span><span class='op'>]</span><span class='op'>]</span> <span class='op'><-</span> <span class='cn'>NULL</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'>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='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 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 class='co'># \dontrun{</span> +<span class='va'>dfop_sfo3_plus</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span> + DMTA <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'>"M23"</span>, <span class='st'>"M27"</span>, <span class='st'>"M31"</span><span class='op'>)</span><span class='op'>)</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>, + 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>, + 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>, + quiet <span class='op'>=</span> <span class='cn'>TRUE</span> +<span class='op'>)</span> +<span class='va'>f_dmta_mkin_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-SFO3+"</span> <span class='op'>=</span> <span class='va'>dfop_sfo3_plus</span><span class='op'>)</span>, + <span class='va'>dmta_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 class='fu'><a href='nlmixr.mmkin.html'>nlmixr_model</a></span><span class='op'>(</span><span class='va'>f_dmta_mkin_tc</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'>With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)</span></div><div class='output co'>#> function () +#> { +#> ini({ +#> DMTA_0 = 98.7697627680706 +#> eta.DMTA_0 ~ 2.35171765917765 +#> log_k_M23 = -3.92162409637283 +#> eta.log_k_M23 ~ 0.549278519419884 +#> log_k_M27 = -4.33774620773911 +#> eta.log_k_M27 ~ 0.864474956685295 +#> log_k_M31 = -4.24767627688461 +#> eta.log_k_M31 ~ 0.750297149164171 +#> log_k1 = -2.2341008812259 +#> eta.log_k1 ~ 0.902976221565793 +#> log_k2 = -3.7762779983269 +#> eta.log_k2 ~ 1.57684519529298 +#> g_qlogis = 0.450175725479389 +#> eta.g_qlogis ~ 3.0851335687675 +#> f_DMTA_tffm0_1_qlogis = -2.09240906629456 +#> eta.f_DMTA_tffm0_1_qlogis ~ 0.3 +#> f_DMTA_tffm0_2_qlogis = -2.18057573598794 +#> eta.f_DMTA_tffm0_2_qlogis ~ 0.3 +#> f_DMTA_tffm0_3_qlogis = -2.14267187609763 +#> eta.f_DMTA_tffm0_3_qlogis ~ 0.3 +#> sigma_low_DMTA = 0.697933852349996 +#> rsd_high_DMTA = 0.0257724286053519 +#> sigma_low_M23 = 0.697933852349996 +#> rsd_high_M23 = 0.0257724286053519 +#> sigma_low_M27 = 0.697933852349996 +#> rsd_high_M27 = 0.0257724286053519 +#> sigma_low_M31 = 0.697933852349996 +#> rsd_high_M31 = 0.0257724286053519 +#> }) +#> model({ +#> DMTA_0_model = DMTA_0 + eta.DMTA_0 +#> DMTA(0) = DMTA_0_model +#> k_M23 = exp(log_k_M23 + eta.log_k_M23) +#> k_M27 = exp(log_k_M27 + eta.log_k_M27) +#> k_M31 = exp(log_k_M31 + eta.log_k_M31) +#> k1 = exp(log_k1 + eta.log_k1) +#> k2 = exp(log_k2 + eta.log_k2) +#> g = expit(g_qlogis + eta.g_qlogis) +#> f_DMTA_tffm0_1 = expit(f_DMTA_tffm0_1_qlogis + eta.f_DMTA_tffm0_1_qlogis) +#> f_DMTA_tffm0_2 = expit(f_DMTA_tffm0_2_qlogis + eta.f_DMTA_tffm0_2_qlogis) +#> f_DMTA_tffm0_3 = expit(f_DMTA_tffm0_3_qlogis + eta.f_DMTA_tffm0_3_qlogis) +#> f_DMTA_to_M23 = f_DMTA_tffm0_1 +#> f_DMTA_to_M27 = f_DMTA_tffm0_2 * (1 - f_DMTA_tffm0_1) +#> f_DMTA_to_M31 = f_DMTA_tffm0_3 * (1 - f_DMTA_tffm0_2) * +#> (1 - f_DMTA_tffm0_1) +#> d/dt(DMTA) = -((k1 * g * exp(-k1 * time) + k2 * (1 - +#> g) * exp(-k2 * time))/(g * exp(-k1 * time) + (1 - +#> g) * exp(-k2 * time))) * DMTA +#> d/dt(M23) = +f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + +#> k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + +#> (1 - g) * exp(-k2 * time))) * DMTA - k_M23 * M23 +#> d/dt(M27) = +f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + +#> k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + +#> (1 - g) * exp(-k2 * time))) * DMTA - k_M27 * M27 + +#> k_M31 * M31 +#> d/dt(M31) = +f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + +#> k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + +#> (1 - g) * exp(-k2 * time))) * DMTA - k_M31 * M31 +#> DMTA ~ add(sigma_low_DMTA) + prop(rsd_high_DMTA) +#> M23 ~ add(sigma_low_M23) + prop(rsd_high_M23) +#> M27 ~ add(sigma_low_M27) + prop(rsd_high_M27) +#> M31 ~ add(sigma_low_M31) + prop(rsd_high_M31) +#> }) +#> } +#> <environment: 0x555559c2bd78></div><div class='input'><span class='va'>f_dmta_nlmixr_focei</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_dmta_mkin_tc</span>, est <span class='op'>=</span> <span class='st'>"focei"</span>, + control <span class='op'>=</span> <span class='fu'>nlmixr</span><span class='fu'>::</span><span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/foceiControl.html'>foceiControl</a></span><span class='op'>(</span>print <span class='op'>=</span> <span class='fl'>500</span><span class='op'>)</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:02 +#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:04 +#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:01 +#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:08 +#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:07 +#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:07 +#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 +#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 +#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> [1] "CMT"</div><div class='output co'>#> <span class='message'>RxODE 1.1.0 using 8 threads (see ?getRxThreads)</span> +#> <span class='message'> no cache: create with `rxCreateCache()`</span></div><div class='output co'>#> <span style='font-weight: bold;'>Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation +#> F: Forward difference gradient approximation +#> C: Central difference gradient approximation +#> M: Mixed forward and central difference gradient approximation +#> Unscaled parameters for Omegas=chol(solve(omega)); +#> Diagonals are transformed, as specified by foceiControl(diagXform=) +#> |-----+---------------+-----------+-----------+-----------+-----------| +#> | #| Objective Fun | DMTA_0 | log_k_M23 | log_k_M27 | log_k_M31 | +#> |.....................| log_k1 | log_k2 | g_qlogis |f_DMTA_tffm0_1_qlogis | +#> |.....................|f_DMTA_tffm0_2_qlogis |f_DMTA_tffm0_3_qlogis | sigma_low | rsd_high | +#> |.....................| o1 | o2 | o3 | o4 | +#> |.....................| o5 | o6 | o7 | o8 | +#> <span style='text-decoration: underline;'>|.....................| o9 | o10 |...........|...........|</span> +#> calculating covariance matrix +#> done</div><div class='output co'>#> <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#> <span class='message'>done</span></div><div class='output co'>#> <span class='warning'>Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span></div><div class='output co'>#> <span class='warning'>Warning: last objective function was not at minimum, possible problems in optimization</span></div><div class='output co'>#> <span class='warning'>Warning: S matrix non-positive definite</span></div><div class='output co'>#> <span class='warning'>Warning: using R matrix to calculate covariance</span></div><div class='output co'>#> <span class='warning'>Warning: gradient problems with initial estimate and covariance; see $scaleInfo</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'>f_dmta_nlmixr_focei</span><span class='op'>)</span> +</div><div class='output co'>#> nlmixr version used for fitting: 2.0.4 +#> mkin version used for pre-fitting: 1.0.5 +#> R version used for fitting: 4.1.0 +#> Date of fit: Thu Jun 17 14:04:58 2021 +#> Date of summary: Thu Jun 17 14:04:58 2021 +#> +#> 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 +#> d_M23/dt = + f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) +#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * +#> exp(-k2 * time))) * DMTA - k_M23 * M23 +#> d_M27/dt = + f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) +#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * +#> exp(-k2 * time))) * DMTA - k_M27 * M27 + k_M31 * M31 +#> d_M31/dt = + f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) +#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * +#> exp(-k2 * time))) * DMTA - k_M31 * M31 +#> +#> Data: +#> 568 observations of 4 variable(s) grouped in 6 datasets +#> +#> Degradation model predictions using RxODE +#> +#> Fitted in 242.937 s +#> +#> Variance model: Two-component variance function +#> +#> Mean of starting values for individual parameters: +#> DMTA_0 log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1 f_DMTA_ilr_2 +#> 98.7698 -3.9216 -4.3377 -4.2477 0.1380 0.1393 +#> f_DMTA_ilr_3 log_k1 log_k2 g_qlogis +#> -1.7571 -2.2341 -3.7763 0.4502 +#> +#> Mean of starting values for error model parameters: +#> sigma_low rsd_high +#> 0.69793 0.02577 +#> +#> Fixed degradation parameter values: +#> None +#> +#> Results: +#> +#> Likelihood calculated by focei +#> AIC BIC logLik +#> 1936 2031 -945.9 +#> +#> Optimised parameters: +#> est. lower upper +#> DMTA_0 98.7698 98.7356 98.8039 +#> log_k_M23 -3.9216 -3.9235 -3.9197 +#> log_k_M27 -4.3377 -4.3398 -4.3357 +#> log_k_M31 -4.2477 -4.2497 -4.2457 +#> log_k1 -2.2341 -2.2353 -2.2329 +#> log_k2 -3.7763 -3.7781 -3.7744 +#> g_qlogis 0.4502 0.4496 0.4507 +#> f_DMTA_tffm0_1_qlogis -2.0924 -2.0936 -2.0912 +#> f_DMTA_tffm0_2_qlogis -2.1806 -2.1818 -2.1794 +#> f_DMTA_tffm0_3_qlogis -2.1427 -2.1439 -2.1415 +#> +#> Correlation: +#> DMTA_0 l__M23 l__M27 l__M31 log_k1 log_k2 g_qlgs +#> log_k_M23 0 +#> log_k_M27 0 0 +#> log_k_M31 0 0 0 +#> log_k1 0 0 0 0 +#> log_k2 0 0 0 0 0 +#> g_qlogis 0 0 0 0 0 0 +#> f_DMTA_tffm0_1_qlogis 0 0 0 0 0 0 0 +#> f_DMTA_tffm0_2_qlogis 0 0 0 0 0 0 0 +#> f_DMTA_tffm0_3_qlogis 0 0 0 0 0 0 0 +#> f_DMTA_0_1 f_DMTA_0_2 +#> log_k_M23 +#> log_k_M27 +#> log_k_M31 +#> log_k1 +#> log_k2 +#> g_qlogis +#> f_DMTA_tffm0_1_qlogis +#> f_DMTA_tffm0_2_qlogis 0 +#> f_DMTA_tffm0_3_qlogis 0 0 +#> +#> Random effects (omega): +#> eta.DMTA_0 eta.log_k_M23 eta.log_k_M27 eta.log_k_M31 +#> eta.DMTA_0 2.352 0.0000 0.0000 0.0000 +#> eta.log_k_M23 0.000 0.5493 0.0000 0.0000 +#> eta.log_k_M27 0.000 0.0000 0.8645 0.0000 +#> eta.log_k_M31 0.000 0.0000 0.0000 0.7503 +#> eta.log_k1 0.000 0.0000 0.0000 0.0000 +#> eta.log_k2 0.000 0.0000 0.0000 0.0000 +#> eta.g_qlogis 0.000 0.0000 0.0000 0.0000 +#> eta.f_DMTA_tffm0_1_qlogis 0.000 0.0000 0.0000 0.0000 +#> eta.f_DMTA_tffm0_2_qlogis 0.000 0.0000 0.0000 0.0000 +#> eta.f_DMTA_tffm0_3_qlogis 0.000 0.0000 0.0000 0.0000 +#> eta.log_k1 eta.log_k2 eta.g_qlogis +#> eta.DMTA_0 0.000 0.000 0.000 +#> eta.log_k_M23 0.000 0.000 0.000 +#> eta.log_k_M27 0.000 0.000 0.000 +#> eta.log_k_M31 0.000 0.000 0.000 +#> eta.log_k1 0.903 0.000 0.000 +#> eta.log_k2 0.000 1.577 0.000 +#> eta.g_qlogis 0.000 0.000 3.085 +#> eta.f_DMTA_tffm0_1_qlogis 0.000 0.000 0.000 +#> eta.f_DMTA_tffm0_2_qlogis 0.000 0.000 0.000 +#> eta.f_DMTA_tffm0_3_qlogis 0.000 0.000 0.000 +#> eta.f_DMTA_tffm0_1_qlogis eta.f_DMTA_tffm0_2_qlogis +#> eta.DMTA_0 0.0 0.0 +#> eta.log_k_M23 0.0 0.0 +#> eta.log_k_M27 0.0 0.0 +#> eta.log_k_M31 0.0 0.0 +#> eta.log_k1 0.0 0.0 +#> eta.log_k2 0.0 0.0 +#> eta.g_qlogis 0.0 0.0 +#> eta.f_DMTA_tffm0_1_qlogis 0.3 0.0 +#> eta.f_DMTA_tffm0_2_qlogis 0.0 0.3 +#> eta.f_DMTA_tffm0_3_qlogis 0.0 0.0 +#> eta.f_DMTA_tffm0_3_qlogis +#> eta.DMTA_0 0.0 +#> eta.log_k_M23 0.0 +#> eta.log_k_M27 0.0 +#> eta.log_k_M31 0.0 +#> eta.log_k1 0.0 +#> eta.log_k2 0.0 +#> eta.g_qlogis 0.0 +#> eta.f_DMTA_tffm0_1_qlogis 0.0 +#> eta.f_DMTA_tffm0_2_qlogis 0.0 +#> eta.f_DMTA_tffm0_3_qlogis 0.3 +#> +#> Variance model: +#> sigma_low rsd_high +#> 0.69793 0.02577 +#> +#> Backtransformed parameters: +#> est. lower upper +#> DMTA_0 98.76976 98.73563 98.80390 +#> k_M23 0.01981 0.01977 0.01985 +#> k_M27 0.01307 0.01304 0.01309 +#> k_M31 0.01430 0.01427 0.01433 +#> f_DMTA_to_M23 0.10984 NA NA +#> f_DMTA_to_M27 0.09036 NA NA +#> f_DMTA_to_M31 0.08399 NA NA +#> k1 0.10709 0.10696 0.10722 +#> k2 0.02291 0.02287 0.02295 +#> g 0.61068 0.61055 0.61081 +#> +#> Resulting formation fractions: +#> ff +#> DMTA_M23 0.10984 +#> DMTA_M27 0.09036 +#> DMTA_M31 0.08399 +#> DMTA_sink 0.71581 +#> +#> Estimated disappearance times: +#> DT50 DT90 DT50back DT50_k1 DT50_k2 +#> DMTA 10.66 59.78 18 6.473 30.26 +#> M23 34.99 116.24 NA NA NA +#> M27 53.05 176.23 NA NA NA +#> M31 48.48 161.05 NA NA NA</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'>f_dmta_nlmixr_focei</span><span class='op'>)</span> +</div><div class='img'><img src='dimethenamid_2018-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># saem has a problem with this model/data combination, maybe because of the</span> +<span class='co'># overparameterised error model, to be investigated</span> +<span class='co'>#f_dmta_nlmixr_saem <- nlmixr(f_dmta_mkin_tc, est = "saem",</span> +<span class='co'># control = saemControl(print = 500))</span> +<span class='co'>#summary(f_dmta_nlmixr_saem)</span> +<span class='co'>#plot(f_dmta_nlmixr_saem)</span> +<span class='co'># }</span> +</div></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> <nav id="toc" data-toggle="toc" class="sticky-top"> diff --git a/docs/dev/reference/endpoints.html b/docs/dev/reference/endpoints.html index db702c2e..dc1d1f17 100644 --- a/docs/dev/reference/endpoints.html +++ b/docs/dev/reference/endpoints.html @@ -78,7 +78,7 @@ advantage that the SFORB model can also be used for metabolites." /> </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> </span> </div> @@ -127,7 +127,7 @@ advantage that the SFORB model can also be used for metabolites." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -165,8 +165,8 @@ advantage that the SFORB model can also be used for metabolites.</p> <colgroup><col class="name" /><col class="desc" /></colgroup> <tr> <th>fit</th> - <td><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 + <td><p>An object of class <a href='mkinfit.html'>mkinfit</a>, <a href='nlme.mmkin.html'>nlme.mmkin</a>, <a href='saem.html'>saem.mmkin</a> or +<a href='nlmixr.mmkin.html'>nlmixr.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></td> 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 24cb54c5..33946ded 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 78e57fb0..9904370f 100644 --- a/docs/dev/reference/experimental_data_for_UBA.html +++ b/docs/dev/reference/experimental_data_for_UBA.html @@ -100,7 +100,7 @@ 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-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -149,7 +149,7 @@ Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> diff --git a/docs/dev/reference/f_time_norm_focus.html b/docs/dev/reference/f_time_norm_focus.html index 3421043d..852e00a0 100644 --- a/docs/dev/reference/f_time_norm_focus.html +++ b/docs/dev/reference/f_time_norm_focus.html @@ -73,7 +73,7 @@ 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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> diff --git a/docs/dev/reference/focus_soil_moisture.html b/docs/dev/reference/focus_soil_moisture.html index c46fd69a..0e6fea28 100644 --- a/docs/dev/reference/focus_soil_moisture.html +++ b/docs/dev/reference/focus_soil_moisture.html @@ -73,7 +73,7 @@ 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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -122,7 +122,7 @@ corresponds to pF2, MWHC to pF 1 and 1/3 bar to pF 2.5." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> diff --git a/docs/dev/reference/get_deg_func.html b/docs/dev/reference/get_deg_func.html index a266bf5f..fb661085 100644 --- a/docs/dev/reference/get_deg_func.html +++ b/docs/dev/reference/get_deg_func.html @@ -72,7 +72,7 @@ </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -121,7 +121,7 @@ <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> diff --git a/docs/dev/reference/ilr.html b/docs/dev/reference/ilr.html index 98e51211..452647d6 100644 --- a/docs/dev/reference/ilr.html +++ b/docs/dev/reference/ilr.html @@ -73,7 +73,7 @@ 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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -122,7 +122,7 @@ transformations." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> diff --git a/docs/dev/reference/index.html b/docs/dev/reference/index.html index e038ef5c..bb030605 100644 --- a/docs/dev/reference/index.html +++ b/docs/dev/reference/index.html @@ -71,7 +71,7 @@ </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> </span> </div> @@ -175,7 +175,7 @@ </tr><tr> <td> - <p><code><a href="mmkin.html">mmkin()</a></code> </p> + <p><code><a href="mmkin.html">mmkin()</a></code> <code><a href="mmkin.html">print(<i><mmkin></i>)</a></code> </p> </td> <td><p>Fit one or more kinetic models with one or more state variables to one or more datasets</p></td> @@ -297,12 +297,6 @@ of an mmkin object</p></td> <p><code><a href="AIC.mmkin.html">AIC(<i><mmkin></i>)</a></code> <code><a href="AIC.mmkin.html">BIC(<i><mmkin></i>)</a></code> </p> </td> <td><p>Calculate the AIC for a column of an mmkin object</p></td> - </tr><tr> - - <td> - <p><code><a href="print.mmkin.html">print(<i><mmkin></i>)</a></code> </p> - </td> - <td><p>Print method for mmkin objects</p></td> </tr> </tbody><tbody> <tr> @@ -331,6 +325,12 @@ of an mmkin object</p></td> </tr><tr> <td> + <p><code><a href="nlmixr.mmkin.html">nlmixr(<i><mmkin></i>)</a></code> <code><a href="nlmixr.mmkin.html">print(<i><nlmixr.mmkin></i>)</a></code> <code><a href="nlmixr.mmkin.html">nlmixr_model()</a></code> <code><a href="nlmixr.mmkin.html">nlmixr_data()</a></code> </p> + </td> + <td><p>Fit nonlinear mixed models using nlmixr</p></td> + </tr><tr> + + <td> <p><code><a href="plot.mixed.mmkin.html">plot(<i><mixed.mmkin></i>)</a></code> </p> </td> <td><p>Plot predictions from a fitted nonlinear mixed model obtained via an mmkin row object</p></td> @@ -343,13 +343,19 @@ of an mmkin object</p></td> </tr><tr> <td> + <p><code><a href="summary.nlmixr.mmkin.html">summary(<i><nlmixr.mmkin></i>)</a></code> <code><a href="summary.nlmixr.mmkin.html">print(<i><summary.nlmixr.mmkin></i>)</a></code> </p> + </td> + <td><p>Summary method for class "nlmixr.mmkin"</p></td> + </tr><tr> + + <td> <p><code><a href="summary.saem.mmkin.html">summary(<i><saem.mmkin></i>)</a></code> <code><a href="summary.saem.mmkin.html">print(<i><summary.saem.mmkin></i>)</a></code> </p> </td> <td><p>Summary method for class "saem.mmkin"</p></td> </tr><tr> <td> - <p><code><a href="nlme.html">nlme_function()</a></code> <code><a href="nlme.html">mean_degparms()</a></code> <code><a href="nlme.html">nlme_data()</a></code> </p> + <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> </tr><tr> @@ -576,6 +582,12 @@ kinetic models fitted with mkinfit</p></td> </tr><tr> <td> + <p><code><a href="tffm0.html">tffm0()</a></code> <code><a href="tffm0.html">invtffm0()</a></code> </p> + </td> + <td><p>Transform formation fractions as in the first published mkin version</p></td> + </tr><tr> + + <td> <p><code><a href="logLik.mkinfit.html">logLik(<i><mkinfit></i>)</a></code> </p> </td> <td><p>Calculated the log-likelihood of a fitted mkinfit object</p></td> @@ -612,6 +624,12 @@ kinetic models fitted with mkinfit</p></td> </tr><tr> <td> + <p><code><a href="mean_degparms.html">mean_degparms()</a></code> </p> + </td> + <td><p>Calculate mean degradation parameters for an mmkin row object</p></td> + </tr><tr> + + <td> <p><code><a href="create_deg_func.html">create_deg_func()</a></code> </p> </td> <td><p>Create degradation functions for known analytical solutions</p></td> diff --git a/docs/dev/reference/loftest-1.png b/docs/dev/reference/loftest-1.png Binary files differindex 6b918fec..d6006ecc 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 60874bd3..4d0dc551 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 4837e7f2..6afd084b 100644 --- a/docs/dev/reference/loftest-3.png +++ b/docs/dev/reference/loftest-3.png diff --git a/docs/dev/reference/loftest-4.png b/docs/dev/reference/loftest-4.png Binary files differindex 9c18ac30..f94eede1 100644 --- a/docs/dev/reference/loftest-4.png +++ b/docs/dev/reference/loftest-4.png diff --git a/docs/dev/reference/loftest-5.png b/docs/dev/reference/loftest-5.png Binary files differindex 11f2bda7..43460a65 100644 --- a/docs/dev/reference/loftest-5.png +++ b/docs/dev/reference/loftest-5.png diff --git a/docs/dev/reference/loftest.html b/docs/dev/reference/loftest.html index abbbd3b9..9dbd547d 100644 --- a/docs/dev/reference/loftest.html +++ b/docs/dev/reference/loftest.html @@ -75,7 +75,7 @@ lrtest.default from the lmtest package." /> </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -124,7 +124,7 @@ lrtest.default from the lmtest package." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> diff --git a/docs/dev/reference/logLik.mkinfit.html b/docs/dev/reference/logLik.mkinfit.html index 66539dbd..3e9452c6 100644 --- a/docs/dev/reference/logLik.mkinfit.html +++ b/docs/dev/reference/logLik.mkinfit.html @@ -76,7 +76,7 @@ the error model." /> </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -125,7 +125,7 @@ the error model." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -196,11 +196,11 @@ and the fitted error model parameters.</p> 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='va'>FOCUS_2006_D</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> -</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='va'>f_obs</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>, error_model <span class='op'>=</span> <span class='st'>"obs"</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='va'>f_tc</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>, 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'><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> + <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 diff --git a/docs/dev/reference/logistic.solution-2.png b/docs/dev/reference/logistic.solution-2.png Binary files differindex 764996df..73e6436d 100644 --- a/docs/dev/reference/logistic.solution-2.png +++ b/docs/dev/reference/logistic.solution-2.png diff --git a/docs/dev/reference/logistic.solution.html b/docs/dev/reference/logistic.solution.html index 950e8a8e..ab68c99e 100644 --- a/docs/dev/reference/logistic.solution.html +++ b/docs/dev/reference/logistic.solution.html @@ -73,7 +73,7 @@ an increasing rate constant, supposedly caused by microbial growth" /> </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -122,7 +122,7 @@ an increasing rate constant, supposedly caused by microbial growth" /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -240,10 +240,10 @@ Version 1.1, 18 December 2014 <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> </div><div class='img'><img src='logistic.solution-2.png' alt='' width='700' height='433' /></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'>m</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 1.057896e+02 1.9023449703 55.610119 3.768361e-16 1.016451e+02 -#> kmax 6.398190e-02 0.0143201031 4.467978 3.841829e-04 3.929235e-02 +#> parent_0 1.057896e+02 1.9023449590 55.610120 3.768360e-16 1.016451e+02 +#> kmax 6.398190e-02 0.0143201029 4.467978 3.841828e-04 3.929235e-02 #> k0 1.612775e-04 0.0005866813 0.274898 3.940351e-01 5.846688e-08 -#> r 2.263946e-01 0.1718110715 1.317695 1.061044e-01 4.335843e-02 +#> r 2.263946e-01 0.1718110662 1.317695 1.061043e-01 4.335843e-02 #> sigma 5.332935e+00 0.9145907310 5.830952 4.036926e-05 3.340213e+00 #> Upper #> parent_0 109.9341588 diff --git a/docs/dev/reference/lrtest.mkinfit.html b/docs/dev/reference/lrtest.mkinfit.html index b76ebc87..f2d8472e 100644 --- a/docs/dev/reference/lrtest.mkinfit.html +++ b/docs/dev/reference/lrtest.mkinfit.html @@ -76,7 +76,7 @@ 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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -125,7 +125,7 @@ and can be expressed by fixing the parameters of the other." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> diff --git a/docs/dev/reference/max_twa_parent.html b/docs/dev/reference/max_twa_parent.html index 25f745e9..a358568a 100644 --- a/docs/dev/reference/max_twa_parent.html +++ b/docs/dev/reference/max_twa_parent.html @@ -78,7 +78,7 @@ soil section of the FOCUS guidance." /> </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -127,7 +127,7 @@ soil section of the FOCUS guidance." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> diff --git a/docs/dev/reference/mccall81_245T-1.png b/docs/dev/reference/mccall81_245T-1.png Binary files differindex 5daa5f18..91fe060e 100644 --- a/docs/dev/reference/mccall81_245T-1.png +++ b/docs/dev/reference/mccall81_245T-1.png diff --git a/docs/dev/reference/mccall81_245T.html b/docs/dev/reference/mccall81_245T.html index 7179533d..f79137be 100644 --- a/docs/dev/reference/mccall81_245T.html +++ b/docs/dev/reference/mccall81_245T.html @@ -74,7 +74,7 @@ </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -181,30 +181,30 @@ <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 2.1847074888 47.537272 4.472189e-18 +#> 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 0.2986993400 1.356073 9.756988e-02 -#> k_anisole 6.678742e-03 0.0008021439 8.326114 2.623176e-07 -#> f_T245_to_phenol 6.227599e-01 0.3985340295 1.562627 6.949412e-02 -#> f_phenol_to_anisole 1.000000e+00 0.6718439378 1.488441 7.867787e-02 -#> sigma 2.514628e+00 0.4907558750 5.123989 6.233156e-05 +#> 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 99.246061370 1.084640e+02 +#> T245_0 99.246061385 1.084640e+02 #> k_T245 0.039631621 4.746194e-02 -#> k_phenol 0.218013878 7.525762e-01 +#> k_phenol 0.218013879 7.525762e-01 #> k_anisole 0.005370739 8.305299e-03 -#> f_T245_to_phenol 0.547559083 6.924813e-01 +#> 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 6.894640e-11 +#> 6.227599e-01 3.772401e-01 1.000000e+00 3.773626e-10 #> #> $distimes #> DT50 DT90 #> T245 15.982025 53.09114 #> phenol 1.711229 5.68458 -#> anisole 103.784092 344.76329 +#> anisole 103.784093 344.76329 #> </div><div class='input'> <span class='co'># formation fraction from phenol to anisol is practically 1. As we cannot</span> <span class='co'># fix formation fractions when using the ilr transformation, we can turn of</span> <span class='co'># the sink in the model generation</span> @@ -215,28 +215,28 @@ quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> </div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>fit.2</span><span class='op'>)</span><span class='op'>$</span><span class='va'>bpar</span> </div><div class='output co'>#> Estimate se_notrans t value Pr(>t) Lower -#> T245_0 1.038550e+02 2.1623653027 48.028439 4.993108e-19 99.271020526 -#> k_T245 4.337042e-02 0.0018343666 23.643268 3.573555e-14 0.039650977 -#> k_phenol 4.050582e-01 0.1177237248 3.440752 1.679252e-03 0.218746585 -#> k_anisole 6.678741e-03 0.0006829745 9.778903 1.872894e-08 0.005377083 -#> f_T245_to_phenol 6.227599e-01 0.0342197865 18.198825 2.039410e-12 0.547975628 +#> 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_0 108.43904097 +#> T245_0 108.43904074 #> k_T245 0.04743877 -#> k_phenol 0.75005577 +#> k_phenol 0.75005585 #> k_anisole 0.00829550 -#> f_T245_to_phenol 0.69212306 +#> 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 6.894640e-11 +#> 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 103.784092 344.76329 +#> 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> diff --git a/docs/dev/reference/mean_degparms.html b/docs/dev/reference/mean_degparms.html new file mode 100644 index 00000000..f63dbc31 --- /dev/null +++ b/docs/dev/reference/mean_degparms.html @@ -0,0 +1,210 @@ +<!-- Generated by pkgdown: do not edit by hand --> +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<meta 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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><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 mmkin row object containing several fits of the same model to different datasets</p></td> + </tr> + <tr> + <th>random</th> + <td><p>Should a list with fixed and random effects be returned?</p></td> + </tr> + <tr> + <th>test_log_parms</th> + <td><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></td> + </tr> + <tr> + <th>conf.level</th> + <td><p>Possibility to adjust the required confidence level +for parameter that are tested if requested by 'test_log_parms'.</p></td> + </tr> + </table> + + <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> + + <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> + + </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>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> +</div> + + </footer> + </div> + + + + + </body> +</html> + + diff --git a/docs/dev/reference/mixed-1.png b/docs/dev/reference/mixed-1.png Binary files differindex 3400c4aa..422ab6a0 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 18a67af8..338480ee 100644 --- a/docs/dev/reference/mixed.html +++ b/docs/dev/reference/mixed.html @@ -72,7 +72,7 @@ </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> </span> </div> @@ -121,7 +121,7 @@ <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -180,6 +180,10 @@ </tr> </table> + <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> + + <p>An object of class 'mixed.mmkin' which has the observed data in a +single dataframe which is convenient for plotting</p> <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> <pre class="examples"><div class='input'><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> @@ -235,18 +239,16 @@ #> Status of individual fits: #> #> dataset -#> model 1 2 3 4 5 6 7 8 -#> DFOP-SFO OK OK OK OK OK C OK OK +#> model 1 2 3 4 5 6 7 8 +#> DFOP-SFO OK OK OK OK OK OK OK OK #> #> OK: No warnings -#> C: Optimisation did not converge: -#> iteration limit reached without convergence (10) #> #> Mean fitted parameters: #> parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 -#> 100.606304 -8.759216 -0.002001 -3.350539 -3.989549 +#> 100.674757 -8.761916 -0.004347 -3.348812 -3.986853 #> g_qlogis -#> -0.090353 </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'>f_mixed</span><span class='op'>)</span> +#> -0.087392 </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'>f_mixed</span><span class='op'>)</span> </div><div class='img'><img src='mixed-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># }</span> </div></pre> </div> diff --git a/docs/dev/reference/mkin_long_to_wide.html b/docs/dev/reference/mkin_long_to_wide.html index 28a37800..6246fbe2 100644 --- a/docs/dev/reference/mkin_long_to_wide.html +++ b/docs/dev/reference/mkin_long_to_wide.html @@ -74,7 +74,7 @@ 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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -123,7 +123,7 @@ variable and several dependent variables as columns." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> diff --git a/docs/dev/reference/mkin_wide_to_long.html b/docs/dev/reference/mkin_wide_to_long.html index f085d162..f2bf00c1 100644 --- a/docs/dev/reference/mkin_wide_to_long.html +++ b/docs/dev/reference/mkin_wide_to_long.html @@ -74,7 +74,7 @@ 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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -123,7 +123,7 @@ mkinfit." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> diff --git a/docs/dev/reference/mkinds.html b/docs/dev/reference/mkinds.html index 907f8ad3..0d1de46d 100644 --- a/docs/dev/reference/mkinds.html +++ b/docs/dev/reference/mkinds.html @@ -75,7 +75,7 @@ provided by this package come as mkinds objects nevertheless." /> </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -124,7 +124,7 @@ provided by this package come as mkinds objects nevertheless." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> diff --git a/docs/dev/reference/mkindsg.html b/docs/dev/reference/mkindsg.html index 3e4dfb39..67c6e5df 100644 --- a/docs/dev/reference/mkindsg.html +++ b/docs/dev/reference/mkindsg.html @@ -75,7 +75,7 @@ dataset if no data are supplied." /> </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -124,7 +124,7 @@ dataset if no data are supplied." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -239,12 +239,12 @@ or covariates like soil pH).</p></dd> <span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>mdsg</span><span class='op'>)</span> </div><div class='output co'>#> <mkindsg> holding 5 mkinds objects #> Title $title: Experimental X -#> Occurrene of observed compounds $observed_n: +#> Occurrence of observed compounds $observed_n: #> parent A1 #> 5 5 </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'>mdsg</span>, verbose <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> </div><div class='output co'>#> <mkindsg> holding 5 mkinds objects #> Title $title: Experimental X -#> Occurrene of observed compounds $observed_n: +#> Occurrence of observed compounds $observed_n: #> parent A1 #> 5 5 #> @@ -290,7 +290,7 @@ or covariates like soil pH).</p></dd> #> Observation unit: \%AR </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'>mdsg</span>, verbose <span class='op'>=</span> <span class='cn'>TRUE</span>, data <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> </div><div class='output co'>#> <mkindsg> holding 5 mkinds objects #> Title $title: Experimental X -#> Occurrene of observed compounds $observed_n: +#> Occurrence of observed compounds $observed_n: #> parent A1 #> 5 5 #> diff --git a/docs/dev/reference/mkinerrmin.html b/docs/dev/reference/mkinerrmin.html index 1cff040d..94c575cb 100644 --- a/docs/dev/reference/mkinerrmin.html +++ b/docs/dev/reference/mkinerrmin.html @@ -73,7 +73,7 @@ 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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -122,7 +122,7 @@ the chi-squared test as defined in the FOCUS kinetics report from 2006." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> diff --git a/docs/dev/reference/mkinerrplot.html b/docs/dev/reference/mkinerrplot.html index 2324b968..7f1fd048 100644 --- a/docs/dev/reference/mkinerrplot.html +++ b/docs/dev/reference/mkinerrplot.html @@ -76,7 +76,7 @@ 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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -125,7 +125,7 @@ using the argument show_errplot = TRUE." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> diff --git a/docs/dev/reference/mkinfit-1.png b/docs/dev/reference/mkinfit-1.png Binary files differindex eed9064f..de2a90a9 100644 --- a/docs/dev/reference/mkinfit-1.png +++ b/docs/dev/reference/mkinfit-1.png diff --git a/docs/dev/reference/mkinfit.html b/docs/dev/reference/mkinfit.html index 6ae7b343..5910038f 100644 --- a/docs/dev/reference/mkinfit.html +++ b/docs/dev/reference/mkinfit.html @@ -80,7 +80,7 @@ likelihood function." /> </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -431,17 +431,17 @@ doi: <a href='https://doi.org/10.3390/environments6120124'>10.3390/environments6 <span class='co'># Use shorthand notation for parent only degradation</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 class='fu'><a href='https://rdrr.io/r/base/summary.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: 0.9.50.4 +</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 Jan 11 12:41:45 2021 -#> Date of summary: Mon Jan 11 12:41:45 2021 +#> Date of fit: Mon Feb 15 17:09:39 2021 +#> Date of summary: Mon Feb 15 17:09:39 2021 #> #> Equations: #> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent #> #> Model predictions using solution type analytical #> -#> Fitted using 222 model solutions performed in 0.046 s +#> Fitted using 222 model solutions performed in 0.045 s #> #> Error model: Constant variance #> @@ -476,10 +476,10 @@ doi: <a href='https://doi.org/10.3390/environments6120124'>10.3390/environments6 #> #> Parameter correlation: #> parent_0 log_alpha log_beta sigma -#> parent_0 1.000e+00 -1.565e-01 -3.142e-01 4.758e-08 -#> log_alpha -1.565e-01 1.000e+00 9.564e-01 1.007e-07 -#> log_beta -3.142e-01 9.564e-01 1.000e+00 8.568e-08 -#> sigma 4.758e-08 1.007e-07 8.568e-08 1.000e+00 +#> parent_0 1.000e+00 -1.565e-01 -3.142e-01 4.772e-08 +#> log_alpha -1.565e-01 1.000e+00 9.564e-01 1.005e-07 +#> log_beta -3.142e-01 9.564e-01 1.000e+00 8.541e-08 +#> sigma 4.772e-08 1.005e-07 8.541e-08 1.000e+00 #> #> Backtransformed parameters: #> Confidence intervals for internally transformed parameters are asymmetric. @@ -548,7 +548,7 @@ doi: <a href='https://doi.org/10.3390/environments6120124'>10.3390/environments6 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</div><div class='input'><span class='fu'><a href='parms.html'>parms</a></span><span class='op'>(</span><span class='va'>fit.tc</span><span class='op'>)</span> </div><div class='output co'>#> parent_0 k_parent k_m1 f_parent_to_m1 sigma_low -#> 1.007343e+02 1.005562e-01 5.166712e-03 5.083933e-01 3.049884e-03 +#> 1.007343e+02 1.005562e-01 5.166712e-03 5.083933e-01 3.049883e-03 #> rsd_high #> 7.928118e-02 </div><div class='input'><span class='fu'><a href='endpoints.html'>endpoints</a></span><span class='op'>(</span><span class='va'>fit.tc</span><span class='op'>)</span> </div><div class='output co'>#> $ff @@ -574,10 +574,10 @@ doi: <a href='https://doi.org/10.3390/environments6120124'>10.3390/environments6 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>, solution_type <span class='op'>=</span> <span class='st'>"analytical"</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>}</span> -</div><div class='output co'>#> <span class='message'>Loading required package: rbenchmark</span></div><div class='output co'>#> test relative elapsed -#> 3 analytical 1.000 0.526 -#> 1 deSolve_compiled 1.903 1.001 -#> 2 eigen 2.308 1.214</div><div class='input'><span class='co'># }</span> +</div><div class='output co'>#> test relative elapsed +#> 3 analytical 1.000 0.563 +#> 1 deSolve_compiled 1.702 0.958 +#> 2 eigen 2.597 1.462</div><div class='input'><span class='co'># }</span> <span class='co'># Use stepwise fitting, using optimised parameters from parent only fit, FOMC-SFO</span> <span class='co'># \dontrun{</span> @@ -587,22 +587,21 @@ doi: <a href='https://doi.org/10.3390/environments6120124'>10.3390/environments6 </div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'><span class='va'>fit.FOMC_SFO</span> <span class='op'><-</span> <span class='fu'>mkinfit</span><span class='op'>(</span><span class='va'>FOMC_SFO</span>, <span class='va'>FOCUS_D</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> <span class='co'># Again, we get a warning and try a more sophisticated error model</span> <span class='va'>fit.FOMC_SFO.tc</span> <span class='op'><-</span> <span class='fu'>mkinfit</span><span class='op'>(</span><span class='va'>FOMC_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 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='co'># This model has a higher likelihood, but not significantly so</span> +<span class='co'># This model has a higher likelihood, but not significantly so</span> <span class='fu'><a href='https://rdrr.io/pkg/lmtest/man/lrtest.html'>lrtest</a></span><span class='op'>(</span><span class='va'>fit.tc</span>, <span class='va'>fit.FOMC_SFO.tc</span><span class='op'>)</span> </div><div class='output co'>#> Likelihood ratio test #> #> Model 1: FOMC_SFO with error model tc and fixed parameter(s) m1_0 #> Model 2: SFO_SFO with error model tc and fixed parameter(s) m1_0 #> #Df LogLik Df Chisq Pr(>Chisq) -#> 1 7 -64.870 -#> 2 6 -64.983 -1 0.2259 0.6346</div><div class='input'><span class='co'># Also, the missing standard error for log_beta and the t-tests for alpha</span> +#> 1 7 -64.829 +#> 2 6 -64.983 -1 0.3075 0.5792</div><div class='input'><span class='co'># Also, the missing standard error for log_beta and the t-tests for alpha</span> <span class='co'># and beta indicate overparameterisation</span> <span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>fit.FOMC_SFO.tc</span>, data <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='warning'>Warning: NaNs produced</span></div><div class='output co'>#> <span class='warning'>Warning: NaNs produced</span></div><div class='output co'>#> <span class='warning'>Warning: NaNs produced</span></div><div class='output co'>#> <span class='warning'>Warning: diag(.) had 0 or NA entries; non-finite result is doubtful</span></div><div class='output co'>#> mkin version used for fitting: 0.9.50.4 +</div><div class='output co'>#> <span class='warning'>Warning: NaNs produced</span></div><div class='output co'>#> <span class='warning'>Warning: NaNs produced</span></div><div class='output co'>#> <span class='warning'>Warning: diag(.) had 0 or NA entries; non-finite result is doubtful</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 Jan 11 12:41:56 2021 -#> Date of summary: Mon Jan 11 12:41:56 2021 +#> Date of fit: Mon Feb 15 17:09:50 2021 +#> Date of summary: Mon Feb 15 17:09:50 2021 #> #> Equations: #> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent @@ -611,12 +610,12 @@ doi: <a href='https://doi.org/10.3390/environments6120124'>10.3390/environments6 #> #> Model predictions using solution type deSolve #> -#> Fitted using 4273 model solutions performed in 3.456 s +#> Fitted using 3729 model solutions performed in 2.815 s #> #> Error model: Two-component variance function #> #> Error model algorithm: d_3 -#> Three-step fitting yielded a higher likelihood than direct fitting +#> Direct fitting and three-step fitting yield approximately the same likelihood #> #> Starting values for parameters to be optimised: #> value type @@ -642,72 +641,67 @@ doi: <a href='https://doi.org/10.3390/environments6120124'>10.3390/environments6 #> value type #> m1_0 0 state #> -#> -#> Warning(s): -#> Optimisation did not converge: -#> iteration limit reached without convergence (10) -#> #> Results: #> -#> AIC BIC logLik -#> 143.7396 155.2027 -64.86982 +#> AIC BIC logLik +#> 143.658 155.1211 -64.82902 #> #> Optimised, transformed parameters with symmetric confidence intervals: -#> Estimate Std. Error Lower Upper -#> parent_0 1.016e+02 1.90600 97.7400 105.5000 -#> log_k_m1 -5.285e+00 0.09286 -5.4740 -5.0950 -#> f_parent_qlogis 6.482e-04 0.06164 -0.1251 0.1264 -#> log_alpha 5.467e+00 NaN NaN NaN -#> log_beta 7.750e+00 NaN NaN NaN -#> sigma_low 0.000e+00 NaN NaN NaN -#> rsd_high 7.989e-02 NaN NaN NaN +#> Estimate Std. Error Lower Upper +#> parent_0 101.600000 2.6400000 96.240000 107.000000 +#> log_k_m1 -5.284000 0.0929100 -5.474000 -5.095000 +#> f_parent_qlogis 0.001426 0.0767000 -0.155000 0.157800 +#> log_alpha 5.522000 0.0077320 5.506000 5.538000 +#> log_beta 7.806000 NaN NaN NaN +#> sigma_low 0.002488 0.0002431 0.001992 0.002984 +#> rsd_high 0.079210 0.0093280 0.060180 0.098230 #> #> Parameter correlation: -#> parent_0 log_k_m1 f_parent_qlogis log_alpha log_beta -#> parent_0 1.0000000 -0.0002167 -0.6060 NaN NaN -#> log_k_m1 -0.0002167 1.0000000 0.5474 NaN NaN -#> f_parent_qlogis -0.6060320 0.5474423 1.0000 NaN NaN -#> log_alpha NaN NaN NaN 1 NaN -#> log_beta NaN NaN NaN NaN 1 -#> sigma_low NaN NaN NaN NaN NaN -#> rsd_high NaN NaN NaN NaN NaN -#> sigma_low rsd_high -#> parent_0 NaN NaN -#> log_k_m1 NaN NaN -#> f_parent_qlogis NaN NaN -#> log_alpha NaN NaN -#> log_beta NaN NaN -#> sigma_low 1 NaN -#> rsd_high NaN 1 +#> parent_0 log_k_m1 f_parent_qlogis log_alpha log_beta +#> parent_0 1.000000 -0.095226 -0.76678 0.70544 NaN +#> log_k_m1 -0.095226 1.000000 0.51432 -0.14387 NaN +#> f_parent_qlogis -0.766780 0.514321 1.00000 -0.61396 NaN +#> log_alpha 0.705444 -0.143872 -0.61396 1.00000 NaN +#> log_beta NaN NaN NaN NaN 1 +#> sigma_low 0.016073 0.001586 0.01548 5.87007 NaN +#> rsd_high 0.006626 -0.011700 -0.05357 0.04849 NaN +#> sigma_low rsd_high +#> parent_0 0.016073 0.006626 +#> log_k_m1 0.001586 -0.011700 +#> f_parent_qlogis 0.015476 -0.053566 +#> log_alpha 5.870075 0.048487 +#> log_beta NaN NaN +#> sigma_low 1.000000 -0.652558 +#> rsd_high -0.652558 1.000000 #> #> 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 1.016e+02 32.5400 7.812e-26 97.740000 1.055e+02 -#> k_m1 5.069e-03 10.0400 1.448e-11 0.004194 6.126e-03 -#> f_parent_to_m1 5.002e-01 20.7300 5.001e-20 0.468800 5.315e-01 -#> alpha 2.367e+02 0.6205 2.697e-01 NA NA -#> beta 2.322e+03 0.6114 2.727e-01 NA NA -#> sigma_low 0.000e+00 NaN NaN NaN NaN -#> rsd_high 7.989e-02 8.6630 4.393e-10 NaN NaN +#> parent_0 1.016e+02 32.7800 6.311e-26 9.624e+01 1.070e+02 +#> k_m1 5.072e-03 10.1200 1.216e-11 4.196e-03 6.130e-03 +#> f_parent_to_m1 5.004e-01 20.8300 4.317e-20 4.613e-01 5.394e-01 +#> alpha 2.502e+02 0.5624 2.889e-01 2.463e+02 2.542e+02 +#> beta 2.455e+03 0.5549 2.915e-01 NA NA +#> sigma_low 2.488e-03 0.4843 3.158e-01 1.992e-03 2.984e-03 +#> rsd_high 7.921e-02 8.4300 8.001e-10 6.018e-02 9.823e-02 #> #> FOCUS Chi2 error levels in percent: #> err.min n.optim df -#> All data 6.782 5 14 -#> parent 7.142 3 6 -#> m1 4.639 2 8 +#> All data 6.781 5 14 +#> parent 7.141 3 6 +#> m1 4.640 2 8 #> #> Resulting formation fractions: #> ff -#> parent_m1 0.5002 -#> parent_sink 0.4998 +#> parent_m1 0.5004 +#> parent_sink 0.4996 #> #> Estimated disappearance times: -#> DT50 DT90 DT50back -#> parent 6.81 22.7 6.833 -#> m1 136.74 454.2 NA</div><div class='input'> +#> DT50 DT90 DT50back +#> parent 6.812 22.7 6.834 +#> m1 136.661 454.0 NA</div><div class='input'> <span class='co'># We can easily use starting parameters from the parent only fit (only for illustration)</span> <span class='va'>fit.FOMC</span> <span class='op'>=</span> <span class='fu'>mkinfit</span><span class='op'>(</span><span class='st'>"FOMC"</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 class='va'>fit.FOMC_SFO</span> <span class='op'><-</span> <span class='fu'>mkinfit</span><span class='op'>(</span><span class='va'>FOMC_SFO</span>, <span class='va'>FOCUS_D</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span>, diff --git a/docs/dev/reference/mkinmod.html b/docs/dev/reference/mkinmod.html index 9a7dac6f..413f0ae1 100644 --- a/docs/dev/reference/mkinmod.html +++ b/docs/dev/reference/mkinmod.html @@ -78,7 +78,7 @@ mkinmod." /> </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -127,7 +127,7 @@ mkinmod." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -348,7 +348,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p> 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>, 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>, 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> -</div><div class='output co'>#> <span class='message'>Copied DLL from /tmp/Rtmpy4eiQb/file554e573761a7.so to /home/jranke/.local/share/mkin/SFO_SFO.so</span></div><div class='input'><span class='co'># Now we can save the model and restore it in a new session</span> +</div><div class='output co'>#> <span class='message'>Copied DLL from /tmp/Rtmp92fCb2/file133ad522561845.so to /home/jranke/.local/share/mkin/SFO_SFO.so</span></div><div class='input'><span class='co'># Now we can save the model and restore it in a new session</span> <span class='fu'><a href='https://rdrr.io/r/base/readRDS.html'>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 class='co'># Terminate the R session here if you would like to check, and then do</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://pkgdown.jrwb.de/mkin/'>mkin</a></span><span class='op'>)</span> @@ -397,7 +397,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p> #> }) #> return(predicted) #> } -#> <environment: 0x55555cac8d00></div><div class='input'> +#> <environment: 0x5555572517f8></div><div class='input'> <span class='co'># If we have several parallel metabolites</span> <span class='co'># (compare tests/testthat/test_synthetic_data_for_UBA_2014.R)</span> <span class='va'>m_synth_DFOP_par</span> <span class='op'><-</span> <span class='fu'>mkinmod</span><span class='op'>(</span> diff --git a/docs/dev/reference/mkinparplot-1.png b/docs/dev/reference/mkinparplot-1.png Binary files differindex dcf3e4b5..c9ed49eb 100644 --- a/docs/dev/reference/mkinparplot-1.png +++ b/docs/dev/reference/mkinparplot-1.png diff --git a/docs/dev/reference/mkinparplot.html b/docs/dev/reference/mkinparplot.html index 0a989ef9..bac6e71c 100644 --- a/docs/dev/reference/mkinparplot.html +++ b/docs/dev/reference/mkinparplot.html @@ -73,7 +73,7 @@ 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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -122,7 +122,7 @@ mkinfit." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -176,7 +176,8 @@ effect, namely to produce a plot.</p> 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='input'><span class='fu'>mkinparplot</span><span class='op'>(</span><span class='va'>fit</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> diff --git a/docs/dev/reference/mkinplot.html b/docs/dev/reference/mkinplot.html index 417c8c73..120bddb3 100644 --- a/docs/dev/reference/mkinplot.html +++ b/docs/dev/reference/mkinplot.html @@ -73,7 +73,7 @@ plot.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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -122,7 +122,7 @@ plot.mkinfit." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> diff --git a/docs/dev/reference/mkinpredict.html b/docs/dev/reference/mkinpredict.html index 3221fc23..1ebaecb5 100644 --- a/docs/dev/reference/mkinpredict.html +++ b/docs/dev/reference/mkinpredict.html @@ -74,7 +74,7 @@ kinetic parameters and initial values for the state variables." /> </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -123,7 +123,7 @@ kinetic parameters and initial values for the state variables." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -408,10 +408,10 @@ as these always return mapped output.</p></td> 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 class='op'>}</span> </div><div class='output co'>#> test relative elapsed +#> 2 deSolve_compiled 1.0 0.005 #> 4 analytical 1.0 0.005 -#> 2 deSolve_compiled 1.2 0.006 -#> 1 eigen 4.0 0.020 -#> 3 deSolve 44.2 0.221</div><div class='input'> +#> 1 eigen 4.4 0.022 +#> 3 deSolve 46.8 0.234</div><div class='input'> <span class='co'># \dontrun{</span> <span class='co'># Predict from a fitted model</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> diff --git a/docs/dev/reference/mkinresplot.html b/docs/dev/reference/mkinresplot.html index 4b2f6bea..30377f2c 100644 --- a/docs/dev/reference/mkinresplot.html +++ b/docs/dev/reference/mkinresplot.html @@ -75,7 +75,7 @@ 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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -124,7 +124,7 @@ argument show_residuals = TRUE." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -242,7 +242,7 @@ lines of the mkinfit object, and <code><a href='plot.mkinfit.html'>plot_res</a>< 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> - <p>Johannes Ranke</p> + <p>Johannes Ranke and Katrin Lindenberger</p> <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> <pre class="examples"><div class='input'> diff --git a/docs/dev/reference/mmkin-1.png b/docs/dev/reference/mmkin-1.png Binary files differindex 7b7da90a..701a6d6a 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 ce2b2af4..5277b389 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 bb96f1b2..2659cd61 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 351b21aa..ae16ee79 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 c1c05eea..2b9dc831 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 651eb9a6..c385bbf6 100644 --- a/docs/dev/reference/mmkin.html +++ b/docs/dev/reference/mmkin.html @@ -75,7 +75,7 @@ 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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> </span> </div> @@ -124,7 +124,7 @@ datasets specified in its first two arguments." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -155,10 +155,13 @@ datasets specified in its first two arguments.</p> <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='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>, + 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></pre> +<span class='op'>)</span> + +<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> <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> <table class="ref-arguments"> @@ -180,7 +183,8 @@ data for <code><a href='mkinfit.html'>mkinfit</a></code>.</p></td> 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.</p></td> +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> @@ -189,7 +193,11 @@ for parallel execution.</p></td> </tr> <tr> <th>...</th> - <td><p>Further arguments that will be passed to <code><a href='mkinfit.html'>mkinfit</a></code>.</p></td> + <td><p>Not used.</p></td> + </tr> + <tr> + <th>x</th> + <td><p>An mmkin object.</p></td> </tr> </table> @@ -227,19 +235,19 @@ plotting.</p></div> <span class='va'>time_default</span> </div><div class='output co'>#> user system elapsed -#> 4.968 0.427 1.342 </div><div class='input'><span class='va'>time_1</span> +#> 4.771 0.576 1.803 </div><div class='input'><span class='va'>time_1</span> </div><div class='output co'>#> user system elapsed -#> 5.365 0.000 5.368 </div><div class='input'> +#> 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 -#> 0.7340478 0.2659522 0.7505687 0.2494313 +#> 0.7340481 0.2659519 0.7505683 0.2494317 #> #> $distimes #> DT50 DT90 #> parent 0.877769 2.915885 -#> M1 2.325746 7.725960 -#> M2 33.720083 112.015691 +#> 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> @@ -266,12 +274,10 @@ plotting.</p></div> #> dataset #> model A B C D #> SFO OK OK OK OK -#> FOMC C OK OK OK +#> FOMC OK OK OK OK #> DFOP OK OK OK OK #> -#> OK: No warnings -#> C: Optimisation did not converge: -#> false convergence (8)</div><div class='input'><span class='co'># We get false convergence for the FOMC fit to FOCUS_2006_A because this</span> +#> 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> diff --git a/docs/dev/reference/nafta-1.png b/docs/dev/reference/nafta-1.png Binary files differindex 4d823d77..4f0d7833 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 bbc8797d..6fb797a5 100644 --- a/docs/dev/reference/nafta.html +++ b/docs/dev/reference/nafta.html @@ -76,7 +76,7 @@ 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-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -125,7 +125,7 @@ order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> diff --git a/docs/dev/reference/nlme-1.png b/docs/dev/reference/nlme-1.png Binary files differindex 193722c7..365aaef0 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 c0fb6dcf..40841404 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 b3e88428..55a94443 100644 --- a/docs/dev/reference/nlme.html +++ b/docs/dev/reference/nlme.html @@ -75,7 +75,7 @@ datasets. They are used internally by the nlme.mmkin() method." /> </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> </span> </div> @@ -124,7 +124,7 @@ datasets. They are used internally by the nlme.mmkin() method." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -155,8 +155,6 @@ datasets. They are used internally by the <code><a href='nlme.mmkin.html'>nlme.m <pre class="usage"><span class='fu'>nlme_function</span><span class='op'>(</span><span class='va'>object</span><span class='op'>)</span> -<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><span class='op'>)</span> - <span class='fu'>nlme_data</span><span class='op'>(</span><span class='va'>object</span><span class='op'>)</span></pre> <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> @@ -166,19 +164,11 @@ datasets. They are used internally by the <code><a href='nlme.mmkin.html'>nlme.m <th>object</th> <td><p>An mmkin row object containing several fits of the same model to different datasets</p></td> </tr> - <tr> - <th>random</th> - <td><p>Should a list with fixed and random effects be returned?</p></td> - </tr> </table> <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> <p>A function that can be used with nlme</p> -<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> <p>A <code><a href='https://rdrr.io/pkg/nlme/man/groupedData.html'>groupedData</a></code> object</p> <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2> @@ -206,12 +196,12 @@ nlme for the case of a single grouping variable ds.</p> <span class='va'>ds</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>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 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 class='va'>mean_dp</span> <span class='op'><-</span> <span class='fu'>mean_degparms</span><span class='op'>(</span><span class='va'>f</span><span class='op'>)</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 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 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 class='co'># These assignments are necessary for these objects to be</span> <span class='co'># visible to nlme and augPred when evaluation is done by</span> -<span class='co'># pkgdown to generated the html docs.</span> +<span class='co'># pkgdown to generate the html docs.</span> <span class='fu'><a href='https://rdrr.io/r/base/assign.html'>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'>globalenv</a></span><span class='op'>(</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='st'>"grouped_data"</span>, <span class='va'>grouped_data</span>, <span class='fu'><a href='https://rdrr.io/r/base/environment.html'>globalenv</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span> @@ -224,37 +214,37 @@ nlme for the case of a single grouping variable ds.</p> <span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>m_nlme</span><span class='op'>)</span> </div><div class='output co'>#> Nonlinear mixed-effects model fit by maximum likelihood #> Model: value ~ nlme_f(name, time, parent_0, log_k_parent_sink) -#> Data: grouped_data +#> Data: grouped_data #> AIC BIC logLik -#> 252.7798 262.1358 -121.3899 +#> 300.6824 310.2426 -145.3412 #> #> Random effects: #> Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1) #> Level: ds #> Structure: Diagonal -#> parent_0 log_k_parent_sink Residual -#> StdDev: 0.0006768135 0.6800777 2.489397 +#> parent_0 log_k_parent_sink Residual +#> StdDev: 1.697361 0.6801209 3.666073 #> -#> Fixed effects: parent_0 + log_k_parent_sink ~ 1 -#> Value Std.Error DF t-value p-value -#> parent_0 101.74884 0.6456014 44 157.60321 0 -#> log_k_parent_sink -3.05575 0.4015811 44 -7.60929 0 +#> Fixed effects: parent_0 + log_k_parent_sink ~ 1 +#> Value Std.Error DF t-value p-value +#> parent_0 100.99378 1.3890416 46 72.70753 0 +#> log_k_parent_sink -3.07521 0.4018589 46 -7.65246 0 #> Correlation: #> prnt_0 -#> log_k_parent_sink 0.026 +#> log_k_parent_sink 0.027 #> #> Standardized Within-Group Residuals: #> Min Q1 Med Q3 Max -#> -2.1317488 -0.6878121 0.0828385 0.8592270 2.9529864 +#> -1.9942823 -0.5622565 0.1791579 0.7165038 2.0704781 #> -#> Number of Observations: 48 +#> Number of Observations: 50 #> Number of Groups: 3 </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='fu'><a href='https://rdrr.io/pkg/nlme/man/augPred.html'>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'>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> </div><div class='img'><img src='nlme-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># augPred does not work on fits with more than one state</span> <span class='co'># variable</span> <span class='co'>#</span> <span class='co'># The procedure is greatly simplified by the nlme.mmkin function</span> <span class='va'>f_nlme</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span><span class='op'>(</span><span class='va'>f</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='warning'>Warning: Iteration 1, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</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'>f_nlme</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_nlme</span><span class='op'>)</span> </div><div class='img'><img src='nlme-2.png' alt='' width='700' height='433' /></div></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> diff --git a/docs/dev/reference/nlme.mmkin-1.png b/docs/dev/reference/nlme.mmkin-1.png Binary files differindex 25bebeca..95adfafb 100644 --- a/docs/dev/reference/nlme.mmkin-1.png +++ b/docs/dev/reference/nlme.mmkin-1.png diff --git a/docs/dev/reference/nlme.mmkin-2.png b/docs/dev/reference/nlme.mmkin-2.png Binary files differindex c314c149..53b6fc76 100644 --- a/docs/dev/reference/nlme.mmkin-2.png +++ b/docs/dev/reference/nlme.mmkin-2.png diff --git a/docs/dev/reference/nlme.mmkin-3.png b/docs/dev/reference/nlme.mmkin-3.png Binary files differindex a40b7cad..8df1e73b 100644 --- a/docs/dev/reference/nlme.mmkin-3.png +++ b/docs/dev/reference/nlme.mmkin-3.png diff --git a/docs/dev/reference/nlme.mmkin.html b/docs/dev/reference/nlme.mmkin.html index a4d7070a..2bbf4f80 100644 --- a/docs/dev/reference/nlme.mmkin.html +++ b/docs/dev/reference/nlme.mmkin.html @@ -74,7 +74,7 @@ have been obtained by fitting the same model to a list of datasets." /> </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> </span> </div> @@ -123,7 +123,7 @@ have been obtained by fitting the same model to a list of datasets." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -155,11 +155,11 @@ have been obtained by fitting the same model to a list of datasets.</p> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>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'>lapply</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>as.list</a></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='fu'><a href='nlme_function.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'>eval</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/parse.html'>parse</a></span><span class='op'>(</span>text <span class='op'>=</span> + fixed <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='fu'><a href='https://rdrr.io/r/base/list.html'>as.list</a></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='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'>eval</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/parse.html'>parse</a></span><span class='op'>(</span>text <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='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'>pdDiag</span><span class='op'>(</span><span class='va'>fixed</span><span class='op'>)</span>, + random <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/pdDiag.html'>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='nlme_function.html'>mean_degparms</a></span><span class='op'>(</span><span class='va'>model</span>, random <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</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>, @@ -262,6 +262,12 @@ parameters taken from the mmkin object are used</p></td> <p>Upon success, a fitted 'nlme.mmkin' object, which is an nlme object with additional elements. It also inherits from 'mixed.mmkin'.</p> + <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2> + + <p>Note that the convergence of the nlme algorithms depends on the quality +of the data. In degradation kinetics, we often only have few datasets +(e.g. data for few soils) and complicated degradation models, which may +make it impossible to obtain convergence with nlme.</p> <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2> <p>As the object inherits from <a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme::nlme</a>, there is a wealth of @@ -284,7 +290,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as <span class='fu'><a href='https://rdrr.io/r/stats/anova.html'>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> </div><div class='output co'>#> Model df AIC BIC logLik Test L.Ratio p-value #> f_nlme_sfo 1 5 625.0539 637.5529 -307.5269 -#> f_nlme_dfop 2 9 495.1270 517.6253 -238.5635 1 vs 2 137.9268 <.0001</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'>f_nlme_dfop</span><span class='op'>)</span> +#> f_nlme_dfop 2 9 495.1270 517.6253 -238.5635 1 vs 2 137.9269 <.0001</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'>f_nlme_dfop</span><span class='op'>)</span> </div><div class='output co'>#> Kinetic nonlinear mixed-effects model fit by maximum likelihood #> #> Structural model: @@ -312,7 +318,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as </div><div class='img'><img src='nlme.mmkin-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'>f_nlme_dfop</span><span class='op'>)</span> </div><div class='output co'>#> $distimes #> DT50 DT90 DT50back DT50_k1 DT50_k2 -#> parent 10.79857 100.7937 30.34192 4.193937 43.85442 +#> parent 10.79857 100.7937 30.34193 4.193938 43.85443 #> </div><div class='input'> <span class='va'>ds_2</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span><span class='op'>(</span><span class='va'>experimental_data_for_UBA_2019</span><span class='op'>[</span><span class='fl'>6</span><span class='op'>:</span><span class='fl'>10</span><span class='op'>]</span>, <span class='kw'>function</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span> <span class='va'>x</span><span class='op'>$</span><span class='va'>data</span><span class='op'>[</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>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> @@ -335,16 +341,17 @@ methods that will automatically work on 'nlme.mmkin' objects, such as <span class='co'># f_nlme_sfo_sfo_ff <- nlme(f_2["SFO-SFO-ff", ])</span> <span class='co'>#plot(f_nlme_sfo_sfo_ff)</span> - <span class='co'># With the log-Cholesky parameterization, this converges in 11</span> - <span class='co'># iterations and around 100 seconds, but without tweaking control</span> - <span class='co'># parameters (with pdDiag, increasing the tolerance and pnlsMaxIter was</span> - <span class='co'># necessary)</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'>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 class='op'>)</span> -</div><div class='output co'>#> <span class='error'>Error in nlme.formula(model = value ~ (mkin::get_deg_func())(name, time, parent_0, log_k_A1, f_parent_qlogis, log_k1, log_k2, g_qlogis), data = structure(list(ds = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L ), .Label = c("1", "2", "3", "4", "5"), class = c("ordered", "factor")), name = c("parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1"), time = c(0, 0, 3, 3, 6, 6, 10, 10, 20, 20, 34, 34, 55, 55, 90, 90, 112, 112, 132, 132, 3, 3, 6, 6, 10, 10, 20, 20, 34, 34, 55, 55, 90, 90, 112, 112, 132, 132, 0, 0, 3, 3, 7, 7, 14, 14, 30, 30, 60, 60, 90, 90, 120, 120, 180, 180, 3, 3, 7, 7, 14, 14, 30, 30, 60, 60, 90, 90, 120, 120, 180, 180, 0, 0, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 0, 0, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 91, 91, 120, 120, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 91, 91, 120, 120, 0, 0, 8, 8, 14, 14, 21, 21, 41, 41, 63, 63, 91, 91, 120, 120, 8, 8, 14, 14, 21, 21, 41, 41, 63, 63, 91, 91, 120, 120), value = c(97.2, 96.4, 71.1, 69.2, 58.1, 56.6, 44.4, 43.4, 33.3, 29.2, 17.6, 18, 10.5, 9.3, 4.5, 4.7, 3, 3.4, 2.3, 2.7, 4.3, 4.6, 7, 7.2, 8.2, 8, 11, 13.7, 11.5, 12.7, 14.9, 14.5, 12.1, 12.3, 9.9, 10.2, 8.8, 7.8, 93.6, 92.3, 87, 82.2, 74, 73.9, 64.2, 69.5, 54, 54.6, 41.1, 38.4, 32.5, 35.5, 28.1, 29, 26.5, 27.6, 3.9, 3.1, 6.9, 6.6, 10.4, 8.3, 14.4, 13.7, 22.1, 22.3, 27.5, 25.4, 28, 26.6, 25.8, 25.3, 91.9, 90.8, 64.9, 66.2, 43.5, 44.1, 18.3, 18.1, 10.2, 10.8, 4.9, 3.3, 1.6, 1.5, 1.1, 0.9, 9.6, 7.7, 15, 15.1, 21.2, 21.1, 19.7, 18.9, 17.5, 15.9, 9.5, 9.8, 6.2, 6.1, 99.8, 98.3, 77.1, 77.2, 59, 58.1, 27.4, 29.2, 19.1, 29.6, 10.1, 18.2, 4.5, 9.1, 2.3, 2.9, 2, 1.8, 2, 2.2, 4.2, 3.9, 7.4, 7.9, 14.5, 13.7, 14.2, 12.2, 13.7, 13.2, 13.6, 15.4, 10.4, 11.6, 10, 9.5, 9.1, 9, 96.1, 94.3, 73.9, 73.9, 69.4, 73.1, 65.6, 65.3, 55.9, 54.4, 47, 49.3, 44.7, 46.7, 42.1, 41.3, 3.3, 3.4, 3.9, 2.9, 6.4, 7.2, 9.1, 8.5, 11.7, 12, 13.3, 13.2, 14.3, 12.1)), row.names = c(NA, -170L), class = c("nfnGroupedData", "nfGroupedData", "groupedData", "data.frame"), formula = value ~ time | ds, FUN = function (x) max(x, na.rm = TRUE), order.groups = FALSE), start = list( fixed = c(parent_0 = 93.8101519326534, log_k_A1 = -9.76474551635931, f_parent_qlogis = -0.971114801595408, log_k1 = -1.87993711571859, log_k2 = -4.27081421366622, g_qlogis = 0.135644115277507 ), random = list(ds = structure(c(2.56569977430371, -3.49441920289139, -3.32614443321494, 4.35347873814922, -0.0986148763466161, 4.65850590018027, 1.8618544764481, 6.12693257601545, 4.91792724701579, -17.5652201996596, -0.466203822618637, 0.746660653597927, 0.282193987271096, -0.42053488943072, -0.142115928819667, 0.369240076779088, -1.38985563501659, 1.02592753494098, 0.73090914081534, -0.736221117518819, 0.768170629350299, -1.89347658079869, 1.72168783460352, 0.844607177798114, -1.44098906095325, -0.377731855445672, 0.168180098477565, 0.469683412912104, 0.500717664434525, -0.760849320378522), .Dim = 5:6, .Dimnames = list(c("1", "2", "3", "4", "5"), c("parent_0", "log_k_A1", "f_parent_qlogis", "log_k1", "log_k2", "g_qlogis"))))), fixed = list(parent_0 ~ 1, log_k_A1 ~ 1, f_parent_qlogis ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1), random = structure(numeric(0), class = c("pdDiag", "pdMat"), formula = structure(list(parent_0 ~ 1, log_k_A1 ~ 1, f_parent_qlogis ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1), class = "listForm"), Dimnames = list(NULL, NULL))): maximum number of iterations (maxIter = 50) reached without convergence</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 49.95 16.5 44.08</span></div><div class='input'> + <span class='co'># For the following, we need to increase pnlsMaxIter and the tolerance</span> + <span class='co'># to get convergence</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'>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>, + control <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>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 class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_nlme_dfop_sfo</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='error'>Error in plot(f_nlme_dfop_sfo): object 'f_nlme_dfop_sfo' not found</span></div><div class='input'> +</div><div class='img'><img src='nlme.mmkin-3.png' alt='' width='700' height='433' /></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/stats/anova.html'>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> -</div><div class='output co'>#> <span class='error'>Error in anova(f_nlme_dfop_sfo, f_nlme_sfo_sfo): object 'f_nlme_dfop_sfo' not found</span></div><div class='input'> +</div><div class='output co'>#> Model df AIC BIC logLik Test L.Ratio p-value +#> f_nlme_dfop_sfo 1 13 843.8547 884.6201 -408.9274 +#> f_nlme_sfo_sfo 2 9 1085.1821 1113.4043 -533.5910 1 vs 2 249.3274 <.0001</div><div class='input'> <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> </div><div class='output co'>#> $ff #> parent_sink parent_A1 A1_sink @@ -355,7 +362,15 @@ methods that will automatically work on 'nlme.mmkin' objects, such as #> parent 19.13518 63.5657 #> A1 66.02155 219.3189 #> </div><div class='input'> <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> -</div><div class='output co'>#> <span class='error'>Error in endpoints(f_nlme_dfop_sfo): object 'f_nlme_dfop_sfo' not found</span></div><div class='input'> +</div><div class='output co'>#> $ff +#> parent_A1 parent_sink +#> 0.2768574 0.7231426 +#> +#> $distimes +#> DT50 DT90 DT50back DT50_k1 DT50_k2 +#> parent 11.07091 104.6320 31.49737 4.462383 46.20825 +#> A1 162.30519 539.1662 NA NA NA +#> </div><div class='input'> <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='co'># tc error model for nlme available</span> <span class='co'># Attempts to fit metabolite kinetics with the tc error model are possible,</span> <span class='co'># but need tweeking of control values and sometimes do not converge</span> @@ -381,7 +396,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as #> Fixed effects: #> list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) #> parent_0 log_k1 log_k2 g_qlogis -#> 94.04775 -1.82340 -4.16715 0.05685 +#> 94.04774 -1.82340 -4.16716 0.05686 #> #> Random effects: #> Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) @@ -395,10 +410,8 @@ methods that will automatically work on 'nlme.mmkin' objects, such as #> Formula: ~fitted(.) #> Parameter estimates: #> const prop -#> 2.23224114 0.01262341 </div><div class='input'> - <span class='va'>f_2_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'>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='st'>"DFOP-SFO"</span> <span class='op'>=</span> <span class='va'>m_dfop_sfo</span><span class='op'>)</span>, - <span class='va'>ds_2</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> +#> 2.23223147 0.01262395 </div><div class='input'> + <span class='va'>f_2_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_2</span>, error_model <span class='op'>=</span> <span class='st'>"obs"</span><span class='op'>)</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'>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 class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>f_nlme_sfo_sfo_obs</span><span class='op'>)</span> </div><div class='output co'>#> Kinetic nonlinear mixed-effects model fit by maximum likelihood @@ -429,18 +442,21 @@ methods that will automatically work on 'nlme.mmkin' objects, such as #> Formula: ~1 | name #> Parameter estimates: #> parent A1 -#> 1.0000000 0.2050003 </div><div class='input'> <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'>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 class='op'>)</span> -</div><div class='output co'>#> <span class='error'>Error in nlme.formula(model = value ~ (mkin::get_deg_func())(name, time, parent_0, log_k_A1, f_parent_qlogis, log_k1, log_k2, g_qlogis), data = structure(list(ds = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L ), .Label = c("1", "2", "3", "4", "5"), class = c("ordered", "factor")), name = c("parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1", "A1"), time = c(0, 0, 3, 3, 6, 6, 10, 10, 20, 20, 34, 34, 55, 55, 90, 90, 112, 112, 132, 132, 3, 3, 6, 6, 10, 10, 20, 20, 34, 34, 55, 55, 90, 90, 112, 112, 132, 132, 0, 0, 3, 3, 7, 7, 14, 14, 30, 30, 60, 60, 90, 90, 120, 120, 180, 180, 3, 3, 7, 7, 14, 14, 30, 30, 60, 60, 90, 90, 120, 120, 180, 180, 0, 0, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 0, 0, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 91, 91, 120, 120, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 91, 91, 120, 120, 0, 0, 8, 8, 14, 14, 21, 21, 41, 41, 63, 63, 91, 91, 120, 120, 8, 8, 14, 14, 21, 21, 41, 41, 63, 63, 91, 91, 120, 120), value = c(97.2, 96.4, 71.1, 69.2, 58.1, 56.6, 44.4, 43.4, 33.3, 29.2, 17.6, 18, 10.5, 9.3, 4.5, 4.7, 3, 3.4, 2.3, 2.7, 4.3, 4.6, 7, 7.2, 8.2, 8, 11, 13.7, 11.5, 12.7, 14.9, 14.5, 12.1, 12.3, 9.9, 10.2, 8.8, 7.8, 93.6, 92.3, 87, 82.2, 74, 73.9, 64.2, 69.5, 54, 54.6, 41.1, 38.4, 32.5, 35.5, 28.1, 29, 26.5, 27.6, 3.9, 3.1, 6.9, 6.6, 10.4, 8.3, 14.4, 13.7, 22.1, 22.3, 27.5, 25.4, 28, 26.6, 25.8, 25.3, 91.9, 90.8, 64.9, 66.2, 43.5, 44.1, 18.3, 18.1, 10.2, 10.8, 4.9, 3.3, 1.6, 1.5, 1.1, 0.9, 9.6, 7.7, 15, 15.1, 21.2, 21.1, 19.7, 18.9, 17.5, 15.9, 9.5, 9.8, 6.2, 6.1, 99.8, 98.3, 77.1, 77.2, 59, 58.1, 27.4, 29.2, 19.1, 29.6, 10.1, 18.2, 4.5, 9.1, 2.3, 2.9, 2, 1.8, 2, 2.2, 4.2, 3.9, 7.4, 7.9, 14.5, 13.7, 14.2, 12.2, 13.7, 13.2, 13.6, 15.4, 10.4, 11.6, 10, 9.5, 9.1, 9, 96.1, 94.3, 73.9, 73.9, 69.4, 73.1, 65.6, 65.3, 55.9, 54.4, 47, 49.3, 44.7, 46.7, 42.1, 41.3, 3.3, 3.4, 3.9, 2.9, 6.4, 7.2, 9.1, 8.5, 11.7, 12, 13.3, 13.2, 14.3, 12.1)), row.names = c(NA, -170L), class = c("nfnGroupedData", "nfGroupedData", "groupedData", "data.frame"), formula = value ~ time | ds, FUN = function (x) max(x, na.rm = TRUE), order.groups = FALSE), start = list( fixed = c(parent_0 = 93.4272167134207, log_k_A1 = -9.71590717106959, f_parent_qlogis = -0.953712099744438, log_k1 = -1.95256957646888, log_k2 = -4.42919226610318, g_qlogis = 0.193023137298073 ), random = list(ds = structure(c(2.85557330683041, -3.87630303729395, -2.78062140212751, 4.82042042600536, -1.01906929341432, 4.613992019697, 2.05871276943309, 6.0766404049189, 4.86471337131288, -17.6140585653619, -0.480721175257541, 0.773079218835614, 0.260464433006093, -0.440615012802434, -0.112207463781733, 0.445812953745225, -1.49588630006094, 1.13602040717272, 0.801850880762046, -0.887797941619048, 0.936480292463262, -2.43093808171905, 1.91256225793793, 0.984827519864443, -1.40293198854659, -0.455176326336681, 0.376355651864385, 0.343919720700401, 0.46329187713133, -0.728390923359434 ), .Dim = 5:6, .Dimnames = list(c("1", "2", "3", "4", "5"), c("parent_0", "log_k_A1", "f_parent_qlogis", "log_k1", "log_k2", "g_qlogis"))))), fixed = list(parent_0 ~ 1, log_k_A1 ~ 1, f_parent_qlogis ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1), random = structure(numeric(0), class = c("pdDiag", "pdMat"), formula = structure(list(parent_0 ~ 1, log_k_A1 ~ 1, f_parent_qlogis ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1), class = "listForm"), Dimnames = list(NULL, NULL)), weights = structure(numeric(0), formula = ~1 | name, class = c("varIdent", "varFunc"))): maximum number of iterations (maxIter = 50) reached without convergence</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 59.38 16.5 53.5</span></div><div class='input'> - <span class='va'>f_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'>"SFO-SFO"</span> <span class='op'>=</span> <span class='va'>m_sfo_sfo</span>, - <span class='st'>"DFOP-SFO"</span> <span class='op'>=</span> <span class='va'>m_dfop_sfo</span><span class='op'>)</span>, - <span class='va'>ds_2</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 class='co'># f_nlme_sfo_sfo_tc <- nlme(f_2_tc["SFO-SFO", ]) # stops with error message</span> - <span class='va'>f_nlme_dfop_sfo_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span><span class='op'>(</span><span class='va'>f_2_tc</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#> <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#> <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#> <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#> <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#> <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#> <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#> <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#> <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#> <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#> <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#> <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#> <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#> <span class='warning'>Warning: longer object length is not a multiple of shorter object length</span></div><div class='output co'>#> <span class='error'>Error in X[, fmap[[nm]]] <- gradnm: number of items to replace is not a multiple of replacement length</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 6.363 2.688 5.469</span></div><div class='input'> <span class='co'># We get warnings about false convergence in the LME step in several iterations</span> - <span class='co'># but as the last such warning occurs in iteration 25 and we have 28 iterations</span> - <span class='co'># we can ignore these</span> - <span class='fu'><a href='https://rdrr.io/r/stats/anova.html'>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='va'>f_nlme_dfop_sfo_tc</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='error'>Error in anova(f_nlme_dfop_sfo, f_nlme_dfop_sfo_obs, f_nlme_dfop_sfo_tc): object 'f_nlme_dfop_sfo' not found</span></div><div class='input'> +#> 1.0000000 0.2049995 </div><div class='input'> <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'>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>, + control <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>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 class='va'>f_2_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_2</span>, error_model <span class='op'>=</span> <span class='st'>"tc"</span><span class='op'>)</span> + <span class='co'># f_nlme_sfo_sfo_tc <- nlme(f_2_tc["SFO-SFO", ]) # No convergence with 50 iterations</span> + <span class='co'># f_nlme_dfop_sfo_tc <- nlme(f_2_tc["DFOP-SFO", ],</span> + <span class='co'># control = list(pnlsMaxIter = 120, tolerance = 5e-4)) # Error in X[, fmap[[nm]]] <- gradnm</span> + + <span class='fu'><a href='https://rdrr.io/r/stats/anova.html'>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> +</div><div class='output co'>#> Model df AIC BIC logLik Test L.Ratio +#> f_nlme_dfop_sfo 1 13 843.8547 884.6201 -408.9274 +#> f_nlme_dfop_sfo_obs 2 14 817.5338 861.4350 -394.7669 1 vs 2 28.32091 +#> p-value +#> f_nlme_dfop_sfo +#> f_nlme_dfop_sfo_obs <.0001</div><div class='input'> <span class='co'># }</span> </div></pre> </div> diff --git a/docs/dev/reference/nlmixr.mmkin-1.png 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+<meta property="og:description" content="This function uses nlmixr::nlmixr() as a backend for fitting nonlinear mixed +effects models created from mmkin row objects using the Stochastic Approximation +Expectation Maximisation algorithm (SAEM)." /> + + +<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">1.0.5</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" aria-expanded="false"> + Articles + + <span class="caret"></span> + </a> + <ul 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navbar-right"> + <li> + <a href="https://github.com/jranke/mkin/"> + <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>Fit nonlinear mixed models using nlmixr</h1> + <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/nlmixr.R'><code>R/nlmixr.R</code></a></small> + <div class="hidden name"><code>nlmixr.mmkin.Rd</code></div> + </div> + + <div class="ref-description"> + <p>This function uses <code><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr::nlmixr()</a></code> as a backend for fitting nonlinear mixed +effects models created from <a href='mmkin.html'>mmkin</a> row objects using the Stochastic Approximation +Expectation Maximisation algorithm (SAEM).</p> + </div> + + <pre class="usage"><span class='co'># S3 method for mmkin</span> +<span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span> + <span class='va'>object</span>, + data <span class='op'>=</span> <span class='cn'>NULL</span>, + est <span class='op'>=</span> <span class='cn'>NULL</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><span class='op'>)</span>, + table <span class='op'>=</span> <span class='fu'>tableControl</span><span class='op'>(</span><span class='op'>)</span>, + error_model <span class='op'>=</span> <span class='va'>object</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'>err_mod</span>, + test_log_parms <span class='op'>=</span> <span class='cn'>TRUE</span>, + conf.level <span class='op'>=</span> <span class='fl'>0.6</span>, + degparms_start <span class='op'>=</span> <span class='st'>"auto"</span>, + eta_start <span class='op'>=</span> <span class='st'>"auto"</span>, + <span class='va'>...</span>, + save <span class='op'>=</span> <span class='cn'>NULL</span>, + envir <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/sys.parent.html'>parent.frame</a></span><span class='op'>(</span><span class='op'>)</span> +<span class='op'>)</span> + +<span class='co'># S3 method for nlmixr.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>, digits <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='fl'>3</span>, <span class='fu'><a href='https://rdrr.io/r/base/options.html'>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='fu'>nlmixr_model</span><span class='op'>(</span> + <span class='va'>object</span>, + est <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'>"saem"</span>, <span class='st'>"focei"</span><span class='op'>)</span>, + degparms_start <span class='op'>=</span> <span class='st'>"auto"</span>, + eta_start <span class='op'>=</span> <span class='st'>"auto"</span>, + test_log_parms <span class='op'>=</span> <span class='cn'>TRUE</span>, + conf.level <span class='op'>=</span> <span class='fl'>0.6</span>, + error_model <span class='op'>=</span> <span class='va'>object</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'>err_mod</span>, + add_attributes <span class='op'>=</span> <span class='cn'>FALSE</span> +<span class='op'>)</span> + +<span class='fu'>nlmixr_data</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 <a href='mmkin.html'>mmkin</a> row object containing several fits of the same +<a href='mkinmod.html'>mkinmod</a> model to different datasets</p></td> + </tr> + <tr> + <th>data</th> + <td><p>Not used, as the data are extracted from the mmkin row object</p></td> + </tr> + <tr> + <th>est</th> + <td><p>Estimation method passed to <a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr::nlmixr</a></p></td> + </tr> + <tr> + <th>control</th> + <td><p>Passed to <a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr::nlmixr</a></p></td> + </tr> + <tr> + <th>table</th> + <td><p>Passed to <a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr::nlmixr</a></p></td> + </tr> + <tr> + <th>error_model</th> + <td><p>Possibility to override the error model which is being +set based on the error model used in the mmkin row object.</p></td> + </tr> + <tr> + <th>test_log_parms</th> + <td><p>If TRUE, an attempt is made to use more robust starting +values for population parameters fitted as log parameters in mkin (like +rate constants) by only considering rate constants that pass the t-test +when calculating mean degradation parameters using <a href='mean_degparms.html'>mean_degparms</a>.</p></td> + </tr> + <tr> + <th>conf.level</th> + <td><p>Possibility to adjust the required confidence level +for parameter that are tested if requested by 'test_log_parms'.</p></td> + </tr> + <tr> + <th>degparms_start</th> + <td><p>Parameter values given as a named numeric vector will +be used to override the starting values obtained from the 'mmkin' object.</p></td> + </tr> + <tr> + <th>eta_start</th> + <td><p>Standard deviations on the transformed scale given as a +named numeric vector will be used to override the starting values obtained +from the 'mmkin' object.</p></td> + </tr> + <tr> + <th>...</th> + <td><p>Passed to nlmixr_model</p></td> + </tr> + <tr> + <th>save</th> + <td><p>Passed to <a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr::nlmixr</a></p></td> + </tr> + <tr> + <th>envir</th> + <td><p>Passed to <a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr::nlmixr</a></p></td> + </tr> + <tr> + <th>x</th> + <td><p>An nlmixr.mmkin object to print</p></td> + </tr> + <tr> + <th>digits</th> + <td><p>Number of digits to use for printing</p></td> + </tr> + <tr> + <th>add_attributes</th> + <td><p>Should the starting values used for degradation model +parameters and their distribution and for the error model parameters +be returned as attributes?</p></td> + </tr> + </table> + + <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> + + <p>An S3 object of class 'nlmixr.mmkin', containing the fitted +<a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr::nlmixr</a> object as a list component named 'nm'. The +object also inherits from 'mixed.mmkin'.</p> +<p>An function defining a model suitable for fitting with <a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr::nlmixr</a>.</p> +<p>An dataframe suitable for use with <a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr::nlmixr</a></p> + <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2> + + <p>An mmkin row object is essentially a list of mkinfit objects that have been +obtained by fitting the same model to a list of datasets using <a href='mkinfit.html'>mkinfit</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='summary.nlmixr.mmkin.html'>summary.nlmixr.mmkin</a> <a href='plot.mixed.mmkin.html'>plot.mixed.mmkin</a></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'>ds</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'>6</span><span class='op'>:</span><span class='fl'>10</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'><a href='https://rdrr.io/r/base/subset.html'>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'>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 class='fu'><a href='https://rdrr.io/r/base/names.html'>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'>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 class='va'>f_mmkin_parent</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='st'>"HS"</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 class='va'>f_mmkin_parent_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'>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>, error_model <span class='op'>=</span> <span class='st'>"tc"</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'>f_nlmixr_sfo_saem</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></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>, est <span class='op'>=</span> <span class='st'>"saem"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'>→ generate SAEM model</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> 1: 86.5083 -3.1968 4.1673 1.7173 48.7028 +#> 2: 87.3628 -3.1468 3.9589 1.6315 45.1225 +#> 3: 86.8866 -3.2249 3.7610 1.8212 43.0034 +#> 4: 85.9210 -3.2427 3.5729 1.7302 39.4197 +#> 5: 85.8539 -3.2018 3.3943 1.7234 38.2933 +#> 6: 85.6934 -3.2262 3.2246 1.6843 39.0348 +#> 7: 85.7421 -3.2696 4.1298 1.7086 39.8152 +#> 8: 85.1605 -3.2190 3.9234 1.7588 41.7476 +#> 9: 84.7745 -3.2389 3.7361 1.6708 41.8512 +#> 10: 84.6549 -3.2078 3.5493 1.6489 41.6110 +#> 11: 84.4739 -3.2788 3.3718 1.5664 42.0076 +#> 12: 84.7871 -3.2674 3.4931 1.6097 40.9060 +#> 13: 84.5267 -3.2635 3.3185 1.6352 39.6914 +#> 14: 84.9806 -3.2353 3.1525 1.6470 39.2556 +#> 15: 84.9752 -3.2566 2.9949 1.6756 39.6152 +#> 16: 85.6293 -3.2232 2.8452 1.7076 39.4391 +#> 17: 85.9944 -3.2268 2.7029 1.6702 40.2731 +#> 18: 86.2811 -3.2260 2.5678 1.7100 41.4854 +#> 19: 86.2617 -3.2476 2.4489 1.7051 41.3066 +#> 20: 85.7552 -3.2032 3.3323 1.8885 42.2273 +#> 21: 85.6493 -3.2685 3.2317 1.7941 39.4198 +#> 22: 86.0133 -3.2457 4.0910 1.7044 39.0319 +#> 23: 86.1636 -3.2528 4.9399 1.6571 38.6728 +#> 24: 86.3086 -3.1708 7.0791 1.8182 39.6791 +#> 25: 85.7316 -3.2203 6.7252 1.7369 38.3546 +#> 26: 85.3476 -3.2341 6.3889 1.6864 38.0521 +#> 27: 85.6328 -3.2543 6.0695 1.6945 37.7990 +#> 28: 85.1715 -3.2191 5.7660 1.7898 38.5662 +#> 29: 85.4945 -3.2264 5.4777 1.7007 40.1659 +#> 30: 85.0864 -3.2463 5.2038 1.6156 39.0718 +#> 31: 85.8220 -3.2347 4.9436 1.6115 39.2011 +#> 32: 85.9869 -3.2400 4.6964 1.6818 41.2956 +#> 33: 85.9899 -3.2041 4.4616 1.6606 40.6657 +#> 34: 85.8353 -3.2065 4.2385 1.6868 41.5006 +#> 35: 85.8113 -3.2366 4.0266 1.8261 41.0403 +#> 36: 85.5233 -3.2389 3.8253 1.7348 39.5202 +#> 37: 85.1751 -3.2657 3.6340 1.6948 39.6097 +#> 38: 85.2768 -3.2380 3.4887 1.6820 38.7641 +#> 39: 84.8240 -3.2264 3.3143 1.5979 39.8074 +#> 40: 85.3754 -3.2147 3.1485 1.5810 39.1710 +#> 41: 85.0277 -3.2347 2.9911 1.7061 39.9948 +#> 42: 85.0113 -3.2651 3.1969 1.6208 39.7266 +#> 43: 85.0772 -3.2729 3.0371 1.6160 40.2919 +#> 44: 85.0769 -3.2272 3.3310 1.7321 38.5229 +#> 45: 85.1638 -3.2546 3.1644 1.6968 40.2382 +#> 46: 84.7966 -3.2597 5.0694 1.6816 38.7996 +#> 47: 85.0588 -3.2247 5.9549 1.7452 39.6569 +#> 48: 85.1769 -3.2557 5.6572 1.7441 37.9050 +#> 49: 84.9296 -3.2425 5.3743 1.6729 37.7885 +#> 50: 85.3414 -3.2421 5.1056 1.6646 38.2243 +#> 51: 84.9127 -3.2674 5.8827 1.7180 40.2859 +#> 52: 85.2014 -3.2471 5.5885 1.7318 39.1745 +#> 53: 85.9330 -3.2228 7.2369 1.8328 39.0461 +#> 54: 86.9718 -3.1447 6.9332 1.8404 39.3098 +#> 55: 87.2708 -3.1595 6.6308 1.8049 39.1338 +#> 56: 87.2006 -3.1746 6.2993 1.7541 38.2780 +#> 57: 87.8013 -3.2306 5.9843 1.6664 40.4876 +#> 58: 87.7294 -3.2120 5.6851 1.5831 41.5056 +#> 59: 87.4898 -3.2207 5.4008 1.5039 41.4401 +#> 60: 86.9156 -3.1861 5.1308 1.6408 39.8972 +#> 61: 86.4508 -3.1870 4.8742 1.5935 39.6871 +#> 62: 86.4028 -3.2191 4.6305 1.6267 39.2092 +#> 63: 86.2536 -3.2491 4.5199 1.5617 39.7603 +#> 64: 85.9775 -3.2650 4.2939 1.6077 39.1909 +#> 65: 85.8907 -3.2430 4.0792 1.6729 37.9420 +#> 66: 85.3450 -3.2888 3.8753 1.6201 40.8998 +#> 67: 85.1869 -3.2940 3.6815 1.6157 40.5107 +#> 68: 84.8029 -3.2830 3.4974 1.6040 40.6254 +#> 69: 85.3549 -3.2425 4.4768 1.5238 40.2418 +#> 70: 85.7957 -3.2296 4.2529 1.7175 40.8618 +#> 71: 85.4200 -3.2381 4.0403 1.6695 41.5731 +#> 72: 85.2950 -3.2566 3.8383 1.5998 40.6494 +#> 73: 85.0683 -3.2464 3.6464 1.5576 39.8095 +#> 74: 85.1667 -3.2436 3.4641 1.6383 39.4925 +#> 75: 84.6547 -3.2300 3.7226 1.6656 40.4684 +#> 76: 84.4882 -3.2521 3.6468 1.6035 40.1800 +#> 77: 84.5250 -3.2398 4.1501 1.6827 40.5269 +#> 78: 84.5191 -3.2372 5.5482 1.6309 41.1739 +#> 79: 84.7471 -3.2581 6.0637 1.6259 41.1003 +#> 80: 85.0581 -3.2680 5.7605 1.6841 40.8918 +#> 81: 84.8468 -3.2564 5.4725 1.6475 39.3456 +#> 82: 84.7614 -3.2385 5.1988 1.7550 38.7275 +#> 83: 85.2921 -3.2657 5.9253 1.6672 39.2423 +#> 84: 85.5760 -3.2261 5.6290 1.7505 39.5500 +#> 85: 85.3215 -3.2277 5.5987 1.8027 39.3145 +#> 86: 85.2656 -3.2023 5.3188 1.8024 40.3098 +#> 87: 84.8950 -3.2551 5.0528 1.7123 39.3470 +#> 88: 84.3157 -3.2661 4.8002 1.6267 38.7095 +#> 89: 84.5442 -3.2870 4.5602 1.5892 39.1735 +#> 90: 85.0956 -3.2195 4.8385 1.5796 39.5164 +#> 91: 84.8619 -3.2621 4.5966 1.6889 39.5512 +#> 92: 84.4901 -3.2735 6.1405 1.6704 39.3358 +#> 93: 84.0819 -3.2609 5.8335 1.6130 38.8618 +#> 94: 84.7585 -3.2336 5.5418 1.6301 38.6591 +#> 95: 85.2669 -3.2358 5.2647 1.6619 38.9136 +#> 96: 85.4955 -3.2064 5.0015 1.7673 39.0495 +#> 97: 85.6591 -3.2016 4.7514 1.7046 40.7861 +#> 98: 86.2097 -3.2833 7.4722 1.6413 42.2938 +#> 99: 85.9645 -3.2570 7.7124 1.5592 41.7216 +#> 100: 85.7018 -3.2605 8.2687 1.6798 40.6639 +#> 101: 85.9905 -3.1956 11.0194 1.7017 39.4324 +#> 102: 87.2679 -3.1741 10.4684 1.7063 38.6812 +#> 103: 86.1910 -3.1709 9.9450 1.7151 38.5198 +#> 104: 86.4413 -3.1544 9.4478 1.7123 38.7428 +#> 105: 85.9840 -3.1921 10.6297 1.8135 38.7775 +#> 106: 85.9926 -3.1839 10.0982 1.7228 40.3136 +#> 107: 85.1792 -3.2343 9.5933 1.6367 40.2709 +#> 108: 84.7583 -3.2332 9.1136 1.6907 41.2122 +#> 109: 85.3756 -3.2311 8.6579 1.7307 39.9303 +#> 110: 84.9686 -3.2365 8.2250 1.7221 40.0379 +#> 111: 84.8527 -3.2448 7.8138 1.6775 39.6794 +#> 112: 84.6271 -3.2609 7.4231 1.7321 41.5666 +#> 113: 84.8515 -3.3056 7.2514 1.7001 41.9758 +#> 114: 84.5991 -3.2319 7.8463 1.7690 41.1386 +#> 115: 85.0535 -3.2864 7.4540 1.7282 40.3883 +#> 116: 85.8661 -3.2355 7.0813 1.7801 39.3078 +#> 117: 85.9911 -3.2357 6.7272 1.6911 38.3913 +#> 118: 86.1894 -3.2424 6.3909 1.6701 38.1915 +#> 119: 85.5637 -3.1992 6.0713 1.7360 38.9386 +#> 120: 86.0733 -3.2069 5.7677 1.7185 36.5189 +#> 121: 86.0168 -3.2181 5.4794 1.7135 38.4044 +#> 122: 86.7470 -3.2319 6.1989 1.6840 38.2615 +#> 123: 86.2918 -3.2089 5.8890 1.6656 38.8486 +#> 124: 85.9387 -3.2124 5.5945 1.6334 37.9425 +#> 125: 86.1519 -3.2717 5.3148 1.7094 38.9708 +#> 126: 85.5194 -3.2391 5.4217 1.6799 39.4876 +#> 127: 85.9691 -3.2205 5.8051 1.6436 40.0593 +#> 128: 85.6171 -3.2309 5.5148 1.6852 39.5398 +#> 129: 84.9252 -3.2495 5.2391 1.7154 40.4020 +#> 130: 85.1496 -3.2882 5.0538 1.7189 40.0908 +#> 131: 85.8552 -3.2474 7.1203 1.6329 39.0547 +#> 132: 86.4666 -3.2151 6.7643 1.7342 38.6596 +#> 133: 86.1550 -3.1895 6.4261 1.7904 38.6211 +#> 134: 86.5040 -3.1785 6.1048 1.7180 39.0804 +#> 135: 85.9752 -3.2116 5.7996 1.6979 38.1745 +#> 136: 86.2161 -3.2075 5.5096 1.7408 38.9002 +#> 137: 85.8408 -3.2604 6.9319 1.7616 39.1657 +#> 138: 86.1261 -3.2179 7.0802 1.8115 37.6614 +#> 139: 85.9082 -3.2374 6.7262 1.7209 38.1986 +#> 140: 85.9556 -3.2641 6.3899 1.8300 39.2071 +#> 141: 86.2052 -3.1928 6.0704 1.7385 38.1745 +#> 142: 86.4062 -3.2076 5.8348 1.6693 38.0271 +#> 143: 86.0680 -3.2372 5.5431 1.7259 39.3885 +#> 144: 86.2001 -3.2040 5.2659 1.6803 38.1606 +#> 145: 86.5820 -3.2306 5.0026 1.6063 38.7208 +#> 146: 86.4522 -3.2072 4.7525 1.6572 37.5206 +#> 147: 85.8311 -3.2320 4.5149 1.7043 39.6955 +#> 148: 86.0754 -3.2072 5.4070 1.6707 38.8858 +#> 149: 87.0038 -3.1954 5.1367 1.7361 37.9862 +#> 150: 86.8647 -3.1903 4.8798 1.7995 39.6906 +#> 151: 86.4913 -3.2101 4.6358 1.7618 39.2462 +#> 152: 86.4667 -3.2254 4.6929 1.7762 38.0665 +#> 153: 86.0176 -3.2241 4.4586 1.7708 37.6367 +#> 154: 85.8680 -3.2359 5.2401 1.7272 37.7322 +#> 155: 85.6560 -3.2147 3.3340 1.7833 38.4605 +#> 156: 85.6927 -3.1987 1.9644 1.8176 39.4958 +#> 157: 86.3686 -3.2294 3.4959 1.6556 39.7058 +#> 158: 86.7614 -3.2051 2.3005 1.6413 40.3968 +#> 159: 86.6393 -3.2243 1.7824 1.6521 40.0846 +#> 160: 86.8686 -3.1850 1.6490 1.7211 39.6362 +#> 161: 86.7853 -3.2071 1.1720 1.6132 39.6921 +#> 162: 86.7337 -3.1825 1.0646 1.5897 41.1027 +#> 163: 86.9192 -3.1365 1.0339 1.6656 40.2410 +#> 164: 86.6652 -3.2052 0.9750 1.5817 40.6189 +#> 165: 86.6154 -3.1870 1.2602 1.6559 40.1832 +#> 166: 86.7300 -3.2096 1.2144 1.6571 39.8989 +#> 167: 86.4536 -3.2135 0.5155 1.7436 39.6313 +#> 168: 86.4848 -3.2315 0.5060 1.6681 39.1479 +#> 169: 86.2641 -3.2444 0.3935 1.6781 40.2903 +#> 170: 86.2482 -3.2628 0.3342 1.6177 40.2600 +#> 171: 86.2833 -3.2338 0.1701 1.6698 39.8946 +#> 172: 86.2155 -3.2175 0.1858 1.6090 39.9709 +#> 173: 86.2916 -3.2313 0.2088 1.6918 41.4421 +#> 174: 86.1920 -3.2050 0.2067 1.7521 40.7724 +#> 175: 86.2771 -3.2071 0.2213 1.5502 40.5055 +#> 176: 86.2589 -3.1867 0.2010 1.5814 40.0963 +#> 177: 86.2740 -3.2209 0.2679 1.6774 40.9479 +#> 178: 86.2210 -3.1896 0.4420 1.5512 40.3238 +#> 179: 86.1769 -3.2036 0.5592 1.6008 40.3873 +#> 180: 85.9366 -3.2046 0.5056 1.6948 41.4254 +#> 181: 85.9173 -3.2167 0.6033 1.6886 39.5784 +#> 182: 85.7077 -3.2508 0.5008 1.7501 40.4224 +#> 183: 85.8084 -3.2743 0.5737 1.7174 40.0576 +#> 184: 85.7776 -3.2518 0.7164 1.7495 39.8748 +#> 185: 85.6192 -3.2378 1.1401 1.7562 39.9841 +#> 186: 85.6951 -3.2460 1.5642 1.7330 39.1282 +#> 187: 85.5281 -3.2309 1.5452 1.7900 38.4833 +#> 188: 85.3476 -3.2018 1.1385 1.8106 39.2842 +#> 189: 85.1914 -3.2180 1.0465 1.7562 40.0715 +#> 190: 85.2759 -3.2275 1.0437 1.7160 39.9928 +#> 191: 85.3630 -3.2728 1.5672 1.7394 39.4749 +#> 192: 85.1334 -3.2467 0.9598 1.6243 39.7385 +#> 193: 84.9313 -3.2401 0.6441 1.6518 39.5447 +#> 194: 84.9097 -3.2361 0.4275 1.6509 40.3383 +#> 195: 84.9131 -3.2241 0.3344 1.5868 39.1438 +#> 196: 84.9117 -3.2419 0.2435 1.6882 40.1132 +#> 197: 84.9569 -3.2776 0.2352 1.6351 40.1070 +#> 198: 84.9113 -3.2334 0.2133 1.6282 39.9988 +#> 199: 84.9028 -3.2637 0.1859 1.6127 38.8695 +#> 200: 84.9020 -3.2456 0.2429 1.6172 40.2644 +#> 201: 84.9327 -3.2292 0.1787 1.6720 40.5826 +#> 202: 84.9313 -3.2363 0.1487 1.6641 40.1952 +#> 203: 84.9208 -3.2350 0.1445 1.6449 40.0176 +#> 204: 84.9312 -3.2296 0.1488 1.6292 40.1353 +#> 205: 84.9302 -3.2277 0.1454 1.6167 40.4137 +#> 206: 84.9378 -3.2314 0.1474 1.6263 40.2241 +#> 207: 84.9190 -3.2369 0.1454 1.6374 40.1459 +#> 208: 84.9085 -3.2385 0.1527 1.6439 40.1931 +#> 209: 84.8920 -3.2411 0.1566 1.6396 40.1558 +#> 210: 84.8787 -3.2435 0.1574 1.6381 40.1872 +#> 211: 84.8784 -3.2460 0.1528 1.6407 40.1825 +#> 212: 84.8745 -3.2469 0.1474 1.6439 40.0865 +#> 213: 84.8702 -3.2474 0.1429 1.6459 40.0164 +#> 214: 84.8592 -3.2476 0.1421 1.6506 39.9852 +#> 215: 84.8558 -3.2479 0.1389 1.6549 39.9882 +#> 216: 84.8542 -3.2488 0.1365 1.6625 39.9461 +#> 217: 84.8594 -3.2488 0.1354 1.6691 39.9751 +#> 218: 84.8634 -3.2487 0.1335 1.6751 39.9844 +#> 219: 84.8653 -3.2485 0.1298 1.6759 39.9263 +#> 220: 84.8722 -3.2496 0.1267 1.6748 39.8897 +#> 221: 84.8782 -3.2496 0.1267 1.6757 39.8504 +#> 222: 84.8772 -3.2483 0.1278 1.6761 39.8406 +#> 223: 84.8765 -3.2490 0.1296 1.6785 39.8138 +#> 224: 84.8750 -3.2492 0.1274 1.6772 39.8278 +#> 225: 84.8767 -3.2493 0.1266 1.6727 39.8642 +#> 226: 84.8741 -3.2495 0.1251 1.6711 39.8208 +#> 227: 84.8678 -3.2502 0.1234 1.6680 39.8193 +#> 228: 84.8618 -3.2509 0.1217 1.6660 39.7846 +#> 229: 84.8567 -3.2504 0.1208 1.6640 39.7538 +#> 230: 84.8559 -3.2503 0.1215 1.6624 39.7184 +#> 231: 84.8548 -3.2501 0.1203 1.6596 39.6840 +#> 232: 84.8528 -3.2505 0.1206 1.6550 39.6882 +#> 233: 84.8510 -3.2499 0.1229 1.6560 39.7083 +#> 234: 84.8479 -3.2502 0.1243 1.6568 39.7116 +#> 235: 84.8443 -3.2509 0.1244 1.6571 39.7504 +#> 236: 84.8391 -3.2515 0.1253 1.6584 39.7761 +#> 237: 84.8390 -3.2522 0.1246 1.6595 39.8188 +#> 238: 84.8433 -3.2520 0.1240 1.6606 39.8393 +#> 239: 84.8453 -3.2517 0.1233 1.6604 39.8360 +#> 240: 84.8439 -3.2519 0.1225 1.6597 39.8355 +#> 241: 84.8423 -3.2516 0.1215 1.6591 39.8154 +#> 242: 84.8403 -3.2521 0.1208 1.6572 39.7956 +#> 243: 84.8378 -3.2514 0.1199 1.6579 39.7842 +#> 244: 84.8375 -3.2501 0.1191 1.6582 39.7851 +#> 245: 84.8367 -3.2497 0.1200 1.6571 39.7873 +#> 246: 84.8348 -3.2499 0.1200 1.6561 39.7972 +#> 247: 84.8344 -3.2490 0.1196 1.6546 39.8425 +#> 248: 84.8320 -3.2485 0.1197 1.6551 39.8607 +#> 249: 84.8330 -3.2477 0.1212 1.6550 39.8643 +#> 250: 84.8348 -3.2481 0.1217 1.6561 39.8570 +#> 251: 84.8384 -3.2483 0.1214 1.6569 39.8535 +#> 252: 84.8394 -3.2487 0.1218 1.6578 39.8584 +#> 253: 84.8408 -3.2490 0.1229 1.6586 39.9146 +#> 254: 84.8414 -3.2497 0.1232 1.6602 39.9561 +#> 255: 84.8424 -3.2502 0.1229 1.6617 39.9734 +#> 256: 84.8428 -3.2506 0.1230 1.6609 39.9959 +#> 257: 84.8425 -3.2507 0.1221 1.6600 40.0029 +#> 258: 84.8420 -3.2513 0.1213 1.6585 40.0135 +#> 259: 84.8411 -3.2512 0.1212 1.6576 40.0261 +#> 260: 84.8404 -3.2513 0.1219 1.6562 40.0238 +#> 261: 84.8382 -3.2514 0.1226 1.6553 40.0140 +#> 262: 84.8358 -3.2511 0.1226 1.6547 40.0022 +#> 263: 84.8337 -3.2513 0.1224 1.6539 40.0037 +#> 264: 84.8318 -3.2511 0.1223 1.6531 39.9986 +#> 265: 84.8316 -3.2504 0.1213 1.6533 40.0094 +#> 266: 84.8325 -3.2503 0.1202 1.6549 40.0179 +#> 267: 84.8328 -3.2501 0.1189 1.6547 40.0438 +#> 268: 84.8324 -3.2505 0.1183 1.6532 40.0734 +#> 269: 84.8315 -3.2505 0.1177 1.6545 40.0714 +#> 270: 84.8304 -3.2508 0.1175 1.6545 40.0698 +#> 271: 84.8293 -3.2512 0.1173 1.6542 40.0623 +#> 272: 84.8279 -3.2512 0.1165 1.6537 40.0659 +#> 273: 84.8260 -3.2512 0.1171 1.6536 40.0580 +#> 274: 84.8241 -3.2512 0.1172 1.6523 40.0540 +#> 275: 84.8245 -3.2508 0.1171 1.6529 40.0513 +#> 276: 84.8240 -3.2510 0.1165 1.6523 40.0407 +#> 277: 84.8240 -3.2509 0.1160 1.6516 40.0290 +#> 278: 84.8250 -3.2507 0.1156 1.6505 40.0255 +#> 279: 84.8253 -3.2507 0.1147 1.6509 40.0301 +#> 280: 84.8252 -3.2507 0.1140 1.6503 40.0278 +#> 281: 84.8255 -3.2508 0.1135 1.6504 40.0238 +#> 282: 84.8246 -3.2506 0.1128 1.6505 40.0212 +#> 283: 84.8237 -3.2508 0.1120 1.6509 40.0206 +#> 284: 84.8235 -3.2507 0.1121 1.6518 40.0316 +#> 285: 84.8236 -3.2499 0.1121 1.6523 40.0330 +#> 286: 84.8230 -3.2490 0.1118 1.6530 40.0435 +#> 287: 84.8222 -3.2485 0.1119 1.6526 40.0428 +#> 288: 84.8211 -3.2486 0.1120 1.6512 40.0446 +#> 289: 84.8196 -3.2490 0.1121 1.6508 40.0355 +#> 290: 84.8189 -3.2494 0.1121 1.6503 40.0319 +#> 291: 84.8183 -3.2495 0.1126 1.6501 40.0263 +#> 292: 84.8174 -3.2496 0.1127 1.6495 40.0226 +#> 293: 84.8163 -3.2499 0.1126 1.6488 40.0255 +#> 294: 84.8165 -3.2499 0.1125 1.6479 40.0207 +#> 295: 84.8165 -3.2502 0.1130 1.6466 40.0406 +#> 296: 84.8158 -3.2508 0.1131 1.6464 40.0428 +#> 297: 84.8162 -3.2506 0.1129 1.6465 40.0432 +#> 298: 84.8166 -3.2501 0.1131 1.6460 40.0415 +#> 299: 84.8184 -3.2499 0.1138 1.6451 40.0513 +#> 300: 84.8205 -3.2499 0.1144 1.6450 40.0615 +#> 301: 84.8216 -3.2496 0.1156 1.6450 40.0591 +#> 302: 84.8225 -3.2498 0.1161 1.6448 40.0618 +#> 303: 84.8232 -3.2493 0.1163 1.6451 40.0612 +#> 304: 84.8233 -3.2488 0.1166 1.6450 40.0669 +#> 305: 84.8230 -3.2485 0.1163 1.6439 40.0714 +#> 306: 84.8221 -3.2482 0.1158 1.6440 40.0838 +#> 307: 84.8217 -3.2479 0.1154 1.6445 40.0835 +#> 308: 84.8219 -3.2477 0.1156 1.6450 40.0829 +#> 309: 84.8224 -3.2477 0.1152 1.6450 40.0836 +#> 310: 84.8224 -3.2480 0.1148 1.6457 40.0873 +#> 311: 84.8225 -3.2480 0.1143 1.6459 40.0894 +#> 312: 84.8219 -3.2482 0.1136 1.6460 40.0835 +#> 313: 84.8214 -3.2484 0.1131 1.6462 40.0810 +#> 314: 84.8208 -3.2485 0.1130 1.6471 40.0786 +#> 315: 84.8211 -3.2485 0.1128 1.6470 40.0707 +#> 316: 84.8211 -3.2483 0.1127 1.6469 40.0628 +#> 317: 84.8210 -3.2482 0.1124 1.6472 40.0580 +#> 318: 84.8201 -3.2484 0.1122 1.6472 40.0602 +#> 319: 84.8196 -3.2484 0.1117 1.6479 40.0555 +#> 320: 84.8183 -3.2480 0.1119 1.6486 40.0659 +#> 321: 84.8173 -3.2479 0.1122 1.6489 40.0713 +#> 322: 84.8164 -3.2479 0.1129 1.6491 40.0781 +#> 323: 84.8159 -3.2480 0.1136 1.6489 40.0790 +#> 324: 84.8158 -3.2480 0.1140 1.6489 40.0746 +#> 325: 84.8158 -3.2480 0.1138 1.6484 40.0845 +#> 326: 84.8157 -3.2482 0.1137 1.6482 40.0953 +#> 327: 84.8155 -3.2482 0.1134 1.6482 40.0955 +#> 328: 84.8156 -3.2482 0.1133 1.6471 40.1167 +#> 329: 84.8152 -3.2483 0.1129 1.6466 40.1195 +#> 330: 84.8152 -3.2482 0.1124 1.6459 40.1280 +#> 331: 84.8151 -3.2478 0.1120 1.6467 40.1282 +#> 332: 84.8147 -3.2477 0.1115 1.6471 40.1265 +#> 333: 84.8145 -3.2477 0.1110 1.6470 40.1333 +#> 334: 84.8144 -3.2479 0.1108 1.6468 40.1474 +#> 335: 84.8141 -3.2481 0.1106 1.6475 40.1549 +#> 336: 84.8135 -3.2481 0.1103 1.6481 40.1664 +#> 337: 84.8134 -3.2481 0.1106 1.6476 40.1837 +#> 338: 84.8129 -3.2479 0.1109 1.6482 40.1855 +#> 339: 84.8126 -3.2478 0.1107 1.6478 40.1830 +#> 340: 84.8120 -3.2482 0.1106 1.6471 40.1893 +#> 341: 84.8120 -3.2482 0.1106 1.6467 40.1931 +#> 342: 84.8119 -3.2482 0.1106 1.6473 40.2091 +#> 343: 84.8135 -3.2483 0.1109 1.6475 40.2113 +#> 344: 84.8153 -3.2483 0.1114 1.6472 40.2116 +#> 345: 84.8165 -3.2484 0.1119 1.6465 40.2110 +#> 346: 84.8171 -3.2481 0.1121 1.6462 40.2099 +#> 347: 84.8184 -3.2483 0.1126 1.6459 40.2120 +#> 348: 84.8189 -3.2483 0.1127 1.6455 40.2115 +#> 349: 84.8198 -3.2483 0.1127 1.6450 40.2087 +#> 350: 84.8202 -3.2482 0.1125 1.6454 40.2118 +#> 351: 84.8208 -3.2483 0.1120 1.6447 40.2094 +#> 352: 84.8213 -3.2483 0.1118 1.6444 40.2070 +#> 353: 84.8218 -3.2481 0.1115 1.6445 40.2077 +#> 354: 84.8226 -3.2482 0.1114 1.6439 40.2077 +#> 355: 84.8230 -3.2481 0.1113 1.6439 40.2072 +#> 356: 84.8232 -3.2479 0.1111 1.6439 40.2075 +#> 357: 84.8239 -3.2477 0.1109 1.6441 40.2021 +#> 358: 84.8245 -3.2476 0.1107 1.6445 40.2028 +#> 359: 84.8251 -3.2476 0.1107 1.6452 40.2032 +#> 360: 84.8252 -3.2474 0.1110 1.6462 40.2012 +#> 361: 84.8258 -3.2473 0.1108 1.6469 40.2043 +#> 362: 84.8260 -3.2475 0.1107 1.6467 40.2056 +#> 363: 84.8262 -3.2474 0.1106 1.6469 40.2028 +#> 364: 84.8266 -3.2472 0.1104 1.6473 40.1979 +#> 365: 84.8270 -3.2469 0.1102 1.6479 40.1923 +#> 366: 84.8273 -3.2469 0.1100 1.6482 40.1872 +#> 367: 84.8267 -3.2468 0.1099 1.6483 40.1836 +#> 368: 84.8263 -3.2470 0.1099 1.6483 40.1850 +#> 369: 84.8269 -3.2471 0.1098 1.6484 40.1864 +#> 370: 84.8274 -3.2472 0.1098 1.6484 40.1856 +#> 371: 84.8282 -3.2471 0.1101 1.6489 40.1839 +#> 372: 84.8288 -3.2469 0.1099 1.6492 40.1804 +#> 373: 84.8294 -3.2467 0.1098 1.6494 40.1806 +#> 374: 84.8301 -3.2466 0.1096 1.6491 40.1855 +#> 375: 84.8301 -3.2467 0.1093 1.6488 40.1951 +#> 376: 84.8302 -3.2467 0.1092 1.6484 40.1921 +#> 377: 84.8302 -3.2467 0.1092 1.6486 40.1842 +#> 378: 84.8300 -3.2467 0.1095 1.6485 40.1760 +#> 379: 84.8296 -3.2468 0.1094 1.6483 40.1701 +#> 380: 84.8297 -3.2469 0.1094 1.6483 40.1738 +#> 381: 84.8299 -3.2469 0.1093 1.6485 40.1801 +#> 382: 84.8302 -3.2470 0.1092 1.6488 40.1857 +#> 383: 84.8299 -3.2469 0.1090 1.6491 40.1859 +#> 384: 84.8297 -3.2470 0.1090 1.6488 40.1903 +#> 385: 84.8289 -3.2469 0.1095 1.6487 40.1978 +#> 386: 84.8282 -3.2470 0.1098 1.6487 40.1976 +#> 387: 84.8277 -3.2471 0.1101 1.6488 40.1910 +#> 388: 84.8270 -3.2471 0.1104 1.6486 40.1863 +#> 389: 84.8263 -3.2471 0.1108 1.6486 40.1837 +#> 390: 84.8259 -3.2472 0.1109 1.6491 40.1881 +#> 391: 84.8250 -3.2472 0.1111 1.6499 40.1919 +#> 392: 84.8248 -3.2471 0.1113 1.6501 40.1961 +#> 393: 84.8247 -3.2471 0.1113 1.6503 40.1941 +#> 394: 84.8241 -3.2470 0.1114 1.6508 40.1933 +#> 395: 84.8239 -3.2469 0.1115 1.6510 40.1916 +#> 396: 84.8239 -3.2468 0.1115 1.6515 40.1946 +#> 397: 84.8239 -3.2466 0.1113 1.6517 40.1979 +#> 398: 84.8241 -3.2467 0.1112 1.6519 40.1966 +#> 399: 84.8244 -3.2466 0.1112 1.6522 40.1975 +#> 400: 84.8248 -3.2466 0.1111 1.6523 40.1919 +#> 401: 84.8255 -3.2466 0.1109 1.6523 40.1889 +#> 402: 84.8259 -3.2468 0.1108 1.6523 40.1836 +#> 403: 84.8257 -3.2470 0.1109 1.6524 40.1787 +#> 404: 84.8251 -3.2470 0.1111 1.6528 40.1788 +#> 405: 84.8244 -3.2472 0.1113 1.6530 40.1761 +#> 406: 84.8235 -3.2472 0.1113 1.6529 40.1763 +#> 407: 84.8231 -3.2471 0.1112 1.6531 40.1742 +#> 408: 84.8229 -3.2471 0.1110 1.6530 40.1728 +#> 409: 84.8229 -3.2471 0.1109 1.6528 40.1698 +#> 410: 84.8233 -3.2473 0.1109 1.6524 40.1701 +#> 411: 84.8235 -3.2474 0.1109 1.6522 40.1714 +#> 412: 84.8236 -3.2474 0.1110 1.6517 40.1716 +#> 413: 84.8241 -3.2474 0.1111 1.6512 40.1741 +#> 414: 84.8238 -3.2476 0.1108 1.6508 40.1809 +#> 415: 84.8238 -3.2477 0.1108 1.6505 40.1803 +#> 416: 84.8234 -3.2475 0.1110 1.6504 40.1880 +#> 417: 84.8232 -3.2475 0.1112 1.6510 40.1938 +#> 418: 84.8232 -3.2475 0.1112 1.6511 40.1944 +#> 419: 84.8231 -3.2476 0.1114 1.6513 40.1921 +#> 420: 84.8226 -3.2477 0.1113 1.6511 40.1880 +#> 421: 84.8220 -3.2478 0.1111 1.6508 40.1859 +#> 422: 84.8213 -3.2478 0.1110 1.6503 40.1897 +#> 423: 84.8207 -3.2479 0.1110 1.6499 40.1876 +#> 424: 84.8203 -3.2479 0.1111 1.6498 40.1860 +#> 425: 84.8198 -3.2479 0.1111 1.6498 40.1817 +#> 426: 84.8191 -3.2479 0.1113 1.6498 40.1796 +#> 427: 84.8186 -3.2478 0.1112 1.6498 40.1781 +#> 428: 84.8183 -3.2478 0.1114 1.6496 40.1738 +#> 429: 84.8177 -3.2477 0.1116 1.6495 40.1695 +#> 430: 84.8172 -3.2477 0.1119 1.6496 40.1739 +#> 431: 84.8169 -3.2478 0.1120 1.6494 40.1741 +#> 432: 84.8169 -3.2479 0.1121 1.6490 40.1758 +#> 433: 84.8170 -3.2479 0.1121 1.6491 40.1793 +#> 434: 84.8171 -3.2480 0.1122 1.6488 40.1808 +#> 435: 84.8173 -3.2481 0.1123 1.6487 40.1845 +#> 436: 84.8176 -3.2481 0.1123 1.6489 40.1866 +#> 437: 84.8178 -3.2480 0.1122 1.6496 40.1872 +#> 438: 84.8183 -3.2480 0.1121 1.6502 40.1869 +#> 439: 84.8185 -3.2481 0.1119 1.6504 40.1834 +#> 440: 84.8185 -3.2480 0.1118 1.6506 40.1831 +#> 441: 84.8188 -3.2480 0.1120 1.6502 40.1893 +#> 442: 84.8192 -3.2480 0.1120 1.6501 40.1930 +#> 443: 84.8196 -3.2480 0.1120 1.6499 40.1917 +#> 444: 84.8202 -3.2478 0.1122 1.6498 40.1966 +#> 445: 84.8207 -3.2476 0.1124 1.6499 40.1977 +#> 446: 84.8210 -3.2473 0.1123 1.6496 40.2017 +#> 447: 84.8217 -3.2472 0.1123 1.6491 40.2030 +#> 448: 84.8221 -3.2473 0.1122 1.6488 40.2025 +#> 449: 84.8225 -3.2474 0.1121 1.6485 40.2069 +#> 450: 84.8224 -3.2473 0.1119 1.6484 40.2078 +#> 451: 84.8221 -3.2473 0.1118 1.6483 40.2032 +#> 452: 84.8220 -3.2472 0.1117 1.6484 40.1989 +#> 453: 84.8220 -3.2472 0.1117 1.6483 40.1953 +#> 454: 84.8220 -3.2473 0.1122 1.6483 40.1942 +#> 455: 84.8220 -3.2472 0.1124 1.6484 40.1932 +#> 456: 84.8220 -3.2470 0.1124 1.6478 40.1972 +#> 457: 84.8222 -3.2469 0.1125 1.6476 40.1989 +#> 458: 84.8226 -3.2468 0.1125 1.6479 40.1989 +#> 459: 84.8228 -3.2467 0.1126 1.6480 40.2035 +#> 460: 84.8231 -3.2467 0.1124 1.6479 40.2032 +#> 461: 84.8236 -3.2466 0.1126 1.6482 40.2030 +#> 462: 84.8238 -3.2466 0.1124 1.6481 40.2052 +#> 463: 84.8238 -3.2467 0.1123 1.6479 40.2023 +#> 464: 84.8233 -3.2467 0.1123 1.6479 40.2004 +#> 465: 84.8230 -3.2468 0.1123 1.6482 40.2043 +#> 466: 84.8233 -3.2469 0.1123 1.6480 40.2062 +#> 467: 84.8236 -3.2468 0.1121 1.6480 40.2026 +#> 468: 84.8238 -3.2468 0.1120 1.6477 40.2034 +#> 469: 84.8239 -3.2468 0.1119 1.6474 40.2035 +#> 470: 84.8241 -3.2469 0.1116 1.6473 40.2015 +#> 471: 84.8241 -3.2470 0.1116 1.6476 40.1993 +#> 472: 84.8240 -3.2469 0.1117 1.6478 40.1977 +#> 473: 84.8239 -3.2468 0.1119 1.6479 40.1949 +#> 474: 84.8239 -3.2466 0.1118 1.6480 40.1946 +#> 475: 84.8239 -3.2464 0.1119 1.6483 40.1941 +#> 476: 84.8237 -3.2462 0.1121 1.6488 40.1930 +#> 477: 84.8235 -3.2462 0.1122 1.6488 40.1901 +#> 478: 84.8235 -3.2462 0.1125 1.6488 40.1837 +#> 479: 84.8238 -3.2463 0.1128 1.6486 40.1814 +#> 480: 84.8238 -3.2464 0.1129 1.6484 40.1794 +#> 481: 84.8239 -3.2464 0.1129 1.6483 40.1783 +#> 482: 84.8237 -3.2465 0.1130 1.6482 40.1784 +#> 483: 84.8234 -3.2465 0.1130 1.6483 40.1764 +#> 484: 84.8227 -3.2465 0.1132 1.6482 40.1775 +#> 485: 84.8223 -3.2465 0.1133 1.6483 40.1764 +#> 486: 84.8219 -3.2465 0.1135 1.6484 40.1781 +#> 487: 84.8215 -3.2465 0.1136 1.6487 40.1770 +#> 488: 84.8214 -3.2466 0.1136 1.6486 40.1796 +#> 489: 84.8214 -3.2466 0.1134 1.6489 40.1801 +#> 490: 84.8214 -3.2466 0.1132 1.6490 40.1786 +#> 491: 84.8218 -3.2466 0.1131 1.6494 40.1805 +#> 492: 84.8220 -3.2465 0.1133 1.6495 40.1805 +#> 493: 84.8223 -3.2465 0.1137 1.6493 40.1791 +#> 494: 84.8223 -3.2465 0.1140 1.6494 40.1774 +#> 495: 84.8224 -3.2465 0.1142 1.6491 40.1764 +#> 496: 84.8225 -3.2465 0.1142 1.6491 40.1750 +#> 497: 84.8229 -3.2465 0.1142 1.6487 40.1742 +#> 498: 84.8230 -3.2466 0.1140 1.6485 40.1712 +#> 499: 84.8229 -3.2466 0.1137 1.6485 40.1688 +#> 500: 84.8228 -3.2468 0.1134 1.6488 40.1690</div><div class='output co'>#> <span class='message'>Calculating covariance matrix</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#> <span class='message'>done</span></div><div class='input'><span class='va'>f_nlmixr_sfo_focei</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></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>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span style='font-weight: bold;'>Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation +#> F: Forward difference gradient approximation +#> C: Central difference gradient approximation +#> M: Mixed forward and central difference gradient approximation +#> Unscaled parameters for Omegas=chol(solve(omega)); +#> Diagonals are transformed, as specified by foceiControl(diagXform=) +#> |-----+---------------+-----------+-----------+-----------+-----------| +#> | #| Objective Fun | parent_0 |log_k_parent | sigma | o1 | +#> <span style='text-decoration: underline;'>|.....................| o2 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 1</span>| 451.22394 | 1.000 | -1.000 | -0.7995 | -0.9125 | +#> <span style='text-decoration: underline;'>|.....................| -0.9081 |...........|...........|...........|</span> +#> | U| 451.22394 | 86.39 | -3.215 | 5.768 | 0.7049 | +#> <span style='text-decoration: underline;'>|.....................| 0.9021 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 451.22394</span> | 86.39 | 0.04015 | 5.768 | 0.7049 | +#> <span style='text-decoration: underline;'>|.....................| 0.9021 |...........|...........|...........|</span> +#> | G| Gill Diff. | 52.79 | 0.01520 | -15.05 | 0.6163 | +#> <span style='text-decoration: underline;'>|.....................| 2.488 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 2</span>| 3099.6543 | 0.03939 | -1.000 | -0.5255 | -0.9237 | +#> <span style='text-decoration: underline;'>|.....................| -0.9534 |...........|...........|...........|</span> +#> | U| 3099.6543 | 3.403 | -3.215 | 6.558 | 0.6970 | +#> <span style='text-decoration: underline;'>|.....................| 0.8613 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 3099.6543</span> | 3.403 | 0.04014 | 6.558 | 0.6970 | +#> <span style='text-decoration: underline;'>|.....................| 0.8613 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 3</span>| 473.10068 | 0.9039 | -1.000 | -0.7721 | -0.9136 | +#> <span style='text-decoration: underline;'>|.....................| -0.9126 |...........|...........|...........|</span> +#> | U| 473.10068 | 78.09 | -3.215 | 5.847 | 0.7041 | +#> <span style='text-decoration: underline;'>|.....................| 0.8980 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 473.10068</span> | 78.09 | 0.04015 | 5.847 | 0.7041 | +#> <span style='text-decoration: underline;'>|.....................| 0.8980 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 4</span>| 450.95086 | 0.9904 | -1.000 | -0.7967 | -0.9126 | +#> <span style='text-decoration: underline;'>|.....................| -0.9086 |...........|...........|...........|</span> +#> | U| 450.95086 | 85.56 | -3.215 | 5.776 | 0.7048 | +#> <span style='text-decoration: underline;'>|.....................| 0.9017 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 450.95086</span> | 85.56 | 0.04015 | 5.776 | 0.7048 | +#> <span style='text-decoration: underline;'>|.....................| 0.9017 |...........|...........|...........|</span> +#> | F| Forward Diff. | -4.520 | 0.09729 | -14.85 | -0.2941 | +#> <span style='text-decoration: underline;'>|.....................| 2.449 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 5</span>| 450.82239 | 0.9932 | -1.000 | -0.7873 | -0.9124 | +#> <span style='text-decoration: underline;'>|.....................| -0.9101 |...........|...........|...........|</span> +#> | U| 450.82239 | 85.81 | -3.215 | 5.804 | 0.7049 | +#> <span style='text-decoration: underline;'>|.....................| 0.9003 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 450.82239</span> | 85.81 | 0.04015 | 5.804 | 0.7049 | +#> <span style='text-decoration: underline;'>|.....................| 0.9003 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 6</span>| 450.73959 | 0.9981 | -1.000 | -0.7712 | -0.9121 | +#> <span style='text-decoration: underline;'>|.....................| -0.9128 |...........|...........|...........|</span> +#> | U| 450.73959 | 86.23 | -3.215 | 5.850 | 0.7051 | +#> <span style='text-decoration: underline;'>|.....................| 0.8979 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 450.73959</span> | 86.23 | 0.04015 | 5.850 | 0.7051 | +#> <span style='text-decoration: underline;'>|.....................| 0.8979 |...........|...........|...........|</span> +#> | F| Forward Diff. | 41.55 | 0.02901 | -12.22 | 0.2553 | +#> <span style='text-decoration: underline;'>|.....................| 2.069 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 7</span>| 450.34694 | 0.9875 | -1.000 | -0.7467 | -0.9114 | +#> <span style='text-decoration: underline;'>|.....................| -0.9169 |...........|...........|...........|</span> +#> | U| 450.34694 | 85.32 | -3.215 | 5.921 | 0.7056 | +#> <span style='text-decoration: underline;'>|.....................| 0.8942 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 450.34694</span> | 85.32 | 0.04014 | 5.921 | 0.7056 | +#> <span style='text-decoration: underline;'>|.....................| 0.8942 |...........|...........|...........|</span> +#> | F| Forward Diff. | -19.58 | 0.1161 | -10.02 | -0.6042 | +#> <span style='text-decoration: underline;'>|.....................| 1.700 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 8</span>| 450.09191 | 0.9931 | -1.001 | -0.7208 | -0.9093 | +#> <span style='text-decoration: underline;'>|.....................| -0.9217 |...........|...........|...........|</span> +#> | U| 450.09191 | 85.80 | -3.216 | 5.995 | 0.7071 | +#> <span style='text-decoration: underline;'>|.....................| 0.8899 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 450.09191</span> | 85.80 | 0.04012 | 5.995 | 0.7071 | +#> <span style='text-decoration: underline;'>|.....................| 0.8899 |...........|...........|...........|</span> +#> | F| Forward Diff. | 13.00 | 0.06566 | -7.570 | -0.3896 | +#> <span style='text-decoration: underline;'>|.....................| 1.273 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 9</span>| 449.93949 | 0.9873 | -1.002 | -0.6965 | -0.8998 | +#> <span style='text-decoration: underline;'>|.....................| -0.9259 |...........|...........|...........|</span> +#> | U| 449.93949 | 85.30 | -3.217 | 6.065 | 0.7138 | +#> <span style='text-decoration: underline;'>|.....................| 0.8861 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 449.93949</span> | 85.30 | 0.04009 | 6.065 | 0.7138 | +#> <span style='text-decoration: underline;'>|.....................| 0.8861 |...........|...........|...........|</span> +#> | F| Forward Diff. | -18.86 | 0.1073 | -5.670 | -0.6860 | +#> <span style='text-decoration: underline;'>|.....................| 0.8878 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 10</span>| 449.82026 | 0.9918 | -1.004 | -0.6799 | -0.8791 | +#> <span style='text-decoration: underline;'>|.....................| -0.9254 |...........|...........|...........|</span> +#> | U| 449.82026 | 85.69 | -3.219 | 6.113 | 0.7284 | +#> <span style='text-decoration: underline;'>|.....................| 0.8865 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 449.82026</span> | 85.69 | 0.04000 | 6.113 | 0.7284 | +#> <span style='text-decoration: underline;'>|.....................| 0.8865 |...........|...........|...........|</span> +#> | F| Forward Diff. | 8.164 | 0.05669 | -4.296 | -0.3775 | +#> <span style='text-decoration: underline;'>|.....................| 0.8823 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 11</span>| 449.76996 | 0.9897 | -1.006 | -0.6720 | -0.8560 | +#> <span style='text-decoration: underline;'>|.....................| -0.9364 |...........|...........|...........|</span> +#> | U| 449.76996 | 85.50 | -3.221 | 6.136 | 0.7447 | +#> <span style='text-decoration: underline;'>|.....................| 0.8766 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 449.76996</span> | 85.50 | 0.03990 | 6.136 | 0.7447 | +#> <span style='text-decoration: underline;'>|.....................| 0.8766 |...........|...........|...........|</span> +#> | F| Forward Diff. | -2.743 | 0.05613 | -3.782 | -0.3486 | +#> <span style='text-decoration: underline;'>|.....................| -0.07732 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 12</span>| 449.73800 | 0.9901 | -1.008 | -0.6600 | -0.8416 | +#> <span style='text-decoration: underline;'>|.....................| -0.9169 |...........|...........|...........|</span> +#> | U| 449.738 | 85.54 | -3.223 | 6.170 | 0.7549 | +#> <span style='text-decoration: underline;'>|.....................| 0.8942 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 449.738</span> | 85.54 | 0.03983 | 6.170 | 0.7549 | +#> <span style='text-decoration: underline;'>|.....................| 0.8942 |...........|...........|...........|</span> +#> | F| Forward Diff. | 0.5907 | 0.04688 | -2.910 | -0.3174 | +#> <span style='text-decoration: underline;'>|.....................| 1.529 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 13</span>| 449.73838 | 0.9854 | -1.008 | -0.6366 | -0.8390 | +#> <span style='text-decoration: underline;'>|.....................| -0.9292 |...........|...........|...........|</span> +#> | U| 449.73838 | 85.13 | -3.224 | 6.238 | 0.7567 | +#> <span style='text-decoration: underline;'>|.....................| 0.8831 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 449.73838</span> | 85.13 | 0.03981 | 6.238 | 0.7567 | +#> <span style='text-decoration: underline;'>|.....................| 0.8831 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 14</span>| 449.71577 | 0.9877 | -1.008 | -0.6484 | -0.8403 | +#> <span style='text-decoration: underline;'>|.....................| -0.9231 |...........|...........|...........|</span> +#> | U| 449.71577 | 85.33 | -3.223 | 6.204 | 0.7558 | +#> <span style='text-decoration: underline;'>|.....................| 0.8886 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 449.71577</span> | 85.33 | 0.03982 | 6.204 | 0.7558 | +#> <span style='text-decoration: underline;'>|.....................| 0.8886 |...........|...........|...........|</span> +#> | F| Forward Diff. | -13.00 | 0.06593 | -2.084 | -0.4341 | +#> <span style='text-decoration: underline;'>|.....................| 1.007 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 15</span>| 449.68436 | 0.9912 | -1.009 | -0.6401 | -0.8344 | +#> <span style='text-decoration: underline;'>|.....................| -0.9311 |...........|...........|...........|</span> +#> | U| 449.68436 | 85.64 | -3.224 | 6.228 | 0.7599 | +#> <span style='text-decoration: underline;'>|.....................| 0.8814 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 449.68436</span> | 85.64 | 0.03979 | 6.228 | 0.7599 | +#> <span style='text-decoration: underline;'>|.....................| 0.8814 |...........|...........|...........|</span> +#> | F| Forward Diff. | 7.939 | 0.02803 | -1.419 | -0.2659 | +#> <span style='text-decoration: underline;'>|.....................| 0.3125 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 16</span>| 449.66988 | 0.9896 | -1.010 | -0.6363 | -0.8221 | +#> <span style='text-decoration: underline;'>|.....................| -0.9344 |...........|...........|...........|</span> +#> | U| 449.66988 | 85.50 | -3.226 | 6.239 | 0.7686 | +#> <span style='text-decoration: underline;'>|.....................| 0.8784 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 449.66988</span> | 85.50 | 0.03973 | 6.239 | 0.7686 | +#> <span style='text-decoration: underline;'>|.....................| 0.8784 |...........|...........|...........|</span> +#> | F| Forward Diff. | -0.8695 | 0.03361 | -1.202 | -0.2917 | +#> <span style='text-decoration: underline;'>|.....................| 0.02327 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 17</span>| 449.66421 | 0.9900 | -1.012 | -0.6343 | -0.8088 | +#> <span style='text-decoration: underline;'>|.....................| -0.9351 |...........|...........|...........|</span> +#> | U| 449.66421 | 85.53 | -3.227 | 6.245 | 0.7779 | +#> <span style='text-decoration: underline;'>|.....................| 0.8778 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 449.66421</span> | 85.53 | 0.03969 | 6.245 | 0.7779 | +#> <span style='text-decoration: underline;'>|.....................| 0.8778 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 18</span>| 449.65407 | 0.9895 | -1.015 | -0.6307 | -0.7728 | +#> <span style='text-decoration: underline;'>|.....................| -0.9370 |...........|...........|...........|</span> +#> | U| 449.65407 | 85.49 | -3.230 | 6.255 | 0.8033 | +#> <span style='text-decoration: underline;'>|.....................| 0.8761 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 449.65407</span> | 85.49 | 0.03957 | 6.255 | 0.8033 | +#> <span style='text-decoration: underline;'>|.....................| 0.8761 |...........|...........|...........|</span> +#> | F| Forward Diff. | 0.6836 | 0.009868 | -0.9456 | -0.1262 | +#> <span style='text-decoration: underline;'>|.....................| -0.2597 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 19</span>| 449.64227 | 0.9890 | -1.006 | -0.6121 | -0.7274 | +#> <span style='text-decoration: underline;'>|.....................| -0.9339 |...........|...........|...........|</span> +#> | U| 449.64227 | 85.45 | -3.222 | 6.309 | 0.8353 | +#> <span style='text-decoration: underline;'>|.....................| 0.8789 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 449.64227</span> | 85.45 | 0.03989 | 6.309 | 0.8353 | +#> <span style='text-decoration: underline;'>|.....................| 0.8789 |...........|...........|...........|</span> +#> | F| Forward Diff. | -0.4372 | 0.06357 | 0.2445 | -0.08318 | +#> <span style='text-decoration: underline;'>|.....................| -0.05696 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 20</span>| 449.64227 | 0.9890 | -1.006 | -0.6121 | -0.7274 | +#> <span style='text-decoration: underline;'>|.....................| -0.9339 |...........|...........|...........|</span> +#> | U| 449.64227 | 85.45 | -3.222 | 6.309 | 0.8353 | +#> <span style='text-decoration: underline;'>|.....................| 0.8789 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 449.64227</span> | 85.45 | 0.03989 | 6.309 | 0.8353 | +#> <span style='text-decoration: underline;'>|.....................| 0.8789 |...........|...........|...........|</span> +#> calculating covariance matrix +#> done</div><div class='output co'>#> <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#> <span class='message'>done</span></div><div class='output co'>#> <span class='warning'>Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span></div><div class='output co'>#> <span class='warning'>Warning: last objective function was not at minimum, possible problems in optimization</span></div><div class='output co'>#> <span class='warning'>Warning: gradient problems with initial estimate and covariance; see $scaleInfo</span></div><div class='input'> +<span class='va'>f_nlmixr_fomc_saem</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></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>, est <span class='op'>=</span> <span class='st'>"saem"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'>→ generate SAEM model</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> 1: 93.6754 -0.2977 2.0254 2.7655 0.7032 0.5111 15.3443 +#> 2: 93.8828 -0.2006 2.0786 2.6886 0.6681 0.4855 7.5256 +#> 3: 94.0494 -0.2006 2.0891 2.9975 0.6347 0.4612 7.0501 +#> 4: 94.1641 -0.2446 2.0103 3.6008 0.6029 0.4382 6.2482 +#> 5: 93.8983 -0.2562 1.9851 4.5637 0.5728 0.4163 6.1507 +#> 6: 93.9311 -0.2542 1.9733 5.7516 0.5441 0.3954 6.1445 +#> 7: 93.8631 -0.2535 1.9876 5.4640 0.5169 0.3757 5.9234 +#> 8: 94.2851 -0.2327 1.9851 5.7884 0.4943 0.3569 5.9887 +#> 9: 94.2114 -0.2348 2.0169 5.4990 0.4733 0.3390 5.9730 +#> 10: 94.0782 -0.1951 2.0678 5.2240 0.4969 0.3221 5.7694 +#> 11: 94.0527 -0.1898 2.0988 4.9628 0.4924 0.3060 5.6429 +#> 12: 93.9845 -0.1795 2.1168 4.7147 0.4748 0.2907 5.4764 +#> 13: 93.9424 -0.1958 2.0924 4.4790 0.4551 0.2762 5.5598 +#> 14: 94.2255 -0.2005 2.0963 4.2910 0.4552 0.2623 5.4520 +#> 15: 94.6065 -0.1964 2.0794 4.0765 0.4516 0.2492 5.5275 +#> 16: 94.8393 -0.1872 2.0825 4.7814 0.4714 0.2368 5.4708 +#> 17: 94.5489 -0.1873 2.0822 5.3772 0.4714 0.2249 5.5790 +#> 18: 94.5797 -0.1994 2.0702 5.1083 0.4563 0.2137 5.5962 +#> 19: 94.7205 -0.1987 2.0942 5.1405 0.4580 0.2030 5.8328 +#> 20: 94.2162 -0.1961 2.0955 7.2352 0.4578 0.2081 5.5730 +#> 21: 94.2688 -0.1935 2.0980 6.8735 0.4539 0.2199 5.6561 +#> 22: 94.4008 -0.2294 2.0430 6.5298 0.4312 0.2528 5.4970 +#> 23: 93.8617 -0.2126 2.0861 6.2033 0.4420 0.2401 5.3679 +#> 24: 93.9223 -0.2173 2.0786 5.8931 0.4419 0.2281 5.4475 +#> 25: 94.1259 -0.2199 2.0790 5.5985 0.4429 0.2167 5.2610 +#> 26: 93.5597 -0.1966 2.1115 5.3186 0.4521 0.2059 5.0971 +#> 27: 93.5468 -0.2077 2.1016 5.0526 0.4458 0.2090 5.2223 +#> 28: 93.6901 -0.2106 2.0884 4.8000 0.4439 0.2114 5.1693 +#> 29: 93.4521 -0.1991 2.1349 4.5600 0.4236 0.2248 5.1834 +#> 30: 93.7678 -0.1998 2.1267 5.5252 0.4212 0.2297 5.0549 +#> 31: 93.5695 -0.2039 2.1244 5.2489 0.4165 0.2334 5.0965 +#> 32: 93.8288 -0.1855 2.1392 5.1872 0.4401 0.2286 5.0321 +#> 33: 93.9053 -0.1827 2.1426 4.9278 0.4479 0.2171 5.0706 +#> 34: 94.0876 -0.1871 2.1151 4.6814 0.4613 0.2063 5.1438 +#> 35: 94.5298 -0.1845 2.1221 4.4474 0.4586 0.2006 5.1897 +#> 36: 94.3221 -0.1765 2.1144 5.3164 0.4401 0.2193 5.0921 +#> 37: 94.3600 -0.1842 2.1021 5.3586 0.4507 0.2210 5.0926 +#> 38: 94.3734 -0.1790 2.1261 5.0907 0.4494 0.2100 5.1494 +#> 39: 94.5052 -0.1806 2.1319 4.8362 0.4514 0.1995 5.0177 +#> 40: 94.1042 -0.1906 2.0983 4.5944 0.4360 0.1984 5.2507 +#> 41: 94.1815 -0.1914 2.1166 4.3646 0.4385 0.1977 5.1065 +#> 42: 93.9837 -0.2144 2.0673 4.1464 0.4378 0.1878 5.1603 +#> 43: 93.8806 -0.2107 2.0840 3.9642 0.4456 0.1848 5.0904 +#> 44: 94.1765 -0.2107 2.0722 3.7660 0.4456 0.1881 5.1562 +#> 45: 94.2089 -0.2018 2.0874 3.5777 0.4482 0.1787 5.1219 +#> 46: 93.8851 -0.2111 2.0869 3.9421 0.4462 0.1697 5.0752 +#> 47: 94.1372 -0.2192 2.0731 3.7450 0.4517 0.1733 5.1784 +#> 48: 94.0436 -0.2157 2.0730 3.5578 0.4577 0.1854 5.1957 +#> 49: 93.9915 -0.2122 2.0740 3.3799 0.4450 0.1829 5.1116 +#> 50: 94.0579 -0.2233 2.0633 3.2109 0.4453 0.1964 5.0295 +#> 51: 94.0044 -0.2283 2.0544 3.9314 0.4563 0.2118 5.0457 +#> 52: 94.1080 -0.2174 2.0551 4.8914 0.4548 0.2182 5.0504 +#> 53: 94.3715 -0.2134 2.0598 6.2569 0.4509 0.2162 4.9574 +#> 54: 94.7344 -0.2119 2.0459 5.9440 0.4563 0.2121 5.1069 +#> 55: 94.2730 -0.2055 2.0625 5.6468 0.4758 0.2125 5.2656 +#> 56: 94.0206 -0.2017 2.0715 5.3645 0.4719 0.2045 5.1400 +#> 57: 94.0409 -0.1986 2.0837 5.0963 0.4801 0.2068 5.0902 +#> 58: 94.2392 -0.2122 2.0652 4.8415 0.4560 0.2334 5.1883 +#> 59: 93.9996 -0.1962 2.0764 4.5994 0.4686 0.2417 5.1242 +#> 60: 94.1448 -0.1840 2.1016 4.3694 0.4916 0.2296 5.0867 +#> 61: 94.4861 -0.1840 2.1239 4.3846 0.4916 0.2181 5.3979 +#> 62: 93.9892 -0.1781 2.1083 5.1623 0.5216 0.2072 5.0944 +#> 63: 94.0641 -0.1822 2.1129 4.9628 0.5123 0.1969 5.4228 +#> 64: 94.1414 -0.1733 2.1343 6.7238 0.5220 0.1879 5.3546 +#> 65: 94.0908 -0.1754 2.1160 8.4197 0.5165 0.1852 5.0873 +#> 66: 94.1490 -0.1753 2.1054 7.9987 0.5183 0.1857 5.0777 +#> 67: 93.8958 -0.1613 2.1295 7.5988 0.5004 0.2102 5.0641 +#> 68: 94.0579 -0.1683 2.1511 7.2188 0.5083 0.2110 5.3362 +#> 69: 94.0001 -0.1581 2.1629 6.8579 0.5225 0.2272 5.4399 +#> 70: 93.9712 -0.1733 2.1393 6.5150 0.5153 0.2403 5.5011 +#> 71: 94.3143 -0.1758 2.0989 6.1893 0.5043 0.2713 5.5366 +#> 72: 94.2138 -0.1842 2.1003 5.8798 0.5130 0.2578 5.2964 +#> 73: 94.1742 -0.1951 2.0773 5.5858 0.5165 0.2449 5.1986 +#> 74: 94.1287 -0.2003 2.0606 5.3065 0.5115 0.2326 4.8815 +#> 75: 94.4113 -0.1918 2.0811 5.6717 0.5153 0.2210 4.8370 +#> 76: 94.5175 -0.1940 2.0773 5.3881 0.5127 0.2127 4.9333 +#> 77: 94.4157 -0.1882 2.0714 5.1187 0.5189 0.2021 5.0162 +#> 78: 94.6190 -0.2000 2.0529 4.8628 0.5057 0.2064 4.9436 +#> 79: 94.8081 -0.2006 2.0458 4.6196 0.5053 0.2177 5.0159 +#> 80: 94.7817 -0.1943 2.0547 4.3886 0.5076 0.2099 5.1427 +#> 81: 94.5410 -0.1990 2.0686 4.8770 0.5032 0.2092 5.1192 +#> 82: 94.9536 -0.1936 2.0879 6.9870 0.4781 0.2068 5.1053 +#> 83: 94.7923 -0.1936 2.0777 6.6377 0.4734 0.2120 5.1233 +#> 84: 94.9314 -0.1881 2.0981 6.3058 0.4701 0.2088 5.2821 +#> 85: 94.8024 -0.1866 2.0975 5.9905 0.4684 0.2150 5.2088 +#> 86: 94.6506 -0.2019 2.0677 5.6910 0.4510 0.2043 5.2488 +#> 87: 94.9460 -0.1868 2.0823 5.4064 0.4625 0.2089 5.2663 +#> 88: 94.6365 -0.1901 2.0791 5.3471 0.4509 0.2203 5.2214 +#> 89: 94.5943 -0.2135 2.0521 5.0798 0.4585 0.2093 5.0161 +#> 90: 94.7957 -0.2131 2.0545 4.8258 0.4502 0.2026 5.1344 +#> 91: 94.6308 -0.2096 2.0565 4.5845 0.4566 0.2108 5.0403 +#> 92: 94.3521 -0.2059 2.0557 4.3553 0.4925 0.2072 5.3715 +#> 93: 94.5188 -0.2130 2.0646 4.1375 0.4980 0.1996 5.5624 +#> 94: 94.5995 -0.2056 2.0593 3.9306 0.4995 0.2167 5.3581 +#> 95: 94.7276 -0.1868 2.0922 3.7341 0.4863 0.2059 5.3610 +#> 96: 94.5986 -0.1900 2.0771 3.5474 0.4998 0.1956 5.2070 +#> 97: 94.2586 -0.1881 2.1051 3.9558 0.4757 0.1858 5.1561 +#> 98: 94.0716 -0.2098 2.0698 5.6441 0.4539 0.2044 5.1802 +#> 99: 94.2657 -0.2065 2.0679 5.6964 0.4679 0.2190 5.3608 +#> 100: 94.2331 -0.2203 2.0679 5.4116 0.4445 0.2256 5.4031 +#> 101: 93.8634 -0.2222 2.0720 5.1410 0.4279 0.2341 5.3774 +#> 102: 93.7675 -0.2496 2.0232 4.8839 0.4103 0.2224 5.1238 +#> 103: 93.9534 -0.2416 2.0249 4.6397 0.4144 0.2113 5.0031 +#> 104: 94.0631 -0.2442 2.0216 4.8203 0.4119 0.2007 5.1163 +#> 105: 94.0324 -0.2464 2.0092 4.5793 0.4135 0.2047 5.1666 +#> 106: 93.9954 -0.2482 2.0256 4.9167 0.4083 0.2052 5.2515 +#> 107: 94.2189 -0.2507 2.0121 4.6709 0.4072 0.2087 5.3430 +#> 108: 94.3707 -0.2448 2.0215 4.4373 0.4119 0.1996 5.1549 +#> 109: 94.1518 -0.2428 2.0197 4.2155 0.4155 0.1958 5.5480 +#> 110: 93.9287 -0.2571 2.0275 4.0047 0.4152 0.1931 5.8482 +#> 111: 93.9743 -0.2488 2.0202 3.8045 0.4171 0.2084 5.9798 +#> 112: 93.6245 -0.2350 2.0346 3.6142 0.4397 0.1980 6.0270 +#> 113: 94.5370 -0.2330 2.0593 3.9090 0.4422 0.1881 5.4431 +#> 114: 94.5052 -0.2289 2.0555 3.7135 0.4391 0.1787 5.5970 +#> 115: 94.5963 -0.2216 2.0579 3.5279 0.4446 0.1727 5.3901 +#> 116: 94.5059 -0.2293 2.0459 3.3515 0.4407 0.1705 5.2788 +#> 117: 94.6315 -0.2211 2.0564 3.1839 0.4279 0.1689 5.3258 +#> 118: 94.4868 -0.2194 2.0508 4.6523 0.4275 0.1604 5.1421 +#> 119: 94.1809 -0.2232 2.0444 7.0101 0.4302 0.1612 5.3468 +#> 120: 94.0950 -0.2231 2.0482 7.2110 0.4304 0.1625 5.1691 +#> 121: 94.1525 -0.2059 2.0682 6.8504 0.4474 0.1875 5.2811 +#> 122: 94.7122 -0.2154 2.0692 6.6747 0.4366 0.1906 5.3851 +#> 123: 94.2915 -0.2311 2.0431 6.9655 0.4351 0.2021 5.2103 +#> 124: 93.9984 -0.2310 2.0401 6.6173 0.4396 0.2091 5.0920 +#> 125: 94.3668 -0.2068 2.0505 6.2864 0.4983 0.1987 5.3263 +#> 126: 94.3570 -0.2043 2.0525 5.9721 0.5006 0.1887 5.3281 +#> 127: 94.7086 -0.2177 2.0377 5.6735 0.4762 0.1958 5.4003 +#> 128: 94.3565 -0.2173 2.0432 5.3898 0.4754 0.2055 5.5196 +#> 129: 94.4862 -0.2066 2.0639 5.1203 0.4807 0.1952 5.4783 +#> 130: 94.6107 -0.2026 2.0908 4.8643 0.4579 0.1855 5.6186 +#> 131: 94.6831 -0.1907 2.0920 4.6211 0.4710 0.1762 5.4859 +#> 132: 94.7035 -0.2052 2.0733 4.6333 0.4492 0.1723 5.2721 +#> 133: 94.1511 -0.2192 2.0615 5.7533 0.4362 0.1905 5.5019 +#> 134: 94.2758 -0.2101 2.0624 5.4656 0.4356 0.1810 5.3233 +#> 135: 94.6546 -0.1960 2.0826 5.1923 0.4281 0.1980 5.2515 +#> 136: 94.0322 -0.2100 2.0770 4.9327 0.4156 0.2103 5.3514 +#> 137: 94.0915 -0.2096 2.0859 5.6044 0.4159 0.2008 5.2755 +#> 138: 94.2452 -0.1983 2.1055 6.0837 0.4213 0.2185 5.0580 +#> 139: 94.5460 -0.1876 2.1093 6.8410 0.4301 0.2288 5.0840 +#> 140: 94.6905 -0.1863 2.1167 7.4689 0.4313 0.2173 5.0868 +#> 141: 94.6425 -0.1703 2.1240 7.0955 0.4522 0.2065 4.9715 +#> 142: 94.2538 -0.1632 2.1514 6.7407 0.4499 0.2059 5.0853 +#> 143: 94.3098 -0.1625 2.1567 6.4037 0.4499 0.2115 5.5860 +#> 144: 94.2802 -0.1716 2.1510 6.0835 0.4535 0.2081 5.1989 +#> 145: 94.1169 -0.1707 2.1523 5.7793 0.4531 0.2109 5.1407 +#> 146: 94.2558 -0.1579 2.1623 5.4903 0.4654 0.2427 5.2652 +#> 147: 93.9440 -0.1587 2.1673 5.2158 0.4611 0.2537 5.2699 +#> 148: 94.4271 -0.1587 2.1586 4.9550 0.4611 0.2595 5.1280 +#> 149: 94.2734 -0.1768 2.1160 4.7073 0.4809 0.2802 4.9251 +#> 150: 94.2406 -0.1928 2.0941 5.4176 0.4626 0.2662 5.0837 +#> 151: 94.4217 -0.1884 2.0965 5.1467 0.4677 0.2538 5.1728 +#> 152: 94.4856 -0.1826 2.1127 5.6736 0.4646 0.2373 5.1522 +#> 153: 94.3458 -0.1686 2.1381 6.3603 0.4760 0.2028 5.2197 +#> 154: 94.3945 -0.1633 2.1370 5.1586 0.4402 0.1955 5.3770 +#> 155: 94.6367 -0.1520 2.1596 6.4738 0.4533 0.1882 5.3345 +#> 156: 94.9050 -0.1521 2.1417 6.8382 0.4532 0.1729 5.2770 +#> 157: 94.5823 -0.1540 2.1326 5.5745 0.4487 0.1813 5.2760 +#> 158: 94.8355 -0.1691 2.1357 5.2979 0.4296 0.1990 5.3177 +#> 159: 94.7330 -0.1740 2.1148 4.0960 0.4476 0.1820 5.3001 +#> 160: 94.4926 -0.1731 2.1123 4.3550 0.4666 0.1817 5.1849 +#> 161: 94.4953 -0.1758 2.1063 4.0311 0.4698 0.1929 5.1371 +#> 162: 94.5639 -0.1753 2.1064 4.3044 0.4692 0.1911 5.1437 +#> 163: 94.5477 -0.1798 2.1041 4.1393 0.4804 0.2002 5.3561 +#> 164: 94.3812 -0.1934 2.1019 3.5760 0.4689 0.1908 5.3231 +#> 165: 94.0978 -0.1924 2.0973 2.2052 0.4743 0.1962 5.2813 +#> 166: 94.1374 -0.2043 2.0834 2.5477 0.4639 0.1904 5.3277 +#> 167: 94.1587 -0.2036 2.0797 2.7035 0.4561 0.1951 5.3106 +#> 168: 94.1518 -0.2166 2.0654 2.4969 0.4405 0.2090 5.3148 +#> 169: 94.3328 -0.2164 2.0652 2.3067 0.4455 0.1993 5.2385 +#> 170: 94.6029 -0.2176 2.0456 1.7913 0.4478 0.2085 5.4589 +#> 171: 94.2690 -0.2189 2.0635 1.8133 0.4496 0.1999 5.4918 +#> 172: 94.3227 -0.2120 2.0643 1.7763 0.4337 0.2063 5.4992 +#> 173: 94.3099 -0.2039 2.0892 1.1103 0.4350 0.2201 5.5148 +#> 174: 94.3192 -0.1895 2.1140 0.9817 0.4454 0.2078 5.5249 +#> 175: 94.2327 -0.1967 2.0939 0.9890 0.4361 0.1876 5.6321 +#> 176: 94.2707 -0.1989 2.0958 1.3001 0.4405 0.1790 5.6494 +#> 177: 94.0762 -0.2024 2.0908 0.9179 0.4426 0.1778 5.7085 +#> 178: 94.1807 -0.2074 2.0761 1.2663 0.4237 0.2064 5.5157 +#> 179: 94.2221 -0.2029 2.1083 2.0148 0.4270 0.2023 5.6770 +#> 180: 94.5889 -0.1975 2.0974 1.5302 0.4223 0.1778 5.7495 +#> 181: 94.4280 -0.2163 2.0648 1.8829 0.3908 0.1994 5.3948 +#> 182: 94.7076 -0.2247 2.0340 2.1148 0.4238 0.2062 5.4167 +#> 183: 94.5127 -0.2292 2.0317 3.0950 0.4302 0.2160 5.5009 +#> 184: 94.2522 -0.2335 2.0515 2.8900 0.4265 0.2038 5.2995 +#> 185: 94.2331 -0.2330 2.0431 3.3282 0.4276 0.2044 5.2220 +#> 186: 94.2207 -0.2259 2.0512 4.0568 0.4253 0.2008 5.2307 +#> 187: 94.5124 -0.2188 2.0603 3.0941 0.4381 0.1962 5.6927 +#> 188: 94.7691 -0.2454 2.0193 3.1090 0.4409 0.2012 5.5051 +#> 189: 94.5693 -0.2399 2.0169 3.1069 0.4292 0.1883 5.4354 +#> 190: 94.5742 -0.2318 2.0256 4.4216 0.4200 0.1932 5.3851 +#> 191: 94.3882 -0.2475 1.9949 4.5490 0.4366 0.1972 5.2470 +#> 192: 94.4267 -0.2478 1.9943 4.3327 0.4281 0.1995 5.2792 +#> 193: 94.6313 -0.2522 1.9703 3.5911 0.4321 0.1944 5.6218 +#> 194: 94.4345 -0.2616 1.9704 3.2209 0.4260 0.1925 5.5199 +#> 195: 94.6135 -0.2614 1.9622 2.1481 0.4264 0.1879 5.5750 +#> 196: 94.7574 -0.2324 2.0049 1.3351 0.4661 0.1738 5.6590 +#> 197: 94.8293 -0.2064 2.0452 1.6807 0.4904 0.1600 5.7639 +#> 198: 94.6372 -0.2157 2.0307 1.6350 0.5008 0.1524 5.6539 +#> 199: 94.5600 -0.2145 2.0318 1.5133 0.4982 0.1604 5.7178 +#> 200: 94.6945 -0.2100 2.0475 1.4526 0.5066 0.1649 5.6094 +#> 201: 94.5335 -0.2025 2.0594 1.3754 0.5066 0.1681 5.6560 +#> 202: 94.4663 -0.1992 2.0657 1.3622 0.5074 0.1665 5.6522 +#> 203: 94.4750 -0.1956 2.0762 1.3218 0.5051 0.1648 5.5985 +#> 204: 94.4206 -0.1916 2.0795 1.3219 0.5066 0.1593 5.5864 +#> 205: 94.4408 -0.1891 2.0816 1.2934 0.5089 0.1553 5.5967 +#> 206: 94.4631 -0.1863 2.0859 1.2768 0.5108 0.1522 5.6212 +#> 207: 94.4742 -0.1825 2.0912 1.3219 0.5122 0.1479 5.6704 +#> 208: 94.4802 -0.1789 2.0950 1.3488 0.5137 0.1450 5.7072 +#> 209: 94.4734 -0.1756 2.1019 1.3165 0.5155 0.1423 5.7458 +#> 210: 94.4589 -0.1742 2.1056 1.3379 0.5156 0.1409 5.7722 +#> 211: 94.4513 -0.1727 2.1083 1.3395 0.5192 0.1395 5.7707 +#> 212: 94.4422 -0.1718 2.1096 1.3506 0.5219 0.1384 5.7602 +#> 213: 94.4503 -0.1704 2.1112 1.3519 0.5233 0.1377 5.7705 +#> 214: 94.4387 -0.1688 2.1143 1.3620 0.5238 0.1374 5.7627 +#> 215: 94.4468 -0.1677 2.1171 1.3815 0.5236 0.1366 5.7552 +#> 216: 94.4314 -0.1671 2.1191 1.4034 0.5217 0.1362 5.7279 +#> 217: 94.4134 -0.1669 2.1206 1.4118 0.5197 0.1363 5.7109 +#> 218: 94.3896 -0.1665 2.1219 1.3959 0.5181 0.1381 5.6979 +#> 219: 94.3836 -0.1667 2.1226 1.3965 0.5160 0.1402 5.6829 +#> 220: 94.3740 -0.1674 2.1219 1.4130 0.5144 0.1419 5.6839 +#> 221: 94.3663 -0.1677 2.1216 1.4134 0.5131 0.1436 5.6717 +#> 222: 94.3498 -0.1683 2.1212 1.4170 0.5117 0.1453 5.6595 +#> 223: 94.3416 -0.1687 2.1219 1.4195 0.5105 0.1467 5.6587 +#> 224: 94.3412 -0.1687 2.1222 1.4245 0.5097 0.1474 5.6517 +#> 225: 94.3323 -0.1685 2.1235 1.4231 0.5093 0.1484 5.6419 +#> 226: 94.3228 -0.1686 2.1239 1.4167 0.5088 0.1493 5.6305 +#> 227: 94.3135 -0.1688 2.1241 1.4162 0.5084 0.1502 5.6197 +#> 228: 94.3088 -0.1686 2.1251 1.4170 0.5088 0.1515 5.6124 +#> 229: 94.2995 -0.1685 2.1257 1.4316 0.5092 0.1527 5.6079 +#> 230: 94.2864 -0.1690 2.1256 1.4492 0.5088 0.1534 5.6042 +#> 231: 94.2783 -0.1688 2.1260 1.4606 0.5085 0.1548 5.6037 +#> 232: 94.2725 -0.1687 2.1267 1.4571 0.5083 0.1557 5.6020 +#> 233: 94.2692 -0.1682 2.1279 1.4649 0.5076 0.1570 5.6027 +#> 234: 94.2697 -0.1678 2.1292 1.4540 0.5070 0.1584 5.5990 +#> 235: 94.2623 -0.1673 2.1302 1.4424 0.5064 0.1593 5.5919 +#> 236: 94.2610 -0.1667 2.1313 1.4255 0.5055 0.1599 5.5953 +#> 237: 94.2660 -0.1663 2.1322 1.4242 0.5053 0.1605 5.5922 +#> 238: 94.2753 -0.1666 2.1320 1.4370 0.5044 0.1611 5.5891 +#> 239: 94.2821 -0.1662 2.1326 1.4395 0.5036 0.1629 5.5864 +#> 240: 94.2886 -0.1661 2.1330 1.4375 0.5028 0.1644 5.5815 +#> 241: 94.2934 -0.1664 2.1329 1.4276 0.5020 0.1661 5.5777 +#> 242: 94.2963 -0.1664 2.1329 1.4247 0.5012 0.1677 5.5704 +#> 243: 94.2931 -0.1666 2.1328 1.4269 0.5008 0.1690 5.5631 +#> 244: 94.2919 -0.1667 2.1326 1.4279 0.5003 0.1701 5.5610 +#> 245: 94.2959 -0.1675 2.1316 1.4289 0.4993 0.1705 5.5524 +#> 246: 94.2992 -0.1683 2.1305 1.4378 0.4986 0.1706 5.5436 +#> 247: 94.2997 -0.1689 2.1296 1.4461 0.4977 0.1707 5.5383 +#> 248: 94.2978 -0.1693 2.1290 1.4430 0.4970 0.1714 5.5362 +#> 249: 94.2991 -0.1697 2.1285 1.4495 0.4963 0.1720 5.5379 +#> 250: 94.3068 -0.1702 2.1279 1.4556 0.4954 0.1723 5.5390 +#> 251: 94.3097 -0.1707 2.1272 1.4588 0.4936 0.1729 5.5342 +#> 252: 94.3104 -0.1711 2.1267 1.4582 0.4919 0.1739 5.5310 +#> 253: 94.3099 -0.1715 2.1262 1.4551 0.4903 0.1746 5.5279 +#> 254: 94.3110 -0.1721 2.1255 1.4592 0.4886 0.1758 5.5223 +#> 255: 94.3111 -0.1731 2.1236 1.4755 0.4878 0.1775 5.5175 +#> 256: 94.3096 -0.1735 2.1227 1.4971 0.4875 0.1784 5.5162 +#> 257: 94.3079 -0.1738 2.1222 1.5277 0.4874 0.1795 5.5132 +#> 258: 94.3103 -0.1741 2.1217 1.5521 0.4872 0.1806 5.5112 +#> 259: 94.3148 -0.1745 2.1212 1.5788 0.4868 0.1817 5.5066 +#> 260: 94.3170 -0.1750 2.1205 1.6038 0.4863 0.1832 5.5007 +#> 261: 94.3158 -0.1756 2.1197 1.6324 0.4857 0.1849 5.4968 +#> 262: 94.3141 -0.1763 2.1186 1.6503 0.4850 0.1866 5.4918 +#> 263: 94.3135 -0.1764 2.1184 1.6658 0.4849 0.1879 5.4910 +#> 264: 94.3121 -0.1767 2.1183 1.6841 0.4848 0.1893 5.4875 +#> 265: 94.3098 -0.1769 2.1184 1.7115 0.4847 0.1903 5.4832 +#> 266: 94.3087 -0.1768 2.1188 1.7162 0.4845 0.1911 5.4783 +#> 267: 94.3082 -0.1767 2.1191 1.7209 0.4842 0.1920 5.4735 +#> 268: 94.3094 -0.1764 2.1198 1.7314 0.4837 0.1926 5.4720 +#> 269: 94.3074 -0.1764 2.1199 1.7340 0.4831 0.1938 5.4718 +#> 270: 94.3025 -0.1764 2.1200 1.7440 0.4832 0.1949 5.4720 +#> 271: 94.3025 -0.1769 2.1194 1.7538 0.4829 0.1958 5.4748 +#> 272: 94.3039 -0.1772 2.1191 1.7664 0.4829 0.1966 5.4773 +#> 273: 94.3046 -0.1773 2.1192 1.7820 0.4826 0.1976 5.4754 +#> 274: 94.3051 -0.1774 2.1193 1.7895 0.4823 0.1988 5.4735 +#> 275: 94.3026 -0.1773 2.1193 1.7891 0.4819 0.1998 5.4749 +#> 276: 94.3034 -0.1771 2.1195 1.7875 0.4812 0.2010 5.4829 +#> 277: 94.3047 -0.1771 2.1197 1.7843 0.4805 0.2026 5.4878 +#> 278: 94.3067 -0.1771 2.1197 1.7747 0.4799 0.2039 5.4888 +#> 279: 94.3066 -0.1768 2.1202 1.7772 0.4795 0.2049 5.4889 +#> 280: 94.3035 -0.1768 2.1203 1.7797 0.4788 0.2062 5.4888 +#> 281: 94.2961 -0.1771 2.1203 1.7789 0.4782 0.2068 5.4874 +#> 282: 94.2893 -0.1772 2.1203 1.7797 0.4777 0.2072 5.4865 +#> 283: 94.2880 -0.1776 2.1198 1.7743 0.4772 0.2074 5.4856 +#> 284: 94.2897 -0.1779 2.1195 1.7717 0.4768 0.2076 5.4836 +#> 285: 94.2922 -0.1781 2.1194 1.7756 0.4765 0.2075 5.4818 +#> 286: 94.2964 -0.1783 2.1190 1.7759 0.4763 0.2074 5.4798 +#> 287: 94.2991 -0.1787 2.1181 1.7884 0.4761 0.2075 5.4769 +#> 288: 94.2980 -0.1793 2.1171 1.7901 0.4756 0.2077 5.4772 +#> 289: 94.2948 -0.1797 2.1166 1.7957 0.4752 0.2077 5.4763 +#> 290: 94.2922 -0.1801 2.1161 1.8012 0.4749 0.2074 5.4752 +#> 291: 94.2891 -0.1803 2.1157 1.8016 0.4747 0.2073 5.4743 +#> 292: 94.2890 -0.1805 2.1155 1.8012 0.4746 0.2072 5.4743 +#> 293: 94.2874 -0.1808 2.1152 1.8012 0.4743 0.2073 5.4743 +#> 294: 94.2841 -0.1811 2.1148 1.8003 0.4740 0.2075 5.4758 +#> 295: 94.2834 -0.1813 2.1143 1.7982 0.4743 0.2075 5.4766 +#> 296: 94.2817 -0.1816 2.1138 1.7997 0.4745 0.2074 5.4756 +#> 297: 94.2772 -0.1820 2.1131 1.8025 0.4747 0.2074 5.4778 +#> 298: 94.2759 -0.1822 2.1125 1.8097 0.4747 0.2073 5.4781 +#> 299: 94.2752 -0.1825 2.1120 1.8176 0.4748 0.2071 5.4784 +#> 300: 94.2758 -0.1828 2.1115 1.8353 0.4750 0.2069 5.4771 +#> 301: 94.2789 -0.1829 2.1113 1.8511 0.4749 0.2066 5.4767 +#> 302: 94.2808 -0.1833 2.1107 1.8541 0.4747 0.2065 5.4785 +#> 303: 94.2832 -0.1836 2.1103 1.8571 0.4745 0.2064 5.4789 +#> 304: 94.2838 -0.1840 2.1097 1.8584 0.4743 0.2064 5.4792 +#> 305: 94.2835 -0.1843 2.1090 1.8633 0.4741 0.2066 5.4790 +#> 306: 94.2868 -0.1847 2.1083 1.8633 0.4738 0.2069 5.4802 +#> 307: 94.2909 -0.1851 2.1076 1.8702 0.4737 0.2072 5.4787 +#> 308: 94.2916 -0.1857 2.1067 1.8754 0.4735 0.2075 5.4773 +#> 309: 94.2889 -0.1860 2.1062 1.8785 0.4732 0.2078 5.4774 +#> 310: 94.2875 -0.1863 2.1059 1.8854 0.4727 0.2082 5.4763 +#> 311: 94.2889 -0.1867 2.1053 1.8873 0.4722 0.2087 5.4746 +#> 312: 94.2889 -0.1870 2.1047 1.8956 0.4717 0.2090 5.4748 +#> 313: 94.2836 -0.1873 2.1044 1.8980 0.4711 0.2093 5.4721 +#> 314: 94.2801 -0.1876 2.1041 1.8924 0.4706 0.2096 5.4718 +#> 315: 94.2768 -0.1880 2.1038 1.8875 0.4701 0.2096 5.4727 +#> 316: 94.2766 -0.1883 2.1035 1.8854 0.4697 0.2097 5.4730 +#> 317: 94.2779 -0.1886 2.1030 1.8808 0.4693 0.2099 5.4725 +#> 318: 94.2806 -0.1889 2.1024 1.8789 0.4688 0.2101 5.4713 +#> 319: 94.2853 -0.1891 2.1018 1.8852 0.4684 0.2104 5.4690 +#> 320: 94.2867 -0.1894 2.1016 1.8898 0.4680 0.2106 5.4677 +#> 321: 94.2883 -0.1897 2.1013 1.8975 0.4676 0.2108 5.4656 +#> 322: 94.2864 -0.1899 2.1011 1.9078 0.4672 0.2109 5.4622 +#> 323: 94.2831 -0.1902 2.1009 1.9181 0.4668 0.2109 5.4593 +#> 324: 94.2799 -0.1904 2.1008 1.9355 0.4665 0.2109 5.4599 +#> 325: 94.2802 -0.1905 2.1007 1.9474 0.4660 0.2112 5.4608 +#> 326: 94.2808 -0.1907 2.1006 1.9656 0.4654 0.2114 5.4606 +#> 327: 94.2815 -0.1907 2.1006 1.9851 0.4649 0.2118 5.4596 +#> 328: 94.2805 -0.1908 2.1007 2.0051 0.4644 0.2120 5.4584 +#> 329: 94.2810 -0.1909 2.1004 2.0162 0.4638 0.2124 5.4566 +#> 330: 94.2812 -0.1912 2.0999 2.0210 0.4632 0.2131 5.4548 +#> 331: 94.2830 -0.1915 2.0994 2.0253 0.4625 0.2136 5.4520 +#> 332: 94.2835 -0.1920 2.0987 2.0288 0.4619 0.2142 5.4493 +#> 333: 94.2832 -0.1924 2.0981 2.0365 0.4615 0.2148 5.4463 +#> 334: 94.2845 -0.1928 2.0976 2.0433 0.4611 0.2153 5.4436 +#> 335: 94.2856 -0.1931 2.0971 2.0423 0.4607 0.2158 5.4405 +#> 336: 94.2886 -0.1936 2.0963 2.0400 0.4606 0.2165 5.4386 +#> 337: 94.2888 -0.1939 2.0957 2.0352 0.4604 0.2171 5.4376 +#> 338: 94.2879 -0.1944 2.0950 2.0360 0.4600 0.2179 5.4361 +#> 339: 94.2860 -0.1947 2.0946 2.0418 0.4599 0.2186 5.4342 +#> 340: 94.2842 -0.1951 2.0940 2.0455 0.4597 0.2192 5.4324 +#> 341: 94.2804 -0.1954 2.0934 2.0535 0.4596 0.2199 5.4310 +#> 342: 94.2772 -0.1958 2.0928 2.0586 0.4594 0.2204 5.4310 +#> 343: 94.2753 -0.1962 2.0921 2.0604 0.4592 0.2209 5.4304 +#> 344: 94.2749 -0.1965 2.0916 2.0591 0.4589 0.2214 5.4305 +#> 345: 94.2757 -0.1969 2.0911 2.0582 0.4586 0.2220 5.4302 +#> 346: 94.2774 -0.1972 2.0906 2.0554 0.4584 0.2225 5.4301 +#> 347: 94.2772 -0.1974 2.0901 2.0533 0.4583 0.2230 5.4298 +#> 348: 94.2769 -0.1977 2.0895 2.0497 0.4581 0.2235 5.4302 +#> 349: 94.2792 -0.1980 2.0890 2.0439 0.4579 0.2241 5.4327 +#> 350: 94.2825 -0.1983 2.0884 2.0391 0.4577 0.2245 5.4358 +#> 351: 94.2849 -0.1985 2.0879 2.0352 0.4576 0.2251 5.4399 +#> 352: 94.2871 -0.1988 2.0874 2.0396 0.4576 0.2257 5.4414 +#> 353: 94.2888 -0.1991 2.0869 2.0407 0.4573 0.2262 5.4417 +#> 354: 94.2914 -0.1994 2.0863 2.0383 0.4571 0.2268 5.4417 +#> 355: 94.2933 -0.1996 2.0859 2.0385 0.4570 0.2275 5.4418 +#> 356: 94.2932 -0.1999 2.0853 2.0377 0.4569 0.2284 5.4426 +#> 357: 94.2944 -0.2001 2.0850 2.0362 0.4566 0.2292 5.4423 +#> 358: 94.2948 -0.2003 2.0847 2.0415 0.4562 0.2299 5.4409 +#> 359: 94.2950 -0.2005 2.0843 2.0452 0.4558 0.2304 5.4393 +#> 360: 94.2967 -0.2008 2.0840 2.0514 0.4554 0.2307 5.4385 +#> 361: 94.2983 -0.2009 2.0839 2.0676 0.4551 0.2308 5.4386 +#> 362: 94.2992 -0.2009 2.0840 2.0770 0.4549 0.2307 5.4370 +#> 363: 94.2991 -0.2008 2.0841 2.0831 0.4550 0.2306 5.4348 +#> 364: 94.2982 -0.2007 2.0843 2.0892 0.4549 0.2304 5.4348 +#> 365: 94.2951 -0.2005 2.0847 2.1002 0.4551 0.2302 5.4347 +#> 366: 94.2938 -0.2004 2.0850 2.1176 0.4553 0.2300 5.4343 +#> 367: 94.2945 -0.2003 2.0850 2.1310 0.4553 0.2298 5.4346 +#> 368: 94.2956 -0.2003 2.0851 2.1436 0.4554 0.2295 5.4323 +#> 369: 94.2960 -0.2003 2.0850 2.1526 0.4555 0.2293 5.4309 +#> 370: 94.2964 -0.2003 2.0848 2.1577 0.4555 0.2292 5.4295 +#> 371: 94.2965 -0.2004 2.0847 2.1621 0.4555 0.2290 5.4278 +#> 372: 94.2972 -0.2004 2.0847 2.1635 0.4556 0.2285 5.4275 +#> 373: 94.2975 -0.2003 2.0848 2.1643 0.4556 0.2282 5.4275 +#> 374: 94.2985 -0.2004 2.0847 2.1648 0.4556 0.2277 5.4270 +#> 375: 94.3001 -0.2004 2.0846 2.1682 0.4555 0.2273 5.4255 +#> 376: 94.3024 -0.2005 2.0845 2.1692 0.4555 0.2268 5.4246 +#> 377: 94.3050 -0.2005 2.0843 2.1700 0.4555 0.2264 5.4239 +#> 378: 94.3041 -0.2005 2.0843 2.1680 0.4555 0.2258 5.4242 +#> 379: 94.3034 -0.2006 2.0842 2.1688 0.4554 0.2255 5.4233 +#> 380: 94.3027 -0.2007 2.0840 2.1754 0.4554 0.2250 5.4222 +#> 381: 94.3015 -0.2008 2.0839 2.1806 0.4553 0.2246 5.4205 +#> 382: 94.3006 -0.2009 2.0837 2.1812 0.4552 0.2242 5.4194 +#> 383: 94.3004 -0.2010 2.0835 2.1835 0.4551 0.2236 5.4178 +#> 384: 94.3001 -0.2011 2.0834 2.1895 0.4550 0.2232 5.4159 +#> 385: 94.3005 -0.2012 2.0834 2.1910 0.4547 0.2228 5.4148 +#> 386: 94.2993 -0.2013 2.0834 2.1926 0.4545 0.2224 5.4139 +#> 387: 94.2974 -0.2014 2.0834 2.1956 0.4543 0.2221 5.4135 +#> 388: 94.2964 -0.2014 2.0835 2.1979 0.4541 0.2218 5.4124 +#> 389: 94.2956 -0.2013 2.0837 2.1974 0.4540 0.2215 5.4117 +#> 390: 94.2962 -0.2013 2.0838 2.1995 0.4538 0.2213 5.4115 +#> 391: 94.2962 -0.2013 2.0838 2.1987 0.4537 0.2211 5.4116 +#> 392: 94.2956 -0.2013 2.0839 2.2007 0.4536 0.2209 5.4111 +#> 393: 94.2954 -0.2012 2.0839 2.2041 0.4535 0.2207 5.4106 +#> 394: 94.2953 -0.2012 2.0840 2.2033 0.4535 0.2205 5.4103 +#> 395: 94.2964 -0.2012 2.0841 2.2052 0.4533 0.2203 5.4098 +#> 396: 94.2950 -0.2012 2.0841 2.2123 0.4532 0.2202 5.4081 +#> 397: 94.2940 -0.2011 2.0843 2.2227 0.4533 0.2201 5.4070 +#> 398: 94.2938 -0.2011 2.0842 2.2283 0.4534 0.2201 5.4065 +#> 399: 94.2930 -0.2012 2.0842 2.2296 0.4535 0.2201 5.4066 +#> 400: 94.2931 -0.2011 2.0844 2.2345 0.4537 0.2199 5.4071 +#> 401: 94.2926 -0.2009 2.0846 2.2414 0.4539 0.2198 5.4067 +#> 402: 94.2916 -0.2008 2.0848 2.2478 0.4541 0.2196 5.4070 +#> 403: 94.2902 -0.2007 2.0849 2.2543 0.4544 0.2194 5.4071 +#> 404: 94.2895 -0.2007 2.0851 2.2578 0.4546 0.2192 5.4079 +#> 405: 94.2896 -0.2006 2.0853 2.2600 0.4548 0.2190 5.4082 +#> 406: 94.2897 -0.2004 2.0855 2.2636 0.4550 0.2188 5.4086 +#> 407: 94.2880 -0.2002 2.0859 2.2670 0.4554 0.2188 5.4079 +#> 408: 94.2883 -0.1999 2.0861 2.2735 0.4556 0.2189 5.4076 +#> 409: 94.2874 -0.1997 2.0865 2.2822 0.4559 0.2190 5.4073 +#> 410: 94.2861 -0.1995 2.0867 2.2861 0.4563 0.2190 5.4062 +#> 411: 94.2861 -0.1993 2.0869 2.2883 0.4566 0.2190 5.4049 +#> 412: 94.2869 -0.1991 2.0872 2.2926 0.4570 0.2190 5.4039 +#> 413: 94.2874 -0.1990 2.0873 2.2936 0.4574 0.2190 5.4031 +#> 414: 94.2881 -0.1988 2.0874 2.2972 0.4577 0.2189 5.4019 +#> 415: 94.2895 -0.1987 2.0876 2.2999 0.4580 0.2188 5.4004 +#> 416: 94.2900 -0.1985 2.0878 2.3003 0.4582 0.2186 5.3997 +#> 417: 94.2917 -0.1984 2.0880 2.2986 0.4583 0.2185 5.3993 +#> 418: 94.2937 -0.1982 2.0882 2.2986 0.4584 0.2183 5.3995 +#> 419: 94.2947 -0.1981 2.0885 2.2993 0.4584 0.2182 5.3995 +#> 420: 94.2954 -0.1979 2.0886 2.2993 0.4585 0.2180 5.3996 +#> 421: 94.2963 -0.1978 2.0888 2.3029 0.4587 0.2180 5.3992 +#> 422: 94.2982 -0.1976 2.0890 2.3074 0.4588 0.2178 5.4000 +#> 423: 94.3001 -0.1975 2.0891 2.3099 0.4589 0.2178 5.3999 +#> 424: 94.3007 -0.1974 2.0891 2.3106 0.4589 0.2177 5.4001 +#> 425: 94.3016 -0.1973 2.0893 2.3107 0.4589 0.2176 5.3997 +#> 426: 94.3021 -0.1972 2.0894 2.3119 0.4590 0.2175 5.3990 +#> 427: 94.3009 -0.1972 2.0894 2.3100 0.4590 0.2175 5.3971 +#> 428: 94.2998 -0.1972 2.0895 2.3070 0.4590 0.2175 5.3966 +#> 429: 94.2988 -0.1973 2.0895 2.3033 0.4590 0.2175 5.3958 +#> 430: 94.2968 -0.1973 2.0895 2.3028 0.4590 0.2174 5.3955 +#> 431: 94.2950 -0.1973 2.0895 2.3004 0.4589 0.2174 5.3954 +#> 432: 94.2944 -0.1973 2.0896 2.2966 0.4589 0.2174 5.3956 +#> 433: 94.2950 -0.1972 2.0897 2.2942 0.4589 0.2176 5.3959 +#> 434: 94.2949 -0.1972 2.0898 2.2911 0.4589 0.2177 5.3955 +#> 435: 94.2943 -0.1971 2.0900 2.2914 0.4588 0.2179 5.3943 +#> 436: 94.2943 -0.1970 2.0902 2.2895 0.4586 0.2180 5.3948 +#> 437: 94.2955 -0.1970 2.0903 2.2890 0.4585 0.2181 5.3954 +#> 438: 94.2961 -0.1969 2.0905 2.2918 0.4584 0.2183 5.3958 +#> 439: 94.2954 -0.1968 2.0906 2.2943 0.4583 0.2185 5.3953 +#> 440: 94.2944 -0.1968 2.0906 2.2977 0.4581 0.2187 5.3949 +#> 441: 94.2931 -0.1968 2.0907 2.2991 0.4578 0.2188 5.3952 +#> 442: 94.2926 -0.1968 2.0908 2.2990 0.4575 0.2188 5.3951 +#> 443: 94.2922 -0.1968 2.0909 2.2990 0.4573 0.2188 5.3938 +#> 444: 94.2917 -0.1969 2.0909 2.2995 0.4571 0.2188 5.3927 +#> 445: 94.2901 -0.1969 2.0910 2.3067 0.4568 0.2187 5.3911 +#> 446: 94.2898 -0.1969 2.0910 2.3082 0.4566 0.2187 5.3891 +#> 447: 94.2897 -0.1969 2.0910 2.3121 0.4564 0.2187 5.3871 +#> 448: 94.2883 -0.1970 2.0911 2.3180 0.4562 0.2188 5.3858 +#> 449: 94.2879 -0.1970 2.0912 2.3210 0.4561 0.2188 5.3851 +#> 450: 94.2874 -0.1970 2.0914 2.3243 0.4559 0.2188 5.3841 +#> 451: 94.2873 -0.1969 2.0915 2.3247 0.4557 0.2188 5.3834 +#> 452: 94.2873 -0.1969 2.0917 2.3249 0.4555 0.2187 5.3839 +#> 453: 94.2868 -0.1968 2.0920 2.3257 0.4554 0.2187 5.3831 +#> 454: 94.2857 -0.1967 2.0922 2.3240 0.4552 0.2187 5.3824 +#> 455: 94.2848 -0.1965 2.0925 2.3214 0.4551 0.2186 5.3822 +#> 456: 94.2838 -0.1964 2.0929 2.3204 0.4550 0.2185 5.3822 +#> 457: 94.2831 -0.1962 2.0932 2.3202 0.4549 0.2184 5.3819 +#> 458: 94.2831 -0.1961 2.0935 2.3174 0.4548 0.2183 5.3810 +#> 459: 94.2829 -0.1960 2.0938 2.3183 0.4546 0.2183 5.3807 +#> 460: 94.2818 -0.1958 2.0941 2.3213 0.4545 0.2183 5.3802 +#> 461: 94.2812 -0.1956 2.0945 2.3292 0.4544 0.2182 5.3785 +#> 462: 94.2813 -0.1955 2.0948 2.3328 0.4544 0.2182 5.3778 +#> 463: 94.2816 -0.1953 2.0951 2.3364 0.4543 0.2181 5.3770 +#> 464: 94.2810 -0.1952 2.0954 2.3365 0.4542 0.2180 5.3764 +#> 465: 94.2797 -0.1950 2.0957 2.3341 0.4541 0.2179 5.3756 +#> 466: 94.2777 -0.1949 2.0960 2.3368 0.4541 0.2178 5.3750 +#> 467: 94.2755 -0.1949 2.0962 2.3417 0.4539 0.2178 5.3738 +#> 468: 94.2741 -0.1948 2.0965 2.3426 0.4537 0.2177 5.3731 +#> 469: 94.2735 -0.1947 2.0967 2.3410 0.4535 0.2175 5.3729 +#> 470: 94.2731 -0.1946 2.0970 2.3440 0.4534 0.2173 5.3733 +#> 471: 94.2727 -0.1945 2.0972 2.3505 0.4533 0.2171 5.3724 +#> 472: 94.2734 -0.1944 2.0973 2.3550 0.4533 0.2169 5.3719 +#> 473: 94.2745 -0.1944 2.0974 2.3593 0.4533 0.2167 5.3715 +#> 474: 94.2746 -0.1944 2.0975 2.3622 0.4533 0.2166 5.3708 +#> 475: 94.2753 -0.1943 2.0975 2.3673 0.4533 0.2165 5.3701 +#> 476: 94.2760 -0.1943 2.0976 2.3745 0.4534 0.2166 5.3698 +#> 477: 94.2771 -0.1942 2.0978 2.3812 0.4535 0.2166 5.3695 +#> 478: 94.2767 -0.1941 2.0981 2.3891 0.4535 0.2166 5.3691 +#> 479: 94.2762 -0.1940 2.0984 2.3931 0.4534 0.2166 5.3691 +#> 480: 94.2754 -0.1939 2.0986 2.3958 0.4533 0.2166 5.3685 +#> 481: 94.2743 -0.1938 2.0987 2.3990 0.4532 0.2165 5.3677 +#> 482: 94.2733 -0.1937 2.0988 2.3996 0.4531 0.2164 5.3670 +#> 483: 94.2724 -0.1937 2.0989 2.4031 0.4531 0.2163 5.3659 +#> 484: 94.2726 -0.1937 2.0989 2.4035 0.4530 0.2162 5.3651 +#> 485: 94.2722 -0.1937 2.0989 2.4033 0.4530 0.2162 5.3649 +#> 486: 94.2716 -0.1936 2.0991 2.4046 0.4529 0.2163 5.3645 +#> 487: 94.2710 -0.1936 2.0992 2.4078 0.4527 0.2165 5.3643 +#> 488: 94.2693 -0.1936 2.0992 2.4088 0.4525 0.2167 5.3653 +#> 489: 94.2689 -0.1936 2.0993 2.4116 0.4523 0.2170 5.3645 +#> 490: 94.2686 -0.1936 2.0993 2.4105 0.4520 0.2172 5.3644 +#> 491: 94.2685 -0.1935 2.0994 2.4097 0.4518 0.2174 5.3651 +#> 492: 94.2677 -0.1935 2.0995 2.4103 0.4517 0.2175 5.3657 +#> 493: 94.2670 -0.1935 2.0996 2.4112 0.4515 0.2177 5.3661 +#> 494: 94.2668 -0.1935 2.0996 2.4140 0.4514 0.2178 5.3662 +#> 495: 94.2667 -0.1936 2.0996 2.4157 0.4513 0.2179 5.3660 +#> 496: 94.2670 -0.1936 2.0996 2.4163 0.4511 0.2180 5.3668 +#> 497: 94.2664 -0.1936 2.0996 2.4170 0.4510 0.2181 5.3676 +#> 498: 94.2654 -0.1937 2.0996 2.4128 0.4509 0.2181 5.3683 +#> 499: 94.2643 -0.1937 2.0996 2.4109 0.4508 0.2181 5.3679 +#> 500: 94.2635 -0.1938 2.0995 2.4122 0.4508 0.2181 5.3682</div><div class='output co'>#> <span class='message'>Calculating covariance matrix</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#> <span class='message'>done</span></div><div class='input'><span class='va'>f_nlmixr_fomc_focei</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></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>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span style='font-weight: bold;'>Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation +#> F: Forward difference gradient approximation +#> C: Central difference gradient approximation +#> M: Mixed forward and central difference gradient approximation +#> Unscaled parameters for Omegas=chol(solve(omega)); +#> Diagonals are transformed, as specified by foceiControl(diagXform=) +#> |-----+---------------+-----------+-----------+-----------+-----------| +#> | #| Objective Fun | parent_0 | log_alpha | log_beta | sigma | +#> <span style='text-decoration: underline;'>|.....................| o1 | o2 | o3 |...........|</span> +#> |<span style='font-weight: bold;'> 1</span>| 296.31831 | 1.000 | -1.000 | -0.9520 | -0.9547 | +#> |.....................| -0.9791 | -0.9725 | -0.9706 |...........| +#> | U| 296.31831 | 94.44 | -0.2226 | 2.048 | 1.920 | +#> |.....................| 0.7656 | 1.078 | 1.168 |...........| +#> | X|<span style='font-weight: bold;'> 296.31831</span> | 94.44 | 0.8004 | 7.754 | 1.920 | +#> <span style='text-decoration: underline;'>|.....................| 0.7656 | 1.078 | 1.168 |...........|</span> +#> | G| Gill Diff. | 9.126 | 0.009097 | -0.01177 | -32.33 | +#> <span style='text-decoration: underline;'>|.....................| 6.099 | -8.436 | -11.35 |...........|</span> +#> |<span style='font-weight: bold;'> 2</span>| 594.25462 | 0.7531 | -1.000 | -0.9517 | -0.07988 | +#> |.....................| -1.144 | -0.7442 | -0.6636 |...........| +#> | U| 594.25462 | 71.12 | -0.2229 | 2.049 | 2.760 | +#> |.....................| 0.6392 | 1.324 | 1.526 |...........| +#> | X|<span style='font-weight: bold;'> 594.25462</span> | 71.12 | 0.8002 | 7.756 | 2.760 | +#> <span style='text-decoration: underline;'>|.....................| 0.6392 | 1.324 | 1.526 |...........|</span> +#> |<span style='font-weight: bold;'> 3</span>| 298.71818 | 0.9753 | -1.000 | -0.9520 | -0.8672 | +#> |.....................| -0.9956 | -0.9497 | -0.9399 |...........| +#> | U| 298.71818 | 92.11 | -0.2226 | 2.048 | 2.004 | +#> |.....................| 0.7529 | 1.103 | 1.204 |...........| +#> | X|<span style='font-weight: bold;'> 298.71818</span> | 92.11 | 0.8004 | 7.754 | 2.004 | +#> <span style='text-decoration: underline;'>|.....................| 0.7529 | 1.103 | 1.204 |...........|</span> +#> |<span style='font-weight: bold;'> 4</span>| 295.79061 | 0.9925 | -1.000 | -0.9520 | -0.9282 | +#> |.....................| -0.9841 | -0.9656 | -0.9613 |...........| +#> | U| 295.79061 | 93.73 | -0.2226 | 2.048 | 1.945 | +#> |.....................| 0.7617 | 1.086 | 1.179 |...........| +#> | X|<span style='font-weight: bold;'> 295.79061</span> | 93.73 | 0.8004 | 7.754 | 1.945 | +#> <span style='text-decoration: underline;'>|.....................| 0.7617 | 1.086 | 1.179 |...........|</span> +#> | F| Forward Diff. | -134.6 | -0.07715 | -0.3541 | -29.37 | +#> <span style='text-decoration: underline;'>|.....................| 6.863 | -7.752 | -10.79 |...........|</span> +#> |<span style='font-weight: bold;'> 5</span>| 294.44078 | 1.001 | -1.000 | -0.9520 | -0.9020 | +#> |.....................| -0.9892 | -0.9588 | -0.9521 |...........| +#> | U| 294.44078 | 94.55 | -0.2226 | 2.048 | 1.970 | +#> |.....................| 0.7578 | 1.093 | 1.189 |...........| +#> | X|<span style='font-weight: bold;'> 294.44078</span> | 94.55 | 0.8004 | 7.754 | 1.970 | +#> <span style='text-decoration: underline;'>|.....................| 0.7578 | 1.093 | 1.189 |...........|</span> +#> | F| Forward Diff. | 30.39 | 0.01643 | 0.02646 | -26.06 | +#> <span style='text-decoration: underline;'>|.....................| 5.336 | -7.397 | -10.44 |...........|</span> +#> |<span style='font-weight: bold;'> 6</span>| 293.62741 | 0.9971 | -1.000 | -0.9519 | -0.8750 | +#> |.....................| -0.9945 | -0.9516 | -0.9423 |...........| +#> | U| 293.62741 | 94.17 | -0.2226 | 2.048 | 1.996 | +#> |.....................| 0.7538 | 1.101 | 1.201 |...........| +#> | X|<span style='font-weight: bold;'> 293.62741</span> | 94.17 | 0.8004 | 7.754 | 1.996 | +#> <span style='text-decoration: underline;'>|.....................| 0.7538 | 1.101 | 1.201 |...........|</span> +#> |<span style='font-weight: bold;'> 7</span>| 292.50099 | 0.9961 | -1.000 | -0.9519 | -0.8316 | +#> |.....................| -1.003 | -0.9401 | -0.9267 |...........| +#> | U| 292.50099 | 94.07 | -0.2226 | 2.048 | 2.038 | +#> |.....................| 0.7474 | 1.113 | 1.219 |...........| +#> | X|<span style='font-weight: bold;'> 292.50099</span> | 94.07 | 0.8004 | 7.755 | 2.038 | +#> <span style='text-decoration: underline;'>|.....................| 0.7474 | 1.113 | 1.219 |...........|</span> +#> |<span style='font-weight: bold;'> 8</span>| 290.76125 | 0.9939 | -1.000 | -0.9518 | -0.7361 | +#> |.....................| -1.021 | -0.9149 | -0.8925 |...........| +#> | U| 290.76125 | 93.87 | -0.2226 | 2.048 | 2.130 | +#> |.....................| 0.7332 | 1.140 | 1.259 |...........| +#> | X|<span style='font-weight: bold;'> 290.76125</span> | 93.87 | 0.8004 | 7.756 | 2.130 | +#> <span style='text-decoration: underline;'>|.....................| 0.7332 | 1.140 | 1.259 |...........|</span> +#> | F| Forward Diff. | -91.20 | -0.08176 | -0.4010 | -10.74 | +#> <span style='text-decoration: underline;'>|.....................| 3.658 | -4.872 | -7.770 |...........|</span> +#> |<span style='font-weight: bold;'> 9</span>| 293.40175 | 1.024 | -0.9990 | -0.9455 | -0.7012 | +#> |.....................| -1.060 | -0.8302 | -0.7398 |...........| +#> | U| 293.40175 | 96.67 | -0.2216 | 2.055 | 2.163 | +#> |.....................| 0.7035 | 1.231 | 1.437 |...........| +#> | X|<span style='font-weight: bold;'> 293.40175</span> | 96.67 | 0.8012 | 7.804 | 2.163 | +#> <span style='text-decoration: underline;'>|.....................| 0.7035 | 1.231 | 1.437 |...........|</span> +#> |<span style='font-weight: bold;'> 10</span>| 292.85583 | 1.019 | -0.9997 | -0.9499 | -0.7242 | +#> |.....................| -1.033 | -0.8898 | -0.8474 |...........| +#> | U| 292.85583 | 96.21 | -0.2223 | 2.050 | 2.141 | +#> |.....................| 0.7242 | 1.167 | 1.312 |...........| +#> | X|<span style='font-weight: bold;'> 292.85583</span> | 96.21 | 0.8007 | 7.770 | 2.141 | +#> <span style='text-decoration: underline;'>|.....................| 0.7242 | 1.167 | 1.312 |...........|</span> +#> |<span style='font-weight: bold;'> 11</span>| 291.55187 | 1.011 | -1.000 | -0.9517 | -0.7341 | +#> |.....................| -1.022 | -0.9140 | -0.8910 |...........| +#> | U| 291.55187 | 95.48 | -0.2226 | 2.048 | 2.132 | +#> |.....................| 0.7326 | 1.141 | 1.261 |...........| +#> | X|<span style='font-weight: bold;'> 291.55187</span> | 95.48 | 0.8004 | 7.756 | 2.132 | +#> <span style='text-decoration: underline;'>|.....................| 0.7326 | 1.141 | 1.261 |...........|</span> +#> |<span style='font-weight: bold;'> 12</span>| 290.49268 | 0.9997 | -1.000 | -0.9518 | -0.7354 | +#> |.....................| -1.022 | -0.9146 | -0.8920 |...........| +#> | U| 290.49268 | 94.41 | -0.2226 | 2.048 | 2.130 | +#> |.....................| 0.7330 | 1.141 | 1.259 |...........| +#> | X|<span style='font-weight: bold;'> 290.49268</span> | 94.41 | 0.8004 | 7.756 | 2.130 | +#> <span style='text-decoration: underline;'>|.....................| 0.7330 | 1.141 | 1.259 |...........|</span> +#> | F| Forward Diff. | 2.619 | -0.007793 | -0.07320 | -10.57 | +#> <span style='text-decoration: underline;'>|.....................| 3.077 | -4.876 | -7.795 |...........|</span> +#> |<span style='font-weight: bold;'> 13</span>| 290.41825 | 0.9986 | -1.000 | -0.9517 | -0.7312 | +#> |.....................| -1.023 | -0.9126 | -0.8889 |...........| +#> | U| 290.41825 | 94.31 | -0.2226 | 2.048 | 2.134 | +#> |.....................| 0.7321 | 1.143 | 1.263 |...........| +#> | X|<span style='font-weight: bold;'> 290.41825</span> | 94.31 | 0.8004 | 7.756 | 2.134 | +#> <span style='text-decoration: underline;'>|.....................| 0.7321 | 1.143 | 1.263 |...........|</span> +#> |<span style='font-weight: bold;'> 14</span>| 290.31205 | 0.9955 | -1.000 | -0.9517 | -0.7186 | +#> |.....................| -1.027 | -0.9068 | -0.8796 |...........| +#> | U| 290.31205 | 94.01 | -0.2226 | 2.049 | 2.146 | +#> |.....................| 0.7292 | 1.149 | 1.274 |...........| +#> | X|<span style='font-weight: bold;'> 290.31205</span> | 94.01 | 0.8004 | 7.757 | 2.146 | +#> <span style='text-decoration: underline;'>|.....................| 0.7292 | 1.149 | 1.274 |...........|</span> +#> | F| Forward Diff. | -64.45 | -0.06351 | -0.3251 | -9.485 | +#> <span style='text-decoration: underline;'>|.....................| 2.861 | -4.414 | -7.225 |...........|</span> +#> |<span style='font-weight: bold;'> 15</span>| 290.00198 | 1.000 | -0.9999 | -0.9510 | -0.7191 | +#> |.....................| -1.030 | -0.8965 | -0.8595 |...........| +#> | U| 290.00198 | 94.46 | -0.2225 | 2.049 | 2.146 | +#> |.....................| 0.7268 | 1.160 | 1.297 |...........| +#> | X|<span style='font-weight: bold;'> 290.00198</span> | 94.46 | 0.8005 | 7.762 | 2.146 | +#> <span style='text-decoration: underline;'>|.....................| 0.7268 | 1.160 | 1.297 |...........|</span> +#> | F| Forward Diff. | 11.27 | -0.003123 | -0.03408 | -9.156 | +#> <span style='text-decoration: underline;'>|.....................| 2.235 | -3.823 | -6.423 |...........|</span> +#> |<span style='font-weight: bold;'> 16</span>| 289.83558 | 0.9983 | -0.9998 | -0.9502 | -0.7180 | +#> |.....................| -1.031 | -0.8872 | -0.8384 |...........| +#> | U| 289.83558 | 94.28 | -0.2224 | 2.050 | 2.147 | +#> |.....................| 0.7259 | 1.170 | 1.322 |...........| +#> | X|<span style='font-weight: bold;'> 289.83558</span> | 94.28 | 0.8006 | 7.768 | 2.147 | +#> <span style='text-decoration: underline;'>|.....................| 0.7259 | 1.170 | 1.322 |...........|</span> +#> |<span style='font-weight: bold;'> 17</span>| 289.63307 | 0.9979 | -0.9995 | -0.9489 | -0.7184 | +#> |.....................| -1.032 | -0.8720 | -0.8037 |...........| +#> | U| 289.63307 | 94.24 | -0.2221 | 2.051 | 2.147 | +#> |.....................| 0.7248 | 1.186 | 1.363 |...........| +#> | X|<span style='font-weight: bold;'> 289.63307</span> | 94.24 | 0.8008 | 7.778 | 2.147 | +#> <span style='text-decoration: underline;'>|.....................| 0.7248 | 1.186 | 1.363 |...........|</span> +#> |<span style='font-weight: bold;'> 18</span>| 289.44450 | 0.9972 | -0.9991 | -0.9468 | -0.7190 | +#> |.....................| -1.035 | -0.8473 | -0.7469 |...........| +#> | U| 289.4445 | 94.18 | -0.2217 | 2.053 | 2.146 | +#> |.....................| 0.7231 | 1.213 | 1.429 |...........| +#> | X|<span style='font-weight: bold;'> 289.4445</span> | 94.18 | 0.8011 | 7.794 | 2.146 | +#> <span style='text-decoration: underline;'>|.....................| 0.7231 | 1.213 | 1.429 |...........|</span> +#> | F| Forward Diff. | -36.76 | -0.05208 | -0.1861 | -9.057 | +#> <span style='text-decoration: underline;'>|.....................| 2.429 | -0.6853 | -1.924 |...........|</span> +#> |<span style='font-weight: bold;'> 19</span>| 288.93351 | 0.9984 | -0.9961 | -0.9370 | -0.6306 | +#> |.....................| -1.080 | -0.9120 | -0.7149 |...........| +#> | U| 288.93351 | 94.29 | -0.2187 | 2.063 | 2.231 | +#> |.....................| 0.6885 | 1.143 | 1.466 |...........| +#> | X|<span style='font-weight: bold;'> 288.93351</span> | 94.29 | 0.8035 | 7.871 | 2.231 | +#> <span style='text-decoration: underline;'>|.....................| 0.6885 | 1.143 | 1.466 |...........|</span> +#> | F| Forward Diff. | -14.48 | -0.02726 | 0.2181 | -3.062 | +#> <span style='text-decoration: underline;'>|.....................| -0.1976 | -4.306 | -0.8806 |...........|</span> +#> |<span style='font-weight: bold;'> 20</span>| 288.85238 | 1.002 | -0.9934 | -0.9444 | -0.5654 | +#> |.....................| -1.062 | -0.8288 | -0.7747 |...........| +#> | U| 288.85238 | 94.67 | -0.2160 | 2.056 | 2.293 | +#> |.....................| 0.7024 | 1.233 | 1.396 |...........| +#> | X|<span style='font-weight: bold;'> 288.85238</span> | 94.67 | 0.8057 | 7.813 | 2.293 | +#> <span style='text-decoration: underline;'>|.....................| 0.7024 | 1.233 | 1.396 |...........|</span> +#> | F| Forward Diff. | 40.49 | 0.1537 | 0.2940 | 0.6524 | +#> <span style='text-decoration: underline;'>|.....................| 0.4942 | 0.3489 | -3.099 |...........|</span> +#> |<span style='font-weight: bold;'> 21</span>| 289.09335 | 0.9960 | -1.025 | -1.050 | -0.5645 | +#> |.....................| -1.111 | -0.8117 | -0.7552 |...........| +#> | U| 289.09335 | 94.07 | -0.2476 | 1.951 | 2.294 | +#> |.....................| 0.6648 | 1.251 | 1.419 |...........| +#> | X|<span style='font-weight: bold;'> 289.09335</span> | 94.07 | 0.7806 | 7.034 | 2.294 | +#> <span style='text-decoration: underline;'>|.....................| 0.6648 | 1.251 | 1.419 |...........|</span> +#> |<span style='font-weight: bold;'> 22</span>| 288.97418 | 0.9945 | -1.003 | -0.9755 | -0.5652 | +#> |.....................| -1.076 | -0.8238 | -0.7685 |...........| +#> | U| 288.97418 | 93.92 | -0.2254 | 2.025 | 2.294 | +#> |.....................| 0.6912 | 1.238 | 1.404 |...........| +#> | X|<span style='font-weight: bold;'> 288.97418</span> | 93.92 | 0.7982 | 7.574 | 2.294 | +#> <span style='text-decoration: underline;'>|.....................| 0.6912 | 1.238 | 1.404 |...........|</span> +#> |<span style='font-weight: bold;'> 23</span>| 288.99640 | 0.9941 | -0.9963 | -0.9538 | -0.5655 | +#> |.....................| -1.066 | -0.8273 | -0.7723 |...........| +#> | U| 288.9964 | 93.88 | -0.2189 | 2.046 | 2.293 | +#> |.....................| 0.6990 | 1.235 | 1.399 |...........| +#> | X|<span style='font-weight: bold;'> 288.9964</span> | 93.88 | 0.8034 | 7.740 | 2.293 | +#> <span style='text-decoration: underline;'>|.....................| 0.6990 | 1.235 | 1.399 |...........|</span> +#> |<span style='font-weight: bold;'> 24</span>| 288.82158 | 0.9975 | -0.9934 | -0.9445 | -0.5655 | +#> |.....................| -1.062 | -0.8288 | -0.7743 |...........| +#> | U| 288.82158 | 94.20 | -0.2160 | 2.056 | 2.293 | +#> |.....................| 0.7023 | 1.233 | 1.397 |...........| +#> | X|<span style='font-weight: bold;'> 288.82158</span> | 94.20 | 0.8057 | 7.813 | 2.293 | +#> <span style='text-decoration: underline;'>|.....................| 0.7023 | 1.233 | 1.397 |...........|</span> +#> | F| Forward Diff. | -27.98 | 0.07663 | -0.09902 | 0.6250 | +#> <span style='text-decoration: underline;'>|.....................| 0.3387 | 0.3777 | -3.049 |...........|</span> +#> |<span style='font-weight: bold;'> 25</span>| 288.78525 | 0.9995 | -0.9943 | -0.9465 | -0.5657 | +#> |.....................| -1.059 | -0.8303 | -0.7716 |...........| +#> | U| 288.78525 | 94.39 | -0.2169 | 2.054 | 2.293 | +#> |.....................| 0.7042 | 1.231 | 1.400 |...........| +#> | X|<span style='font-weight: bold;'> 288.78525</span> | 94.39 | 0.8050 | 7.797 | 2.293 | +#> <span style='text-decoration: underline;'>|.....................| 0.7042 | 1.231 | 1.400 |...........|</span> +#> | F| Forward Diff. | -0.7037 | 0.08814 | -0.009566 | 0.5597 | +#> <span style='text-decoration: underline;'>|.....................| 0.2999 | 0.2778 | -2.968 |...........|</span> +#> |<span style='font-weight: bold;'> 26</span>| 288.77680 | 1.000 | -0.9946 | -0.9467 | -0.5664 | +#> |.....................| -1.059 | -0.8311 | -0.7670 |...........| +#> | U| 288.7768 | 94.48 | -0.2172 | 2.053 | 2.292 | +#> |.....................| 0.7047 | 1.231 | 1.405 |...........| +#> | X|<span style='font-weight: bold;'> 288.7768</span> | 94.48 | 0.8048 | 7.795 | 2.292 | +#> <span style='text-decoration: underline;'>|.....................| 0.7047 | 1.231 | 1.405 |...........|</span> +#> | F| Forward Diff. | 12.46 | 0.09472 | 0.05753 | 0.4960 | +#> <span style='text-decoration: underline;'>|.....................| 0.3156 | 0.2411 | -2.796 |...........|</span> +#> |<span style='font-weight: bold;'> 27</span>| 288.76499 | 0.9995 | -0.9954 | -0.9482 | -0.5665 | +#> |.....................| -1.055 | -0.8326 | -0.7642 |...........| +#> | U| 288.76499 | 94.39 | -0.2180 | 2.052 | 2.292 | +#> |.....................| 0.7071 | 1.229 | 1.409 |...........| +#> | X|<span style='font-weight: bold;'> 288.76499</span> | 94.39 | 0.8042 | 7.783 | 2.292 | +#> <span style='text-decoration: underline;'>|.....................| 0.7071 | 1.229 | 1.409 |...........|</span> +#> | F| Forward Diff. | -0.8358 | 0.06465 | -0.06858 | 0.5747 | +#> <span style='text-decoration: underline;'>|.....................| 0.6430 | 0.1630 | -2.683 |...........|</span> +#> |<span style='font-weight: bold;'> 28</span>| 288.75697 | 1.000 | -0.9957 | -0.9484 | -0.5681 | +#> |.....................| -1.059 | -0.8325 | -0.7609 |...........| +#> | U| 288.75697 | 94.45 | -0.2183 | 2.052 | 2.291 | +#> |.....................| 0.7046 | 1.229 | 1.413 |...........| +#> | X|<span style='font-weight: bold;'> 288.75697</span> | 94.45 | 0.8039 | 7.782 | 2.291 | +#> <span style='text-decoration: underline;'>|.....................| 0.7046 | 1.229 | 1.413 |...........|</span> +#> | F| Forward Diff. | 8.673 | 0.06496 | -0.02049 | 0.4885 | +#> <span style='text-decoration: underline;'>|.....................| 0.5066 | 0.1747 | -2.560 |...........|</span> +#> |<span style='font-weight: bold;'> 29</span>| 288.75050 | 0.9994 | -0.9958 | -0.9480 | -0.5696 | +#> |.....................| -1.063 | -0.8317 | -0.7600 |...........| +#> | U| 288.7505 | 94.38 | -0.2184 | 2.052 | 2.289 | +#> |.....................| 0.7012 | 1.230 | 1.414 |...........| +#> | X|<span style='font-weight: bold;'> 288.7505</span> | 94.38 | 0.8038 | 7.785 | 2.289 | +#> <span style='text-decoration: underline;'>|.....................| 0.7012 | 1.230 | 1.414 |...........|</span> +#> | F| Forward Diff. | -2.463 | 0.04955 | -0.07455 | 0.3979 | +#> <span style='text-decoration: underline;'>|.....................| 0.1788 | 0.2263 | -2.511 |...........|</span> +#> |<span style='font-weight: bold;'> 30</span>| 288.74110 | 0.9997 | -0.9954 | -0.9459 | -0.5705 | +#> |.....................| -1.061 | -0.8331 | -0.7562 |...........| +#> | U| 288.7411 | 94.41 | -0.2180 | 2.054 | 2.289 | +#> |.....................| 0.7025 | 1.228 | 1.418 |...........| +#> | X|<span style='font-weight: bold;'> 288.7411</span> | 94.41 | 0.8041 | 7.801 | 2.289 | +#> <span style='text-decoration: underline;'>|.....................| 0.7025 | 1.228 | 1.418 |...........|</span> +#> |<span style='font-weight: bold;'> 31</span>| 288.72064 | 0.9993 | -0.9939 | -0.9392 | -0.5730 | +#> |.....................| -1.056 | -0.8374 | -0.7455 |...........| +#> | U| 288.72064 | 94.37 | -0.2166 | 2.061 | 2.286 | +#> |.....................| 0.7068 | 1.224 | 1.431 |...........| +#> | X|<span style='font-weight: bold;'> 288.72064</span> | 94.37 | 0.8053 | 7.854 | 2.286 | +#> <span style='text-decoration: underline;'>|.....................| 0.7068 | 1.224 | 1.431 |...........|</span> +#> |<span style='font-weight: bold;'> 32</span>| 288.70690 | 0.9989 | -0.9915 | -0.9277 | -0.5774 | +#> |.....................| -1.046 | -0.8449 | -0.7267 |...........| +#> | U| 288.7069 | 94.33 | -0.2141 | 2.072 | 2.282 | +#> |.....................| 0.7141 | 1.216 | 1.453 |...........| +#> | X|<span style='font-weight: bold;'> 288.7069</span> | 94.33 | 0.8073 | 7.944 | 2.282 | +#> <span style='text-decoration: underline;'>|.....................| 0.7141 | 1.216 | 1.453 |...........|</span> +#> | F| Forward Diff. | -8.246 | 0.08782 | 0.6230 | -0.2261 | +#> <span style='text-decoration: underline;'>|.....................| 0.9054 | -0.5290 | -1.268 |...........|</span> +#> |<span style='font-weight: bold;'> 33</span>| 288.68146 | 1.000 | -0.9932 | -0.9567 | -0.5899 | +#> |.....................| -1.067 | -0.8479 | -0.7019 |...........| +#> | U| 288.68146 | 94.46 | -0.2158 | 2.043 | 2.270 | +#> |.....................| 0.6982 | 1.212 | 1.481 |...........| +#> | X|<span style='font-weight: bold;'> 288.68146</span> | 94.46 | 0.8059 | 7.717 | 2.270 | +#> <span style='text-decoration: underline;'>|.....................| 0.6982 | 1.212 | 1.481 |...........|</span> +#> | F| Forward Diff. | 8.603 | 0.1068 | -0.4021 | -0.6499 | +#> <span style='text-decoration: underline;'>|.....................| 0.1745 | -0.5873 | -0.4459 |...........|</span> +#> |<span style='font-weight: bold;'> 34</span>| 288.70236 | 1.001 | -1.018 | -0.9264 | -0.5930 | +#> |.....................| -1.088 | -0.8392 | -0.6985 |...........| +#> | U| 288.70236 | 94.50 | -0.2403 | 2.074 | 2.267 | +#> |.....................| 0.6822 | 1.222 | 1.485 |...........| +#> | X|<span style='font-weight: bold;'> 288.70236</span> | 94.50 | 0.7864 | 7.955 | 2.267 | +#> <span style='text-decoration: underline;'>|.....................| 0.6822 | 1.222 | 1.485 |...........|</span> +#> |<span style='font-weight: bold;'> 35</span>| 288.67546 | 0.9997 | -0.9992 | -0.9493 | -0.5906 | +#> |.....................| -1.072 | -0.8457 | -0.7010 |...........| +#> | U| 288.67546 | 94.41 | -0.2218 | 2.051 | 2.269 | +#> |.....................| 0.6943 | 1.215 | 1.482 |...........| +#> | X|<span style='font-weight: bold;'> 288.67546</span> | 94.41 | 0.8011 | 7.775 | 2.269 | +#> <span style='text-decoration: underline;'>|.....................| 0.6943 | 1.215 | 1.482 |...........|</span> +#> | F| Forward Diff. | 1.309 | -0.03968 | -0.1448 | -0.6596 | +#> <span style='text-decoration: underline;'>|.....................| 0.05856 | -0.4617 | -0.3123 |...........|</span> +#> |<span style='font-weight: bold;'> 36</span>| 288.67323 | 0.9995 | -0.9891 | -0.9462 | -0.5890 | +#> |.....................| -1.074 | -0.8436 | -0.6999 |...........| +#> | U| 288.67323 | 94.40 | -0.2117 | 2.054 | 2.271 | +#> |.....................| 0.6929 | 1.217 | 1.484 |...........| +#> | X|<span style='font-weight: bold;'> 288.67323</span> | 94.40 | 0.8092 | 7.800 | 2.271 | +#> <span style='text-decoration: underline;'>|.....................| 0.6929 | 1.217 | 1.484 |...........|</span> +#> | F| Forward Diff. | -0.3529 | 0.1695 | -0.04594 | -0.6688 | +#> <span style='text-decoration: underline;'>|.....................| -0.2932 | -0.3576 | -0.2566 |...........|</span> +#> |<span style='font-weight: bold;'> 37</span>| 288.67323 | 0.9995 | -0.9891 | -0.9462 | -0.5890 | +#> |.....................| -1.074 | -0.8436 | -0.6999 |...........| +#> | U| 288.67323 | 94.40 | -0.2117 | 2.054 | 2.271 | +#> |.....................| 0.6929 | 1.217 | 1.484 |...........| +#> | X|<span style='font-weight: bold;'> 288.67323</span> | 94.40 | 0.8092 | 7.800 | 2.271 | +#> <span style='text-decoration: underline;'>|.....................| 0.6929 | 1.217 | 1.484 |...........|</span> +#> calculating covariance matrix +#> done</div><div class='output co'>#> <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#> <span class='message'>done</span></div><div class='output co'>#> <span class='warning'>Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span></div><div class='output co'>#> <span class='warning'>Warning: ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))</span></div><div class='output co'>#> <span class='warning'>Warning: last objective function was not at minimum, possible problems in optimization</span></div><div class='output co'>#> <span class='warning'>Warning: gradient problems with initial estimate and covariance; see $scaleInfo</span></div><div class='input'> +<span class='va'>f_nlmixr_dfop_saem</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></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>, est <span class='op'>=</span> <span class='st'>"saem"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'>→ generate SAEM model</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> 1: 93.2375 -1.6690 -4.0126 0.0336 3.3441 0.9789 2.1220 0.5342 18.1447 +#> 2: 92.9778 -1.6369 -3.9297 0.0067 3.1769 1.2515 2.0460 0.5166 11.1022 +#> 3: 92.9382 -1.6747 -3.9496 -0.0050 3.0181 1.1889 1.9437 0.4908 9.5980 +#> 4: 93.4481 -1.8083 -3.9734 -0.0250 2.8672 1.1295 1.8797 0.4662 8.6240 +#> 5: 93.4584 -1.8288 -4.0221 0.0414 2.7238 1.0730 1.8467 0.5161 8.1404 +#> 6: 93.7533 -1.8675 -4.0215 0.0158 2.5876 1.0194 1.8017 0.4911 7.5848 +#> 7: 93.6006 -1.8542 -4.0241 -0.0026 2.4582 0.9684 1.7860 0.4916 7.0796 +#> 8: 93.6918 -1.8416 -3.9940 0.0121 2.3353 0.9200 1.7061 0.4681 6.9985 +#> 9: 93.4789 -1.8738 -3.9845 0.0318 3.1307 0.8740 1.7845 0.4553 6.8335 +#> 10: 93.6048 -1.8723 -4.0154 0.0112 3.1962 0.8303 1.7434 0.4325 7.0681 +#> 11: 93.5135 -1.8675 -3.9905 0.0295 3.2177 0.7888 1.6910 0.4619 6.9572 +#> 12: 93.4407 -1.8790 -3.9877 0.0509 3.4194 0.7493 1.6324 0.5060 6.5755 +#> 13: 93.5033 -1.9250 -4.0416 0.0734 3.2485 0.7295 1.7369 0.4807 6.3881 +#> 14: 93.4276 -1.9082 -4.0516 0.0558 3.0860 0.7281 1.7241 0.4567 5.9840 +#> 15: 93.3041 -1.9256 -4.0718 0.0854 3.4389 0.7293 1.7446 0.4524 5.8195 +#> 16: 93.2979 -1.9297 -4.0624 0.0730 3.2670 0.7239 1.7476 0.4298 5.7629 +#> 17: 93.3522 -1.9570 -4.0876 0.1304 3.3053 0.7020 1.7402 0.4083 5.6926 +#> 18: 93.3500 -1.9652 -4.0816 0.1350 3.1400 0.7130 1.7217 0.3879 5.5714 +#> 19: 93.3822 -1.9519 -4.0961 0.1322 2.9830 0.7087 1.7228 0.3745 5.4176 +#> 20: 93.2823 -1.9490 -4.0841 0.1238 2.8339 0.6988 1.7659 0.3753 5.5279 +#> 21: 93.5951 -1.9298 -4.0874 0.1345 2.6922 0.6665 1.7724 0.3645 5.4414 +#> 22: 93.5052 -1.9469 -4.0739 0.1260 3.1244 0.6776 1.7629 0.3618 5.5395 +#> 23: 93.4734 -1.9952 -4.0909 0.1472 3.0340 0.7225 1.8104 0.3437 5.5072 +#> 24: 93.8816 -1.9639 -4.0914 0.1511 2.8824 0.7215 1.8586 0.3324 5.6009 +#> 25: 93.5874 -1.9750 -4.1026 0.1296 2.7383 0.7178 1.8209 0.3680 5.6274 +#> 26: 93.4057 -1.9316 -4.0922 0.1224 3.8103 0.7331 1.7796 0.3639 5.6861 +#> 27: 93.5013 -1.9188 -4.0698 0.0758 3.7127 0.7670 1.8750 0.3457 5.6624 +#> 28: 93.5703 -1.9523 -4.0758 0.0731 4.6390 0.7489 1.8583 0.3445 5.8077 +#> 29: 93.4694 -1.9559 -4.0566 0.0444 5.1290 0.8062 1.9344 0.3273 5.8688 +#> 30: 93.2290 -1.9824 -4.0475 0.0674 4.8726 0.8702 2.0343 0.3109 5.7579 +#> 31: 93.8652 -1.9771 -4.0510 0.0679 4.6289 0.8565 2.0529 0.2954 5.5526 +#> 32: 93.5854 -1.9573 -4.0510 0.0643 5.1320 0.8417 2.0138 0.2806 5.4199 +#> 33: 93.9870 -1.9503 -4.0513 0.0542 4.8754 0.8412 2.0433 0.2666 5.6945 +#> 34: 93.6884 -1.9172 -4.0633 0.0556 4.6317 0.8847 2.0861 0.2702 5.2687 +#> 35: 94.0375 -1.9365 -4.0576 0.0753 5.2320 0.8404 2.0791 0.2582 5.2760 +#> 36: 94.1588 -1.9423 -4.0499 0.0792 4.9704 0.8221 2.1145 0.2669 5.2050 +#> 37: 93.8626 -1.9356 -4.0538 0.0591 5.2723 0.8360 2.1407 0.2536 5.3218 +#> 38: 93.7237 -1.9357 -4.0611 0.0543 5.0087 0.8361 2.0788 0.2710 5.2866 +#> 39: 93.6513 -1.9327 -4.0408 0.0712 4.7582 0.8408 2.0051 0.2899 5.4693 +#> 40: 93.4619 -1.9634 -4.0360 0.1232 4.5203 0.8317 2.0367 0.3288 5.4324 +#> 41: 93.4809 -1.9601 -4.0351 0.1261 4.2943 0.8424 2.0081 0.3306 5.4573 +#> 42: 93.5851 -1.9745 -4.0428 0.1250 4.9744 0.8003 1.9818 0.3141 5.5168 +#> 43: 93.7820 -1.9597 -4.0401 0.1305 5.9118 0.7603 2.1332 0.2984 5.4899 +#> 44: 93.7419 -1.9509 -4.0495 0.1345 5.6162 0.7743 2.0459 0.2998 5.5344 +#> 45: 93.6967 -1.9366 -4.0522 0.1215 5.3354 0.7968 2.0566 0.2848 5.7738 +#> 46: 93.3665 -1.9553 -4.0018 0.0951 5.0686 0.7583 2.1124 0.2706 5.3850 +#> 47: 93.2974 -1.9332 -4.0091 0.0869 5.2792 0.8149 2.1009 0.2597 5.6743 +#> 48: 93.3967 -1.9540 -4.0218 0.0623 5.0152 0.8006 2.1538 0.2467 5.5889 +#> 49: 93.1652 -1.9724 -4.0350 0.0506 4.7645 0.8055 2.1445 0.2344 5.3586 +#> 50: 93.1464 -1.9377 -4.0185 0.0591 5.3658 0.8149 2.1523 0.2226 5.2483 +#> 51: 93.5217 -1.9246 -4.0272 0.0423 5.8579 0.8368 2.1596 0.2115 5.2746 +#> 52: 93.5512 -1.9257 -4.0204 0.0307 7.2345 0.8463 2.1903 0.2065 5.2405 +#> 53: 93.5400 -1.9428 -4.0300 0.0572 6.8728 0.8268 2.0807 0.2139 5.4127 +#> 54: 93.9868 -1.9502 -4.0129 0.0282 9.6651 0.8468 2.0823 0.2032 5.0396 +#> 55: 94.0505 -1.9393 -4.0073 0.0390 10.0994 0.8375 2.1018 0.2016 4.9147 +#> 56: 93.8010 -1.9493 -4.0026 0.0415 10.1741 0.8816 2.1117 0.2207 5.0723 +#> 57: 93.7596 -1.9762 -4.0154 0.0651 9.6654 0.8952 2.1662 0.2096 5.2311 +#> 58: 94.3399 -1.9353 -4.0095 0.0446 9.1821 0.9498 2.2103 0.1991 5.1009 +#> 59: 94.4036 -1.9283 -4.0279 0.0475 8.7230 0.9480 2.3209 0.1892 4.9930 +#> 60: 94.6395 -1.9260 -4.0348 0.0457 8.8651 0.9006 2.2565 0.1797 5.1751 +#> 61: 94.6499 -1.9291 -4.0216 0.0297 8.4218 0.9206 2.2220 0.1843 5.1124 +#> 62: 94.3847 -1.9010 -4.0300 0.0257 9.0591 0.9331 2.2795 0.1816 5.0834 +#> 63: 94.5510 -1.9120 -4.0116 0.0179 8.6061 0.9256 2.1791 0.1736 5.1513 +#> 64: 94.2510 -1.9213 -4.0184 0.0204 8.1758 0.9124 2.2131 0.1682 5.0698 +#> 65: 94.1173 -1.9044 -4.0279 0.0286 8.6773 0.9211 2.2202 0.1598 5.1120 +#> 66: 94.2093 -1.9098 -4.0206 0.0160 8.2435 0.9230 2.2475 0.1750 5.0175 +#> 67: 94.2814 -1.9339 -4.0041 0.0146 7.8313 0.9377 2.2350 0.1709 5.1478 +#> 68: 94.3001 -1.9079 -4.0127 -0.0103 7.4397 0.9163 2.2245 0.1640 5.2529 +#> 69: 94.3820 -1.9167 -4.0176 0.0296 7.0678 0.8704 2.2236 0.1888 5.2574 +#> 70: 94.2691 -1.9037 -4.0156 0.0388 6.7144 0.8601 2.1833 0.2128 5.0230 +#> 71: 94.3827 -1.9183 -4.0056 0.0485 6.3786 0.8491 2.2147 0.2345 5.1212 +#> 72: 94.3104 -1.9291 -4.0099 0.0330 6.0597 0.9007 2.2316 0.2255 5.3748 +#> 73: 94.1778 -1.9238 -4.0054 0.0222 5.7567 0.9479 2.2969 0.2142 5.2827 +#> 74: 94.1022 -1.9149 -4.0017 0.0497 5.4689 0.9305 2.3058 0.2035 5.3117 +#> 75: 94.2343 -1.9045 -4.0141 0.0189 5.1954 0.9141 2.3227 0.1933 5.1047 +#> 76: 94.0905 -1.9019 -4.0166 0.0170 5.5411 0.8978 2.3315 0.1836 5.1233 +#> 77: 94.2772 -1.9117 -4.0053 0.0584 5.2641 0.9238 2.3678 0.1866 4.9803 +#> 78: 94.7235 -1.9141 -4.0464 0.0758 5.0735 0.9308 2.3720 0.2062 5.0544 +#> 79: 94.4674 -1.9287 -4.0494 0.0724 5.7355 0.9063 2.3680 0.1959 5.0910 +#> 80: 93.9895 -1.9271 -4.0456 0.0366 7.2150 0.8857 2.4000 0.1861 5.0612 +#> 81: 94.3190 -1.9358 -4.0402 0.0506 7.5591 0.8891 2.3317 0.1814 4.8617 +#> 82: 94.1898 -1.9126 -4.0552 0.0595 7.6462 0.9157 2.3848 0.1854 4.7335 +#> 83: 94.2044 -1.9145 -4.0359 0.0295 7.8610 0.9451 2.4305 0.1871 4.9258 +#> 84: 93.8197 -1.9058 -3.9879 -0.0409 10.4218 0.9604 2.3848 0.2177 5.0619 +#> 85: 94.0219 -1.8957 -3.9753 -0.0441 9.9007 0.9637 2.4476 0.2219 5.0532 +#> 86: 94.0737 -1.8889 -3.9753 -0.0220 9.4056 0.9675 2.4476 0.2284 5.2694 +#> 87: 93.8548 -1.8755 -3.9707 -0.0024 8.9354 1.0066 2.4895 0.2340 5.4019 +#> 88: 93.7578 -1.9046 -3.9804 -0.0042 8.4886 0.9656 2.5006 0.2271 5.3724 +#> 89: 93.6848 -1.8936 -3.9689 -0.0396 10.6813 0.9805 2.4561 0.2254 5.2615 +#> 90: 93.3617 -1.9167 -3.9801 -0.0221 10.1472 1.0147 2.3589 0.2141 5.4193 +#> 91: 93.7419 -1.8964 -3.9888 -0.0363 9.6398 1.0077 2.3748 0.2066 5.3463 +#> 92: 93.8635 -1.8994 -3.9783 -0.0625 9.1578 1.0028 2.3282 0.2239 5.3026 +#> 93: 94.0864 -1.8648 -3.9426 -0.0813 8.8693 1.0348 2.3654 0.2127 5.2637 +#> 94: 93.9789 -1.8949 -3.9840 -0.0549 10.0871 1.0752 2.4551 0.2021 5.4225 +#> 95: 93.9008 -1.9141 -4.0080 -0.0644 9.8584 1.1599 2.4184 0.1920 5.2179 +#> 96: 93.6926 -2.0270 -3.8911 -0.0777 10.3968 1.1019 3.0518 0.1824 5.3163 +#> 97: 93.2478 -2.0074 -3.9034 -0.0427 10.7200 1.0468 2.9960 0.1732 5.5172 +#> 98: 93.4556 -2.0118 -3.9034 -0.0294 10.1840 1.0007 2.9960 0.1646 5.5887 +#> 99: 93.7548 -2.0076 -3.8894 -0.0157 9.7519 0.9507 3.0357 0.1569 5.7139 +#> 100: 93.8962 -2.0112 -3.8887 -0.0406 9.2643 0.9048 3.0369 0.1491 5.6145 +#> 101: 94.0889 -2.0221 -3.8612 -0.0145 8.8011 0.8728 3.1466 0.1499 5.4224 +#> 102: 94.5428 -2.0206 -3.8489 0.0115 8.3611 0.8292 3.1577 0.1473 5.6634 +#> 103: 94.4882 -2.0447 -3.8594 0.0514 7.9430 0.8202 3.1812 0.1562 5.5136 +#> 104: 94.3185 -2.0389 -3.8584 0.0258 8.3364 0.8346 3.1801 0.1484 5.3612 +#> 105: 94.2858 -2.0345 -3.8738 -0.0001 10.6008 0.8415 3.2251 0.1410 5.3359 +#> 106: 94.1264 -2.0415 -3.8756 0.0411 10.0707 0.8554 3.2182 0.1658 5.2280 +#> 107: 93.9801 -2.0574 -3.8674 0.0403 10.0269 0.8807 3.2628 0.1744 5.0299 +#> 108: 93.6911 -2.0295 -3.8693 0.0355 9.5255 0.8683 3.2708 0.1803 5.1880 +#> 109: 94.0646 -2.0260 -3.8806 0.0506 9.0493 0.8729 3.3140 0.1759 5.1927 +#> 110: 94.4591 -2.0378 -3.8962 0.0360 8.5968 0.8890 3.3076 0.1675 4.8961 +#> 111: 94.3748 -2.0319 -3.9053 0.0397 8.1670 0.8995 3.3254 0.1591 4.8066 +#> 112: 94.2370 -2.0338 -3.9017 0.0603 7.7586 0.8545 3.2484 0.1512 4.8856 +#> 113: 94.1242 -2.0237 -3.8954 0.0795 7.3707 0.8980 3.2127 0.1530 5.1859 +#> 114: 94.1452 -2.0298 -3.9197 0.0530 7.0021 0.8771 3.0744 0.1628 5.1303 +#> 115: 94.1403 -2.0410 -3.9093 0.0476 6.9173 0.9383 3.0223 0.1621 5.2563 +#> 116: 94.1612 -2.0424 -3.9063 0.0593 7.6367 0.8914 3.0420 0.1856 5.1566 +#> 117: 94.2018 -2.0488 -3.9041 0.0539 7.2549 0.8549 3.0204 0.1796 5.2119 +#> 118: 94.1315 -2.0579 -3.9139 0.0564 6.8922 0.8121 3.0180 0.1948 5.0302 +#> 119: 93.7398 -2.0747 -3.9202 0.0570 6.7510 0.7838 3.0084 0.1906 5.0863 +#> 120: 93.5945 -2.0511 -3.9388 0.0534 6.4134 0.7885 3.0100 0.2128 5.0597 +#> 121: 93.9845 -2.0613 -3.9338 0.0568 6.0928 0.7793 2.9944 0.2022 5.3179 +#> 122: 93.7779 -2.0831 -3.9338 0.0630 5.7881 0.7778 2.9944 0.1921 5.2399 +#> 123: 93.9128 -2.0623 -3.9135 0.0493 5.4987 0.8329 2.9729 0.1825 5.0752 +#> 124: 93.5190 -2.0804 -3.9315 0.0538 5.2238 0.8581 3.0220 0.1733 4.9713 +#> 125: 93.7427 -2.0649 -3.9309 0.0499 4.9626 0.8431 3.0260 0.1882 5.0718 +#> 126: 9.3540e+01 -2.0238e+00 -3.9006e+00 -6.8989e-05 4.7145e+00 8.3548e-01 2.9498e+00 1.9993e-01 5.2080e+00 +#> 127: 93.4310 -2.0496 -3.8898 -0.0173 4.4788 0.8864 2.9614 0.2302 5.6432 +#> 128: 93.7512 -2.0285 -3.9180 -0.0096 4.2548 0.8653 3.0768 0.2312 5.3906 +#> 129: 93.6908 -2.0718 -3.9113 -0.0194 4.0421 0.9022 3.0506 0.2386 5.3278 +#> 130: 93.5805 -1.9753 -4.0480 0.0065 3.8400 1.0388 2.8980 0.2276 5.2583 +#> 131: 93.8050 -1.9501 -4.0447 0.0040 3.8738 1.0957 2.7531 0.2162 5.3026 +#> 132: 93.6470 -1.9322 -4.0411 0.0048 3.6801 1.0618 2.6155 0.2159 5.2552 +#> 133: 94.2927 -1.9445 -4.0067 -0.0040 5.6903 1.0378 2.5425 0.2094 5.2430 +#> 134: 94.2814 -1.9286 -4.0021 -0.0144 6.9123 1.1316 2.5172 0.1990 5.3877 +#> 135: 94.0440 -1.9285 -4.0415 0.0254 6.5667 1.1416 2.4394 0.1975 5.3248 +#> 136: 94.0122 -1.9256 -4.0542 0.0156 6.6147 1.1511 2.4728 0.1954 5.2109 +#> 137: 93.8613 -1.9095 -4.0629 -0.0007 6.2840 1.1789 2.5078 0.2045 5.2876 +#> 138: 93.7410 -1.9345 -4.0788 -0.0005 6.0718 1.1507 2.5026 0.2086 5.3284 +#> 139: 93.6437 -1.9499 -4.0788 0.0159 5.7682 1.0932 2.5026 0.1982 5.4211 +#> 140: 93.4066 -1.9591 -4.0720 0.0472 6.9432 1.0883 2.4756 0.1947 5.4439 +#> 141: 93.6086 -1.9625 -4.1026 0.0785 7.7204 1.1027 2.3974 0.2084 5.6595 +#> 142: 93.8693 -1.9640 -4.1003 0.0670 10.1206 1.1196 2.2775 0.1980 5.4918 +#> 143: 93.6954 -1.9890 -4.0792 0.0824 9.6146 1.0636 2.3366 0.1881 5.2818 +#> 144: 93.5119 -1.9888 -4.0603 0.0645 9.1339 1.0557 2.3380 0.1787 5.3491 +#> 145: 93.3539 -1.9874 -4.0563 0.0764 8.6772 1.0340 2.3573 0.1697 5.4214 +#> 146: 93.2812 -1.9734 -4.0620 0.0674 8.4698 1.0504 2.3604 0.1641 5.5968 +#> 147: 93.8919 -1.9657 -4.0863 0.0596 8.0463 1.0288 2.4569 0.1668 5.3476 +#> 148: 93.7841 -1.9719 -4.0688 0.0880 9.4571 1.0719 2.4020 0.1692 5.1664 +#> 149: 93.6361 -1.9912 -4.0523 0.0895 8.9842 1.0183 2.5236 0.1671 5.5060 +#> 150: 93.6402 -1.9940 -4.0365 0.0730 9.1100 0.9674 2.3974 0.1669 5.5402 +#> 151: 93.4283 -1.9861 -4.0594 0.0805 8.6545 0.9567 2.4304 0.1652 5.3571 +#> 152: 93.7431 -1.9444 -4.0833 0.0612 9.2738 0.9789 2.3602 0.1571 5.0632 +#> 153: 93.7239 -1.9307 -4.0780 0.0780 8.9915 0.9995 2.3398 0.1600 5.1077 +#> 154: 94.0115 -1.9655 -4.0978 0.0859 9.0507 0.9894 2.3313 0.1628 5.2272 +#> 155: 94.3207 -1.9792 -4.0905 0.1114 8.5756 0.9988 2.3790 0.1873 5.0916 +#> 156: 94.3160 -1.9811 -4.0894 0.0906 5.0717 0.9968 2.3662 0.2003 4.9973 +#> 157: 94.3042 -1.9641 -4.1031 0.0966 5.1875 0.9911 2.3908 0.1943 4.9993 +#> 158: 94.0102 -1.9635 -4.1047 0.1003 5.2398 0.9834 2.3905 0.1809 5.2765 +#> 159: 94.5686 -2.0012 -4.1459 0.1212 6.8800 1.0317 2.5969 0.1215 5.3943 +#> 160: 94.2433 -1.9673 -4.1420 0.1165 8.0930 1.0286 2.5827 0.1092 5.2904 +#> 161: 94.1327 -1.9644 -4.1595 0.1196 9.5810 1.0786 2.7063 0.1123 5.1723 +#> 162: 94.0779 -1.9525 -4.1608 0.1103 6.6456 1.0562 2.7111 0.1277 5.0224 +#> 163: 94.0995 -1.9687 -4.1910 0.1320 8.2582 1.0701 2.8394 0.1232 5.1593 +#> 164: 94.4575 -1.9800 -4.1936 0.1208 6.4860 1.1603 2.8332 0.1254 5.1325 +#> 165: 94.3298 -1.9968 -4.1963 0.1506 5.7592 1.1484 2.9143 0.1196 5.3059 +#> 166: 94.2531 -1.9977 -4.1748 0.1566 5.3810 1.1262 2.8044 0.1142 5.2569 +#> 167: 94.4593 -1.9985 -4.1758 0.1435 7.0082 1.1247 2.8542 0.1125 5.4332 +#> 168: 94.0868 -2.0117 -4.2259 0.1345 7.5364 1.1395 3.0314 0.1137 5.2790 +#> 169: 93.7927 -2.0072 -4.2177 0.1276 6.7023 1.1292 3.0535 0.1135 5.1357 +#> 170: 93.8094 -2.0309 -4.2244 0.1298 6.7343 1.0975 3.2542 0.1065 5.2372 +#> 171: 93.7263 -2.0349 -4.2115 0.1204 8.2555 1.0626 3.2292 0.1020 5.4467 +#> 172: 93.3380 -2.0022 -4.2262 0.1193 6.6891 1.0822 3.2762 0.0989 5.3641 +#> 173: 93.5334 -2.0224 -4.2488 0.1145 6.0685 1.0328 3.4694 0.0978 5.4780 +#> 174: 93.1805 -2.0207 -4.2344 0.1025 6.1648 1.0612 3.2079 0.0976 5.2570 +#> 175: 93.3423 -2.0255 -4.1644 0.1070 5.9418 1.0701 2.8555 0.1059 5.3415 +#> 176: 93.3387 -2.0192 -4.1473 0.0786 4.7649 1.0508 2.9102 0.1101 5.3381 +#> 177: 93.4640 -2.0177 -4.1504 0.0709 4.5672 1.0590 2.9447 0.1103 5.3245 +#> 178: 93.4930 -2.0147 -4.1568 0.0777 4.5325 1.1063 2.7902 0.1247 5.2036 +#> 179: 93.7455 -2.0101 -4.1580 0.0823 4.2094 1.1020 2.8075 0.1246 5.1184 +#> 180: 93.4838 -1.9989 -4.1631 0.0868 3.6999 1.0782 2.8790 0.1280 5.2677 +#> 181: 93.5207 -1.9975 -4.1926 0.1013 4.5693 1.0706 2.9216 0.1375 5.3783 +#> 182: 93.6695 -2.0251 -4.1717 0.0809 3.8373 1.0341 2.9954 0.1328 5.3774 +#> 183: 93.7238 -2.0095 -4.1222 0.0861 3.8354 1.0138 2.7536 0.1512 5.2600 +#> 184: 93.7106 -2.0032 -4.1244 0.0853 4.1968 1.0250 2.6849 0.1590 5.1996 +#> 185: 93.2862 -2.0028 -4.1628 0.0743 5.4347 1.0373 2.6528 0.1640 5.3269 +#> 186: 93.5567 -2.0040 -4.1438 0.0807 6.5150 1.0562 2.6486 0.1589 5.4158 +#> 187: 93.7894 -2.0023 -4.1137 0.1288 5.1401 1.0207 2.5217 0.1745 5.6484 +#> 188: 93.4911 -1.8872 -4.2405 0.1324 4.3165 0.8176 2.2483 0.1870 5.5214 +#> 189: 93.9184 -1.8982 -4.2936 0.1606 3.7995 0.8383 2.2555 0.1766 5.6320 +#> 190: 93.7487 -1.8878 -4.2872 0.1651 3.6764 0.8860 2.2088 0.1748 5.4829 +#> 191: 93.8940 -1.8715 -4.3244 0.1650 2.8119 0.9024 2.1141 0.1903 5.7768 +#> 192: 93.9378 -1.9105 -4.3010 0.1954 2.5239 0.8232 2.1331 0.1831 5.8507 +#> 193: 94.5609 -1.8766 -4.3303 0.2042 3.9595 0.8413 2.0662 0.2095 5.6119 +#> 194: 94.7465 -1.9036 -4.3363 0.2112 5.0784 0.8176 2.1071 0.2149 5.6051 +#> 195: 94.4761 -1.8852 -4.3375 0.2021 4.7026 0.7615 2.0556 0.2333 5.3997 +#> 196: 93.7678 -1.9037 -4.3676 0.2273 5.6976 0.7824 2.1487 0.2478 5.2531 +#> 197: 94.0788 -1.9208 -4.3670 0.2203 3.8352 0.7644 2.0893 0.2354 5.2196 +#> 198: 94.3424 -1.8825 -4.3288 0.2075 4.9447 0.7304 1.9525 0.2502 5.1387 +#> 199: 94.0613 -1.9911 -4.1676 0.2379 3.6248 0.6126 2.8184 0.2801 5.3421 +#> 200: 94.4814 -2.0045 -4.1782 0.2245 3.5637 0.6427 2.7132 0.3014 5.3984 +#> 201: 94.3903 -1.9973 -4.1773 0.2165 3.4686 0.6525 2.7040 0.2901 5.4178 +#> 202: 94.1840 -1.9928 -4.1742 0.2117 3.6920 0.6576 2.7046 0.2870 5.3743 +#> 203: 94.1832 -1.9865 -4.1670 0.2025 3.8180 0.6618 2.7097 0.2758 5.3389 +#> 204: 94.1550 -1.9832 -4.1631 0.1955 3.9449 0.6613 2.6998 0.2691 5.2948 +#> 205: 94.1853 -1.9824 -4.1602 0.1948 4.1753 0.6598 2.6909 0.2695 5.2556 +#> 206: 94.1775 -1.9800 -4.1564 0.1918 4.1962 0.6581 2.6778 0.2678 5.2316 +#> 207: 94.1754 -1.9736 -4.1532 0.1864 4.2107 0.6580 2.6645 0.2694 5.2553 +#> 208: 94.1591 -1.9695 -4.1498 0.1811 4.2621 0.6596 2.6537 0.2712 5.2543 +#> 209: 94.1225 -1.9675 -4.1454 0.1744 4.1977 0.6651 2.6519 0.2687 5.3075 +#> 210: 94.1047 -1.9628 -4.1424 0.1666 4.1981 0.6663 2.6570 0.2717 5.3160 +#> 211: 94.1161 -1.9587 -4.1398 0.1600 4.1858 0.6674 2.6614 0.2728 5.3307 +#> 212: 94.0976 -1.9551 -4.1379 0.1529 4.2002 0.6693 2.6737 0.2709 5.3288 +#> 213: 94.0845 -1.9511 -4.1365 0.1449 4.1381 0.6710 2.6727 0.2680 5.3322 +#> 214: 94.0582 -1.9493 -4.1351 0.1394 4.0630 0.6733 2.6729 0.2663 5.3504 +#> 215: 94.0449 -1.9493 -4.1338 0.1340 3.9607 0.6733 2.6719 0.2641 5.3681 +#> 216: 94.0030 -1.9496 -4.1321 0.1299 4.0200 0.6742 2.6727 0.2622 5.3619 +#> 217: 93.9560 -1.9514 -4.1315 0.1267 4.0642 0.6778 2.6764 0.2612 5.3584 +#> 218: 93.9485 -1.9520 -4.1297 0.1235 4.1822 0.6795 2.6745 0.2599 5.3471 +#> 219: 93.9650 -1.9523 -4.1289 0.1211 4.3244 0.6807 2.6851 0.2591 5.3531 +#> 220: 93.9961 -1.9519 -4.1284 0.1193 4.4276 0.6837 2.6936 0.2577 5.3528 +#> 221: 94.0080 -1.9517 -4.1275 0.1183 4.5303 0.6866 2.6979 0.2578 5.3538 +#> 222: 94.0143 -1.9505 -4.1272 0.1159 4.5882 0.6887 2.7039 0.2570 5.3489 +#> 223: 94.0189 -1.9491 -4.1269 0.1138 4.5674 0.6910 2.7092 0.2562 5.3424 +#> 224: 94.0136 -1.9464 -4.1270 0.1126 4.5582 0.6923 2.7161 0.2548 5.3421 +#> 225: 94.0118 -1.9444 -4.1276 0.1112 4.6000 0.6929 2.7269 0.2533 5.3525 +#> 226: 93.9884 -1.9428 -4.1260 0.1099 4.6720 0.6935 2.7428 0.2530 5.3427 +#> 227: 93.9657 -1.9416 -4.1247 0.1097 4.7197 0.6937 2.7581 0.2529 5.3455 +#> 228: 93.9586 -1.9410 -4.1234 0.1105 4.7731 0.6945 2.7801 0.2528 5.3408 +#> 229: 93.9574 -1.9409 -4.1215 0.1102 4.7898 0.6963 2.7970 0.2518 5.3366 +#> 230: 93.9495 -1.9410 -4.1201 0.1096 4.7966 0.6982 2.8117 0.2505 5.3301 +#> 231: 93.9378 -1.9416 -4.1193 0.1093 4.7947 0.6993 2.8274 0.2492 5.3270 +#> 232: 93.9362 -1.9421 -4.1184 0.1086 4.8132 0.7011 2.8411 0.2477 5.3191 +#> 233: 93.9412 -1.9424 -4.1167 0.1074 4.8188 0.7028 2.8514 0.2459 5.3134 +#> 234: 93.9436 -1.9424 -4.1152 0.1061 4.7865 0.7040 2.8618 0.2440 5.3153 +#> 235: 93.9413 -1.9425 -4.1134 0.1051 4.8017 0.7062 2.8679 0.2426 5.3137 +#> 236: 93.9480 -1.9423 -4.1119 0.1033 4.8537 0.7085 2.8730 0.2416 5.3089 +#> 237: 93.9560 -1.9408 -4.1105 0.1020 4.9091 0.7098 2.8777 0.2411 5.2970 +#> 238: 93.9610 -1.9393 -4.1091 0.1003 4.9394 0.7113 2.8824 0.2409 5.2902 +#> 239: 93.9634 -1.9378 -4.1080 0.0993 4.9304 0.7121 2.8875 0.2407 5.2868 +#> 240: 93.9727 -1.9360 -4.1063 0.0980 4.9651 0.7128 2.8918 0.2404 5.2825 +#> 241: 93.9736 -1.9348 -4.1045 0.0969 5.0080 0.7139 2.8917 0.2395 5.2751 +#> 242: 93.9779 -1.9334 -4.1030 0.0959 5.0856 0.7150 2.8923 0.2389 5.2656 +#> 243: 93.9807 -1.9322 -4.1015 0.0953 5.1490 0.7158 2.8929 0.2385 5.2560 +#> 244: 93.9858 -1.9317 -4.0998 0.0942 5.2172 0.7171 2.8922 0.2380 5.2514 +#> 245: 93.9798 -1.9309 -4.0984 0.0920 5.2903 0.7172 2.8892 0.2383 5.2502 +#> 246: 93.9782 -1.9296 -4.0971 0.0903 5.3132 0.7180 2.8866 0.2384 5.2482 +#> 247: 93.9809 -1.9290 -4.0958 0.0886 5.3342 0.7188 2.8839 0.2386 5.2466 +#> 248: 93.9731 -1.9281 -4.0944 0.0873 5.3438 0.7187 2.8812 0.2393 5.2480 +#> 249: 93.9594 -1.9273 -4.0932 0.0852 5.3449 0.7181 2.8781 0.2401 5.2489 +#> 250: 93.9508 -1.9261 -4.0919 0.0835 5.3194 0.7173 2.8752 0.2406 5.2495 +#> 251: 93.9421 -1.9248 -4.0903 0.0812 5.3051 0.7180 2.8714 0.2410 5.2480 +#> 252: 93.9291 -1.9240 -4.0888 0.0793 5.3122 0.7175 2.8681 0.2415 5.2447 +#> 253: 93.9233 -1.9232 -4.0876 0.0777 5.3289 0.7170 2.8636 0.2420 5.2423 +#> 254: 93.9189 -1.9217 -4.0863 0.0760 5.3708 0.7165 2.8593 0.2425 5.2395 +#> 255: 93.9130 -1.9205 -4.0850 0.0743 5.4093 0.7157 2.8548 0.2428 5.2393 +#> 256: 93.9031 -1.9195 -4.0837 0.0731 5.4400 0.7153 2.8501 0.2432 5.2417 +#> 257: 93.9079 -1.9183 -4.0821 0.0720 5.4612 0.7138 2.8454 0.2434 5.2469 +#> 258: 93.9117 -1.9173 -4.0807 0.0711 5.4979 0.7126 2.8412 0.2439 5.2491 +#> 259: 93.9199 -1.9164 -4.0797 0.0708 5.5145 0.7107 2.8364 0.2449 5.2481 +#> 260: 93.9300 -1.9150 -4.0782 0.0699 5.5067 0.7086 2.8316 0.2453 5.2501 +#> 261: 93.9382 -1.9140 -4.0768 0.0689 5.5191 0.7070 2.8271 0.2455 5.2518 +#> 262: 93.9467 -1.9126 -4.0755 0.0681 5.5261 0.7049 2.8227 0.2454 5.2564 +#> 263: 93.9594 -1.9110 -4.0739 0.0667 5.5365 0.7039 2.8196 0.2455 5.2613 +#> 264: 93.9697 -1.9096 -4.0718 0.0650 5.5589 0.7033 2.8174 0.2459 5.2628 +#> 265: 93.9784 -1.9080 -4.0698 0.0631 5.5668 0.7025 2.8153 0.2458 5.2627 +#> 266: 93.9865 -1.9068 -4.0686 0.0615 5.5819 0.7012 2.8114 0.2456 5.2638 +#> 267: 93.9940 -1.9055 -4.0673 0.0599 5.5887 0.7000 2.8076 0.2452 5.2644 +#> 268: 93.9991 -1.9045 -4.0660 0.0584 5.5989 0.6986 2.8039 0.2453 5.2657 +#> 269: 94.0034 -1.9036 -4.0649 0.0573 5.6276 0.6972 2.7990 0.2453 5.2648 +#> 270: 94.0104 -1.9028 -4.0639 0.0561 5.6456 0.6959 2.7945 0.2453 5.2614 +#> 271: 94.0190 -1.9022 -4.0629 0.0550 5.6409 0.6950 2.7900 0.2451 5.2606 +#> 272: 94.0244 -1.9017 -4.0623 0.0542 5.6452 0.6944 2.7863 0.2449 5.2626 +#> 273: 94.0312 -1.9010 -4.0620 0.0531 5.6581 0.6939 2.7821 0.2450 5.2620 +#> 274: 94.0387 -1.9004 -4.0615 0.0520 5.6569 0.6932 2.7774 0.2456 5.2657 +#> 275: 94.0381 -1.9000 -4.0611 0.0510 5.6525 0.6938 2.7727 0.2463 5.2662 +#> 276: 94.0426 -1.8994 -4.0606 0.0498 5.6664 0.6955 2.7682 0.2472 5.2687 +#> 277: 94.0437 -1.8988 -4.0604 0.0486 5.6705 0.6969 2.7646 0.2479 5.2699 +#> 278: 94.0470 -1.8982 -4.0606 0.0476 5.6495 0.6983 2.7620 0.2487 5.2741 +#> 279: 94.0475 -1.8980 -4.0608 0.0470 5.6561 0.6990 2.7590 0.2494 5.2749 +#> 280: 94.0485 -1.8977 -4.0609 0.0462 5.6510 0.6997 2.7565 0.2501 5.2755 +#> 281: 94.0473 -1.8975 -4.0609 0.0456 5.6493 0.6998 2.7529 0.2504 5.2764 +#> 282: 94.0448 -1.8972 -4.0608 0.0448 5.6523 0.7003 2.7495 0.2506 5.2773 +#> 283: 94.0392 -1.8975 -4.0608 0.0440 5.6543 0.7011 2.7463 0.2507 5.2772 +#> 284: 94.0315 -1.8976 -4.0609 0.0432 5.6575 0.7017 2.7431 0.2506 5.2792 +#> 285: 94.0262 -1.8980 -4.0611 0.0427 5.6632 0.7018 2.7402 0.2505 5.2805 +#> 286: 94.0255 -1.8986 -4.0615 0.0427 5.6683 0.7018 2.7371 0.2507 5.2795 +#> 287: 94.0234 -1.8992 -4.0619 0.0427 5.6533 0.7014 2.7340 0.2513 5.2803 +#> 288: 94.0227 -1.9000 -4.0631 0.0431 5.6485 0.7016 2.7352 0.2517 5.2802 +#> 289: 94.0179 -1.9008 -4.0641 0.0433 5.6553 0.7016 2.7358 0.2523 5.2808 +#> 290: 94.0135 -1.9017 -4.0650 0.0435 5.6776 0.7015 2.7363 0.2528 5.2839 +#> 291: 94.0101 -1.9025 -4.0660 0.0440 5.7028 0.7012 2.7372 0.2531 5.2883 +#> 292: 94.0066 -1.9034 -4.0672 0.0442 5.7277 0.7007 2.7369 0.2536 5.2890 +#> 293: 94.0002 -1.9042 -4.0681 0.0441 5.7462 0.7004 2.7366 0.2538 5.2906 +#> 294: 93.9917 -1.9049 -4.0690 0.0440 5.7707 0.7001 2.7363 0.2539 5.2927 +#> 295: 93.9864 -1.9055 -4.0703 0.0440 5.7816 0.7001 2.7362 0.2542 5.2950 +#> 296: 93.9807 -1.9060 -4.0716 0.0441 5.7884 0.7000 2.7362 0.2545 5.2974 +#> 297: 93.9749 -1.9063 -4.0729 0.0442 5.7926 0.7005 2.7362 0.2548 5.3032 +#> 298: 93.9700 -1.9070 -4.0735 0.0442 5.7850 0.7005 2.7323 0.2553 5.3067 +#> 299: 93.9668 -1.9075 -4.0740 0.0442 5.7688 0.7000 2.7293 0.2558 5.3100 +#> 300: 93.9654 -1.9080 -4.0742 0.0441 5.7541 0.6993 2.7260 0.2563 5.3123 +#> 301: 93.9678 -1.9082 -4.0744 0.0439 5.7383 0.6980 2.7217 0.2568 5.3165 +#> 302: 93.9687 -1.9087 -4.0747 0.0435 5.7262 0.6977 2.7175 0.2574 5.3179 +#> 303: 93.9675 -1.9090 -4.0751 0.0430 5.7050 0.6966 2.7137 0.2580 5.3197 +#> 304: 93.9641 -1.9092 -4.0755 0.0428 5.6977 0.6954 2.7097 0.2583 5.3215 +#> 305: 93.9624 -1.9095 -4.0759 0.0427 5.6986 0.6947 2.7061 0.2585 5.3200 +#> 306: 93.9623 -1.9098 -4.0763 0.0428 5.7065 0.6941 2.7025 0.2587 5.3174 +#> 307: 93.9635 -1.9105 -4.0767 0.0430 5.7229 0.6938 2.6992 0.2585 5.3153 +#> 308: 93.9658 -1.9112 -4.0778 0.0435 5.7340 0.6935 2.6992 0.2580 5.3131 +#> 309: 93.9671 -1.9119 -4.0784 0.0440 5.7510 0.6929 2.6990 0.2576 5.3113 +#> 310: 93.9669 -1.9124 -4.0791 0.0441 5.7560 0.6926 2.6988 0.2569 5.3128 +#> 311: 93.9670 -1.9129 -4.0795 0.0443 5.7557 0.6922 2.6972 0.2563 5.3134 +#> 312: 93.9689 -1.9132 -4.0799 0.0446 5.7554 0.6921 2.6959 0.2559 5.3125 +#> 313: 93.9685 -1.9136 -4.0806 0.0448 5.7489 0.6921 2.6960 0.2553 5.3110 +#> 314: 93.9673 -1.9138 -4.0812 0.0447 5.7562 0.6925 2.6964 0.2545 5.3107 +#> 315: 93.9635 -1.9139 -4.0818 0.0447 5.7392 0.6931 2.6971 0.2539 5.3127 +#> 316: 93.9581 -1.9139 -4.0823 0.0442 5.7376 0.6937 2.6974 0.2532 5.3140 +#> 317: 93.9541 -1.9140 -4.0826 0.0437 5.7426 0.6946 2.6968 0.2526 5.3155 +#> 318: 93.9521 -1.9141 -4.0829 0.0432 5.7378 0.6951 2.6970 0.2521 5.3158 +#> 319: 93.9520 -1.9139 -4.0829 0.0423 5.7366 0.6959 2.6977 0.2516 5.3138 +#> 320: 93.9538 -1.9136 -4.0828 0.0414 5.7416 0.6964 2.6980 0.2510 5.3135 +#> 321: 93.9557 -1.9132 -4.0827 0.0406 5.7539 0.6969 2.6983 0.2503 5.3141 +#> 322: 93.9568 -1.9130 -4.0825 0.0399 5.7460 0.6971 2.6988 0.2497 5.3155 +#> 323: 93.9594 -1.9125 -4.0824 0.0393 5.7274 0.6972 2.6993 0.2492 5.3166 +#> 324: 93.9608 -1.9122 -4.0823 0.0386 5.7161 0.6973 2.7006 0.2487 5.3156 +#> 325: 93.9601 -1.9120 -4.0822 0.0379 5.7036 0.6973 2.7019 0.2483 5.3161 +#> 326: 93.9602 -1.9118 -4.0822 0.0372 5.6817 0.6977 2.7023 0.2480 5.3182 +#> 327: 93.9615 -1.9115 -4.0820 0.0364 5.6682 0.6986 2.7024 0.2476 5.3203 +#> 328: 93.9601 -1.9114 -4.0814 0.0355 5.6746 0.6999 2.7012 0.2472 5.3224 +#> 329: 93.9580 -1.9112 -4.0809 0.0348 5.6670 0.7014 2.7003 0.2469 5.3229 +#> 330: 93.9577 -1.9111 -4.0808 0.0341 5.6613 0.7023 2.7007 0.2466 5.3224 +#> 331: 93.9570 -1.9109 -4.0808 0.0334 5.6607 0.7029 2.7020 0.2463 5.3223 +#> 332: 93.9599 -1.9106 -4.0806 0.0328 5.6610 0.7037 2.7023 0.2459 5.3212 +#> 333: 93.9638 -1.9102 -4.0806 0.0320 5.6751 0.7043 2.7029 0.2458 5.3187 +#> 334: 93.9672 -1.9096 -4.0805 0.0311 5.6801 0.7051 2.7033 0.2456 5.3168 +#> 335: 93.9714 -1.9093 -4.0805 0.0302 5.6855 0.7058 2.7038 0.2453 5.3156 +#> 336: 93.9755 -1.9090 -4.0804 0.0294 5.6979 0.7062 2.7040 0.2452 5.3158 +#> 337: 93.9796 -1.9088 -4.0803 0.0286 5.7025 0.7069 2.7038 0.2447 5.3159 +#> 338: 93.9845 -1.9087 -4.0803 0.0278 5.7100 0.7074 2.7042 0.2443 5.3166 +#> 339: 93.9889 -1.9084 -4.0803 0.0273 5.7123 0.7080 2.7045 0.2438 5.3165 +#> 340: 93.9916 -1.9082 -4.0801 0.0267 5.7289 0.7086 2.7045 0.2434 5.3167 +#> 341: 93.9938 -1.9080 -4.0800 0.0263 5.7602 0.7091 2.7048 0.2430 5.3173 +#> 342: 93.9971 -1.9076 -4.0799 0.0257 5.7951 0.7096 2.7046 0.2427 5.3171 +#> 343: 93.9979 -1.9073 -4.0794 0.0251 5.8156 0.7101 2.7044 0.2424 5.3157 +#> 344: 94.0015 -1.9070 -4.0792 0.0246 5.8378 0.7105 2.7047 0.2420 5.3153 +#> 345: 94.0040 -1.9067 -4.0789 0.0241 5.8559 0.7111 2.7046 0.2414 5.3149 +#> 346: 94.0073 -1.9066 -4.0787 0.0237 5.8810 0.7119 2.7045 0.2409 5.3131 +#> 347: 94.0084 -1.9066 -4.0785 0.0232 5.8815 0.7127 2.7044 0.2406 5.3125 +#> 348: 94.0084 -1.9067 -4.0785 0.0229 5.8870 0.7132 2.7051 0.2403 5.3110 +#> 349: 94.0079 -1.9068 -4.0785 0.0225 5.8882 0.7136 2.7048 0.2401 5.3127 +#> 350: 94.0075 -1.9067 -4.0785 0.0220 5.8857 0.7137 2.7045 0.2396 5.3133 +#> 351: 94.0068 -1.9068 -4.0786 0.0218 5.8849 0.7140 2.7041 0.2393 5.3135 +#> 352: 94.0059 -1.9067 -4.0788 0.0216 5.8778 0.7141 2.7039 0.2390 5.3139 +#> 353: 94.0073 -1.9067 -4.0792 0.0215 5.8709 0.7140 2.7047 0.2388 5.3129 +#> 354: 94.0078 -1.9065 -4.0795 0.0214 5.8623 0.7139 2.7054 0.2386 5.3135 +#> 355: 94.0065 -1.9064 -4.0795 0.0211 5.8637 0.7137 2.7048 0.2383 5.3122 +#> 356: 94.0080 -1.9063 -4.0796 0.0209 5.8613 0.7134 2.7041 0.2380 5.3121 +#> 357: 94.0105 -1.9061 -4.0797 0.0206 5.8613 0.7132 2.7036 0.2379 5.3119 +#> 358: 94.0114 -1.9059 -4.0798 0.0205 5.8539 0.7130 2.7029 0.2377 5.3107 +#> 359: 94.0154 -1.9058 -4.0799 0.0203 5.8559 0.7126 2.7024 0.2374 5.3112 +#> 360: 94.0165 -1.9057 -4.0800 0.0201 5.8544 0.7124 2.7020 0.2372 5.3099 +#> 361: 94.0198 -1.9056 -4.0802 0.0199 5.8511 0.7121 2.7018 0.2370 5.3089 +#> 362: 94.0224 -1.9054 -4.0811 0.0198 5.8509 0.7122 2.7071 0.2368 5.3077 +#> 363: 94.0241 -1.9053 -4.0821 0.0197 5.8582 0.7121 2.7135 0.2366 5.3073 +#> 364: 94.0254 -1.9052 -4.0824 0.0195 5.8606 0.7122 2.7147 0.2362 5.3079 +#> 365: 94.0276 -1.9052 -4.0831 0.0195 5.8668 0.7119 2.7197 0.2359 5.3081 +#> 366: 94.0276 -1.9052 -4.0836 0.0195 5.8765 0.7121 2.7217 0.2357 5.3074 +#> 367: 94.0276 -1.9051 -4.0842 0.0194 5.8627 0.7120 2.7240 0.2354 5.3083 +#> 368: 94.0292 -1.9050 -4.0847 0.0195 5.8579 0.7120 2.7254 0.2352 5.3096 +#> 369: 94.0289 -1.9049 -4.0852 0.0195 5.8590 0.7122 2.7271 0.2350 5.3095 +#> 370: 94.0300 -1.9049 -4.0855 0.0194 5.8712 0.7123 2.7284 0.2348 5.3094 +#> 371: 94.0309 -1.9050 -4.0858 0.0194 5.8766 0.7122 2.7295 0.2346 5.3095 +#> 372: 94.0306 -1.9050 -4.0860 0.0196 5.8800 0.7121 2.7306 0.2344 5.3101 +#> 373: 94.0315 -1.9051 -4.0861 0.0196 5.8840 0.7120 2.7305 0.2341 5.3091 +#> 374: 94.0323 -1.9052 -4.0862 0.0194 5.8755 0.7120 2.7301 0.2337 5.3101 +#> 375: 94.0344 -1.9055 -4.0863 0.0193 5.8744 0.7122 2.7308 0.2333 5.3121 +#> 376: 94.0341 -1.9056 -4.0865 0.0191 5.8738 0.7122 2.7311 0.2327 5.3136 +#> 377: 94.0320 -1.9055 -4.0868 0.0188 5.8703 0.7121 2.7311 0.2322 5.3161 +#> 378: 94.0291 -1.9058 -4.0869 0.0186 5.8771 0.7124 2.7311 0.2317 5.3187 +#> 379: 94.0273 -1.9062 -4.0872 0.0184 5.8829 0.7127 2.7316 0.2312 5.3206 +#> 380: 94.0259 -1.9067 -4.0875 0.0181 5.8786 0.7130 2.7321 0.2306 5.3235 +#> 381: 94.0231 -1.9068 -4.0877 0.0178 5.8716 0.7132 2.7331 0.2300 5.3231 +#> 382: 94.0210 -1.9069 -4.0879 0.0172 5.8636 0.7134 2.7340 0.2294 5.3240 +#> 383: 94.0189 -1.9070 -4.0880 0.0167 5.8596 0.7140 2.7351 0.2287 5.3246 +#> 384: 94.0171 -1.9070 -4.0882 0.0161 5.8588 0.7147 2.7365 0.2281 5.3251 +#> 385: 94.0141 -1.9070 -4.0880 0.0154 5.8659 0.7152 2.7365 0.2276 5.3263 +#> 386: 94.0116 -1.9070 -4.0879 0.0148 5.8785 0.7158 2.7364 0.2270 5.3272 +#> 387: 94.0090 -1.9070 -4.0877 0.0142 5.8874 0.7164 2.7363 0.2264 5.3286 +#> 388: 94.0068 -1.9069 -4.0875 0.0136 5.9016 0.7169 2.7364 0.2258 5.3299 +#> 389: 94.0063 -1.9067 -4.0873 0.0131 5.9114 0.7175 2.7363 0.2253 5.3332 +#> 390: 94.0074 -1.9064 -4.0872 0.0126 5.9258 0.7175 2.7362 0.2249 5.3353 +#> 391: 94.0092 -1.9061 -4.0870 0.0121 5.9426 0.7174 2.7359 0.2245 5.3370 +#> 392: 94.0112 -1.9060 -4.0870 0.0119 5.9499 0.7175 2.7358 0.2242 5.3375 +#> 393: 94.0120 -1.9058 -4.0869 0.0116 5.9514 0.7177 2.7351 0.2237 5.3364 +#> 394: 94.0137 -1.9056 -4.0867 0.0112 5.9560 0.7179 2.7342 0.2234 5.3371 +#> 395: 94.0150 -1.9054 -4.0866 0.0109 5.9566 0.7184 2.7340 0.2229 5.3376 +#> 396: 94.0175 -1.9054 -4.0866 0.0106 5.9564 0.7189 2.7341 0.2226 5.3370 +#> 397: 94.0195 -1.9055 -4.0866 0.0104 5.9447 0.7193 2.7344 0.2223 5.3378 +#> 398: 94.0201 -1.9056 -4.0867 0.0102 5.9353 0.7197 2.7348 0.2220 5.3380 +#> 399: 94.0204 -1.9056 -4.0868 0.0101 5.9282 0.7201 2.7350 0.2217 5.3387 +#> 400: 94.0198 -1.9058 -4.0867 0.0099 5.9243 0.7206 2.7348 0.2214 5.3383 +#> 401: 94.0194 -1.9059 -4.0867 0.0097 5.9225 0.7210 2.7345 0.2211 5.3379 +#> 402: 94.0176 -1.9060 -4.0868 0.0096 5.9237 0.7215 2.7342 0.2209 5.3370 +#> 403: 94.0172 -1.9061 -4.0869 0.0095 5.9259 0.7220 2.7337 0.2206 5.3371 +#> 404: 94.0147 -1.9062 -4.0870 0.0093 5.9322 0.7226 2.7330 0.2203 5.3382 +#> 405: 94.0131 -1.9065 -4.0872 0.0092 5.9354 0.7232 2.7326 0.2202 5.3385 +#> 406: 94.0117 -1.9066 -4.0872 0.0091 5.9399 0.7237 2.7318 0.2200 5.3388 +#> 407: 94.0114 -1.9069 -4.0871 0.0090 5.9495 0.7238 2.7314 0.2199 5.3397 +#> 408: 94.0133 -1.9071 -4.0870 0.0089 5.9505 0.7238 2.7310 0.2197 5.3401 +#> 409: 94.0159 -1.9074 -4.0868 0.0090 5.9523 0.7237 2.7309 0.2196 5.3417 +#> 410: 94.0171 -1.9076 -4.0864 0.0087 5.9503 0.7235 2.7307 0.2195 5.3449 +#> 411: 94.0193 -1.9078 -4.0862 0.0086 5.9528 0.7234 2.7304 0.2194 5.3476 +#> 412: 94.0193 -1.9082 -4.0860 0.0088 5.9516 0.7236 2.7303 0.2195 5.3509 +#> 413: 94.0192 -1.9085 -4.0859 0.0087 5.9491 0.7235 2.7302 0.2195 5.3517 +#> 414: 94.0175 -1.9086 -4.0860 0.0087 5.9453 0.7237 2.7297 0.2196 5.3523 +#> 415: 94.0156 -1.9088 -4.0861 0.0088 5.9408 0.7238 2.7289 0.2196 5.3528 +#> 416: 94.0145 -1.9090 -4.0861 0.0088 5.9442 0.7236 2.7281 0.2197 5.3540 +#> 417: 94.0140 -1.9093 -4.0862 0.0092 5.9459 0.7235 2.7274 0.2198 5.3549 +#> 418: 94.0144 -1.9097 -4.0864 0.0095 5.9495 0.7233 2.7269 0.2199 5.3551 +#> 419: 94.0142 -1.9102 -4.0866 0.0099 5.9425 0.7233 2.7265 0.2200 5.3555 +#> 420: 94.0134 -1.9107 -4.0867 0.0102 5.9338 0.7234 2.7260 0.2200 5.3563 +#> 421: 94.0096 -1.9113 -4.0869 0.0105 5.9272 0.7236 2.7260 0.2200 5.3571 +#> 422: 94.0069 -1.9118 -4.0872 0.0108 5.9238 0.7238 2.7261 0.2200 5.3576 +#> 423: 94.0034 -1.9124 -4.0874 0.0111 5.9217 0.7240 2.7260 0.2200 5.3579 +#> 424: 94.0009 -1.9129 -4.0876 0.0114 5.9258 0.7240 2.7259 0.2200 5.3578 +#> 425: 94.0000 -1.9134 -4.0879 0.0119 5.9330 0.7240 2.7258 0.2199 5.3572 +#> 426: 93.9991 -1.9138 -4.0881 0.0122 5.9526 0.7243 2.7256 0.2198 5.3572 +#> 427: 93.9969 -1.9140 -4.0882 0.0124 5.9692 0.7247 2.7258 0.2196 5.3587 +#> 428: 93.9940 -1.9143 -4.0883 0.0124 5.9777 0.7247 2.7259 0.2194 5.3591 +#> 429: 93.9935 -1.9145 -4.0882 0.0123 5.9781 0.7247 2.7260 0.2192 5.3601 +#> 430: 93.9925 -1.9147 -4.0881 0.0122 5.9772 0.7247 2.7260 0.2190 5.3606 +#> 431: 93.9928 -1.9150 -4.0879 0.0120 5.9824 0.7249 2.7262 0.2189 5.3616 +#> 432: 93.9930 -1.9152 -4.0879 0.0120 5.9797 0.7251 2.7267 0.2188 5.3618 +#> 433: 93.9930 -1.9154 -4.0878 0.0119 5.9785 0.7254 2.7271 0.2187 5.3626 +#> 434: 93.9930 -1.9156 -4.0878 0.0120 5.9711 0.7255 2.7273 0.2186 5.3638 +#> 435: 93.9935 -1.9157 -4.0878 0.0120 5.9659 0.7255 2.7269 0.2186 5.3643 +#> 436: 93.9951 -1.9158 -4.0876 0.0120 5.9570 0.7253 2.7263 0.2184 5.3667 +#> 437: 93.9980 -1.9158 -4.0874 0.0119 5.9492 0.7252 2.7259 0.2182 5.3680 +#> 438: 93.9999 -1.9158 -4.0872 0.0117 5.9361 0.7250 2.7255 0.2179 5.3700 +#> 439: 93.9990 -1.9159 -4.0868 0.0115 5.9312 0.7249 2.7247 0.2177 5.3700 +#> 440: 93.9986 -1.9160 -4.0865 0.0114 5.9280 0.7248 2.7235 0.2175 5.3698 +#> 441: 93.9996 -1.9160 -4.0863 0.0114 5.9248 0.7246 2.7222 0.2173 5.3696 +#> 442: 94.0001 -1.9160 -4.0861 0.0114 5.9266 0.7243 2.7213 0.2171 5.3702 +#> 443: 94.0004 -1.9159 -4.0859 0.0113 5.9228 0.7241 2.7202 0.2169 5.3707 +#> 444: 93.9989 -1.9161 -4.0858 0.0113 5.9200 0.7239 2.7194 0.2166 5.3722 +#> 445: 93.9971 -1.9162 -4.0857 0.0114 5.9257 0.7238 2.7182 0.2165 5.3736 +#> 446: 93.9970 -1.9164 -4.0858 0.0114 5.9286 0.7238 2.7177 0.2164 5.3738 +#> 447: 93.9959 -1.9163 -4.0858 0.0113 5.9407 0.7237 2.7166 0.2165 5.3731 +#> 448: 93.9947 -1.9163 -4.0856 0.0113 5.9442 0.7237 2.7159 0.2167 5.3723 +#> 449: 93.9948 -1.9164 -4.0854 0.0114 5.9386 0.7234 2.7151 0.2170 5.3730 +#> 450: 93.9937 -1.9164 -4.0853 0.0115 5.9368 0.7231 2.7142 0.2172 5.3732 +#> 451: 93.9929 -1.9164 -4.0851 0.0114 5.9312 0.7229 2.7135 0.2173 5.3735 +#> 452: 93.9923 -1.9163 -4.0850 0.0112 5.9288 0.7227 2.7121 0.2175 5.3747 +#> 453: 93.9918 -1.9162 -4.0849 0.0111 5.9339 0.7225 2.7112 0.2178 5.3759 +#> 454: 93.9912 -1.9164 -4.0849 0.0111 5.9355 0.7224 2.7103 0.2181 5.3777 +#> 455: 93.9902 -1.9164 -4.0849 0.0111 5.9412 0.7223 2.7097 0.2183 5.3784 +#> 456: 93.9894 -1.9164 -4.0848 0.0110 5.9554 0.7223 2.7076 0.2186 5.3801 +#> 457: 93.9902 -1.9161 -4.0846 0.0110 5.9675 0.7219 2.7054 0.2188 5.3807 +#> 458: 93.9907 -1.9159 -4.0845 0.0109 5.9710 0.7216 2.7032 0.2191 5.3815 +#> 459: 93.9926 -1.9157 -4.0844 0.0108 5.9751 0.7213 2.7011 0.2193 5.3817 +#> 460: 93.9930 -1.9155 -4.0845 0.0107 5.9788 0.7210 2.6985 0.2197 5.3818 +#> 461: 93.9933 -1.9153 -4.0845 0.0106 5.9809 0.7208 2.6959 0.2200 5.3822 +#> 462: 93.9941 -1.9153 -4.0845 0.0105 5.9904 0.7205 2.6935 0.2203 5.3820 +#> 463: 93.9945 -1.9152 -4.0844 0.0105 5.9971 0.7201 2.6913 0.2206 5.3817 +#> 464: 93.9942 -1.9151 -4.0844 0.0104 6.0010 0.7198 2.6892 0.2209 5.3818 +#> 465: 93.9931 -1.9152 -4.0843 0.0103 6.0113 0.7193 2.6872 0.2212 5.3823 +#> 466: 93.9937 -1.9152 -4.0840 0.0101 6.0145 0.7188 2.6853 0.2215 5.3828 +#> 467: 93.9939 -1.9152 -4.0838 0.0099 6.0189 0.7182 2.6835 0.2218 5.3832 +#> 468: 93.9933 -1.9153 -4.0835 0.0097 6.0247 0.7177 2.6818 0.2221 5.3830 +#> 469: 93.9933 -1.9153 -4.0832 0.0095 6.0251 0.7173 2.6801 0.2224 5.3822 +#> 470: 93.9914 -1.9153 -4.0829 0.0092 6.0332 0.7169 2.6785 0.2226 5.3823 +#> 471: 93.9894 -1.9153 -4.0826 0.0089 6.0455 0.7165 2.6769 0.2230 5.3822 +#> 472: 93.9869 -1.9152 -4.0824 0.0086 6.0454 0.7161 2.6754 0.2232 5.3836 +#> 473: 93.9852 -1.9152 -4.0822 0.0084 6.0501 0.7159 2.6740 0.2234 5.3832 +#> 474: 93.9829 -1.9152 -4.0821 0.0082 6.0579 0.7155 2.6725 0.2235 5.3831 +#> 475: 93.9826 -1.9152 -4.0819 0.0082 6.0661 0.7150 2.6711 0.2238 5.3829 +#> 476: 93.9837 -1.9152 -4.0819 0.0082 6.0774 0.7147 2.6696 0.2241 5.3824 +#> 477: 93.9852 -1.9151 -4.0819 0.0081 6.0890 0.7145 2.6681 0.2244 5.3817 +#> 478: 93.9851 -1.9151 -4.0820 0.0080 6.0957 0.7144 2.6665 0.2246 5.3827 +#> 479: 93.9857 -1.9150 -4.0820 0.0079 6.0981 0.7144 2.6651 0.2250 5.3838 +#> 480: 93.9856 -1.9151 -4.0821 0.0080 6.0944 0.7144 2.6638 0.2255 5.3854 +#> 481: 93.9864 -1.9152 -4.0823 0.0081 6.0912 0.7144 2.6624 0.2258 5.3865 +#> 482: 93.9870 -1.9153 -4.0825 0.0081 6.0954 0.7142 2.6613 0.2262 5.3864 +#> 483: 93.9888 -1.9153 -4.0826 0.0081 6.0888 0.7141 2.6602 0.2267 5.3870 +#> 484: 93.9903 -1.9154 -4.0828 0.0082 6.0848 0.7139 2.6592 0.2272 5.3861 +#> 485: 93.9914 -1.9154 -4.0831 0.0085 6.0851 0.7138 2.6586 0.2275 5.3858 +#> 486: 93.9909 -1.9154 -4.0834 0.0088 6.0824 0.7137 2.6581 0.2278 5.3850 +#> 487: 93.9899 -1.9155 -4.0838 0.0091 6.0870 0.7137 2.6577 0.2281 5.3838 +#> 488: 93.9882 -1.9156 -4.0842 0.0095 6.0877 0.7135 2.6574 0.2284 5.3835 +#> 489: 93.9865 -1.9163 -4.0841 0.0099 6.0839 0.7139 2.6581 0.2287 5.3835 +#> 490: 93.9859 -1.9170 -4.0841 0.0104 6.0783 0.7143 2.6587 0.2290 5.3830 +#> 491: 93.9847 -1.9177 -4.0838 0.0108 6.0773 0.7148 2.6596 0.2293 5.3824 +#> 492: 93.9840 -1.9183 -4.0836 0.0110 6.0833 0.7152 2.6606 0.2295 5.3817 +#> 493: 93.9832 -1.9188 -4.0834 0.0113 6.0832 0.7157 2.6613 0.2297 5.3814 +#> 494: 93.9824 -1.9195 -4.0832 0.0115 6.0859 0.7163 2.6620 0.2299 5.3819 +#> 495: 93.9813 -1.9200 -4.0830 0.0117 6.0878 0.7169 2.6633 0.2300 5.3820 +#> 496: 93.9798 -1.9206 -4.0827 0.0118 6.0871 0.7173 2.6644 0.2302 5.3825 +#> 497: 93.9787 -1.9213 -4.0824 0.0120 6.0856 0.7178 2.6653 0.2304 5.3834 +#> 498: 93.9771 -1.9220 -4.0822 0.0123 6.0759 0.7181 2.6660 0.2308 5.3850 +#> 499: 93.9744 -1.9225 -4.0819 0.0125 6.0692 0.7183 2.6666 0.2311 5.3868 +#> 500: 93.9728 -1.9229 -4.0816 0.0129 6.0609 0.7184 2.6675 0.2314 5.3884</div><div class='output co'>#> <span class='message'>Calculating covariance matrix</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#> <span class='message'>done</span></div><div class='input'><span class='va'>f_nlmixr_dfop_focei</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></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>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span style='font-weight: bold;'>Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation +#> F: Forward difference gradient approximation +#> C: Central difference gradient approximation +#> M: Mixed forward and central difference gradient approximation +#> Unscaled parameters for Omegas=chol(solve(omega)); +#> Diagonals are transformed, as specified by foceiControl(diagXform=) +#> |-----+---------------+-----------+-----------+-----------+-----------| +#> | #| Objective Fun | parent_0 | log_k1 | log_k2 | g_qlogis | +#> |.....................| sigma | o1 | o2 | o3 | +#> <span style='text-decoration: underline;'>|.....................| o4 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 1</span>| 319.20504 | 1.000 | -0.9516 | -1.000 | -0.9121 | +#> |.....................| -0.8747 | -0.8993 | -0.8937 | -0.8958 | +#> <span style='text-decoration: underline;'>|.....................| -0.8908 |...........|...........|...........|</span> +#> | U| 319.20504 | 94.08 | -1.834 | -4.211 | 0.1100 | +#> |.....................| 1.947 | 0.7373 | 1.015 | 0.9087 | +#> <span style='text-decoration: underline;'>|.....................| 1.155 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 319.20504</span> | 94.08 | 0.1597 | 0.01483 | 0.5275 | +#> |.....................| 1.947 | 0.7373 | 1.015 | 0.9087 | +#> <span style='text-decoration: underline;'>|.....................| 1.155 |...........|...........|...........|</span> +#> | G| Gill Diff. | 17.25 | -0.06517 | -0.2231 | 0.05323 | +#> |.....................| -31.06 | 10.54 | -5.521 | 3.149 | +#> <span style='text-decoration: underline;'>|.....................| -10.19 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 2</span>| 930.59637 | 0.5572 | -0.9500 | -0.9943 | -0.9135 | +#> |.....................| -0.07749 | -1.170 | -0.7520 | -0.9767 | +#> <span style='text-decoration: underline;'>|.....................| -0.6292 |...........|...........|...........|</span> +#> | U| 930.59637 | 52.42 | -1.832 | -4.205 | 0.1099 | +#> |.....................| 2.723 | 0.5378 | 1.159 | 0.8352 | +#> <span style='text-decoration: underline;'>|.....................| 1.457 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 930.59637</span> | 52.42 | 0.1600 | 0.01492 | 0.5274 | +#> |.....................| 2.723 | 0.5378 | 1.159 | 0.8352 | +#> <span style='text-decoration: underline;'>|.....................| 1.457 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 3</span>| 366.81009 | 0.9557 | -0.9515 | -0.9994 | -0.9122 | +#> |.....................| -0.7950 | -0.9264 | -0.8795 | -0.9039 | +#> <span style='text-decoration: underline;'>|.....................| -0.8647 |...........|...........|...........|</span> +#> | U| 366.81009 | 89.92 | -1.834 | -4.210 | 0.1100 | +#> |.....................| 2.024 | 0.7174 | 1.030 | 0.9013 | +#> <span style='text-decoration: underline;'>|.....................| 1.185 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 366.81009</span> | 89.92 | 0.1598 | 0.01484 | 0.5275 | +#> |.....................| 2.024 | 0.7174 | 1.030 | 0.9013 | +#> <span style='text-decoration: underline;'>|.....................| 1.185 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 4</span>| 354.05577 | 0.9956 | -0.9516 | -0.9999 | -0.9121 | +#> |.....................| -0.8667 | -0.9020 | -0.8922 | -0.8966 | +#> <span style='text-decoration: underline;'>|.....................| -0.8882 |...........|...........|...........|</span> +#> | U| 354.05577 | 93.67 | -1.834 | -4.211 | 0.1100 | +#> |.....................| 1.954 | 0.7353 | 1.017 | 0.9079 | +#> <span style='text-decoration: underline;'>|.....................| 1.158 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 354.05577</span> | 93.67 | 0.1597 | 0.01484 | 0.5275 | +#> |.....................| 1.954 | 0.7353 | 1.017 | 0.9079 | +#> <span style='text-decoration: underline;'>|.....................| 1.158 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 5</span>| 354.18966 | 0.9996 | -0.9516 | -1.000 | -0.9121 | +#> |.....................| -0.8739 | -0.8996 | -0.8935 | -0.8959 | +#> <span style='text-decoration: underline;'>|.....................| -0.8906 |...........|...........|...........|</span> +#> | U| 354.18966 | 94.04 | -1.834 | -4.211 | 0.1100 | +#> |.....................| 1.947 | 0.7371 | 1.015 | 0.9086 | +#> <span style='text-decoration: underline;'>|.....................| 1.155 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 354.18966</span> | 94.04 | 0.1597 | 0.01483 | 0.5275 | +#> |.....................| 1.947 | 0.7371 | 1.015 | 0.9086 | +#> <span style='text-decoration: underline;'>|.....................| 1.155 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 6</span>| 354.21855 | 1.000 | -0.9516 | -1.000 | -0.9121 | +#> |.....................| -0.8746 | -0.8993 | -0.8937 | -0.8958 | +#> <span style='text-decoration: underline;'>|.....................| -0.8908 |...........|...........|...........|</span> +#> | U| 354.21855 | 94.08 | -1.834 | -4.211 | 0.1100 | +#> |.....................| 1.947 | 0.7373 | 1.015 | 0.9087 | +#> <span style='text-decoration: underline;'>|.....................| 1.155 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 354.21855</span> | 94.08 | 0.1597 | 0.01483 | 0.5275 | +#> |.....................| 1.947 | 0.7373 | 1.015 | 0.9087 | +#> <span style='text-decoration: underline;'>|.....................| 1.155 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 7</span>| 354.22159 | 1.000 | -0.9516 | -1.000 | -0.9121 | +#> |.....................| -0.8747 | -0.8993 | -0.8937 | -0.8958 | +#> <span style='text-decoration: underline;'>|.....................| -0.8908 |...........|...........|...........|</span> +#> | U| 354.22159 | 94.08 | -1.834 | -4.211 | 0.1100 | +#> |.....................| 1.947 | 0.7373 | 1.015 | 0.9087 | +#> <span style='text-decoration: underline;'>|.....................| 1.155 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 354.22159</span> | 94.08 | 0.1597 | 0.01483 | 0.5275 | +#> |.....................| 1.947 | 0.7373 | 1.015 | 0.9087 | +#> <span style='text-decoration: underline;'>|.....................| 1.155 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 8</span>| 354.22201 | 1.000 | -0.9516 | -1.000 | -0.9121 | +#> |.....................| -0.8747 | -0.8993 | -0.8937 | -0.8958 | +#> <span style='text-decoration: underline;'>|.....................| -0.8908 |...........|...........|...........|</span> +#> | U| 354.22201 | 94.08 | -1.834 | -4.211 | 0.1100 | +#> |.....................| 1.947 | 0.7373 | 1.015 | 0.9087 | +#> <span style='text-decoration: underline;'>|.....................| 1.155 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 354.22201</span> | 94.08 | 0.1597 | 0.01483 | 0.5275 | +#> |.....................| 1.947 | 0.7373 | 1.015 | 0.9087 | +#> <span style='text-decoration: underline;'>|.....................| 1.155 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 9</span>| 354.22204 | 1.000 | -0.9516 | -1.000 | -0.9121 | +#> |.....................| -0.8747 | -0.8993 | -0.8937 | -0.8958 | +#> <span style='text-decoration: underline;'>|.....................| -0.8908 |...........|...........|...........|</span> +#> | U| 354.22204 | 94.08 | -1.834 | -4.211 | 0.1100 | +#> |.....................| 1.947 | 0.7373 | 1.015 | 0.9087 | +#> <span style='text-decoration: underline;'>|.....................| 1.155 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 354.22204</span> | 94.08 | 0.1597 | 0.01483 | 0.5275 | +#> |.....................| 1.947 | 0.7373 | 1.015 | 0.9087 | +#> <span style='text-decoration: underline;'>|.....................| 1.155 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 10</span>| 354.22204 | 1.000 | -0.9516 | -1.000 | -0.9121 | +#> |.....................| -0.8747 | -0.8993 | -0.8937 | -0.8958 | +#> <span style='text-decoration: underline;'>|.....................| -0.8908 |...........|...........|...........|</span> +#> | U| 354.22204 | 94.08 | -1.834 | -4.211 | 0.1100 | +#> |.....................| 1.947 | 0.7373 | 1.015 | 0.9087 | +#> <span style='text-decoration: underline;'>|.....................| 1.155 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 354.22204</span> | 94.08 | 0.1597 | 0.01483 | 0.5275 | +#> |.....................| 1.947 | 0.7373 | 1.015 | 0.9087 | +#> <span style='text-decoration: underline;'>|.....................| 1.155 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 11</span>| 354.22204 | 1.000 | -0.9516 | -1.000 | -0.9121 | +#> |.....................| -0.8747 | -0.8993 | -0.8937 | -0.8958 | +#> <span style='text-decoration: underline;'>|.....................| -0.8908 |...........|...........|...........|</span> +#> | U| 354.22204 | 94.08 | -1.834 | -4.211 | 0.1100 | +#> |.....................| 1.947 | 0.7373 | 1.015 | 0.9087 | +#> <span style='text-decoration: underline;'>|.....................| 1.155 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 354.22204</span> | 94.08 | 0.1597 | 0.01483 | 0.5275 | +#> |.....................| 1.947 | 0.7373 | 1.015 | 0.9087 | +#> <span style='text-decoration: underline;'>|.....................| 1.155 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 12</span>| 354.22204 | 1.000 | -0.9516 | -1.000 | -0.9121 | +#> |.....................| -0.8747 | -0.8993 | -0.8937 | -0.8958 | +#> <span style='text-decoration: underline;'>|.....................| -0.8908 |...........|...........|...........|</span> +#> | U| 354.22204 | 94.08 | -1.834 | -4.211 | 0.1100 | +#> |.....................| 1.947 | 0.7373 | 1.015 | 0.9087 | +#> <span style='text-decoration: underline;'>|.....................| 1.155 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 354.22204</span> | 94.08 | 0.1597 | 0.01483 | 0.5275 | +#> |.....................| 1.947 | 0.7373 | 1.015 | 0.9087 | +#> <span style='text-decoration: underline;'>|.....................| 1.155 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 13</span>| 354.22204 | 1.000 | -0.9516 | -1.000 | -0.9121 | +#> |.....................| -0.8747 | -0.8993 | -0.8937 | -0.8958 | +#> <span style='text-decoration: underline;'>|.....................| -0.8908 |...........|...........|...........|</span> +#> | U| 354.22204 | 94.08 | -1.834 | -4.211 | 0.1100 | +#> |.....................| 1.947 | 0.7373 | 1.015 | 0.9087 | +#> <span style='text-decoration: underline;'>|.....................| 1.155 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 354.22204</span> | 94.08 | 0.1597 | 0.01483 | 0.5275 | +#> |.....................| 1.947 | 0.7373 | 1.015 | 0.9087 | +#> <span style='text-decoration: underline;'>|.....................| 1.155 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 14</span>| 354.22204 | 1.000 | -0.9516 | -1.000 | -0.9121 | +#> |.....................| -0.8747 | -0.8993 | -0.8937 | -0.8958 | +#> <span style='text-decoration: underline;'>|.....................| -0.8908 |...........|...........|...........|</span> +#> | U| 354.22204 | 94.08 | -1.834 | -4.211 | 0.1100 | +#> |.....................| 1.947 | 0.7373 | 1.015 | 0.9087 | +#> <span style='text-decoration: underline;'>|.....................| 1.155 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 354.22204</span> | 94.08 | 0.1597 | 0.01483 | 0.5275 | +#> |.....................| 1.947 | 0.7373 | 1.015 | 0.9087 | +#> <span style='text-decoration: underline;'>|.....................| 1.155 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 15</span>| 354.22200 | 1.000 | -0.9516 | -1.000 | -0.9121 | +#> |.....................| -0.8747 | -0.8993 | -0.8937 | -0.8958 | +#> <span style='text-decoration: underline;'>|.....................| -0.8908 |...........|...........|...........|</span> +#> | U| 354.222 | 94.08 | -1.834 | -4.211 | 0.1100 | +#> |.....................| 1.947 | 0.7373 | 1.015 | 0.9087 | +#> <span style='text-decoration: underline;'>|.....................| 1.155 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 354.222</span> | 94.08 | 0.1597 | 0.01483 | 0.5275 | +#> |.....................| 1.947 | 0.7373 | 1.015 | 0.9087 | +#> <span style='text-decoration: underline;'>|.....................| 1.155 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 16</span>| 354.22200 | 1.000 | -0.9516 | -1.000 | -0.9121 | +#> |.....................| -0.8747 | -0.8993 | -0.8937 | -0.8958 | +#> <span style='text-decoration: underline;'>|.....................| -0.8908 |...........|...........|...........|</span> +#> | U| 354.222 | 94.08 | -1.834 | -4.211 | 0.1100 | +#> |.....................| 1.947 | 0.7373 | 1.015 | 0.9087 | +#> <span style='text-decoration: underline;'>|.....................| 1.155 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 354.222</span> | 94.08 | 0.1597 | 0.01483 | 0.5275 | +#> |.....................| 1.947 | 0.7373 | 1.015 | 0.9087 | +#> <span style='text-decoration: underline;'>|.....................| 1.155 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 17</span>| 354.22200 | 1.000 | -0.9516 | -1.000 | -0.9121 | +#> |.....................| -0.8747 | -0.8993 | -0.8937 | -0.8958 | +#> <span style='text-decoration: underline;'>|.....................| -0.8908 |...........|...........|...........|</span> +#> | U| 354.222 | 94.08 | -1.834 | -4.211 | 0.1100 | +#> |.....................| 1.947 | 0.7373 | 1.015 | 0.9087 | +#> <span style='text-decoration: underline;'>|.....................| 1.155 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 354.222</span> | 94.08 | 0.1597 | 0.01483 | 0.5275 | +#> |.....................| 1.947 | 0.7373 | 1.015 | 0.9087 | +#> <span style='text-decoration: underline;'>|.....................| 1.155 |...........|...........|...........|</span> +#> calculating covariance matrix +#> done</div><div class='output co'>#> <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#> <span class='message'>done</span></div><div class='output co'>#> <span class='warning'>Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span></div><div class='output co'>#> <span class='warning'>Warning: last objective function was not at minimum, possible problems in optimization</span></div><div class='output co'>#> <span class='warning'>Warning: gradient problems with initial estimate and covariance; see $scaleInfo</span></div><div class='input'> +<span class='va'>f_nlmixr_hs_saem</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_parent</span><span class='op'>[</span><span class='st'>"HS"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"saem"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'>→ generate SAEM model</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> 1: 93.5894 -2.4029 -3.9815 2.0318 3.0448 0.8581 1.0844 0.3182 21.0327 +#> 2: 93.5363 -2.3652 -3.9374 1.9473 2.8925 0.8152 1.0302 0.3023 14.7642 +#> 3: 93.3061 -2.3950 -3.8630 1.9537 2.7479 0.7744 1.0729 0.2872 12.2332 +#> 4: 93.4757 -2.3967 -3.8509 1.9504 2.6105 0.7357 1.1580 0.2729 11.6140 +#> 5: 93.6045 -2.3957 -3.8593 1.9732 2.4800 0.6989 1.1001 0.2592 11.0776 +#> 6: 93.6138 -2.4089 -3.9577 1.9557 2.8119 0.6640 1.0451 0.2463 11.5001 +#> 7: 93.4125 -2.3879 -3.8924 1.9950 3.1015 0.6308 1.0649 0.2339 10.6133 +#> 8: 93.5798 -2.3850 -3.9314 1.9888 3.0019 0.5992 1.0116 0.2222 10.4278 +#> 9: 93.1493 -2.3918 -3.9011 2.0040 4.3802 0.5693 1.0723 0.2111 10.2172 +#> 10: 93.5411 -2.3906 -3.8778 1.9664 4.5606 0.5408 1.0616 0.2006 10.1244 +#> 11: 93.3749 -2.4011 -3.8586 1.9682 4.3326 0.5138 1.0696 0.1905 10.1145 +#> 12: 93.0136 -2.3943 -3.8530 1.9633 4.1160 0.4881 1.0606 0.1810 10.0091 +#> 13: 93.1809 -2.4059 -3.9088 1.9821 3.9102 0.5448 1.0076 0.1720 9.8058 +#> 14: 93.3891 -2.4107 -3.9285 1.9894 3.7147 0.5504 0.9810 0.1634 10.2784 +#> 15: 93.4041 -2.4114 -3.9711 2.0216 4.4250 0.6070 0.9495 0.1552 9.4036 +#> 16: 93.4244 -2.4191 -4.0366 2.0511 4.2037 0.6035 0.9020 0.1474 10.0835 +#> 17: 93.6295 -2.4103 -4.0143 2.0509 4.0926 0.5997 0.8599 0.1401 9.7686 +#> 18: 93.6653 -2.4165 -3.9724 2.0405 3.8880 0.5979 0.9046 0.1331 9.6299 +#> 19: 93.6510 -2.4088 -3.9969 2.0328 3.6936 0.5934 0.9181 0.1264 9.3236 +#> 20: 93.6048 -2.4117 -3.9552 2.0268 3.9084 0.5879 1.0078 0.1201 9.6618 +#> 21: 94.0961 -2.4193 -3.9812 2.0552 3.7456 0.5743 0.9574 0.1141 9.6510 +#> 22: 93.9157 -2.4202 -3.9102 2.0263 5.0447 0.6198 0.9742 0.1294 9.6463 +#> 23: 94.1580 -2.4286 -3.9223 2.0441 4.7925 0.5981 0.9312 0.1230 9.8346 +#> 24: 94.4405 -2.4141 -3.9564 2.0383 4.5529 0.5925 0.9173 0.1168 10.6161 +#> 25: 93.8846 -2.3958 -4.0122 2.0053 4.9956 0.5677 0.8715 0.1173 10.3823 +#> 26: 93.6815 -2.3835 -3.9801 1.9872 5.6625 0.5514 0.8368 0.1114 9.8283 +#> 27: 93.6463 -2.3779 -3.9731 1.9833 5.3794 0.5566 0.8650 0.1059 9.5439 +#> 28: 93.7974 -2.3980 -3.9583 1.9657 6.4804 0.5366 0.8756 0.1006 9.7998 +#> 29: 93.6921 -2.4221 -3.8982 1.9701 6.1564 0.6070 0.9713 0.0955 9.2988 +#> 30: 93.3112 -2.4200 -3.8916 1.9702 6.6968 0.6110 0.9538 0.0908 9.1812 +#> 31: 93.9900 -2.4282 -3.9448 2.0257 6.3620 0.6071 0.9061 0.0862 9.4865 +#> 32: 93.8014 -2.4241 -3.9364 2.0053 6.8497 0.6173 0.8608 0.0819 9.5589 +#> 33: 94.0330 -2.4215 -3.9888 2.0034 6.5072 0.6142 0.8178 0.0778 10.2023 +#> 34: 93.5811 -2.4215 -3.9917 2.0170 6.1819 0.5907 0.8314 0.0842 10.2204 +#> 35: 93.9308 -2.4210 -3.8798 2.0046 6.7593 0.5877 1.1132 0.0800 9.2384 +#> 36: 94.0000 -2.4325 -3.8970 2.0457 6.4213 0.5886 1.0731 0.0835 8.8987 +#> 37: 93.4010 -2.4325 -3.9306 2.0550 7.2268 0.5886 1.0220 0.0969 9.1261 +#> 38: 93.3896 -2.4291 -3.9250 2.0148 6.8655 0.5885 0.9709 0.1039 9.2989 +#> 39: 93.3821 -2.4349 -3.9148 2.0368 6.5222 0.6059 0.9647 0.1095 9.2864 +#> 40: 93.1382 -2.4685 -3.9384 2.1083 8.6249 0.6287 1.0265 0.1066 9.6411 +#> 41: 92.7963 -2.4643 -3.8992 2.0585 8.1937 0.6376 1.1117 0.1234 9.4738 +#> 42: 92.7160 -2.4545 -3.9652 2.0680 7.7840 0.6068 1.0561 0.1173 9.4776 +#> 43: 93.0070 -2.4360 -4.0223 2.0624 7.9556 0.5840 1.0033 0.1114 9.7197 +#> 44: 93.3836 -2.4207 -4.0739 2.0872 7.5578 0.5788 0.9531 0.1058 10.3515 +#> 45: 93.3240 -2.4382 -4.0210 2.1103 7.1799 0.6211 0.9055 0.1165 10.5050 +#> 46: 93.1921 -2.4438 -4.0330 2.0842 7.3884 0.6159 0.8602 0.1107 10.7251 +#> 47: 92.9710 -2.4351 -4.0155 2.1117 7.0189 0.5998 0.8519 0.1091 10.2972 +#> 48: 93.0129 -2.4395 -3.9677 2.0986 6.6680 0.5804 0.8775 0.1058 10.8515 +#> 49: 92.6562 -2.4474 -4.0295 2.0877 6.3346 0.6338 0.8723 0.1155 10.0641 +#> 50: 92.5101 -2.4612 -4.0295 2.0845 6.0179 0.6197 0.8742 0.1097 9.9048 +#> 51: 92.9446 -2.4615 -3.9927 2.1199 5.7170 0.6165 0.9311 0.1042 9.8383 +#> 52: 92.8362 -2.4525 -3.9682 2.0787 5.4311 0.6329 0.9647 0.0990 9.0726 +#> 53: 92.8579 -2.4598 -3.9324 2.0529 5.1596 0.6057 0.9192 0.0940 9.5677 +#> 54: 92.8667 -2.4858 -3.9104 2.0454 5.0661 0.6304 1.0025 0.0893 9.0977 +#> 55: 93.2327 -2.4650 -3.8323 2.0628 6.8188 0.6499 1.1366 0.0852 8.5677 +#> 56: 92.9319 -2.4794 -3.8376 2.0490 6.4778 0.6635 1.1141 0.1064 9.0723 +#> 57: 93.1126 -2.5128 -3.8223 2.0834 6.1539 0.6637 1.1361 0.1010 9.2678 +#> 58: 93.5085 -2.4894 -3.8723 2.0650 5.8462 0.6745 1.0793 0.0960 9.0367 +#> 59: 93.7882 -2.4614 -3.9241 2.0707 5.5539 0.6898 1.0254 0.0912 8.7466 +#> 60: 94.1492 -2.4386 -3.9415 2.0599 5.2762 0.6711 0.9741 0.0932 8.4466 +#> 61: 94.4215 -2.4272 -3.9647 2.0482 5.0124 0.6549 0.9254 0.0911 8.7870 +#> 62: 94.3607 -2.4053 -3.9633 1.9966 4.7618 0.6534 0.8878 0.1221 9.0404 +#> 63: 94.3958 -2.4179 -3.9386 2.0041 4.5237 0.6462 0.9360 0.1245 9.0491 +#> 64: 94.5204 -2.4175 -3.9411 2.0106 4.2975 0.6532 0.9657 0.1183 8.9115 +#> 65: 94.5674 -2.4117 -3.9701 2.0546 4.0826 0.6438 0.9238 0.1247 8.7293 +#> 66: 94.2199 -2.4337 -3.9298 2.0287 4.7686 0.6582 0.9262 0.1185 9.0519 +#> 67: 94.2756 -2.4305 -3.9706 2.0782 4.5301 0.6512 0.8799 0.1126 9.1397 +#> 68: 94.4195 -2.4193 -4.0049 2.0643 4.3036 0.6804 0.8359 0.1220 9.5306 +#> 69: 94.5255 -2.4183 -4.0119 2.0733 4.0884 0.6784 0.8577 0.1297 9.4535 +#> 70: 94.5668 -2.4117 -3.9662 2.0762 4.2149 0.6511 0.9325 0.1475 9.1637 +#> 71: 94.7464 -2.4147 -3.9937 2.0942 4.2418 0.6571 0.9524 0.1540 9.6576 +#> 72: 94.4869 -2.4160 -4.0050 2.1075 4.8520 0.6687 1.0119 0.1488 9.4234 +#> 73: 94.3747 -2.4423 -4.0072 2.1484 6.4364 0.6948 1.0011 0.1438 9.1490 +#> 74: 94.3997 -2.4464 -4.0147 2.1965 6.1146 0.7030 1.0566 0.1521 9.0697 +#> 75: 94.4187 -2.4566 -3.9611 2.1337 5.8089 0.6866 1.1666 0.1656 8.9436 +#> 76: 94.4381 -2.4502 -3.9816 2.1209 5.6488 0.7266 1.1449 0.1573 8.9289 +#> 77: 94.6421 -2.4446 -3.9603 2.1544 5.3663 0.6968 1.2087 0.1662 8.5186 +#> 78: 94.8397 -2.4420 -3.9690 2.1380 5.0980 0.6969 1.1833 0.1578 8.9071 +#> 79: 94.4296 -2.4547 -3.9576 2.1569 6.3095 0.6829 1.1850 0.1544 9.1345 +#> 80: 93.9628 -2.4530 -3.9312 2.0956 8.5844 0.6880 1.2548 0.1835 8.6936 +#> 81: 94.2900 -2.4687 -3.8570 2.0779 9.0596 0.6993 1.2012 0.1743 8.9092 +#> 82: 93.9652 -2.4742 -3.9261 2.0913 8.6066 0.6970 1.1667 0.1656 8.4359 +#> 83: 94.0828 -2.4739 -3.8603 2.0587 8.1763 0.7123 1.2575 0.1638 8.5431 +#> 84: 93.5926 -2.4645 -3.8993 2.0391 9.8721 0.7178 1.1947 0.1556 8.5623 +#> 85: 93.7052 -2.4692 -3.8411 2.0448 9.3785 0.7251 1.1349 0.1478 8.5558 +#> 86: 93.8043 -2.4726 -3.9028 2.0745 8.9096 0.7064 1.0782 0.1404 9.1308 +#> 87: 93.5704 -2.4836 -3.8694 2.0999 12.3224 0.7284 1.0922 0.1334 8.8645 +#> 88: 93.5715 -2.4827 -3.9202 2.0861 11.7063 0.7541 1.0376 0.1267 9.2433 +#> 89: 93.6894 -2.4720 -3.8964 2.1093 12.4610 0.7727 1.0218 0.1325 9.0321 +#> 90: 93.2881 -2.4787 -3.9464 2.1137 11.8380 0.7850 0.9707 0.1258 8.8265 +#> 91: 93.8454 -2.4626 -3.9566 2.1181 11.2461 0.7620 0.9579 0.1396 8.8279 +#> 92: 93.8268 -2.4639 -3.8951 2.0936 10.6838 0.7618 1.1083 0.1553 8.4609 +#> 93: 94.0622 -2.4853 -3.8531 2.0740 10.1496 0.7493 1.1237 0.1596 8.2057 +#> 94: 93.6190 -2.4843 -3.8857 2.0625 9.6421 0.7596 1.1104 0.1686 8.3522 +#> 95: 93.6352 -2.4725 -3.9243 2.0582 9.1600 0.7732 1.0549 0.1694 8.3993 +#> 96: 93.5291 -2.4707 -3.9318 2.0612 8.7020 0.7853 1.0639 0.1609 8.2908 +#> 97: 93.0626 -2.4639 -3.9255 2.0887 8.4092 0.7717 1.1477 0.1685 8.2710 +#> 98: 93.3712 -2.4677 -3.9642 2.1350 7.9888 0.7703 1.0903 0.1921 8.5468 +#> 99: 93.7108 -2.4848 -3.9775 2.1733 7.5893 0.7490 1.0367 0.1825 8.5629 +#> 100: 94.1114 -2.4867 -4.0111 2.1705 7.2099 0.7446 0.9849 0.1832 8.6964 +#> 101: 93.7547 -2.4897 -3.9793 2.1817 7.1755 0.7513 0.9899 0.1774 8.5077 +#> 102: 93.8818 -2.5029 -3.9929 2.2028 6.8167 0.7137 1.0045 0.1685 8.3706 +#> 103: 94.0026 -2.5094 -3.9680 2.2059 6.4759 0.7073 1.0498 0.1601 8.3087 +#> 104: 93.5946 -2.5260 -3.9640 2.2209 6.2674 0.7688 1.0548 0.1531 8.3444 +#> 105: 93.3863 -2.5431 -4.0087 2.2211 7.1040 0.7987 1.0020 0.1454 8.2210 +#> 106: 93.1536 -2.5365 -4.0243 2.2457 6.7488 0.7909 0.9519 0.1389 8.0950 +#> 107: 93.2220 -2.5446 -4.0016 2.2508 6.4114 0.8108 0.9483 0.1364 8.5629 +#> 108: 93.0778 -2.5470 -3.9678 2.2329 6.4774 0.8077 1.0081 0.1850 9.2740 +#> 109: 93.8925 -2.5453 -3.9560 2.2193 6.1535 0.8079 1.0608 0.2111 9.2651 +#> 110: 94.3171 -2.5179 -4.0040 2.2145 5.8458 0.7874 1.0520 0.2135 8.9788 +#> 111: 94.0655 -2.5069 -3.9752 2.2009 5.5536 0.8056 1.1206 0.2192 8.9410 +#> 112: 93.8552 -2.4994 -3.9791 2.1597 5.2759 0.8012 1.0646 0.2365 8.9570 +#> 113: 93.5190 -2.5053 -3.9760 2.1727 5.0121 0.8326 1.0114 0.2246 9.2154 +#> 114: 93.5531 -2.5083 -3.9569 2.1636 4.7615 0.8255 0.9879 0.2134 9.1197 +#> 115: 93.4780 -2.5217 -3.9467 2.1529 4.5234 0.8314 1.0392 0.2027 8.7850 +#> 116: 93.5707 -2.5216 -3.9098 2.1667 4.2972 0.8261 1.1213 0.1926 9.2991 +#> 117: 93.6610 -2.5445 -3.8775 2.1473 4.0824 0.8122 1.1232 0.1830 9.2054 +#> 118: 93.4315 -2.5251 -3.9166 2.1365 4.6012 0.7933 1.0690 0.1738 8.8061 +#> 119: 93.2491 -2.5265 -3.9236 2.1671 5.0672 0.8046 1.0711 0.1709 8.2293 +#> 120: 93.2605 -2.5327 -3.9714 2.1984 4.8138 0.8025 1.0176 0.1623 7.9088 +#> 121: 93.5831 -2.5448 -3.9669 2.2195 4.5731 0.8079 0.9921 0.1542 8.2211 +#> 122: 93.3408 -2.5460 -3.9710 2.2235 4.6838 0.8053 1.0377 0.1658 8.2934 +#> 123: 93.4581 -2.5395 -3.9487 2.2279 4.4496 0.8298 1.0338 0.1732 8.2859 +#> 124: 93.0562 -2.5565 -3.9587 2.2299 4.2272 0.8590 1.0531 0.1964 8.1244 +#> 125: 93.0576 -2.5660 -3.9434 2.2457 4.0158 0.8564 1.0768 0.1866 8.3730 +#> 126: 92.8366 -2.5571 -3.9463 2.2096 3.8150 0.8551 1.0476 0.1773 8.3820 +#> 127: 92.9607 -2.5595 -3.9773 2.2325 3.6243 0.8497 0.9952 0.1684 9.2276 +#> 128: 93.0655 -2.5463 -3.9731 2.1901 3.4430 0.8903 0.9454 0.1600 8.8096 +#> 129: 93.0669 -2.5467 -3.9713 2.2204 3.2709 0.8905 0.9234 0.1520 8.8686 +#> 130: 93.2036 -2.5524 -3.9702 2.2070 3.1073 0.8719 0.9514 0.1578 8.8433 +#> 131: 93.3565 -2.5544 -3.9809 2.1654 2.9520 0.8777 0.9117 0.1764 8.9770 +#> 132: 93.0371 -2.5364 -3.9250 2.1761 2.8044 0.8338 1.0518 0.1731 8.5405 +#> 133: 93.5727 -2.5388 -3.8759 2.1580 3.6769 0.8616 1.0981 0.1858 8.5303 +#> 134: 93.4962 -2.5341 -3.9006 2.1394 4.3695 0.8904 1.0432 0.1765 8.7067 +#> 135: 93.3219 -2.5413 -3.8922 2.1888 4.1510 0.8971 1.0435 0.1857 8.4977 +#> 136: 93.3582 -2.5477 -3.8412 2.1957 3.9435 0.8816 1.1954 0.2102 8.1330 +#> 137: 93.2791 -2.5313 -3.8936 2.1570 3.7463 0.8875 1.1356 0.1997 8.3094 +#> 138: 93.0890 -2.5428 -3.8910 2.1414 3.5590 0.8826 1.1008 0.2120 8.2653 +#> 139: 93.2404 -2.5407 -3.8926 2.1727 3.3810 0.8829 1.1068 0.2014 8.3739 +#> 140: 93.0870 -2.5514 -3.9131 2.2182 3.2120 0.8712 1.0870 0.1914 8.6179 +#> 141: 93.2715 -2.5499 -3.9460 2.2216 3.4383 0.8470 1.0662 0.1900 8.4034 +#> 142: 93.1915 -2.5583 -3.9990 2.2475 5.2653 0.8607 1.0129 0.2061 7.9891 +#> 143: 93.3709 -2.5650 -3.9422 2.2369 5.0020 0.8748 1.2043 0.2248 8.0084 +#> 144: 93.2092 -2.5706 -3.9016 2.1930 4.7519 0.8667 1.1977 0.2179 8.2733 +#> 145: 92.6640 -2.5733 -3.9225 2.1859 4.5143 0.8636 1.1695 0.2070 8.6212 +#> 146: 92.7581 -2.5695 -3.9055 2.1801 5.4209 0.8589 1.1678 0.1967 8.9378 +#> 147: 93.1089 -2.5707 -3.9825 2.2113 7.6640 0.8710 1.1094 0.1934 9.0543 +#> 148: 93.0803 -2.5672 -3.9461 2.2066 9.9043 0.8648 1.1043 0.1863 8.6209 +#> 149: 92.6332 -2.5468 -3.9425 2.1881 9.4091 0.8278 1.1313 0.1769 8.4652 +#> 150: 92.9068 -2.5440 -3.9531 2.2005 8.9386 0.8189 1.1104 0.1681 8.4196 +#> 151: 92.7324 -2.5497 -3.9648 2.2387 8.4917 0.8205 1.1421 0.1597 8.4228 +#> 152: 93.0394 -2.5282 -3.9916 2.2251 3.9029 0.8190 1.0320 0.1612 8.3453 +#> 153: 93.3137 -2.5268 -3.9993 2.2294 3.7951 0.8187 1.0311 0.1780 8.4258 +#> 154: 93.6677 -2.5264 -3.9756 2.2615 4.8704 0.8177 1.1355 0.1799 8.7204 +#> 155: 94.0822 -2.5409 -4.0456 2.2507 5.1202 0.8032 0.9930 0.1613 8.8844 +#> 156: 93.6289 -2.5388 -4.1150 2.2777 4.6367 0.8080 0.8336 0.1817 8.4370 +#> 157: 93.9171 -2.5327 -4.0218 2.2696 3.1121 0.8069 1.0394 0.1800 8.5006 +#> 158: 94.0010 -2.5357 -4.0036 2.2695 3.1485 0.8087 1.1132 0.2048 8.7160 +#> 159: 94.1277 -2.5541 -3.9717 2.2773 5.1432 0.8088 1.0732 0.1980 8.5378 +#> 160: 94.0075 -2.5436 -3.9550 2.2796 4.7826 0.8286 1.0820 0.1953 8.3885 +#> 161: 93.6793 -2.5471 -3.9675 2.2713 3.9366 0.8603 1.0682 0.1972 8.3026 +#> 162: 93.2649 -2.5429 -3.9564 2.2406 2.7349 0.8469 1.0889 0.1929 8.3765 +#> 163: 93.2072 -2.5519 -3.9786 2.2535 3.1500 0.8361 1.1240 0.1997 8.4527 +#> 164: 93.4059 -2.5471 -4.0398 2.2257 2.8708 0.8284 1.0541 0.2105 8.4984 +#> 165: 93.2579 -2.5407 -3.9665 2.2305 2.7397 0.8251 1.1355 0.2302 7.9794 +#> 166: 93.4900 -2.5465 -3.9565 2.2316 1.9775 0.8359 1.0939 0.2243 8.1279 +#> 167: 93.3825 -2.5567 -3.9784 2.2276 2.3737 0.8251 1.0894 0.2254 8.6657 +#> 168: 93.2568 -2.5681 -3.9993 2.2818 2.6721 0.8237 1.1398 0.2207 8.4894 +#> 169: 93.0484 -2.5468 -3.9693 2.2586 1.9105 0.8518 1.1911 0.1917 8.5627 +#> 170: 93.2703 -2.5730 -3.9059 2.2512 2.1481 0.8068 1.3267 0.2198 8.2260 +#> 171: 93.2041 -2.5720 -3.8992 2.2227 2.7790 0.8045 1.2387 0.2059 8.1401 +#> 172: 92.7596 -2.5722 -3.8802 2.2537 2.9977 0.8049 1.2807 0.1831 8.3375 +#> 173: 92.7734 -2.5716 -3.8811 2.1987 3.0176 0.8063 1.3070 0.2285 8.5061 +#> 174: 92.5561 -2.5700 -3.9236 2.2351 3.0286 0.8250 1.2000 0.2200 8.0725 +#> 175: 92.5072 -2.5724 -3.9968 2.2479 2.4287 0.8333 1.0169 0.2235 8.2600 +#> 176: 92.3531 -2.5787 -3.9977 2.2407 2.9999 0.8167 0.9813 0.2451 8.7505 +#> 177: 92.4672 -2.5746 -4.0095 2.2733 2.8040 0.8361 0.9794 0.2363 8.5176 +#> 178: 92.5747 -2.5981 -3.9921 2.2835 1.8203 0.8411 0.9795 0.2112 8.8034 +#> 179: 92.7101 -2.5766 -3.9697 2.2337 1.7808 0.8348 1.0402 0.2247 8.3952 +#> 180: 92.5348 -2.5714 -3.9595 2.2236 1.2661 0.8361 1.0107 0.2375 8.7156 +#> 181: 92.7241 -2.5730 -3.9205 2.2162 1.1047 0.8321 1.1192 0.2147 8.8821 +#> 182: 92.9177 -2.5864 -3.9351 2.2280 1.2069 0.8108 1.1022 0.2163 8.5703 +#> 183: 92.8646 -2.5704 -3.9755 2.2192 1.5680 0.8232 0.9400 0.1848 8.6586 +#> 184: 92.8081 -2.5759 -3.9981 2.2411 1.7739 0.8394 0.8711 0.1788 8.6327 +#> 185: 92.6830 -2.5700 -4.0110 2.2360 1.5375 0.8093 0.9114 0.1782 8.6703 +#> 186: 92.7691 -2.5764 -3.9671 2.2148 1.8813 0.8117 0.9794 0.1901 8.4813 +#> 187: 92.7540 -2.5659 -3.9695 2.2543 1.3755 0.8130 1.0332 0.1960 8.5371 +#> 188: 92.5722 -2.5650 -3.9527 2.2552 1.4000 0.8142 1.1013 0.1881 8.2025 +#> 189: 92.9404 -2.5644 -3.9446 2.2579 1.3589 0.8157 1.1262 0.1741 8.2347 +#> 190: 92.8142 -2.5628 -3.9397 2.2549 1.1871 0.8241 1.1571 0.1728 8.1590 +#> 191: 92.7352 -2.5682 -3.9476 2.2502 0.8302 0.7954 1.1448 0.1859 8.6148 +#> 192: 92.7380 -2.5574 -3.9273 2.2318 0.6692 0.8185 1.1124 0.1932 8.5279 +#> 193: 92.9199 -2.5652 -3.9586 2.2184 0.9877 0.8097 1.1689 0.1709 8.7071 +#> 194: 93.0042 -2.5651 -3.9699 2.2302 1.3311 0.8135 1.1202 0.1832 8.8051 +#> 195: 92.8090 -2.5890 -3.9799 2.2360 0.9251 0.8313 1.0192 0.1806 9.3110 +#> 196: 92.5114 -2.5894 -3.9883 2.2553 0.8504 0.8299 1.0665 0.1855 8.9668 +#> 197: 92.6704 -2.5845 -3.9577 2.2490 0.3567 0.8365 1.0893 0.1896 8.5856 +#> 198: 92.7249 -2.5753 -3.9775 2.2327 0.4282 0.8506 1.0736 0.2003 8.7110 +#> 199: 92.5538 -2.5696 -3.9550 2.2382 0.3177 0.8550 1.1060 0.2132 8.5431 +#> 200: 92.6352 -2.5716 -3.9921 2.2372 0.2500 0.8592 1.0083 0.2057 8.5811 +#> 201: 92.6440 -2.5663 -3.9931 2.2219 0.2611 0.8647 1.0130 0.1931 8.6428 +#> 202: 92.6090 -2.5633 -3.9837 2.2198 0.2389 0.8680 1.0373 0.1958 8.6818 +#> 203: 92.6180 -2.5627 -3.9823 2.2185 0.2315 0.8627 1.0398 0.1939 8.6310 +#> 204: 92.6140 -2.5628 -3.9783 2.2176 0.2289 0.8588 1.0462 0.1923 8.5391 +#> 205: 92.6337 -2.5619 -3.9802 2.2190 0.2227 0.8579 1.0407 0.1965 8.5514 +#> 206: 92.6373 -2.5615 -3.9835 2.2175 0.2313 0.8580 1.0330 0.2006 8.5635 +#> 207: 92.6403 -2.5594 -3.9836 2.2189 0.2365 0.8608 1.0282 0.2017 8.5721 +#> 208: 92.6415 -2.5587 -3.9862 2.2192 0.2480 0.8615 1.0221 0.2001 8.5738 +#> 209: 92.6303 -2.5586 -3.9872 2.2180 0.2544 0.8608 1.0127 0.1966 8.6159 +#> 210: 92.6278 -2.5584 -3.9829 2.2178 0.2577 0.8576 1.0149 0.1932 8.6336 +#> 211: 92.6320 -2.5580 -3.9844 2.2163 0.2614 0.8544 1.0057 0.1902 8.6594 +#> 212: 92.6266 -2.5576 -3.9802 2.2140 0.2554 0.8515 1.0125 0.1891 8.6549 +#> 213: 92.6226 -2.5570 -3.9771 2.2114 0.2491 0.8468 1.0201 0.1879 8.6612 +#> 214: 92.6217 -2.5570 -3.9759 2.2119 0.2430 0.8429 1.0289 0.1859 8.6700 +#> 215: 92.6212 -2.5573 -3.9743 2.2121 0.2354 0.8394 1.0383 0.1853 8.6796 +#> 216: 92.6151 -2.5566 -3.9736 2.2125 0.2329 0.8378 1.0446 0.1850 8.7036 +#> 217: 92.6073 -2.5558 -3.9759 2.2133 0.2311 0.8373 1.0459 0.1854 8.7185 +#> 218: 92.6090 -2.5556 -3.9771 2.2142 0.2312 0.8373 1.0499 0.1866 8.7181 +#> 219: 92.6166 -2.5553 -3.9764 2.2142 0.2358 0.8376 1.0624 0.1882 8.7228 +#> 220: 92.6268 -2.5549 -3.9770 2.2150 0.2404 0.8395 1.0671 0.1899 8.7325 +#> 221: 92.6337 -2.5548 -3.9765 2.2172 0.2460 0.8412 1.0713 0.1900 8.7409 +#> 222: 92.6383 -2.5563 -3.9796 2.2211 0.2499 0.8412 1.0667 0.1898 8.7456 +#> 223: 92.6399 -2.5575 -3.9806 2.2259 0.2494 0.8406 1.0665 0.1898 8.7564 +#> 224: 92.6424 -2.5589 -3.9840 2.2296 0.2451 0.8412 1.0624 0.1894 8.7571 +#> 225: 92.6431 -2.5599 -3.9883 2.2336 0.2427 0.8423 1.0555 0.1885 8.7754 +#> 226: 92.6393 -2.5612 -3.9919 2.2371 0.2384 0.8431 1.0488 0.1886 8.7904 +#> 227: 92.6354 -2.5630 -3.9918 2.2406 0.2361 0.8432 1.0501 0.1892 8.8070 +#> 228: 92.6328 -2.5650 -3.9926 2.2437 0.2336 0.8434 1.0524 0.1908 8.8133 +#> 229: 92.6328 -2.5672 -3.9913 2.2462 0.2318 0.8439 1.0578 0.1926 8.8314 +#> 230: 92.6322 -2.5684 -3.9911 2.2482 0.2269 0.8426 1.0621 0.1952 8.8464 +#> 231: 92.6263 -2.5698 -3.9910 2.2500 0.2240 0.8418 1.0628 0.1963 8.8734 +#> 232: 92.6228 -2.5710 -3.9908 2.2515 0.2218 0.8411 1.0644 0.1977 8.9056 +#> 233: 92.6235 -2.5721 -3.9919 2.2545 0.2192 0.8409 1.0649 0.1983 8.9192 +#> 234: 92.6232 -2.5727 -3.9927 2.2551 0.2171 0.8397 1.0649 0.1981 8.9294 +#> 235: 92.6219 -2.5733 -3.9924 2.2562 0.2155 0.8390 1.0646 0.1978 8.9242 +#> 236: 92.6212 -2.5737 -3.9924 2.2574 0.2145 0.8384 1.0639 0.1975 8.9292 +#> 237: 92.6211 -2.5738 -3.9938 2.2588 0.2142 0.8379 1.0607 0.1970 8.9400 +#> 238: 92.6194 -2.5735 -3.9931 2.2589 0.2155 0.8373 1.0630 0.1969 8.9371 +#> 239: 92.6175 -2.5734 -3.9928 2.2593 0.2155 0.8371 1.0648 0.1967 8.9315 +#> 240: 92.6175 -2.5729 -3.9923 2.2593 0.2143 0.8367 1.0673 0.1963 8.9180 +#> 241: 92.6155 -2.5728 -3.9917 2.2591 0.2133 0.8372 1.0695 0.1960 8.9139 +#> 242: 92.6135 -2.5726 -3.9923 2.2588 0.2136 0.8375 1.0681 0.1965 8.9191 +#> 243: 92.6115 -2.5726 -3.9930 2.2592 0.2127 0.8375 1.0683 0.1969 8.9117 +#> 244: 92.6106 -2.5726 -3.9925 2.2588 0.2123 0.8381 1.0704 0.1975 8.9124 +#> 245: 92.6065 -2.5730 -3.9930 2.2586 0.2127 0.8388 1.0691 0.1982 8.9140 +#> 246: 92.6046 -2.5734 -3.9931 2.2588 0.2109 0.8397 1.0701 0.1986 8.9132 +#> 247: 92.6048 -2.5737 -3.9938 2.2597 0.2081 0.8404 1.0708 0.1989 8.9224 +#> 248: 92.6029 -2.5739 -3.9932 2.2599 0.2056 0.8410 1.0718 0.1993 8.9198 +#> 249: 92.6006 -2.5743 -3.9934 2.2598 0.2052 0.8419 1.0705 0.1996 8.9244 +#> 250: 92.5984 -2.5740 -3.9930 2.2595 0.2037 0.8417 1.0709 0.1997 8.9208 +#> 251: 92.5967 -2.5739 -3.9932 2.2595 0.2018 0.8418 1.0700 0.1996 8.9143 +#> 252: 92.5943 -2.5737 -3.9920 2.2594 0.2009 0.8412 1.0734 0.1992 8.9090 +#> 253: 92.5944 -2.5736 -3.9904 2.2588 0.1997 0.8405 1.0769 0.1995 8.9035 +#> 254: 92.5941 -2.5732 -3.9896 2.2582 0.1987 0.8394 1.0788 0.1993 8.8940 +#> 255: 92.5916 -2.5728 -3.9892 2.2571 0.1983 0.8387 1.0794 0.1988 8.8894 +#> 256: 92.5889 -2.5724 -3.9880 2.2562 0.1988 0.8382 1.0813 0.1992 8.8834 +#> 257: 92.5889 -2.5719 -3.9872 2.2557 0.2003 0.8378 1.0831 0.1995 8.8806 +#> 258: 92.5889 -2.5717 -3.9866 2.2556 0.2021 0.8377 1.0858 0.1995 8.8792 +#> 259: 92.5898 -2.5715 -3.9867 2.2556 0.2033 0.8373 1.0884 0.1999 8.8785 +#> 260: 92.5924 -2.5709 -3.9868 2.2556 0.2033 0.8367 1.0891 0.2006 8.8743 +#> 261: 92.5956 -2.5703 -3.9866 2.2552 0.2045 0.8360 1.0908 0.2014 8.8635 +#> 262: 92.5985 -2.5698 -3.9859 2.2546 0.2054 0.8354 1.0940 0.2022 8.8551 +#> 263: 92.6014 -2.5694 -3.9857 2.2544 0.2067 0.8347 1.0964 0.2028 8.8479 +#> 264: 92.6041 -2.5690 -3.9858 2.2543 0.2069 0.8338 1.0977 0.2028 8.8421 +#> 265: 92.6063 -2.5687 -3.9861 2.2541 0.2079 0.8327 1.0976 0.2029 8.8394 +#> 266: 92.6087 -2.5684 -3.9867 2.2540 0.2107 0.8318 1.0968 0.2027 8.8351 +#> 267: 92.6108 -2.5682 -3.9863 2.2534 0.2118 0.8314 1.0970 0.2032 8.8283 +#> 268: 92.6130 -2.5680 -3.9860 2.2530 0.2131 0.8309 1.0970 0.2034 8.8263 +#> 269: 92.6139 -2.5678 -3.9851 2.2526 0.2155 0.8306 1.0979 0.2040 8.8240 +#> 270: 92.6144 -2.5676 -3.9851 2.2521 0.2176 0.8303 1.0972 0.2044 8.8283 +#> 271: 92.6153 -2.5675 -3.9855 2.2518 0.2190 0.8305 1.0961 0.2049 8.8310 +#> 272: 92.6163 -2.5674 -3.9859 2.2521 0.2196 0.8305 1.0965 0.2051 8.8378 +#> 273: 92.6178 -2.5672 -3.9862 2.2520 0.2198 0.8302 1.0959 0.2051 8.8421 +#> 274: 92.6193 -2.5670 -3.9870 2.2524 0.2195 0.8299 1.0955 0.2051 8.8441 +#> 275: 92.6197 -2.5669 -3.9874 2.2526 0.2194 0.8295 1.0947 0.2052 8.8477 +#> 276: 92.6215 -2.5665 -3.9875 2.2523 0.2206 0.8288 1.0956 0.2058 8.8462 +#> 277: 92.6225 -2.5660 -3.9878 2.2522 0.2231 0.8282 1.0974 0.2064 8.8450 +#> 278: 92.6237 -2.5655 -3.9883 2.2522 0.2240 0.8277 1.0988 0.2074 8.8501 +#> 279: 92.6249 -2.5651 -3.9888 2.2524 0.2244 0.8274 1.0994 0.2083 8.8504 +#> 280: 92.6259 -2.5647 -3.9891 2.2523 0.2235 0.8270 1.0992 0.2087 8.8514 +#> 281: 92.6264 -2.5643 -3.9889 2.2522 0.2225 0.8262 1.1001 0.2090 8.8559 +#> 282: 92.6270 -2.5639 -3.9889 2.2516 0.2223 0.8255 1.0997 0.2090 8.8593 +#> 283: 92.6280 -2.5633 -3.9885 2.2503 0.2214 0.8248 1.0999 0.2101 8.8586 +#> 284: 92.6281 -2.5627 -3.9883 2.2491 0.2212 0.8241 1.0993 0.2110 8.8580 +#> 285: 92.6283 -2.5621 -3.9881 2.2481 0.2213 0.8235 1.0986 0.2118 8.8590 +#> 286: 92.6288 -2.5615 -3.9886 2.2475 0.2219 0.8231 1.0973 0.2123 8.8602 +#> 287: 92.6291 -2.5611 -3.9890 2.2470 0.2217 0.8230 1.0961 0.2133 8.8577 +#> 288: 92.6292 -2.5607 -3.9893 2.2468 0.2202 0.8229 1.0960 0.2142 8.8570 +#> 289: 92.6275 -2.5602 -3.9895 2.2464 0.2192 0.8226 1.0964 0.2151 8.8554 +#> 290: 92.6262 -2.5598 -3.9892 2.2457 0.2189 0.8223 1.0977 0.2161 8.8578 +#> 291: 92.6246 -2.5596 -3.9890 2.2454 0.2183 0.8218 1.0999 0.2165 8.8596 +#> 292: 92.6223 -2.5593 -3.9892 2.2451 0.2183 0.8213 1.1003 0.2173 8.8575 +#> 293: 92.6201 -2.5590 -3.9896 2.2447 0.2193 0.8209 1.1003 0.2175 8.8569 +#> 294: 92.6169 -2.5587 -3.9902 2.2445 0.2202 0.8204 1.0998 0.2176 8.8568 +#> 295: 92.6144 -2.5584 -3.9906 2.2442 0.2217 0.8197 1.0994 0.2176 8.8565 +#> 296: 92.6126 -2.5581 -3.9913 2.2441 0.2223 0.8188 1.0983 0.2175 8.8585 +#> 297: 92.6112 -2.5576 -3.9920 2.2439 0.2235 0.8182 1.0969 0.2175 8.8600 +#> 298: 92.6108 -2.5572 -3.9921 2.2433 0.2250 0.8174 1.0964 0.2177 8.8612 +#> 299: 92.6101 -2.5567 -3.9919 2.2425 0.2254 0.8169 1.0960 0.2178 8.8626 +#> 300: 92.6097 -2.5562 -3.9913 2.2415 0.2257 0.8163 1.0974 0.2182 8.8632 +#> 301: 92.6102 -2.5556 -3.9913 2.2407 0.2255 0.8156 1.0972 0.2183 8.8600 +#> 302: 92.6102 -2.5551 -3.9916 2.2400 0.2252 0.8156 1.0966 0.2186 8.8586 +#> 303: 92.6099 -2.5546 -3.9915 2.2391 0.2250 0.8152 1.0978 0.2189 8.8589 +#> 304: 92.6096 -2.5541 -3.9913 2.2387 0.2242 0.8149 1.0987 0.2194 8.8570 +#> 305: 92.6100 -2.5538 -3.9914 2.2383 0.2247 0.8144 1.0995 0.2202 8.8553 +#> 306: 92.6109 -2.5533 -3.9915 2.2378 0.2255 0.8144 1.1001 0.2212 8.8531 +#> 307: 92.6119 -2.5529 -3.9913 2.2371 0.2252 0.8143 1.1007 0.2217 8.8498 +#> 308: 92.6128 -2.5525 -3.9912 2.2366 0.2249 0.8142 1.1012 0.2219 8.8490 +#> 309: 92.6143 -2.5519 -3.9905 2.2357 0.2251 0.8138 1.1018 0.2224 8.8449 +#> 310: 92.6160 -2.5513 -3.9900 2.2346 0.2255 0.8136 1.1020 0.2230 8.8403 +#> 311: 92.6177 -2.5506 -3.9891 2.2333 0.2258 0.8132 1.1031 0.2236 8.8392 +#> 312: 92.6190 -2.5499 -3.9881 2.2319 0.2267 0.8130 1.1047 0.2242 8.8382 +#> 313: 92.6192 -2.5493 -3.9872 2.2305 0.2273 0.8127 1.1057 0.2249 8.8350 +#> 314: 92.6196 -2.5490 -3.9864 2.2300 0.2279 0.8129 1.1067 0.2257 8.8315 +#> 315: 92.6197 -2.5488 -3.9858 2.2295 0.2277 0.8132 1.1072 0.2266 8.8285 +#> 316: 92.6192 -2.5485 -3.9850 2.2284 0.2276 0.8133 1.1087 0.2275 8.8278 +#> 317: 92.6190 -2.5482 -3.9840 2.2275 0.2278 0.8135 1.1105 0.2282 8.8296 +#> 318: 92.6193 -2.5480 -3.9833 2.2266 0.2274 0.8133 1.1120 0.2289 8.8313 +#> 319: 92.6200 -2.5476 -3.9827 2.2257 0.2265 0.8129 1.1133 0.2297 8.8326 +#> 320: 92.6211 -2.5472 -3.9820 2.2250 0.2260 0.8124 1.1150 0.2302 8.8359 +#> 321: 92.6226 -2.5468 -3.9816 2.2246 0.2254 0.8118 1.1158 0.2308 8.8396 +#> 322: 92.6238 -2.5464 -3.9808 2.2238 0.2249 0.8114 1.1169 0.2316 8.8424 +#> 323: 92.6248 -2.5461 -3.9805 2.2231 0.2241 0.8109 1.1173 0.2320 8.8458 +#> 324: 92.6252 -2.5458 -3.9801 2.2224 0.2233 0.8103 1.1182 0.2324 8.8474 +#> 325: 92.6248 -2.5455 -3.9799 2.2216 0.2225 0.8096 1.1192 0.2328 8.8507 +#> 326: 92.6247 -2.5451 -3.9802 2.2209 0.2216 0.8091 1.1186 0.2331 8.8519 +#> 327: 92.6248 -2.5446 -3.9806 2.2203 0.2203 0.8088 1.1179 0.2335 8.8535 +#> 328: 92.6242 -2.5442 -3.9808 2.2198 0.2196 0.8084 1.1175 0.2339 8.8533 +#> 329: 92.6234 -2.5437 -3.9809 2.2192 0.2188 0.8081 1.1176 0.2342 8.8550 +#> 330: 92.6229 -2.5433 -3.9806 2.2187 0.2182 0.8078 1.1187 0.2346 8.8574 +#> 331: 92.6220 -2.5429 -3.9801 2.2181 0.2183 0.8075 1.1210 0.2352 8.8599 +#> 332: 92.6214 -2.5425 -3.9796 2.2175 0.2185 0.8072 1.1235 0.2360 8.8612 +#> 333: 92.6215 -2.5421 -3.9794 2.2170 0.2184 0.8068 1.1248 0.2365 8.8660 +#> 334: 92.6218 -2.5417 -3.9795 2.2168 0.2180 0.8065 1.1257 0.2369 8.8675 +#> 335: 92.6220 -2.5413 -3.9793 2.2164 0.2177 0.8062 1.1269 0.2374 8.8683 +#> 336: 92.6228 -2.5410 -3.9792 2.2159 0.2173 0.8059 1.1275 0.2378 8.8707 +#> 337: 92.6244 -2.5405 -3.9792 2.2153 0.2175 0.8057 1.1278 0.2387 8.8734 +#> 338: 92.6266 -2.5401 -3.9792 2.2146 0.2184 0.8057 1.1283 0.2396 8.8757 +#> 339: 92.6290 -2.5398 -3.9790 2.2144 0.2191 0.8060 1.1294 0.2403 8.8770 +#> 340: 92.6309 -2.5396 -3.9790 2.2142 0.2200 0.8061 1.1295 0.2405 8.8766 +#> 341: 92.6328 -2.5394 -3.9788 2.2142 0.2211 0.8061 1.1300 0.2406 8.8752 +#> 342: 92.6347 -2.5392 -3.9788 2.2140 0.2223 0.8062 1.1291 0.2405 8.8744 +#> 343: 92.6365 -2.5390 -3.9787 2.2139 0.2233 0.8063 1.1288 0.2405 8.8732 +#> 344: 92.6383 -2.5388 -3.9785 2.2136 0.2242 0.8060 1.1295 0.2404 8.8730 +#> 345: 92.6392 -2.5386 -3.9781 2.2133 0.2248 0.8055 1.1303 0.2401 8.8737 +#> 346: 92.6401 -2.5384 -3.9780 2.2129 0.2249 0.8051 1.1302 0.2399 8.8739 +#> 347: 92.6411 -2.5381 -3.9777 2.2124 0.2248 0.8049 1.1302 0.2399 8.8794 +#> 348: 92.6427 -2.5380 -3.9777 2.2122 0.2251 0.8047 1.1306 0.2398 8.8814 +#> 349: 92.6444 -2.5378 -3.9777 2.2119 0.2252 0.8047 1.1304 0.2397 8.8834 +#> 350: 92.6462 -2.5376 -3.9776 2.2115 0.2260 0.8043 1.1300 0.2395 8.8859 +#> 351: 92.6470 -2.5375 -3.9772 2.2110 0.2265 0.8041 1.1303 0.2392 8.8883 +#> 352: 92.6478 -2.5373 -3.9772 2.2106 0.2266 0.8037 1.1293 0.2386 8.8926 +#> 353: 92.6493 -2.5373 -3.9772 2.2103 0.2268 0.8032 1.1285 0.2381 8.8928 +#> 354: 92.6504 -2.5372 -3.9772 2.2100 0.2264 0.8028 1.1274 0.2376 8.8946 +#> 355: 92.6512 -2.5370 -3.9771 2.2096 0.2267 0.8023 1.1273 0.2373 8.8951 +#> 356: 92.6522 -2.5367 -3.9767 2.2089 0.2275 0.8018 1.1272 0.2370 8.8947 +#> 357: 92.6534 -2.5364 -3.9765 2.2080 0.2290 0.8015 1.1268 0.2369 8.8932 +#> 358: 92.6545 -2.5362 -3.9761 2.2072 0.2301 0.8011 1.1270 0.2368 8.8919 +#> 359: 92.6566 -2.5360 -3.9757 2.2064 0.2310 0.8008 1.1269 0.2369 8.8928 +#> 360: 92.6584 -2.5358 -3.9751 2.2059 0.2311 0.8005 1.1272 0.2368 8.8924 +#> 361: 92.6611 -2.5356 -3.9744 2.2051 0.2317 0.8004 1.1280 0.2369 8.8932 +#> 362: 92.6639 -2.5353 -3.9740 2.2043 0.2321 0.8003 1.1284 0.2370 8.8914 +#> 363: 92.6662 -2.5349 -3.9733 2.2033 0.2328 0.8001 1.1289 0.2371 8.8902 +#> 364: 92.6679 -2.5345 -3.9729 2.2025 0.2325 0.7998 1.1292 0.2372 8.8883 +#> 365: 92.6695 -2.5341 -3.9725 2.2019 0.2321 0.7994 1.1297 0.2373 8.8865 +#> 366: 92.6709 -2.5337 -3.9722 2.2011 0.2321 0.7990 1.1297 0.2373 8.8860 +#> 367: 92.6724 -2.5334 -3.9720 2.2005 0.2317 0.7987 1.1295 0.2372 8.8848 +#> 368: 92.6736 -2.5330 -3.9719 2.1999 0.2314 0.7985 1.1288 0.2371 8.8844 +#> 369: 92.6745 -2.5326 -3.9717 2.1994 0.2310 0.7982 1.1283 0.2371 8.8840 +#> 370: 92.6758 -2.5323 -3.9714 2.1990 0.2312 0.7980 1.1283 0.2370 8.8854 +#> 371: 92.6776 -2.5321 -3.9708 2.1984 0.2313 0.7977 1.1286 0.2369 8.8852 +#> 372: 92.6791 -2.5317 -3.9704 2.1978 0.2311 0.7973 1.1282 0.2367 8.8865 +#> 373: 92.6804 -2.5312 -3.9701 2.1969 0.2308 0.7969 1.1280 0.2366 8.8884 +#> 374: 92.6814 -2.5308 -3.9699 2.1962 0.2305 0.7965 1.1279 0.2364 8.8898 +#> 375: 92.6827 -2.5304 -3.9698 2.1954 0.2305 0.7961 1.1271 0.2360 8.8938 +#> 376: 92.6832 -2.5301 -3.9695 2.1947 0.2301 0.7957 1.1268 0.2359 8.8930 +#> 377: 92.6835 -2.5298 -3.9692 2.1941 0.2300 0.7953 1.1269 0.2357 8.8933 +#> 378: 92.6831 -2.5295 -3.9693 2.1935 0.2303 0.7950 1.1266 0.2357 8.8990 +#> 379: 92.6827 -2.5293 -3.9694 2.1933 0.2307 0.7948 1.1265 0.2356 8.9027 +#> 380: 92.6826 -2.5291 -3.9695 2.1931 0.2307 0.7947 1.1262 0.2356 8.9045 +#> 381: 92.6822 -2.5290 -3.9695 2.1929 0.2307 0.7945 1.1259 0.2355 8.9040 +#> 382: 92.6817 -2.5289 -3.9694 2.1925 0.2305 0.7943 1.1258 0.2357 8.9033 +#> 383: 92.6812 -2.5288 -3.9695 2.1922 0.2305 0.7942 1.1255 0.2358 8.9045 +#> 384: 92.6810 -2.5288 -3.9695 2.1920 0.2302 0.7940 1.1253 0.2360 8.9058 +#> 385: 92.6806 -2.5287 -3.9694 2.1918 0.2301 0.7938 1.1254 0.2361 8.9052 +#> 386: 92.6801 -2.5286 -3.9692 2.1914 0.2298 0.7936 1.1256 0.2362 8.9039 +#> 387: 92.6800 -2.5285 -3.9687 2.1914 0.2294 0.7934 1.1261 0.2361 8.9032 +#> 388: 92.6801 -2.5284 -3.9683 2.1913 0.2293 0.7931 1.1267 0.2360 8.9027 +#> 389: 92.6802 -2.5283 -3.9684 2.1912 0.2288 0.7928 1.1261 0.2360 8.9028 +#> 390: 92.6805 -2.5281 -3.9684 2.1910 0.2283 0.7925 1.1258 0.2360 8.9044 +#> 391: 92.6806 -2.5280 -3.9685 2.1908 0.2285 0.7921 1.1254 0.2360 8.9047 +#> 392: 92.6810 -2.5278 -3.9682 2.1907 0.2288 0.7918 1.1257 0.2360 8.9057 +#> 393: 92.6810 -2.5275 -3.9681 2.1906 0.2290 0.7916 1.1257 0.2360 8.9049 +#> 394: 92.6811 -2.5272 -3.9682 2.1904 0.2292 0.7913 1.1253 0.2360 8.9056 +#> 395: 92.6812 -2.5269 -3.9682 2.1900 0.2295 0.7911 1.1251 0.2362 8.9044 +#> 396: 92.6817 -2.5269 -3.9683 2.1901 0.2292 0.7911 1.1251 0.2361 8.9031 +#> 397: 92.6824 -2.5269 -3.9686 2.1903 0.2292 0.7911 1.1250 0.2361 8.9043 +#> 398: 92.6828 -2.5270 -3.9688 2.1907 0.2291 0.7913 1.1248 0.2359 8.9035 +#> 399: 92.6829 -2.5271 -3.9689 2.1909 0.2292 0.7916 1.1248 0.2358 8.9043 +#> 400: 92.6829 -2.5273 -3.9688 2.1909 0.2295 0.7919 1.1250 0.2356 8.9037 +#> 401: 92.6827 -2.5274 -3.9687 2.1910 0.2299 0.7922 1.1249 0.2356 8.9035 +#> 402: 92.6822 -2.5276 -3.9687 2.1911 0.2303 0.7926 1.1248 0.2355 8.9033 +#> 403: 92.6821 -2.5277 -3.9686 2.1913 0.2307 0.7929 1.1250 0.2354 8.9009 +#> 404: 92.6817 -2.5279 -3.9684 2.1914 0.2314 0.7930 1.1249 0.2352 8.9012 +#> 405: 92.6813 -2.5281 -3.9683 2.1915 0.2318 0.7930 1.1252 0.2349 8.9015 +#> 406: 92.6811 -2.5283 -3.9680 2.1916 0.2321 0.7930 1.1255 0.2345 8.9019 +#> 407: 92.6817 -2.5285 -3.9677 2.1918 0.2329 0.7930 1.1255 0.2343 8.9014 +#> 408: 92.6824 -2.5287 -3.9675 2.1919 0.2330 0.7930 1.1253 0.2341 8.9019 +#> 409: 92.6833 -2.5289 -3.9674 2.1922 0.2331 0.7931 1.1249 0.2338 8.9031 +#> 410: 92.6840 -2.5291 -3.9673 2.1924 0.2331 0.7930 1.1245 0.2335 8.9054 +#> 411: 92.6848 -2.5292 -3.9672 2.1926 0.2333 0.7929 1.1243 0.2333 8.9083 +#> 412: 92.6852 -2.5293 -3.9671 2.1928 0.2333 0.7931 1.1243 0.2333 8.9107 +#> 413: 92.6858 -2.5293 -3.9671 2.1929 0.2332 0.7932 1.1246 0.2332 8.9119 +#> 414: 92.6863 -2.5293 -3.9671 2.1928 0.2332 0.7934 1.1252 0.2333 8.9112 +#> 415: 92.6868 -2.5293 -3.9671 2.1928 0.2330 0.7935 1.1253 0.2332 8.9109 +#> 416: 92.6872 -2.5293 -3.9672 2.1928 0.2327 0.7935 1.1247 0.2330 8.9101 +#> 417: 92.6875 -2.5293 -3.9674 2.1929 0.2324 0.7934 1.1241 0.2330 8.9126 +#> 418: 92.6881 -2.5294 -3.9675 2.1929 0.2322 0.7935 1.1238 0.2332 8.9148 +#> 419: 92.6885 -2.5295 -3.9674 2.1929 0.2322 0.7936 1.1236 0.2331 8.9179 +#> 420: 92.6890 -2.5297 -3.9674 2.1929 0.2322 0.7936 1.1235 0.2331 8.9194 +#> 421: 92.6891 -2.5299 -3.9672 2.1930 0.2318 0.7937 1.1236 0.2330 8.9192 +#> 422: 92.6894 -2.5301 -3.9670 2.1930 0.2318 0.7937 1.1239 0.2329 8.9183 +#> 423: 92.6898 -2.5302 -3.9667 2.1931 0.2318 0.7937 1.1242 0.2327 8.9190 +#> 424: 92.6905 -2.5304 -3.9667 2.1931 0.2316 0.7937 1.1243 0.2326 8.9190 +#> 425: 92.6910 -2.5305 -3.9667 2.1932 0.2316 0.7936 1.1240 0.2327 8.9203 +#> 426: 92.6917 -2.5306 -3.9668 2.1935 0.2318 0.7937 1.1237 0.2326 8.9200 +#> 427: 92.6918 -2.5308 -3.9671 2.1939 0.2322 0.7938 1.1227 0.2326 8.9224 +#> 428: 92.6912 -2.5310 -3.9670 2.1941 0.2319 0.7939 1.1225 0.2325 8.9268 +#> 429: 92.6912 -2.5312 -3.9670 2.1944 0.2316 0.7939 1.1225 0.2324 8.9301 +#> 430: 92.6910 -2.5314 -3.9674 2.1948 0.2314 0.7940 1.1217 0.2322 8.9314 +#> 431: 92.6911 -2.5315 -3.9675 2.1950 0.2314 0.7942 1.1210 0.2320 8.9320 +#> 432: 92.6911 -2.5316 -3.9677 2.1953 0.2312 0.7944 1.1204 0.2320 8.9327 +#> 433: 92.6910 -2.5317 -3.9681 2.1955 0.2309 0.7946 1.1196 0.2320 8.9336 +#> 434: 92.6910 -2.5318 -3.9683 2.1957 0.2306 0.7949 1.1188 0.2320 8.9337 +#> 435: 92.6912 -2.5319 -3.9687 2.1960 0.2302 0.7951 1.1178 0.2319 8.9343 +#> 436: 92.6914 -2.5320 -3.9688 2.1961 0.2300 0.7953 1.1173 0.2319 8.9345 +#> 437: 92.6919 -2.5321 -3.9688 2.1962 0.2299 0.7955 1.1168 0.2318 8.9335 +#> 438: 92.6920 -2.5323 -3.9688 2.1964 0.2296 0.7957 1.1164 0.2318 8.9334 +#> 439: 92.6917 -2.5324 -3.9689 2.1965 0.2292 0.7959 1.1165 0.2317 8.9322 +#> 440: 92.6910 -2.5326 -3.9688 2.1969 0.2289 0.7960 1.1170 0.2316 8.9319 +#> 441: 92.6907 -2.5328 -3.9688 2.1973 0.2283 0.7961 1.1175 0.2316 8.9317 +#> 442: 92.6909 -2.5330 -3.9689 2.1976 0.2280 0.7962 1.1174 0.2315 8.9326 +#> 443: 92.6911 -2.5332 -3.9689 2.1980 0.2277 0.7963 1.1180 0.2315 8.9338 +#> 444: 92.6906 -2.5332 -3.9690 2.1981 0.2275 0.7963 1.1181 0.2315 8.9354 +#> 445: 92.6897 -2.5333 -3.9691 2.1982 0.2276 0.7962 1.1181 0.2315 8.9364 +#> 446: 92.6896 -2.5333 -3.9692 2.1982 0.2272 0.7962 1.1176 0.2314 8.9363 +#> 447: 92.6893 -2.5334 -3.9693 2.1982 0.2272 0.7961 1.1173 0.2313 8.9365 +#> 448: 92.6890 -2.5334 -3.9693 2.1982 0.2271 0.7961 1.1173 0.2313 8.9364 +#> 449: 92.6888 -2.5335 -3.9693 2.1982 0.2267 0.7961 1.1170 0.2313 8.9372 +#> 450: 92.6884 -2.5335 -3.9693 2.1982 0.2262 0.7959 1.1166 0.2312 8.9364 +#> 451: 92.6885 -2.5335 -3.9691 2.1981 0.2261 0.7958 1.1167 0.2312 8.9350 +#> 452: 92.6887 -2.5335 -3.9691 2.1980 0.2260 0.7957 1.1164 0.2311 8.9349 +#> 453: 92.6888 -2.5335 -3.9691 2.1979 0.2258 0.7957 1.1163 0.2310 8.9375 +#> 454: 92.6890 -2.5335 -3.9689 2.1977 0.2258 0.7957 1.1160 0.2308 8.9385 +#> 455: 92.6894 -2.5334 -3.9687 2.1975 0.2259 0.7956 1.1158 0.2307 8.9382 +#> 456: 92.6898 -2.5334 -3.9685 2.1974 0.2261 0.7957 1.1154 0.2306 8.9380 +#> 457: 92.6904 -2.5334 -3.9685 2.1975 0.2265 0.7956 1.1146 0.2304 8.9391 +#> 458: 92.6908 -2.5334 -3.9687 2.1975 0.2266 0.7956 1.1137 0.2303 8.9418 +#> 459: 92.6911 -2.5335 -3.9689 2.1975 0.2270 0.7956 1.1129 0.2303 8.9442 +#> 460: 92.6912 -2.5335 -3.9687 2.1976 0.2274 0.7957 1.1126 0.2301 8.9461 +#> 461: 92.6913 -2.5336 -3.9687 2.1975 0.2276 0.7958 1.1125 0.2300 8.9463 +#> 462: 92.6914 -2.5336 -3.9686 2.1974 0.2280 0.7959 1.1126 0.2299 8.9456 +#> 463: 92.6917 -2.5336 -3.9684 2.1973 0.2280 0.7960 1.1127 0.2297 8.9452 +#> 464: 92.6918 -2.5336 -3.9683 2.1970 0.2280 0.7961 1.1127 0.2295 8.9444 +#> 465: 92.6921 -2.5336 -3.9682 2.1967 0.2277 0.7962 1.1127 0.2294 8.9447 +#> 466: 92.6924 -2.5336 -3.9679 2.1967 0.2275 0.7964 1.1127 0.2291 8.9454 +#> 467: 92.6930 -2.5336 -3.9677 2.1966 0.2273 0.7967 1.1128 0.2290 8.9453 +#> 468: 92.6935 -2.5337 -3.9675 2.1966 0.2275 0.7970 1.1128 0.2289 8.9458 +#> 469: 92.6937 -2.5338 -3.9676 2.1967 0.2278 0.7972 1.1123 0.2287 8.9455 +#> 470: 92.6938 -2.5338 -3.9677 2.1967 0.2283 0.7974 1.1122 0.2285 8.9451 +#> 471: 92.6940 -2.5339 -3.9676 2.1969 0.2290 0.7976 1.1124 0.2283 8.9448 +#> 472: 92.6938 -2.5339 -3.9676 2.1972 0.2293 0.7977 1.1125 0.2281 8.9460 +#> 473: 92.6937 -2.5340 -3.9676 2.1972 0.2298 0.7978 1.1121 0.2278 8.9461 +#> 474: 92.6934 -2.5341 -3.9677 2.1974 0.2308 0.7978 1.1118 0.2276 8.9470 +#> 475: 92.6936 -2.5342 -3.9677 2.1978 0.2316 0.7979 1.1113 0.2273 8.9486 +#> 476: 92.6940 -2.5345 -3.9679 2.1983 0.2324 0.7981 1.1106 0.2271 8.9491 +#> 477: 92.6945 -2.5347 -3.9681 2.1989 0.2332 0.7983 1.1099 0.2269 8.9502 +#> 478: 92.6951 -2.5349 -3.9682 2.1992 0.2344 0.7986 1.1093 0.2267 8.9502 +#> 479: 92.6958 -2.5352 -3.9683 2.1995 0.2357 0.7987 1.1088 0.2266 8.9521 +#> 480: 92.6967 -2.5354 -3.9684 2.1998 0.2370 0.7988 1.1083 0.2265 8.9524 +#> 481: 92.6977 -2.5355 -3.9685 2.2001 0.2383 0.7990 1.1079 0.2263 8.9521 +#> 482: 92.6985 -2.5357 -3.9687 2.2004 0.2395 0.7992 1.1073 0.2262 8.9518 +#> 483: 92.6992 -2.5359 -3.9690 2.2008 0.2403 0.7995 1.1066 0.2262 8.9524 +#> 484: 92.7000 -2.5361 -3.9691 2.2010 0.2406 0.7998 1.1061 0.2260 8.9516 +#> 485: 92.7009 -2.5362 -3.9693 2.2015 0.2410 0.8001 1.1057 0.2261 8.9508 +#> 486: 92.7010 -2.5363 -3.9695 2.2019 0.2412 0.8004 1.1051 0.2261 8.9502 +#> 487: 92.7008 -2.5365 -3.9698 2.2023 0.2413 0.8009 1.1048 0.2260 8.9502 +#> 488: 92.7006 -2.5366 -3.9700 2.2026 0.2411 0.8012 1.1044 0.2260 8.9501 +#> 489: 92.7006 -2.5367 -3.9701 2.2029 0.2410 0.8015 1.1041 0.2261 8.9504 +#> 490: 92.7006 -2.5368 -3.9702 2.2031 0.2407 0.8015 1.1043 0.2260 8.9498 +#> 491: 92.7007 -2.5369 -3.9701 2.2034 0.2405 0.8016 1.1047 0.2261 8.9484 +#> 492: 92.7008 -2.5370 -3.9702 2.2035 0.2406 0.8017 1.1046 0.2261 8.9473 +#> 493: 92.7010 -2.5370 -3.9704 2.2037 0.2406 0.8018 1.1044 0.2261 8.9469 +#> 494: 92.7015 -2.5371 -3.9707 2.2038 0.2408 0.8019 1.1040 0.2261 8.9453 +#> 495: 92.7017 -2.5371 -3.9708 2.2039 0.2407 0.8021 1.1042 0.2262 8.9447 +#> 496: 92.7016 -2.5371 -3.9708 2.2039 0.2407 0.8022 1.1042 0.2262 8.9433 +#> 497: 92.7015 -2.5371 -3.9709 2.2039 0.2408 0.8023 1.1044 0.2262 8.9431 +#> 498: 92.7013 -2.5371 -3.9709 2.2040 0.2409 0.8024 1.1047 0.2262 8.9452 +#> 499: 92.7011 -2.5371 -3.9710 2.2039 0.2409 0.8023 1.1049 0.2261 8.9481 +#> 500: 92.7010 -2.5371 -3.9712 2.2040 0.2412 0.8022 1.1049 0.2260 8.9498</div><div class='output co'>#> <span class='message'>Calculating covariance matrix</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#> <span class='message'>done</span></div><div class='input'><span class='va'>f_nlmixr_hs_focei</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_parent</span><span class='op'>[</span><span class='st'>"HS"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span style='font-weight: bold;'>Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation +#> F: Forward difference gradient approximation +#> C: Central difference gradient approximation +#> M: Mixed forward and central difference gradient approximation +#> Unscaled parameters for Omegas=chol(solve(omega)); +#> Diagonals are transformed, as specified by foceiControl(diagXform=) +#> |-----+---------------+-----------+-----------+-----------+-----------| +#> | #| Objective Fun | parent_0 | log_k1 | log_k2 | log_tb | +#> |.....................| sigma | o1 | o2 | o3 | +#> <span style='text-decoration: underline;'>|.....................| o4 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 1</span>| 360.27275 | 1.000 | -0.9661 | -1.000 | -0.8790 | +#> |.....................| -0.8669 | -0.9051 | -0.8997 | -0.9009 | +#> <span style='text-decoration: underline;'>|.....................| -0.8938 |...........|...........|...........|</span> +#> | U| 360.27275 | 94.06 | -2.229 | -3.887 | 2.039 | +#> |.....................| 2.632 | 0.7629 | 1.026 | 0.9675 | +#> <span style='text-decoration: underline;'>|.....................| 1.314 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 360.27275</span> | 94.06 | 0.1077 | 0.02051 | 7.683 | +#> |.....................| 2.632 | 0.7629 | 1.026 | 0.9675 | +#> <span style='text-decoration: underline;'>|.....................| 1.314 |...........|...........|...........|</span> +#> | G| Gill Diff. | 106.2 | 0.7918 | 0.06750 | 10.50 | +#> |.....................| -26.04 | 2.358 | -5.196 | -2.491 | +#> <span style='text-decoration: underline;'>|.....................| -12.13 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 2</span>| 7055.7467 | 0.04059 | -0.9733 | -1.001 | -0.9739 | +#> |.....................| -0.6317 | -0.9263 | -0.8528 | -0.8784 | +#> <span style='text-decoration: underline;'>|.....................| -0.7843 |...........|...........|...........|</span> +#> | U| 7055.7467 | 3.818 | -2.236 | -3.887 | 1.944 | +#> |.....................| 2.941 | 0.7466 | 1.074 | 0.9892 | +#> <span style='text-decoration: underline;'>|.....................| 1.458 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 7055.7467</span> | 3.818 | 0.1069 | 0.02050 | 6.988 | +#> |.....................| 2.941 | 0.7466 | 1.074 | 0.9892 | +#> <span style='text-decoration: underline;'>|.....................| 1.458 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 3</span>| 499.76989 | 0.9041 | -0.9669 | -1.000 | -0.8885 | +#> |.....................| -0.8434 | -0.9072 | -0.8950 | -0.8986 | +#> <span style='text-decoration: underline;'>|.....................| -0.8828 |...........|...........|...........|</span> +#> | U| 499.76989 | 85.03 | -2.229 | -3.887 | 2.030 | +#> |.....................| 2.663 | 0.7612 | 1.031 | 0.9696 | +#> <span style='text-decoration: underline;'>|.....................| 1.329 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 499.76989</span> | 85.03 | 0.1076 | 0.02051 | 7.611 | +#> |.....................| 2.663 | 0.7612 | 1.031 | 0.9696 | +#> <span style='text-decoration: underline;'>|.....................| 1.329 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 4</span>| 360.48011 | 0.9904 | -0.9662 | -1.000 | -0.8799 | +#> |.....................| -0.8645 | -0.9053 | -0.8992 | -0.9007 | +#> <span style='text-decoration: underline;'>|.....................| -0.8927 |...........|...........|...........|</span> +#> | U| 360.48011 | 93.15 | -2.229 | -3.887 | 2.038 | +#> |.....................| 2.635 | 0.7627 | 1.026 | 0.9677 | +#> <span style='text-decoration: underline;'>|.....................| 1.316 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 360.48011</span> | 93.15 | 0.1077 | 0.02051 | 7.676 | +#> |.....................| 2.635 | 0.7627 | 1.026 | 0.9677 | +#> <span style='text-decoration: underline;'>|.....................| 1.316 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 5</span>| 360.80998 | 0.9960 | -0.9662 | -1.000 | -0.8794 | +#> |.....................| -0.8659 | -0.9051 | -0.8995 | -0.9008 | +#> <span style='text-decoration: underline;'>|.....................| -0.8933 |...........|...........|...........|</span> +#> | U| 360.80998 | 93.68 | -2.229 | -3.887 | 2.039 | +#> |.....................| 2.633 | 0.7628 | 1.026 | 0.9676 | +#> <span style='text-decoration: underline;'>|.....................| 1.315 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 360.80998</span> | 93.68 | 0.1077 | 0.02051 | 7.680 | +#> |.....................| 2.633 | 0.7628 | 1.026 | 0.9676 | +#> <span style='text-decoration: underline;'>|.....................| 1.315 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 6</span>| 361.20154 | 0.9991 | -0.9661 | -1.000 | -0.8791 | +#> |.....................| -0.8667 | -0.9051 | -0.8996 | -0.9009 | +#> <span style='text-decoration: underline;'>|.....................| -0.8937 |...........|...........|...........|</span> +#> | U| 361.20154 | 93.97 | -2.229 | -3.887 | 2.039 | +#> |.....................| 2.632 | 0.7629 | 1.026 | 0.9675 | +#> <span style='text-decoration: underline;'>|.....................| 1.315 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 361.20154</span> | 93.97 | 0.1077 | 0.02051 | 7.683 | +#> |.....................| 2.632 | 0.7629 | 1.026 | 0.9675 | +#> <span style='text-decoration: underline;'>|.....................| 1.315 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 7</span>| 361.33469 | 0.9999 | -0.9661 | -1.000 | -0.8790 | +#> |.....................| -0.8669 | -0.9051 | -0.8997 | -0.9009 | +#> <span style='text-decoration: underline;'>|.....................| -0.8938 |...........|...........|...........|</span> +#> | U| 361.33469 | 94.05 | -2.229 | -3.887 | 2.039 | +#> |.....................| 2.632 | 0.7629 | 1.026 | 0.9675 | +#> <span style='text-decoration: underline;'>|.....................| 1.314 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 361.33469</span> | 94.05 | 0.1077 | 0.02051 | 7.683 | +#> |.....................| 2.632 | 0.7629 | 1.026 | 0.9675 | +#> <span style='text-decoration: underline;'>|.....................| 1.314 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 8</span>| 361.34878 | 1.000 | -0.9661 | -1.000 | -0.8790 | +#> |.....................| -0.8669 | -0.9051 | -0.8997 | -0.9009 | +#> <span style='text-decoration: underline;'>|.....................| -0.8938 |...........|...........|...........|</span> +#> | U| 361.34878 | 94.06 | -2.229 | -3.887 | 2.039 | +#> |.....................| 2.632 | 0.7629 | 1.026 | 0.9675 | +#> <span style='text-decoration: underline;'>|.....................| 1.314 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 361.34878</span> | 94.06 | 0.1077 | 0.02051 | 7.683 | +#> |.....................| 2.632 | 0.7629 | 1.026 | 0.9675 | +#> <span style='text-decoration: underline;'>|.....................| 1.314 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 9</span>| 361.35091 | 1.000 | -0.9661 | -1.000 | -0.8790 | +#> |.....................| -0.8669 | -0.9051 | -0.8997 | -0.9009 | +#> <span style='text-decoration: underline;'>|.....................| -0.8938 |...........|...........|...........|</span> +#> | U| 361.35091 | 94.06 | -2.229 | -3.887 | 2.039 | +#> |.....................| 2.632 | 0.7629 | 1.026 | 0.9675 | +#> <span style='text-decoration: underline;'>|.....................| 1.314 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 361.35091</span> | 94.06 | 0.1077 | 0.02051 | 7.683 | +#> |.....................| 2.632 | 0.7629 | 1.026 | 0.9675 | +#> <span style='text-decoration: underline;'>|.....................| 1.314 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 10</span>| 361.35004 | 1.000 | -0.9661 | -1.000 | -0.8790 | +#> |.....................| -0.8669 | -0.9051 | -0.8997 | -0.9009 | +#> <span style='text-decoration: underline;'>|.....................| -0.8938 |...........|...........|...........|</span> +#> | U| 361.35004 | 94.06 | -2.229 | -3.887 | 2.039 | +#> |.....................| 2.632 | 0.7629 | 1.026 | 0.9675 | +#> <span style='text-decoration: underline;'>|.....................| 1.314 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 361.35004</span> | 94.06 | 0.1077 | 0.02051 | 7.683 | +#> |.....................| 2.632 | 0.7629 | 1.026 | 0.9675 | +#> <span style='text-decoration: underline;'>|.....................| 1.314 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 11</span>| 361.35006 | 1.000 | -0.9661 | -1.000 | -0.8790 | +#> |.....................| -0.8669 | -0.9051 | -0.8997 | -0.9009 | +#> <span style='text-decoration: underline;'>|.....................| -0.8938 |...........|...........|...........|</span> +#> | U| 361.35006 | 94.06 | -2.229 | -3.887 | 2.039 | +#> |.....................| 2.632 | 0.7629 | 1.026 | 0.9675 | +#> <span style='text-decoration: underline;'>|.....................| 1.314 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 361.35006</span> | 94.06 | 0.1077 | 0.02051 | 7.683 | +#> |.....................| 2.632 | 0.7629 | 1.026 | 0.9675 | +#> <span style='text-decoration: underline;'>|.....................| 1.314 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 12</span>| 361.35006 | 1.000 | -0.9661 | -1.000 | -0.8790 | +#> |.....................| -0.8669 | -0.9051 | -0.8997 | -0.9009 | +#> <span style='text-decoration: underline;'>|.....................| -0.8938 |...........|...........|...........|</span> +#> | U| 361.35006 | 94.06 | -2.229 | -3.887 | 2.039 | +#> |.....................| 2.632 | 0.7629 | 1.026 | 0.9675 | +#> <span style='text-decoration: underline;'>|.....................| 1.314 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 361.35006</span> | 94.06 | 0.1077 | 0.02051 | 7.683 | +#> |.....................| 2.632 | 0.7629 | 1.026 | 0.9675 | +#> <span style='text-decoration: underline;'>|.....................| 1.314 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 13</span>| 361.35006 | 1.000 | -0.9661 | -1.000 | -0.8790 | +#> |.....................| -0.8669 | -0.9051 | -0.8997 | -0.9009 | +#> <span style='text-decoration: underline;'>|.....................| -0.8938 |...........|...........|...........|</span> +#> | U| 361.35006 | 94.06 | -2.229 | -3.887 | 2.039 | +#> |.....................| 2.632 | 0.7629 | 1.026 | 0.9675 | +#> <span style='text-decoration: underline;'>|.....................| 1.314 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 361.35006</span> | 94.06 | 0.1077 | 0.02051 | 7.683 | +#> |.....................| 2.632 | 0.7629 | 1.026 | 0.9675 | +#> <span style='text-decoration: underline;'>|.....................| 1.314 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 14</span>| 361.35006 | 1.000 | -0.9661 | -1.000 | -0.8790 | +#> |.....................| -0.8669 | -0.9051 | -0.8997 | -0.9009 | +#> <span style='text-decoration: underline;'>|.....................| -0.8938 |...........|...........|...........|</span> +#> | U| 361.35006 | 94.06 | -2.229 | -3.887 | 2.039 | +#> |.....................| 2.632 | 0.7629 | 1.026 | 0.9675 | +#> <span style='text-decoration: underline;'>|.....................| 1.314 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 361.35006</span> | 94.06 | 0.1077 | 0.02051 | 7.683 | +#> |.....................| 2.632 | 0.7629 | 1.026 | 0.9675 | +#> <span style='text-decoration: underline;'>|.....................| 1.314 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 15</span>| 361.35006 | 1.000 | -0.9661 | -1.000 | -0.8790 | +#> |.....................| -0.8669 | -0.9051 | -0.8997 | -0.9009 | +#> <span style='text-decoration: underline;'>|.....................| -0.8938 |...........|...........|...........|</span> +#> | U| 361.35006 | 94.06 | -2.229 | -3.887 | 2.039 | +#> |.....................| 2.632 | 0.7629 | 1.026 | 0.9675 | +#> <span style='text-decoration: underline;'>|.....................| 1.314 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 361.35006</span> | 94.06 | 0.1077 | 0.02051 | 7.683 | +#> |.....................| 2.632 | 0.7629 | 1.026 | 0.9675 | +#> <span style='text-decoration: underline;'>|.....................| 1.314 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 16</span>| 361.35006 | 1.000 | -0.9661 | -1.000 | -0.8790 | +#> |.....................| -0.8669 | -0.9051 | -0.8997 | -0.9009 | +#> <span style='text-decoration: underline;'>|.....................| -0.8938 |...........|...........|...........|</span> +#> | U| 361.35006 | 94.06 | -2.229 | -3.887 | 2.039 | +#> |.....................| 2.632 | 0.7629 | 1.026 | 0.9675 | +#> <span style='text-decoration: underline;'>|.....................| 1.314 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 361.35006</span> | 94.06 | 0.1077 | 0.02051 | 7.683 | +#> |.....................| 2.632 | 0.7629 | 1.026 | 0.9675 | +#> <span style='text-decoration: underline;'>|.....................| 1.314 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 17</span>| 361.35006 | 1.000 | -0.9661 | -1.000 | -0.8790 | +#> |.....................| -0.8669 | -0.9051 | -0.8997 | -0.9009 | +#> <span style='text-decoration: underline;'>|.....................| -0.8938 |...........|...........|...........|</span> +#> | U| 361.35006 | 94.06 | -2.229 | -3.887 | 2.039 | +#> |.....................| 2.632 | 0.7629 | 1.026 | 0.9675 | +#> <span style='text-decoration: underline;'>|.....................| 1.314 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 361.35006</span> | 94.06 | 0.1077 | 0.02051 | 7.683 | +#> |.....................| 2.632 | 0.7629 | 1.026 | 0.9675 | +#> <span style='text-decoration: underline;'>|.....................| 1.314 |...........|...........|...........|</span> +#> |<span style='font-weight: bold;'> 18</span>| 361.35006 | 1.000 | -0.9661 | -1.000 | -0.8790 | +#> |.....................| -0.8669 | -0.9051 | -0.8997 | -0.9009 | +#> <span style='text-decoration: underline;'>|.....................| -0.8938 |...........|...........|...........|</span> +#> | U| 361.35006 | 94.06 | -2.229 | -3.887 | 2.039 | +#> |.....................| 2.632 | 0.7629 | 1.026 | 0.9675 | +#> <span style='text-decoration: underline;'>|.....................| 1.314 |...........|...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 361.35006</span> | 94.06 | 0.1077 | 0.02051 | 7.683 | +#> |.....................| 2.632 | 0.7629 | 1.026 | 0.9675 | +#> <span style='text-decoration: underline;'>|.....................| 1.314 |...........|...........|...........|</span> +#> calculating covariance matrix +#> done</div><div class='output co'>#> <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#> <span class='message'>done</span></div><div class='output co'>#> <span class='warning'>Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span></div><div class='output co'>#> <span class='warning'>Warning: last objective function was not at minimum, possible problems in optimization</span></div><div class='output co'>#> <span class='warning'>Warning: gradient problems with initial estimate and covariance; see $scaleInfo</span></div><div class='input'> +<span class='va'>f_nlmixr_fomc_saem_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></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>, est <span class='op'>=</span> <span class='st'>"saem"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> 1: 92.2167 0.0936 1.9256 3.3974 0.7958 0.7197 11.8539 0.0004 +#> 2: 92.5446 0.0892 2.4952 3.3516 0.8528 0.6837 4.5197 0.0001 +#> 3: 9.2720e+01 1.3849e-01 2.5917e+00 3.9204e+00 9.5883e-01 6.4953e-01 4.0268e+00 5.9554e-05 +#> 4: 92.6098 0.1052 2.5494 5.0533 1.0968 0.6171 3.2396 0.0200 +#> 5: 92.6795 0.0406 2.4151 5.6729 1.0420 0.5862 3.1558 0.0183 +#> 6: 92.6580 0.0258 2.3640 5.7014 0.9899 0.5569 3.0212 0.0140 +#> 7: 93.0532 -0.0754 2.2262 7.3582 0.9404 0.5291 2.5591 0.0180 +#> 8: 92.8372 -0.0760 2.2080 6.9903 0.8934 0.5026 2.5653 0.0187 +#> 9: 93.0757 -0.1322 2.1663 6.6408 0.8487 0.4775 2.4943 0.0182 +#> 10: 93.0704 -0.1520 2.1410 6.3087 0.8063 0.4536 2.4004 0.0225 +#> 11: 93.1611 -0.1366 2.1740 5.9933 0.7659 0.4309 2.4242 0.0199 +#> 12: 92.7195 -0.0787 2.2947 5.6936 0.7277 0.4094 2.4532 0.0205 +#> 13: 92.6573 -0.1543 2.1929 5.4089 0.6913 0.3889 2.3750 0.0244 +#> 14: 93.1138 -0.1547 2.1924 5.1385 0.6567 0.3695 2.3590 0.0187 +#> 15: 93.5083 -0.1625 2.1831 4.8816 0.6239 0.3510 2.4420 0.0125 +#> 16: 93.2086 -0.1667 2.1516 4.6375 0.5927 0.3334 2.4527 0.0004 +#> 17: 93.3988 -0.1766 2.1521 4.4056 0.5630 0.3168 2.4527 0.0004 +#> 18: 93.4526 -0.1748 2.1461 4.1853 0.5349 0.3009 2.3775 0.0116 +#> 19: 93.5953 -0.1963 2.1167 3.9761 0.5081 0.2859 2.4693 0.0031 +#> 20: 9.3404e+01 -2.4408e-01 2.0453e+00 3.7773e+00 4.8274e-01 2.7158e-01 2.4789e+00 2.0760e-05 +#> 21: 9.3624e+01 -2.4691e-01 2.0524e+00 3.5884e+00 4.5860e-01 2.5800e-01 2.4789e+00 2.0760e-05 +#> 22: 9.3821e+01 -2.5932e-01 2.0021e+00 3.4090e+00 4.3567e-01 2.8670e-01 2.4182e+00 9.3297e-06 +#> 23: 9.3572e+01 -2.3703e-01 2.0725e+00 3.2385e+00 4.4889e-01 2.7237e-01 2.4525e+00 1.5592e-06 +#> 24: 9.3496e+01 -2.2704e-01 2.0746e+00 3.0766e+00 4.3674e-01 2.5875e-01 2.4569e+00 6.1365e-05 +#> 25: 9.3772e+01 -2.2211e-01 2.0762e+00 2.9228e+00 4.4843e-01 2.6194e-01 2.4015e+00 8.3288e-05 +#> 26: 9.3266e+01 -1.9408e-01 2.1345e+00 2.7766e+00 4.6952e-01 2.4885e-01 2.3827e+00 2.9029e-05 +#> 27: 9.3472e+01 -1.9793e-01 2.1141e+00 3.5922e+00 4.7687e-01 2.3640e-01 2.3827e+00 2.9029e-05 +#> 28: 9.3411e+01 -1.7721e-01 2.1334e+00 3.4125e+00 4.8209e-01 2.2458e-01 2.3864e+00 6.5503e-06 +#> 29: 93.6868 -0.1863 2.1258 4.5379 0.4744 0.2134 2.3001 0.0045 +#> 30: 9.4054e+01 -1.8122e-01 2.1287e+00 4.9729e+00 4.7945e-01 2.0269e-01 2.2979e+00 5.8327e-05 +#> 31: 9.3955e+01 -1.9131e-01 2.1202e+00 5.6375e+00 4.7965e-01 1.9255e-01 2.2671e+00 1.6931e-05 +#> 32: 9.4376e+01 -1.6810e-01 2.1287e+00 5.3556e+00 4.7567e-01 1.8972e-01 2.2483e+00 1.1778e-05 +#> 33: 9.4067e+01 -1.5819e-01 2.1656e+00 5.0878e+00 4.5710e-01 1.9389e-01 2.2696e+00 2.4282e-05 +#> 34: 9.4526e+01 -1.6367e-01 2.1473e+00 4.8334e+00 4.7085e-01 1.8419e-01 2.2919e+00 8.8644e-06 +#> 35: 9.4972e+01 -1.6784e-01 2.1353e+00 4.5917e+00 4.7510e-01 1.7498e-01 2.3129e+00 2.2851e-05 +#> 36: 9.4744e+01 -1.5973e-01 2.1281e+00 5.2356e+00 4.5695e-01 1.8499e-01 2.2896e+00 6.8824e-05 +#> 37: 9.4721e+01 -1.6756e-01 2.1168e+00 5.1111e+00 4.6804e-01 1.8407e-01 2.3035e+00 1.5534e-06 +#> 38: 9.4613e+01 -1.5952e-01 2.1385e+00 4.8555e+00 4.6107e-01 1.7720e-01 2.2650e+00 1.2489e-05 +#> 39: 9.4787e+01 -1.6113e-01 2.1458e+00 4.6128e+00 4.6317e-01 1.8378e-01 2.2831e+00 1.3668e-05 +#> 40: 94.5315 -0.1765 2.1186 4.3821 0.4428 0.1902 2.3132 0.0001 +#> 41: 9.4336e+01 -1.8333e-01 2.1285e+00 4.1630e+00 4.4521e-01 1.9913e-01 2.3092e+00 1.3482e-05 +#> 42: 94.0780 -0.2031 2.0724 3.9549 0.4405 0.1892 2.2704 0.0056 +#> 43: 93.9276 -0.1896 2.1191 3.7571 0.4590 0.1797 2.2396 0.0080 +#> 44: 94.2491 -0.1896 2.1006 3.8473 0.4590 0.1764 2.2774 0.0083 +#> 45: 94.4073 -0.1811 2.1156 3.6550 0.4519 0.1676 2.2682 0.0078 +#> 46: 93.9736 -0.1882 2.1196 3.4722 0.4545 0.1633 2.2775 0.0035 +#> 47: 94.1930 -0.1965 2.1102 3.2986 0.4599 0.1664 2.3243 0.0005 +#> 48: 9.4147e+01 -1.9494e-01 2.1118e+00 3.3188e+00 4.7005e-01 1.7181e-01 2.3345e+00 1.1669e-05 +#> 49: 9.4139e+01 -1.7920e-01 2.1199e+00 3.1528e+00 4.7417e-01 1.6322e-01 2.2794e+00 3.5582e-05 +#> 50: 9.4031e+01 -1.9074e-01 2.1098e+00 2.9952e+00 4.7498e-01 1.6695e-01 2.2574e+00 2.7302e-06 +#> 51: 9.3982e+01 -1.9369e-01 2.1058e+00 2.8848e+00 4.8158e-01 1.8408e-01 2.2447e+00 1.8188e-05 +#> 52: 9.3924e+01 -2.0726e-01 2.0809e+00 3.6064e+00 4.7008e-01 2.0029e-01 2.2319e+00 6.8301e-06 +#> 53: 9.4094e+01 -1.9609e-01 2.0780e+00 4.4341e+00 4.7556e-01 1.9742e-01 2.2701e+00 2.1343e-06 +#> 54: 9.4351e+01 -1.9839e-01 2.0746e+00 4.2124e+00 4.7456e-01 1.8866e-01 2.2778e+00 4.8058e-06 +#> 55: 93.9450 -0.1876 2.1059 4.0017 0.4892 0.1792 2.2720 0.0001 +#> 56: 9.3741e+01 -1.8208e-01 2.1172e+00 3.8017e+00 4.7696e-01 1.7027e-01 2.2332e+00 2.1237e-05 +#> 57: 9.3668e+01 -1.8580e-01 2.1181e+00 3.9224e+00 4.7704e-01 1.7425e-01 2.2512e+00 2.2766e-05 +#> 58: 9.3811e+01 -1.8324e-01 2.1178e+00 3.7263e+00 4.7945e-01 1.7939e-01 2.2512e+00 2.2766e-05 +#> 59: 9.3800e+01 -1.6691e-01 2.1250e+00 3.8213e+00 4.9353e-01 1.8464e-01 2.2763e+00 4.6129e-06 +#> 60: 9.3997e+01 -1.5920e-01 2.1489e+00 3.6303e+00 5.0788e-01 1.7541e-01 2.2466e+00 4.1975e-06 +#> 61: 9.4215e+01 -1.6445e-01 2.1482e+00 3.9303e+00 5.1966e-01 1.6664e-01 2.3053e+00 5.8982e-07 +#> 62: 9.3936e+01 -1.6721e-01 2.1376e+00 4.0316e+00 5.3719e-01 1.7228e-01 2.2841e+00 7.8603e-05 +#> 63: 9.3832e+01 -1.6209e-01 2.1334e+00 3.8698e+00 5.4370e-01 1.7064e-01 2.3046e+00 6.4415e-07 +#> 64: 93.9042 -0.1617 2.1563 5.5384 0.5430 0.1622 2.2988 0.0002 +#> 65: 93.8613 -0.1723 2.1239 6.2143 0.5304 0.1541 2.2949 0.0001 +#> 66: 9.4113e+01 -1.9168e-01 2.1019e+00 7.3588e+00 5.1287e-01 1.4641e-01 2.3164e+00 1.2580e-05 +#> 67: 9.3954e+01 -1.8141e-01 2.1199e+00 6.9909e+00 5.0278e-01 1.4993e-01 2.2676e+00 1.1126e-05 +#> 68: 93.8741 -0.1852 2.1343 6.6414 0.4997 0.1493 2.2706 0.0001 +#> 69: 9.3657e+01 -1.8345e-01 2.1375e+00 6.3093e+00 5.0292e-01 1.6326e-01 2.2809e+00 1.7299e-07 +#> 70: 9.3762e+01 -1.7493e-01 2.1512e+00 5.9938e+00 5.1042e-01 1.5509e-01 2.2837e+00 4.5745e-05 +#> 71: 9.4060e+01 -1.6516e-01 2.1440e+00 5.6941e+00 5.1615e-01 1.4734e-01 2.3001e+00 3.9993e-07 +#> 72: 9.3927e+01 -1.7365e-01 2.1347e+00 5.4094e+00 5.2582e-01 1.3997e-01 2.3075e+00 6.3748e-06 +#> 73: 9.4049e+01 -1.8080e-01 2.1254e+00 5.1390e+00 5.1154e-01 1.3297e-01 2.3042e+00 9.5858e-06 +#> 74: 9.3917e+01 -1.9083e-01 2.1051e+00 4.8820e+00 5.0104e-01 1.4605e-01 2.2733e+00 7.4923e-05 +#> 75: 9.4271e+01 -1.8281e-01 2.1059e+00 5.0872e+00 5.0773e-01 1.5322e-01 2.2387e+00 1.4240e-05 +#> 76: 9.4205e+01 -1.8352e-01 2.1160e+00 4.8328e+00 5.0684e-01 1.5669e-01 2.2708e+00 3.6346e-05 +#> 77: 9.4480e+01 -1.8352e-01 2.0942e+00 4.9009e+00 5.0684e-01 1.4885e-01 2.3098e+00 1.8186e-06 +#> 78: 9.4699e+01 -1.9686e-01 2.0671e+00 4.6559e+00 4.9182e-01 1.4503e-01 2.2806e+00 7.9443e-08 +#> 79: 9.4785e+01 -2.0649e-01 2.0500e+00 6.0608e+00 4.6723e-01 1.6185e-01 2.2607e+00 2.1557e-07 +#> 80: 9.4782e+01 -2.0045e-01 2.0680e+00 5.7578e+00 4.5759e-01 1.5747e-01 2.2926e+00 8.6381e-06 +#> 81: 9.4339e+01 -2.0435e-01 2.0885e+00 6.9051e+00 4.5054e-01 1.7410e-01 2.2796e+00 1.5517e-05 +#> 82: 9.4805e+01 -2.1032e-01 2.0658e+00 7.1580e+00 4.7091e-01 1.6539e-01 2.3013e+00 1.3893e-05 +#> 83: 9.4650e+01 -2.0507e-01 2.0485e+00 6.8001e+00 4.7624e-01 1.5938e-01 2.3104e+00 6.6569e-06 +#> 84: 9.4766e+01 -1.9959e-01 2.0667e+00 6.4601e+00 4.7322e-01 1.5619e-01 2.3359e+00 1.8890e-09 +#> 85: 9.4714e+01 -1.9959e-01 2.0756e+00 6.1371e+00 4.7322e-01 1.6894e-01 2.2738e+00 4.9578e-06 +#> 86: 9.4466e+01 -2.0544e-01 2.0626e+00 5.8302e+00 4.6340e-01 1.6050e-01 2.2773e+00 3.6221e-07 +#> 87: 9.4786e+01 -1.9292e-01 2.0703e+00 5.5387e+00 4.6881e-01 1.5641e-01 2.2746e+00 2.3326e-05 +#> 88: 9.4573e+01 -1.9488e-01 2.0597e+00 5.2618e+00 4.6538e-01 1.5079e-01 2.3225e+00 4.7054e-05 +#> 89: 94.8466 -0.2040 2.0603 4.9987 0.4620 0.1456 2.2807 0.0002 +#> 90: 9.4839e+01 -2.0359e-01 2.0673e+00 4.7488e+00 4.5379e-01 1.4729e-01 2.3099e+00 2.7922e-05 +#> 91: 9.4897e+01 -2.0635e-01 2.0496e+00 4.5113e+00 4.4018e-01 1.3993e-01 2.2924e+00 1.7074e-05 +#> 92: 9.4740e+01 -2.0567e-01 2.0518e+00 4.2858e+00 4.6190e-01 1.3293e-01 2.3396e+00 9.0471e-05 +#> 93: 94.9558 -0.2033 2.0824 4.0715 0.4877 0.1263 2.3785 0.0082 +#> 94: 95.1673 -0.1801 2.0900 3.8679 0.5150 0.1200 2.4128 0.0106 +#> 95: 95.3129 -0.1686 2.1057 3.6745 0.4892 0.1140 2.4135 0.0147 +#> 96: 9.5235e+01 -1.6834e-01 2.1069e+00 3.4908e+00 4.9584e-01 1.0827e-01 2.4408e+00 2.5829e-06 +#> 97: 9.4892e+01 -1.5911e-01 2.1277e+00 3.3162e+00 4.7518e-01 1.0286e-01 2.4658e+00 1.8589e-05 +#> 98: 9.4749e+01 -1.6133e-01 2.1204e+00 4.5926e+00 4.6435e-01 1.0192e-01 2.4716e+00 5.8808e-09 +#> 99: 9.4546e+01 -1.5627e-01 2.1358e+00 5.5648e+00 4.8843e-01 1.0047e-01 2.5033e+00 2.5865e-05 +#> 100: 9.4544e+01 -1.6341e-01 2.1317e+00 5.2974e+00 4.7076e-01 1.1065e-01 2.4711e+00 4.4438e-05 +#> 101: 94.2461 -0.1640 2.1458 5.0325 0.4750 0.1107 2.5297 0.0002 +#> 102: 9.4039e+01 -1.6946e-01 2.1490e+00 4.9929e+00 4.7265e-01 1.2109e-01 2.3907e+00 2.3093e-05 +#> 103: 9.4132e+01 -1.6840e-01 2.1250e+00 5.3879e+00 4.7062e-01 1.2389e-01 2.3401e+00 5.4840e-07 +#> 104: 9.4376e+01 -1.6842e-01 2.1239e+00 7.9826e+00 4.7053e-01 1.1769e-01 2.3663e+00 1.9617e-05 +#> 105: 9.4370e+01 -1.7024e-01 2.1187e+00 7.5834e+00 4.6738e-01 1.1683e-01 2.3471e+00 1.4035e-05 +#> 106: 9.4462e+01 -1.6562e-01 2.1406e+00 7.5466e+00 4.6364e-01 1.2640e-01 2.3140e+00 4.7933e-05 +#> 107: 9.4541e+01 -1.6582e-01 2.1308e+00 7.1692e+00 4.6457e-01 1.2964e-01 2.3395e+00 1.8489e-05 +#> 108: 9.4709e+01 -1.6157e-01 2.1484e+00 6.8108e+00 4.5925e-01 1.3393e-01 2.3340e+00 1.5230e-06 +#> 109: 9.4450e+01 -1.8900e-01 2.0799e+00 6.4702e+00 4.4801e-01 1.4728e-01 2.3847e+00 3.2613e-05 +#> 110: 9.4180e+01 -1.9104e-01 2.1172e+00 6.1467e+00 4.5389e-01 1.4273e-01 2.3775e+00 6.0285e-05 +#> 111: 9.4366e+01 -1.8908e-01 2.1031e+00 5.8394e+00 4.4875e-01 1.4983e-01 2.3898e+00 7.2653e-05 +#> 112: 9.4008e+01 -1.8144e-01 2.1008e+00 5.5474e+00 4.6433e-01 1.4234e-01 2.3705e+00 9.9395e-06 +#> 113: 9.4372e+01 -1.8885e-01 2.1154e+00 5.2700e+00 4.7983e-01 1.3522e-01 2.3641e+00 1.8643e-05 +#> 114: 94.1292 -0.1872 2.1134 5.0065 0.4824 0.1285 2.3163 0.0001 +#> 115: 9.4510e+01 -1.7805e-01 2.1185e+00 4.7562e+00 4.8451e-01 1.2204e-01 2.3568e+00 1.6277e-07 +#> 116: 9.4234e+01 -1.8613e-01 2.1214e+00 4.5184e+00 4.7967e-01 1.2275e-01 2.3388e+00 3.4361e-05 +#> 117: 9.4438e+01 -1.7276e-01 2.1218e+00 4.2925e+00 4.6200e-01 1.1662e-01 2.3686e+00 5.6594e-06 +#> 118: 9.4498e+01 -1.7628e-01 2.1143e+00 6.2395e+00 4.5445e-01 1.2378e-01 2.3303e+00 6.5645e-05 +#> 119: 9.4303e+01 -1.8107e-01 2.1120e+00 7.5774e+00 4.6102e-01 1.2829e-01 2.3595e+00 9.4057e-06 +#> 120: 9.4022e+01 -1.7626e-01 2.1258e+00 9.9044e+00 4.6505e-01 1.5188e-01 2.3582e+00 3.6907e-05 +#> 121: 9.4103e+01 -1.5976e-01 2.1354e+00 9.4091e+00 4.7792e-01 1.5429e-01 2.3729e+00 4.6749e-05 +#> 122: 9.4727e+01 -1.9092e-01 2.0956e+00 8.9387e+00 4.5402e-01 1.6371e-01 2.3118e+00 6.8573e-06 +#> 123: 9.4447e+01 -1.9139e-01 2.0898e+00 8.4918e+00 4.3317e-01 1.7569e-01 2.3083e+00 4.4068e-05 +#> 124: 9.4422e+01 -1.9130e-01 2.0920e+00 8.0672e+00 4.3952e-01 1.7160e-01 2.3003e+00 1.7162e-05 +#> 125: 9.4608e+01 -1.7777e-01 2.1007e+00 7.6638e+00 4.8253e-01 1.6302e-01 2.3244e+00 1.9896e-05 +#> 126: 9.4512e+01 -1.6596e-01 2.1139e+00 7.2806e+00 4.7648e-01 1.7588e-01 2.2913e+00 4.9747e-05 +#> 127: 9.4983e+01 -1.6562e-01 2.1290e+00 6.9166e+00 4.7028e-01 1.6709e-01 2.3141e+00 3.3357e-05 +#> 128: 94.3910 -0.1728 2.1159 6.5708 0.4914 0.1850 2.3173 0.0001 +#> 129: 9.4578e+01 -1.7211e-01 2.1177e+00 6.2422e+00 4.8295e-01 1.7709e-01 2.2815e+00 4.8158e-05 +#> 130: 9.4646e+01 -1.6785e-01 2.1333e+00 5.9301e+00 4.6360e-01 1.6823e-01 2.3140e+00 2.1204e-05 +#> 131: 9.4670e+01 -1.4897e-01 2.1480e+00 5.6336e+00 4.8826e-01 1.5982e-01 2.3436e+00 1.3221e-05 +#> 132: 9.4625e+01 -1.6160e-01 2.1599e+00 5.3519e+00 4.6385e-01 1.6125e-01 2.2830e+00 9.6815e-06 +#> 133: 9.3985e+01 -1.7636e-01 2.1299e+00 5.8178e+00 4.4885e-01 1.5389e-01 2.2810e+00 6.7789e-06 +#> 134: 9.4105e+01 -1.7389e-01 2.1199e+00 5.5269e+00 4.5848e-01 1.4628e-01 2.2992e+00 7.6542e-06 +#> 135: 9.4387e+01 -1.5936e-01 2.1418e+00 5.2506e+00 4.5002e-01 1.6349e-01 2.3403e+00 7.6250e-05 +#> 136: 94.3595 -0.1696 2.1493 4.9880 0.4407 0.1722 2.3121 0.0001 +#> 137: 9.4056e+01 -1.6030e-01 2.1720e+00 5.5600e+00 4.4954e-01 1.8233e-01 2.3099e+00 2.1195e-05 +#> 138: 9.4043e+01 -1.4848e-01 2.1876e+00 5.2820e+00 4.5696e-01 1.8542e-01 2.2876e+00 8.7271e-06 +#> 139: 94.3020 -0.1374 2.1965 5.6428 0.4668 0.1927 2.3341 0.0001 +#> 140: 9.4260e+01 -1.3603e-01 2.2014e+00 5.7727e+00 4.6823e-01 1.8302e-01 2.3248e+00 1.4731e-06 +#> 141: 9.4302e+01 -1.2134e-01 2.1992e+00 5.4841e+00 4.8967e-01 1.7947e-01 2.3212e+00 1.6339e-05 +#> 142: 94.0970 -0.1143 2.2570 5.4173 0.4766 0.1726 2.3581 0.0077 +#> 143: 94.2078 -0.1162 2.2460 5.1464 0.4745 0.1874 2.3551 0.0152 +#> 144: 94.2085 -0.0953 2.2685 4.8891 0.5010 0.1780 2.3881 0.0095 +#> 145: 94.1483 -0.0906 2.2751 5.0705 0.4959 0.1770 2.3103 0.0143 +#> 146: 94.4257 -0.0859 2.2735 5.0201 0.5331 0.2050 2.3104 0.0160 +#> 147: 93.8072 -0.0887 2.2766 4.7691 0.5253 0.2200 2.2903 0.0180 +#> 148: 94.4354 -0.0901 2.2770 4.5306 0.5237 0.2147 2.3108 0.0150 +#> 149: 94.1171 -0.1126 2.2342 4.3041 0.5412 0.2300 2.3454 0.0126 +#> 150: 94.0704 -0.1267 2.2071 4.0889 0.5324 0.2185 2.3673 0.0097 +#> 151: 93.9860 -0.1480 2.1852 3.8844 0.5101 0.2529 2.3280 0.0056 +#> 152: 93.9500 -0.1419 2.1940 4.4687 0.5066 0.2371 2.3617 0.0002 +#> 153: 93.8058 -0.1368 2.1917 4.2493 0.5068 0.2057 2.3481 0.0002 +#> 154: 9.4043e+01 -1.3331e-01 2.1972e+00 4.3921e+00 4.7605e-01 1.9689e-01 2.3952e+00 9.5657e-05 +#> 155: 94.2500 -0.1223 2.2260 5.7786 0.4848 0.1829 2.3361 0.0075 +#> 156: 94.5035 -0.1223 2.2091 6.1344 0.4848 0.1558 2.3951 0.0048 +#> 157: 9.4448e+01 -1.3268e-01 2.1797e+00 6.4746e+00 4.6934e-01 1.6035e-01 2.3496e+00 5.2771e-05 +#> 158: 94.7438 -0.1401 2.1904 6.0162 0.4589 0.1692 2.3444 0.0001 +#> 159: 94.2681 -0.1430 2.1852 4.6165 0.4774 0.1617 2.3601 0.0001 +#> 160: 9.3911e+01 -1.1659e-01 2.2267e+00 4.9756e+00 4.9349e-01 1.7150e-01 2.3500e+00 9.0374e-06 +#> 161: 9.3914e+01 -1.1938e-01 2.2233e+00 4.8238e+00 4.9674e-01 1.8358e-01 2.3536e+00 1.1877e-06 +#> 162: 93.9974 -0.1188 2.2349 5.1092 0.4967 0.1714 2.3237 0.0041 +#> 163: 9.3939e+01 -1.2170e-01 2.2147e+00 4.8315e+00 5.0622e-01 1.8195e-01 2.3823e+00 2.4030e-05 +#> 164: 93.8015 -0.1362 2.2166 3.9112 0.4958 0.1684 2.3488 0.0072 +#> 165: 93.4082 -0.1398 2.2132 3.2992 0.5087 0.1734 2.2861 0.0125 +#> 166: 93.4680 -0.1421 2.2077 3.3232 0.5075 0.1643 2.2792 0.0149 +#> 167: 93.5455 -0.1443 2.2080 3.7465 0.4972 0.1685 2.2194 0.0191 +#> 168: 93.5603 -0.1711 2.1421 3.2407 0.5201 0.1940 2.3029 0.0198 +#> 169: 93.7281 -0.1578 2.1553 2.5110 0.4988 0.1836 2.3343 0.0134 +#> 170: 93.9675 -0.1564 2.1532 2.2507 0.5049 0.1753 2.3089 0.0110 +#> 171: 93.8255 -0.1543 2.1647 2.7302 0.5114 0.1691 2.2959 0.0113 +#> 172: 93.8071 -0.1536 2.1689 2.5849 0.5069 0.1751 2.3047 0.0099 +#> 173: 93.7137 -0.1403 2.2096 1.5160 0.5204 0.1622 2.3452 0.0155 +#> 174: 93.7182 -0.1376 2.1975 1.3366 0.5222 0.1700 2.3311 0.0149 +#> 175: 93.5957 -0.1587 2.1613 1.3539 0.5321 0.1470 2.3893 0.0156 +#> 176: 93.6058 -0.1587 2.1602 1.4588 0.5321 0.1412 2.4323 0.0116 +#> 177: 93.4496 -0.1858 2.1323 1.2423 0.4987 0.1460 2.3491 0.0167 +#> 178: 93.5894 -0.1935 2.1217 1.7812 0.4776 0.1643 2.3046 0.0168 +#> 179: 93.6386 -0.1887 2.1445 2.8813 0.4808 0.1585 2.2689 0.0192 +#> 180: 93.9288 -0.1950 2.1015 2.0905 0.4681 0.1557 2.2783 0.0170 +#> 181: 93.8165 -0.1950 2.0840 2.6302 0.4681 0.1592 2.3643 0.0173 +#> 182: 94.2132 -0.1936 2.0866 3.0185 0.5131 0.1712 2.3164 0.0147 +#> 183: 94.0929 -0.1896 2.0782 3.0716 0.5288 0.1644 2.5169 0.0066 +#> 184: 93.8694 -0.1968 2.0946 2.4734 0.5121 0.1709 2.3795 0.0071 +#> 185: 93.8138 -0.1970 2.0987 2.9707 0.4957 0.1500 2.3995 0.0034 +#> 186: 9.4047e+01 -2.1045e-01 2.0791e+00 3.6686e+00 4.8764e-01 1.4347e-01 2.3654e+00 3.8127e-05 +#> 187: 9.4498e+01 -1.9649e-01 2.0949e+00 2.0912e+00 4.7479e-01 1.6122e-01 2.3873e+00 4.7739e-06 +#> 188: 9.4650e+01 -1.8508e-01 2.1132e+00 2.1529e+00 4.6244e-01 1.4403e-01 2.3367e+00 3.5345e-06 +#> 189: 9.4301e+01 -1.8137e-01 2.1132e+00 2.6433e+00 4.4894e-01 1.3537e-01 2.3145e+00 9.6836e-06 +#> 190: 9.4501e+01 -1.8209e-01 2.0962e+00 3.1460e+00 4.4908e-01 1.2006e-01 2.3563e+00 3.3387e-05 +#> 191: 9.4156e+01 -2.0214e-01 2.0803e+00 3.2334e+00 4.7635e-01 1.1917e-01 2.3782e+00 6.6641e-06 +#> 192: 9.3981e+01 -2.1562e-01 2.0492e+00 3.0526e+00 5.0505e-01 1.3669e-01 2.3412e+00 7.3871e-05 +#> 193: 9.4085e+01 -2.2693e-01 2.0318e+00 2.9855e+00 4.9563e-01 1.4371e-01 2.3727e+00 8.6443e-05 +#> 194: 9.3922e+01 -2.3089e-01 2.0323e+00 2.9709e+00 4.9151e-01 1.4470e-01 2.3667e+00 3.5941e-05 +#> 195: 9.4180e+01 -2.2865e-01 2.0284e+00 2.2426e+00 4.8793e-01 1.5283e-01 2.3442e+00 1.8882e-05 +#> 196: 9.4259e+01 -2.0053e-01 2.0541e+00 1.5155e+00 5.1571e-01 1.5596e-01 2.3638e+00 2.9015e-05 +#> 197: 9.4225e+01 -2.0144e-01 2.0551e+00 1.6032e+00 5.0920e-01 1.6454e-01 2.3564e+00 2.7823e-05 +#> 198: 9.4166e+01 -1.9411e-01 2.0602e+00 1.8793e+00 5.5190e-01 1.8338e-01 2.3611e+00 1.6669e-05 +#> 199: 9.4230e+01 -1.9621e-01 2.0737e+00 1.8847e+00 5.4082e-01 1.7340e-01 2.3488e+00 5.8282e-07 +#> 200: 9.4215e+01 -1.9629e-01 2.0888e+00 1.9185e+00 5.4293e-01 1.7502e-01 2.3563e+00 5.7303e-06 +#> 201: 94.0654 -0.1931 2.0901 1.8074 0.5373 0.1886 2.3869 0.0025 +#> 202: 93.9801 -0.1898 2.0990 1.6823 0.5318 0.1841 2.4043 0.0016 +#> 203: 94.0246 -0.1893 2.1004 1.6503 0.5286 0.1855 2.3971 0.0012 +#> 204: 93.9893 -0.1870 2.1014 1.6166 0.5276 0.1846 2.3900 0.0010 +#> 205: 94.0154 -0.1854 2.1006 1.5294 0.5286 0.1828 2.3939 0.0009 +#> 206: 94.0468 -0.1833 2.1024 1.5102 0.5295 0.1807 2.3967 0.0007 +#> 207: 94.0641 -0.1810 2.1049 1.5136 0.5289 0.1798 2.4037 0.0008 +#> 208: 94.0794 -0.1790 2.1062 1.5078 0.5286 0.1790 2.4139 0.0007 +#> 209: 94.0892 -0.1799 2.1049 1.4549 0.5261 0.1793 2.4144 0.0006 +#> 210: 94.0911 -0.1817 2.1042 1.4537 0.5217 0.1810 2.4069 0.0012 +#> 211: 94.1011 -0.1828 2.1016 1.4582 0.5235 0.1825 2.4049 0.0011 +#> 212: 94.1081 -0.1839 2.0989 1.4657 0.5255 0.1838 2.4031 0.0010 +#> 213: 94.1264 -0.1842 2.0973 1.4527 0.5263 0.1851 2.4026 0.0010 +#> 214: 94.1287 -0.1844 2.0974 1.4405 0.5270 0.1869 2.4006 0.0009 +#> 215: 94.1440 -0.1850 2.0973 1.4556 0.5269 0.1876 2.3985 0.0009 +#> 216: 94.1352 -0.1863 2.0970 1.4698 0.5258 0.1885 2.3977 0.0008 +#> 217: 94.1261 -0.1868 2.0962 1.4850 0.5244 0.1897 2.3946 0.0008 +#> 218: 94.1100 -0.1858 2.0987 1.4673 0.5230 0.1934 2.3924 0.0007 +#> 219: 94.1073 -0.1845 2.1013 1.4630 0.5218 0.1993 2.3890 0.0011 +#> 220: 94.1026 -0.1836 2.1030 1.4705 0.5205 0.2028 2.3904 0.0010 +#> 221: 94.0972 -0.1824 2.1046 1.4732 0.5198 0.2065 2.3907 0.0010 +#> 222: 94.0898 -0.1824 2.1052 1.4952 0.5180 0.2083 2.3892 0.0010 +#> 223: 94.0975 -0.1830 2.1050 1.5035 0.5161 0.2107 2.3888 0.0011 +#> 224: 94.1027 -0.1831 2.1050 1.5196 0.5148 0.2124 2.3878 0.0011 +#> 225: 94.0977 -0.1828 2.1065 1.5153 0.5142 0.2141 2.3856 0.0013 +#> 226: 94.0907 -0.1831 2.1066 1.5287 0.5130 0.2151 2.3828 0.0014 +#> 227: 94.0831 -0.1833 2.1065 1.5535 0.5119 0.2159 2.3814 0.0014 +#> 228: 94.0834 -0.1832 2.1072 1.5713 0.5114 0.2174 2.3813 0.0014 +#> 229: 94.0793 -0.1832 2.1076 1.6041 0.5111 0.2184 2.3811 0.0015 +#> 230: 94.0701 -0.1843 2.1064 1.6177 0.5096 0.2181 2.3803 0.0017 +#> 231: 94.0598 -0.1853 2.1052 1.6254 0.5085 0.2180 2.3818 0.0016 +#> 232: 94.0539 -0.1862 2.1045 1.6254 0.5074 0.2175 2.3824 0.0017 +#> 233: 94.0498 -0.1869 2.1034 1.6380 0.5065 0.2169 2.3826 0.0017 +#> 234: 94.0514 -0.1872 2.1035 1.6300 0.5050 0.2160 2.3829 0.0017 +#> 235: 94.0521 -0.1876 2.1026 1.6263 0.5041 0.2148 2.3825 0.0018 +#> 236: 94.0587 -0.1876 2.1024 1.6277 0.5023 0.2134 2.3834 0.0020 +#> 237: 94.0741 -0.1873 2.1025 1.6349 0.5013 0.2120 2.3828 0.0019 +#> 238: 94.0898 -0.1876 2.1022 1.6509 0.4997 0.2107 2.3837 0.0019 +#> 239: 94.1055 -0.1880 2.1016 1.6596 0.4979 0.2098 2.3836 0.0018 +#> 240: 94.1209 -0.1885 2.1007 1.6627 0.4958 0.2092 2.3831 0.0018 +#> 241: 94.1322 -0.1893 2.0992 1.6563 0.4945 0.2085 2.3825 0.0017 +#> 242: 94.1404 -0.1904 2.0976 1.6574 0.4930 0.2082 2.3814 0.0017 +#> 243: 94.1428 -0.1914 2.0961 1.6412 0.4918 0.2078 2.3800 0.0017 +#> 244: 94.1477 -0.1923 2.0945 1.6287 0.4907 0.2071 2.3795 0.0016 +#> 245: 94.1525 -0.1931 2.0933 1.6225 0.4897 0.2064 2.3791 0.0016 +#> 246: 94.1557 -0.1938 2.0927 1.6243 0.4890 0.2048 2.3780 0.0016 +#> 247: 94.1576 -0.1943 2.0919 1.6333 0.4881 0.2034 2.3777 0.0015 +#> 248: 94.1603 -0.1951 2.0909 1.6328 0.4863 0.2026 2.3775 0.0015 +#> 249: 94.1648 -0.1957 2.0898 1.6427 0.4847 0.2018 2.3774 0.0015 +#> 250: 94.1766 -0.1963 2.0889 1.6482 0.4829 0.2012 2.3770 0.0015 +#> 251: 94.1854 -0.1971 2.0875 1.6536 0.4806 0.2011 2.3769 0.0016 +#> 252: 94.1906 -0.1980 2.0861 1.6527 0.4785 0.2013 2.3763 0.0017 +#> 253: 94.1913 -0.1982 2.0857 1.6459 0.4772 0.2014 2.3751 0.0019 +#> 254: 94.1945 -0.1985 2.0852 1.6413 0.4759 0.2019 2.3755 0.0019 +#> 255: 94.1972 -0.1989 2.0837 1.6451 0.4754 0.2027 2.3753 0.0018 +#> 256: 94.1994 -0.1989 2.0833 1.6548 0.4758 0.2024 2.3752 0.0018 +#> 257: 94.2014 -0.1987 2.0833 1.6708 0.4765 0.2024 2.3752 0.0018 +#> 258: 94.2081 -0.1984 2.0836 1.6903 0.4768 0.2023 2.3749 0.0017 +#> 259: 94.2151 -0.1982 2.0839 1.7169 0.4767 0.2023 2.3737 0.0017 +#> 260: 94.2212 -0.1980 2.0838 1.7426 0.4766 0.2031 2.3725 0.0017 +#> 261: 94.2229 -0.1981 2.0835 1.7696 0.4764 0.2038 2.3714 0.0017 +#> 262: 94.2213 -0.1983 2.0832 1.7977 0.4762 0.2045 2.3704 0.0016 +#> 263: 94.2220 -0.1984 2.0830 1.8277 0.4764 0.2051 2.3700 0.0016 +#> 264: 94.2230 -0.1983 2.0830 1.8430 0.4766 0.2057 2.3690 0.0016 +#> 265: 94.2235 -0.1983 2.0832 1.8679 0.4768 0.2060 2.3674 0.0016 +#> 266: 94.2242 -0.1982 2.0833 1.8705 0.4769 0.2064 2.3658 0.0015 +#> 267: 94.2267 -0.1982 2.0832 1.8715 0.4769 0.2070 2.3643 0.0015 +#> 268: 94.2312 -0.1980 2.0834 1.8824 0.4769 0.2074 2.3631 0.0015 +#> 269: 94.2329 -0.1981 2.0829 1.8843 0.4766 0.2084 2.3628 0.0015 +#> 270: 94.2321 -0.1982 2.0825 1.8904 0.4770 0.2093 2.3629 0.0015 +#> 271: 94.2349 -0.1985 2.0820 1.8942 0.4769 0.2101 2.3633 0.0016 +#> 272: 94.2388 -0.1989 2.0818 1.9099 0.4767 0.2110 2.3634 0.0018 +#> 273: 94.2420 -0.1992 2.0816 1.9259 0.4765 0.2118 2.3632 0.0018 +#> 274: 94.2454 -0.1994 2.0813 1.9330 0.4763 0.2128 2.3629 0.0017 +#> 275: 94.2456 -0.1997 2.0810 1.9316 0.4761 0.2138 2.3624 0.0018 +#> 276: 94.2472 -0.1999 2.0807 1.9306 0.4758 0.2146 2.3624 0.0019 +#> 277: 94.2492 -0.2001 2.0808 1.9326 0.4756 0.2153 2.3623 0.0020 +#> 278: 94.2493 -0.2003 2.0807 1.9225 0.4752 0.2163 2.3628 0.0020 +#> 279: 94.2481 -0.2002 2.0808 1.9206 0.4750 0.2168 2.3628 0.0019 +#> 280: 94.2433 -0.2002 2.0810 1.9257 0.4749 0.2173 2.3626 0.0019 +#> 281: 94.2358 -0.2004 2.0809 1.9217 0.4748 0.2173 2.3620 0.0019 +#> 282: 94.2307 -0.2005 2.0807 1.9209 0.4748 0.2172 2.3617 0.0019 +#> 283: 94.2302 -0.2008 2.0803 1.9131 0.4748 0.2172 2.3615 0.0019 +#> 284: 94.2309 -0.2009 2.0802 1.9085 0.4749 0.2171 2.3610 0.0018 +#> 285: 94.2344 -0.2010 2.0799 1.9135 0.4749 0.2170 2.3603 0.0018 +#> 286: 94.2381 -0.2013 2.0794 1.9099 0.4749 0.2167 2.3596 0.0018 +#> 287: 94.2420 -0.2016 2.0786 1.9105 0.4749 0.2164 2.3596 0.0018 +#> 288: 94.2425 -0.2020 2.0778 1.9081 0.4749 0.2161 2.3590 0.0019 +#> 289: 94.2386 -0.2023 2.0773 1.9136 0.4749 0.2158 2.3586 0.0019 +#> 290: 94.2357 -0.2026 2.0768 1.9171 0.4750 0.2154 2.3581 0.0019 +#> 291: 94.2326 -0.2026 2.0765 1.9162 0.4750 0.2150 2.3577 0.0019 +#> 292: 94.2305 -0.2026 2.0766 1.9178 0.4753 0.2144 2.3577 0.0020 +#> 293: 94.2268 -0.2023 2.0771 1.9257 0.4754 0.2138 2.3574 0.0022 +#> 294: 94.2216 -0.2023 2.0773 1.9326 0.4754 0.2132 2.3565 0.0023 +#> 295: 94.2193 -0.2024 2.0769 1.9378 0.4762 0.2125 2.3565 0.0024 +#> 296: 94.2160 -0.2025 2.0765 1.9463 0.4771 0.2117 2.3565 0.0025 +#> 297: 94.2106 -0.2026 2.0761 1.9523 0.4779 0.2109 2.3569 0.0026 +#> 298: 94.2089 -0.2028 2.0756 1.9622 0.4787 0.2099 2.3578 0.0025 +#> 299: 94.2077 -0.2029 2.0753 1.9721 0.4794 0.2090 2.3586 0.0026 +#> 300: 94.2064 -0.2030 2.0749 1.9838 0.4802 0.2080 2.3589 0.0026 +#> 301: 94.2086 -0.2029 2.0747 1.9942 0.4806 0.2071 2.3587 0.0025 +#> 302: 94.2111 -0.2031 2.0744 1.9938 0.4810 0.2063 2.3593 0.0025 +#> 303: 94.2133 -0.2031 2.0743 1.9923 0.4811 0.2056 2.3593 0.0025 +#> 304: 94.2151 -0.2032 2.0739 1.9885 0.4811 0.2049 2.3595 0.0025 +#> 305: 94.2159 -0.2035 2.0735 1.9872 0.4813 0.2044 2.3594 0.0024 +#> 306: 94.2192 -0.2038 2.0729 1.9806 0.4813 0.2041 2.3592 0.0024 +#> 307: 94.2226 -0.2040 2.0724 1.9796 0.4814 0.2038 2.3588 0.0024 +#> 308: 94.2224 -0.2042 2.0723 1.9828 0.4814 0.2036 2.3589 0.0024 +#> 309: 94.2200 -0.2043 2.0723 1.9859 0.4812 0.2034 2.3587 0.0024 +#> 310: 94.2183 -0.2044 2.0723 1.9892 0.4810 0.2034 2.3583 0.0023 +#> 311: 94.2175 -0.2044 2.0724 1.9895 0.4805 0.2033 2.3580 0.0023 +#> 312: 94.2171 -0.2044 2.0725 1.9977 0.4800 0.2032 2.3581 0.0023 +#> 313: 94.2108 -0.2044 2.0728 1.9995 0.4795 0.2030 2.3578 0.0023 +#> 314: 94.2068 -0.2045 2.0730 1.9929 0.4790 0.2029 2.3576 0.0024 +#> 315: 94.2031 -0.2047 2.0730 1.9884 0.4784 0.2028 2.3579 0.0024 +#> 316: 94.2018 -0.2048 2.0731 1.9860 0.4779 0.2026 2.3582 0.0024 +#> 317: 94.2015 -0.2050 2.0729 1.9836 0.4773 0.2025 2.3582 0.0024 +#> 318: 94.2025 -0.2052 2.0728 1.9814 0.4768 0.2024 2.3580 0.0023 +#> 319: 94.2066 -0.2053 2.0726 1.9867 0.4764 0.2024 2.3577 0.0023 +#> 320: 94.2074 -0.2055 2.0727 1.9896 0.4760 0.2024 2.3575 0.0023 +#> 321: 94.2097 -0.2055 2.0728 1.9985 0.4758 0.2026 2.3573 0.0023 +#> 322: 94.2080 -0.2054 2.0731 2.0108 0.4759 0.2028 2.3570 0.0023 +#> 323: 94.2042 -0.2054 2.0732 2.0253 0.4762 0.2030 2.3566 0.0023 +#> 324: 94.2005 -0.2054 2.0733 2.0514 0.4765 0.2032 2.3566 0.0023 +#> 325: 94.2000 -0.2053 2.0735 2.0719 0.4767 0.2034 2.3570 0.0023 +#> 326: 94.2002 -0.2052 2.0738 2.0907 0.4769 0.2034 2.3573 0.0023 +#> 327: 94.1997 -0.2051 2.0741 2.1140 0.4770 0.2035 2.3571 0.0023 +#> 328: 94.1976 -0.2050 2.0743 2.1379 0.4770 0.2035 2.3569 0.0023 +#> 329: 94.1969 -0.2051 2.0741 2.1485 0.4769 0.2038 2.3566 0.0022 +#> 330: 94.1959 -0.2053 2.0738 2.1533 0.4767 0.2042 2.3561 0.0022 +#> 331: 94.1962 -0.2055 2.0733 2.1588 0.4763 0.2046 2.3555 0.0022 +#> 332: 94.1967 -0.2059 2.0727 2.1626 0.4760 0.2051 2.3551 0.0022 +#> 333: 94.1964 -0.2062 2.0721 2.1666 0.4757 0.2056 2.3547 0.0022 +#> 334: 94.1978 -0.2064 2.0718 2.1703 0.4756 0.2063 2.3543 0.0022 +#> 335: 94.1985 -0.2066 2.0715 2.1698 0.4755 0.2068 2.3538 0.0021 +#> 336: 94.1999 -0.2068 2.0711 2.1705 0.4757 0.2075 2.3534 0.0021 +#> 337: 94.1990 -0.2069 2.0708 2.1690 0.4759 0.2080 2.3530 0.0021 +#> 338: 94.1965 -0.2071 2.0706 2.1708 0.4760 0.2085 2.3525 0.0021 +#> 339: 94.1934 -0.2071 2.0704 2.1769 0.4761 0.2088 2.3518 0.0021 +#> 340: 94.1908 -0.2072 2.0704 2.1794 0.4763 0.2091 2.3515 0.0021 +#> 341: 94.1875 -0.2072 2.0706 2.1859 0.4762 0.2092 2.3512 0.0021 +#> 342: 94.1840 -0.2071 2.0707 2.1903 0.4762 0.2093 2.3513 0.0021 +#> 343: 94.1816 -0.2072 2.0706 2.1909 0.4761 0.2093 2.3512 0.0020 +#> 344: 94.1815 -0.2070 2.0708 2.1877 0.4758 0.2091 2.3514 0.0021 +#> 345: 94.1839 -0.2070 2.0710 2.1844 0.4757 0.2090 2.3517 0.0021 +#> 346: 94.1868 -0.2068 2.0713 2.1787 0.4756 0.2088 2.3520 0.0020 +#> 347: 94.1871 -0.2066 2.0717 2.1762 0.4756 0.2086 2.3519 0.0020 +#> 348: 94.1868 -0.2064 2.0722 2.1724 0.4756 0.2084 2.3521 0.0020 +#> 349: 94.1892 -0.2062 2.0725 2.1673 0.4755 0.2080 2.3524 0.0020 +#> 350: 94.1921 -0.2060 2.0726 2.1632 0.4754 0.2076 2.3527 0.0020 +#> 351: 94.1947 -0.2060 2.0726 2.1613 0.4752 0.2073 2.3530 0.0020 +#> 352: 94.1988 -0.2060 2.0725 2.1647 0.4751 0.2069 2.3530 0.0020 +#> 353: 94.2036 -0.2061 2.0721 2.1684 0.4749 0.2067 2.3530 0.0020 +#> 354: 94.2082 -0.2061 2.0719 2.1670 0.4747 0.2063 2.3529 0.0020 +#> 355: 94.2111 -0.2061 2.0718 2.1645 0.4747 0.2062 2.3526 0.0020 +#> 356: 94.2123 -0.2061 2.0717 2.1628 0.4747 0.2063 2.3525 0.0020 +#> 357: 94.2146 -0.2062 2.0716 2.1610 0.4746 0.2064 2.3523 0.0020 +#> 358: 94.2161 -0.2062 2.0715 2.1656 0.4744 0.2065 2.3520 0.0020 +#> 359: 94.2178 -0.2063 2.0714 2.1684 0.4743 0.2065 2.3516 0.0020 +#> 360: 94.2194 -0.2063 2.0713 2.1687 0.4742 0.2065 2.3512 0.0019 +#> 361: 94.2191 -0.2064 2.0713 2.1738 0.4741 0.2065 2.3508 0.0019 +#> 362: 94.2186 -0.2064 2.0713 2.1762 0.4740 0.2065 2.3502 0.0019 +#> 363: 94.2179 -0.2064 2.0714 2.1754 0.4740 0.2065 2.3495 0.0019 +#> 364: 94.2165 -0.2063 2.0715 2.1740 0.4741 0.2064 2.3495 0.0019 +#> 365: 94.2149 -0.2063 2.0716 2.1736 0.4741 0.2062 2.3495 0.0020 +#> 366: 94.2141 -0.2062 2.0717 2.1813 0.4740 0.2064 2.3490 0.0020 +#> 367: 94.2158 -0.2062 2.0717 2.1905 0.4739 0.2063 2.3491 0.0019 +#> 368: 94.2173 -0.2062 2.0718 2.1963 0.4737 0.2063 2.3485 0.0019 +#> 369: 94.2183 -0.2062 2.0717 2.2005 0.4736 0.2064 2.3481 0.0019 +#> 370: 94.2194 -0.2062 2.0716 2.2016 0.4735 0.2063 2.3477 0.0019 +#> 371: 94.2192 -0.2063 2.0715 2.1997 0.4733 0.2064 2.3476 0.0019 +#> 372: 94.2202 -0.2062 2.0716 2.1957 0.4733 0.2065 2.3479 0.0019 +#> 373: 94.2208 -0.2061 2.0717 2.1913 0.4733 0.2065 2.3480 0.0019 +#> 374: 94.2209 -0.2061 2.0719 2.1870 0.4731 0.2065 2.3479 0.0019 +#> 375: 94.2219 -0.2061 2.0719 2.1864 0.4729 0.2064 2.3477 0.0019 +#> 376: 94.2231 -0.2061 2.0720 2.1849 0.4726 0.2063 2.3473 0.0019 +#> 377: 94.2251 -0.2061 2.0720 2.1835 0.4724 0.2063 2.3471 0.0019 +#> 378: 94.2238 -0.2062 2.0719 2.1777 0.4721 0.2062 2.3472 0.0018 +#> 379: 94.2226 -0.2064 2.0717 2.1741 0.4717 0.2063 2.3471 0.0018 +#> 380: 94.2216 -0.2066 2.0714 2.1759 0.4714 0.2063 2.3468 0.0018 +#> 381: 94.2206 -0.2068 2.0711 2.1784 0.4711 0.2063 2.3465 0.0018 +#> 382: 94.2200 -0.2071 2.0707 2.1753 0.4707 0.2062 2.3462 0.0018 +#> 383: 94.2205 -0.2073 2.0704 2.1757 0.4703 0.2061 2.3461 0.0018 +#> 384: 94.2201 -0.2076 2.0702 2.1802 0.4698 0.2060 2.3458 0.0018 +#> 385: 94.2210 -0.2078 2.0701 2.1795 0.4693 0.2061 2.3457 0.0018 +#> 386: 94.2199 -0.2079 2.0700 2.1788 0.4688 0.2061 2.3455 0.0018 +#> 387: 94.2181 -0.2081 2.0699 2.1801 0.4683 0.2061 2.3454 0.0018 +#> 388: 94.2169 -0.2082 2.0699 2.1850 0.4679 0.2061 2.3452 0.0017 +#> 389: 94.2158 -0.2083 2.0699 2.1881 0.4674 0.2063 2.3449 0.0017 +#> 390: 94.2162 -0.2084 2.0696 2.1928 0.4671 0.2064 2.3447 0.0017 +#> 391: 94.2172 -0.2085 2.0696 2.1921 0.4669 0.2063 2.3444 0.0017 +#> 392: 94.2175 -0.2085 2.0695 2.1933 0.4667 0.2063 2.3442 0.0017 +#> 393: 94.2174 -0.2086 2.0695 2.1972 0.4666 0.2062 2.3440 0.0017 +#> 394: 94.2179 -0.2087 2.0694 2.1972 0.4664 0.2061 2.3439 0.0017 +#> 395: 94.2200 -0.2087 2.0694 2.2009 0.4663 0.2059 2.3438 0.0017 +#> 396: 94.2189 -0.2086 2.0695 2.2062 0.4662 0.2058 2.3434 0.0017 +#> 397: 94.2183 -0.2085 2.0696 2.2151 0.4663 0.2056 2.3431 0.0017 +#> 398: 94.2186 -0.2085 2.0696 2.2200 0.4664 0.2056 2.3430 0.0017 +#> 399: 94.2183 -0.2084 2.0698 2.2204 0.4664 0.2056 2.3428 0.0017 +#> 400: 94.2184 -0.2082 2.0703 2.2252 0.4665 0.2054 2.3428 0.0016 +#> 401: 94.2176 -0.2080 2.0707 2.2323 0.4666 0.2052 2.3427 0.0016 +#> 402: 94.2167 -0.2078 2.0712 2.2397 0.4668 0.2050 2.3426 0.0016 +#> 403: 94.2157 -0.2075 2.0716 2.2464 0.4669 0.2049 2.3426 0.0016 +#> 404: 94.2152 -0.2074 2.0719 2.2508 0.4670 0.2047 2.3427 0.0016 +#> 405: 94.2152 -0.2072 2.0723 2.2537 0.4671 0.2046 2.3427 0.0016 +#> 406: 94.2151 -0.2070 2.0726 2.2565 0.4672 0.2044 2.3427 0.0016 +#> 407: 94.2132 -0.2067 2.0731 2.2568 0.4673 0.2044 2.3426 0.0016 +#> 408: 94.2142 -0.2065 2.0734 2.2579 0.4674 0.2046 2.3424 0.0016 +#> 409: 94.2136 -0.2063 2.0738 2.2630 0.4674 0.2046 2.3420 0.0016 +#> 410: 94.2125 -0.2062 2.0739 2.2635 0.4674 0.2047 2.3417 0.0016 +#> 411: 94.2131 -0.2061 2.0741 2.2634 0.4674 0.2048 2.3413 0.0016 +#> 412: 94.2132 -0.2060 2.0742 2.2662 0.4674 0.2048 2.3409 0.0016 +#> 413: 94.2143 -0.2059 2.0743 2.2666 0.4673 0.2048 2.3407 0.0016 +#> 414: 94.2156 -0.2058 2.0743 2.2710 0.4672 0.2048 2.3404 0.0015 +#> 415: 94.2174 -0.2057 2.0745 2.2751 0.4671 0.2049 2.3400 0.0015 +#> 416: 94.2185 -0.2057 2.0746 2.2762 0.4669 0.2049 2.3399 0.0015 +#> 417: 94.2208 -0.2056 2.0748 2.2759 0.4667 0.2049 2.3397 0.0015 +#> 418: 94.2231 -0.2054 2.0751 2.2772 0.4664 0.2050 2.3398 0.0015 +#> 419: 94.2249 -0.2053 2.0754 2.2783 0.4663 0.2050 2.3396 0.0015 +#> 420: 94.2255 -0.2052 2.0757 2.2798 0.4660 0.2050 2.3395 0.0015 +#> 421: 94.2265 -0.2051 2.0759 2.2848 0.4659 0.2050 2.3392 0.0016 +#> 422: 94.2288 -0.2049 2.0761 2.2929 0.4659 0.2050 2.3390 0.0016 +#> 423: 94.2307 -0.2048 2.0762 2.2988 0.4657 0.2051 2.3390 0.0016 +#> 424: 94.2313 -0.2047 2.0764 2.3017 0.4656 0.2051 2.3391 0.0016 +#> 425: 94.2322 -0.2046 2.0765 2.3028 0.4655 0.2050 2.3388 0.0016 +#> 426: 94.2327 -0.2046 2.0765 2.3049 0.4654 0.2050 2.3386 0.0016 +#> 427: 94.2323 -0.2045 2.0768 2.3053 0.4655 0.2049 2.3386 0.0016 +#> 428: 94.2324 -0.2044 2.0770 2.3016 0.4655 0.2048 2.3387 0.0017 +#> 429: 94.2322 -0.2043 2.0772 2.2984 0.4656 0.2047 2.3386 0.0017 +#> 430: 94.2306 -0.2042 2.0774 2.2971 0.4656 0.2046 2.3384 0.0017 +#> 431: 94.2295 -0.2042 2.0775 2.2931 0.4657 0.2044 2.3384 0.0017 +#> 432: 94.2298 -0.2040 2.0778 2.2896 0.4656 0.2044 2.3383 0.0018 +#> 433: 94.2311 -0.2039 2.0780 2.2885 0.4656 0.2044 2.3383 0.0018 +#> 434: 94.2311 -0.2037 2.0783 2.2854 0.4655 0.2044 2.3381 0.0018 +#> 435: 94.2314 -0.2036 2.0786 2.2838 0.4654 0.2044 2.3378 0.0018 +#> 436: 94.2315 -0.2035 2.0788 2.2817 0.4653 0.2044 2.3377 0.0018 +#> 437: 94.2326 -0.2034 2.0790 2.2801 0.4652 0.2044 2.3378 0.0018 +#> 438: 94.2338 -0.2034 2.0791 2.2802 0.4650 0.2046 2.3380 0.0018 +#> 439: 94.2340 -0.2033 2.0791 2.2810 0.4649 0.2046 2.3377 0.0018 +#> 440: 94.2330 -0.2034 2.0791 2.2822 0.4646 0.2046 2.3376 0.0018 +#> 441: 94.2323 -0.2035 2.0790 2.2818 0.4644 0.2046 2.3375 0.0018 +#> 442: 94.2321 -0.2034 2.0792 2.2804 0.4642 0.2043 2.3375 0.0018 +#> 443: 94.2313 -0.2033 2.0794 2.2812 0.4641 0.2041 2.3372 0.0018 +#> 444: 94.2301 -0.2032 2.0796 2.2820 0.4640 0.2040 2.3369 0.0018 +#> 445: 94.2279 -0.2031 2.0799 2.2872 0.4639 0.2039 2.3366 0.0018 +#> 446: 94.2272 -0.2030 2.0801 2.2874 0.4639 0.2037 2.3363 0.0018 +#> 447: 94.2262 -0.2029 2.0803 2.2881 0.4639 0.2036 2.3359 0.0018 +#> 448: 94.2248 -0.2028 2.0806 2.2905 0.4639 0.2036 2.3358 0.0018 +#> 449: 94.2245 -0.2027 2.0808 2.2914 0.4638 0.2035 2.3356 0.0018 +#> 450: 94.2237 -0.2027 2.0809 2.2928 0.4638 0.2035 2.3356 0.0018 +#> 451: 94.2233 -0.2025 2.0813 2.2917 0.4639 0.2033 2.3355 0.0018 +#> 452: 94.2232 -0.2023 2.0816 2.2898 0.4640 0.2031 2.3356 0.0018 +#> 453: 94.2230 -0.2021 2.0819 2.2890 0.4641 0.2030 2.3356 0.0018 +#> 454: 94.2222 -0.2020 2.0822 2.2851 0.4641 0.2029 2.3357 0.0018 +#> 455: 94.2214 -0.2018 2.0824 2.2820 0.4640 0.2028 2.3357 0.0017 +#> 456: 94.2212 -0.2017 2.0827 2.2797 0.4640 0.2026 2.3357 0.0017 +#> 457: 94.2216 -0.2016 2.0829 2.2771 0.4640 0.2024 2.3358 0.0017 +#> 458: 94.2220 -0.2015 2.0831 2.2740 0.4639 0.2022 2.3358 0.0017 +#> 459: 94.2229 -0.2013 2.0834 2.2765 0.4638 0.2021 2.3358 0.0017 +#> 460: 94.2226 -0.2012 2.0837 2.2810 0.4637 0.2020 2.3359 0.0017 +#> 461: 94.2227 -0.2009 2.0841 2.2893 0.4637 0.2018 2.3358 0.0017 +#> 462: 94.2235 -0.2007 2.0844 2.2942 0.4637 0.2016 2.3357 0.0017 +#> 463: 94.2241 -0.2005 2.0848 2.2971 0.4637 0.2014 2.3358 0.0017 +#> 464: 94.2236 -0.2002 2.0853 2.2953 0.4637 0.2012 2.3360 0.0017 +#> 465: 94.2230 -0.2000 2.0858 2.2946 0.4638 0.2010 2.3360 0.0017 +#> 466: 94.2215 -0.1997 2.0863 2.2995 0.4638 0.2009 2.3363 0.0017 +#> 467: 94.2193 -0.1995 2.0868 2.3051 0.4637 0.2008 2.3363 0.0017 +#> 468: 94.2174 -0.1992 2.0874 2.3086 0.4636 0.2006 2.3363 0.0018 +#> 469: 94.2160 -0.1989 2.0881 2.3072 0.4636 0.2006 2.3361 0.0018 +#> 470: 94.2152 -0.1985 2.0887 2.3075 0.4637 0.2005 2.3363 0.0018 +#> 471: 94.2139 -0.1982 2.0891 2.3126 0.4638 0.2004 2.3361 0.0018 +#> 472: 94.2134 -0.1980 2.0895 2.3151 0.4640 0.2002 2.3360 0.0018 +#> 473: 94.2141 -0.1979 2.0897 2.3149 0.4640 0.2001 2.3360 0.0018 +#> 474: 94.2144 -0.1978 2.0900 2.3140 0.4640 0.2001 2.3358 0.0018 +#> 475: 94.2151 -0.1977 2.0901 2.3151 0.4640 0.2000 2.3358 0.0018 +#> 476: 94.2154 -0.1975 2.0903 2.3195 0.4641 0.2001 2.3357 0.0018 +#> 477: 94.2167 -0.1974 2.0905 2.3253 0.4642 0.2002 2.3358 0.0018 +#> 478: 94.2163 -0.1972 2.0909 2.3324 0.4641 0.2004 2.3357 0.0017 +#> 479: 94.2156 -0.1970 2.0912 2.3364 0.4640 0.2006 2.3355 0.0017 +#> 480: 94.2149 -0.1969 2.0915 2.3395 0.4638 0.2007 2.3353 0.0017 +#> 481: 94.2140 -0.1968 2.0918 2.3431 0.4637 0.2008 2.3350 0.0017 +#> 482: 94.2137 -0.1967 2.0919 2.3440 0.4635 0.2010 2.3349 0.0017 +#> 483: 94.2139 -0.1966 2.0920 2.3468 0.4634 0.2011 2.3348 0.0017 +#> 484: 94.2149 -0.1966 2.0921 2.3488 0.4633 0.2012 2.3346 0.0017 +#> 485: 94.2153 -0.1966 2.0921 2.3486 0.4632 0.2012 2.3345 0.0017 +#> 486: 94.2148 -0.1965 2.0923 2.3483 0.4631 0.2015 2.3345 0.0017 +#> 487: 94.2140 -0.1965 2.0923 2.3492 0.4628 0.2018 2.3345 0.0017 +#> 488: 94.2121 -0.1965 2.0923 2.3489 0.4625 0.2020 2.3347 0.0017 +#> 489: 94.2119 -0.1966 2.0923 2.3497 0.4622 0.2023 2.3346 0.0017 +#> 490: 94.2120 -0.1966 2.0923 2.3476 0.4618 0.2025 2.3346 0.0017 +#> 491: 94.2124 -0.1966 2.0923 2.3462 0.4615 0.2028 2.3346 0.0017 +#> 492: 94.2118 -0.1966 2.0923 2.3453 0.4613 0.2029 2.3346 0.0017 +#> 493: 94.2113 -0.1967 2.0923 2.3452 0.4610 0.2030 2.3347 0.0017 +#> 494: 94.2118 -0.1968 2.0922 2.3488 0.4608 0.2030 2.3347 0.0017 +#> 495: 94.2122 -0.1969 2.0920 2.3530 0.4605 0.2029 2.3347 0.0017 +#> 496: 94.2138 -0.1969 2.0919 2.3540 0.4603 0.2028 2.3350 0.0017 +#> 497: 94.2148 -0.1970 2.0917 2.3554 0.4601 0.2029 2.3352 0.0017 +#> 498: 94.2152 -0.1971 2.0916 2.3534 0.4600 0.2029 2.3356 0.0017 +#> 499: 94.2157 -0.1972 2.0914 2.3519 0.4598 0.2029 2.3357 0.0016 +#> 500: 94.2162 -0.1973 2.0912 2.3498 0.4596 0.2030 2.3358 0.0016</div><div class='output co'>#> <span class='message'>Calculating covariance matrix</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#> <span class='message'>done</span></div><div class='input'><span class='va'>f_nlmixr_fomc_focei_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></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>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span style='font-weight: bold;'>Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation +#> F: Forward difference gradient approximation +#> C: Central difference gradient approximation +#> M: Mixed forward and central difference gradient approximation +#> Unscaled parameters for Omegas=chol(solve(omega)); +#> Diagonals are transformed, as specified by foceiControl(diagXform=) +#> |-----+---------------+-----------+-----------+-----------+-----------| +#> | #| Objective Fun | parent_0 | log_alpha | log_beta | sigma_low | +#> |.....................| rsd_high | o1 | o2 | o3 | +#> |<span style='font-weight: bold;'> 1</span>| 356.08238 | 1.000 | -1.000 | -0.9495 | -0.9739 | +#> |.....................| -0.9969 | -0.9818 | -0.9750 | -0.9744 | +#> | U| 356.08238 | 93.10 | -0.1209 | 2.232 | 1.095 | +#> |.....................| 0.02509 | 0.7272 | 1.045 | 1.072 | +#> | X|<span style='font-weight: bold;'> 356.08238</span> | 93.10 | 0.8861 | 9.321 | 1.095 | +#> |.....................| 0.02509 | 0.7272 | 1.045 | 1.072 | +#> | G| Gill Diff. | -85.81 | 0.5929 | 0.9043 | -97.79 | +#> |.....................| -28.71 | -0.07427 | -8.550 | -12.99 | +#> |<span style='font-weight: bold;'> 2</span>| 1940.7752 | 1.640 | -1.004 | -0.9563 | -0.2449 | +#> |.....................| -0.7829 | -0.9813 | -0.9112 | -0.8775 | +#> | U| 1940.7752 | 152.7 | -0.1253 | 2.226 | 1.495 | +#> |.....................| 0.02778 | 0.7276 | 1.112 | 1.176 | +#> | X|<span style='font-weight: bold;'> 1940.7752</span> | 152.7 | 0.8822 | 9.258 | 1.495 | +#> |.....................| 0.02778 | 0.7276 | 1.112 | 1.176 | +#> |<span style='font-weight: bold;'> 3</span>| 370.78508 | 1.064 | -1.000 | -0.9502 | -0.9010 | +#> |.....................| -0.9755 | -0.9817 | -0.9686 | -0.9647 | +#> | U| 370.78508 | 99.05 | -0.1213 | 2.232 | 1.135 | +#> |.....................| 0.02536 | 0.7272 | 1.052 | 1.082 | +#> | X|<span style='font-weight: bold;'> 370.78508</span> | 99.05 | 0.8857 | 9.315 | 1.135 | +#> |.....................| 0.02536 | 0.7272 | 1.052 | 1.082 | +#> |<span style='font-weight: bold;'> 4</span>| 354.52588 | 1.015 | -1.000 | -0.9497 | -0.9565 | +#> |.....................| -0.9918 | -0.9818 | -0.9735 | -0.9721 | +#> | U| 354.52588 | 94.52 | -0.1210 | 2.232 | 1.105 | +#> |.....................| 0.02516 | 0.7272 | 1.047 | 1.074 | +#> | X|<span style='font-weight: bold;'> 354.52588</span> | 94.52 | 0.8860 | 9.319 | 1.105 | +#> |.....................| 0.02516 | 0.7272 | 1.047 | 1.074 | +#> | F| Forward Diff. | 126.3 | 0.7329 | 1.391 | -95.71 | +#> |.....................| -26.58 | 0.4812 | -8.528 | -12.76 | +#> |<span style='font-weight: bold;'> 5</span>| 352.43362 | 0.9998 | -1.000 | -0.9499 | -0.9392 | +#> |.....................| -0.9869 | -0.9819 | -0.9719 | -0.9698 | +#> | U| 352.43362 | 93.08 | -0.1211 | 2.232 | 1.114 | +#> |.....................| 0.02522 | 0.7271 | 1.048 | 1.077 | +#> | X|<span style='font-weight: bold;'> 352.43362</span> | 93.08 | 0.8859 | 9.317 | 1.114 | +#> |.....................| 0.02522 | 0.7271 | 1.048 | 1.077 | +#> | F| Forward Diff. | -88.58 | 0.5971 | 0.9141 | -92.65 | +#> |.....................| -26.61 | -0.01862 | -8.458 | -12.78 | +#> |<span style='font-weight: bold;'> 6</span>| 350.82994 | 1.015 | -1.000 | -0.9501 | -0.9214 | +#> |.....................| -0.9818 | -0.9819 | -0.9703 | -0.9673 | +#> | U| 350.82994 | 94.46 | -0.1213 | 2.232 | 1.124 | +#> |.....................| 0.02528 | 0.7271 | 1.050 | 1.079 | +#> | X|<span style='font-weight: bold;'> 350.82994</span> | 94.46 | 0.8858 | 9.315 | 1.124 | +#> |.....................| 0.02528 | 0.7271 | 1.050 | 1.079 | +#> | F| Forward Diff. | 115.7 | 0.7442 | 1.407 | -90.51 | +#> |.....................| -24.67 | 0.2416 | -8.378 | -12.59 | +#> |<span style='font-weight: bold;'> 7</span>| 348.85697 | 1.000 | -1.000 | -0.9503 | -0.9035 | +#> |.....................| -0.9769 | -0.9819 | -0.9686 | -0.9649 | +#> | U| 348.85697 | 93.10 | -0.1214 | 2.231 | 1.134 | +#> |.....................| 0.02534 | 0.7271 | 1.052 | 1.082 | +#> | X|<span style='font-weight: bold;'> 348.85697</span> | 93.10 | 0.8857 | 9.313 | 1.134 | +#> |.....................| 0.02534 | 0.7271 | 1.052 | 1.082 | +#> | F| Forward Diff. | -86.89 | 0.6078 | 0.9395 | -87.49 | +#> |.....................| -24.70 | -0.2033 | -8.301 | -12.59 | +#> |<span style='font-weight: bold;'> 8</span>| 347.23757 | 1.014 | -1.001 | -0.9506 | -0.8852 | +#> |.....................| -0.9717 | -0.9819 | -0.9669 | -0.9622 | +#> | U| 347.23757 | 94.41 | -0.1215 | 2.231 | 1.144 | +#> |.....................| 0.02541 | 0.7271 | 1.054 | 1.085 | +#> | X|<span style='font-weight: bold;'> 347.23757</span> | 94.41 | 0.8856 | 9.311 | 1.144 | +#> |.....................| 0.02541 | 0.7271 | 1.054 | 1.085 | +#> | F| Forward Diff. | 106.0 | 0.7499 | 1.419 | -85.67 | +#> |.....................| -22.89 | -0.09812 | -8.213 | -12.39 | +#> |<span style='font-weight: bold;'> 9</span>| 345.37317 | 1.000 | -1.001 | -0.9508 | -0.8667 | +#> |.....................| -0.9667 | -0.9818 | -0.9651 | -0.9596 | +#> | U| 345.37317 | 93.12 | -0.1217 | 2.231 | 1.154 | +#> |.....................| 0.02547 | 0.7272 | 1.056 | 1.088 | +#> | X|<span style='font-weight: bold;'> 345.37317</span> | 93.12 | 0.8854 | 9.308 | 1.154 | +#> |.....................| 0.02547 | 0.7272 | 1.056 | 1.088 | +#> | F| Forward Diff. | -84.47 | 0.6193 | 0.9668 | -82.72 | +#> |.....................| -22.87 | -0.2860 | -8.128 | -12.38 | +#> |<span style='font-weight: bold;'> 10</span>| 343.77522 | 1.014 | -1.001 | -0.9511 | -0.8479 | +#> |.....................| -0.9616 | -0.9818 | -0.9633 | -0.9568 | +#> | U| 343.77522 | 94.37 | -0.1218 | 2.231 | 1.164 | +#> |.....................| 0.02554 | 0.7272 | 1.057 | 1.091 | +#> | X|<span style='font-weight: bold;'> 343.77522</span> | 94.37 | 0.8853 | 9.306 | 1.164 | +#> |.....................| 0.02554 | 0.7272 | 1.057 | 1.091 | +#> | F| Forward Diff. | 98.54 | 0.7582 | 1.440 | -80.80 | +#> |.....................| -21.11 | -0.2480 | -8.037 | -12.18 | +#> |<span style='font-weight: bold;'> 11</span>| 342.01002 | 1.000 | -1.001 | -0.9514 | -0.8290 | +#> |.....................| -0.9566 | -0.9817 | -0.9614 | -0.9539 | +#> | U| 342.01002 | 93.14 | -0.1220 | 2.230 | 1.175 | +#> |.....................| 0.02560 | 0.7273 | 1.059 | 1.094 | +#> | X|<span style='font-weight: bold;'> 342.01002</span> | 93.14 | 0.8852 | 9.303 | 1.175 | +#> |.....................| 0.02560 | 0.7273 | 1.059 | 1.094 | +#> | F| Forward Diff. | -81.78 | 0.6281 | 0.9934 | -78.17 | +#> |.....................| -21.11 | -0.4903 | -7.943 | -12.16 | +#> |<span style='font-weight: bold;'> 12</span>| 340.43696 | 1.013 | -1.001 | -0.9517 | -0.8098 | +#> |.....................| -0.9515 | -0.9816 | -0.9595 | -0.9509 | +#> | U| 340.43696 | 94.32 | -0.1222 | 2.230 | 1.185 | +#> |.....................| 0.02566 | 0.7274 | 1.062 | 1.097 | +#> | X|<span style='font-weight: bold;'> 340.43696</span> | 94.32 | 0.8850 | 9.301 | 1.185 | +#> |.....................| 0.02566 | 0.7274 | 1.062 | 1.097 | +#> | F| Forward Diff. | 90.87 | 0.7671 | 1.462 | -75.86 | +#> |.....................| -19.30 | -0.2119 | -7.851 | -11.96 | +#> |<span style='font-weight: bold;'> 13</span>| 338.78414 | 1.001 | -1.001 | -0.9520 | -0.7906 | +#> |.....................| -0.9465 | -0.9815 | -0.9574 | -0.9478 | +#> | U| 338.78414 | 93.15 | -0.1223 | 2.230 | 1.196 | +#> |.....................| 0.02572 | 0.7274 | 1.064 | 1.100 | +#> | X|<span style='font-weight: bold;'> 338.78414</span> | 93.15 | 0.8848 | 9.298 | 1.196 | +#> |.....................| 0.02572 | 0.7274 | 1.064 | 1.100 | +#> | F| Forward Diff. | -80.47 | 0.6431 | 1.023 | -73.28 | +#> |.....................| -19.27 | -0.2791 | -7.739 | -11.92 | +#> |<span style='font-weight: bold;'> 14</span>| 337.22825 | 1.013 | -1.002 | -0.9523 | -0.7710 | +#> |.....................| -0.9415 | -0.9814 | -0.9553 | -0.9445 | +#> | U| 337.22825 | 94.28 | -0.1225 | 2.229 | 1.206 | +#> |.....................| 0.02579 | 0.7275 | 1.066 | 1.104 | +#> | X|<span style='font-weight: bold;'> 337.22825</span> | 94.28 | 0.8847 | 9.295 | 1.206 | +#> |.....................| 0.02579 | 0.7275 | 1.066 | 1.104 | +#> | F| Forward Diff. | 82.17 | 0.7754 | 1.480 | -71.69 | +#> |.....................| -17.81 | -0.5846 | -7.635 | -11.71 | +#> |<span style='font-weight: bold;'> 15</span>| 335.66851 | 1.001 | -1.002 | -0.9527 | -0.7512 | +#> |.....................| -0.9367 | -0.9812 | -0.9531 | -0.9411 | +#> | U| 335.66851 | 93.18 | -0.1228 | 2.229 | 1.217 | +#> |.....................| 0.02585 | 0.7276 | 1.068 | 1.108 | +#> | X|<span style='font-weight: bold;'> 335.66851</span> | 93.18 | 0.8845 | 9.291 | 1.217 | +#> |.....................| 0.02585 | 0.7276 | 1.068 | 1.108 | +#> | F| Forward Diff. | -77.03 | 0.6546 | 1.055 | -69.28 | +#> |.....................| -17.76 | -0.6126 | -7.531 | -11.66 | +#> |<span style='font-weight: bold;'> 16</span>| 334.17549 | 1.012 | -1.002 | -0.9531 | -0.7314 | +#> |.....................| -0.9319 | -0.9810 | -0.9509 | -0.9376 | +#> | U| 334.17549 | 94.25 | -0.1230 | 2.229 | 1.228 | +#> |.....................| 0.02591 | 0.7278 | 1.070 | 1.111 | +#> | X|<span style='font-weight: bold;'> 334.17549</span> | 94.25 | 0.8843 | 9.287 | 1.228 | +#> |.....................| 0.02591 | 0.7278 | 1.070 | 1.111 | +#> | F| Forward Diff. | 77.34 | 0.7869 | 1.511 | -67.40 | +#> |.....................| -16.23 | -0.6338 | -7.414 | -11.45 | +#> |<span style='font-weight: bold;'> 17</span>| 332.70253 | 1.001 | -1.002 | -0.9536 | -0.7113 | +#> |.....................| -0.9273 | -0.9807 | -0.9485 | -0.9339 | +#> | U| 332.70253 | 93.20 | -0.1232 | 2.228 | 1.239 | +#> |.....................| 0.02597 | 0.7280 | 1.073 | 1.115 | +#> | X|<span style='font-weight: bold;'> 332.70253</span> | 93.20 | 0.8841 | 9.283 | 1.239 | +#> |.....................| 0.02597 | 0.7280 | 1.073 | 1.115 | +#> | F| Forward Diff. | -74.42 | 0.6680 | 1.089 | -65.07 | +#> |.....................| -16.20 | -0.6067 | -7.288 | -11.39 | +#> |<span style='font-weight: bold;'> 18</span>| 331.26057 | 1.012 | -1.003 | -0.9540 | -0.6912 | +#> |.....................| -0.9227 | -0.9804 | -0.9461 | -0.9301 | +#> | U| 331.26057 | 94.22 | -0.1235 | 2.228 | 1.250 | +#> |.....................| 0.02602 | 0.7282 | 1.076 | 1.119 | +#> | X|<span style='font-weight: bold;'> 331.26057</span> | 94.22 | 0.8838 | 9.279 | 1.250 | +#> |.....................| 0.02602 | 0.7282 | 1.076 | 1.119 | +#> | F| Forward Diff. | 71.33 | 0.7962 | 1.537 | -63.45 | +#> |.....................| -14.84 | -0.8466 | -7.169 | -11.16 | +#> |<span style='font-weight: bold;'> 19</span>| 329.86877 | 1.001 | -1.003 | -0.9546 | -0.6708 | +#> |.....................| -0.9184 | -0.9799 | -0.9435 | -0.9260 | +#> | U| 329.86877 | 93.23 | -0.1238 | 2.227 | 1.261 | +#> |.....................| 0.02608 | 0.7285 | 1.078 | 1.124 | +#> | X|<span style='font-weight: bold;'> 329.86877</span> | 93.23 | 0.8836 | 9.273 | 1.261 | +#> |.....................| 0.02608 | 0.7285 | 1.078 | 1.124 | +#> | F| Forward Diff. | -70.96 | 0.6825 | 1.126 | -60.92 | +#> |.....................| -14.66 | -0.5289 | -7.027 | -11.08 | +#> |<span style='font-weight: bold;'> 20</span>| 328.50031 | 1.012 | -1.003 | -0.9552 | -0.6504 | +#> |.....................| -0.9143 | -0.9795 | -0.9408 | -0.9217 | +#> | U| 328.50031 | 94.20 | -0.1241 | 2.227 | 1.272 | +#> |.....................| 0.02613 | 0.7288 | 1.081 | 1.128 | +#> | X|<span style='font-weight: bold;'> 328.50031</span> | 94.20 | 0.8833 | 9.268 | 1.272 | +#> |.....................| 0.02613 | 0.7288 | 1.081 | 1.128 | +#> | F| Forward Diff. | 67.86 | 0.8082 | 1.577 | -59.49 | +#> |.....................| -13.42 | -0.7986 | -6.899 | -10.84 | +#> |<span style='font-weight: bold;'> 21</span>| 327.16645 | 1.002 | -1.004 | -0.9559 | -0.6298 | +#> |.....................| -0.9105 | -0.9791 | -0.9380 | -0.9171 | +#> | U| 327.16645 | 93.27 | -0.1245 | 2.226 | 1.284 | +#> |.....................| 0.02618 | 0.7291 | 1.084 | 1.133 | +#> | X|<span style='font-weight: bold;'> 327.16645</span> | 93.27 | 0.8829 | 9.261 | 1.284 | +#> |.....................| 0.02618 | 0.7291 | 1.084 | 1.133 | +#> | F| Forward Diff. | -65.39 | 0.6978 | 1.172 | -57.48 | +#> |.....................| -13.36 | -0.7754 | -6.743 | -10.73 | +#> |<span style='font-weight: bold;'> 22</span>| 325.87373 | 1.012 | -1.004 | -0.9567 | -0.6091 | +#> |.....................| -0.9070 | -0.9785 | -0.9351 | -0.9123 | +#> | U| 325.87373 | 94.19 | -0.1249 | 2.225 | 1.295 | +#> |.....................| 0.02622 | 0.7296 | 1.087 | 1.138 | +#> | X|<span style='font-weight: bold;'> 325.87373</span> | 94.19 | 0.8826 | 9.255 | 1.295 | +#> |.....................| 0.02622 | 0.7296 | 1.087 | 1.138 | +#> | F| Forward Diff. | 64.00 | 0.8187 | 1.613 | -55.46 | +#> |.....................| -12.01 | -0.6347 | -6.615 | -10.48 | +#> |<span style='font-weight: bold;'> 23</span>| 324.62990 | 1.002 | -1.004 | -0.9576 | -0.5884 | +#> |.....................| -0.9040 | -0.9780 | -0.9320 | -0.9071 | +#> | U| 324.6299 | 93.29 | -0.1254 | 2.224 | 1.306 | +#> |.....................| 0.02626 | 0.7300 | 1.090 | 1.144 | +#> | X|<span style='font-weight: bold;'> 324.6299</span> | 93.29 | 0.8822 | 9.246 | 1.306 | +#> |.....................| 0.02626 | 0.7300 | 1.090 | 1.144 | +#> | F| Forward Diff. | -64.25 | 0.7091 | 1.205 | -53.86 | +#> |.....................| -12.06 | -0.7132 | -6.446 | -10.35 | +#> |<span style='font-weight: bold;'> 24</span>| 323.37595 | 1.011 | -1.005 | -0.9586 | -0.5676 | +#> |.....................| -0.9015 | -0.9774 | -0.9287 | -0.9014 | +#> | U| 323.37595 | 94.14 | -0.1259 | 2.223 | 1.318 | +#> |.....................| 0.02629 | 0.7304 | 1.094 | 1.150 | +#> | X|<span style='font-weight: bold;'> 323.37595</span> | 94.14 | 0.8817 | 9.236 | 1.318 | +#> |.....................| 0.02629 | 0.7304 | 1.094 | 1.150 | +#> | F| Forward Diff. | 56.04 | 0.8254 | 1.637 | -52.44 | +#> |.....................| -10.96 | -0.9420 | -6.280 | -10.07 | +#> |<span style='font-weight: bold;'> 25</span>| 322.22752 | 1.002 | -1.006 | -0.9598 | -0.5467 | +#> |.....................| -0.8995 | -0.9764 | -0.9254 | -0.8957 | +#> | U| 322.22752 | 93.30 | -0.1265 | 2.222 | 1.329 | +#> |.....................| 0.02631 | 0.7311 | 1.097 | 1.156 | +#> | X|<span style='font-weight: bold;'> 322.22752</span> | 93.30 | 0.8812 | 9.225 | 1.329 | +#> |.....................| 0.02631 | 0.7311 | 1.097 | 1.156 | +#> | F| Forward Diff. | -62.58 | 0.7198 | 1.238 | -50.46 | +#> |.....................| -10.85 | -0.6563 | -6.111 | -9.931 | +#> |<span style='font-weight: bold;'> 26</span>| 321.05050 | 1.011 | -1.006 | -0.9612 | -0.5258 | +#> |.....................| -0.8983 | -0.9755 | -0.9219 | -0.8894 | +#> | U| 321.0505 | 94.13 | -0.1272 | 2.221 | 1.341 | +#> |.....................| 0.02633 | 0.7318 | 1.101 | 1.163 | +#> | X|<span style='font-weight: bold;'> 321.0505</span> | 94.13 | 0.8805 | 9.213 | 1.341 | +#> |.....................| 0.02633 | 0.7318 | 1.101 | 1.163 | +#> | F| Forward Diff. | 53.55 | 0.8319 | 1.674 | -49.18 | +#> |.....................| -9.827 | -0.8926 | -5.944 | -9.631 | +#> |<span style='font-weight: bold;'> 27</span>| 319.96320 | 1.003 | -1.007 | -0.9629 | -0.5048 | +#> |.....................| -0.8978 | -0.9744 | -0.9184 | -0.8829 | +#> | U| 319.9632 | 93.35 | -0.1280 | 2.219 | 1.352 | +#> |.....................| 0.02633 | 0.7325 | 1.104 | 1.170 | +#> | X|<span style='font-weight: bold;'> 319.9632</span> | 93.35 | 0.8798 | 9.197 | 1.352 | +#> |.....................| 0.02633 | 0.7325 | 1.104 | 1.170 | +#> | F| Forward Diff. | -57.14 | 0.7318 | 1.284 | -47.52 | +#> |.....................| -9.778 | -0.7040 | -5.744 | -9.448 | +#> |<span style='font-weight: bold;'> 28</span>| 318.87595 | 1.011 | -1.008 | -0.9647 | -0.4840 | +#> |.....................| -0.8984 | -0.9733 | -0.9148 | -0.8761 | +#> | U| 318.87595 | 94.12 | -0.1289 | 2.217 | 1.364 | +#> |.....................| 0.02633 | 0.7334 | 1.108 | 1.177 | +#> | X|<span style='font-weight: bold;'> 318.87595</span> | 94.12 | 0.8790 | 9.180 | 1.364 | +#> |.....................| 0.02633 | 0.7334 | 1.108 | 1.177 | +#> | F| Forward Diff. | 50.84 | 0.8352 | 1.706 | -46.29 | +#> |.....................| -8.837 | -0.9158 | -5.564 | -9.134 | +#> |<span style='font-weight: bold;'> 29</span>| 317.86528 | 1.003 | -1.009 | -0.9669 | -0.4631 | +#> |.....................| -0.9000 | -0.9719 | -0.9113 | -0.8691 | +#> | U| 317.86528 | 93.39 | -0.1300 | 2.215 | 1.375 | +#> |.....................| 0.02631 | 0.7344 | 1.112 | 1.185 | +#> | X|<span style='font-weight: bold;'> 317.86528</span> | 93.39 | 0.8781 | 9.160 | 1.375 | +#> |.....................| 0.02631 | 0.7344 | 1.112 | 1.185 | +#> | F| Forward Diff. | -53.64 | 0.7337 | 1.307 | -44.73 | +#> |.....................| -8.788 | -0.7242 | -5.380 | -8.940 | +#> |<span style='font-weight: bold;'> 30</span>| 316.86653 | 1.011 | -1.010 | -0.9694 | -0.4424 | +#> |.....................| -0.9029 | -0.9703 | -0.9078 | -0.8619 | +#> | U| 316.86653 | 94.11 | -0.1312 | 2.212 | 1.386 | +#> |.....................| 0.02627 | 0.7355 | 1.115 | 1.192 | +#> | X|<span style='font-weight: bold;'> 316.86653</span> | 94.11 | 0.8771 | 9.137 | 1.386 | +#> |.....................| 0.02627 | 0.7355 | 1.115 | 1.192 | +#> | F| Forward Diff. | 47.91 | 0.8298 | 1.717 | -43.37 | +#> |.....................| -7.860 | -0.7095 | -5.221 | -8.628 | +#> |<span style='font-weight: bold;'> 31</span>| 315.94581 | 1.003 | -1.012 | -0.9723 | -0.4219 | +#> |.....................| -0.9070 | -0.9693 | -0.9044 | -0.8547 | +#> | U| 315.94581 | 93.42 | -0.1325 | 2.209 | 1.398 | +#> |.....................| 0.02622 | 0.7363 | 1.119 | 1.200 | +#> | X|<span style='font-weight: bold;'> 315.94581</span> | 93.42 | 0.8759 | 9.111 | 1.398 | +#> |.....................| 0.02622 | 0.7363 | 1.119 | 1.200 | +#> | F| Forward Diff. | -50.84 | 0.7268 | 1.307 | -41.97 | +#> |.....................| -7.840 | -0.5502 | -5.032 | -8.421 | +#> |<span style='font-weight: bold;'> 32</span>| 315.03994 | 1.011 | -1.013 | -0.9754 | -0.4018 | +#> |.....................| -0.9129 | -0.9687 | -0.9011 | -0.8473 | +#> | U| 315.03994 | 94.09 | -0.1340 | 2.206 | 1.409 | +#> |.....................| 0.02615 | 0.7367 | 1.122 | 1.208 | +#> | X|<span style='font-weight: bold;'> 315.03994</span> | 94.09 | 0.8746 | 9.082 | 1.409 | +#> |.....................| 0.02615 | 0.7367 | 1.122 | 1.208 | +#> | F| Forward Diff. | 43.50 | 0.8139 | 1.698 | -41.38 | +#> |.....................| -7.196 | -0.9249 | -4.882 | -8.108 | +#> |<span style='font-weight: bold;'> 33</span>| 314.20198 | 1.004 | -1.015 | -0.9788 | -0.3816 | +#> |.....................| -0.9197 | -0.9671 | -0.8983 | -0.8406 | +#> | U| 314.20198 | 93.47 | -0.1355 | 2.203 | 1.420 | +#> |.....................| 0.02606 | 0.7379 | 1.125 | 1.215 | +#> | X|<span style='font-weight: bold;'> 314.20198</span> | 93.47 | 0.8733 | 9.052 | 1.420 | +#> |.....................| 0.02606 | 0.7379 | 1.125 | 1.215 | +#> | F| Forward Diff. | -46.04 | 0.7133 | 1.286 | -40.35 | +#> |.....................| -7.243 | -0.8268 | -4.724 | -7.917 | +#> |<span style='font-weight: bold;'> 34</span>| 313.39087 | 1.011 | -1.016 | -0.9822 | -0.3616 | +#> |.....................| -0.9277 | -0.9641 | -0.8960 | -0.8348 | +#> | U| 313.39087 | 94.10 | -0.1371 | 2.200 | 1.431 | +#> |.....................| 0.02596 | 0.7401 | 1.128 | 1.221 | +#> | X|<span style='font-weight: bold;'> 313.39087</span> | 94.10 | 0.8719 | 9.021 | 1.431 | +#> |.....................| 0.02596 | 0.7401 | 1.128 | 1.221 | +#> | F| Forward Diff. | 42.44 | 0.7936 | 1.657 | -38.93 | +#> |.....................| -6.417 | -0.6060 | -4.631 | -7.687 | +#> |<span style='font-weight: bold;'> 35</span>| 312.65204 | 1.004 | -1.018 | -0.9857 | -0.3421 | +#> |.....................| -0.9371 | -0.9626 | -0.8936 | -0.8290 | +#> | U| 312.65204 | 93.49 | -0.1387 | 2.196 | 1.441 | +#> |.....................| 0.02584 | 0.7411 | 1.130 | 1.228 | +#> | X|<span style='font-weight: bold;'> 312.65204</span> | 93.49 | 0.8705 | 8.989 | 1.441 | +#> |.....................| 0.02584 | 0.7411 | 1.130 | 1.228 | +#> | F| Forward Diff. | -46.74 | 0.6875 | 1.233 | -38.07 | +#> |.....................| -6.520 | -0.5247 | -4.495 | -7.518 | +#> |<span style='font-weight: bold;'> 36</span>| 311.92333 | 1.010 | -1.020 | -0.9894 | -0.3235 | +#> |.....................| -0.9483 | -0.9627 | -0.8910 | -0.8230 | +#> | U| 311.92333 | 94.07 | -0.1404 | 2.192 | 1.452 | +#> |.....................| 0.02570 | 0.7411 | 1.133 | 1.234 | +#> | X|<span style='font-weight: bold;'> 311.92333</span> | 94.07 | 0.8690 | 8.957 | 1.452 | +#> |.....................| 0.02570 | 0.7411 | 1.133 | 1.234 | +#> | F| Forward Diff. | 35.63 | 0.7624 | 1.583 | -37.23 | +#> |.....................| -5.893 | -0.6222 | -4.382 | -7.287 | +#> |<span style='font-weight: bold;'> 37</span>| 311.27355 | 1.004 | -1.021 | -0.9929 | -0.3046 | +#> |.....................| -0.9595 | -0.9623 | -0.8888 | -0.8177 | +#> | U| 311.27355 | 93.51 | -0.1420 | 2.189 | 1.462 | +#> |.....................| 0.02556 | 0.7413 | 1.135 | 1.240 | +#> | X|<span style='font-weight: bold;'> 311.27355</span> | 93.51 | 0.8676 | 8.925 | 1.462 | +#> |.....................| 0.02556 | 0.7413 | 1.135 | 1.240 | +#> | F| Forward Diff. | -45.98 | 0.6631 | 1.170 | -36.31 | +#> |.....................| -5.950 | -0.4376 | -4.255 | -7.133 | +#> |<span style='font-weight: bold;'> 38</span>| 310.62439 | 1.010 | -1.023 | -0.9963 | -0.2868 | +#> |.....................| -0.9728 | -0.9625 | -0.8869 | -0.8128 | +#> | U| 310.62439 | 94.07 | -0.1437 | 2.185 | 1.472 | +#> |.....................| 0.02539 | 0.7412 | 1.137 | 1.245 | +#> | X|<span style='font-weight: bold;'> 310.62439</span> | 94.07 | 0.8661 | 8.895 | 1.472 | +#> |.....................| 0.02539 | 0.7412 | 1.137 | 1.245 | +#> | F| Forward Diff. | 33.19 | 0.7369 | 1.513 | -35.63 | +#> |.....................| -5.399 | -0.5527 | -4.174 | -6.950 | +#> |<span style='font-weight: bold;'> 39</span>| 310.04420 | 1.005 | -1.024 | -0.9995 | -0.2687 | +#> |.....................| -0.9859 | -0.9628 | -0.8850 | -0.8081 | +#> | U| 310.0442 | 93.55 | -0.1453 | 2.182 | 1.482 | +#> |.....................| 0.02523 | 0.7410 | 1.139 | 1.250 | +#> | X|<span style='font-weight: bold;'> 310.0442</span> | 93.55 | 0.8648 | 8.866 | 1.482 | +#> |.....................| 0.02523 | 0.7410 | 1.139 | 1.250 | +#> | F| Forward Diff. | -43.63 | 0.6390 | 1.117 | -34.92 | +#> |.....................| -5.491 | -0.4082 | -4.072 | -6.814 | +#> |<span style='font-weight: bold;'> 40</span>| 309.46411 | 1.010 | -1.026 | -1.003 | -0.2518 | +#> |.....................| -1.001 | -0.9632 | -0.8835 | -0.8040 | +#> | U| 309.46411 | 94.07 | -0.1468 | 2.179 | 1.491 | +#> |.....................| 0.02504 | 0.7407 | 1.141 | 1.254 | +#> | X|<span style='font-weight: bold;'> 309.46411</span> | 94.07 | 0.8634 | 8.839 | 1.491 | +#> |.....................| 0.02504 | 0.7407 | 1.141 | 1.254 | +#> | F| Forward Diff. | 30.94 | 0.7075 | 1.451 | -34.14 | +#> |.....................| -4.970 | -0.4915 | -4.021 | -6.668 | +#> |<span style='font-weight: bold;'> 41</span>| 308.94397 | 1.005 | -1.027 | -1.005 | -0.2344 | +#> |.....................| -1.015 | -0.9639 | -0.8817 | -0.7999 | +#> | U| 308.94397 | 93.57 | -0.1483 | 2.176 | 1.500 | +#> |.....................| 0.02486 | 0.7402 | 1.143 | 1.259 | +#> | X|<span style='font-weight: bold;'> 308.94397</span> | 93.57 | 0.8622 | 8.814 | 1.500 | +#> |.....................| 0.02486 | 0.7402 | 1.143 | 1.259 | +#> | F| Forward Diff. | -43.40 | 0.6150 | 1.062 | -33.15 | +#> |.....................| -4.981 | -0.1275 | -3.914 | -6.542 | +#> |<span style='font-weight: bold;'> 42</span>| 308.42636 | 1.010 | -1.029 | -1.008 | -0.2188 | +#> |.....................| -1.031 | -0.9663 | -0.8797 | -0.7956 | +#> | U| 308.42636 | 94.07 | -0.1498 | 2.174 | 1.509 | +#> |.....................| 0.02466 | 0.7384 | 1.145 | 1.264 | +#> | X|<span style='font-weight: bold;'> 308.42636</span> | 94.07 | 0.8609 | 8.789 | 1.509 | +#> |.....................| 0.02466 | 0.7384 | 1.145 | 1.264 | +#> | F| Forward Diff. | 28.94 | 0.6832 | 1.395 | -33.36 | +#> |.....................| -4.720 | -0.6585 | -3.841 | -6.387 | +#> |<span style='font-weight: bold;'> 43</span>| 307.94294 | 1.006 | -1.030 | -1.011 | -0.2019 | +#> |.....................| -1.047 | -0.9672 | -0.8783 | -0.7922 | +#> | U| 307.94294 | 93.62 | -0.1511 | 2.171 | 1.518 | +#> |.....................| 0.02447 | 0.7378 | 1.146 | 1.267 | +#> | X|<span style='font-weight: bold;'> 307.94294</span> | 93.62 | 0.8597 | 8.766 | 1.518 | +#> |.....................| 0.02447 | 0.7378 | 1.146 | 1.267 | +#> | F| Forward Diff. | -38.44 | 0.5985 | 1.037 | -32.41 | +#> |.....................| -4.734 | -0.3663 | -3.762 | -6.284 | +#> |<span style='font-weight: bold;'> 44</span>| 307.46797 | 1.011 | -1.032 | -1.013 | -0.1861 | +#> |.....................| -1.063 | -0.9666 | -0.8774 | -0.7896 | +#> | U| 307.46797 | 94.11 | -0.1524 | 2.169 | 1.527 | +#> |.....................| 0.02426 | 0.7383 | 1.147 | 1.270 | +#> | X|<span style='font-weight: bold;'> 307.46797</span> | 94.11 | 0.8586 | 8.746 | 1.527 | +#> |.....................| 0.02426 | 0.7383 | 1.147 | 1.270 | +#> | F| Forward Diff. | 31.70 | 0.6652 | 1.367 | -32.07 | +#> |.....................| -4.364 | -0.4841 | -3.739 | -6.200 | +#> |<span style='font-weight: bold;'> 45</span>| 307.02197 | 1.006 | -1.033 | -1.016 | -0.1702 | +#> |.....................| -1.080 | -0.9671 | -0.8762 | -0.7866 | +#> | U| 307.02197 | 93.66 | -0.1537 | 2.166 | 1.536 | +#> |.....................| 0.02405 | 0.7379 | 1.149 | 1.273 | +#> | X|<span style='font-weight: bold;'> 307.02197</span> | 93.66 | 0.8575 | 8.725 | 1.536 | +#> |.....................| 0.02405 | 0.7379 | 1.149 | 1.273 | +#> | F| Forward Diff. | -34.81 | 0.5817 | 1.015 | -31.25 | +#> |.....................| -4.413 | -0.2597 | -3.670 | -6.117 | +#> |<span style='font-weight: bold;'> 46</span>| 306.58875 | 1.011 | -1.034 | -1.018 | -0.1551 | +#> |.....................| -1.097 | -0.9684 | -0.8747 | -0.7833 | +#> | U| 306.58875 | 94.13 | -0.1549 | 2.164 | 1.544 | +#> |.....................| 0.02384 | 0.7369 | 1.150 | 1.277 | +#> | X|<span style='font-weight: bold;'> 306.58875</span> | 94.13 | 0.8565 | 8.705 | 1.544 | +#> |.....................| 0.02384 | 0.7369 | 1.150 | 1.277 | +#> | F| Forward Diff. | 31.47 | 0.6484 | 1.332 | -31.08 | +#> |.....................| -4.101 | -0.4354 | -3.617 | -5.999 | +#> |<span style='font-weight: bold;'> 47</span>| 306.17343 | 1.006 | -1.035 | -1.020 | -0.1399 | +#> |.....................| -1.114 | -0.9699 | -0.8732 | -0.7802 | +#> | U| 306.17343 | 93.70 | -0.1561 | 2.162 | 1.552 | +#> |.....................| 0.02362 | 0.7358 | 1.152 | 1.280 | +#> | X|<span style='font-weight: bold;'> 306.17343</span> | 93.70 | 0.8554 | 8.686 | 1.552 | +#> |.....................| 0.02362 | 0.7358 | 1.152 | 1.280 | +#> | F| Forward Diff. | -31.81 | 0.5683 | 0.9956 | -30.69 | +#> |.....................| -4.225 | -0.4059 | -3.540 | -5.903 | +#> |<span style='font-weight: bold;'> 48</span>| 305.76609 | 1.011 | -1.036 | -1.022 | -0.1248 | +#> |.....................| -1.132 | -0.9702 | -0.8722 | -0.7778 | +#> | U| 305.76609 | 94.14 | -0.1573 | 2.160 | 1.560 | +#> |.....................| 0.02340 | 0.7356 | 1.153 | 1.283 | +#> | X|<span style='font-weight: bold;'> 305.76609</span> | 94.14 | 0.8545 | 8.668 | 1.560 | +#> |.....................| 0.02340 | 0.7356 | 1.153 | 1.283 | +#> | F| Forward Diff. | 30.78 | 0.6301 | 1.297 | -30.24 | +#> |.....................| -3.891 | -0.4278 | -3.502 | -5.825 | +#> |<span style='font-weight: bold;'> 49</span>| 305.37620 | 1.007 | -1.037 | -1.024 | -0.1098 | +#> |.....................| -1.149 | -0.9705 | -0.8714 | -0.7755 | +#> | U| 305.3762 | 93.72 | -0.1584 | 2.158 | 1.569 | +#> |.....................| 0.02318 | 0.7354 | 1.154 | 1.285 | +#> | X|<span style='font-weight: bold;'> 305.3762</span> | 93.72 | 0.8535 | 8.651 | 1.569 | +#> |.....................| 0.02318 | 0.7354 | 1.154 | 1.285 | +#> | F| Forward Diff. | -32.45 | 0.5512 | 0.9611 | -29.28 | +#> |.....................| -3.904 | -0.09870 | -3.459 | -5.767 | +#> |<span style='font-weight: bold;'> 50</span>| 304.99974 | 1.011 | -1.039 | -1.026 | -0.09561 | +#> |.....................| -1.167 | -0.9731 | -0.8699 | -0.7723 | +#> | U| 304.99974 | 94.15 | -0.1595 | 2.156 | 1.576 | +#> |.....................| 0.02295 | 0.7335 | 1.155 | 1.288 | +#> | X|<span style='font-weight: bold;'> 304.99974</span> | 94.15 | 0.8526 | 8.633 | 1.576 | +#> |.....................| 0.02295 | 0.7335 | 1.155 | 1.288 | +#> | F| Forward Diff. | 30.20 | 0.6130 | 1.265 | -28.57 | +#> |.....................| -3.511 | -0.04200 | -3.403 | -5.652 | +#> |<span style='font-weight: bold;'> 51</span>| 304.64794 | 1.007 | -1.040 | -1.028 | -0.08217 | +#> |.....................| -1.185 | -0.9783 | -0.8678 | -0.7682 | +#> | U| 304.64794 | 93.75 | -0.1607 | 2.153 | 1.584 | +#> |.....................| 0.02273 | 0.7297 | 1.157 | 1.293 | +#> | X|<span style='font-weight: bold;'> 304.64794</span> | 93.75 | 0.8516 | 8.614 | 1.584 | +#> |.....................| 0.02273 | 0.7297 | 1.157 | 1.293 | +#> | F| Forward Diff. | -30.08 | 0.5385 | 0.9408 | -28.96 | +#> |.....................| -3.779 | -0.3908 | -3.281 | -5.515 | +#> |<span style='font-weight: bold;'> 52</span>| 304.28931 | 1.011 | -1.041 | -1.030 | -0.06828 | +#> |.....................| -1.203 | -0.9811 | -0.8668 | -0.7655 | +#> | U| 304.28931 | 94.14 | -0.1618 | 2.151 | 1.591 | +#> |.....................| 0.02250 | 0.7277 | 1.158 | 1.296 | +#> | X|<span style='font-weight: bold;'> 304.28931</span> | 94.14 | 0.8506 | 8.597 | 1.591 | +#> |.....................| 0.02250 | 0.7277 | 1.158 | 1.296 | +#> |<span style='font-weight: bold;'> 53</span>| 304.03244 | 1.011 | -1.042 | -1.033 | -0.05709 | +#> |.....................| -1.225 | -0.9843 | -0.8662 | -0.7633 | +#> | U| 304.03244 | 94.13 | -0.1630 | 2.149 | 1.597 | +#> |.....................| 0.02223 | 0.7253 | 1.159 | 1.298 | +#> | X|<span style='font-weight: bold;'> 304.03244</span> | 94.13 | 0.8496 | 8.578 | 1.597 | +#> |.....................| 0.02223 | 0.7253 | 1.159 | 1.298 | +#> |<span style='font-weight: bold;'> 54</span>| 302.98899 | 1.011 | -1.047 | -1.041 | -0.01055 | +#> |.....................| -1.314 | -0.9977 | -0.8638 | -0.7544 | +#> | U| 302.98899 | 94.10 | -0.1678 | 2.140 | 1.623 | +#> |.....................| 0.02111 | 0.7156 | 1.161 | 1.308 | +#> | X|<span style='font-weight: bold;'> 302.98899</span> | 94.10 | 0.8455 | 8.503 | 1.623 | +#> |.....................| 0.02111 | 0.7156 | 1.161 | 1.308 | +#> |<span style='font-weight: bold;'> 55</span>| 298.89653 | 1.010 | -1.068 | -1.080 | 0.1944 | +#> |.....................| -1.708 | -1.057 | -0.8531 | -0.7150 | +#> | U| 298.89653 | 93.99 | -0.1892 | 2.101 | 1.735 | +#> |.....................| 0.01618 | 0.6726 | 1.173 | 1.350 | +#> | X|<span style='font-weight: bold;'> 298.89653</span> | 93.99 | 0.8276 | 8.177 | 1.735 | +#> |.....................| 0.01618 | 0.6726 | 1.173 | 1.350 | +#> |<span style='font-weight: bold;'> 56</span>| 292.24425 | 1.012 | -1.205 | -1.331 | 1.218 | +#> |.....................| -2.997 | -1.313 | -0.8095 | -0.4981 | +#> | U| 292.24425 | 94.21 | -0.3257 | 1.851 | 2.296 | +#> |.....................| 5.960e-07 | 0.4863 | 1.218 | 1.582 | +#> | X|<span style='font-weight: bold;'> 292.24425</span> | 94.21 | 0.7221 | 6.365 | 2.296 | +#> |.....................| 5.960e-07 | 0.4863 | 1.218 | 1.582 | +#> | F| Forward Diff. | -17.20 | -1.896 | -10.23 | 0.3663 | +#> |.....................| 0.002021 | -17.85 | 0.1528 | 5.292 | +#> |<span style='font-weight: bold;'> 57</span>| 309.71599 | 0.9897 | -1.187 | -0.4357 | 2.442 | +#> |.....................| -2.997 | 0.5394 | -0.6812 | -0.7129 | +#> | U| 309.71599 | 92.14 | -0.3076 | 2.746 | 2.966 | +#> |.....................| 5.960e-07 | 1.833 | 1.352 | 1.352 | +#> | X|<span style='font-weight: bold;'> 309.71599</span> | 92.14 | 0.7352 | 15.58 | 2.966 | +#> |.....................| 5.960e-07 | 1.833 | 1.352 | 1.352 | +#> |<span style='font-weight: bold;'> 58</span>| 292.01474 | 1.005 | -1.198 | -1.013 | 1.651 | +#> |.....................| -2.997 | -0.6561 | -0.7641 | -0.5745 | +#> | U| 292.01474 | 93.60 | -0.3191 | 2.168 | 2.533 | +#> |.....................| 5.960e-07 | 0.9640 | 1.266 | 1.501 | +#> | X|<span style='font-weight: bold;'> 292.01474</span> | 93.60 | 0.7268 | 8.745 | 2.533 | +#> |.....................| 5.960e-07 | 0.9640 | 1.266 | 1.501 | +#> | F| Forward Diff. | -172.4 | -2.986 | 3.411 | 4.977 | +#> |.....................| 0.05585 | 3.841 | 3.028 | 0.3322 | +#> |<span style='font-weight: bold;'> 59</span>| 292.30890 | 1.013 | -0.8632 | -1.158 | 1.672 | +#> |.....................| -2.997 | -0.5770 | -0.9665 | -0.6082 | +#> | U| 292.3089 | 94.28 | 0.01586 | 2.024 | 2.544 | +#> |.....................| 5.960e-07 | 1.022 | 1.054 | 1.464 | +#> | X|<span style='font-weight: bold;'> 292.3089</span> | 94.28 | 1.016 | 7.565 | 2.544 | +#> |.....................| 5.960e-07 | 1.022 | 1.054 | 1.464 | +#> |<span style='font-weight: bold;'> 60</span>| 291.20170 | 1.015 | -1.046 | -1.079 | 1.660 | +#> |.....................| -2.997 | -0.6203 | -0.8561 | -0.5898 | +#> | U| 291.2017 | 94.51 | -0.1669 | 2.103 | 2.538 | +#> |.....................| 5.960e-07 | 0.9900 | 1.170 | 1.484 | +#> | X|<span style='font-weight: bold;'> 291.2017</span> | 94.51 | 0.8462 | 8.187 | 2.538 | +#> |.....................| 5.960e-07 | 0.9900 | 1.170 | 1.484 | +#> | F| Forward Diff. | 39.51 | 0.9033 | 2.112 | 5.106 | +#> |.....................| 0.03418 | 2.863 | -2.696 | -0.7695 | +#> |<span style='font-weight: bold;'> 61</span>| 291.43833 | 1.017 | -1.033 | -1.136 | 1.600 | +#> |.....................| -2.997 | -0.6066 | -0.6851 | -0.5537 | +#> | U| 291.43833 | 94.73 | -0.1542 | 2.046 | 2.505 | +#> |.....................| 5.960e-07 | 1.000 | 1.348 | 1.523 | +#> | X|<span style='font-weight: bold;'> 291.43833</span> | 94.73 | 0.8571 | 7.739 | 2.505 | +#> |.....................| 5.960e-07 | 1.000 | 1.348 | 1.523 | +#> |<span style='font-weight: bold;'> 62</span>| 290.99248 | 1.014 | -1.041 | -1.101 | 1.637 | +#> |.....................| -2.997 | -0.6152 | -0.7907 | -0.5760 | +#> | U| 290.99248 | 94.43 | -0.1621 | 2.081 | 2.525 | +#> |.....................| 5.960e-07 | 0.9938 | 1.238 | 1.499 | +#> | X|<span style='font-weight: bold;'> 290.99248</span> | 94.43 | 0.8503 | 8.012 | 2.525 | +#> |.....................| 5.960e-07 | 0.9938 | 1.238 | 1.499 | +#> | F| Forward Diff. | 14.98 | 1.278 | 1.101 | 4.858 | +#> |.....................| 0.03639 | 3.021 | 0.9673 | -0.2780 | +#> |<span style='font-weight: bold;'> 63</span>| 291.02454 | 1.009 | -1.102 | -1.088 | 1.608 | +#> |.....................| -2.997 | -0.6330 | -0.7900 | -0.5542 | +#> | U| 291.02454 | 93.95 | -0.2228 | 2.094 | 2.510 | +#> |.....................| 5.960e-07 | 0.9808 | 1.239 | 1.522 | +#> | X|<span style='font-weight: bold;'> 291.02454</span> | 93.95 | 0.8003 | 8.118 | 2.510 | +#> |.....................| 5.960e-07 | 0.9808 | 1.239 | 1.522 | +#> |<span style='font-weight: bold;'> 64</span>| 291.12722 | 1.009 | -1.068 | -1.095 | 1.623 | +#> |.....................| -2.997 | -0.6237 | -0.7906 | -0.5663 | +#> | U| 291.12722 | 93.94 | -0.1892 | 2.087 | 2.518 | +#> |.....................| 5.960e-07 | 0.9876 | 1.238 | 1.509 | +#> | X|<span style='font-weight: bold;'> 291.12722</span> | 93.94 | 0.8276 | 8.057 | 2.518 | +#> |.....................| 5.960e-07 | 0.9876 | 1.238 | 1.509 | +#> |<span style='font-weight: bold;'> 65</span>| 291.20836 | 1.009 | -1.048 | -1.100 | 1.633 | +#> |.....................| -2.997 | -0.6180 | -0.7910 | -0.5738 | +#> | U| 291.20836 | 93.93 | -0.1686 | 2.082 | 2.523 | +#> |.....................| 5.960e-07 | 0.9918 | 1.238 | 1.501 | +#> | X|<span style='font-weight: bold;'> 291.20836</span> | 93.93 | 0.8449 | 8.020 | 2.523 | +#> |.....................| 5.960e-07 | 0.9918 | 1.238 | 1.501 | +#> |<span style='font-weight: bold;'> 66</span>| 290.99661 | 1.013 | -1.041 | -1.101 | 1.637 | +#> |.....................| -2.997 | -0.6156 | -0.7909 | -0.5760 | +#> | U| 290.99661 | 94.27 | -0.1623 | 2.081 | 2.525 | +#> |.....................| 5.960e-07 | 0.9935 | 1.238 | 1.499 | +#> | X|<span style='font-weight: bold;'> 290.99661</span> | 94.27 | 0.8502 | 8.011 | 2.525 | +#> |.....................| 5.960e-07 | 0.9935 | 1.238 | 1.499 | +#> |<span style='font-weight: bold;'> 67</span>| 290.98636 | 1.014 | -1.041 | -1.101 | 1.637 | +#> |.....................| -2.997 | -0.6154 | -0.7908 | -0.5760 | +#> | U| 290.98636 | 94.36 | -0.1622 | 2.081 | 2.525 | +#> |.....................| 5.960e-07 | 0.9936 | 1.238 | 1.499 | +#> | X|<span style='font-weight: bold;'> 290.98636</span> | 94.36 | 0.8503 | 8.012 | 2.525 | +#> |.....................| 5.960e-07 | 0.9936 | 1.238 | 1.499 | +#> | F| Forward Diff. | -1.956 | 1.256 | 0.9523 | 4.835 | +#> |.....................| 0.03649 | 3.031 | 0.9657 | -0.2695 | +#> |<span style='font-weight: bold;'> 68</span>| 290.98211 | 1.014 | -1.041 | -1.101 | 1.636 | +#> |.....................| -2.997 | -0.6157 | -0.7909 | -0.5760 | +#> | U| 290.98211 | 94.38 | -0.1623 | 2.081 | 2.525 | +#> |.....................| 5.960e-07 | 0.9934 | 1.238 | 1.499 | +#> | X|<span style='font-weight: bold;'> 290.98211</span> | 94.38 | 0.8502 | 8.011 | 2.525 | +#> |.....................| 5.960e-07 | 0.9934 | 1.238 | 1.499 | +#> |<span style='font-weight: bold;'> 69</span>| 290.97746 | 1.014 | -1.042 | -1.101 | 1.635 | +#> |.....................| -2.997 | -0.6167 | -0.7912 | -0.5759 | +#> | U| 290.97746 | 94.44 | -0.1627 | 2.081 | 2.524 | +#> |.....................| 5.960e-07 | 0.9927 | 1.237 | 1.499 | +#> | X|<span style='font-weight: bold;'> 290.97746</span> | 94.44 | 0.8498 | 8.009 | 2.524 | +#> |.....................| 5.960e-07 | 0.9927 | 1.237 | 1.499 | +#> | F| Forward Diff. | 17.70 | 1.268 | 1.108 | 4.855 | +#> |.....................| 0.04257 | 3.066 | 0.9427 | -0.2771 | +#> |<span style='font-weight: bold;'> 70</span>| 290.96180 | 1.014 | -1.044 | -1.101 | 1.634 | +#> |.....................| -2.997 | -0.6175 | -0.7910 | -0.5752 | +#> | U| 290.9618 | 94.36 | -0.1647 | 2.081 | 2.523 | +#> |.....................| 5.960e-07 | 0.9921 | 1.238 | 1.500 | +#> | X|<span style='font-weight: bold;'> 290.9618</span> | 94.36 | 0.8481 | 8.013 | 2.523 | +#> |.....................| 5.960e-07 | 0.9921 | 1.238 | 1.500 | +#> | F| Forward Diff. | -1.598 | 1.197 | 0.9704 | 4.824 | +#> |.....................| 0.03731 | 2.941 | 0.9551 | -0.2334 | +#> |<span style='font-weight: bold;'> 71</span>| 290.95083 | 1.014 | -1.044 | -1.101 | 1.632 | +#> |.....................| -2.997 | -0.6188 | -0.7915 | -0.5751 | +#> | U| 290.95083 | 94.43 | -0.1653 | 2.081 | 2.522 | +#> |.....................| 5.960e-07 | 0.9912 | 1.237 | 1.500 | +#> | X|<span style='font-weight: bold;'> 290.95083</span> | 94.43 | 0.8477 | 8.010 | 2.522 | +#> |.....................| 5.960e-07 | 0.9912 | 1.237 | 1.500 | +#> | F| Forward Diff. | 14.81 | 1.204 | 1.097 | 4.820 | +#> |.....................| 0.03908 | 3.014 | 0.9116 | -0.2462 | +#> |<span style='font-weight: bold;'> 72</span>| 290.93714 | 1.014 | -1.046 | -1.101 | 1.630 | +#> |.....................| -2.997 | -0.6196 | -0.7913 | -0.5744 | +#> | U| 290.93714 | 94.36 | -0.1673 | 2.081 | 2.522 | +#> |.....................| 5.960e-07 | 0.9906 | 1.237 | 1.501 | +#> | X|<span style='font-weight: bold;'> 290.93714</span> | 94.36 | 0.8459 | 8.014 | 2.522 | +#> |.....................| 5.960e-07 | 0.9906 | 1.237 | 1.501 | +#> | F| Forward Diff. | -1.943 | 1.135 | 0.9791 | 4.793 | +#> |.....................| 0.03360 | 3.051 | 0.9080 | -0.2200 | +#> |<span style='font-weight: bold;'> 73</span>| 290.92845 | 1.014 | -1.047 | -1.101 | 1.628 | +#> |.....................| -2.997 | -0.6209 | -0.7917 | -0.5743 | +#> | U| 290.92845 | 94.44 | -0.1678 | 2.081 | 2.521 | +#> |.....................| 5.960e-07 | 0.9896 | 1.237 | 1.501 | +#> | X|<span style='font-weight: bold;'> 290.92845</span> | 94.44 | 0.8455 | 8.011 | 2.521 | +#> |.....................| 5.960e-07 | 0.9896 | 1.237 | 1.501 | +#> | F| Forward Diff. | 17.70 | 1.147 | 1.134 | 4.752 | +#> |.....................| 0.02729 | 3.018 | 0.8867 | -0.2229 | +#> |<span style='font-weight: bold;'> 74</span>| 290.91300 | 1.014 | -1.049 | -1.100 | 1.627 | +#> |.....................| -2.997 | -0.6219 | -0.7915 | -0.5737 | +#> | U| 290.913 | 94.36 | -0.1698 | 2.081 | 2.520 | +#> |.....................| 5.960e-07 | 0.9889 | 1.237 | 1.501 | +#> | X|<span style='font-weight: bold;'> 290.913</span> | 94.36 | 0.8439 | 8.016 | 2.520 | +#> |.....................| 5.960e-07 | 0.9889 | 1.237 | 1.501 | +#> | F| Forward Diff. | -1.940 | 1.078 | 0.9981 | 4.722 | +#> |.....................| 0.04064 | 3.105 | 0.9143 | -0.1849 | +#> |<span style='font-weight: bold;'> 75</span>| 290.90444 | 1.014 | -1.049 | -1.101 | 1.625 | +#> |.....................| -2.997 | -0.6232 | -0.7919 | -0.5736 | +#> | U| 290.90444 | 94.44 | -0.1702 | 2.081 | 2.519 | +#> |.....................| 5.960e-07 | 0.9879 | 1.237 | 1.501 | +#> | X|<span style='font-weight: bold;'> 290.90444</span> | 94.44 | 0.8435 | 8.013 | 2.519 | +#> |.....................| 5.960e-07 | 0.9879 | 1.237 | 1.501 | +#> | F| Forward Diff. | 17.76 | 1.091 | 1.153 | 4.713 | +#> |.....................| 0.03198 | 2.950 | 0.8627 | -0.2001 | +#> |<span style='font-weight: bold;'> 76</span>| 290.88905 | 1.014 | -1.051 | -1.100 | 1.624 | +#> |.....................| -2.997 | -0.6243 | -0.7916 | -0.5732 | +#> | U| 290.88905 | 94.36 | -0.1722 | 2.082 | 2.518 | +#> |.....................| 5.960e-07 | 0.9872 | 1.237 | 1.502 | +#> | X|<span style='font-weight: bold;'> 290.88905</span> | 94.36 | 0.8418 | 8.019 | 2.518 | +#> |.....................| 5.960e-07 | 0.9872 | 1.237 | 1.502 | +#> | F| Forward Diff. | -2.112 | 1.022 | 1.016 | 4.749 | +#> |.....................| 0.03990 | 3.117 | 0.8810 | -0.1779 | +#> |<span style='font-weight: bold;'> 77</span>| 290.87937 | 1.014 | -1.052 | -1.100 | 1.622 | +#> |.....................| -2.997 | -0.6257 | -0.7918 | -0.5730 | +#> | U| 290.87937 | 94.43 | -0.1731 | 2.082 | 2.517 | +#> |.....................| 5.960e-07 | 0.9861 | 1.237 | 1.502 | +#> | X|<span style='font-weight: bold;'> 290.87937</span> | 94.43 | 0.8411 | 8.018 | 2.517 | +#> |.....................| 5.960e-07 | 0.9861 | 1.237 | 1.502 | +#> | F| Forward Diff. | 15.72 | 1.022 | 1.168 | 4.728 | +#> |.....................| 0.04036 | 3.118 | 0.8621 | -0.1806 | +#> |<span style='font-weight: bold;'> 78</span>| 290.86528 | 1.014 | -1.054 | -1.099 | 1.621 | +#> |.....................| -2.997 | -0.6269 | -0.7915 | -0.5727 | +#> | U| 290.86528 | 94.36 | -0.1749 | 2.083 | 2.516 | +#> |.....................| 5.960e-07 | 0.9853 | 1.237 | 1.502 | +#> | X|<span style='font-weight: bold;'> 290.86528</span> | 94.36 | 0.8396 | 8.025 | 2.516 | +#> |.....................| 5.960e-07 | 0.9853 | 1.237 | 1.502 | +#> | F| Forward Diff. | -2.089 | 0.9583 | 1.055 | 4.711 | +#> |.....................| 0.04161 | 3.089 | 0.8790 | -0.1555 | +#> |<span style='font-weight: bold;'> 79</span>| 290.85625 | 1.014 | -1.055 | -1.099 | 1.619 | +#> |.....................| -2.997 | -0.6283 | -0.7918 | -0.5726 | +#> | U| 290.85625 | 94.44 | -0.1756 | 2.082 | 2.515 | +#> |.....................| 5.960e-07 | 0.9842 | 1.237 | 1.503 | +#> | X|<span style='font-weight: bold;'> 290.85625</span> | 94.44 | 0.8389 | 8.023 | 2.515 | +#> |.....................| 5.960e-07 | 0.9842 | 1.237 | 1.503 | +#> | F| Forward Diff. | 16.77 | 0.9641 | 1.212 | 4.706 | +#> |.....................| 0.04215 | 3.138 | 0.8554 | -0.1643 | +#> |<span style='font-weight: bold;'> 80</span>| 290.84140 | 1.014 | -1.056 | -1.099 | 1.618 | +#> |.....................| -2.997 | -0.6296 | -0.7915 | -0.5724 | +#> | U| 290.8414 | 94.36 | -0.1774 | 2.083 | 2.515 | +#> |.....................| 5.960e-07 | 0.9833 | 1.237 | 1.503 | +#> | X|<span style='font-weight: bold;'> 290.8414</span> | 94.36 | 0.8375 | 8.030 | 2.515 | +#> |.....................| 5.960e-07 | 0.9833 | 1.237 | 1.503 | +#> | F| Forward Diff. | -1.641 | 0.9006 | 1.093 | 4.694 | +#> |.....................| 0.04205 | 3.147 | 0.8775 | -0.1452 | +#> |<span style='font-weight: bold;'> 81</span>| 290.83107 | 1.014 | -1.057 | -1.099 | 1.616 | +#> |.....................| -2.997 | -0.6310 | -0.7919 | -0.5723 | +#> | U| 290.83107 | 94.43 | -0.1778 | 2.083 | 2.514 | +#> |.....................| 5.960e-07 | 0.9823 | 1.237 | 1.503 | +#> | X|<span style='font-weight: bold;'> 290.83107</span> | 94.43 | 0.8371 | 8.026 | 2.514 | +#> |.....................| 5.960e-07 | 0.9823 | 1.237 | 1.503 | +#> | F| Forward Diff. | 15.22 | 0.9116 | 1.221 | 4.655 | +#> |.....................| 0.04015 | 3.140 | 0.8393 | -0.1501 | +#> |<span style='font-weight: bold;'> 82</span>| 290.81725 | 1.014 | -1.059 | -1.098 | 1.615 | +#> |.....................| -2.997 | -0.6323 | -0.7916 | -0.5722 | +#> | U| 290.81725 | 94.36 | -0.1795 | 2.084 | 2.513 | +#> |.....................| 5.960e-07 | 0.9813 | 1.237 | 1.503 | +#> | X|<span style='font-weight: bold;'> 290.81725</span> | 94.36 | 0.8357 | 8.034 | 2.513 | +#> |.....................| 5.960e-07 | 0.9813 | 1.237 | 1.503 | +#> | F| Forward Diff. | -2.105 | 0.8517 | 1.114 | 4.660 | +#> |.....................| 0.03878 | 3.162 | 0.8666 | -0.1313 | +#> |<span style='font-weight: bold;'> 83</span>| 290.80795 | 1.014 | -1.059 | -1.098 | 1.613 | +#> |.....................| -2.997 | -0.6339 | -0.7918 | -0.5722 | +#> | U| 290.80795 | 94.43 | -0.1802 | 2.084 | 2.512 | +#> |.....................| 5.960e-07 | 0.9802 | 1.237 | 1.503 | +#> | X|<span style='font-weight: bold;'> 290.80795</span> | 94.43 | 0.8351 | 8.033 | 2.512 | +#> |.....................| 5.960e-07 | 0.9802 | 1.237 | 1.503 | +#> | F| Forward Diff. | 16.11 | 0.8564 | 1.267 | 4.653 | +#> |.....................| 0.04303 | 3.178 | 0.8469 | -0.1413 | +#> |<span style='font-weight: bold;'> 84</span>| 290.79348 | 1.014 | -1.061 | -1.097 | 1.611 | +#> |.....................| -2.997 | -0.6353 | -0.7914 | -0.5722 | +#> | U| 290.79348 | 94.36 | -0.1817 | 2.084 | 2.511 | +#> |.....................| 5.960e-07 | 0.9792 | 1.237 | 1.503 | +#> | X|<span style='font-weight: bold;'> 290.79348</span> | 94.36 | 0.8338 | 8.041 | 2.511 | +#> |.....................| 5.960e-07 | 0.9792 | 1.237 | 1.503 | +#> | F| Forward Diff. | -1.840 | 0.7976 | 1.155 | 4.587 | +#> |.....................| 0.02723 | 3.115 | 0.8603 | -0.1275 | +#> |<span style='font-weight: bold;'> 85</span>| 290.78474 | 1.014 | -1.061 | -1.098 | 1.609 | +#> |.....................| -2.997 | -0.6367 | -0.7918 | -0.5721 | +#> | U| 290.78474 | 94.44 | -0.1821 | 2.084 | 2.510 | +#> |.....................| 5.960e-07 | 0.9781 | 1.237 | 1.503 | +#> | X|<span style='font-weight: bold;'> 290.78474</span> | 94.44 | 0.8335 | 8.036 | 2.510 | +#> |.....................| 5.960e-07 | 0.9781 | 1.237 | 1.503 | +#> | F| Forward Diff. | 17.19 | 0.8130 | 1.300 | 4.618 | +#> |.....................| 0.03919 | 3.190 | 0.8345 | -0.1328 | +#> |<span style='font-weight: bold;'> 86</span>| 290.76934 | 1.014 | -1.063 | -1.097 | 1.608 | +#> |.....................| -2.997 | -0.6382 | -0.7915 | -0.5722 | +#> | U| 290.76934 | 94.36 | -0.1836 | 2.085 | 2.510 | +#> |.....................| 5.960e-07 | 0.9771 | 1.237 | 1.503 | +#> | X|<span style='font-weight: bold;'> 290.76934</span> | 94.36 | 0.8322 | 8.044 | 2.510 | +#> |.....................| 5.960e-07 | 0.9771 | 1.237 | 1.503 | +#> | F| Forward Diff. | -1.203 | 0.7543 | 1.182 | 4.565 | +#> |.....................| 0.03490 | 3.166 | 0.8589 | -0.1256 | +#> |<span style='font-weight: bold;'> 87</span>| 290.75687 | 1.014 | -1.063 | -1.097 | 1.606 | +#> |.....................| -2.997 | -0.6397 | -0.7919 | -0.5722 | +#> | U| 290.75687 | 94.41 | -0.1840 | 2.084 | 2.508 | +#> |.....................| 5.960e-07 | 0.9760 | 1.237 | 1.503 | +#> | X|<span style='font-weight: bold;'> 290.75687</span> | 94.41 | 0.8319 | 8.039 | 2.508 | +#> |.....................| 5.960e-07 | 0.9760 | 1.237 | 1.503 | +#> |<span style='font-weight: bold;'> 88</span>| 290.75123 | 1.015 | -1.063 | -1.098 | 1.604 | +#> |.....................| -2.997 | -0.6414 | -0.7924 | -0.5721 | +#> | U| 290.75123 | 94.47 | -0.1844 | 2.084 | 2.507 | +#> |.....................| 5.960e-07 | 0.9747 | 1.236 | 1.503 | +#> | X|<span style='font-weight: bold;'> 290.75123</span> | 94.47 | 0.8316 | 8.034 | 2.507 | +#> |.....................| 5.960e-07 | 0.9747 | 1.236 | 1.503 | +#> | F| Forward Diff. | 26.23 | 0.7709 | 1.374 | 4.560 | +#> |.....................| 0.04194 | 3.213 | 0.7966 | -0.1353 | +#> |<span style='font-weight: bold;'> 89</span>| 290.71744 | 1.014 | -1.067 | -1.096 | 1.601 | +#> |.....................| -2.997 | -0.6448 | -0.7915 | -0.5726 | +#> | U| 290.71744 | 94.37 | -0.1875 | 2.086 | 2.506 | +#> |.....................| 5.960e-07 | 0.9722 | 1.237 | 1.503 | +#> | X|<span style='font-weight: bold;'> 290.71744</span> | 94.37 | 0.8291 | 8.054 | 2.506 | +#> |.....................| 5.960e-07 | 0.9722 | 1.237 | 1.503 | +#> | F| Forward Diff. | 0.1928 | 0.6670 | 1.256 | 4.555 | +#> |.....................| 0.04212 | 3.227 | 0.8436 | -0.1302 | +#> |<span style='font-weight: bold;'> 90</span>| 290.68496 | 1.013 | -1.067 | -1.097 | 1.597 | +#> |.....................| -2.997 | -0.6481 | -0.7924 | -0.5725 | +#> | U| 290.68496 | 94.35 | -0.1881 | 2.085 | 2.503 | +#> |.....................| 5.960e-07 | 0.9698 | 1.236 | 1.503 | +#> | X|<span style='font-weight: bold;'> 290.68496</span> | 94.35 | 0.8285 | 8.044 | 2.503 | +#> |.....................| 5.960e-07 | 0.9698 | 1.236 | 1.503 | +#> |<span style='font-weight: bold;'> 91</span>| 290.59496 | 1.013 | -1.069 | -1.101 | 1.583 | +#> |.....................| -2.997 | -0.6580 | -0.7950 | -0.5721 | +#> | U| 290.59496 | 94.29 | -0.1902 | 2.081 | 2.496 | +#> |.....................| 5.960e-07 | 0.9627 | 1.233 | 1.503 | +#> | X|<span style='font-weight: bold;'> 290.59496</span> | 94.29 | 0.8268 | 8.013 | 2.496 | +#> |.....................| 5.960e-07 | 0.9627 | 1.233 | 1.503 | +#> |<span style='font-weight: bold;'> 92</span>| 290.34408 | 1.010 | -1.077 | -1.116 | 1.527 | +#> |.....................| -2.997 | -0.6974 | -0.8053 | -0.5705 | +#> | U| 290.34408 | 94.08 | -0.1983 | 2.066 | 2.465 | +#> |.....................| 5.960e-07 | 0.9340 | 1.223 | 1.505 | +#> | X|<span style='font-weight: bold;'> 290.34408</span> | 94.08 | 0.8201 | 7.891 | 2.465 | +#> |.....................| 5.960e-07 | 0.9340 | 1.223 | 1.505 | +#> | F| Forward Diff. | -74.08 | 0.3588 | -0.1794 | 3.803 | +#> |.....................| 0.04205 | 3.779 | 0.06785 | -0.005437 | +#> |<span style='font-weight: bold;'> 93</span>| 289.95778 | 1.012 | -1.081 | -1.068 | 1.490 | +#> |.....................| -2.997 | -0.7670 | -0.7909 | -0.5845 | +#> | U| 289.95778 | 94.18 | -0.2020 | 2.114 | 2.445 | +#> |.....................| 5.960e-07 | 0.8834 | 1.238 | 1.490 | +#> | X|<span style='font-weight: bold;'> 289.95778</span> | 94.18 | 0.8171 | 8.282 | 2.445 | +#> |.....................| 5.960e-07 | 0.8834 | 1.238 | 1.490 | +#> |<span style='font-weight: bold;'> 94</span>| 289.83089 | 1.009 | -1.086 | -1.006 | 1.442 | +#> |.....................| -2.997 | -0.8563 | -0.7725 | -0.6025 | +#> | U| 289.83089 | 93.98 | -0.2067 | 2.176 | 2.418 | +#> |.....................| 5.960e-07 | 0.8185 | 1.257 | 1.470 | +#> | X|<span style='font-weight: bold;'> 289.83089</span> | 93.98 | 0.8132 | 8.812 | 2.418 | +#> |.....................| 5.960e-07 | 0.8185 | 1.257 | 1.470 | +#> | F| Forward Diff. | -65.01 | -0.01626 | 4.198 | 3.297 | +#> |.....................| 0.05097 | 3.562 | 1.909 | -0.3175 | +#> |<span style='font-weight: bold;'> 95</span>| 290.63229 | 1.014 | -1.226 | -1.068 | 1.287 | +#> |.....................| -2.997 | -1.101 | -0.7595 | -0.8853 | +#> | U| 290.63229 | 94.43 | -0.3467 | 2.113 | 2.333 | +#> |.....................| 5.960e-07 | 0.6407 | 1.271 | 1.167 | +#> | X|<span style='font-weight: bold;'> 290.63229</span> | 94.43 | 0.7070 | 8.277 | 2.333 | +#> |.....................| 5.960e-07 | 0.6407 | 1.271 | 1.167 | +#> |<span style='font-weight: bold;'> 96</span>| 289.56584 | 1.017 | -1.134 | -1.028 | 1.388 | +#> |.....................| -2.997 | -0.9416 | -0.7681 | -0.7007 | +#> | U| 289.56584 | 94.70 | -0.2554 | 2.154 | 2.389 | +#> |.....................| 5.960e-07 | 0.7564 | 1.261 | 1.365 | +#> | X|<span style='font-weight: bold;'> 289.56584</span> | 94.70 | 0.7746 | 8.619 | 2.389 | +#> |.....................| 5.960e-07 | 0.7564 | 1.261 | 1.365 | +#> | F| Forward Diff. | 59.80 | -0.9076 | 3.450 | 2.884 | +#> |.....................| 0.04168 | 2.247 | 1.868 | -3.338 | +#> |<span style='font-weight: bold;'> 97</span>| 289.16078 | 1.017 | -1.094 | -1.010 | 1.317 | +#> |.....................| -2.997 | -0.9798 | -0.7948 | -0.5837 | +#> | U| 289.16078 | 94.64 | -0.2152 | 2.172 | 2.350 | +#> |.....................| 5.960e-07 | 0.7287 | 1.234 | 1.491 | +#> | X|<span style='font-weight: bold;'> 289.16078</span> | 94.64 | 0.8063 | 8.773 | 2.350 | +#> |.....................| 5.960e-07 | 0.7287 | 1.234 | 1.491 | +#> | F| Forward Diff. | 50.77 | -0.08196 | 5.132 | 1.948 | +#> |.....................| 0.04608 | 1.474 | 0.6389 | 0.4459 | +#> |<span style='font-weight: bold;'> 98</span>| 290.19527 | 1.002 | -1.018 | -1.037 | 1.157 | +#> |.....................| -2.997 | -1.195 | -0.7989 | -0.6967 | +#> | U| 290.19527 | 93.32 | -0.1385 | 2.145 | 2.263 | +#> |.....................| 5.960e-07 | 0.5724 | 1.229 | 1.370 | +#> | X|<span style='font-weight: bold;'> 290.19527</span> | 93.32 | 0.8707 | 8.542 | 2.263 | +#> |.....................| 5.960e-07 | 0.5724 | 1.229 | 1.370 | +#> |<span style='font-weight: bold;'> 99</span>| 289.65582 | 1.003 | -1.072 | -1.019 | 1.270 | +#> |.....................| -2.997 | -1.043 | -0.7961 | -0.6170 | +#> | U| 289.65582 | 93.34 | -0.1926 | 2.163 | 2.324 | +#> |.....................| 5.960e-07 | 0.6825 | 1.232 | 1.455 | +#> | X|<span style='font-weight: bold;'> 289.65582</span> | 93.34 | 0.8248 | 8.696 | 2.324 | +#> |.....................| 5.960e-07 | 0.6825 | 1.232 | 1.455 | +#> |<span style='font-weight: bold;'> 100</span>| 289.77865 | 1.003 | -1.088 | -1.014 | 1.303 | +#> |.....................| -2.997 | -0.9984 | -0.7953 | -0.5934 | +#> | U| 289.77865 | 93.35 | -0.2087 | 2.168 | 2.342 | +#> |.....................| 5.960e-07 | 0.7151 | 1.233 | 1.480 | +#> | X|<span style='font-weight: bold;'> 289.77865</span> | 93.35 | 0.8116 | 8.742 | 2.342 | +#> |.....................| 5.960e-07 | 0.7151 | 1.233 | 1.480 | +#> |<span style='font-weight: bold;'> 101</span>| 289.23886 | 1.008 | -1.094 | -1.011 | 1.317 | +#> |.....................| -2.997 | -0.9800 | -0.7949 | -0.5837 | +#> | U| 289.23886 | 93.87 | -0.2152 | 2.171 | 2.350 | +#> |.....................| 5.960e-07 | 0.7285 | 1.234 | 1.491 | +#> | X|<span style='font-weight: bold;'> 289.23886</span> | 93.87 | 0.8064 | 8.765 | 2.350 | +#> |.....................| 5.960e-07 | 0.7285 | 1.234 | 1.491 | +#> |<span style='font-weight: bold;'> 102</span>| 289.07165 | 1.013 | -1.094 | -1.010 | 1.317 | +#> |.....................| -2.997 | -0.9799 | -0.7948 | -0.5837 | +#> | U| 289.07165 | 94.31 | -0.2152 | 2.171 | 2.350 | +#> |.....................| 5.960e-07 | 0.7286 | 1.234 | 1.491 | +#> | X|<span style='font-weight: bold;'> 289.07165</span> | 94.31 | 0.8063 | 8.770 | 2.350 | +#> |.....................| 5.960e-07 | 0.7286 | 1.234 | 1.491 | +#> | F| Forward Diff. | -0.3607 | -0.1394 | 4.728 | 1.937 | +#> |.....................| 0.04518 | 1.333 | 0.6601 | 0.3686 | +#> |<span style='font-weight: bold;'> 103</span>| 289.05383 | 1.013 | -1.094 | -1.014 | 1.315 | +#> |.....................| -2.997 | -0.9807 | -0.7952 | -0.5839 | +#> | U| 289.05383 | 94.33 | -0.2152 | 2.168 | 2.349 | +#> |.....................| 5.960e-07 | 0.7280 | 1.233 | 1.490 | +#> | X|<span style='font-weight: bold;'> 289.05383</span> | 94.33 | 0.8064 | 8.742 | 2.349 | +#> |.....................| 5.960e-07 | 0.7280 | 1.233 | 1.490 | +#> |<span style='font-weight: bold;'> 104</span>| 289.00706 | 1.014 | -1.094 | -1.023 | 1.312 | +#> |.....................| -2.997 | -0.9834 | -0.7965 | -0.5847 | +#> | U| 289.00706 | 94.40 | -0.2149 | 2.159 | 2.347 | +#> |.....................| 5.960e-07 | 0.7260 | 1.232 | 1.490 | +#> | X|<span style='font-weight: bold;'> 289.00706</span> | 94.40 | 0.8066 | 8.661 | 2.347 | +#> |.....................| 5.960e-07 | 0.7260 | 1.232 | 1.490 | +#> |<span style='font-weight: bold;'> 105</span>| 288.92149 | 1.016 | -1.093 | -1.055 | 1.299 | +#> |.....................| -2.997 | -0.9924 | -0.8010 | -0.5872 | +#> | U| 288.92149 | 94.63 | -0.2139 | 2.127 | 2.340 | +#> |.....................| 5.960e-07 | 0.7195 | 1.227 | 1.487 | +#> | X|<span style='font-weight: bold;'> 288.92149</span> | 94.63 | 0.8074 | 8.388 | 2.340 | +#> |.....................| 5.960e-07 | 0.7195 | 1.227 | 1.487 | +#> | F| Forward Diff. | 43.21 | 0.03028 | 3.221 | 1.557 | +#> |.....................| 0.008151 | 1.175 | 0.2057 | -0.1154 | +#> |<span style='font-weight: bold;'> 106</span>| 288.79118 | 1.014 | -1.096 | -1.061 | 1.264 | +#> |.....................| -2.997 | -1.027 | -0.7973 | -0.5956 | +#> | U| 288.79118 | 94.43 | -0.2174 | 2.120 | 2.321 | +#> |.....................| 5.960e-07 | 0.6943 | 1.231 | 1.478 | +#> | X|<span style='font-weight: bold;'> 288.79118</span> | 94.43 | 0.8046 | 8.334 | 2.321 | +#> |.....................| 5.960e-07 | 0.6943 | 1.231 | 1.478 | +#> | F| Forward Diff. | 10.81 | -0.06252 | 2.679 | 1.204 | +#> |.....................| 0.03262 | -0.1240 | 0.4322 | -0.2470 | +#> |<span style='font-weight: bold;'> 107</span>| 288.75294 | 1.013 | -1.132 | -1.081 | 1.252 | +#> |.....................| -2.997 | -1.011 | -0.7930 | -0.5741 | +#> | U| 288.75294 | 94.35 | -0.2531 | 2.101 | 2.314 | +#> |.....................| 5.960e-07 | 0.7060 | 1.235 | 1.501 | +#> | X|<span style='font-weight: bold;'> 288.75294</span> | 94.35 | 0.7764 | 8.173 | 2.314 | +#> |.....................| 5.960e-07 | 0.7060 | 1.235 | 1.501 | +#> | F| Forward Diff. | -3.091 | -0.8602 | 1.971 | 1.009 | +#> |.....................| 0.04475 | 0.5130 | 0.7746 | 0.2303 | +#> |<span style='font-weight: bold;'> 108</span>| 288.69834 | 1.013 | -1.093 | -1.104 | 1.232 | +#> |.....................| -2.997 | -1.011 | -0.7973 | -0.5721 | +#> | U| 288.69834 | 94.27 | -0.2136 | 2.078 | 2.303 | +#> |.....................| 5.960e-07 | 0.7061 | 1.231 | 1.503 | +#> | X|<span style='font-weight: bold;'> 288.69834</span> | 94.27 | 0.8077 | 7.987 | 2.303 | +#> |.....................| 5.960e-07 | 0.7061 | 1.231 | 1.503 | +#> | F| Forward Diff. | -16.61 | 0.06814 | 0.8311 | 0.6184 | +#> |.....................| 0.03151 | 0.5612 | 0.4558 | 0.3067 | +#> |<span style='font-weight: bold;'> 109</span>| 288.67099 | 1.014 | -1.108 | -1.122 | 1.197 | +#> |.....................| -2.997 | -1.038 | -0.8030 | -0.5758 | +#> | U| 288.67099 | 94.36 | -0.2285 | 2.060 | 2.284 | +#> |.....................| 5.960e-07 | 0.6866 | 1.225 | 1.499 | +#> | X|<span style='font-weight: bold;'> 288.67099</span> | 94.36 | 0.7957 | 7.847 | 2.284 | +#> |.....................| 5.960e-07 | 0.6866 | 1.225 | 1.499 | +#> | F| Forward Diff. | -4.975 | -0.2154 | 0.1983 | 0.1047 | +#> |.....................| 0.03564 | -0.4652 | 0.1266 | 0.2269 | +#> |<span style='font-weight: bold;'> 110</span>| 288.66432 | 1.014 | -1.097 | -1.128 | 1.196 | +#> |.....................| -2.997 | -1.027 | -0.8055 | -0.5813 | +#> | U| 288.66432 | 94.40 | -0.2184 | 2.053 | 2.283 | +#> |.....................| 5.960e-07 | 0.6941 | 1.222 | 1.493 | +#> | X|<span style='font-weight: bold;'> 288.66432</span> | 94.40 | 0.8038 | 7.793 | 2.283 | +#> |.....................| 5.960e-07 | 0.6941 | 1.222 | 1.493 | +#> | F| Forward Diff. | 0.3927 | 0.02780 | -0.05986 | 0.04997 | +#> |.....................| 0.03453 | -0.01180 | -0.03408 | 0.03556 | +#> |<span style='font-weight: bold;'> 111</span>| 288.66432 | 1.014 | -1.097 | -1.128 | 1.196 | +#> |.....................| -2.997 | -1.027 | -0.8055 | -0.5813 | +#> | U| 288.66432 | 94.40 | -0.2184 | 2.053 | 2.283 | +#> |.....................| 5.960e-07 | 0.6941 | 1.222 | 1.493 | +#> | X|<span style='font-weight: bold;'> 288.66432</span> | 94.40 | 0.8038 | 7.793 | 2.283 | +#> |.....................| 5.960e-07 | 0.6941 | 1.222 | 1.493 | +#> done</div><div class='output co'>#> <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#> <span class='message'>done</span></div><div class='output co'>#> <span class='warning'>Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span></div><div class='output co'>#> <span class='warning'>Warning: ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))</span></div><div class='output co'>#> <span class='warning'>Warning: last objective function was not at minimum, possible problems in optimization</span></div><div class='output co'>#> <span class='warning'>Warning: parameter estimate near boundary; covariance not calculated</span> +#> <span class='warning'> use 'getVarCov' to calculate anyway</span></div><div class='output co'>#> <span class='warning'>Warning: gradient problems with initial estimate; see $scaleInfo</span></div><div class='input'> +<span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span> + <span class='va'>f_nlmixr_sfo_saem</span><span class='op'>$</span><span class='va'>nm</span>, <span class='va'>f_nlmixr_sfo_focei</span><span class='op'>$</span><span class='va'>nm</span>, + <span class='va'>f_nlmixr_fomc_saem</span><span class='op'>$</span><span class='va'>nm</span>, <span class='va'>f_nlmixr_fomc_focei</span><span class='op'>$</span><span class='va'>nm</span>, + <span class='va'>f_nlmixr_dfop_saem</span><span class='op'>$</span><span class='va'>nm</span>, <span class='va'>f_nlmixr_dfop_focei</span><span class='op'>$</span><span class='va'>nm</span>, + <span class='va'>f_nlmixr_hs_saem</span><span class='op'>$</span><span class='va'>nm</span>, <span class='va'>f_nlmixr_hs_focei</span><span class='op'>$</span><span class='va'>nm</span>, + <span class='va'>f_nlmixr_fomc_saem_tc</span><span class='op'>$</span><span class='va'>nm</span>, <span class='va'>f_nlmixr_fomc_focei_tc</span><span class='op'>$</span><span class='va'>nm</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'>Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span></div><div class='output co'>#> </div><div class='output co'>#> df AIC +#> f_nlmixr_sfo_saem$nm 5 627.9197 +#> f_nlmixr_sfo_focei$nm 5 625.0512 +#> f_nlmixr_fomc_saem$nm 7 463.7245 +#> f_nlmixr_fomc_focei$nm 7 468.0822 +#> f_nlmixr_dfop_saem$nm 9 518.5794 +#> f_nlmixr_dfop_focei$nm 9 537.6309 +#> f_nlmixr_hs_saem$nm 9 535.9011 +#> f_nlmixr_hs_focei$nm 9 544.7590 +#> f_nlmixr_fomc_saem_tc$nm 8 463.5871 +#> f_nlmixr_fomc_focei_tc$nm 8 470.0733</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='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></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 class='op'>)</span> +</div><div class='output co'>#> [1] 468.0781</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='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span><span class='op'>(</span><span class='va'>f_mmkin_parent</span><span class='op'>[</span><span class='st'>"HS"</span>, <span class='op'>]</span><span class='op'>)</span><span class='op'>)</span> +</div><div class='output co'>#> [1] 535.609</div><div class='input'> +<span class='co'># nlme is comparable to nlmixr with focei, saem finds a better</span> +<span class='co'># solution, the two-component error model does not improve it</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_nlmixr_fomc_saem</span><span class='op'>)</span> +</div><div class='img'><img src='nlmixr.mmkin-1.png' alt='' width='700' height='433' /></div><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>, <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><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'>fomc_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'>"FOMC"</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><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>, <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><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_mmkin_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/list.html'>list</a></span><span class='op'>(</span> + <span class='st'>"SFO-SFO"</span> <span class='op'>=</span> <span class='va'>sfo_sfo</span>, <span class='st'>"FOMC-SFO"</span> <span class='op'>=</span> <span class='va'>fomc_sfo</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'>"const"</span><span class='op'>)</span> +<span class='va'>f_mmkin_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'>list</a></span><span class='op'>(</span> + <span class='st'>"SFO-SFO"</span> <span class='op'>=</span> <span class='va'>sfo_sfo</span>, <span class='st'>"FOMC-SFO"</span> <span class='op'>=</span> <span class='va'>fomc_sfo</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 class='va'>f_mmkin_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'>"SFO-SFO"</span> <span class='op'>=</span> <span class='va'>sfo_sfo</span>, <span class='st'>"FOMC-SFO"</span> <span class='op'>=</span> <span class='va'>fomc_sfo</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'>"tc"</span><span class='op'>)</span> + +<span class='co'># A single constant variance is currently only possible with est = 'focei' in nlmixr</span> +<span class='va'>f_nlmixr_sfo_sfo_focei_const</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_const</span><span class='op'>[</span><span class='st'>"SFO-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Model:</span></div><div class='output co'>#> <span class='message'>cmt(parent);</span> +#> <span class='message'>cmt(A1);</span> +#> <span class='message'>rx_expr_6~ETA[1]+THETA[1];</span> +#> <span class='message'>parent(0)=rx_expr_6;</span> +#> <span class='message'>rx_expr_7~ETA[2]+THETA[2];</span> +#> <span class='message'>rx_expr_10~exp(rx_expr_7);</span> +#> <span class='message'>d/dt(parent)=-rx_expr_10*parent;</span> +#> <span class='message'>rx_expr_8~ETA[3]+THETA[3];</span> +#> <span class='message'>rx_expr_11~exp(rx_expr_8);</span> +#> <span class='message'>d/dt(A1)=-rx_expr_11*A1+rx_expr_10*parent*f_parent_to_A1;</span> +#> <span class='message'>rx_expr_0~CMT==2;</span> +#> <span class='message'>rx_expr_1~CMT==1;</span> +#> <span class='message'>rx_expr_2~1-(rx_expr_0);</span> +#> <span class='message'>rx_yj_~2*(rx_expr_2)*(rx_expr_1)+2*(rx_expr_0);</span> +#> <span class='message'>rx_expr_3~(rx_expr_0);</span> +#> <span class='message'>rx_expr_5~(rx_expr_2);</span> +#> <span class='message'>rx_expr_13~rx_expr_5*(rx_expr_1);</span> +#> <span class='message'>rx_lambda_~rx_expr_13+rx_expr_3;</span> +#> <span class='message'>rx_hi_~rx_expr_13+rx_expr_3;</span> +#> <span class='message'>rx_low_~0;</span> +#> <span class='message'>rx_expr_4~A1*(rx_expr_0);</span> +#> <span class='message'>rx_expr_9~parent*(rx_expr_2);</span> +#> <span class='message'>rx_expr_14~rx_expr_9*(rx_expr_1);</span> +#> <span class='message'>rx_pred_=(rx_expr_4+rx_expr_14)*(rx_expr_0)+(rx_expr_4+rx_expr_14)*(rx_expr_2)*(rx_expr_1);</span> +#> <span class='message'>rx_expr_12~Rx_pow_di(THETA[5],2);</span> +#> <span class='message'>rx_r_=(rx_expr_0)*rx_expr_12+(rx_expr_2)*(rx_expr_1)*rx_expr_12;</span> +#> <span class='message'>parent_0=THETA[1];</span> +#> <span class='message'>log_k_parent=THETA[2];</span> +#> <span class='message'>log_k_A1=THETA[3];</span> +#> <span class='message'>f_parent_qlogis=THETA[4];</span> +#> <span class='message'>sigma=THETA[5];</span> +#> <span class='message'>eta.parent_0=ETA[1];</span> +#> <span class='message'>eta.log_k_parent=ETA[2];</span> +#> <span class='message'>eta.log_k_A1=ETA[3];</span> +#> <span class='message'>eta.f_parent_qlogis=ETA[4];</span> +#> <span class='message'>parent_0_model=rx_expr_6;</span> +#> <span class='message'>k_parent=rx_expr_10;</span> +#> <span class='message'>k_A1=rx_expr_11;</span> +#> <span class='message'>f_parent=1/(1+exp(-(ETA[4]+THETA[4])));</span> +#> <span class='message'>tad=tad();</span> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 5.607 0.474 6.078</span></div><div class='input'><span class='va'>f_nlmixr_fomc_sfo_focei_const</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_const</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Model:</span></div><div class='output co'>#> <span class='message'>cmt(parent);</span> +#> <span class='message'>cmt(A1);</span> +#> <span class='message'>rx_expr_6~ETA[1]+THETA[1];</span> +#> <span class='message'>parent(0)=rx_expr_6;</span> +#> <span class='message'>rx_expr_7~ETA[4]+THETA[4];</span> +#> <span class='message'>rx_expr_8~ETA[5]+THETA[5];</span> +#> <span class='message'>rx_expr_13~exp(-(rx_expr_8));</span> +#> <span class='message'>rx_expr_15~t*rx_expr_13;</span> +#> <span class='message'>rx_expr_16~1+rx_expr_15;</span> +#> <span class='message'>rx_expr_18~rx_expr_7-(rx_expr_8);</span> +#> <span class='message'>rx_expr_20~exp(rx_expr_18);</span> +#> <span class='message'>d/dt(parent)=-rx_expr_20*parent/(rx_expr_16);</span> +#> <span class='message'>rx_expr_9~ETA[2]+THETA[2];</span> +#> <span class='message'>rx_expr_11~exp(rx_expr_9);</span> +#> <span class='message'>d/dt(A1)=-rx_expr_11*A1+rx_expr_20*parent*f_parent_to_A1/(rx_expr_16);</span> +#> <span class='message'>rx_expr_0~CMT==2;</span> +#> <span class='message'>rx_expr_1~CMT==1;</span> +#> <span class='message'>rx_expr_2~1-(rx_expr_0);</span> +#> <span class='message'>rx_yj_~2*(rx_expr_2)*(rx_expr_1)+2*(rx_expr_0);</span> +#> <span class='message'>rx_expr_3~(rx_expr_0);</span> +#> <span class='message'>rx_expr_5~(rx_expr_2);</span> +#> <span class='message'>rx_expr_14~rx_expr_5*(rx_expr_1);</span> +#> <span class='message'>rx_lambda_~rx_expr_14+rx_expr_3;</span> +#> <span class='message'>rx_hi_~rx_expr_14+rx_expr_3;</span> +#> <span class='message'>rx_low_~0;</span> +#> <span class='message'>rx_expr_4~A1*(rx_expr_0);</span> +#> <span class='message'>rx_expr_10~parent*(rx_expr_2);</span> +#> <span class='message'>rx_expr_17~rx_expr_10*(rx_expr_1);</span> +#> <span class='message'>rx_pred_=(rx_expr_4+rx_expr_17)*(rx_expr_0)+(rx_expr_4+rx_expr_17)*(rx_expr_2)*(rx_expr_1);</span> +#> <span class='message'>rx_expr_12~Rx_pow_di(THETA[6],2);</span> +#> <span class='message'>rx_r_=(rx_expr_0)*rx_expr_12+(rx_expr_2)*(rx_expr_1)*rx_expr_12;</span> +#> <span class='message'>parent_0=THETA[1];</span> +#> <span class='message'>log_k_A1=THETA[2];</span> +#> <span class='message'>f_parent_qlogis=THETA[3];</span> +#> <span class='message'>log_alpha=THETA[4];</span> +#> <span class='message'>log_beta=THETA[5];</span> +#> <span class='message'>sigma=THETA[6];</span> +#> <span class='message'>eta.parent_0=ETA[1];</span> +#> <span class='message'>eta.log_k_A1=ETA[2];</span> +#> <span class='message'>eta.f_parent_qlogis=ETA[3];</span> +#> <span class='message'>eta.log_alpha=ETA[4];</span> +#> <span class='message'>eta.log_beta=ETA[5];</span> +#> <span class='message'>parent_0_model=rx_expr_6;</span> +#> <span class='message'>k_A1=rx_expr_11;</span> +#> <span class='message'>alpha=exp(rx_expr_7);</span> +#> <span class='message'>beta=exp(rx_expr_8);</span> +#> <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span> +#> <span class='message'>tad=tad();</span> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 6.853 0.393 7.242</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_const</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_const</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Model:</span></div><div class='output co'>#> <span class='message'>cmt(parent);</span> +#> <span class='message'>cmt(A1);</span> +#> <span class='message'>rx_expr_6~ETA[1]+THETA[1];</span> +#> <span class='message'>parent(0)=rx_expr_6;</span> +#> <span class='message'>rx_expr_7~ETA[4]+THETA[4];</span> +#> <span class='message'>rx_expr_8~ETA[6]+THETA[6];</span> +#> <span class='message'>rx_expr_9~ETA[5]+THETA[5];</span> +#> <span class='message'>rx_expr_12~exp(rx_expr_7);</span> +#> <span class='message'>rx_expr_13~exp(rx_expr_9);</span> +#> <span class='message'>rx_expr_15~t*rx_expr_12;</span> +#> <span class='message'>rx_expr_16~t*rx_expr_13;</span> +#> <span class='message'>rx_expr_18~exp(-(rx_expr_8));</span> +#> <span class='message'>rx_expr_20~1+rx_expr_18;</span> +#> <span class='message'>rx_expr_25~1/(rx_expr_20);</span> +#> <span class='message'>rx_expr_27~(rx_expr_25);</span> +#> <span class='message'>rx_expr_28~1-rx_expr_27;</span> +#> <span class='message'>d/dt(parent)=-parent*(exp(rx_expr_7-rx_expr_15)/(rx_expr_20)+exp(rx_expr_9-rx_expr_16)*(rx_expr_28))/(exp(-t*rx_expr_12)/(rx_expr_20)+exp(-t*rx_expr_13)*(rx_expr_28));</span> +#> <span class='message'>rx_expr_10~ETA[2]+THETA[2];</span> +#> <span class='message'>rx_expr_14~exp(rx_expr_10);</span> +#> <span class='message'>d/dt(A1)=-rx_expr_14*A1+parent*f_parent_to_A1*(exp(rx_expr_7-rx_expr_15)/(rx_expr_20)+exp(rx_expr_9-rx_expr_16)*(rx_expr_28))/(exp(-t*rx_expr_12)/(rx_expr_20)+exp(-t*rx_expr_13)*(rx_expr_28));</span> +#> <span class='message'>rx_expr_0~CMT==2;</span> +#> <span class='message'>rx_expr_1~CMT==1;</span> +#> <span class='message'>rx_expr_2~1-(rx_expr_0);</span> +#> <span class='message'>rx_yj_~2*(rx_expr_2)*(rx_expr_1)+2*(rx_expr_0);</span> +#> <span class='message'>rx_expr_3~(rx_expr_0);</span> +#> <span class='message'>rx_expr_5~(rx_expr_2);</span> +#> <span class='message'>rx_expr_19~rx_expr_5*(rx_expr_1);</span> +#> <span class='message'>rx_lambda_~rx_expr_19+rx_expr_3;</span> +#> <span class='message'>rx_hi_~rx_expr_19+rx_expr_3;</span> +#> <span class='message'>rx_low_~0;</span> +#> <span class='message'>rx_expr_4~A1*(rx_expr_0);</span> +#> <span class='message'>rx_expr_11~parent*(rx_expr_2);</span> +#> <span class='message'>rx_expr_23~rx_expr_11*(rx_expr_1);</span> +#> <span class='message'>rx_pred_=(rx_expr_4+rx_expr_23)*(rx_expr_0)+(rx_expr_4+rx_expr_23)*(rx_expr_2)*(rx_expr_1);</span> +#> <span class='message'>rx_expr_17~Rx_pow_di(THETA[7],2);</span> +#> <span class='message'>rx_r_=(rx_expr_0)*rx_expr_17+(rx_expr_2)*(rx_expr_1)*rx_expr_17;</span> +#> <span class='message'>parent_0=THETA[1];</span> +#> <span class='message'>log_k_A1=THETA[2];</span> +#> <span class='message'>f_parent_qlogis=THETA[3];</span> +#> <span class='message'>log_k1=THETA[4];</span> +#> <span class='message'>log_k2=THETA[5];</span> +#> <span class='message'>g_qlogis=THETA[6];</span> +#> <span class='message'>sigma=THETA[7];</span> +#> <span class='message'>eta.parent_0=ETA[1];</span> +#> <span class='message'>eta.log_k_A1=ETA[2];</span> +#> <span class='message'>eta.f_parent_qlogis=ETA[3];</span> +#> <span class='message'>eta.log_k1=ETA[4];</span> +#> <span class='message'>eta.log_k2=ETA[5];</span> +#> <span class='message'>eta.g_qlogis=ETA[6];</span> +#> <span class='message'>parent_0_model=rx_expr_6;</span> +#> <span class='message'>k_A1=rx_expr_14;</span> +#> <span class='message'>k1=rx_expr_12;</span> +#> <span class='message'>k2=rx_expr_13;</span> +#> <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span> +#> <span class='message'>g=1/(rx_expr_20);</span> +#> <span class='message'>tad=tad();</span> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 15.18 0.414 15.6</span></div><div class='input'> +<span class='co'># Variance by variable is supported by 'saem' and 'focei'</span> +<span class='va'>f_nlmixr_fomc_sfo_saem_obs</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_obs</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"saem"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'>→ generate SAEM model</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='error'>Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 1.22 0.089 1.31</span></div><div class='input'><span class='va'>f_nlmixr_fomc_sfo_focei_obs</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_obs</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Model:</span></div><div class='output co'>#> <span class='message'>cmt(parent);</span> +#> <span class='message'>cmt(A1);</span> +#> <span class='message'>rx_expr_6~ETA[1]+THETA[1];</span> +#> <span class='message'>parent(0)=rx_expr_6;</span> +#> <span class='message'>rx_expr_7~ETA[4]+THETA[4];</span> +#> <span class='message'>rx_expr_8~ETA[5]+THETA[5];</span> +#> <span class='message'>rx_expr_12~exp(-(rx_expr_8));</span> +#> <span class='message'>rx_expr_14~t*rx_expr_12;</span> +#> <span class='message'>rx_expr_15~1+rx_expr_14;</span> +#> <span class='message'>rx_expr_17~rx_expr_7-(rx_expr_8);</span> +#> <span class='message'>rx_expr_19~exp(rx_expr_17);</span> +#> <span class='message'>d/dt(parent)=-rx_expr_19*parent/(rx_expr_15);</span> +#> <span class='message'>rx_expr_9~ETA[2]+THETA[2];</span> +#> <span class='message'>rx_expr_11~exp(rx_expr_9);</span> +#> <span class='message'>d/dt(A1)=-rx_expr_11*A1+rx_expr_19*parent*f_parent_to_A1/(rx_expr_15);</span> +#> <span class='message'>rx_expr_0~CMT==2;</span> +#> <span class='message'>rx_expr_1~CMT==1;</span> +#> <span class='message'>rx_expr_2~1-(rx_expr_0);</span> +#> <span class='message'>rx_yj_~2*(rx_expr_2)*(rx_expr_1)+2*(rx_expr_0);</span> +#> <span class='message'>rx_expr_3~(rx_expr_0);</span> +#> <span class='message'>rx_expr_5~(rx_expr_2);</span> +#> <span class='message'>rx_expr_13~rx_expr_5*(rx_expr_1);</span> +#> <span class='message'>rx_lambda_~rx_expr_13+rx_expr_3;</span> +#> <span class='message'>rx_hi_~rx_expr_13+rx_expr_3;</span> +#> <span class='message'>rx_low_~0;</span> +#> <span class='message'>rx_expr_4~A1*(rx_expr_0);</span> +#> <span class='message'>rx_expr_10~parent*(rx_expr_2);</span> +#> <span class='message'>rx_expr_16~rx_expr_10*(rx_expr_1);</span> +#> <span class='message'>rx_pred_=(rx_expr_4+rx_expr_16)*(rx_expr_0)+(rx_expr_4+rx_expr_16)*(rx_expr_2)*(rx_expr_1);</span> +#> <span class='message'>rx_r_=(rx_expr_0)*Rx_pow_di(THETA[7],2)+(rx_expr_2)*(rx_expr_1)*Rx_pow_di(THETA[6],2);</span> +#> <span class='message'>parent_0=THETA[1];</span> +#> <span class='message'>log_k_A1=THETA[2];</span> +#> <span class='message'>f_parent_qlogis=THETA[3];</span> +#> <span class='message'>log_alpha=THETA[4];</span> +#> <span class='message'>log_beta=THETA[5];</span> +#> <span class='message'>sigma_parent=THETA[6];</span> +#> <span class='message'>sigma_A1=THETA[7];</span> +#> <span class='message'>eta.parent_0=ETA[1];</span> +#> <span class='message'>eta.log_k_A1=ETA[2];</span> +#> <span class='message'>eta.f_parent_qlogis=ETA[3];</span> +#> <span class='message'>eta.log_alpha=ETA[4];</span> +#> <span class='message'>eta.log_beta=ETA[5];</span> +#> <span class='message'>parent_0_model=rx_expr_6;</span> +#> <span class='message'>k_A1=rx_expr_11;</span> +#> <span class='message'>alpha=exp(rx_expr_7);</span> +#> <span class='message'>beta=exp(rx_expr_8);</span> +#> <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span> +#> <span class='message'>tad=tad();</span> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 6.784 0.418 7.2</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_saem_obs</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_obs</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"saem"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'>→ generate SAEM model</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='error'>Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 1.357 0.096 1.452</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_obs</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_obs</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Model:</span></div><div class='output co'>#> <span class='message'>cmt(parent);</span> +#> <span class='message'>cmt(A1);</span> +#> <span class='message'>rx_expr_6~ETA[1]+THETA[1];</span> +#> <span class='message'>parent(0)=rx_expr_6;</span> +#> <span class='message'>rx_expr_7~ETA[4]+THETA[4];</span> +#> <span class='message'>rx_expr_8~ETA[6]+THETA[6];</span> +#> <span class='message'>rx_expr_9~ETA[5]+THETA[5];</span> +#> <span class='message'>rx_expr_12~exp(rx_expr_7);</span> +#> <span class='message'>rx_expr_13~exp(rx_expr_9);</span> +#> <span class='message'>rx_expr_15~t*rx_expr_12;</span> +#> <span class='message'>rx_expr_16~t*rx_expr_13;</span> +#> <span class='message'>rx_expr_17~exp(-(rx_expr_8));</span> +#> <span class='message'>rx_expr_19~1+rx_expr_17;</span> +#> <span class='message'>rx_expr_24~1/(rx_expr_19);</span> +#> <span class='message'>rx_expr_26~(rx_expr_24);</span> +#> <span class='message'>rx_expr_27~1-rx_expr_26;</span> +#> <span class='message'>d/dt(parent)=-parent*(exp(rx_expr_7-rx_expr_15)/(rx_expr_19)+exp(rx_expr_9-rx_expr_16)*(rx_expr_27))/(exp(-t*rx_expr_12)/(rx_expr_19)+exp(-t*rx_expr_13)*(rx_expr_27));</span> +#> <span class='message'>rx_expr_10~ETA[2]+THETA[2];</span> +#> <span class='message'>rx_expr_14~exp(rx_expr_10);</span> +#> <span class='message'>d/dt(A1)=-rx_expr_14*A1+parent*f_parent_to_A1*(exp(rx_expr_7-rx_expr_15)/(rx_expr_19)+exp(rx_expr_9-rx_expr_16)*(rx_expr_27))/(exp(-t*rx_expr_12)/(rx_expr_19)+exp(-t*rx_expr_13)*(rx_expr_27));</span> +#> <span class='message'>rx_expr_0~CMT==2;</span> +#> <span class='message'>rx_expr_1~CMT==1;</span> +#> <span class='message'>rx_expr_2~1-(rx_expr_0);</span> +#> <span class='message'>rx_yj_~2*(rx_expr_2)*(rx_expr_1)+2*(rx_expr_0);</span> +#> <span class='message'>rx_expr_3~(rx_expr_0);</span> +#> <span class='message'>rx_expr_5~(rx_expr_2);</span> +#> <span class='message'>rx_expr_18~rx_expr_5*(rx_expr_1);</span> +#> <span class='message'>rx_lambda_~rx_expr_18+rx_expr_3;</span> +#> <span class='message'>rx_hi_~rx_expr_18+rx_expr_3;</span> +#> <span class='message'>rx_low_~0;</span> +#> <span class='message'>rx_expr_4~A1*(rx_expr_0);</span> +#> <span class='message'>rx_expr_11~parent*(rx_expr_2);</span> +#> <span class='message'>rx_expr_22~rx_expr_11*(rx_expr_1);</span> +#> <span class='message'>rx_pred_=(rx_expr_4+rx_expr_22)*(rx_expr_0)+(rx_expr_4+rx_expr_22)*(rx_expr_2)*(rx_expr_1);</span> +#> <span class='message'>rx_r_=(rx_expr_0)*Rx_pow_di(THETA[8],2)+(rx_expr_2)*(rx_expr_1)*Rx_pow_di(THETA[7],2);</span> +#> <span class='message'>parent_0=THETA[1];</span> +#> <span class='message'>log_k_A1=THETA[2];</span> +#> <span class='message'>f_parent_qlogis=THETA[3];</span> +#> <span class='message'>log_k1=THETA[4];</span> +#> <span class='message'>log_k2=THETA[5];</span> +#> <span class='message'>g_qlogis=THETA[6];</span> +#> <span class='message'>sigma_parent=THETA[7];</span> +#> <span class='message'>sigma_A1=THETA[8];</span> +#> <span class='message'>eta.parent_0=ETA[1];</span> +#> <span class='message'>eta.log_k_A1=ETA[2];</span> +#> <span class='message'>eta.f_parent_qlogis=ETA[3];</span> +#> <span class='message'>eta.log_k1=ETA[4];</span> +#> <span class='message'>eta.log_k2=ETA[5];</span> +#> <span class='message'>eta.g_qlogis=ETA[6];</span> +#> <span class='message'>parent_0_model=rx_expr_6;</span> +#> <span class='message'>k_A1=rx_expr_14;</span> +#> <span class='message'>k1=rx_expr_12;</span> +#> <span class='message'>k2=rx_expr_13;</span> +#> <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span> +#> <span class='message'>g=1/(rx_expr_19);</span> +#> <span class='message'>tad=tad();</span> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 15.17 0.353 15.52</span></div><div class='input'> +<span class='co'># Identical two-component error for all variables is only possible with</span> +<span class='co'># est = 'focei' in nlmixr</span> +<span class='va'>f_nlmixr_fomc_sfo_focei_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Model:</span></div><div class='output co'>#> <span class='message'>cmt(parent);</span> +#> <span class='message'>cmt(A1);</span> +#> <span class='message'>rx_expr_6~ETA[1]+THETA[1];</span> +#> <span class='message'>parent(0)=rx_expr_6;</span> +#> <span class='message'>rx_expr_7~ETA[4]+THETA[4];</span> +#> <span class='message'>rx_expr_8~ETA[5]+THETA[5];</span> +#> <span class='message'>rx_expr_14~exp(-(rx_expr_8));</span> +#> <span class='message'>rx_expr_16~t*rx_expr_14;</span> +#> <span class='message'>rx_expr_17~1+rx_expr_16;</span> +#> <span class='message'>rx_expr_19~rx_expr_7-(rx_expr_8);</span> +#> <span class='message'>rx_expr_21~exp(rx_expr_19);</span> +#> <span class='message'>d/dt(parent)=-rx_expr_21*parent/(rx_expr_17);</span> +#> <span class='message'>rx_expr_9~ETA[2]+THETA[2];</span> +#> <span class='message'>rx_expr_11~exp(rx_expr_9);</span> +#> <span class='message'>d/dt(A1)=-rx_expr_11*A1+rx_expr_21*parent*f_parent_to_A1/(rx_expr_17);</span> +#> <span class='message'>rx_expr_0~CMT==2;</span> +#> <span class='message'>rx_expr_1~CMT==1;</span> +#> <span class='message'>rx_expr_2~1-(rx_expr_0);</span> +#> <span class='message'>rx_yj_~2*(rx_expr_2)*(rx_expr_1)+2*(rx_expr_0);</span> +#> <span class='message'>rx_expr_3~(rx_expr_0);</span> +#> <span class='message'>rx_expr_5~(rx_expr_2);</span> +#> <span class='message'>rx_expr_15~rx_expr_5*(rx_expr_1);</span> +#> <span class='message'>rx_lambda_~rx_expr_15+rx_expr_3;</span> +#> <span class='message'>rx_hi_~rx_expr_15+rx_expr_3;</span> +#> <span class='message'>rx_low_~0;</span> +#> <span class='message'>rx_expr_4~A1*(rx_expr_0);</span> +#> <span class='message'>rx_expr_10~parent*(rx_expr_2);</span> +#> <span class='message'>rx_expr_18~rx_expr_10*(rx_expr_1);</span> +#> <span class='message'>rx_pred_=(rx_expr_4+rx_expr_18)*(rx_expr_0)+(rx_expr_4+rx_expr_18)*(rx_expr_2)*(rx_expr_1);</span> +#> <span class='message'>rx_expr_12~Rx_pow_di(THETA[7],2);</span> +#> <span class='message'>rx_expr_13~Rx_pow_di(THETA[6],2);</span> +#> <span class='message'>rx_r_=(Rx_pow_di(((rx_expr_4+rx_expr_18)*(rx_expr_0)+(rx_expr_4+rx_expr_18)*(rx_expr_2)*(rx_expr_1)),2)*rx_expr_12+rx_expr_13)*(rx_expr_0)+(rx_expr_12*Rx_pow_di(((rx_expr_4+rx_expr_18)*(rx_expr_1)),2)+rx_expr_13)*(rx_expr_2)*(rx_expr_1);</span> +#> <span class='message'>parent_0=THETA[1];</span> +#> <span class='message'>log_k_A1=THETA[2];</span> +#> <span class='message'>f_parent_qlogis=THETA[3];</span> +#> <span class='message'>log_alpha=THETA[4];</span> +#> <span class='message'>log_beta=THETA[5];</span> +#> <span class='message'>sigma_low=THETA[6];</span> +#> <span class='message'>rsd_high=THETA[7];</span> +#> <span class='message'>eta.parent_0=ETA[1];</span> +#> <span class='message'>eta.log_k_A1=ETA[2];</span> +#> <span class='message'>eta.f_parent_qlogis=ETA[3];</span> +#> <span class='message'>eta.log_alpha=ETA[4];</span> +#> <span class='message'>eta.log_beta=ETA[5];</span> +#> <span class='message'>parent_0_model=rx_expr_6;</span> +#> <span class='message'>k_A1=rx_expr_11;</span> +#> <span class='message'>alpha=exp(rx_expr_7);</span> +#> <span class='message'>beta=exp(rx_expr_8);</span> +#> <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span> +#> <span class='message'>tad=tad();</span> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 8.708 0.429 9.135</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Model:</span></div><div class='output co'>#> <span class='message'>cmt(parent);</span> +#> <span class='message'>cmt(A1);</span> +#> <span class='message'>rx_expr_6~ETA[1]+THETA[1];</span> +#> <span class='message'>parent(0)=rx_expr_6;</span> +#> <span class='message'>rx_expr_7~ETA[4]+THETA[4];</span> +#> <span class='message'>rx_expr_8~ETA[6]+THETA[6];</span> +#> <span class='message'>rx_expr_9~ETA[5]+THETA[5];</span> +#> <span class='message'>rx_expr_12~exp(rx_expr_7);</span> +#> <span class='message'>rx_expr_13~exp(rx_expr_9);</span> +#> <span class='message'>rx_expr_15~t*rx_expr_12;</span> +#> <span class='message'>rx_expr_16~t*rx_expr_13;</span> +#> <span class='message'>rx_expr_19~exp(-(rx_expr_8));</span> +#> <span class='message'>rx_expr_21~1+rx_expr_19;</span> +#> <span class='message'>rx_expr_26~1/(rx_expr_21);</span> +#> <span class='message'>rx_expr_28~(rx_expr_26);</span> +#> <span class='message'>rx_expr_29~1-rx_expr_28;</span> +#> <span class='message'>d/dt(parent)=-parent*(exp(rx_expr_7-rx_expr_15)/(rx_expr_21)+exp(rx_expr_9-rx_expr_16)*(rx_expr_29))/(exp(-t*rx_expr_12)/(rx_expr_21)+exp(-t*rx_expr_13)*(rx_expr_29));</span> +#> <span class='message'>rx_expr_10~ETA[2]+THETA[2];</span> +#> <span class='message'>rx_expr_14~exp(rx_expr_10);</span> +#> <span class='message'>d/dt(A1)=-rx_expr_14*A1+parent*f_parent_to_A1*(exp(rx_expr_7-rx_expr_15)/(rx_expr_21)+exp(rx_expr_9-rx_expr_16)*(rx_expr_29))/(exp(-t*rx_expr_12)/(rx_expr_21)+exp(-t*rx_expr_13)*(rx_expr_29));</span> +#> <span class='message'>rx_expr_0~CMT==2;</span> +#> <span class='message'>rx_expr_1~CMT==1;</span> +#> <span class='message'>rx_expr_2~1-(rx_expr_0);</span> +#> <span class='message'>rx_yj_~2*(rx_expr_2)*(rx_expr_1)+2*(rx_expr_0);</span> +#> <span class='message'>rx_expr_3~(rx_expr_0);</span> +#> <span class='message'>rx_expr_5~(rx_expr_2);</span> +#> <span class='message'>rx_expr_20~rx_expr_5*(rx_expr_1);</span> +#> <span class='message'>rx_lambda_~rx_expr_20+rx_expr_3;</span> +#> <span class='message'>rx_hi_~rx_expr_20+rx_expr_3;</span> +#> <span class='message'>rx_low_~0;</span> +#> <span class='message'>rx_expr_4~A1*(rx_expr_0);</span> +#> <span class='message'>rx_expr_11~parent*(rx_expr_2);</span> +#> <span class='message'>rx_expr_24~rx_expr_11*(rx_expr_1);</span> +#> <span class='message'>rx_pred_=(rx_expr_4+rx_expr_24)*(rx_expr_0)+(rx_expr_4+rx_expr_24)*(rx_expr_2)*(rx_expr_1);</span> +#> <span class='message'>rx_expr_17~Rx_pow_di(THETA[8],2);</span> +#> <span class='message'>rx_expr_18~Rx_pow_di(THETA[7],2);</span> +#> <span class='message'>rx_r_=(Rx_pow_di(((rx_expr_4+rx_expr_24)*(rx_expr_0)+(rx_expr_4+rx_expr_24)*(rx_expr_2)*(rx_expr_1)),2)*rx_expr_17+rx_expr_18)*(rx_expr_0)+(rx_expr_17*Rx_pow_di(((rx_expr_4+rx_expr_24)*(rx_expr_1)),2)+rx_expr_18)*(rx_expr_2)*(rx_expr_1);</span> +#> <span class='message'>parent_0=THETA[1];</span> +#> <span class='message'>log_k_A1=THETA[2];</span> +#> <span class='message'>f_parent_qlogis=THETA[3];</span> +#> <span class='message'>log_k1=THETA[4];</span> +#> <span class='message'>log_k2=THETA[5];</span> +#> <span class='message'>g_qlogis=THETA[6];</span> +#> <span class='message'>sigma_low=THETA[7];</span> +#> <span class='message'>rsd_high=THETA[8];</span> +#> <span class='message'>eta.parent_0=ETA[1];</span> +#> <span class='message'>eta.log_k_A1=ETA[2];</span> +#> <span class='message'>eta.f_parent_qlogis=ETA[3];</span> +#> <span class='message'>eta.log_k1=ETA[4];</span> +#> <span class='message'>eta.log_k2=ETA[5];</span> +#> <span class='message'>eta.g_qlogis=ETA[6];</span> +#> <span class='message'>parent_0_model=rx_expr_6;</span> +#> <span class='message'>k_A1=rx_expr_14;</span> +#> <span class='message'>k1=rx_expr_12;</span> +#> <span class='message'>k2=rx_expr_13;</span> +#> <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span> +#> <span class='message'>g=1/(rx_expr_21);</span> +#> <span class='message'>tad=tad();</span> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 18.05 0.446 18.5</span></div><div class='input'> +<span class='co'># Two-component error by variable is possible with both estimation methods</span> +<span class='co'># Variance by variable is supported by 'saem' and 'focei'</span> +<span class='va'>f_nlmixr_fomc_sfo_saem_obs_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"saem"</span>, + error_model <span class='op'>=</span> <span class='st'>"obs_tc"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='error'>Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 0.763 0.036 0.799</span></div><div class='input'><span class='va'>f_nlmixr_fomc_sfo_focei_obs_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span>, + error_model <span class='op'>=</span> <span class='st'>"obs_tc"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Model:</span></div><div class='output co'>#> <span class='message'>cmt(parent);</span> +#> <span class='message'>cmt(A1);</span> +#> <span class='message'>rx_expr_6~ETA[1]+THETA[1];</span> +#> <span class='message'>parent(0)=rx_expr_6;</span> +#> <span class='message'>rx_expr_7~ETA[4]+THETA[4];</span> +#> <span class='message'>rx_expr_8~ETA[5]+THETA[5];</span> +#> <span class='message'>rx_expr_12~exp(-(rx_expr_8));</span> +#> <span class='message'>rx_expr_14~t*rx_expr_12;</span> +#> <span class='message'>rx_expr_15~1+rx_expr_14;</span> +#> <span class='message'>rx_expr_17~rx_expr_7-(rx_expr_8);</span> +#> <span class='message'>rx_expr_19~exp(rx_expr_17);</span> +#> <span class='message'>d/dt(parent)=-rx_expr_19*parent/(rx_expr_15);</span> +#> <span class='message'>rx_expr_9~ETA[2]+THETA[2];</span> +#> <span class='message'>rx_expr_11~exp(rx_expr_9);</span> +#> <span class='message'>d/dt(A1)=-rx_expr_11*A1+rx_expr_19*parent*f_parent_to_A1/(rx_expr_15);</span> +#> <span class='message'>rx_expr_0~CMT==2;</span> +#> <span class='message'>rx_expr_1~CMT==1;</span> +#> <span class='message'>rx_expr_2~1-(rx_expr_0);</span> +#> <span class='message'>rx_yj_~2*(rx_expr_2)*(rx_expr_1)+2*(rx_expr_0);</span> +#> <span class='message'>rx_expr_3~(rx_expr_0);</span> +#> <span class='message'>rx_expr_5~(rx_expr_2);</span> +#> <span class='message'>rx_expr_13~rx_expr_5*(rx_expr_1);</span> +#> <span class='message'>rx_lambda_~rx_expr_13+rx_expr_3;</span> +#> <span class='message'>rx_hi_~rx_expr_13+rx_expr_3;</span> +#> <span class='message'>rx_low_~0;</span> +#> <span class='message'>rx_expr_4~A1*(rx_expr_0);</span> +#> <span class='message'>rx_expr_10~parent*(rx_expr_2);</span> +#> <span class='message'>rx_expr_16~rx_expr_10*(rx_expr_1);</span> +#> <span class='message'>rx_pred_=(rx_expr_4+rx_expr_16)*(rx_expr_0)+(rx_expr_4+rx_expr_16)*(rx_expr_2)*(rx_expr_1);</span> +#> <span class='message'>rx_r_=(Rx_pow_di(((rx_expr_4+rx_expr_16)*(rx_expr_0)+(rx_expr_4+rx_expr_16)*(rx_expr_2)*(rx_expr_1)),2)*Rx_pow_di(THETA[9],2)+Rx_pow_di(THETA[8],2))*(rx_expr_0)+(Rx_pow_di(THETA[7],2)*Rx_pow_di(((rx_expr_4+rx_expr_16)*(rx_expr_1)),2)+Rx_pow_di(THETA[6],2))*(rx_expr_2)*(rx_expr_1);</span> +#> <span class='message'>parent_0=THETA[1];</span> +#> <span class='message'>log_k_A1=THETA[2];</span> +#> <span class='message'>f_parent_qlogis=THETA[3];</span> +#> <span class='message'>log_alpha=THETA[4];</span> +#> <span class='message'>log_beta=THETA[5];</span> +#> <span class='message'>sigma_low_parent=THETA[6];</span> +#> <span class='message'>rsd_high_parent=THETA[7];</span> +#> <span class='message'>sigma_low_A1=THETA[8];</span> +#> <span class='message'>rsd_high_A1=THETA[9];</span> +#> <span class='message'>eta.parent_0=ETA[1];</span> +#> <span class='message'>eta.log_k_A1=ETA[2];</span> +#> <span class='message'>eta.f_parent_qlogis=ETA[3];</span> +#> <span class='message'>eta.log_alpha=ETA[4];</span> +#> <span class='message'>eta.log_beta=ETA[5];</span> +#> <span class='message'>parent_0_model=rx_expr_6;</span> +#> <span class='message'>k_A1=rx_expr_11;</span> +#> <span class='message'>alpha=exp(rx_expr_7);</span> +#> <span class='message'>beta=exp(rx_expr_8);</span> +#> <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span> +#> <span class='message'>tad=tad();</span> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 8.196 0.388 8.584</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_saem_obs_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"saem"</span>, + error_model <span class='op'>=</span> <span class='st'>"obs_tc"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='error'>Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 0.843 0.028 0.871</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_obs_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span>, + error_model <span class='op'>=</span> <span class='st'>"obs_tc"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Model:</span></div><div class='output co'>#> <span class='message'>cmt(parent);</span> +#> <span class='message'>cmt(A1);</span> +#> <span class='message'>rx_expr_6~ETA[1]+THETA[1];</span> +#> <span class='message'>parent(0)=rx_expr_6;</span> +#> <span class='message'>rx_expr_7~ETA[4]+THETA[4];</span> +#> <span class='message'>rx_expr_8~ETA[6]+THETA[6];</span> +#> <span class='message'>rx_expr_9~ETA[5]+THETA[5];</span> +#> <span class='message'>rx_expr_12~exp(rx_expr_7);</span> +#> <span class='message'>rx_expr_13~exp(rx_expr_9);</span> +#> <span class='message'>rx_expr_15~t*rx_expr_12;</span> +#> <span class='message'>rx_expr_16~t*rx_expr_13;</span> +#> <span class='message'>rx_expr_17~exp(-(rx_expr_8));</span> +#> <span class='message'>rx_expr_19~1+rx_expr_17;</span> +#> <span class='message'>rx_expr_24~1/(rx_expr_19);</span> +#> <span class='message'>rx_expr_26~(rx_expr_24);</span> +#> <span class='message'>rx_expr_27~1-rx_expr_26;</span> +#> <span class='message'>d/dt(parent)=-parent*(exp(rx_expr_7-rx_expr_15)/(rx_expr_19)+exp(rx_expr_9-rx_expr_16)*(rx_expr_27))/(exp(-t*rx_expr_12)/(rx_expr_19)+exp(-t*rx_expr_13)*(rx_expr_27));</span> +#> <span class='message'>rx_expr_10~ETA[2]+THETA[2];</span> +#> <span class='message'>rx_expr_14~exp(rx_expr_10);</span> +#> <span class='message'>d/dt(A1)=-rx_expr_14*A1+parent*f_parent_to_A1*(exp(rx_expr_7-rx_expr_15)/(rx_expr_19)+exp(rx_expr_9-rx_expr_16)*(rx_expr_27))/(exp(-t*rx_expr_12)/(rx_expr_19)+exp(-t*rx_expr_13)*(rx_expr_27));</span> +#> <span class='message'>rx_expr_0~CMT==2;</span> +#> <span class='message'>rx_expr_1~CMT==1;</span> +#> <span class='message'>rx_expr_2~1-(rx_expr_0);</span> +#> <span class='message'>rx_yj_~2*(rx_expr_2)*(rx_expr_1)+2*(rx_expr_0);</span> +#> <span class='message'>rx_expr_3~(rx_expr_0);</span> +#> <span class='message'>rx_expr_5~(rx_expr_2);</span> +#> <span class='message'>rx_expr_18~rx_expr_5*(rx_expr_1);</span> +#> <span class='message'>rx_lambda_~rx_expr_18+rx_expr_3;</span> +#> <span class='message'>rx_hi_~rx_expr_18+rx_expr_3;</span> +#> <span class='message'>rx_low_~0;</span> +#> <span class='message'>rx_expr_4~A1*(rx_expr_0);</span> +#> <span class='message'>rx_expr_11~parent*(rx_expr_2);</span> +#> <span class='message'>rx_expr_22~rx_expr_11*(rx_expr_1);</span> +#> <span class='message'>rx_pred_=(rx_expr_4+rx_expr_22)*(rx_expr_0)+(rx_expr_4+rx_expr_22)*(rx_expr_2)*(rx_expr_1);</span> +#> <span class='message'>rx_r_=(rx_expr_0)*(Rx_pow_di(((rx_expr_4+rx_expr_22)*(rx_expr_0)+(rx_expr_4+rx_expr_22)*(rx_expr_2)*(rx_expr_1)),2)*Rx_pow_di(THETA[10],2)+Rx_pow_di(THETA[9],2))+(Rx_pow_di(THETA[8],2)*Rx_pow_di(((rx_expr_4+rx_expr_22)*(rx_expr_1)),2)+Rx_pow_di(THETA[7],2))*(rx_expr_2)*(rx_expr_1);</span> +#> <span class='message'>parent_0=THETA[1];</span> +#> <span class='message'>log_k_A1=THETA[2];</span> +#> <span class='message'>f_parent_qlogis=THETA[3];</span> +#> <span class='message'>log_k1=THETA[4];</span> +#> <span class='message'>log_k2=THETA[5];</span> +#> <span class='message'>g_qlogis=THETA[6];</span> +#> <span class='message'>sigma_low_parent=THETA[7];</span> +#> <span class='message'>rsd_high_parent=THETA[8];</span> +#> <span class='message'>sigma_low_A1=THETA[9];</span> +#> <span class='message'>rsd_high_A1=THETA[10];</span> +#> <span class='message'>eta.parent_0=ETA[1];</span> +#> <span class='message'>eta.log_k_A1=ETA[2];</span> +#> <span class='message'>eta.f_parent_qlogis=ETA[3];</span> +#> <span class='message'>eta.log_k1=ETA[4];</span> +#> <span class='message'>eta.log_k2=ETA[5];</span> +#> <span class='message'>eta.g_qlogis=ETA[6];</span> +#> <span class='message'>parent_0_model=rx_expr_6;</span> +#> <span class='message'>k_A1=rx_expr_14;</span> +#> <span class='message'>k1=rx_expr_12;</span> +#> <span class='message'>k2=rx_expr_13;</span> +#> <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span> +#> <span class='message'>g=1/(rx_expr_19);</span> +#> <span class='message'>tad=tad();</span> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 17.73 0.411 18.14</span></div><div class='input'> +<span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span> + <span class='va'>f_nlmixr_sfo_sfo_focei_const</span><span class='op'>$</span><span class='va'>nm</span>, + <span class='va'>f_nlmixr_fomc_sfo_focei_const</span><span class='op'>$</span><span class='va'>nm</span>, + <span class='va'>f_nlmixr_dfop_sfo_focei_const</span><span class='op'>$</span><span class='va'>nm</span>, + <span class='va'>f_nlmixr_fomc_sfo_saem_obs</span><span class='op'>$</span><span class='va'>nm</span>, + <span class='va'>f_nlmixr_fomc_sfo_focei_obs</span><span class='op'>$</span><span class='va'>nm</span>, + <span class='va'>f_nlmixr_dfop_sfo_saem_obs</span><span class='op'>$</span><span class='va'>nm</span>, + <span class='va'>f_nlmixr_dfop_sfo_focei_obs</span><span class='op'>$</span><span class='va'>nm</span>, + <span class='va'>f_nlmixr_fomc_sfo_focei_tc</span><span class='op'>$</span><span class='va'>nm</span>, + <span class='va'>f_nlmixr_dfop_sfo_focei_tc</span><span class='op'>$</span><span class='va'>nm</span>, + <span class='va'>f_nlmixr_fomc_sfo_saem_obs_tc</span><span class='op'>$</span><span class='va'>nm</span>, + <span class='va'>f_nlmixr_fomc_sfo_focei_obs_tc</span><span class='op'>$</span><span class='va'>nm</span>, + <span class='va'>f_nlmixr_dfop_sfo_saem_obs_tc</span><span class='op'>$</span><span class='va'>nm</span>, + <span class='va'>f_nlmixr_dfop_sfo_focei_obs_tc</span><span class='op'>$</span><span class='va'>nm</span> +<span class='op'>)</span> +</div><div class='output co'>#> <span class='error'>Error in AIC(f_nlmixr_sfo_sfo_focei_const$nm, f_nlmixr_fomc_sfo_focei_const$nm, f_nlmixr_dfop_sfo_focei_const$nm, f_nlmixr_fomc_sfo_saem_obs$nm, f_nlmixr_fomc_sfo_focei_obs$nm, f_nlmixr_dfop_sfo_saem_obs$nm, f_nlmixr_dfop_sfo_focei_obs$nm, f_nlmixr_fomc_sfo_focei_tc$nm, f_nlmixr_dfop_sfo_focei_tc$nm, f_nlmixr_fomc_sfo_saem_obs_tc$nm, f_nlmixr_fomc_sfo_focei_obs_tc$nm, f_nlmixr_dfop_sfo_saem_obs_tc$nm, f_nlmixr_dfop_sfo_focei_obs_tc$nm): object 'f_nlmixr_sfo_sfo_focei_const' not found</span></div><div class='input'><span class='co'># Currently, FOMC-SFO with two-component error by variable fitted by focei gives the</span> +<span class='co'># lowest AIC</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_nlmixr_fomc_sfo_focei_obs_tc</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='error'>Error in plot(f_nlmixr_fomc_sfo_focei_obs_tc): object 'f_nlmixr_fomc_sfo_focei_obs_tc' not found</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'>f_nlmixr_fomc_sfo_focei_obs_tc</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='error'>Error in summary(f_nlmixr_fomc_sfo_focei_obs_tc): object 'f_nlmixr_fomc_sfo_focei_obs_tc' not found</span></div><div class='input'><span class='co'># }</span> +</div></pre> + </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>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> +</div> + + </footer> + </div> + + + + + </body> +</html> + + diff --git a/docs/dev/reference/nobs.mkinfit.html b/docs/dev/reference/nobs.mkinfit.html index 621eb622..0b6c963c 100644 --- a/docs/dev/reference/nobs.mkinfit.html +++ b/docs/dev/reference/nobs.mkinfit.html @@ -72,7 +72,7 @@ </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -121,7 +121,7 @@ <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> diff --git a/docs/dev/reference/parms.html b/docs/dev/reference/parms.html index ba0e89bb..9f6f4225 100644 --- a/docs/dev/reference/parms.html +++ b/docs/dev/reference/parms.html @@ -74,7 +74,7 @@ considering the error structure that was assumed for the 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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -123,7 +123,7 @@ considering the error structure that was assumed for the fit." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -219,10 +219,10 @@ such matrices is returned.</p> #> #> $DFOP #> Dataset 7 -#> parent_0 91.058971597 +#> parent_0 91.058971589 #> k1 0.044946770 #> k2 0.002868336 -#> g 0.526942414 +#> g 0.526942415 #> sigma 2.221302196 #> </div><div class='input'><span class='fu'>parms</span><span class='op'>(</span><span class='va'>fits</span><span class='op'>)</span> </div><div class='output co'>#> $SFO @@ -233,17 +233,17 @@ such matrices is returned.</p> #> #> $FOMC #> Dataset 6 Dataset 7 Dataset 8 Dataset 9 Dataset 10 -#> parent_0 95.558575 92.6837649 90.719787 98.383939 94.8481458 +#> parent_0 95.558575 92.6837649 90.719787 98.383939 94.8481459 #> alpha 1.338667 0.4967832 1.639099 1.074460 0.2805272 #> beta 13.033315 14.1451255 5.007077 4.397126 6.9052224 #> sigma 1.847671 1.9167519 1.066063 3.146056 1.6222778 #> #> $DFOP #> Dataset 6 Dataset 7 Dataset 8 Dataset 9 Dataset 10 -#> parent_0 96.55213663 91.058971597 90.34509493 98.14858820 94.311323733 +#> parent_0 96.55213663 91.058971589 90.34509493 98.14858820 94.311323734 #> k1 0.21954588 0.044946770 0.41232288 0.31697588 0.080663857 #> k2 0.02957934 0.002868336 0.07581766 0.03260384 0.003425417 -#> g 0.44845068 0.526942414 0.66091967 0.65322767 0.342652880 +#> g 0.44845068 0.526942415 0.66091967 0.65322767 0.342652880 #> sigma 1.35690468 2.221302196 1.34169076 2.87159846 1.942067831 #> </div><div class='input'><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> </div><div class='output co'>#> $SFO diff --git a/docs/dev/reference/plot.mixed.mmkin-1.png b/docs/dev/reference/plot.mixed.mmkin-1.png Binary files differindex 5cb33214..be3c664a 100644 --- a/docs/dev/reference/plot.mixed.mmkin-1.png +++ b/docs/dev/reference/plot.mixed.mmkin-1.png diff --git a/docs/dev/reference/plot.mixed.mmkin-2.png b/docs/dev/reference/plot.mixed.mmkin-2.png Binary files differindex c0d67204..b0e43b11 100644 --- a/docs/dev/reference/plot.mixed.mmkin-2.png +++ b/docs/dev/reference/plot.mixed.mmkin-2.png diff --git a/docs/dev/reference/plot.mixed.mmkin-3.png b/docs/dev/reference/plot.mixed.mmkin-3.png Binary files differindex 5e00afe6..a9b96726 100644 --- a/docs/dev/reference/plot.mixed.mmkin-3.png +++ b/docs/dev/reference/plot.mixed.mmkin-3.png diff --git a/docs/dev/reference/plot.mixed.mmkin-4.png b/docs/dev/reference/plot.mixed.mmkin-4.png Binary files differindex 6a5f3b9c..22219e5e 100644 --- a/docs/dev/reference/plot.mixed.mmkin-4.png +++ b/docs/dev/reference/plot.mixed.mmkin-4.png diff --git a/docs/dev/reference/plot.mixed.mmkin.html b/docs/dev/reference/plot.mixed.mmkin.html index 55c411e7..a4222991 100644 --- a/docs/dev/reference/plot.mixed.mmkin.html +++ b/docs/dev/reference/plot.mixed.mmkin.html @@ -72,7 +72,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> </span> </div> @@ -157,11 +157,13 @@ xlim <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/range.html'>range</a></span><span class='op'>(</span><span class='va'>x</span><span class='op'>$</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'>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>, 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'>ifelse</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'>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'>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'>ifelse</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'>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'>ceiling</a></span><span class='op'>(</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'>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'>0.03</span> <span class='op'>+</span> <span class='fl'>0.08</span> <span class='op'>*</span> <span class='va'>nrow.legend</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'>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>, @@ -212,6 +214,16 @@ predicted values?</p></td> from <a href='mkinpredict.html'>mkinpredict</a> with a compatible <a href='mkinmod.html'>mkinmod</a>.</p></td> </tr> <tr> + <th>test_log_parms</th> + <td><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></td> + </tr> + <tr> + <th>conf.level</th> + <td><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></td> + </tr> + <tr> <th>ymax</th> <td><p>Vector of maximum y axis values</p></td> </tr> @@ -278,16 +290,21 @@ corresponding model prediction lines for the different datasets.</p></td> </div><div class='img'><img src='plot.mixed.mmkin-1.png' alt='' width='700' height='433' /></div><div class='input'> <span class='co'># For this fit we need to increase pnlsMaxiter, and we increase the</span> <span class='co'># tolerance in order to speed up the fit for this example evaluation</span> +<span class='co'># It still takes 20 seconds to run</span> <span class='va'>f_nlme</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>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'>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 class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_nlme</span><span class='op'>)</span> </div><div class='img'><img src='plot.mixed.mmkin-2.png' alt='' width='700' height='433' /></div><div class='input'> <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> </div><div class='output co'>#> Running main SAEM algorithm -#> [1] "Mon Dec 21 05:58:23 2020" +#> [1] "Fri Jun 11 10:56:37 2021" #> .... #> Minimisation finished -#> [1] "Mon Dec 21 05:58:30 2020"</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'>f_saem</span><span class='op'>)</span> +#> [1] "Fri Jun 11 10:56:44 2021"</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'>f_saem</span><span class='op'>)</span> </div><div class='img'><img src='plot.mixed.mmkin-3.png' alt='' width='700' height='433' /></div><div class='input'> +<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'>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 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> +</div><div class='output co'>#> <span class='error'>Error in nlmix(f_obs): could not find function "nlmix"</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'>f_nlmix</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='error'>Error in plot(f_nlmix): object 'f_nlmix' not found</span></div><div class='input'> <span class='co'># We can overlay the two variants if we generate predictions</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 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>, diff --git a/docs/dev/reference/plot.mkinfit-2.png b/docs/dev/reference/plot.mkinfit-2.png Binary files differindex 376c812f..a11d1680 100644 --- a/docs/dev/reference/plot.mkinfit-2.png +++ b/docs/dev/reference/plot.mkinfit-2.png diff --git a/docs/dev/reference/plot.mkinfit-5.png b/docs/dev/reference/plot.mkinfit-5.png Binary files differindex bc44de88..6631aa68 100644 --- a/docs/dev/reference/plot.mkinfit-5.png +++ b/docs/dev/reference/plot.mkinfit-5.png diff --git a/docs/dev/reference/plot.mkinfit-6.png b/docs/dev/reference/plot.mkinfit-6.png Binary files differindex eb8cbd92..946b20c5 100644 --- a/docs/dev/reference/plot.mkinfit-6.png +++ b/docs/dev/reference/plot.mkinfit-6.png diff --git a/docs/dev/reference/plot.mkinfit-7.png b/docs/dev/reference/plot.mkinfit-7.png Binary files differindex 92a664f4..10807ea8 100644 --- a/docs/dev/reference/plot.mkinfit-7.png +++ b/docs/dev/reference/plot.mkinfit-7.png diff --git a/docs/dev/reference/plot.mkinfit.html b/docs/dev/reference/plot.mkinfit.html index c5bfb528..c5249ecd 100644 --- a/docs/dev/reference/plot.mkinfit.html +++ b/docs/dev/reference/plot.mkinfit.html @@ -74,7 +74,7 @@ observed data together with the solution of the fitted model." /> </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> diff --git a/docs/dev/reference/plot.mmkin-1.png b/docs/dev/reference/plot.mmkin-1.png Binary files differindex f12b7907..647dfb8a 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 e3127554..1bc1c9db 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 5448976e..50d6ffac 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 9a25fc50..e049fa16 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 82b422b5..2421995b 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 ee80c6e4..9ca0df94 100644 --- a/docs/dev/reference/plot.mmkin.html +++ b/docs/dev/reference/plot.mmkin.html @@ -76,7 +76,7 @@ the fit of at least one model to the same dataset is shown." /> </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> diff --git a/docs/dev/reference/plot.nafta.html b/docs/dev/reference/plot.nafta.html index 9fc59f94..c24fba99 100644 --- a/docs/dev/reference/plot.nafta.html +++ b/docs/dev/reference/plot.nafta.html @@ -73,7 +73,7 @@ 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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -122,7 +122,7 @@ function (SFO, then IORE, then DFOP)." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> diff --git a/docs/dev/reference/reexports.html b/docs/dev/reference/reexports.html index f12a2690..f5ace044 100644 --- a/docs/dev/reference/reexports.html +++ b/docs/dev/reference/reexports.html @@ -47,6 +47,8 @@ below to see their documentation. nlmenlme + nlmixrnlmixr + " /> @@ -79,7 +81,7 @@ below to see their documentation. </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> </span> </div> @@ -128,7 +130,7 @@ below to see their documentation. <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -146,7 +148,7 @@ below to see their documentation. <div class="col-md-9 contents"> <div class="page-header"> <h1>Objects exported from other packages</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>, <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/master/R/lrtest.mkinfit.R'><code>R/lrtest.mkinfit.R</code></a>, <a href='https://github.com/jranke/mkin/blob/master/R/nlme.mmkin.R'><code>R/nlme.mmkin.R</code></a>, <a href='https://github.com/jranke/mkin/blob/master/R/nlmixr.R'><code>R/nlmixr.R</code></a></small> <div class="hidden name"><code>reexports.Rd</code></div> </div> @@ -158,6 +160,8 @@ below to see their documentation.</p> <dt>nlme</dt><dd><p><code><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></code></p></dd> + <dt>nlmixr</dt><dd><p><code><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></code></p></dd> + </dl> </div> diff --git a/docs/dev/reference/residuals.mkinfit.html b/docs/dev/reference/residuals.mkinfit.html index 95114dae..3f518ab7 100644 --- a/docs/dev/reference/residuals.mkinfit.html +++ b/docs/dev/reference/residuals.mkinfit.html @@ -72,7 +72,7 @@ </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -121,7 +121,7 @@ <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -175,7 +175,7 @@ standard deviation obtained from the fitted error model?</p></td> </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] -0.04695354 2.08761977 -1.27002287</div></pre> +#> [7] -0.04695355 2.08761977 -1.27002287</div></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> <nav id="toc" data-toggle="toc" class="sticky-top"> diff --git a/docs/dev/reference/saem-1.png b/docs/dev/reference/saem-1.png Binary files differindex 2df248bb..0e87d741 100644 --- a/docs/dev/reference/saem-1.png +++ b/docs/dev/reference/saem-1.png diff --git a/docs/dev/reference/saem-2.png b/docs/dev/reference/saem-2.png Binary files differindex d4a2c1be..456a4c58 100644 --- a/docs/dev/reference/saem-2.png +++ b/docs/dev/reference/saem-2.png diff --git a/docs/dev/reference/saem-3.png b/docs/dev/reference/saem-3.png Binary files differindex 6a32cda1..27d43e53 100644 --- a/docs/dev/reference/saem-3.png +++ b/docs/dev/reference/saem-3.png diff --git a/docs/dev/reference/saem-4.png b/docs/dev/reference/saem-4.png Binary files differindex bf24d6b0..5c089bbc 100644 --- a/docs/dev/reference/saem-4.png +++ b/docs/dev/reference/saem-4.png diff --git a/docs/dev/reference/saem-5.png b/docs/dev/reference/saem-5.png Binary files differindex 6e6e0f91..8212ec67 100644 --- a/docs/dev/reference/saem-5.png +++ b/docs/dev/reference/saem-5.png diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html index 59589378..98faad6f 100644 --- a/docs/dev/reference/saem.html +++ b/docs/dev/reference/saem.html @@ -74,7 +74,7 @@ Expectation Maximisation algorithm (SAEM)." /> </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> </span> </div> @@ -158,9 +158,13 @@ Expectation Maximisation algorithm (SAEM).</p> <span class='va'>object</span>, transformations <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'>"mkin"</span>, <span class='st'>"saemix"</span><span class='op'>)</span>, degparms_start <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/numeric.html'>numeric</a></span><span class='op'>(</span><span class='op'>)</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>, solution_type <span class='op'>=</span> <span class='st'>"auto"</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>displayProgress <span class='op'>=</span> <span class='cn'>FALSE</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><span class='op'>)</span>, + nbiter.saemix <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'>300</span>, <span class='fl'>100</span><span class='op'>)</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>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>, + 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>, + fail_with_errors <span class='op'>=</span> <span class='cn'>TRUE</span>, verbose <span class='op'>=</span> <span class='cn'>FALSE</span>, quiet <span class='op'>=</span> <span class='cn'>FALSE</span>, <span class='va'>...</span> @@ -174,6 +178,7 @@ Expectation Maximisation algorithm (SAEM).</p> solution_type <span class='op'>=</span> <span class='st'>"auto"</span>, transformations <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'>"mkin"</span>, <span class='st'>"saemix"</span><span class='op'>)</span>, degparms_start <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/numeric.html'>numeric</a></span><span class='op'>(</span><span class='op'>)</span>, + test_log_parms <span class='op'>=</span> <span class='cn'>FALSE</span>, verbose <span class='op'>=</span> <span class='cn'>FALSE</span>, <span class='va'>...</span> <span class='op'>)</span> @@ -206,13 +211,35 @@ SFO or DFOP is used for the parent and there is either no metabolite or one.</p> be used to override the starting values obtained from the 'mmkin' object.</p></td> </tr> <tr> + <th>test_log_parms</th> + <td><p>If TRUE, an attempt is made to use more robust starting +values for population parameters fitted as log parameters in mkin (like +rate constants) by only considering rate constants that pass the t-test +when calculating mean degradation parameters using <a href='mean_degparms.html'>mean_degparms</a>.</p></td> + </tr> + <tr> + <th>conf.level</th> + <td><p>Possibility to adjust the required confidence level +for parameter that are tested if requested by 'test_log_parms'.</p></td> + </tr> + <tr> <th>solution_type</th> <td><p>Possibility to specify the solution type in case the automatic choice is not desired</p></td> </tr> <tr> + <th>nbiter.saemix</th> + <td><p>Convenience option to increase the number of +iterations</p></td> + </tr> + <tr> <th>control</th> - <td><p>Passed to <a href='https://rdrr.io/pkg/saemix/man/saemix.html'>saemix::saemix</a></p></td> + <td><p>Passed to <a href='https://rdrr.io/pkg/saemix/man/saemix.html'>saemix::saemix</a>.</p></td> + </tr> + <tr> + <th>fail_with_errors</th> + <td><p>Should a failure to compute standard errors +from the inverse of the Fisher Information Matrix be a failure?</p></td> </tr> <tr> <th>verbose</th> @@ -261,33 +288,39 @@ using <a href='mmkin.html'>mmkin</a>.</p> state.ini <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><span class='op'>)</span>, fixed_initials <span class='op'>=</span> <span class='st'>"parent"</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> <span class='va'>f_saem_p0_fixed</span> <span class='op'><-</span> <span class='fu'>saem</span><span class='op'>(</span><span class='va'>f_mmkin_parent_p0_fixed</span><span class='op'>)</span> </div><div class='output co'>#> Running main SAEM algorithm -#> [1] "Mon Jan 25 14:41:42 2021" +#> [1] "Fri Jun 11 10:56:49 2021" #> .... #> Minimisation finished -#> [1] "Mon Jan 25 14:41:43 2021"</div><div class='input'> +#> [1] "Fri Jun 11 10:56:51 2021"</div><div class='input'> <span class='va'>f_mmkin_parent</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='va'>ds</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</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> </div><div class='output co'>#> Running main SAEM algorithm -#> [1] "Mon Jan 25 14:41:45 2021" +#> [1] "Fri Jun 11 10:56:53 2021" #> .... #> Minimisation finished -#> [1] "Mon Jan 25 14:41:46 2021"</div><div class='input'><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> +#> [1] "Fri Jun 11 10:56:54 2021"</div><div class='input'><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> </div><div class='output co'>#> Running main SAEM algorithm -#> [1] "Mon Jan 25 14:41:47 2021" +#> [1] "Fri Jun 11 10:56:54 2021" #> .... #> Minimisation finished -#> [1] "Mon Jan 25 14:41:49 2021"</div><div class='input'><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> +#> [1] "Fri Jun 11 10:56:57 2021"</div><div class='input'><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> </div><div class='output co'>#> Running main SAEM algorithm -#> [1] "Mon Jan 25 14:41:49 2021" +#> [1] "Fri Jun 11 10:56:57 2021" #> .... #> Minimisation finished -#> [1] "Mon Jan 25 14:41:52 2021"</div><div class='input'> +#> [1] "Fri Jun 11 10:57:00 2021"</div><div class='input'> <span class='co'># The returned saem.mmkin object contains an SaemixObject, therefore we can use</span> <span class='co'># functions from saemix</span> <span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'>saemix</span><span class='op'>)</span> </div><div class='output co'>#> <span class='message'>Package saemix, version 3.1.9000</span> -#> <span class='message'> please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/compare.saemix.html'>compare.saemix</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='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'>$</span><span class='va'>so</span><span class='op'>)</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='error'>Error in compare.saemix(list(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so)): 'compare.saemix' requires at least two models.</span></div><div class='input'><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'>f_saem_fomc</span><span class='op'>$</span><span class='va'>so</span>, plot.type <span class='op'>=</span> <span class='st'>"convergence"</span><span class='op'>)</span> +#> <span class='message'> please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr</span></div><div class='output co'>#> <span class='message'></span> +#> <span class='message'>Attaching package: ‘saemix’</span></div><div class='output co'>#> <span class='message'>The following object is masked from ‘package:RxODE’:</span> +#> <span class='message'></span> +#> <span class='message'> phi</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/compare.saemix.html'>compare.saemix</a></span><span class='op'>(</span><span class='va'>f_saem_sfo</span><span class='op'>$</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> +</div><div class='output co'>#> <span class='message'>Likelihoods calculated by importance sampling</span></div><div class='output co'>#> AIC BIC +#> 1 624.2484 622.2956 +#> 2 467.7096 464.9757 +#> 3 495.4373 491.9222</div><div class='input'><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'>f_saem_fomc</span><span class='op'>$</span><span class='va'>so</span>, plot.type <span class='op'>=</span> <span class='st'>"convergence"</span><span class='op'>)</span> </div><div class='output co'>#> Plotting convergence plots</div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html'>plot</a></span><span class='op'>(</span><span class='va'>f_saem_fomc</span><span class='op'>$</span><span class='va'>so</span>, plot.type <span class='op'>=</span> <span class='st'>"individual.fit"</span><span class='op'>)</span> </div><div class='img'><img src='saem-1.png' alt='' width='700' height='433' /></div><div class='output co'>#> Plotting individual fits</div><div class='input'><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'>f_saem_fomc</span><span class='op'>$</span><span class='va'>so</span>, plot.type <span class='op'>=</span> <span class='st'>"npde"</span><span class='op'>)</span> </div><div class='img'><img src='saem-2.png' alt='' width='700' height='433' /></div><div class='output co'>#> Simulating data using nsim = 1000 simulated datasets @@ -324,11 +357,13 @@ using <a href='mmkin.html'>mmkin</a>.</p> <span class='va'>f_mmkin_parent_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/update.html'>update</a></span><span class='op'>(</span><span class='va'>f_mmkin_parent</span>, error_model <span class='op'>=</span> <span class='st'>"tc"</span><span class='op'>)</span> <span class='va'>f_saem_fomc_tc</span> <span class='op'><-</span> <span class='fu'>saem</span><span class='op'>(</span><span class='va'>f_mmkin_parent_tc</span><span class='op'>[</span><span class='st'>"FOMC"</span>, <span class='op'>]</span><span class='op'>)</span> </div><div class='output co'>#> Running main SAEM algorithm -#> [1] "Mon Jan 25 14:41:55 2021" +#> [1] "Fri Jun 11 10:57:03 2021" #> .... #> Minimisation finished -#> [1] "Mon Jan 25 14:42:00 2021"</div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/compare.saemix.html'>compare.saemix</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>f_saem_fomc</span><span class='op'>$</span><span class='va'>so</span>, <span class='va'>f_saem_fomc_tc</span><span class='op'>$</span><span class='va'>so</span><span class='op'>)</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='error'>Error in compare.saemix(list(f_saem_fomc$so, f_saem_fomc_tc$so)): 'compare.saemix' requires at least two models.</span></div><div class='input'> +#> [1] "Fri Jun 11 10:57:09 2021"</div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/compare.saemix.html'>compare.saemix</a></span><span class='op'>(</span><span class='va'>f_saem_fomc</span><span class='op'>$</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> +</div><div class='output co'>#> <span class='message'>Likelihoods calculated by importance sampling</span></div><div class='output co'>#> AIC BIC +#> 1 467.7096 464.9757 +#> 2 469.6831 466.5586</div><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>, <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><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'>fomc_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'>"FOMC"</span>, <span class='st'>"A1"</span><span class='op'>)</span>, @@ -346,15 +381,15 @@ using <a href='mmkin.html'>mmkin</a>.</p> <span class='co'># four minutes</span> <span class='va'>f_saem_sfo_sfo</span> <span class='op'><-</span> <span class='fu'>saem</span><span class='op'>(</span><span class='va'>f_mmkin</span><span class='op'>[</span><span class='st'>"SFO-SFO"</span>, <span class='op'>]</span><span class='op'>)</span> </div><div class='output co'>#> Running main SAEM algorithm -#> [1] "Mon Jan 25 14:42:02 2021" +#> [1] "Fri Jun 11 10:57:12 2021" #> .... #> Minimisation finished -#> [1] "Mon Jan 25 14:42:07 2021"</div><div class='input'><span class='va'>f_saem_dfop_sfo</span> <span class='op'><-</span> <span class='fu'>saem</span><span class='op'>(</span><span class='va'>f_mmkin</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span><span class='op'>)</span> +#> [1] "Fri Jun 11 10:57:17 2021"</div><div class='input'><span class='va'>f_saem_dfop_sfo</span> <span class='op'><-</span> <span class='fu'>saem</span><span class='op'>(</span><span class='va'>f_mmkin</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span><span class='op'>)</span> </div><div class='output co'>#> Running main SAEM algorithm -#> [1] "Mon Jan 25 14:42:08 2021" +#> [1] "Fri Jun 11 10:57:17 2021" #> .... #> Minimisation finished -#> [1] "Mon Jan 25 14:42:17 2021"</div><div class='input'><span class='co'># We can use print, plot and summary methods to check the results</span> +#> [1] "Fri Jun 11 10:57:26 2021"</div><div class='input'><span class='co'># We can use print, plot and summary methods to check the results</span> <span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>f_saem_dfop_sfo</span><span class='op'>)</span> </div><div class='output co'>#> Kinetic nonlinear mixed-effects model fit by SAEM #> Structural model: @@ -395,10 +430,10 @@ using <a href='mmkin.html'>mmkin</a>.</p> #> SD.g_qlogis 0.44771 -0.86417 1.7596</div><div class='input'><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'>f_saem_dfop_sfo</span><span class='op'>)</span> </div><div class='img'><img src='saem-5.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_saem_dfop_sfo</span>, data <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> </div><div class='output co'>#> saemix version used for fitting: 3.1.9000 -#> mkin version used for pre-fitting: 0.9.50.4 -#> R version used for fitting: 4.0.3 -#> Date of fit: Mon Jan 25 14:42:18 2021 -#> Date of summary: Mon Jan 25 14:42:18 2021 +#> mkin version used for pre-fitting: 1.0.5 +#> R version used for fitting: 4.1.0 +#> Date of fit: Fri Jun 11 10:57:27 2021 +#> Date of summary: Fri Jun 11 10:57:27 2021 #> #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -413,7 +448,7 @@ using <a href='mmkin.html'>mmkin</a>.</p> #> #> Model predictions using solution type analytical #> -#> Fitted in 9.954 s using 300, 100 iterations +#> Fitted in 9.712 s using 300, 100 iterations #> #> Variance model: Constant variance #> @@ -483,176 +518,176 @@ using <a href='mmkin.html'>mmkin</a>.</p> #> #> Data: #> ds name time observed predicted residual std standardized -#> Dataset 6 parent 0 97.2 95.79523 -1.40477 1.883 -0.745888 -#> Dataset 6 parent 0 96.4 95.79523 -0.60477 1.883 -0.321114 -#> Dataset 6 parent 3 71.1 71.32042 0.22042 1.883 0.117035 -#> Dataset 6 parent 3 69.2 71.32042 2.12042 1.883 1.125873 -#> Dataset 6 parent 6 58.1 56.45256 -1.64744 1.883 -0.874739 -#> Dataset 6 parent 6 56.6 56.45256 -0.14744 1.883 -0.078288 -#> Dataset 6 parent 10 44.4 44.48523 0.08523 1.883 0.045256 -#> Dataset 6 parent 10 43.4 44.48523 1.08523 1.883 0.576224 -#> Dataset 6 parent 20 33.3 29.75774 -3.54226 1.883 -1.880826 -#> Dataset 6 parent 20 29.2 29.75774 0.55774 1.883 0.296141 -#> Dataset 6 parent 34 17.6 19.35710 1.75710 1.883 0.932966 -#> Dataset 6 parent 34 18.0 19.35710 1.35710 1.883 0.720578 -#> Dataset 6 parent 55 10.5 10.48443 -0.01557 1.883 -0.008266 -#> Dataset 6 parent 55 9.3 10.48443 1.18443 1.883 0.628895 -#> Dataset 6 parent 90 4.5 3.78622 -0.71378 1.883 -0.378995 -#> Dataset 6 parent 90 4.7 3.78622 -0.91378 1.883 -0.485188 -#> Dataset 6 parent 112 3.0 1.99608 -1.00392 1.883 -0.533048 -#> Dataset 6 parent 112 3.4 1.99608 -1.40392 1.883 -0.745435 -#> Dataset 6 parent 132 2.3 1.11539 -1.18461 1.883 -0.628990 -#> Dataset 6 parent 132 2.7 1.11539 -1.58461 1.883 -0.841377 -#> Dataset 6 A1 3 4.3 4.66132 0.36132 1.883 0.191849 -#> Dataset 6 A1 3 4.6 4.66132 0.06132 1.883 0.032559 -#> Dataset 6 A1 6 7.0 7.41087 0.41087 1.883 0.218157 -#> Dataset 6 A1 6 7.2 7.41087 0.21087 1.883 0.111964 -#> Dataset 6 A1 10 8.2 9.50878 1.30878 1.883 0.694921 -#> Dataset 6 A1 10 8.0 9.50878 1.50878 1.883 0.801114 -#> Dataset 6 A1 20 11.0 11.69902 0.69902 1.883 0.371157 -#> Dataset 6 A1 20 13.7 11.69902 -2.00098 1.883 -1.062455 -#> Dataset 6 A1 34 11.5 12.67784 1.17784 1.883 0.625396 -#> Dataset 6 A1 34 12.7 12.67784 -0.02216 1.883 -0.011765 -#> Dataset 6 A1 55 14.9 12.78556 -2.11444 1.883 -1.122701 -#> Dataset 6 A1 55 14.5 12.78556 -1.71444 1.883 -0.910314 -#> Dataset 6 A1 90 12.1 11.52954 -0.57046 1.883 -0.302898 -#> Dataset 6 A1 90 12.3 11.52954 -0.77046 1.883 -0.409092 -#> Dataset 6 A1 112 9.9 10.43825 0.53825 1.883 0.285793 -#> Dataset 6 A1 112 10.2 10.43825 0.23825 1.883 0.126503 -#> Dataset 6 A1 132 8.8 9.42830 0.62830 1.883 0.333609 -#> Dataset 6 A1 132 7.8 9.42830 1.62830 1.883 0.864577 -#> Dataset 7 parent 0 93.6 90.91477 -2.68523 1.883 -1.425772 -#> Dataset 7 parent 0 92.3 90.91477 -1.38523 1.883 -0.735514 -#> Dataset 7 parent 3 87.0 84.76874 -2.23126 1.883 -1.184726 -#> Dataset 7 parent 3 82.2 84.76874 2.56874 1.883 1.363919 -#> Dataset 7 parent 7 74.0 77.62735 3.62735 1.883 1.926003 -#> Dataset 7 parent 7 73.9 77.62735 3.72735 1.883 1.979100 -#> Dataset 7 parent 14 64.2 67.52266 3.32266 1.883 1.764224 -#> Dataset 7 parent 14 69.5 67.52266 -1.97734 1.883 -1.049904 -#> Dataset 7 parent 30 54.0 52.41949 -1.58051 1.883 -0.839202 -#> Dataset 7 parent 30 54.6 52.41949 -2.18051 1.883 -1.157783 -#> Dataset 7 parent 60 41.1 39.36582 -1.73418 1.883 -0.920794 -#> Dataset 7 parent 60 38.4 39.36582 0.96582 1.883 0.512818 -#> Dataset 7 parent 90 32.5 33.75388 1.25388 1.883 0.665771 -#> Dataset 7 parent 90 35.5 33.75388 -1.74612 1.883 -0.927132 -#> Dataset 7 parent 120 28.1 30.41716 2.31716 1.883 1.230335 -#> Dataset 7 parent 120 29.0 30.41716 1.41716 1.883 0.752464 -#> Dataset 7 parent 180 26.5 25.66046 -0.83954 1.883 -0.445767 -#> Dataset 7 parent 180 27.6 25.66046 -1.93954 1.883 -1.029832 -#> Dataset 7 A1 3 3.9 2.69355 -1.20645 1.883 -0.640585 -#> Dataset 7 A1 3 3.1 2.69355 -0.40645 1.883 -0.215811 -#> Dataset 7 A1 7 6.9 5.81807 -1.08193 1.883 -0.574470 -#> Dataset 7 A1 7 6.6 5.81807 -0.78193 1.883 -0.415180 -#> Dataset 7 A1 14 10.4 10.22529 -0.17471 1.883 -0.092767 -#> Dataset 7 A1 14 8.3 10.22529 1.92529 1.883 1.022265 -#> Dataset 7 A1 30 14.4 16.75484 2.35484 1.883 1.250345 -#> Dataset 7 A1 30 13.7 16.75484 3.05484 1.883 1.622022 -#> Dataset 7 A1 60 22.1 22.22540 0.12540 1.883 0.066583 -#> Dataset 7 A1 60 22.3 22.22540 -0.07460 1.883 -0.039610 -#> Dataset 7 A1 90 27.5 24.38799 -3.11201 1.883 -1.652376 -#> Dataset 7 A1 90 25.4 24.38799 -1.01201 1.883 -0.537344 -#> Dataset 7 A1 120 28.0 25.53294 -2.46706 1.883 -1.309927 -#> Dataset 7 A1 120 26.6 25.53294 -1.06706 1.883 -0.566572 -#> Dataset 7 A1 180 25.8 26.94943 1.14943 1.883 0.610309 -#> Dataset 7 A1 180 25.3 26.94943 1.64943 1.883 0.875793 -#> Dataset 8 parent 0 91.9 91.53246 -0.36754 1.883 -0.195151 -#> Dataset 8 parent 0 90.8 91.53246 0.73246 1.883 0.388914 -#> Dataset 8 parent 1 64.9 67.73197 2.83197 1.883 1.503686 -#> Dataset 8 parent 1 66.2 67.73197 1.53197 1.883 0.813428 -#> Dataset 8 parent 3 43.5 41.58448 -1.91552 1.883 -1.017081 -#> Dataset 8 parent 3 44.1 41.58448 -2.51552 1.883 -1.335661 -#> Dataset 8 parent 8 18.3 19.62286 1.32286 1.883 0.702395 -#> Dataset 8 parent 8 18.1 19.62286 1.52286 1.883 0.808589 -#> Dataset 8 parent 14 10.2 10.77819 0.57819 1.883 0.306999 -#> Dataset 8 parent 14 10.8 10.77819 -0.02181 1.883 -0.011582 -#> Dataset 8 parent 27 4.9 3.26977 -1.63023 1.883 -0.865599 -#> Dataset 8 parent 27 3.3 3.26977 -0.03023 1.883 -0.016051 -#> Dataset 8 parent 48 1.6 0.48024 -1.11976 1.883 -0.594557 -#> Dataset 8 parent 48 1.5 0.48024 -1.01976 1.883 -0.541460 -#> Dataset 8 parent 70 1.1 0.06438 -1.03562 1.883 -0.549881 -#> Dataset 8 parent 70 0.9 0.06438 -0.83562 1.883 -0.443688 -#> Dataset 8 A1 1 9.6 7.61539 -1.98461 1.883 -1.053761 -#> Dataset 8 A1 1 7.7 7.61539 -0.08461 1.883 -0.044923 -#> Dataset 8 A1 3 15.0 15.47954 0.47954 1.883 0.254622 -#> Dataset 8 A1 3 15.1 15.47954 0.37954 1.883 0.201525 -#> Dataset 8 A1 8 21.2 20.22616 -0.97384 1.883 -0.517076 -#> Dataset 8 A1 8 21.1 20.22616 -0.87384 1.883 -0.463979 -#> Dataset 8 A1 14 19.7 20.00067 0.30067 1.883 0.159645 -#> Dataset 8 A1 14 18.9 20.00067 1.10067 1.883 0.584419 -#> Dataset 8 A1 27 17.5 16.38142 -1.11858 1.883 -0.593929 -#> Dataset 8 A1 27 15.9 16.38142 0.48142 1.883 0.255619 -#> Dataset 8 A1 48 9.5 10.25357 0.75357 1.883 0.400123 -#> Dataset 8 A1 48 9.8 10.25357 0.45357 1.883 0.240833 -#> Dataset 8 A1 70 6.2 5.95728 -0.24272 1.883 -0.128878 -#> Dataset 8 A1 70 6.1 5.95728 -0.14272 1.883 -0.075781 -#> Dataset 9 parent 0 99.8 97.47274 -2.32726 1.883 -1.235697 -#> Dataset 9 parent 0 98.3 97.47274 -0.82726 1.883 -0.439246 -#> Dataset 9 parent 1 77.1 79.72257 2.62257 1.883 1.392500 -#> Dataset 9 parent 1 77.2 79.72257 2.52257 1.883 1.339404 -#> Dataset 9 parent 3 59.0 56.26497 -2.73503 1.883 -1.452212 -#> Dataset 9 parent 3 58.1 56.26497 -1.83503 1.883 -0.974342 -#> Dataset 9 parent 8 27.4 31.66985 4.26985 1.883 2.267151 -#> Dataset 9 parent 8 29.2 31.66985 2.46985 1.883 1.311410 -#> Dataset 9 parent 14 19.1 22.39789 3.29789 1.883 1.751071 -#> Dataset 9 parent 14 29.6 22.39789 -7.20211 1.883 -3.824090 -#> Dataset 9 parent 27 10.1 14.21758 4.11758 1.883 2.186301 -#> Dataset 9 parent 27 18.2 14.21758 -3.98242 1.883 -2.114537 -#> Dataset 9 parent 48 4.5 7.27921 2.77921 1.883 1.475671 -#> Dataset 9 parent 48 9.1 7.27921 -1.82079 1.883 -0.966780 -#> Dataset 9 parent 70 2.3 3.61470 1.31470 1.883 0.698065 -#> Dataset 9 parent 70 2.9 3.61470 0.71470 1.883 0.379485 -#> Dataset 9 parent 91 2.0 1.85303 -0.14697 1.883 -0.078038 -#> Dataset 9 parent 91 1.8 1.85303 0.05303 1.883 0.028155 -#> Dataset 9 parent 120 2.0 0.73645 -1.26355 1.883 -0.670906 -#> Dataset 9 parent 120 2.2 0.73645 -1.46355 1.883 -0.777099 -#> Dataset 9 A1 1 4.2 3.87843 -0.32157 1.883 -0.170743 -#> Dataset 9 A1 1 3.9 3.87843 -0.02157 1.883 -0.011453 -#> Dataset 9 A1 3 7.4 8.90535 1.50535 1.883 0.799291 -#> Dataset 9 A1 3 7.9 8.90535 1.00535 1.883 0.533807 -#> Dataset 9 A1 8 14.5 13.75172 -0.74828 1.883 -0.397312 -#> Dataset 9 A1 8 13.7 13.75172 0.05172 1.883 0.027462 -#> Dataset 9 A1 14 14.2 14.97541 0.77541 1.883 0.411715 -#> Dataset 9 A1 14 12.2 14.97541 2.77541 1.883 1.473650 -#> Dataset 9 A1 27 13.7 14.94728 1.24728 1.883 0.662266 -#> Dataset 9 A1 27 13.2 14.94728 1.74728 1.883 0.927750 -#> Dataset 9 A1 48 13.6 13.66078 0.06078 1.883 0.032272 -#> Dataset 9 A1 48 15.4 13.66078 -1.73922 1.883 -0.923470 -#> Dataset 9 A1 70 10.4 11.84899 1.44899 1.883 0.769365 -#> Dataset 9 A1 70 11.6 11.84899 0.24899 1.883 0.132204 -#> Dataset 9 A1 91 10.0 10.09177 0.09177 1.883 0.048727 -#> Dataset 9 A1 91 9.5 10.09177 0.59177 1.883 0.314211 -#> Dataset 9 A1 120 9.1 7.91379 -1.18621 1.883 -0.629841 -#> Dataset 9 A1 120 9.0 7.91379 -1.08621 1.883 -0.576745 -#> Dataset 10 parent 0 96.1 93.65257 -2.44743 1.883 -1.299505 -#> Dataset 10 parent 0 94.3 93.65257 -0.64743 1.883 -0.343763 -#> Dataset 10 parent 8 73.9 77.85906 3.95906 1.883 2.102132 -#> Dataset 10 parent 8 73.9 77.85906 3.95906 1.883 2.102132 -#> Dataset 10 parent 14 69.4 70.17143 0.77143 1.883 0.409606 -#> Dataset 10 parent 14 73.1 70.17143 -2.92857 1.883 -1.554974 -#> Dataset 10 parent 21 65.6 63.99188 -1.60812 1.883 -0.853862 -#> Dataset 10 parent 21 65.3 63.99188 -1.30812 1.883 -0.694572 -#> Dataset 10 parent 41 55.9 54.64292 -1.25708 1.883 -0.667467 -#> Dataset 10 parent 41 54.4 54.64292 0.24292 1.883 0.128985 -#> Dataset 10 parent 63 47.0 49.61303 2.61303 1.883 1.387433 -#> Dataset 10 parent 63 49.3 49.61303 0.31303 1.883 0.166207 -#> Dataset 10 parent 91 44.7 45.17807 0.47807 1.883 0.253839 -#> Dataset 10 parent 91 46.7 45.17807 -1.52193 1.883 -0.808096 -#> Dataset 10 parent 120 42.1 41.27970 -0.82030 1.883 -0.435552 -#> Dataset 10 parent 120 41.3 41.27970 -0.02030 1.883 -0.010778 -#> Dataset 10 A1 8 3.3 3.99294 0.69294 1.883 0.367929 -#> Dataset 10 A1 8 3.4 3.99294 0.59294 1.883 0.314832 -#> Dataset 10 A1 14 3.9 5.92756 2.02756 1.883 1.076570 -#> Dataset 10 A1 14 2.9 5.92756 3.02756 1.883 1.607538 -#> Dataset 10 A1 21 6.4 7.47313 1.07313 1.883 0.569799 -#> Dataset 10 A1 21 7.2 7.47313 0.27313 1.883 0.145025 -#> Dataset 10 A1 41 9.1 9.76819 0.66819 1.883 0.354786 -#> Dataset 10 A1 41 8.5 9.76819 1.26819 1.883 0.673367 -#> Dataset 10 A1 63 11.7 10.94733 -0.75267 1.883 -0.399643 -#> Dataset 10 A1 63 12.0 10.94733 -1.05267 1.883 -0.558933 -#> Dataset 10 A1 91 13.3 11.93773 -1.36227 1.883 -0.723321 -#> Dataset 10 A1 91 13.2 11.93773 -1.26227 1.883 -0.670224 -#> Dataset 10 A1 120 14.3 12.77666 -1.52334 1.883 -0.808847 -#> Dataset 10 A1 120 12.1 12.77666 0.67666 1.883 0.359282</div><div class='input'> +#> Dataset 6 parent 0 97.2 95.79523 1.40477 1.883 0.745888 +#> Dataset 6 parent 0 96.4 95.79523 0.60477 1.883 0.321114 +#> Dataset 6 parent 3 71.1 71.32042 -0.22042 1.883 -0.117035 +#> Dataset 6 parent 3 69.2 71.32042 -2.12042 1.883 -1.125873 +#> Dataset 6 parent 6 58.1 56.45256 1.64744 1.883 0.874739 +#> Dataset 6 parent 6 56.6 56.45256 0.14744 1.883 0.078288 +#> Dataset 6 parent 10 44.4 44.48523 -0.08523 1.883 -0.045257 +#> Dataset 6 parent 10 43.4 44.48523 -1.08523 1.883 -0.576224 +#> Dataset 6 parent 20 33.3 29.75774 3.54226 1.883 1.880826 +#> Dataset 6 parent 20 29.2 29.75774 -0.55774 1.883 -0.296141 +#> Dataset 6 parent 34 17.6 19.35710 -1.75710 1.883 -0.932966 +#> Dataset 6 parent 34 18.0 19.35710 -1.35710 1.883 -0.720579 +#> Dataset 6 parent 55 10.5 10.48443 0.01557 1.883 0.008266 +#> Dataset 6 parent 55 9.3 10.48443 -1.18443 1.883 -0.628895 +#> Dataset 6 parent 90 4.5 3.78622 0.71378 1.883 0.378995 +#> Dataset 6 parent 90 4.7 3.78622 0.91378 1.883 0.485188 +#> Dataset 6 parent 112 3.0 1.99608 1.00392 1.883 0.533048 +#> Dataset 6 parent 112 3.4 1.99608 1.40392 1.883 0.745435 +#> Dataset 6 parent 132 2.3 1.11539 1.18461 1.883 0.628990 +#> Dataset 6 parent 132 2.7 1.11539 1.58461 1.883 0.841377 +#> Dataset 6 A1 3 4.3 4.66132 -0.36132 1.883 -0.191849 +#> Dataset 6 A1 3 4.6 4.66132 -0.06132 1.883 -0.032559 +#> Dataset 6 A1 6 7.0 7.41087 -0.41087 1.883 -0.218157 +#> Dataset 6 A1 6 7.2 7.41087 -0.21087 1.883 -0.111964 +#> Dataset 6 A1 10 8.2 9.50878 -1.30878 1.883 -0.694921 +#> Dataset 6 A1 10 8.0 9.50878 -1.50878 1.883 -0.801114 +#> Dataset 6 A1 20 11.0 11.69902 -0.69902 1.883 -0.371157 +#> Dataset 6 A1 20 13.7 11.69902 2.00098 1.883 1.062455 +#> Dataset 6 A1 34 11.5 12.67784 -1.17784 1.883 -0.625396 +#> Dataset 6 A1 34 12.7 12.67784 0.02216 1.883 0.011765 +#> Dataset 6 A1 55 14.9 12.78556 2.11444 1.883 1.122701 +#> Dataset 6 A1 55 14.5 12.78556 1.71444 1.883 0.910314 +#> Dataset 6 A1 90 12.1 11.52954 0.57046 1.883 0.302898 +#> Dataset 6 A1 90 12.3 11.52954 0.77046 1.883 0.409092 +#> Dataset 6 A1 112 9.9 10.43825 -0.53825 1.883 -0.285793 +#> Dataset 6 A1 112 10.2 10.43825 -0.23825 1.883 -0.126503 +#> Dataset 6 A1 132 8.8 9.42830 -0.62830 1.883 -0.333609 +#> Dataset 6 A1 132 7.8 9.42830 -1.62830 1.883 -0.864577 +#> Dataset 7 parent 0 93.6 90.91477 2.68523 1.883 1.425772 +#> Dataset 7 parent 0 92.3 90.91477 1.38523 1.883 0.735514 +#> Dataset 7 parent 3 87.0 84.76874 2.23126 1.883 1.184726 +#> Dataset 7 parent 3 82.2 84.76874 -2.56874 1.883 -1.363919 +#> Dataset 7 parent 7 74.0 77.62735 -3.62735 1.883 -1.926003 +#> Dataset 7 parent 7 73.9 77.62735 -3.72735 1.883 -1.979100 +#> Dataset 7 parent 14 64.2 67.52266 -3.32266 1.883 -1.764224 +#> Dataset 7 parent 14 69.5 67.52266 1.97734 1.883 1.049904 +#> Dataset 7 parent 30 54.0 52.41949 1.58051 1.883 0.839202 +#> Dataset 7 parent 30 54.6 52.41949 2.18051 1.883 1.157783 +#> Dataset 7 parent 60 41.1 39.36582 1.73418 1.883 0.920794 +#> Dataset 7 parent 60 38.4 39.36582 -0.96582 1.883 -0.512818 +#> Dataset 7 parent 90 32.5 33.75388 -1.25388 1.883 -0.665771 +#> Dataset 7 parent 90 35.5 33.75388 1.74612 1.883 0.927132 +#> Dataset 7 parent 120 28.1 30.41716 -2.31716 1.883 -1.230335 +#> Dataset 7 parent 120 29.0 30.41716 -1.41716 1.883 -0.752464 +#> Dataset 7 parent 180 26.5 25.66046 0.83954 1.883 0.445767 +#> Dataset 7 parent 180 27.6 25.66046 1.93954 1.883 1.029832 +#> Dataset 7 A1 3 3.9 2.69355 1.20645 1.883 0.640585 +#> Dataset 7 A1 3 3.1 2.69355 0.40645 1.883 0.215811 +#> Dataset 7 A1 7 6.9 5.81807 1.08193 1.883 0.574470 +#> Dataset 7 A1 7 6.6 5.81807 0.78193 1.883 0.415180 +#> Dataset 7 A1 14 10.4 10.22529 0.17471 1.883 0.092767 +#> Dataset 7 A1 14 8.3 10.22529 -1.92529 1.883 -1.022265 +#> Dataset 7 A1 30 14.4 16.75484 -2.35484 1.883 -1.250345 +#> Dataset 7 A1 30 13.7 16.75484 -3.05484 1.883 -1.622022 +#> Dataset 7 A1 60 22.1 22.22540 -0.12540 1.883 -0.066583 +#> Dataset 7 A1 60 22.3 22.22540 0.07460 1.883 0.039610 +#> Dataset 7 A1 90 27.5 24.38799 3.11201 1.883 1.652376 +#> Dataset 7 A1 90 25.4 24.38799 1.01201 1.883 0.537344 +#> Dataset 7 A1 120 28.0 25.53294 2.46706 1.883 1.309927 +#> Dataset 7 A1 120 26.6 25.53294 1.06706 1.883 0.566572 +#> Dataset 7 A1 180 25.8 26.94943 -1.14943 1.883 -0.610309 +#> Dataset 7 A1 180 25.3 26.94943 -1.64943 1.883 -0.875793 +#> Dataset 8 parent 0 91.9 91.53246 0.36754 1.883 0.195151 +#> Dataset 8 parent 0 90.8 91.53246 -0.73246 1.883 -0.388914 +#> Dataset 8 parent 1 64.9 67.73197 -2.83197 1.883 -1.503686 +#> Dataset 8 parent 1 66.2 67.73197 -1.53197 1.883 -0.813428 +#> Dataset 8 parent 3 43.5 41.58448 1.91552 1.883 1.017081 +#> Dataset 8 parent 3 44.1 41.58448 2.51552 1.883 1.335662 +#> Dataset 8 parent 8 18.3 19.62286 -1.32286 1.883 -0.702395 +#> Dataset 8 parent 8 18.1 19.62286 -1.52286 1.883 -0.808588 +#> Dataset 8 parent 14 10.2 10.77819 -0.57819 1.883 -0.306999 +#> Dataset 8 parent 14 10.8 10.77819 0.02181 1.883 0.011582 +#> Dataset 8 parent 27 4.9 3.26977 1.63023 1.883 0.865599 +#> Dataset 8 parent 27 3.3 3.26977 0.03023 1.883 0.016051 +#> Dataset 8 parent 48 1.6 0.48024 1.11976 1.883 0.594557 +#> Dataset 8 parent 48 1.5 0.48024 1.01976 1.883 0.541460 +#> Dataset 8 parent 70 1.1 0.06438 1.03562 1.883 0.549881 +#> Dataset 8 parent 70 0.9 0.06438 0.83562 1.883 0.443688 +#> Dataset 8 A1 1 9.6 7.61539 1.98461 1.883 1.053761 +#> Dataset 8 A1 1 7.7 7.61539 0.08461 1.883 0.044923 +#> Dataset 8 A1 3 15.0 15.47954 -0.47954 1.883 -0.254622 +#> Dataset 8 A1 3 15.1 15.47954 -0.37954 1.883 -0.201525 +#> Dataset 8 A1 8 21.2 20.22616 0.97384 1.883 0.517075 +#> Dataset 8 A1 8 21.1 20.22616 0.87384 1.883 0.463979 +#> Dataset 8 A1 14 19.7 20.00067 -0.30067 1.883 -0.159645 +#> Dataset 8 A1 14 18.9 20.00067 -1.10067 1.883 -0.584419 +#> Dataset 8 A1 27 17.5 16.38142 1.11858 1.883 0.593928 +#> Dataset 8 A1 27 15.9 16.38142 -0.48142 1.883 -0.255620 +#> Dataset 8 A1 48 9.5 10.25357 -0.75357 1.883 -0.400124 +#> Dataset 8 A1 48 9.8 10.25357 -0.45357 1.883 -0.240833 +#> Dataset 8 A1 70 6.2 5.95728 0.24272 1.883 0.128878 +#> Dataset 8 A1 70 6.1 5.95728 0.14272 1.883 0.075781 +#> Dataset 9 parent 0 99.8 97.47274 2.32726 1.883 1.235697 +#> Dataset 9 parent 0 98.3 97.47274 0.82726 1.883 0.439246 +#> Dataset 9 parent 1 77.1 79.72257 -2.62257 1.883 -1.392500 +#> Dataset 9 parent 1 77.2 79.72257 -2.52257 1.883 -1.339404 +#> Dataset 9 parent 3 59.0 56.26497 2.73503 1.883 1.452212 +#> Dataset 9 parent 3 58.1 56.26497 1.83503 1.883 0.974342 +#> Dataset 9 parent 8 27.4 31.66985 -4.26985 1.883 -2.267151 +#> Dataset 9 parent 8 29.2 31.66985 -2.46985 1.883 -1.311410 +#> Dataset 9 parent 14 19.1 22.39789 -3.29789 1.883 -1.751071 +#> Dataset 9 parent 14 29.6 22.39789 7.20211 1.883 3.824090 +#> Dataset 9 parent 27 10.1 14.21758 -4.11758 1.883 -2.186301 +#> Dataset 9 parent 27 18.2 14.21758 3.98242 1.883 2.114537 +#> Dataset 9 parent 48 4.5 7.27921 -2.77921 1.883 -1.475671 +#> Dataset 9 parent 48 9.1 7.27921 1.82079 1.883 0.966780 +#> Dataset 9 parent 70 2.3 3.61470 -1.31470 1.883 -0.698065 +#> Dataset 9 parent 70 2.9 3.61470 -0.71470 1.883 -0.379485 +#> Dataset 9 parent 91 2.0 1.85303 0.14697 1.883 0.078038 +#> Dataset 9 parent 91 1.8 1.85303 -0.05303 1.883 -0.028155 +#> Dataset 9 parent 120 2.0 0.73645 1.26355 1.883 0.670906 +#> Dataset 9 parent 120 2.2 0.73645 1.46355 1.883 0.777099 +#> Dataset 9 A1 1 4.2 3.87843 0.32157 1.883 0.170743 +#> Dataset 9 A1 1 3.9 3.87843 0.02157 1.883 0.011453 +#> Dataset 9 A1 3 7.4 8.90535 -1.50535 1.883 -0.799291 +#> Dataset 9 A1 3 7.9 8.90535 -1.00535 1.883 -0.533807 +#> Dataset 9 A1 8 14.5 13.75172 0.74828 1.883 0.397312 +#> Dataset 9 A1 8 13.7 13.75172 -0.05172 1.883 -0.027462 +#> Dataset 9 A1 14 14.2 14.97541 -0.77541 1.883 -0.411715 +#> Dataset 9 A1 14 12.2 14.97541 -2.77541 1.883 -1.473650 +#> Dataset 9 A1 27 13.7 14.94728 -1.24728 1.883 -0.662266 +#> Dataset 9 A1 27 13.2 14.94728 -1.74728 1.883 -0.927750 +#> Dataset 9 A1 48 13.6 13.66078 -0.06078 1.883 -0.032272 +#> Dataset 9 A1 48 15.4 13.66078 1.73922 1.883 0.923470 +#> Dataset 9 A1 70 10.4 11.84899 -1.44899 1.883 -0.769365 +#> Dataset 9 A1 70 11.6 11.84899 -0.24899 1.883 -0.132204 +#> Dataset 9 A1 91 10.0 10.09177 -0.09177 1.883 -0.048727 +#> Dataset 9 A1 91 9.5 10.09177 -0.59177 1.883 -0.314211 +#> Dataset 9 A1 120 9.1 7.91379 1.18621 1.883 0.629841 +#> Dataset 9 A1 120 9.0 7.91379 1.08621 1.883 0.576744 +#> Dataset 10 parent 0 96.1 93.65257 2.44743 1.883 1.299505 +#> Dataset 10 parent 0 94.3 93.65257 0.64743 1.883 0.343763 +#> Dataset 10 parent 8 73.9 77.85906 -3.95906 1.883 -2.102132 +#> Dataset 10 parent 8 73.9 77.85906 -3.95906 1.883 -2.102132 +#> Dataset 10 parent 14 69.4 70.17143 -0.77143 1.883 -0.409606 +#> Dataset 10 parent 14 73.1 70.17143 2.92857 1.883 1.554974 +#> Dataset 10 parent 21 65.6 63.99188 1.60812 1.883 0.853862 +#> Dataset 10 parent 21 65.3 63.99188 1.30812 1.883 0.694572 +#> Dataset 10 parent 41 55.9 54.64292 1.25708 1.883 0.667467 +#> Dataset 10 parent 41 54.4 54.64292 -0.24292 1.883 -0.128985 +#> Dataset 10 parent 63 47.0 49.61303 -2.61303 1.883 -1.387433 +#> Dataset 10 parent 63 49.3 49.61303 -0.31303 1.883 -0.166207 +#> Dataset 10 parent 91 44.7 45.17807 -0.47807 1.883 -0.253839 +#> Dataset 10 parent 91 46.7 45.17807 1.52193 1.883 0.808096 +#> Dataset 10 parent 120 42.1 41.27970 0.82030 1.883 0.435552 +#> Dataset 10 parent 120 41.3 41.27970 0.02030 1.883 0.010778 +#> Dataset 10 A1 8 3.3 3.99294 -0.69294 1.883 -0.367929 +#> Dataset 10 A1 8 3.4 3.99294 -0.59294 1.883 -0.314832 +#> Dataset 10 A1 14 3.9 5.92756 -2.02756 1.883 -1.076570 +#> Dataset 10 A1 14 2.9 5.92756 -3.02756 1.883 -1.607538 +#> Dataset 10 A1 21 6.4 7.47313 -1.07313 1.883 -0.569799 +#> Dataset 10 A1 21 7.2 7.47313 -0.27313 1.883 -0.145025 +#> Dataset 10 A1 41 9.1 9.76819 -0.66819 1.883 -0.354786 +#> Dataset 10 A1 41 8.5 9.76819 -1.26819 1.883 -0.673367 +#> Dataset 10 A1 63 11.7 10.94733 0.75267 1.883 0.399643 +#> Dataset 10 A1 63 12.0 10.94733 1.05267 1.883 0.558933 +#> Dataset 10 A1 91 13.3 11.93773 1.36227 1.883 0.723321 +#> Dataset 10 A1 91 13.2 11.93773 1.26227 1.883 0.670224 +#> Dataset 10 A1 120 14.3 12.77666 1.52334 1.883 0.808847 +#> Dataset 10 A1 120 12.1 12.77666 -0.67666 1.883 -0.359282</div><div class='input'> <span class='co'># The following takes about 6 minutes</span> <span class='co'>#f_saem_dfop_sfo_deSolve <- saem(f_mmkin["DFOP-SFO", ], solution_type = "deSolve",</span> <span class='co'># control = list(nbiter.saemix = c(200, 80), nbdisplay = 10))</span> diff --git a/docs/dev/reference/schaefer07_complex_case-1.png b/docs/dev/reference/schaefer07_complex_case-1.png Binary files differindex 16e657dd..96aab2dc 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 83555b99..4ccad5c4 100644 --- a/docs/dev/reference/schaefer07_complex_case.html +++ b/docs/dev/reference/schaefer07_complex_case.html @@ -74,7 +74,7 @@ </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -123,7 +123,7 @@ <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -186,15 +186,15 @@ </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 0.1954665 0.4235715 0.0000000 0.4479662 0.5520338 +#> 0.3809620 0.1954667 0.4235713 0.0000000 0.4479619 0.5520381 #> #> $distimes #> DT50 DT90 #> parent 13.95078 46.34350 -#> A1 49.75343 165.27731 -#> B1 37.26912 123.80533 -#> C1 11.23131 37.30959 -#> A2 28.50569 94.69386 +#> 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> diff --git a/docs/dev/reference/sigma_twocomp-1.png b/docs/dev/reference/sigma_twocomp-1.png Binary files differindex 21db3145..6e61684e 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 8212e480..b7d295b2 100644 --- a/docs/dev/reference/sigma_twocomp.html +++ b/docs/dev/reference/sigma_twocomp.html @@ -73,7 +73,7 @@ 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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -122,7 +122,7 @@ dependence of the measured value \(y\):" /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -188,6 +188,10 @@ Additive, Multiplicative, and Mixed Analytical Errors. Clinical Chemistry 24(11), 1895-1898.</p> <p>Rocke, David M. and Lorenzato, Stefan (1995) A two-component model for 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> +.</p> <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> diff --git a/docs/dev/reference/summary.mkinfit.html b/docs/dev/reference/summary.mkinfit.html index f314dfa8..494731e9 100644 --- a/docs/dev/reference/summary.mkinfit.html +++ b/docs/dev/reference/summary.mkinfit.html @@ -76,7 +76,7 @@ values." /> </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -125,7 +125,7 @@ values." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -236,17 +236,17 @@ EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, <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/pkg/saemix/man/summary-methods.html'>summary</a></span><span class='op'>(</span><span class='fu'><a href='mkinfit.html'>mkinfit</a></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 class='va'>FOCUS_2006_A</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span> -</div><div class='output co'>#> mkin version used for fitting: 0.9.50.4 +</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 Nov 30 16:01:20 2020 -#> Date of summary: Mon Nov 30 16:01:20 2020 +#> Date of fit: Mon Feb 15 17:13:09 2021 +#> Date of summary: Mon Feb 15 17:13:09 2021 #> #> Equations: #> d_parent/dt = - k_parent * parent #> #> Model predictions using solution type analytical #> -#> Fitted using 131 model solutions performed in 0.028 s +#> Fitted using 131 model solutions performed in 0.027 s #> #> Error model: Constant variance #> diff --git a/docs/dev/reference/summary.nlme.mmkin.html b/docs/dev/reference/summary.nlme.mmkin.html index 2aeadc46..b2f6624a 100644 --- a/docs/dev/reference/summary.nlme.mmkin.html +++ b/docs/dev/reference/summary.nlme.mmkin.html @@ -76,7 +76,7 @@ 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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -125,7 +125,7 @@ endpoints such as formation fractions and DT50 values. Optionally <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -263,12 +263,12 @@ José Pinheiro and Douglas Bates for the components inherited from nlme</p> <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> </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='va'>f_nlme</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span><span class='op'>(</span><span class='va'>f_mmkin</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='warning'>Warning: Iteration 1, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></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_nlme</span>, data <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -</div><div class='output co'>#> nlme version used for fitting: 3.1.150.1 -#> mkin version used for pre-fitting: 0.9.50.4 +</div><div class='output co'>#> <span class='warning'>Warning: Iteration 4, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></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_nlme</span>, data <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> +</div><div class='output co'>#> nlme version used for fitting: 3.1.152 +#> mkin version used for pre-fitting: 1.0.3.9000 #> R version used for fitting: 4.0.3 -#> Date of fit: Mon Nov 30 16:01:23 2020 -#> Date of summary: Mon Nov 30 16:01:23 2020 +#> Date of fit: Mon Feb 15 17:13:12 2021 +#> Date of summary: Mon Feb 15 17:13:12 2021 #> #> Equations: #> d_parent/dt = - k_parent * parent @@ -278,148 +278,146 @@ José Pinheiro and Douglas Bates for the components inherited from nlme</p> #> #> Model predictions using solution type analytical #> -#> Fitted in 0.996 s using 6 iterations +#> Fitted in 0.544 s using 4 iterations #> #> Variance model: Two-component variance function #> #> Mean of starting values for individual parameters: #> parent_0 log_k_parent -#> 101.568773 -4.454103 +#> 101.569 -4.454 #> #> Fixed degradation parameter values: #> None #> #> Results: #> -#> AIC BIC logLik -#> 586.4159 603.9145 -286.2079 +#> AIC BIC logLik +#> 584.5 599.5 -286.2 #> #> Optimised, transformed parameters with symmetric confidence intervals: #> lower est. upper -#> parent_0 99.360 101.593 103.825 -#> log_k_parent -4.979 -4.451 -3.924 +#> parent_0 99.371 101.592 103.814 +#> log_k_parent -4.973 -4.449 -3.926 #> #> Correlation: #> prnt_0 -#> log_k_parent 0.155 +#> log_k_parent 0.051 #> #> Random effects: #> Formula: list(parent_0 ~ 1, log_k_parent ~ 1) #> Level: ds -#> Structure: General positive-definite, Log-Cholesky parametrization -#> StdDev Corr -#> parent_0 0.2624248 prnt_0 -#> log_k_parent 0.5907146 0.999 -#> Residual 1.0000000 +#> Structure: Diagonal +#> parent_0 log_k_parent Residual +#> StdDev: 6.924e-05 0.5863 1 #> #> Variance function: #> Structure: Constant plus proportion of variance covariate #> Formula: ~fitted(.) #> Parameter estimates: -#> const prop -#> 6.56706e-24 7.89583e-02 +#> const prop +#> 0.0001208853 0.0789968036 #> #> Backtransformed parameters with asymmetric confidence intervals: #> lower est. upper -#> parent_0 99.360213 101.59280 103.82539 -#> k_parent 0.006883 0.01166 0.01976 +#> parent_0 99.370882 101.59243 103.81398 +#> k_parent 0.006923 0.01168 0.01972 #> #> Estimated disappearance times: #> DT50 DT90 -#> parent 59.44 197.4 +#> parent 59.32 197.1 #> #> Data: #> ds name time observed predicted residual std standardized -#> ds 1 parent 0 104.1 101.417 2.68283 8.0077 0.335030 -#> ds 1 parent 0 105.0 101.417 3.58283 8.0077 0.447422 -#> ds 1 parent 1 98.5 100.624 -2.12400 7.9451 -0.267334 -#> ds 1 parent 1 96.1 100.624 -4.52400 7.9451 -0.569407 -#> ds 1 parent 3 101.9 99.056 2.84379 7.8213 0.363595 -#> ds 1 parent 3 85.2 99.056 -13.85621 7.8213 -1.771597 -#> ds 1 parent 7 99.1 95.994 3.10646 7.5795 0.409850 -#> ds 1 parent 7 93.0 95.994 -2.99354 7.5795 -0.394953 -#> ds 1 parent 14 88.1 90.860 -2.75997 7.1741 -0.384710 -#> ds 1 parent 14 84.1 90.860 -6.75997 7.1741 -0.942268 -#> ds 1 parent 28 80.2 81.402 -1.20174 6.4273 -0.186973 -#> ds 1 parent 28 91.3 81.402 9.89826 6.4273 1.540024 -#> ds 1 parent 60 65.1 63.316 1.78361 4.9994 0.356767 -#> ds 1 parent 60 65.8 63.316 2.48361 4.9994 0.496785 -#> ds 1 parent 90 47.8 50.029 -2.22862 3.9502 -0.564182 -#> ds 1 parent 90 53.5 50.029 3.47138 3.9502 0.878792 -#> ds 1 parent 120 37.6 39.529 -1.92946 3.1212 -0.618182 -#> ds 1 parent 120 39.3 39.529 -0.22946 3.1212 -0.073516 -#> ds 2 parent 0 107.9 101.711 6.18875 8.0309 0.770613 -#> ds 2 parent 0 102.1 101.711 0.38875 8.0309 0.048406 -#> ds 2 parent 1 103.8 100.174 3.62597 7.9096 0.458428 -#> ds 2 parent 1 108.6 100.174 8.42597 7.9096 1.065287 -#> ds 2 parent 3 91.0 97.169 -6.16895 7.6723 -0.804055 -#> ds 2 parent 3 84.9 97.169 -12.26895 7.6723 -1.599124 -#> ds 2 parent 7 79.3 91.427 -12.12652 7.2189 -1.679833 -#> ds 2 parent 7 100.9 91.427 9.47348 7.2189 1.312320 -#> ds 2 parent 14 77.3 82.182 -4.88174 6.4889 -0.752318 -#> ds 2 parent 14 83.5 82.182 1.31826 6.4889 0.203155 -#> ds 2 parent 28 66.8 66.402 0.39792 5.2430 0.075895 -#> ds 2 parent 28 63.3 66.402 -3.10208 5.2430 -0.591662 -#> ds 2 parent 60 40.8 40.789 0.01138 3.2206 0.003532 -#> ds 2 parent 60 44.8 40.789 4.01138 3.2206 1.245537 -#> ds 2 parent 90 27.8 25.830 1.97002 2.0395 0.965937 -#> ds 2 parent 90 27.0 25.830 1.17002 2.0395 0.573682 -#> ds 2 parent 120 15.2 16.357 -1.15721 1.2915 -0.895991 -#> ds 2 parent 120 15.5 16.357 -0.85721 1.2915 -0.663710 -#> ds 3 parent 0 97.7 101.907 -4.20726 8.0464 -0.522873 -#> ds 3 parent 0 88.2 101.907 -13.70726 8.0464 -1.703521 -#> ds 3 parent 1 109.9 99.522 10.37805 7.8581 1.320685 -#> ds 3 parent 1 97.8 99.522 -1.72195 7.8581 -0.219130 -#> ds 3 parent 3 100.5 94.918 5.58248 7.4945 0.744875 -#> ds 3 parent 3 77.4 94.918 -17.51752 7.4945 -2.337375 -#> ds 3 parent 7 78.3 86.338 -8.03788 6.8171 -1.179077 -#> ds 3 parent 7 90.3 86.338 3.96212 6.8171 0.581204 -#> ds 3 parent 14 76.0 73.147 2.85281 5.7756 0.493944 -#> ds 3 parent 14 79.1 73.147 5.95281 5.7756 1.030687 -#> ds 3 parent 28 46.0 52.504 -6.50373 4.1456 -1.568826 -#> ds 3 parent 28 53.4 52.504 0.89627 4.1456 0.216197 -#> ds 3 parent 60 25.1 24.605 0.49461 1.9428 0.254586 -#> ds 3 parent 60 21.4 24.605 -3.20539 1.9428 -1.649882 -#> ds 3 parent 90 11.0 12.090 -1.09046 0.9546 -1.142271 -#> ds 3 parent 90 14.2 12.090 2.10954 0.9546 2.209770 -#> ds 3 parent 120 5.8 5.941 -0.14094 0.4691 -0.300464 -#> ds 3 parent 120 6.1 5.941 0.15906 0.4691 0.339077 -#> ds 4 parent 0 95.3 101.177 -5.87672 7.9887 -0.735625 -#> ds 4 parent 0 102.0 101.177 0.82328 7.9887 0.103056 -#> ds 4 parent 1 104.4 100.716 3.68438 7.9523 0.463307 -#> ds 4 parent 1 105.4 100.716 4.68438 7.9523 0.589057 -#> ds 4 parent 3 113.7 99.800 13.90026 7.8800 1.763989 -#> ds 4 parent 3 82.3 99.800 -17.49974 7.8800 -2.220774 -#> ds 4 parent 7 98.1 97.993 0.10713 7.7374 0.013846 -#> ds 4 parent 7 87.8 97.993 -10.19287 7.7374 -1.317359 -#> ds 4 parent 14 97.9 94.909 2.99079 7.4939 0.399098 -#> ds 4 parent 14 104.8 94.909 9.89079 7.4939 1.319851 -#> ds 4 parent 28 85.0 89.030 -4.02995 7.0297 -0.573279 -#> ds 4 parent 28 77.2 89.030 -11.82995 7.0297 -1.682864 -#> ds 4 parent 60 82.2 76.923 5.27690 6.0737 0.868808 -#> ds 4 parent 60 86.1 76.923 9.17690 6.0737 1.510919 -#> ds 4 parent 90 70.5 67.073 3.42743 5.2959 0.647182 -#> ds 4 parent 90 61.7 67.073 -5.37257 5.2959 -1.014470 -#> ds 4 parent 120 60.0 58.483 1.51654 4.6178 0.328416 -#> ds 4 parent 120 56.4 58.483 -2.08346 4.6178 -0.451184 -#> ds 5 parent 0 92.6 101.752 -9.15161 8.0341 -1.139091 -#> ds 5 parent 0 116.5 101.752 14.74839 8.0341 1.835716 -#> ds 5 parent 1 108.0 100.069 7.93112 7.9013 1.003778 -#> ds 5 parent 1 104.9 100.069 4.83112 7.9013 0.611436 -#> ds 5 parent 3 100.5 96.786 3.71355 7.6421 0.485934 -#> ds 5 parent 3 89.5 96.786 -7.28645 7.6421 -0.953462 -#> ds 5 parent 7 91.7 90.541 1.15895 7.1490 0.162114 -#> ds 5 parent 7 95.1 90.541 4.55895 7.1490 0.637707 -#> ds 5 parent 14 82.2 80.566 1.63437 6.3613 0.256923 -#> ds 5 parent 14 84.5 80.566 3.93437 6.3613 0.618483 -#> ds 5 parent 28 60.5 63.791 -3.29084 5.0368 -0.653357 -#> ds 5 parent 28 72.8 63.791 9.00916 5.0368 1.788662 -#> ds 5 parent 60 38.3 37.412 0.88840 2.9540 0.300748 -#> ds 5 parent 60 40.7 37.412 3.28840 2.9540 1.113217 -#> ds 5 parent 90 22.5 22.685 -0.18500 1.7912 -0.103287 -#> ds 5 parent 90 20.8 22.685 -1.88500 1.7912 -1.052387 -#> ds 5 parent 120 13.4 13.755 -0.35534 1.0861 -0.327173 -#> ds 5 parent 120 13.8 13.755 0.04466 1.0861 0.041118</div><div class='input'> +#> ds 1 parent 0 104.1 101.592 2.50757 8.0255 0.312451 +#> ds 1 parent 0 105.0 101.592 3.40757 8.0255 0.424594 +#> ds 1 parent 1 98.5 100.796 -2.29571 7.9625 -0.288313 +#> ds 1 parent 1 96.1 100.796 -4.69571 7.9625 -0.589725 +#> ds 1 parent 3 101.9 99.221 2.67904 7.8381 0.341796 +#> ds 1 parent 3 85.2 99.221 -14.02096 7.8381 -1.788812 +#> ds 1 parent 7 99.1 96.145 2.95512 7.5951 0.389081 +#> ds 1 parent 7 93.0 96.145 -3.14488 7.5951 -0.414065 +#> ds 1 parent 14 88.1 90.989 -2.88944 7.1879 -0.401987 +#> ds 1 parent 14 84.1 90.989 -6.88944 7.1879 -0.958480 +#> ds 1 parent 28 80.2 81.493 -1.29305 6.4377 -0.200857 +#> ds 1 parent 28 91.3 81.493 9.80695 6.4377 1.523364 +#> ds 1 parent 60 65.1 63.344 1.75642 5.0039 0.351008 +#> ds 1 parent 60 65.8 63.344 2.45642 5.0039 0.490898 +#> ds 1 parent 90 47.8 50.018 -2.21764 3.9512 -0.561252 +#> ds 1 parent 90 53.5 50.018 3.48236 3.9512 0.881335 +#> ds 1 parent 120 37.6 39.495 -1.89515 3.1200 -0.607423 +#> ds 1 parent 120 39.3 39.495 -0.19515 3.1200 -0.062549 +#> ds 2 parent 0 107.9 101.592 6.30757 8.0255 0.785943 +#> ds 2 parent 0 102.1 101.592 0.50757 8.0255 0.063245 +#> ds 2 parent 1 103.8 100.058 3.74159 7.9043 0.473361 +#> ds 2 parent 1 108.6 100.058 8.54159 7.9043 1.080626 +#> ds 2 parent 3 91.0 97.060 -6.05952 7.6674 -0.790297 +#> ds 2 parent 3 84.9 97.060 -12.15952 7.6674 -1.585874 +#> ds 2 parent 7 79.3 91.329 -12.02867 7.2147 -1.667251 +#> ds 2 parent 7 100.9 91.329 9.57133 7.2147 1.326647 +#> ds 2 parent 14 77.3 82.102 -4.80185 6.4858 -0.740366 +#> ds 2 parent 14 83.5 82.102 1.39815 6.4858 0.215571 +#> ds 2 parent 28 66.8 66.351 0.44945 5.2415 0.085748 +#> ds 2 parent 28 63.3 66.351 -3.05055 5.2415 -0.582002 +#> ds 2 parent 60 40.8 40.775 0.02474 3.2211 0.007679 +#> ds 2 parent 60 44.8 40.775 4.02474 3.2211 1.249485 +#> ds 2 parent 90 27.8 25.832 1.96762 2.0407 0.964198 +#> ds 2 parent 90 27.0 25.832 1.16762 2.0407 0.572171 +#> ds 2 parent 120 15.2 16.366 -1.16561 1.2928 -0.901595 +#> ds 2 parent 120 15.5 16.366 -0.86561 1.2928 -0.669547 +#> ds 3 parent 0 97.7 101.592 -3.89243 8.0255 -0.485009 +#> ds 3 parent 0 88.2 101.592 -13.39243 8.0255 -1.668739 +#> ds 3 parent 1 109.9 99.218 10.68196 7.8379 1.362858 +#> ds 3 parent 1 97.8 99.218 -1.41804 7.8379 -0.180921 +#> ds 3 parent 3 100.5 94.634 5.86555 7.4758 0.784603 +#> ds 3 parent 3 77.4 94.634 -17.23445 7.4758 -2.305360 +#> ds 3 parent 7 78.3 86.093 -7.79273 6.8011 -1.145813 +#> ds 3 parent 7 90.3 86.093 4.20727 6.8011 0.618620 +#> ds 3 parent 14 76.0 72.958 3.04222 5.7634 0.527848 +#> ds 3 parent 14 79.1 72.958 6.14222 5.7634 1.065722 +#> ds 3 parent 28 46.0 52.394 -6.39404 4.1390 -1.544842 +#> ds 3 parent 28 53.4 52.394 1.00596 4.1390 0.243046 +#> ds 3 parent 60 25.1 24.582 0.51786 1.9419 0.266676 +#> ds 3 parent 60 21.4 24.582 -3.18214 1.9419 -1.638664 +#> ds 3 parent 90 11.0 12.092 -1.09202 0.9552 -1.143199 +#> ds 3 parent 90 14.2 12.092 2.10798 0.9552 2.206776 +#> ds 3 parent 120 5.8 5.948 -0.14810 0.4699 -0.315178 +#> ds 3 parent 120 6.1 5.948 0.15190 0.4699 0.323282 +#> ds 4 parent 0 95.3 101.592 -6.29243 8.0255 -0.784057 +#> ds 4 parent 0 102.0 101.592 0.40757 8.0255 0.050784 +#> ds 4 parent 1 104.4 101.125 3.27549 7.9885 0.410025 +#> ds 4 parent 1 105.4 101.125 4.27549 7.9885 0.535205 +#> ds 4 parent 3 113.7 100.195 13.50487 7.9151 1.706218 +#> ds 4 parent 3 82.3 100.195 -17.89513 7.9151 -2.260886 +#> ds 4 parent 7 98.1 98.362 -0.26190 7.7703 -0.033706 +#> ds 4 parent 7 87.8 98.362 -10.56190 7.7703 -1.359270 +#> ds 4 parent 14 97.9 95.234 2.66590 7.5232 0.354357 +#> ds 4 parent 14 104.8 95.234 9.56590 7.5232 1.271521 +#> ds 4 parent 28 85.0 89.274 -4.27372 7.0523 -0.606001 +#> ds 4 parent 28 77.2 89.274 -12.07372 7.0523 -1.712017 +#> ds 4 parent 60 82.2 77.013 5.18661 6.0838 0.852526 +#> ds 4 parent 60 86.1 77.013 9.08661 6.0838 1.493571 +#> ds 4 parent 90 70.5 67.053 3.44692 5.2970 0.650733 +#> ds 4 parent 90 61.7 67.053 -5.35308 5.2970 -1.010591 +#> ds 4 parent 120 60.0 58.381 1.61905 4.6119 0.351058 +#> ds 4 parent 120 56.4 58.381 -1.98095 4.6119 -0.429530 +#> ds 5 parent 0 92.6 101.592 -8.99243 8.0255 -1.120485 +#> ds 5 parent 0 116.5 101.592 14.90757 8.0255 1.857531 +#> ds 5 parent 1 108.0 99.914 8.08560 7.8929 1.024413 +#> ds 5 parent 1 104.9 99.914 4.98560 7.8929 0.631655 +#> ds 5 parent 3 100.5 96.641 3.85898 7.6343 0.505477 +#> ds 5 parent 3 89.5 96.641 -7.14102 7.6343 -0.935382 +#> ds 5 parent 7 91.7 90.412 1.28752 7.1423 0.180267 +#> ds 5 parent 7 95.1 90.412 4.68752 7.1423 0.656304 +#> ds 5 parent 14 82.2 80.463 1.73715 6.3563 0.273295 +#> ds 5 parent 14 84.5 80.463 4.03715 6.3563 0.635141 +#> ds 5 parent 28 60.5 63.728 -3.22788 5.0343 -0.641178 +#> ds 5 parent 28 72.8 63.728 9.07212 5.0343 1.802062 +#> ds 5 parent 60 38.3 37.399 0.90061 2.9544 0.304835 +#> ds 5 parent 60 40.7 37.399 3.30061 2.9544 1.117174 +#> ds 5 parent 90 22.5 22.692 -0.19165 1.7926 -0.106913 +#> ds 5 parent 90 20.8 22.692 -1.89165 1.7926 -1.055273 +#> ds 5 parent 120 13.4 13.768 -0.36790 1.0876 -0.338259 +#> ds 5 parent 120 13.8 13.768 0.03210 1.0876 0.029517</div><div class='input'> </div></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> diff --git a/docs/dev/reference/summary.nlmixr.mmkin.html b/docs/dev/reference/summary.nlmixr.mmkin.html new file mode 100644 index 00000000..0fead0df --- 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Optionally +(default is FALSE), the data are listed in full.</p> + </div> + + <pre class="usage"><span class='co'># S3 method for nlmixr.mmkin</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'>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 class='co'># S3 method for summary.nlmixr.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>, digits <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='fl'>3</span>, <span class='fu'><a href='https://rdrr.io/r/base/options.html'>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></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 <a href='nlmixr.mmkin.html'>nlmixr.mmkin</a></p></td> + </tr> + <tr> + <th>data</th> + <td><p>logical, indicating whether the full data should be included in +the summary.</p></td> + </tr> + <tr> + <th>verbose</th> + <td><p>Should the summary be verbose?</p></td> + </tr> + <tr> + <th>distimes</th> + <td><p>logical, indicating whether DT50 and DT90 values should be +included.</p></td> + </tr> + <tr> + <th>...</th> + <td><p>optional arguments passed to methods like <code>print</code>.</p></td> + </tr> + <tr> + <th>x</th> + <td><p>an object of class summary.nlmixr.mmkin</p></td> + </tr> + <tr> + <th>digits</th> + <td><p>Number of digits to use for printing</p></td> + </tr> + </table> + + <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> + + <p>The summary function returns a list obtained in the fit, with at +least the following additional components</p> +<dt>nlmixrversion, mkinversion, Rversion</dt><dd><p>The nlmixr, 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_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> +The print method is called for its side effect, i.e. printing the summary. + + <h2 class="hasAnchor" id="author"><a class="anchor" href="#author"></a>Author</h2> + + <p>Johannes Ranke for the mkin specific parts +nlmixr authors for the parts inherited from nlmixr.</p> + + <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> + <pre class="examples"><div class='input'><span class='co'># Generate five datasets following DFOP-SFO kinetics</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'>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'>"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>, quiet <span class='op'>=</span> <span class='cn'>TRUE</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'>1234</span><span class='op'>)</span> +<span class='va'>k1_in</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/Lognormal.html'>rlnorm</a></span><span class='op'>(</span><span class='fl'>5</span>, <span class='fu'><a href='https://rdrr.io/r/base/Log.html'>log</a></span><span class='op'>(</span><span class='fl'>0.1</span><span class='op'>)</span>, <span class='fl'>0.3</span><span class='op'>)</span> +<span class='va'>k2_in</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/Lognormal.html'>rlnorm</a></span><span class='op'>(</span><span class='fl'>5</span>, <span class='fu'><a href='https://rdrr.io/r/base/Log.html'>log</a></span><span class='op'>(</span><span class='fl'>0.02</span><span class='op'>)</span>, <span class='fl'>0.3</span><span class='op'>)</span> +<span class='va'>g_in</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/Logistic.html'>plogis</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/stats/Normal.html'>rnorm</a></span><span class='op'>(</span><span class='fl'>5</span>, <span class='fu'><a href='https://rdrr.io/r/stats/Logistic.html'>qlogis</a></span><span class='op'>(</span><span class='fl'>0.5</span><span class='op'>)</span>, <span class='fl'>0.3</span><span class='op'>)</span><span class='op'>)</span> +<span class='va'>f_parent_to_m1_in</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/Logistic.html'>plogis</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/stats/Normal.html'>rnorm</a></span><span class='op'>(</span><span class='fl'>5</span>, <span class='fu'><a href='https://rdrr.io/r/stats/Logistic.html'>qlogis</a></span><span class='op'>(</span><span class='fl'>0.3</span><span class='op'>)</span>, <span class='fl'>0.3</span><span class='op'>)</span><span class='op'>)</span> +<span class='va'>k_m1_in</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/Lognormal.html'>rlnorm</a></span><span class='op'>(</span><span class='fl'>5</span>, <span class='fu'><a href='https://rdrr.io/r/base/Log.html'>log</a></span><span class='op'>(</span><span class='fl'>0.02</span><span class='op'>)</span>, <span class='fl'>0.3</span><span class='op'>)</span> + +<span class='va'>pred_dfop_sfo</span> <span class='op'><-</span> <span class='kw'>function</span><span class='op'>(</span><span class='va'>k1</span>, <span class='va'>k2</span>, <span class='va'>g</span>, <span class='va'>f_parent_to_m1</span>, <span class='va'>k_m1</span><span class='op'>)</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 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='va'>k1</span>, k2 <span class='op'>=</span> <span class='va'>k2</span>, g <span class='op'>=</span> <span class='va'>g</span>, f_parent_to_m1 <span class='op'>=</span> <span class='va'>f_parent_to_m1</span>, k_m1 <span class='op'>=</span> <span class='va'>k_m1</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='op'>}</span> + +<span class='va'>ds_mean_dfop_sfo</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='fl'>1</span><span class='op'>:</span><span class='fl'>5</span>, <span class='kw'>function</span><span class='op'>(</span><span class='va'>i</span><span class='op'>)</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 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='va'>k1_in</span><span class='op'>[</span><span class='va'>i</span><span class='op'>]</span>, k2 <span class='op'>=</span> <span class='va'>k2_in</span><span class='op'>[</span><span class='va'>i</span><span class='op'>]</span>, g <span class='op'>=</span> <span class='va'>g_in</span><span class='op'>[</span><span class='va'>i</span><span class='op'>]</span>, + f_parent_to_m1 <span class='op'>=</span> <span class='va'>f_parent_to_m1_in</span><span class='op'>[</span><span class='va'>i</span><span class='op'>]</span>, k_m1 <span class='op'>=</span> <span class='va'>k_m1_in</span><span class='op'>[</span><span class='va'>i</span><span class='op'>]</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='op'>}</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'>ds_mean_dfop_sfo</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'>"ds"</span>, <span class='fl'>1</span><span class='op'>:</span><span class='fl'>5</span><span class='op'>)</span> + +<span class='va'>ds_syn_dfop_sfo</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'>ds_mean_dfop_sfo</span>, <span class='kw'>function</span><span class='op'>(</span><span class='va'>ds</span><span class='op'>)</span> <span class='op'>{</span> + <span class='fu'><a href='add_err.html'>add_err</a></span><span class='op'>(</span><span class='va'>ds</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'>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>, + 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='op'>}</span><span class='op'>)</span> + +<span class='co'># \dontrun{</span> +<span class='co'># Evaluate using mmkin and nlmixr</span> +<span class='va'>f_mmkin_dfop_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='va'>dfop_sfo</span><span class='op'>)</span>, <span class='va'>ds_syn_dfop_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'>5</span><span class='op'>)</span> +<span class='va'>f_saemix_dfop_sfo</span> <span class='op'><-</span> <span class='fu'>mkin</span><span class='fu'>::</span><span class='fu'><a href='saem.html'>saem</a></span><span class='op'>(</span><span class='va'>f_mmkin_dfop_sfo</span><span class='op'>)</span> +</div><div class='output co'>#> Running main SAEM algorithm +#> [1] "Fri Jun 11 10:57:31 2021" +#> .... +#> Minimisation finished +#> [1] "Fri Jun 11 10:57:43 2021"</div><div class='input'><span class='va'>f_nlme_dfop_sfo</span> <span class='op'><-</span> <span class='fu'>mkin</span><span class='fu'>::</span><span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span><span class='op'>(</span><span class='va'>f_mmkin_dfop_sfo</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='warning'>Warning: Iteration 4, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 6, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_saem</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_dfop_sfo</span>, est <span class='op'>=</span> <span class='st'>"saem"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'>With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'>→ generate SAEM model</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> 1: 1.0127e+02 -3.8515e+00 -2.0719e+00 -3.7271e+00 -1.9335e+00 4.0311e-01 6.9594e+00 1.5021e-01 5.3947e-01 1.9686e-01 3.7429e-01 5.4209e-01 8.4121e+00 7.3391e-02 7.1185e+00 2.5869e-01 +#> 2: 1.0136e+02 -3.8005e+00 -2.3424e+00 -4.0759e+00 -1.6475e+00 1.1598e-01 6.6115e+00 1.4406e-01 5.1249e-01 1.8701e-01 3.5786e-01 5.1499e-01 4.9102e+00 6.2829e-02 4.7230e+00 7.8901e-02 +#> 3: 1.0126e+02 -4.0285e+00 -2.3629e+00 -4.1271e+00 -1.1733e+00 1.7634e-02 6.2809e+00 1.6892e-01 4.8687e-01 1.7766e-01 3.3997e-01 4.8924e-01 3.2256e+00 6.6693e-02 3.3261e+00 8.7190e-02 +#> 4: 1.0105e+02 -4.0894e+00 -2.5516e+00 -4.1037e+00 -1.0816e+00 4.5377e-02 5.9668e+00 1.6048e-01 4.6252e-01 1.6878e-01 3.2297e-01 4.6478e-01 2.4343e+00 7.0557e-02 2.2610e+00 9.2498e-02 +#> 5: 1.0101e+02 -4.1364e+00 -2.4605e+00 -4.0737e+00 -1.0920e+00 -4.7953e-03 5.9593e+00 1.5245e-01 4.3940e-01 1.8078e-01 3.0682e-01 5.4688e-01 1.7424e+00 7.4776e-02 1.5144e+00 1.0787e-01 +#> 6: 1.0042e+02 -4.0933e+00 -2.4472e+00 -4.1090e+00 -9.7996e-01 -9.0472e-02 6.0175e+00 1.4483e-01 4.1743e-01 1.8824e-01 2.9148e-01 5.3033e-01 1.5545e+00 6.8588e-02 1.3401e+00 9.8865e-02 +#> 7: 1.0078e+02 -4.0911e+00 -2.4335e+00 -4.0758e+00 -9.9422e-01 -7.8849e-02 6.6318e+00 1.3759e-01 3.9656e-01 1.7882e-01 2.7691e-01 5.0381e-01 1.3780e+00 6.9978e-02 1.1346e+00 9.6162e-02 +#> 8: 1.0077e+02 -4.0196e+00 -2.4345e+00 -4.0444e+00 -9.3483e-01 -1.1032e-01 6.3002e+00 1.3071e-01 3.7673e-01 1.6988e-01 2.6306e-01 4.8191e-01 1.1774e+00 7.4232e-02 1.0270e+00 9.5616e-02 +#> 9: 1.0118e+02 -4.0436e+00 -2.4649e+00 -4.0207e+00 -8.9829e-01 -1.7784e-01 5.9852e+00 1.2417e-01 3.5789e-01 1.6139e-01 2.4991e-01 5.5466e-01 1.1040e+00 7.1515e-02 1.0342e+00 9.3972e-02 +#> 10: 1.0143e+02 -4.0523e+00 -2.3737e+00 -4.0184e+00 -9.1167e-01 -2.3828e-01 5.8520e+00 1.1797e-01 3.4196e-01 1.5332e-01 2.3741e-01 5.2849e-01 1.0510e+00 7.5719e-02 1.0638e+00 9.3973e-02 +#> 11: 1.0119e+02 -4.0699e+00 -2.3680e+00 -4.0191e+00 -9.4858e-01 -1.7310e-01 6.9958e+00 1.1207e-01 3.6891e-01 1.4565e-01 2.2554e-01 5.0206e-01 1.0247e+00 7.5497e-02 1.0292e+00 9.3707e-02 +#> 12: 1.0121e+02 -4.0189e+00 -2.4198e+00 -4.0139e+00 -9.1693e-01 -2.0613e-01 6.6460e+00 1.0646e-01 3.5046e-01 1.3837e-01 2.1427e-01 5.7696e-01 1.1046e+00 7.6090e-02 9.3689e-01 9.4115e-02 +#> 13: 1.0083e+02 -4.0451e+00 -2.4395e+00 -4.0235e+00 -9.4535e-01 -1.4723e-01 6.3137e+00 1.0114e-01 3.3294e-01 1.3145e-01 2.0355e-01 5.4811e-01 1.0360e+00 7.3381e-02 9.7078e-01 9.1659e-02 +#> 14: 1.0056e+02 -4.0401e+00 -2.4045e+00 -4.0054e+00 -9.4191e-01 -1.3928e-01 5.9980e+00 9.6084e-02 3.4934e-01 1.2488e-01 1.9338e-01 5.2071e-01 1.0303e+00 7.7118e-02 8.8372e-01 9.0469e-02 +#> 15: 1.0070e+02 -4.0388e+00 -2.4210e+00 -4.0113e+00 -9.1136e-01 -1.2702e-01 5.6981e+00 9.1279e-02 3.3187e-01 1.1864e-01 1.8371e-01 4.9467e-01 1.0486e+00 7.2427e-02 7.8179e-01 9.1572e-02 +#> 16: 1.0078e+02 -4.0175e+00 -2.4766e+00 -4.0191e+00 -9.0733e-01 -1.1952e-01 5.4132e+00 8.6716e-02 3.1528e-01 1.1270e-01 1.7452e-01 4.8928e-01 9.7799e-01 8.1464e-02 8.2935e-01 8.6520e-02 +#> 17: 1.0069e+02 -4.0533e+00 -2.5110e+00 -4.0294e+00 -9.1841e-01 -6.8363e-03 5.1426e+00 8.2380e-02 2.9952e-01 1.0707e-01 1.6580e-01 4.6482e-01 9.1609e-01 8.1008e-02 8.1783e-01 8.8818e-02 +#> 18: 99.9647 -4.0672 -2.5327 -4.0416 -0.9273 0.0097 4.8854 0.0783 0.2970 0.1280 0.1941 0.5053 0.9306 0.0764 0.8097 0.0881 +#> 19: 1.0027e+02 -4.0667e+00 -2.4653e+00 -4.0579e+00 -9.2776e-01 3.0417e-02 4.6412e+00 7.4348e-02 3.3694e-01 1.2164e-01 1.8435e-01 5.1797e-01 9.7386e-01 7.4954e-02 7.9297e-01 8.9915e-02 +#> 20: 1.0006e+02 -4.0935e+00 -2.4804e+00 -4.0721e+00 -9.3737e-01 1.9496e-02 4.4091e+00 7.0630e-02 3.3728e-01 1.2544e-01 1.7513e-01 6.0925e-01 1.0232e+00 7.4618e-02 7.9988e-01 8.9642e-02 +#> 21: 1.0043e+02 -4.0542e+00 -2.5168e+00 -4.0623e+00 -9.1553e-01 3.9474e-02 4.1887e+00 6.7099e-02 3.4553e-01 1.1917e-01 1.6638e-01 6.0827e-01 1.0155e+00 8.0771e-02 7.8424e-01 8.6213e-02 +#> 22: 1.0049e+02 -4.0449e+00 -2.5082e+00 -4.0849e+00 -9.2553e-01 4.5424e-02 3.9792e+00 6.3744e-02 3.2825e-01 1.2365e-01 1.5806e-01 5.8922e-01 8.2860e-01 8.3384e-02 8.2525e-01 8.9218e-02 +#> 23: 1.0067e+02 -4.0411e+00 -2.5460e+00 -4.0736e+00 -9.2578e-01 5.2422e-02 3.7803e+00 6.0557e-02 3.1661e-01 1.2306e-01 1.5016e-01 5.8274e-01 9.3412e-01 8.0508e-02 8.1829e-01 8.6377e-02 +#> 24: 1.0091e+02 -4.0314e+00 -2.5298e+00 -4.0566e+00 -8.9743e-01 3.7634e-02 3.5913e+00 5.7529e-02 3.5267e-01 1.2194e-01 1.4265e-01 5.5360e-01 9.6271e-01 7.6960e-02 8.8466e-01 8.5693e-02 +#> 25: 1.0100e+02 -4.0442e+00 -2.5399e+00 -4.0568e+00 -8.9494e-01 1.7415e-02 3.4117e+00 5.4652e-02 3.3504e-01 1.2781e-01 1.3552e-01 5.2592e-01 9.6040e-01 7.7299e-02 8.9561e-01 8.6893e-02 +#> 26: 1.0111e+02 -4.0354e+00 -2.5182e+00 -4.0899e+00 -9.0799e-01 7.6464e-02 4.8614e+00 5.1920e-02 3.1829e-01 1.2142e-01 1.3110e-01 4.9963e-01 9.6997e-01 7.4932e-02 8.2521e-01 9.3659e-02 +#> 27: 1.0159e+02 -4.0653e+00 -2.4934e+00 -4.0803e+00 -9.5632e-01 2.8659e-03 4.6184e+00 4.9324e-02 3.0237e-01 1.1535e-01 1.4743e-01 4.7465e-01 9.4314e-01 7.7860e-02 8.9820e-01 8.8210e-02 +#> 28: 1.0154e+02 -4.0487e+00 -2.4844e+00 -4.0511e+00 -9.6473e-01 -4.7382e-02 4.3874e+00 4.6858e-02 3.2049e-01 1.0958e-01 1.5243e-01 4.5091e-01 9.8808e-01 7.4786e-02 8.6833e-01 8.8720e-02 +#> 29: 1.0144e+02 -4.0414e+00 -2.4105e+00 -4.0504e+00 -9.4039e-01 -3.6753e-02 4.1681e+00 4.4515e-02 3.2754e-01 1.0410e-01 1.4940e-01 4.2837e-01 9.5520e-01 7.8507e-02 8.2408e-01 8.5998e-02 +#> 30: 1.0137e+02 -4.0292e+00 -2.4174e+00 -4.0382e+00 -9.3180e-01 -7.1482e-02 5.4636e+00 4.2289e-02 3.2074e-01 9.8896e-02 1.6877e-01 4.0695e-01 8.8153e-01 7.5106e-02 8.5239e-01 8.8266e-02 +#> 31: 1.0105e+02 -4.0387e+00 -2.4368e+00 -4.0346e+00 -9.1098e-01 -5.4730e-02 5.1904e+00 4.0175e-02 3.0470e-01 9.3951e-02 1.6034e-01 3.8660e-01 8.7853e-01 8.0278e-02 8.7981e-01 8.6404e-02 +#> 32: 1.0147e+02 -4.0435e+00 -2.4530e+00 -4.0365e+00 -9.1241e-01 -7.1281e-02 4.9309e+00 3.8166e-02 2.8947e-01 9.4694e-02 1.7475e-01 3.6727e-01 8.7005e-01 8.1398e-02 8.7784e-01 8.8976e-02 +#> 33: 1.0144e+02 -4.0092e+00 -2.4279e+00 -4.0090e+00 -8.8656e-01 -1.4017e-01 5.2945e+00 3.6258e-02 2.9770e-01 1.0169e-01 1.6601e-01 3.4891e-01 9.2202e-01 7.8841e-02 8.7551e-01 8.4011e-02 +#> 34: 1.0157e+02 -3.9839e+00 -2.4469e+00 -4.0180e+00 -8.3877e-01 -1.4664e-01 6.3506e+00 3.4445e-02 2.8282e-01 1.0831e-01 1.6850e-01 3.3146e-01 8.4403e-01 7.9056e-02 8.4620e-01 8.6363e-02 +#> 35: 1.0149e+02 -3.9928e+00 -2.4771e+00 -4.0106e+00 -8.6974e-01 -1.4219e-01 6.2039e+00 3.2722e-02 2.8123e-01 1.1283e-01 1.6008e-01 3.1489e-01 9.1308e-01 7.8685e-02 7.8939e-01 8.7289e-02 +#> 36: 1.0162e+02 -4.0099e+00 -2.4822e+00 -3.9880e+00 -8.7959e-01 -1.3237e-01 5.8937e+00 3.1086e-02 3.2200e-01 1.0719e-01 1.6077e-01 2.9914e-01 9.0821e-01 8.4066e-02 7.5559e-01 8.4838e-02 +#> 37: 1.0102e+02 -3.9962e+00 -2.4852e+00 -3.9954e+00 -8.8307e-01 -9.2070e-02 5.5991e+00 2.9532e-02 3.3713e-01 1.0183e-01 1.5333e-01 2.8419e-01 8.3918e-01 8.5231e-02 7.6007e-01 8.9541e-02 +#> 38: 1.0102e+02 -3.9987e+00 -2.5129e+00 -3.9833e+00 -8.7454e-01 -1.6469e-01 5.3191e+00 2.8055e-02 3.2027e-01 1.0792e-01 1.4707e-01 2.6998e-01 9.1490e-01 8.4715e-02 7.6778e-01 8.9241e-02 +#> 39: 1.0054e+02 -3.9875e+00 -2.4301e+00 -3.9797e+00 -8.7222e-01 -1.9597e-01 7.3800e+00 2.6653e-02 3.0426e-01 1.0801e-01 1.4393e-01 2.5648e-01 9.5901e-01 7.8320e-02 8.1559e-01 9.2429e-02 +#> 40: 1.0077e+02 -4.0057e+00 -2.4630e+00 -3.9849e+00 -8.6788e-01 -1.9606e-01 7.0110e+00 2.5320e-02 3.0385e-01 1.3164e-01 1.4567e-01 3.0284e-01 9.7123e-01 7.6328e-02 8.3681e-01 8.9349e-02 +#> 41: 1.0069e+02 -4.0143e+00 -2.3805e+00 -3.9962e+00 -8.7503e-01 -1.8532e-01 6.6604e+00 2.4054e-02 3.0707e-01 1.4668e-01 1.5021e-01 3.0404e-01 1.0072e+00 7.3629e-02 9.4494e-01 8.4745e-02 +#> 42: 1.0073e+02 -3.9861e+00 -2.4464e+00 -3.9919e+00 -8.7912e-01 -1.8435e-01 6.3274e+00 2.2851e-02 2.9171e-01 1.3935e-01 1.5080e-01 2.8883e-01 9.6502e-01 7.7470e-02 9.4221e-01 8.2459e-02 +#> 43: 1.0104e+02 -3.9881e+00 -2.4156e+00 -3.9688e+00 -8.9448e-01 -2.3739e-01 6.0110e+00 2.1709e-02 2.7713e-01 1.3238e-01 1.5603e-01 2.7439e-01 9.7714e-01 7.1720e-02 8.5890e-01 8.6635e-02 +#> 44: 1.0084e+02 -4.0117e+00 -2.4455e+00 -3.9753e+00 -8.8716e-01 -2.0112e-01 5.7105e+00 2.0623e-02 2.6327e-01 1.2741e-01 1.5200e-01 2.6067e-01 9.3289e-01 8.0543e-02 8.5055e-01 8.2921e-02 +#> 45: 1.0071e+02 -3.9996e+00 -2.4359e+00 -3.9764e+00 -9.1082e-01 -2.4578e-01 5.4250e+00 1.9592e-02 2.5011e-01 1.3254e-01 1.6132e-01 2.8273e-01 9.5805e-01 7.7734e-02 7.8171e-01 8.4571e-02 +#> 46: 1.0018e+02 -4.0077e+00 -2.4835e+00 -3.9739e+00 -8.6079e-01 -1.6592e-01 5.1537e+00 1.8613e-02 2.3760e-01 1.3830e-01 1.5392e-01 3.0295e-01 1.0931e+00 7.3274e-02 8.9544e-01 8.8388e-02 +#> 47: 99.9834 -3.9991 -2.5292 -3.9863 -0.8820 -0.0796 4.8960 0.0177 0.2348 0.1376 0.1639 0.2878 0.9864 0.0837 0.9094 0.0832 +#> 48: 99.9155 -4.0224 -2.5422 -3.9854 -0.8719 -0.0750 4.6512 0.0184 0.2251 0.1307 0.1596 0.2734 0.9841 0.0835 0.8696 0.0843 +#> 49: 99.6136 -4.0397 -2.5172 -4.0115 -0.8774 -0.0922 5.2402 0.0175 0.2558 0.1242 0.1551 0.2597 0.9060 0.0816 0.8365 0.0869 +#> 50: 99.4747 -4.0542 -2.4192 -3.9834 -0.9041 -0.1798 4.9782 0.0219 0.2695 0.1234 0.1474 0.2468 0.9269 0.0783 0.8593 0.0854 +#> 51: 99.3401 -4.0386 -2.3951 -3.9661 -0.9181 -0.1887 4.7574 0.0213 0.2746 0.1522 0.1400 0.2344 0.9901 0.0781 0.8863 0.0928 +#> 52: 99.7109 -4.0509 -2.4227 -3.9770 -0.9247 -0.1431 4.9004 0.0203 0.2688 0.1446 0.1330 0.2227 0.8999 0.0791 1.0265 0.0890 +#> 53: 99.6496 -4.0397 -2.4398 -3.9752 -0.9193 -0.2119 5.1106 0.0193 0.2795 0.1527 0.1325 0.2116 0.8949 0.0788 0.9447 0.0872 +#> 54: 99.9071 -4.0211 -2.3887 -3.9812 -0.9233 -0.1946 5.0887 0.0183 0.2763 0.1450 0.1365 0.2010 0.8793 0.0875 0.8643 0.0903 +#> 55: 1.0012e+02 -4.0401e+00 -2.4203e+00 -3.9511e+00 -9.0712e-01 -2.5566e-01 5.7301e+00 1.7375e-02 2.7324e-01 1.3780e-01 1.6204e-01 1.9094e-01 9.7803e-01 7.6146e-02 9.0756e-01 8.7636e-02 +#> 56: 1.0032e+02 -4.0207e+00 -2.4263e+00 -3.9533e+00 -8.7574e-01 -2.3076e-01 6.5321e+00 1.6507e-02 3.0821e-01 1.3091e-01 1.5394e-01 1.8139e-01 8.8520e-01 7.6350e-02 9.2796e-01 8.5283e-02 +#> 57: 1.0028e+02 -4.0037e+00 -2.4301e+00 -3.9655e+00 -8.8472e-01 -1.8969e-01 9.8969e+00 1.5681e-02 2.9280e-01 1.2436e-01 1.4624e-01 1.7232e-01 9.2902e-01 7.4974e-02 8.9204e-01 8.4563e-02 +#> 58: 1.0048e+02 -3.9928e+00 -2.4961e+00 -3.9709e+00 -9.0263e-01 -1.4516e-01 9.4021e+00 1.6151e-02 2.7816e-01 1.1814e-01 1.4165e-01 1.6370e-01 9.5145e-01 8.0233e-02 8.2896e-01 8.3498e-02 +#> 59: 1.0060e+02 -4.0181e+00 -2.4963e+00 -3.9751e+00 -9.0684e-01 -1.1186e-01 8.9320e+00 1.9914e-02 3.0097e-01 1.1224e-01 1.4109e-01 1.5552e-01 9.9121e-01 7.3120e-02 8.6454e-01 8.2239e-02 +#> 60: 1.0047e+02 -3.9976e+00 -2.4797e+00 -3.9780e+00 -8.9328e-01 -1.0814e-01 8.4854e+00 1.8918e-02 3.2275e-01 1.1591e-01 1.3404e-01 1.4774e-01 9.6968e-01 7.4984e-02 8.9831e-01 8.1655e-02 +#> 61: 1.0040e+02 -4.0068e+00 -2.5217e+00 -3.9844e+00 -8.6447e-01 -1.0567e-01 8.0611e+00 1.7972e-02 3.1372e-01 1.1011e-01 1.2973e-01 1.4036e-01 9.1698e-01 7.8118e-02 9.1811e-01 8.4420e-02 +#> 62: 1.0076e+02 -4.0080e+00 -2.4931e+00 -3.9623e+00 -8.9789e-01 -8.3896e-02 7.6580e+00 1.7073e-02 3.0460e-01 1.1254e-01 1.2324e-01 1.3334e-01 9.9032e-01 7.7618e-02 8.3808e-01 8.5031e-02 +#> 63: 1.0064e+02 -4.0129e+00 -2.4731e+00 -3.9561e+00 -8.9103e-01 -8.8987e-02 7.2751e+00 1.6220e-02 2.8944e-01 1.1647e-01 1.4845e-01 1.2667e-01 1.0745e+00 7.6375e-02 8.4316e-01 8.6681e-02 +#> 64: 1.0098e+02 -4.0094e+00 -2.4541e+00 -3.9604e+00 -9.1524e-01 -9.3413e-02 6.9114e+00 1.5409e-02 2.7497e-01 1.2065e-01 1.7095e-01 1.2034e-01 1.0963e+00 7.8304e-02 8.7104e-01 8.5727e-02 +#> 65: 1.0070e+02 -4.0433e+00 -2.4793e+00 -3.9722e+00 -9.3012e-01 -6.5917e-02 6.5658e+00 1.4638e-02 2.7040e-01 1.1462e-01 1.9067e-01 1.1432e-01 9.7444e-01 8.4510e-02 8.7028e-01 8.6292e-02 +#> 66: 1.0049e+02 -4.0656e+00 -2.4659e+00 -3.9898e+00 -9.4278e-01 -7.5929e-02 6.2375e+00 1.3906e-02 2.9347e-01 1.1997e-01 1.8114e-01 1.0860e-01 9.9830e-01 8.0902e-02 9.3551e-01 8.5261e-02 +#> 67: 1.0046e+02 -4.0477e+00 -2.4685e+00 -3.9907e+00 -9.1503e-01 -9.8019e-02 5.9256e+00 1.3211e-02 3.2166e-01 1.1506e-01 1.7208e-01 1.0317e-01 8.6453e-01 9.0533e-02 8.3598e-01 8.6343e-02 +#> 68: 1.0077e+02 -4.0575e+00 -2.4709e+00 -3.9523e+00 -9.2903e-01 -8.1099e-02 5.6294e+00 1.2818e-02 3.1005e-01 1.3665e-01 1.6347e-01 9.8015e-02 9.0181e-01 8.7058e-02 8.4937e-01 8.3248e-02 +#> 69: 1.0086e+02 -4.0626e+00 -2.3922e+00 -3.9557e+00 -9.6741e-01 -3.5986e-02 5.3479e+00 1.2844e-02 3.3024e-01 1.2982e-01 1.5530e-01 9.3115e-02 9.8180e-01 8.3132e-02 8.6549e-01 8.8939e-02 +#> 70: 1.0082e+02 -4.0640e+00 -2.4449e+00 -3.9787e+00 -9.5159e-01 -3.2904e-02 5.0805e+00 1.4346e-02 3.1373e-01 1.2333e-01 1.4754e-01 8.8459e-02 1.0129e+00 7.4856e-02 8.6688e-01 8.4769e-02 +#> 71: 1.0072e+02 -4.0642e+00 -2.5069e+00 -3.9493e+00 -9.3453e-01 -4.4116e-02 4.8265e+00 1.3628e-02 3.0428e-01 1.2122e-01 1.4091e-01 8.4036e-02 1.0454e+00 7.7023e-02 8.9566e-01 8.1639e-02 +#> 72: 1.0049e+02 -4.0609e+00 -2.4472e+00 -3.9669e+00 -9.3972e-01 -7.7498e-02 4.5852e+00 1.4441e-02 3.2552e-01 1.3911e-01 1.4144e-01 8.1899e-02 1.0114e+00 7.7019e-02 8.2312e-01 8.2494e-02 +#> 73: 1.0022e+02 -4.0598e+00 -2.4410e+00 -3.9952e+00 -9.2810e-01 -1.1309e-01 4.3559e+00 1.3719e-02 3.3556e-01 1.3303e-01 1.4990e-01 1.1303e-01 9.6726e-01 7.6776e-02 8.6331e-01 8.3048e-02 +#> 74: 1.0024e+02 -4.0628e+00 -2.4358e+00 -3.9977e+00 -9.1347e-01 -9.1966e-02 4.1381e+00 1.3033e-02 3.4332e-01 1.3418e-01 1.8099e-01 1.0738e-01 1.0158e+00 7.4697e-02 8.6366e-01 8.4370e-02 +#> 75: 99.7847 -4.0500 -2.4401 -4.0018 -0.9252 -0.1013 4.4651 0.0124 0.3365 0.1399 0.1817 0.1020 1.0278 0.0779 0.9008 0.0841 +#> 76: 99.9526 -4.0482 -2.4819 -3.9947 -0.9049 -0.0557 4.2419 0.0126 0.3248 0.1494 0.1726 0.1135 1.0493 0.0778 0.9341 0.0804 +#> 77: 99.9982 -4.0184 -2.4951 -4.0043 -0.8927 -0.0688 5.2538 0.0120 0.3696 0.1419 0.1817 0.1078 1.0402 0.0839 0.9605 0.0848 +#> 78: 1.0007e+02 -4.0210e+00 -2.4725e+00 -4.0040e+00 -8.9827e-01 2.3164e-03 6.4464e+00 1.1395e-02 3.7410e-01 1.3481e-01 2.0294e-01 1.0879e-01 9.7822e-01 8.7445e-02 9.9990e-01 8.2845e-02 +#> 79: 99.3513 -4.0171 -2.5065 -4.0078 -0.8962 -0.0029 7.7527 0.0108 0.3554 0.1281 0.1928 0.1069 1.0455 0.0866 0.9982 0.0870 +#> 80: 98.9945 -4.0172 -2.5412 -4.0341 -0.8891 -0.0187 9.8218 0.0103 0.3376 0.1217 0.1831 0.1457 0.9733 0.0894 1.0164 0.0832 +#> 81: 99.0936 -4.0275 -2.5134 -4.0127 -0.8552 -0.0614 12.1567 0.0098 0.3494 0.1156 0.1740 0.1384 0.9509 0.0843 1.0171 0.0855 +#> 82: 99.2481 -3.9996 -2.4945 -4.0011 -0.8914 -0.0492 11.5489 0.0128 0.3792 0.1098 0.1653 0.1315 0.9915 0.0818 1.0405 0.0928 +#> 83: 99.6941 -3.9998 -2.4851 -3.9845 -0.8802 -0.0560 10.9714 0.0146 0.3602 0.1043 0.1570 0.1249 0.9934 0.0852 0.9707 0.0866 +#> 84: 99.2185 -3.9920 -2.4843 -4.0051 -0.8546 -0.0642 10.4228 0.0153 0.3422 0.0991 0.1492 0.1187 0.9923 0.0833 0.9799 0.0873 +#> 85: 98.8470 -3.9956 -2.4652 -4.0201 -0.8483 -0.0414 9.9017 0.0146 0.3251 0.0941 0.1417 0.1128 0.9732 0.0901 0.9035 0.0858 +#> 86: 98.5012 -3.9841 -2.5148 -4.0250 -0.8408 -0.0551 9.4066 0.0148 0.3088 0.0962 0.1346 0.1071 0.8570 0.0932 0.8532 0.0896 +#> 87: 99.0868 -4.0055 -2.5058 -4.0249 -0.8522 -0.0311 10.3528 0.0175 0.2934 0.1013 0.1411 0.1018 0.8802 0.0838 0.8849 0.0862 +#> 88: 99.5158 -4.0031 -2.4437 -3.9866 -0.8894 -0.0963 9.9832 0.0167 0.3049 0.1030 0.1447 0.0967 0.9955 0.0834 0.8861 0.0893 +#> 89: 99.5538 -4.0347 -2.4494 -4.0213 -0.8695 -0.0494 9.4841 0.0158 0.2897 0.0978 0.1543 0.0918 0.8597 0.0904 0.8959 0.0880 +#> 90: 99.4422 -4.0453 -2.4398 -4.0114 -0.9279 -0.0745 9.8221 0.0150 0.2842 0.0929 0.1466 0.0944 0.9009 0.0871 0.8696 0.0924 +#> 91: 98.8721 -4.0328 -2.4996 -4.0041 -0.8832 -0.0689 9.3310 0.0143 0.2700 0.0896 0.1444 0.1137 0.9567 0.0904 0.8680 0.0891 +#> 92: 99.8390 -4.0418 -2.4914 -4.0182 -0.9279 -0.0460 10.9801 0.0136 0.2585 0.0949 0.1461 0.1210 1.0043 0.0908 0.8310 0.0939 +#> 93: 1.0029e+02 -4.0313e+00 -2.4620e+00 -4.0187e+00 -8.9083e-01 -1.0908e-01 1.0431e+01 1.2890e-02 2.4559e-01 9.5757e-02 1.3878e-01 1.1565e-01 9.9174e-01 9.0056e-02 8.9538e-01 8.8925e-02 +#> 94: 99.3285 -4.0295 -2.4523 -4.0235 -0.8828 -0.1190 10.9003 0.0137 0.2333 0.0915 0.1318 0.1212 1.0729 0.0779 0.9543 0.0907 +#> 95: 99.4117 -4.0422 -2.3807 -4.0870 -0.8960 -0.0889 10.3553 0.0130 0.2216 0.0870 0.1253 0.1366 0.9127 0.0864 0.8901 0.0911 +#> 96: 99.3348 -4.0401 -2.4009 -4.0698 -0.8730 -0.0622 9.8375 0.0123 0.2106 0.0826 0.1241 0.1297 0.8504 0.0836 0.9140 0.0881 +#> 97: 99.4898 -4.0419 -2.4310 -4.0589 -0.8932 -0.0634 9.3456 0.0132 0.2000 0.0785 0.1224 0.1233 0.8770 0.0836 0.8715 0.0837 +#> 98: 99.3750 -4.0704 -2.4353 -4.0616 -0.9333 -0.0846 8.8783 0.0136 0.1900 0.0746 0.1245 0.1171 0.8907 0.0838 0.9066 0.0832 +#> 99: 99.6234 -4.0366 -2.3740 -4.0657 -0.9242 -0.0675 8.4344 0.0129 0.1805 0.0708 0.1182 0.1112 0.8814 0.0808 0.9511 0.0863 +#> 100: 1.0025e+02 -4.0420e+00 -2.3557e+00 -4.0579e+00 -9.5051e-01 -6.3418e-02 8.0319e+00 1.2286e-02 1.7150e-01 6.7291e-02 1.1232e-01 1.0568e-01 8.5851e-01 8.7881e-02 8.9363e-01 8.5897e-02 +#> 101: 1.0041e+02 -4.0461e+00 -2.3840e+00 -4.0384e+00 -9.3752e-01 -7.7594e-02 9.5649e+00 1.1672e-02 1.7509e-01 6.3926e-02 1.2760e-01 1.0039e-01 8.6733e-01 8.2748e-02 9.6277e-01 8.4274e-02 +#> 102: 1.0095e+02 -4.0372e+00 -2.3633e+00 -4.0286e+00 -9.1961e-01 -6.5350e-02 1.1428e+01 1.1088e-02 1.8557e-01 6.0730e-02 1.3211e-01 9.5374e-02 9.3928e-01 8.0161e-02 9.7913e-01 8.4081e-02 +#> 103: 1.0019e+02 -4.0236e+00 -2.4105e+00 -4.0337e+00 -9.1362e-01 -7.3859e-02 1.0856e+01 1.0534e-02 1.7629e-01 5.7693e-02 1.2695e-01 9.1362e-02 9.8491e-01 8.1430e-02 9.7682e-01 8.2250e-02 +#> 104: 99.7755 -4.0280 -2.4452 -4.0197 -0.9112 -0.0810 11.0317 0.0100 0.1796 0.0548 0.1301 0.0868 0.9418 0.0816 0.9170 0.0806 +#> 105: 1.0010e+02 -4.0418e+00 -2.4294e+00 -4.0225e+00 -9.1111e-01 -8.9920e-02 1.0480e+01 9.5070e-03 1.7060e-01 5.2068e-02 1.3987e-01 8.2454e-02 9.1944e-01 7.8110e-02 8.9266e-01 8.7228e-02 +#> 106: 1.0025e+02 -4.0507e+00 -2.4134e+00 -4.0343e+00 -9.0244e-01 -8.4683e-02 1.3506e+01 9.0316e-03 1.6207e-01 4.9465e-02 1.5337e-01 7.8331e-02 9.9609e-01 8.4473e-02 8.7046e-01 8.5479e-02 +#> 107: 1.0014e+02 -4.0468e+00 -2.3972e+00 -4.0196e+00 -9.3650e-01 -2.4087e-02 1.2830e+01 8.5801e-03 1.6027e-01 4.6992e-02 1.5429e-01 8.2493e-02 9.8959e-01 8.2626e-02 8.3427e-01 8.8197e-02 +#> 108: 1.0114e+02 -4.0338e+00 -2.4307e+00 -4.0724e+00 -9.1363e-01 1.1952e-02 1.2189e+01 8.1511e-03 1.5563e-01 4.4854e-02 1.7315e-01 7.8368e-02 9.8589e-01 7.8130e-02 9.0460e-01 8.2870e-02 +#> 109: 1.0066e+02 -4.0550e+00 -2.4094e+00 -4.0641e+00 -9.0945e-01 -1.5401e-03 1.3149e+01 7.7435e-03 1.4785e-01 4.2612e-02 1.7232e-01 7.4450e-02 1.0942e+00 7.4816e-02 9.1706e-01 8.5333e-02 +#> 110: 1.0111e+02 -4.0266e+00 -2.4047e+00 -4.0646e+00 -9.0541e-01 -1.7212e-02 1.2492e+01 7.3563e-03 1.4046e-01 4.0481e-02 1.8132e-01 7.0727e-02 1.0508e+00 7.9457e-02 9.8990e-01 8.2975e-02 +#> 111: 1.0155e+02 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-2.3511e-02 1.1665e+01 7.0759e-03 1.2912e-01 2.9757e-02 1.5870e-01 7.2453e-02 9.3580e-01 8.2952e-02 9.3341e-01 8.3302e-02 +#> 117: 1.0112e+02 -4.0326e+00 -2.4093e+00 -4.0559e+00 -8.9743e-01 -2.0572e-02 1.1082e+01 6.7221e-03 1.2266e-01 2.8269e-02 1.5339e-01 6.8831e-02 9.0879e-01 8.4441e-02 9.1432e-01 8.0538e-02 +#> 118: 1.0123e+02 -4.0411e+00 -2.4077e+00 -4.0556e+00 -9.2971e-01 -2.1885e-02 1.0528e+01 6.3860e-03 1.1653e-01 3.3123e-02 1.6947e-01 6.5389e-02 9.7140e-01 8.6671e-02 8.9874e-01 8.1670e-02 +#> 119: 1.0098e+02 -4.0538e+00 -2.3515e+00 -4.0607e+00 -9.5433e-01 -7.5743e-02 1.0001e+01 6.0667e-03 1.1070e-01 3.1467e-02 1.8338e-01 6.2120e-02 9.1537e-01 8.4827e-02 9.2420e-01 8.2769e-02 +#> 120: 1.0076e+02 -4.0573e+00 -2.3627e+00 -4.0329e+00 -9.3251e-01 -6.7669e-02 9.5011e+00 5.7634e-03 1.0517e-01 3.2868e-02 1.7422e-01 6.6096e-02 9.5247e-01 8.5343e-02 9.4678e-01 8.5335e-02 +#> 121: 1.0085e+02 -4.0450e+00 -2.3478e+00 -4.0692e+00 -9.2333e-01 -9.8005e-03 9.0261e+00 5.4752e-03 9.9911e-02 3.1225e-02 1.6550e-01 7.1593e-02 8.5572e-01 8.8654e-02 1.0248e+00 8.0646e-02 +#> 122: 1.0164e+02 -4.0325e+00 -2.3562e+00 -4.0680e+00 -9.4287e-01 -1.2103e-02 8.5748e+00 5.3493e-03 9.4915e-02 2.9663e-02 1.6347e-01 6.8014e-02 8.4872e-01 8.6803e-02 1.0282e+00 8.0381e-02 +#> 123: 1.0184e+02 -4.0521e+00 -2.3504e+00 -4.0714e+00 -9.5966e-01 -9.1996e-05 8.1460e+00 5.0818e-03 9.8247e-02 3.0007e-02 1.7746e-01 6.4613e-02 9.7181e-01 8.0986e-02 9.8860e-01 8.0317e-02 +#> 124: 1.0235e+02 -4.0674e+00 -2.3315e+00 -4.0874e+00 -9.9802e-01 3.8818e-02 7.7387e+00 4.8277e-03 9.3335e-02 2.8506e-02 1.7611e-01 6.8940e-02 9.7376e-01 7.6658e-02 9.9156e-01 8.4407e-02 +#> 125: 1.0257e+02 -4.0718e+00 -2.3604e+00 -4.0627e+00 -1.0591e+00 2.4685e-02 7.3518e+00 4.5863e-03 8.8668e-02 3.0650e-02 1.8671e-01 6.5493e-02 1.0275e+00 8.2278e-02 1.0896e+00 8.0976e-02 +#> 126: 1.0287e+02 -4.0691e+00 -2.3103e+00 -4.0552e+00 -1.0174e+00 2.1863e-02 7.5644e+00 4.3570e-03 1.0937e-01 2.9117e-02 1.7738e-01 6.2218e-02 9.2668e-01 7.9560e-02 9.5409e-01 8.4671e-02 +#> 127: 1.0327e+02 -4.0528e+00 -2.3141e+00 -4.0522e+00 -1.0108e+00 4.4779e-03 7.1862e+00 4.1392e-03 1.2239e-01 2.7661e-02 1.6925e-01 5.9107e-02 9.1372e-01 7.9536e-02 9.9164e-01 8.2999e-02 +#> 128: 1.0352e+02 -4.0496e+00 -2.2880e+00 -4.0496e+00 -1.0063e+00 -1.3248e-02 7.6721e+00 3.9613e-03 1.1627e-01 2.6278e-02 1.7517e-01 8.0231e-02 8.4407e-01 8.5078e-02 9.4382e-01 8.7530e-02 +#> 129: 1.0345e+02 -4.0715e+00 -2.3090e+00 -4.0400e+00 -1.0276e+00 -1.8301e-02 8.2197e+00 3.7633e-03 1.1046e-01 2.7141e-02 1.9366e-01 7.6220e-02 9.3357e-01 8.2674e-02 9.7064e-01 8.6011e-02 +#> 130: 1.0245e+02 -4.0787e+00 -2.3263e+00 -4.0106e+00 -1.0200e+00 -8.5976e-02 7.8087e+00 4.0830e-03 1.3607e-01 2.6631e-02 2.2700e-01 7.2409e-02 9.8233e-01 7.9348e-02 9.6780e-01 8.2658e-02 +#> 131: 1.0217e+02 -4.0760e+00 -2.2525e+00 -4.0082e+00 -1.0099e+00 -1.6111e-01 7.4183e+00 3.8789e-03 1.3972e-01 2.5299e-02 2.2508e-01 6.8788e-02 1.0066e+00 7.8692e-02 9.4684e-01 8.4349e-02 +#> 132: 1.0185e+02 -4.0792e+00 -2.2309e+00 -3.9996e+00 -9.8302e-01 -2.2504e-01 7.0474e+00 4.0356e-03 1.3743e-01 2.4034e-02 2.1383e-01 7.7346e-02 9.4225e-01 7.9110e-02 9.5160e-01 8.4398e-02 +#> 133: 1.0135e+02 -4.0818e+00 -2.2219e+00 -4.0054e+00 -9.7264e-01 -1.8912e-01 7.1932e+00 3.8338e-03 1.3056e-01 2.2833e-02 2.0314e-01 7.6769e-02 1.0031e+00 8.5400e-02 1.0034e+00 8.4805e-02 +#> 134: 1.0148e+02 -4.0782e+00 -2.2492e+00 -3.9886e+00 -9.5184e-01 -1.5049e-01 6.8336e+00 3.6422e-03 1.2403e-01 2.3398e-02 1.9298e-01 7.2931e-02 9.3696e-01 8.3566e-02 9.4742e-01 8.9137e-02 +#> 135: 1.0145e+02 -4.0852e+00 -2.3062e+00 -4.0011e+00 -9.4444e-01 -1.6803e-01 6.4919e+00 3.4600e-03 1.1783e-01 2.2228e-02 1.8333e-01 6.9284e-02 9.4846e-01 8.3087e-02 9.7774e-01 8.2610e-02 +#> 136: 1.0177e+02 -4.0861e+00 -2.2785e+00 -3.9890e+00 -9.9625e-01 -1.8938e-01 6.1673e+00 3.2870e-03 1.1752e-01 2.1116e-02 1.8815e-01 6.5820e-02 9.3634e-01 8.5255e-02 1.1001e+00 8.5332e-02 +#> 137: 1.0200e+02 -4.0928e+00 -2.1946e+00 -3.9974e+00 -1.0098e+00 -1.8810e-01 5.8589e+00 3.1227e-03 1.2394e-01 2.1203e-02 1.7874e-01 7.2232e-02 1.0048e+00 7.3422e-02 1.0222e+00 8.3484e-02 +#> 138: 1.0214e+02 -4.0820e+00 -2.2052e+00 -3.9737e+00 -1.0420e+00 -2.0594e-01 5.5660e+00 3.8937e-03 1.9164e-01 2.0143e-02 1.6980e-01 6.8621e-02 1.0126e+00 7.6106e-02 1.0780e+00 8.2960e-02 +#> 139: 1.0249e+02 -4.0785e+00 -2.1649e+00 -3.9567e+00 -1.0095e+00 -2.8807e-01 5.2877e+00 3.6990e-03 1.8647e-01 1.9135e-02 1.6131e-01 6.5190e-02 1.0030e+00 7.9858e-02 1.0611e+00 8.4109e-02 +#> 140: 1.0184e+02 -4.0847e+00 -2.1800e+00 -3.9565e+00 -9.9415e-01 -2.8869e-01 5.0233e+00 4.0857e-03 1.9502e-01 1.8179e-02 1.6676e-01 6.2879e-02 9.5962e-01 7.8117e-02 9.9649e-01 8.4914e-02 +#> 141: 1.0195e+02 -4.1012e+00 -2.1831e+00 -3.9488e+00 -9.9515e-01 -3.1864e-01 4.7721e+00 3.8814e-03 1.8527e-01 1.7270e-02 1.6797e-01 6.1084e-02 9.0969e-01 8.2722e-02 1.0122e+00 8.2518e-02 +#> 142: 1.0233e+02 -4.1139e+00 -2.1692e+00 -3.9542e+00 -1.0023e+00 -3.3242e-01 4.5335e+00 3.6873e-03 2.0662e-01 1.6406e-02 1.5957e-01 5.8030e-02 9.4761e-01 8.4629e-02 1.0342e+00 8.3954e-02 +#> 143: 1.0217e+02 -4.1103e+00 -2.1380e+00 -3.9511e+00 -1.0300e+00 -2.5992e-01 5.2035e+00 4.7053e-03 1.9629e-01 1.5586e-02 1.5979e-01 5.5128e-02 8.9255e-01 7.9042e-02 1.0461e+00 8.6952e-02 +#> 144: 1.0185e+02 -4.1335e+00 -2.1911e+00 -3.9650e+00 -1.0440e+00 -2.4451e-01 5.0998e+00 4.4700e-03 1.8648e-01 1.9590e-02 1.5534e-01 5.2372e-02 9.7863e-01 8.3932e-02 1.0197e+00 8.7673e-02 +#> 145: 1.0242e+02 -4.1445e+00 -2.1203e+00 -3.9616e+00 -1.0426e+00 -2.7120e-01 4.8448e+00 4.2465e-03 1.7715e-01 1.8611e-02 1.4757e-01 4.9753e-02 1.0024e+00 8.4131e-02 1.0768e+00 8.5388e-02 +#> 146: 1.0236e+02 -4.1519e+00 -2.1958e+00 -3.9779e+00 -9.8615e-01 -2.5863e-01 4.6026e+00 4.0718e-03 1.6829e-01 1.7680e-02 1.6407e-01 4.7266e-02 1.0740e+00 8.2413e-02 1.0706e+00 8.3410e-02 +#> 147: 1.0251e+02 -4.1465e+00 -2.2042e+00 -3.9775e+00 -1.0317e+00 -2.2757e-01 4.3725e+00 3.8682e-03 1.5988e-01 1.6796e-02 1.7016e-01 4.4902e-02 9.7748e-01 8.3376e-02 1.0880e+00 8.1968e-02 +#> 148: 1.0244e+02 -4.1432e+00 -2.1786e+00 -3.9792e+00 -1.0442e+00 -2.2002e-01 4.9671e+00 3.6748e-03 1.5189e-01 1.5956e-02 2.2196e-01 4.2657e-02 1.0412e+00 7.8051e-02 1.1051e+00 8.1618e-02 +#> 149: 1.0219e+02 -4.1384e+00 -2.2318e+00 -3.9757e+00 -1.0438e+00 -2.4124e-01 4.7187e+00 3.4910e-03 1.4429e-01 1.6061e-02 2.1086e-01 4.0524e-02 1.0082e+00 8.0377e-02 1.1455e+00 8.0545e-02 +#> 150: 1.0264e+02 -4.1498e+00 -2.2352e+00 -3.9915e+00 -1.0669e+00 -2.1255e-01 4.4828e+00 3.3165e-03 1.3708e-01 1.7218e-02 2.0032e-01 3.8498e-02 9.5031e-01 8.7248e-02 9.8770e-01 8.3250e-02 +#> 151: 1.0250e+02 -4.1365e+00 -2.1876e+00 -3.9939e+00 -1.0568e+00 -1.8159e-01 4.2587e+00 3.1507e-03 1.3022e-01 1.7383e-02 1.9030e-01 3.6573e-02 9.6938e-01 8.0203e-02 1.0578e+00 8.3430e-02 +#> 152: 1.0256e+02 -4.1370e+00 -2.2238e+00 -4.0047e+00 -1.0406e+00 -1.8764e-01 1.9609e+00 1.4191e-03 1.1882e-01 1.7924e-02 1.6889e-01 4.1216e-02 9.1972e-01 7.8573e-02 1.0717e+00 8.0882e-02 +#> 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-3.9781e+00 -1.0384e+00 -2.6750e-01 2.5310e+00 4.8270e-04 6.5662e-02 1.8085e-02 1.7665e-01 4.4020e-02 8.8632e-01 8.6004e-02 1.0425e+00 8.2894e-02 +#> 159: 1.0270e+02 -4.1204e+00 -2.1837e+00 -3.9530e+00 -1.0587e+00 -2.5809e-01 3.4348e+00 5.6788e-04 6.5500e-02 1.9540e-02 1.8629e-01 4.0730e-02 9.5079e-01 8.2399e-02 9.9316e-01 8.3381e-02 +#> 160: 1.0282e+02 -4.1223e+00 -2.1325e+00 -3.9734e+00 -1.0068e+00 -2.8751e-01 3.9652e+00 7.6565e-04 8.5246e-02 1.7068e-02 1.7587e-01 3.0778e-02 9.1802e-01 8.0158e-02 9.9642e-01 8.1564e-02 +#> 161: 1.0330e+02 -4.1180e+00 -2.1879e+00 -3.9743e+00 -1.0268e+00 -2.8812e-01 4.9153e+00 5.8033e-04 8.0457e-02 1.8555e-02 1.7312e-01 3.3941e-02 8.6920e-01 8.2509e-02 9.5632e-01 8.1798e-02 +#> 162: 1.0335e+02 -4.1182e+00 -2.2089e+00 -3.9566e+00 -1.0409e+00 -2.7390e-01 3.6169e+00 2.8392e-04 1.0776e-01 1.9589e-02 1.6479e-01 2.8481e-02 8.8603e-01 8.7799e-02 9.5197e-01 7.9563e-02 +#> 163: 1.0294e+02 -4.1181e+00 -2.2025e+00 -3.9462e+00 -9.9783e-01 -3.0753e-01 3.7234e+00 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3.0582e-01 7.5239e-06 8.2812e-02 2.9556e-02 1.9725e-01 1.3753e-01 9.0862e-01 8.1319e-02 9.0031e-01 8.3491e-02 +#> 185: 1.0178e+02 -4.1208e+00 -2.2858e+00 -4.0272e+00 -1.0063e+00 -1.6155e-01 3.0856e-01 4.5894e-06 8.8870e-02 2.5817e-02 1.9251e-01 1.0670e-01 9.1157e-01 7.7834e-02 9.6258e-01 7.8990e-02 +#> 186: 1.0198e+02 -4.1208e+00 -2.2682e+00 -4.0401e+00 -9.8523e-01 -1.1556e-01 2.4761e-01 3.2640e-06 7.5614e-02 2.1067e-02 1.9085e-01 9.0045e-02 8.5090e-01 8.6621e-02 1.0145e+00 8.1864e-02 +#> 187: 1.0197e+02 -4.1208e+00 -2.2788e+00 -4.0281e+00 -1.0066e+00 -1.0149e-01 2.0460e-01 4.5073e-06 7.8797e-02 2.3861e-02 2.0725e-01 7.9771e-02 9.6253e-01 8.2363e-02 9.3855e-01 8.3939e-02 +#> 188: 1.0196e+02 -4.1207e+00 -2.3105e+00 -4.0149e+00 -1.0217e+00 -9.0603e-02 2.2178e-01 3.6903e-06 8.9793e-02 2.1775e-02 1.9248e-01 8.2415e-02 9.4078e-01 8.1247e-02 9.1756e-01 8.2786e-02 +#> 189: 1.0202e+02 -4.1204e+00 -2.2702e+00 -4.0430e+00 -1.0032e+00 -1.1308e-01 2.2944e-01 3.5141e-06 7.8575e-02 2.4885e-02 2.0968e-01 8.2380e-02 9.5115e-01 8.1619e-02 9.2134e-01 8.9958e-02 +#> 190: 1.0195e+02 -4.1207e+00 -2.3126e+00 -4.0312e+00 -1.0154e+00 -6.3842e-02 2.5129e-01 2.6517e-06 4.2267e-02 2.2084e-02 1.9361e-01 7.0492e-02 9.3985e-01 8.5817e-02 9.3893e-01 8.7011e-02 +#> 191: 1.0203e+02 -4.1206e+00 -2.2758e+00 -4.0290e+00 -1.0102e+00 -3.1042e-02 1.7935e-01 3.4489e-06 5.7444e-02 2.3544e-02 1.9651e-01 7.9509e-02 9.5213e-01 8.2030e-02 1.0054e+00 8.7523e-02 +#> 192: 1.0199e+02 -4.1205e+00 -2.2969e+00 -4.0329e+00 -1.0364e+00 -8.3705e-02 1.5785e-01 3.5081e-06 7.4305e-02 2.2992e-02 1.9662e-01 7.7684e-02 9.2601e-01 8.3027e-02 9.8642e-01 8.3428e-02 +#> 193: 1.0196e+02 -4.1205e+00 -2.2661e+00 -4.0513e+00 -9.9271e-01 -4.6516e-02 1.2084e-01 2.6911e-06 6.8360e-02 3.5444e-02 1.9649e-01 7.5188e-02 9.1949e-01 7.9194e-02 1.0046e+00 8.5964e-02 +#> 194: 1.0198e+02 -4.1207e+00 -2.2817e+00 -4.0520e+00 -9.9852e-01 -8.4466e-02 1.3596e-01 1.5511e-06 6.5142e-02 4.1562e-02 1.9137e-01 9.6992e-02 9.6709e-01 7.6757e-02 9.7566e-01 8.3784e-02 +#> 195: 1.0200e+02 -4.1207e+00 -2.3076e+00 -4.0637e+00 -1.0028e+00 -7.2489e-02 1.0942e-01 1.6451e-06 6.1364e-02 4.6242e-02 1.9470e-01 9.3546e-02 9.9614e-01 8.1292e-02 9.7814e-01 8.1909e-02 +#> 196: 1.0194e+02 -4.1205e+00 -2.2970e+00 -4.0482e+00 -9.8816e-01 -6.8493e-02 1.1918e-01 1.2629e-06 4.2775e-02 3.6925e-02 2.3565e-01 7.7784e-02 8.9524e-01 9.2250e-02 9.8003e-01 8.2408e-02 +#> 197: 1.0199e+02 -4.1205e+00 -2.3075e+00 -4.0418e+00 -1.0196e+00 -6.8458e-02 1.7674e-01 7.5205e-07 5.2125e-02 2.9288e-02 2.1892e-01 8.4416e-02 8.9857e-01 9.1154e-02 1.0377e+00 8.3604e-02 +#> 198: 1.0197e+02 -4.1206e+00 -2.3051e+00 -4.0367e+00 -1.0252e+00 -6.9200e-02 9.1625e-02 6.6068e-07 4.7665e-02 2.8907e-02 1.8679e-01 7.5787e-02 9.0272e-01 8.8077e-02 9.2929e-01 8.0385e-02 +#> 199: 1.0192e+02 -4.1204e+00 -2.3163e+00 -4.0506e+00 -1.0152e+00 -5.3872e-02 6.8196e-02 5.5789e-07 6.0471e-02 3.1730e-02 2.0053e-01 6.8557e-02 9.0478e-01 8.5910e-02 9.3814e-01 8.2211e-02 +#> 200: 1.0195e+02 -4.1205e+00 -2.3141e+00 -4.0728e+00 -1.0010e+00 -2.5675e-03 6.5235e-02 6.9762e-07 5.8458e-02 2.8504e-02 2.0377e-01 4.9513e-02 8.5640e-01 8.6640e-02 9.5731e-01 8.4390e-02 +#> 201: 1.0195e+02 -4.1205e+00 -2.3106e+00 -4.0774e+00 -9.9012e-01 5.1724e-03 5.1225e-02 5.4222e-07 6.0577e-02 3.3554e-02 2.0505e-01 4.4738e-02 8.8073e-01 8.5488e-02 9.6928e-01 8.4895e-02 +#> 202: 1.0194e+02 -4.1205e+00 -2.3078e+00 -4.0767e+00 -9.9283e-01 3.9328e-03 4.5461e-02 4.8520e-07 6.7405e-02 3.4599e-02 2.1312e-01 4.6664e-02 9.0528e-01 8.4189e-02 9.8043e-01 8.5266e-02 +#> 203: 1.0193e+02 -4.1205e+00 -2.3029e+00 -4.0790e+00 -9.8990e-01 -9.1380e-03 4.7128e-02 5.0468e-07 6.8524e-02 3.6050e-02 2.1378e-01 5.1774e-02 9.0923e-01 8.4899e-02 9.8928e-01 8.4613e-02 +#> 204: 1.0192e+02 -4.1205e+00 -2.3080e+00 -4.0760e+00 -9.8833e-01 -1.2434e-02 4.8184e-02 4.9472e-07 6.4152e-02 3.5604e-02 2.0954e-01 5.1354e-02 9.1294e-01 8.5219e-02 9.8301e-01 8.4374e-02 +#> 205: 1.0192e+02 -4.1205e+00 -2.3100e+00 -4.0712e+00 -9.9253e-01 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9.8903e-01 8.2705e-02 +#> 369: 1.0187e+02 -4.1204e+00 -2.3530e+00 -4.0650e+00 -1.0088e+00 6.1767e-03 3.6436e-02 5.0046e-07 9.0617e-02 2.2226e-02 1.8787e-01 9.4410e-02 9.3504e-01 8.2361e-02 9.8843e-01 8.2694e-02 +#> 370: 1.0187e+02 -4.1204e+00 -2.3528e+00 -4.0651e+00 -1.0088e+00 6.2532e-03 3.6467e-02 5.0024e-07 9.0741e-02 2.2223e-02 1.8794e-01 9.4288e-02 9.3472e-01 8.2374e-02 9.8781e-01 8.2703e-02 +#> 371: 1.0186e+02 -4.1204e+00 -2.3525e+00 -4.0652e+00 -1.0088e+00 6.2117e-03 3.6465e-02 4.9964e-07 9.0904e-02 2.2220e-02 1.8788e-01 9.4310e-02 9.3470e-01 8.2367e-02 9.8730e-01 8.2731e-02 +#> 372: 1.0186e+02 -4.1204e+00 -2.3524e+00 -4.0651e+00 -1.0089e+00 6.1363e-03 3.6367e-02 5.0037e-07 9.1177e-02 2.2230e-02 1.8783e-01 9.4288e-02 9.3496e-01 8.2365e-02 9.8699e-01 8.2729e-02 +#> 373: 1.0186e+02 -4.1204e+00 -2.3523e+00 -4.0650e+00 -1.0089e+00 6.0384e-03 3.6353e-02 5.0195e-07 9.1402e-02 2.2219e-02 1.8764e-01 9.4343e-02 9.3478e-01 8.2430e-02 9.8641e-01 8.2747e-02 +#> 374: 1.0186e+02 -4.1204e+00 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9.3422e-02 9.3444e-01 8.2277e-02 9.8639e-01 8.2670e-02 +#> 385: 1.0186e+02 -4.1204e+00 -2.3505e+00 -4.0638e+00 -1.0089e+00 1.7048e-03 3.6391e-02 5.0788e-07 9.1717e-02 2.2160e-02 1.8746e-01 9.3178e-02 9.3457e-01 8.2261e-02 9.8616e-01 8.2636e-02 +#> 386: 1.0186e+02 -4.1204e+00 -2.3504e+00 -4.0637e+00 -1.0089e+00 1.4309e-03 3.6372e-02 5.0847e-07 9.1895e-02 2.2157e-02 1.8754e-01 9.2918e-02 9.3439e-01 8.2246e-02 9.8601e-01 8.2617e-02 +#> 387: 1.0186e+02 -4.1204e+00 -2.3505e+00 -4.0636e+00 -1.0089e+00 1.3524e-03 3.6446e-02 5.0896e-07 9.2022e-02 2.2182e-02 1.8768e-01 9.2684e-02 9.3470e-01 8.2216e-02 9.8593e-01 8.2620e-02 +#> 388: 1.0186e+02 -4.1204e+00 -2.3506e+00 -4.0635e+00 -1.0089e+00 1.2887e-03 3.6478e-02 5.0904e-07 9.2117e-02 2.2174e-02 1.8761e-01 9.2506e-02 9.3463e-01 8.2221e-02 9.8563e-01 8.2609e-02 +#> 389: 1.0186e+02 -4.1204e+00 -2.3505e+00 -4.0635e+00 -1.0089e+00 1.2044e-03 3.6479e-02 5.0969e-07 9.2068e-02 2.2180e-02 1.8751e-01 9.2308e-02 9.3438e-01 8.2241e-02 9.8516e-01 8.2592e-02 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-1.0095e+00 5.2506e-05 3.6667e-02 5.0955e-07 9.1548e-02 2.2269e-02 1.8733e-01 9.2151e-02 9.3450e-01 8.2256e-02 9.8389e-01 8.2612e-02 +#> 396: 1.0186e+02 -4.1204e+00 -2.3497e+00 -4.0630e+00 -1.0097e+00 1.6581e-05 3.6789e-02 5.1002e-07 9.1431e-02 2.2299e-02 1.8742e-01 9.2120e-02 9.3450e-01 8.2252e-02 9.8367e-01 8.2620e-02 +#> 397: 1.0186e+02 -4.1205e+00 -2.3495e+00 -4.0629e+00 -1.0098e+00 -5.0310e-05 3.6860e-02 5.0949e-07 9.1311e-02 2.2323e-02 1.8738e-01 9.2130e-02 9.3467e-01 8.2250e-02 9.8388e-01 8.2628e-02 +#> 398: 1.0186e+02 -4.1205e+00 -2.3494e+00 -4.0629e+00 -1.0097e+00 -1.4918e-04 3.6902e-02 5.0935e-07 9.1211e-02 2.2330e-02 1.8747e-01 9.2144e-02 9.3478e-01 8.2260e-02 9.8420e-01 8.2632e-02 +#> 399: 1.0186e+02 -4.1205e+00 -2.3497e+00 -4.0628e+00 -1.0097e+00 -2.2152e-04 3.6932e-02 5.0927e-07 9.1209e-02 2.2377e-02 1.8750e-01 9.2136e-02 9.3481e-01 8.2286e-02 9.8431e-01 8.2622e-02 +#> 400: 1.0186e+02 -4.1205e+00 -2.3499e+00 -4.0629e+00 -1.0097e+00 3.2878e-05 3.6943e-02 5.0892e-07 9.1092e-02 2.2388e-02 1.8752e-01 9.2072e-02 9.3534e-01 8.2276e-02 9.8472e-01 8.2615e-02 +#> 401: 1.0186e+02 -4.1205e+00 -2.3501e+00 -4.0630e+00 -1.0097e+00 2.6776e-04 3.6950e-02 5.0860e-07 9.1038e-02 2.2395e-02 1.8740e-01 9.1911e-02 9.3515e-01 8.2331e-02 9.8459e-01 8.2615e-02 +#> 402: 1.0186e+02 -4.1205e+00 -2.3502e+00 -4.0632e+00 -1.0097e+00 3.9988e-04 3.6912e-02 5.0849e-07 9.0944e-02 2.2401e-02 1.8737e-01 9.1701e-02 9.3494e-01 8.2353e-02 9.8479e-01 8.2609e-02 +#> 403: 1.0186e+02 -4.1205e+00 -2.3503e+00 -4.0633e+00 -1.0098e+00 4.9714e-04 3.6935e-02 5.0805e-07 9.0895e-02 2.2404e-02 1.8741e-01 9.1609e-02 9.3444e-01 8.2372e-02 9.8505e-01 8.2638e-02 +#> 404: 1.0186e+02 -4.1205e+00 -2.3504e+00 -4.0633e+00 -1.0100e+00 5.8465e-04 3.6978e-02 5.0889e-07 9.0862e-02 2.2453e-02 1.8746e-01 9.1650e-02 9.3491e-01 8.2364e-02 9.8484e-01 8.2653e-02 +#> 405: 1.0186e+02 -4.1205e+00 -2.3505e+00 -4.0634e+00 -1.0099e+00 5.5970e-04 3.6999e-02 5.0964e-07 9.0930e-02 2.2480e-02 1.8742e-01 9.1823e-02 9.3458e-01 8.2371e-02 9.8465e-01 8.2670e-02 +#> 406: 1.0186e+02 -4.1205e+00 -2.3507e+00 -4.0634e+00 -1.0098e+00 5.4464e-04 3.7123e-02 5.1046e-07 9.1008e-02 2.2478e-02 1.8749e-01 9.1930e-02 9.3449e-01 8.2361e-02 9.8440e-01 8.2666e-02 +#> 407: 1.0186e+02 -4.1205e+00 -2.3506e+00 -4.0634e+00 -1.0097e+00 3.5564e-04 3.7226e-02 5.0978e-07 9.0891e-02 2.2469e-02 1.8751e-01 9.2130e-02 9.3444e-01 8.2380e-02 9.8462e-01 8.2660e-02 +#> 408: 1.0186e+02 -4.1205e+00 -2.3506e+00 -4.0635e+00 -1.0097e+00 3.8362e-04 3.7354e-02 5.0967e-07 9.0892e-02 2.2461e-02 1.8747e-01 9.2230e-02 9.3453e-01 8.2363e-02 9.8466e-01 8.2661e-02 +#> 409: 1.0186e+02 -4.1205e+00 -2.3505e+00 -4.0635e+00 -1.0097e+00 2.6671e-04 3.7473e-02 5.0928e-07 9.0894e-02 2.2519e-02 1.8751e-01 9.2243e-02 9.3447e-01 8.2347e-02 9.8449e-01 8.2667e-02 +#> 410: 1.0186e+02 -4.1205e+00 -2.3506e+00 -4.0634e+00 -1.0098e+00 1.7963e-04 3.7438e-02 5.0981e-07 9.0898e-02 2.2600e-02 1.8764e-01 9.2237e-02 9.3502e-01 8.2320e-02 9.8430e-01 8.2663e-02 +#> 411: 1.0186e+02 -4.1205e+00 -2.3506e+00 -4.0634e+00 -1.0098e+00 1.0085e-04 3.7381e-02 5.0970e-07 9.0820e-02 2.2618e-02 1.8767e-01 9.2103e-02 9.3480e-01 8.2324e-02 9.8427e-01 8.2652e-02 +#> 412: 1.0186e+02 -4.1205e+00 -2.3508e+00 -4.0633e+00 -1.0097e+00 1.9452e-04 3.7315e-02 5.0984e-07 9.0784e-02 2.2605e-02 1.8772e-01 9.2118e-02 9.3504e-01 8.2314e-02 9.8431e-01 8.2636e-02 +#> 413: 1.0186e+02 -4.1205e+00 -2.3508e+00 -4.0632e+00 -1.0097e+00 1.8432e-04 3.7243e-02 5.0946e-07 9.0798e-02 2.2604e-02 1.8765e-01 9.2206e-02 9.3499e-01 8.2299e-02 9.8426e-01 8.2636e-02 +#> 414: 1.0186e+02 -4.1205e+00 -2.3505e+00 -4.0632e+00 -1.0097e+00 2.1744e-04 3.7203e-02 5.0880e-07 9.0769e-02 2.2604e-02 1.8757e-01 9.2403e-02 9.3516e-01 8.2279e-02 9.8414e-01 8.2659e-02 +#> 415: 1.0186e+02 -4.1205e+00 -2.3505e+00 -4.0633e+00 -1.0097e+00 1.9330e-04 3.7197e-02 5.0896e-07 9.0657e-02 2.2618e-02 1.8764e-01 9.2565e-02 9.3514e-01 8.2264e-02 9.8435e-01 8.2655e-02 +#> 416: 1.0186e+02 -4.1205e+00 -2.3501e+00 -4.0634e+00 -1.0097e+00 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8.2094e-02 9.7985e-01 8.2716e-02 +#> 469: 1.0186e+02 -4.1205e+00 -2.3483e+00 -4.0630e+00 -1.0096e+00 -7.4343e-04 3.6891e-02 5.1401e-07 9.2204e-02 2.3114e-02 1.8700e-01 9.4639e-02 9.3643e-01 8.2078e-02 9.7996e-01 8.2709e-02 +#> 470: 1.0186e+02 -4.1205e+00 -2.3482e+00 -4.0630e+00 -1.0096e+00 -7.8250e-04 3.6874e-02 5.1370e-07 9.2209e-02 2.3083e-02 1.8702e-01 9.4728e-02 9.3646e-01 8.2073e-02 9.7989e-01 8.2703e-02 +#> 471: 1.0186e+02 -4.1205e+00 -2.3480e+00 -4.0629e+00 -1.0095e+00 -1.0440e-03 3.6843e-02 5.1358e-07 9.2194e-02 2.3062e-02 1.8710e-01 9.4741e-02 9.3649e-01 8.2082e-02 9.8003e-01 8.2701e-02 +#> 472: 1.0186e+02 -4.1205e+00 -2.3481e+00 -4.0629e+00 -1.0096e+00 -9.5438e-04 3.6869e-02 5.1330e-07 9.2176e-02 2.3050e-02 1.8712e-01 9.4766e-02 9.3666e-01 8.2080e-02 9.7996e-01 8.2691e-02 +#> 473: 1.0186e+02 -4.1205e+00 -2.3481e+00 -4.0630e+00 -1.0096e+00 -8.2178e-04 3.6877e-02 5.1283e-07 9.2191e-02 2.3021e-02 1.8703e-01 9.4747e-02 9.3670e-01 8.2072e-02 9.8007e-01 8.2693e-02 +#> 474: 1.0186e+02 -4.1205e+00 -2.3481e+00 -4.0630e+00 -1.0096e+00 -7.0189e-04 3.6927e-02 5.1196e-07 9.2195e-02 2.2989e-02 1.8702e-01 9.4746e-02 9.3669e-01 8.2054e-02 9.8029e-01 8.2689e-02 +#> 475: 1.0186e+02 -4.1205e+00 -2.3481e+00 -4.0630e+00 -1.0095e+00 -7.1989e-04 3.6993e-02 5.1125e-07 9.2159e-02 2.2963e-02 1.8700e-01 9.4813e-02 9.3681e-01 8.2051e-02 9.8027e-01 8.2680e-02 +#> 476: 1.0186e+02 -4.1205e+00 -2.3481e+00 -4.0629e+00 -1.0096e+00 -7.1806e-04 3.7018e-02 5.1105e-07 9.2091e-02 2.2933e-02 1.8696e-01 9.4837e-02 9.3713e-01 8.2067e-02 9.8033e-01 8.2674e-02 +#> 477: 1.0186e+02 -4.1205e+00 -2.3479e+00 -4.0630e+00 -1.0097e+00 -7.3438e-04 3.6986e-02 5.1121e-07 9.2046e-02 2.2909e-02 1.8698e-01 9.4809e-02 9.3743e-01 8.2059e-02 9.8045e-01 8.2693e-02 +#> 478: 1.0186e+02 -4.1205e+00 -2.3478e+00 -4.0630e+00 -1.0096e+00 -7.9338e-04 3.6912e-02 5.1224e-07 9.2056e-02 2.2881e-02 1.8698e-01 9.4791e-02 9.3757e-01 8.2042e-02 9.8072e-01 8.2682e-02 +#> 479: 1.0186e+02 -4.1205e+00 -2.3476e+00 -4.0629e+00 -1.0096e+00 -8.6158e-04 3.6882e-02 5.1284e-07 9.2159e-02 2.2867e-02 1.8694e-01 9.4774e-02 9.3749e-01 8.2051e-02 9.8088e-01 8.2679e-02 +#> 480: 1.0186e+02 -4.1205e+00 -2.3474e+00 -4.0629e+00 -1.0096e+00 -1.1334e-03 3.6851e-02 5.1423e-07 9.2253e-02 2.2869e-02 1.8696e-01 9.4820e-02 9.3751e-01 8.2063e-02 9.8097e-01 8.2693e-02 +#> 481: 1.0186e+02 -4.1205e+00 -2.3470e+00 -4.0629e+00 -1.0096e+00 -1.2444e-03 3.6785e-02 5.1490e-07 9.2397e-02 2.2853e-02 1.8694e-01 9.4838e-02 9.3770e-01 8.2031e-02 9.8124e-01 8.2707e-02 +#> 482: 1.0186e+02 -4.1205e+00 -2.3467e+00 -4.0629e+00 -1.0095e+00 -1.3612e-03 3.6750e-02 5.1658e-07 9.2440e-02 2.2842e-02 1.8683e-01 9.4800e-02 9.3786e-01 8.2041e-02 9.8107e-01 8.2719e-02 +#> 483: 1.0186e+02 -4.1205e+00 -2.3467e+00 -4.0628e+00 -1.0096e+00 -1.5168e-03 3.6783e-02 5.1708e-07 9.2590e-02 2.2804e-02 1.8674e-01 9.4804e-02 9.3790e-01 8.2042e-02 9.8116e-01 8.2719e-02 +#> 484: 1.0186e+02 -4.1205e+00 -2.3466e+00 -4.0628e+00 -1.0097e+00 -1.5218e-03 3.6848e-02 5.1669e-07 9.2717e-02 2.2775e-02 1.8670e-01 9.4940e-02 9.3798e-01 8.2028e-02 9.8106e-01 8.2719e-02 +#> 485: 1.0186e+02 -4.1205e+00 -2.3464e+00 -4.0628e+00 -1.0097e+00 -1.4177e-03 3.6867e-02 5.1615e-07 9.2806e-02 2.2765e-02 1.8669e-01 9.5018e-02 9.3816e-01 8.2020e-02 9.8090e-01 8.2721e-02 +#> 486: 1.0186e+02 -4.1205e+00 -2.3462e+00 -4.0628e+00 -1.0098e+00 -1.5257e-03 3.6968e-02 5.1513e-07 9.3019e-02 2.2762e-02 1.8663e-01 9.5111e-02 9.3816e-01 8.2013e-02 9.8071e-01 8.2732e-02 +#> 487: 1.0186e+02 -4.1205e+00 -2.3460e+00 -4.0628e+00 -1.0097e+00 -1.7055e-03 3.7021e-02 5.1446e-07 9.3161e-02 2.2732e-02 1.8652e-01 9.5373e-02 9.3832e-01 8.1997e-02 9.8078e-01 8.2737e-02 +#> 488: 1.0186e+02 -4.1205e+00 -2.3459e+00 -4.0628e+00 -1.0097e+00 -1.8502e-03 3.7069e-02 5.1391e-07 9.3282e-02 2.2741e-02 1.8641e-01 9.5414e-02 9.3818e-01 8.2001e-02 9.8064e-01 8.2738e-02 +#> 489: 1.0186e+02 -4.1205e+00 -2.3458e+00 -4.0628e+00 -1.0097e+00 -1.9091e-03 3.7017e-02 5.1291e-07 9.3286e-02 2.2738e-02 1.8639e-01 9.5453e-02 9.3808e-01 8.1991e-02 9.8047e-01 8.2728e-02 +#> 490: 1.0186e+02 -4.1205e+00 -2.3456e+00 -4.0628e+00 -1.0097e+00 -1.8766e-03 3.6969e-02 5.1220e-07 9.3297e-02 2.2728e-02 1.8635e-01 9.5468e-02 9.3793e-01 8.1988e-02 9.8034e-01 8.2726e-02 +#> 491: 1.0186e+02 -4.1205e+00 -2.3457e+00 -4.0627e+00 -1.0097e+00 -1.7736e-03 3.6915e-02 5.1153e-07 9.3298e-02 2.2716e-02 1.8634e-01 9.5548e-02 9.3772e-01 8.2005e-02 9.8025e-01 8.2722e-02 +#> 492: 1.0186e+02 -4.1205e+00 -2.3457e+00 -4.0627e+00 -1.0097e+00 -1.7747e-03 3.6877e-02 5.1077e-07 9.3336e-02 2.2697e-02 1.8635e-01 9.5593e-02 9.3778e-01 8.2001e-02 9.8013e-01 8.2725e-02 +#> 493: 1.0186e+02 -4.1205e+00 -2.3456e+00 -4.0628e+00 -1.0094e+00 -1.6324e-03 3.6857e-02 5.1020e-07 9.3348e-02 2.2668e-02 1.8636e-01 9.5735e-02 9.3764e-01 8.1984e-02 9.8019e-01 8.2723e-02 +#> 494: 1.0186e+02 -4.1205e+00 -2.3456e+00 -4.0629e+00 -1.0094e+00 -1.5393e-03 3.6842e-02 5.1022e-07 9.3359e-02 2.2649e-02 1.8637e-01 9.5812e-02 9.3739e-01 8.1982e-02 9.8033e-01 8.2708e-02 +#> 495: 1.0186e+02 -4.1205e+00 -2.3456e+00 -4.0629e+00 -1.0094e+00 -1.5166e-03 3.6841e-02 5.1004e-07 9.3321e-02 2.2642e-02 1.8640e-01 9.5849e-02 9.3716e-01 8.1979e-02 9.8016e-01 8.2700e-02 +#> 496: 1.0186e+02 -4.1205e+00 -2.3456e+00 -4.0630e+00 -1.0095e+00 -1.4947e-03 3.6841e-02 5.0969e-07 9.3236e-02 2.2646e-02 1.8640e-01 9.5916e-02 9.3719e-01 8.1963e-02 9.8028e-01 8.2702e-02 +#> 497: 1.0186e+02 -4.1205e+00 -2.3457e+00 -4.0629e+00 -1.0094e+00 -1.4507e-03 3.6827e-02 5.0937e-07 9.3185e-02 2.2663e-02 1.8638e-01 9.5991e-02 9.3707e-01 8.1954e-02 9.8047e-01 8.2718e-02 +#> 498: 1.0186e+02 -4.1205e+00 -2.3459e+00 -4.0630e+00 -1.0094e+00 -1.2569e-03 3.6805e-02 5.0854e-07 9.3089e-02 2.2677e-02 1.8634e-01 9.5931e-02 9.3719e-01 8.1952e-02 9.8051e-01 8.2718e-02 +#> 499: 1.0186e+02 -4.1205e+00 -2.3460e+00 -4.0630e+00 -1.0093e+00 -1.0466e-03 3.6769e-02 5.0789e-07 9.3029e-02 2.2690e-02 1.8631e-01 9.5862e-02 9.3729e-01 8.1956e-02 9.8046e-01 8.2731e-02 +#> 500: 1.0186e+02 -4.1205e+00 -2.3464e+00 -4.0630e+00 -1.0093e+00 -7.3346e-04 3.6766e-02 5.0769e-07 9.3093e-02 2.2701e-02 1.8633e-01 9.5687e-02 9.3739e-01 8.1977e-02 9.8039e-01 8.2728e-02</div><div class='output co'>#> <span class='message'>Calculating covariance matrix</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> [1] "CMT"</div><div class='output co'>#> <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#> <span class='message'>done</span></div><div class='input'><span class='co'># The following takes a very long time but gives</span> +<span class='va'>f_nlmixr_dfop_sfo_focei</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_dfop_sfo</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> [1] "CMT" +#> <span style='font-weight: bold;'>Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation +#> F: Forward difference gradient approximation +#> C: Central difference gradient approximation +#> M: Mixed forward and central difference gradient approximation +#> Unscaled parameters for Omegas=chol(solve(omega)); +#> Diagonals are transformed, as specified by foceiControl(diagXform=) +#> |-----+---------------+-----------+-----------+-----------+-----------| +#> | #| Objective Fun | parent_0 | log_k_m1 |f_parent_qlogis | log_k1 | +#> |.....................| log_k2 | g_qlogis | sigma_low | rsd_high | +#> |.....................| o1 | o2 | o3 | o4 | +#> <span style='text-decoration: underline;'>|.....................| o5 | o6 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 1</span>| 496.98032 | 1.000 | -0.9997 | -0.9408 | -0.9676 | +#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | +#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | +#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> +#> | U| 496.98032 | 101.7 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 496.98032</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | G| Gill Diff. | 57.10 | -0.1453 | -0.1275 | 0.2854 | +#> |.....................| -0.6156 | 0.007043 | -23.49 | -32.87 | +#> |.....................| 3.669 | -17.46 | -13.05 | -13.08 | +#> <span style='text-decoration: underline;'>|.....................| -16.16 | -9.766 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 2</span>| 3094.8373 | 0.2572 | -0.9978 | -0.9392 | -0.9714 | +#> |.....................| -0.9920 | -0.9233 | -0.6037 | -0.4942 | +#> |.....................| -0.9579 | -0.6658 | -0.7293 | -0.7310 | +#> <span style='text-decoration: underline;'>|.....................| -0.6848 | -0.7742 |...........|...........|</span> +#> | U| 3094.8373 | 26.15 | -4.052 | -0.9415 | -2.363 | +#> |.....................| -4.062 | -0.01133 | 0.8386 | 0.08074 | +#> |.....................| 0.6445 | 1.946 | 1.477 | 1.348 | +#> <span style='text-decoration: underline;'>|.....................| 1.794 | 1.297 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 3094.8373</span> | 26.15 | 0.01739 | 0.2806 | 0.09412 | +#> |.....................| 0.01721 | 0.4972 | 0.8386 | 0.08074 | +#> |.....................| 0.6445 | 1.946 | 1.477 | 1.348 | +#> <span style='text-decoration: underline;'>|.....................| 1.794 | 1.297 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 3</span>| 557.60681 | 0.9257 | -0.9995 | -0.9407 | -0.9680 | +#> |.....................| -0.9992 | -0.9232 | -0.8787 | -0.8790 | +#> |.....................| -0.9150 | -0.8703 | -0.8821 | -0.8842 | +#> <span style='text-decoration: underline;'>|.....................| -0.8739 | -0.8885 |...........|...........|</span> +#> | U| 557.60681 | 94.11 | -4.053 | -0.9430 | -2.360 | +#> |.....................| -4.069 | -0.01133 | 0.7386 | 0.06794 | +#> |.....................| 0.6735 | 1.622 | 1.284 | 1.172 | +#> <span style='text-decoration: underline;'>|.....................| 1.513 | 1.165 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 557.60681</span> | 94.11 | 0.01736 | 0.2803 | 0.09444 | +#> |.....................| 0.01709 | 0.4972 | 0.7386 | 0.06794 | +#> |.....................| 0.6735 | 1.622 | 1.284 | 1.172 | +#> <span style='text-decoration: underline;'>|.....................| 1.513 | 1.165 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 4</span>| 543.47785 | 0.9926 | -0.9997 | -0.9408 | -0.9677 | +#> |.....................| -0.9999 | -0.9232 | -0.9062 | -0.9175 | +#> |.....................| -0.9107 | -0.8907 | -0.8974 | -0.8995 | +#> <span style='text-decoration: underline;'>|.....................| -0.8929 | -0.9000 |...........|...........|</span> +#> | U| 543.47785 | 100.9 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7286 | 0.06666 | +#> |.....................| 0.6764 | 1.589 | 1.264 | 1.154 | +#> <span style='text-decoration: underline;'>|.....................| 1.485 | 1.152 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 543.47785</span> | 100.9 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7286 | 0.06666 | +#> |.....................| 0.6764 | 1.589 | 1.264 | 1.154 | +#> <span style='text-decoration: underline;'>|.....................| 1.485 | 1.152 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 5</span>| 544.09017 | 0.9993 | -0.9997 | -0.9408 | -0.9676 | +#> |.....................| -1.000 | -0.9232 | -0.9089 | -0.9213 | +#> |.....................| -0.9103 | -0.8928 | -0.8990 | -0.9010 | +#> <span style='text-decoration: underline;'>|.....................| -0.8948 | -0.9011 |...........|...........|</span> +#> | U| 544.09017 | 101.6 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7276 | 0.06654 | +#> |.....................| 0.6767 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 544.09017</span> | 101.6 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7276 | 0.06654 | +#> |.....................| 0.6767 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 6</span>| 544.17109 | 0.9999 | -0.9997 | -0.9408 | -0.9676 | +#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | +#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | +#> <span style='text-decoration: underline;'>|.....................| -0.8949 | -0.9012 |...........|...........|</span> +#> | U| 544.17109 | 101.6 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 544.17109</span> | 101.6 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 7</span>| 544.17937 | 1.000 | -0.9997 | -0.9408 | -0.9676 | +#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | +#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | +#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> +#> | U| 544.17937 | 101.7 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 544.17937</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 8</span>| 544.18025 | 1.000 | -0.9997 | -0.9408 | -0.9676 | +#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | +#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | +#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> +#> | U| 544.18025 | 101.7 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 544.18025</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 9</span>| 544.18033 | 1.000 | -0.9997 | -0.9408 | -0.9676 | +#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | +#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | +#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> +#> | U| 544.18033 | 101.7 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 544.18033</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 10</span>| 544.18034 | 1.000 | -0.9997 | -0.9408 | -0.9676 | +#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | +#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | +#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> +#> | U| 544.18034 | 101.7 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 544.18034</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 11</span>| 544.18036 | 1.000 | -0.9997 | -0.9408 | -0.9676 | +#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | +#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | +#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> +#> | U| 544.18036 | 101.7 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 544.18036</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 12</span>| 544.18036 | 1.000 | -0.9997 | -0.9408 | -0.9676 | +#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | +#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | +#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> +#> | U| 544.18036 | 101.7 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 544.18036</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 13</span>| 544.18036 | 1.000 | -0.9997 | -0.9408 | -0.9676 | +#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | +#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | +#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> +#> | U| 544.18036 | 101.7 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 544.18036</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 14</span>| 544.18036 | 1.000 | -0.9997 | -0.9408 | -0.9676 | +#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | +#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | +#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> +#> | U| 544.18036 | 101.7 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 544.18036</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 15</span>| 544.18036 | 1.000 | -0.9997 | -0.9408 | -0.9676 | +#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | +#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | +#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> +#> | U| 544.18036 | 101.7 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 544.18036</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 16</span>| 544.18036 | 1.000 | -0.9997 | -0.9408 | -0.9676 | +#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | +#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | +#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> +#> | U| 544.18036 | 101.7 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 544.18036</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> |<span style='font-weight: bold;'> 17</span>| 544.18036 | 1.000 | -0.9997 | -0.9408 | -0.9676 | +#> |.....................| -1.000 | -0.9232 | -0.9092 | -0.9217 | +#> |.....................| -0.9102 | -0.8930 | -0.8991 | -0.9012 | +#> <span style='text-decoration: underline;'>|.....................| -0.8950 | -0.9012 |...........|...........|</span> +#> | U| 544.18036 | 101.7 | -4.054 | -0.9431 | -2.359 | +#> |.....................| -4.070 | -0.01132 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> | X|<span style='font-weight: bold;'> 544.18036</span> | 101.7 | 0.01736 | 0.2803 | 0.09447 | +#> |.....................| 0.01708 | 0.4972 | 0.7275 | 0.06652 | +#> |.....................| 0.6768 | 1.586 | 1.262 | 1.152 | +#> <span style='text-decoration: underline;'>|.....................| 1.482 | 1.151 |...........|...........|</span> +#> calculating covariance matrix +#> done</div><div class='output co'>#> <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#> <span class='message'>done</span></div><div class='output co'>#> <span class='warning'>Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span></div><div class='output co'>#> <span class='warning'>Warning: last objective function was not at minimum, possible problems in optimization</span></div><div class='output co'>#> <span class='warning'>Warning: using R matrix to calculate covariance, can check sandwich or S matrix with $covRS and $covS</span></div><div class='output co'>#> <span class='warning'>Warning: gradient problems with initial estimate and covariance; see $scaleInfo</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span><span class='va'>f_nlmixr_dfop_sfo_saem</span><span class='op'>$</span><span class='va'>nm</span>, <span class='va'>f_nlmixr_dfop_sfo_focei</span><span class='op'>$</span><span class='va'>nm</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'>Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span></div><div class='output co'>#> </div><div class='output co'>#> df AIC +#> f_nlmixr_dfop_sfo_saem$nm 16 Inf +#> f_nlmixr_dfop_sfo_focei$nm 14 886.4573</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_nlmixr_dfop_sfo_sfo</span>, data <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='error'>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'f_nlmixr_dfop_sfo_sfo' not found</span></div><div class='input'><span class='co'># }</span> + +</div></pre> + </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>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> +</div> + + </footer> + </div> + + + + + </body> +</html> + + diff --git a/docs/dev/reference/summary.saem.mmkin.html b/docs/dev/reference/summary.saem.mmkin.html index 722415fb..fdfdaf4b 100644 --- a/docs/dev/reference/summary.saem.mmkin.html +++ b/docs/dev/reference/summary.saem.mmkin.html @@ -76,7 +76,7 @@ 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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> </span> </div> @@ -260,15 +260,15 @@ saemix authors for the parts inherited from saemix.</p> 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'>5</span><span class='op'>)</span> <span class='va'>f_saem_dfop_sfo</span> <span class='op'><-</span> <span class='fu'><a href='saem.html'>saem</a></span><span class='op'>(</span><span class='va'>f_mmkin_dfop_sfo</span><span class='op'>)</span> </div><div class='output co'>#> Running main SAEM algorithm -#> [1] "Mon Jan 11 12:42:40 2021" +#> [1] "Fri Jun 11 10:58:28 2021" #> .... #> Minimisation finished -#> [1] "Mon Jan 11 12:42:53 2021"</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_saem_dfop_sfo</span>, data <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> +#> [1] "Fri Jun 11 10:58:40 2021"</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_saem_dfop_sfo</span>, data <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> </div><div class='output co'>#> saemix version used for fitting: 3.1.9000 -#> mkin version used for pre-fitting: 0.9.50.4 -#> R version used for fitting: 4.0.3 -#> Date of fit: Mon Jan 11 12:42:54 2021 -#> Date of summary: Mon Jan 11 12:42:54 2021 +#> mkin version used for pre-fitting: 1.0.5 +#> R version used for fitting: 4.1.0 +#> Date of fit: Fri Jun 11 10:58:41 2021 +#> Date of summary: Fri Jun 11 10:58:41 2021 #> #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -283,7 +283,7 @@ saemix authors for the parts inherited from saemix.</p> #> #> Model predictions using solution type analytical #> -#> Fitted in 13.298 s using 300, 100 iterations +#> Fitted in 12.75 s using 300, 100 iterations #> #> Variance model: Two-component variance function #> @@ -291,7 +291,7 @@ saemix authors for the parts inherited from saemix.</p> #> parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 #> 101.65645 -4.05368 -0.94311 -2.35943 -4.07006 #> g_qlogis -#> -0.01133 +#> -0.01132 #> #> Fixed degradation parameter values: #> None @@ -299,232 +299,232 @@ saemix authors for the parts inherited from saemix.</p> #> Results: #> #> Likelihood computed by importance sampling -#> AIC BIC logLik -#> 830 824.5 -401 +#> AIC BIC logLik +#> 825.9 820.4 -398.9 #> #> Optimised parameters: -#> est. lower upper -#> parent_0 101.4423 97.862 105.0224 -#> log_k_m1 -4.0703 -4.191 -3.9495 -#> f_parent_qlogis -0.9539 -1.313 -0.5949 -#> log_k1 -2.9724 -3.811 -2.1342 -#> log_k2 -3.4977 -4.206 -2.7895 -#> g_qlogis -0.0449 -1.116 1.0262 +#> est. lower upper +#> parent_0 101.118986 97.368 104.8695 +#> log_k_m1 -4.057591 -4.177 -3.9379 +#> f_parent_qlogis -0.933087 -1.290 -0.5763 +#> log_k1 -2.945520 -3.833 -2.0576 +#> log_k2 -3.531954 -4.310 -2.7542 +#> g_qlogis -0.009584 -1.688 1.6687 #> #> Correlation: #> prnt_0 lg_k_1 f_prn_ log_k1 log_k2 -#> log_k_m1 -0.207 -#> f_parent_qlogis -0.148 0.202 -#> log_k1 0.040 -0.038 -0.022 -#> log_k2 0.022 -0.015 -0.009 0.001 -#> g_qlogis -0.012 0.005 0.011 -0.173 -0.130 +#> log_k_m1 -0.198 +#> f_parent_qlogis -0.153 0.184 +#> log_k1 0.080 -0.077 -0.045 +#> log_k2 0.005 0.008 -0.003 -0.019 +#> g_qlogis -0.059 0.048 0.041 -0.334 -0.253 #> #> Random effects: -#> est. lower upper -#> SD.parent_0 2.88564 -0.5163 6.2876 -#> SD.log_k_m1 0.08502 -0.0427 0.2127 -#> SD.f_parent_qlogis 0.38857 0.1350 0.6421 -#> SD.log_k1 0.92338 0.3296 1.5172 -#> SD.log_k2 0.78644 0.2817 1.2912 -#> SD.g_qlogis 0.34614 -0.8727 1.5650 +#> est. lower upper +#> SD.parent_0 2.97797 -0.62927 6.5852 +#> SD.log_k_m1 0.09235 -0.02448 0.2092 +#> SD.f_parent_qlogis 0.38712 0.13469 0.6396 +#> SD.log_k1 0.88671 0.27052 1.5029 +#> SD.log_k2 0.80497 0.25587 1.3541 +#> SD.g_qlogis 0.36812 -3.56188 4.2981 #> #> Variance model: #> est. lower upper -#> a.1 0.65859 0.49250 0.82469 -#> b.1 0.06411 0.05006 0.07817 +#> a.1 0.85879 0.68143 1.03615 +#> b.1 0.07787 0.06288 0.09286 #> #> Backtransformed parameters: #> est. lower upper -#> parent_0 101.44231 97.86220 105.02241 -#> k_m1 0.01707 0.01513 0.01926 -#> f_parent_to_m1 0.27811 0.21201 0.35551 -#> k1 0.05118 0.02213 0.11834 -#> k2 0.03027 0.01491 0.06145 -#> g 0.48878 0.24675 0.73618 +#> parent_0 101.11899 97.36850 104.86947 +#> k_m1 0.01729 0.01534 0.01949 +#> f_parent_to_m1 0.28230 0.21587 0.35979 +#> k1 0.05257 0.02163 0.12776 +#> k2 0.02925 0.01344 0.06366 +#> g 0.49760 0.15606 0.84140 #> #> Resulting formation fractions: #> ff -#> parent_m1 0.2781 -#> parent_sink 0.7219 +#> parent_m1 0.2823 +#> parent_sink 0.7177 #> #> Estimated disappearance times: #> DT50 DT90 DT50back DT50_k1 DT50_k2 -#> parent 17.53 61.64 18.55 13.54 22.9 -#> m1 40.60 134.88 NA NA NA +#> parent 17.47 62.31 18.76 13.18 23.7 +#> m1 40.09 133.17 NA NA NA #> #> Data: -#> ds name time observed predicted residual std standardized -#> ds 1 parent 0 89.8 9.869e+01 8.894553 6.3618 1.398124 -#> ds 1 parent 0 104.1 9.869e+01 -5.405447 6.3618 -0.849676 -#> ds 1 parent 1 88.7 9.413e+01 5.426448 6.0706 0.893897 -#> ds 1 parent 1 95.5 9.413e+01 -1.373552 6.0706 -0.226265 -#> ds 1 parent 3 81.8 8.576e+01 3.961821 5.5377 0.715422 -#> ds 1 parent 3 94.5 8.576e+01 -8.738179 5.5377 -1.577932 -#> ds 1 parent 7 71.5 7.168e+01 0.184828 4.6429 0.039809 -#> ds 1 parent 7 70.3 7.168e+01 1.384828 4.6429 0.298270 -#> ds 1 parent 14 54.2 5.351e+01 -0.688235 3.4934 -0.197008 -#> ds 1 parent 14 49.6 5.351e+01 3.911765 3.4934 1.119747 -#> ds 1 parent 28 31.5 3.209e+01 0.590445 2.1603 0.273322 -#> ds 1 parent 28 28.8 3.209e+01 3.290445 2.1603 1.523177 -#> ds 1 parent 60 12.1 1.272e+01 0.618158 1.0481 0.589761 -#> ds 1 parent 60 13.6 1.272e+01 -0.881842 1.0481 -0.841332 -#> ds 1 parent 90 6.2 6.085e+00 -0.115212 0.7655 -0.150512 -#> ds 1 parent 90 8.3 6.085e+00 -2.215212 0.7655 -2.893953 -#> ds 1 parent 120 2.2 3.009e+00 0.809439 0.6863 1.179470 -#> ds 1 parent 120 2.4 3.009e+00 0.609439 0.6863 0.888041 -#> ds 1 m1 1 0.3 1.129e+00 0.828817 0.6626 1.250938 -#> ds 1 m1 1 0.2 1.129e+00 0.928817 0.6626 1.401869 -#> ds 1 m1 3 2.2 3.141e+00 0.940880 0.6887 1.366187 -#> ds 1 m1 3 3.0 3.141e+00 0.140880 0.6887 0.204562 -#> ds 1 m1 7 6.5 6.326e+00 -0.174162 0.7735 -0.225175 -#> ds 1 m1 7 5.0 6.326e+00 1.325838 0.7735 1.714181 -#> ds 1 m1 14 10.2 9.883e+00 -0.317417 0.9139 -0.347326 -#> ds 1 m1 14 9.5 9.883e+00 0.382583 0.9139 0.418631 -#> ds 1 m1 28 12.2 1.251e+01 0.309856 1.0378 0.298572 -#> ds 1 m1 28 13.4 1.251e+01 -0.890144 1.0378 -0.857726 -#> ds 1 m1 60 11.8 1.086e+01 -0.940009 0.9584 -0.980812 -#> ds 1 m1 60 13.2 1.086e+01 -2.340009 0.9584 -2.441581 -#> ds 1 m1 90 6.6 7.823e+00 1.222977 0.8278 1.477332 -#> ds 1 m1 90 9.3 7.823e+00 -1.477023 0.8278 -1.784214 -#> ds 1 m1 120 3.5 5.315e+00 1.815201 0.7415 2.447906 -#> ds 1 m1 120 5.4 5.315e+00 -0.084799 0.7415 -0.114356 -#> ds 2 parent 0 118.0 1.031e+02 -14.876736 6.6443 -2.239038 -#> ds 2 parent 0 99.8 1.031e+02 3.323264 6.6443 0.500171 -#> ds 2 parent 1 90.2 9.757e+01 7.371379 6.2902 1.171891 -#> ds 2 parent 1 94.6 9.757e+01 2.971379 6.2902 0.472386 -#> ds 2 parent 3 96.1 8.788e+01 -8.222746 5.6724 -1.449599 -#> ds 2 parent 3 78.4 8.788e+01 9.477254 5.6724 1.670758 -#> ds 2 parent 7 77.9 7.293e+01 -4.972272 4.7218 -1.053054 -#> ds 2 parent 7 77.7 7.293e+01 -4.772272 4.7218 -1.010697 -#> ds 2 parent 14 56.0 5.602e+01 0.016773 3.6513 0.004594 -#> ds 2 parent 14 54.7 5.602e+01 1.316773 3.6513 0.360633 -#> ds 2 parent 28 36.6 3.855e+01 1.945779 2.5575 0.760803 -#> ds 2 parent 28 36.8 3.855e+01 1.745779 2.5575 0.682603 -#> ds 2 parent 60 22.1 2.101e+01 -1.086693 1.4996 -0.724663 -#> ds 2 parent 60 24.7 2.101e+01 -3.686693 1.4996 -2.458475 -#> ds 2 parent 90 12.4 1.246e+01 0.058759 1.0353 0.056757 -#> ds 2 parent 90 10.8 1.246e+01 1.658759 1.0353 1.602256 -#> ds 2 parent 120 6.8 7.406e+00 0.606226 0.8119 0.746659 -#> ds 2 parent 120 7.9 7.406e+00 -0.493774 0.8119 -0.608157 -#> ds 2 m1 1 1.3 1.438e+00 0.138236 0.6650 0.207869 -#> ds 2 m1 3 3.7 3.879e+00 0.178617 0.7040 0.253726 -#> ds 2 m1 3 4.7 3.879e+00 -0.821383 0.7040 -1.166780 -#> ds 2 m1 7 8.1 7.389e+00 -0.710951 0.8113 -0.876337 -#> ds 2 m1 7 7.9 7.389e+00 -0.510951 0.8113 -0.629812 -#> ds 2 m1 14 10.1 1.069e+01 0.593533 0.9507 0.624328 -#> ds 2 m1 14 10.3 1.069e+01 0.393533 0.9507 0.413951 -#> ds 2 m1 28 10.7 1.240e+01 1.703647 1.0325 1.649956 -#> ds 2 m1 28 12.2 1.240e+01 0.203647 1.0325 0.197229 -#> ds 2 m1 60 10.7 1.055e+01 -0.147672 0.9442 -0.156405 -#> ds 2 m1 60 12.5 1.055e+01 -1.947672 0.9442 -2.062848 -#> ds 2 m1 90 9.1 8.010e+00 -1.090041 0.8351 -1.305210 -#> ds 2 m1 90 7.4 8.010e+00 0.609959 0.8351 0.730362 -#> ds 2 m1 120 6.1 5.793e+00 -0.306797 0.7561 -0.405759 -#> ds 2 m1 120 4.5 5.793e+00 1.293203 0.7561 1.710347 -#> ds 3 parent 0 106.2 1.035e+02 -2.712344 6.6675 -0.406801 -#> ds 3 parent 0 106.9 1.035e+02 -3.412344 6.6675 -0.511788 -#> ds 3 parent 1 107.4 9.548e+01 -11.924044 6.1566 -1.936801 -#> ds 3 parent 1 96.1 9.548e+01 -0.624044 6.1566 -0.101362 -#> ds 3 parent 3 79.4 8.246e+01 3.056105 5.3274 0.573662 -#> ds 3 parent 3 82.6 8.246e+01 -0.143895 5.3274 -0.027010 -#> ds 3 parent 7 63.9 6.489e+01 0.991141 4.2122 0.235304 -#> ds 3 parent 7 62.4 6.489e+01 2.491141 4.2122 0.591416 -#> ds 3 parent 14 51.0 4.869e+01 -2.306824 3.1906 -0.723013 -#> ds 3 parent 14 47.1 4.869e+01 1.593176 3.1906 0.499338 -#> ds 3 parent 28 36.1 3.480e+01 -1.304261 2.3260 -0.560722 -#> ds 3 parent 28 36.6 3.480e+01 -1.804261 2.3260 -0.775679 -#> ds 3 parent 60 20.1 1.988e+01 -0.221952 1.4346 -0.154719 -#> ds 3 parent 60 19.8 1.988e+01 0.078048 1.4346 0.054406 -#> ds 3 parent 90 11.3 1.194e+01 0.642458 1.0099 0.636132 -#> ds 3 parent 90 10.7 1.194e+01 1.242458 1.0099 1.230224 -#> ds 3 parent 120 8.2 7.176e+00 -1.023847 0.8034 -1.274423 -#> ds 3 parent 120 7.3 7.176e+00 -0.123847 0.8034 -0.154158 -#> ds 3 m1 0 0.8 8.527e-13 -0.800000 0.6586 -1.214712 -#> ds 3 m1 1 1.8 1.856e+00 0.055925 0.6693 0.083562 -#> ds 3 m1 1 2.3 1.856e+00 -0.444075 0.6693 -0.663537 -#> ds 3 m1 3 4.2 4.780e+00 0.580164 0.7264 0.798676 -#> ds 3 m1 3 4.1 4.780e+00 0.680164 0.7264 0.936340 -#> ds 3 m1 7 6.8 8.410e+00 1.609920 0.8512 1.891455 -#> ds 3 m1 7 10.1 8.410e+00 -1.690080 0.8512 -1.985633 -#> ds 3 m1 14 11.4 1.098e+01 -0.424444 0.9638 -0.440389 -#> ds 3 m1 14 12.8 1.098e+01 -1.824444 0.9638 -1.892979 -#> ds 3 m1 28 11.5 1.142e+01 -0.079336 0.9848 -0.080558 -#> ds 3 m1 28 10.6 1.142e+01 0.820664 0.9848 0.833311 -#> ds 3 m1 60 7.5 9.110e+00 1.610231 0.8803 1.829222 -#> ds 3 m1 60 8.6 9.110e+00 0.510231 0.8803 0.579622 -#> ds 3 m1 90 7.3 6.799e+00 -0.501085 0.7898 -0.634463 -#> ds 3 m1 90 8.1 6.799e+00 -1.301085 0.7898 -1.647404 -#> ds 3 m1 120 5.3 4.868e+00 -0.431505 0.7288 -0.592064 -#> ds 3 m1 120 3.8 4.868e+00 1.068495 0.7288 1.466073 -#> ds 4 parent 0 104.7 9.926e+01 -5.444622 6.3975 -0.851049 -#> ds 4 parent 0 88.3 9.926e+01 10.955378 6.3975 1.712436 -#> ds 4 parent 1 94.2 9.618e+01 1.978413 6.2013 0.319030 -#> ds 4 parent 1 94.6 9.618e+01 1.578413 6.2013 0.254527 -#> ds 4 parent 3 78.1 9.037e+01 12.268550 5.8311 2.103985 -#> ds 4 parent 3 96.5 9.037e+01 -6.131450 5.8311 -1.051508 -#> ds 4 parent 7 76.2 7.999e+01 3.794958 5.1708 0.733918 -#> ds 4 parent 7 77.8 7.999e+01 2.194958 5.1708 0.424489 -#> ds 4 parent 14 70.8 6.518e+01 -5.624996 4.2301 -1.329742 -#> ds 4 parent 14 67.3 6.518e+01 -2.124996 4.2301 -0.502346 -#> ds 4 parent 28 43.1 4.462e+01 1.517860 2.9354 0.517085 -#> ds 4 parent 28 45.1 4.462e+01 -0.482140 2.9354 -0.164249 -#> ds 4 parent 60 21.3 2.130e+01 -0.003305 1.5159 -0.002180 -#> ds 4 parent 60 23.5 2.130e+01 -2.203305 1.5159 -1.453435 -#> ds 4 parent 90 11.8 1.180e+01 0.002834 1.0032 0.002825 -#> ds 4 parent 90 12.1 1.180e+01 -0.297166 1.0032 -0.296226 -#> ds 4 parent 120 7.0 6.868e+00 -0.132251 0.7922 -0.166937 -#> ds 4 parent 120 6.2 6.868e+00 0.667749 0.7922 0.842879 -#> ds 4 m1 0 1.6 0.000e+00 -1.600000 0.6586 -2.429424 -#> ds 4 m1 1 0.9 6.826e-01 -0.217363 0.6600 -0.329315 -#> ds 4 m1 3 3.7 1.935e+00 -1.765082 0.6702 -2.633768 -#> ds 4 m1 3 2.0 1.935e+00 -0.065082 0.6702 -0.097112 -#> ds 4 m1 7 3.6 4.035e+00 0.434805 0.7076 0.614501 -#> ds 4 m1 7 3.8 4.035e+00 0.234805 0.7076 0.331845 -#> ds 4 m1 14 7.1 6.652e+00 -0.448187 0.7846 -0.571220 -#> ds 4 m1 14 6.6 6.652e+00 0.051813 0.7846 0.066036 -#> ds 4 m1 28 9.5 9.156e+00 -0.343805 0.8822 -0.389696 -#> ds 4 m1 28 9.3 9.156e+00 -0.143805 0.8822 -0.163000 -#> ds 4 m1 60 8.3 8.848e+00 0.547762 0.8692 0.630185 -#> ds 4 m1 60 9.0 8.848e+00 -0.152238 0.8692 -0.175146 -#> ds 4 m1 90 6.6 6.674e+00 0.073979 0.7854 0.094194 -#> ds 4 m1 90 7.7 6.674e+00 -1.026021 0.7854 -1.306390 -#> ds 4 m1 120 3.7 4.668e+00 0.967537 0.7234 1.337503 -#> ds 4 m1 120 3.5 4.668e+00 1.167537 0.7234 1.613979 -#> ds 5 parent 0 110.4 1.022e+02 -8.170986 6.5872 -1.240433 -#> ds 5 parent 0 112.1 1.022e+02 -9.870986 6.5872 -1.498509 -#> ds 5 parent 1 93.5 9.513e+01 1.630764 6.1346 0.265832 -#> ds 5 parent 1 91.0 9.513e+01 4.130764 6.1346 0.673359 -#> ds 5 parent 3 71.0 8.296e+01 11.964279 5.3597 2.232268 -#> ds 5 parent 3 89.7 8.296e+01 -6.735721 5.3597 -1.256735 -#> ds 5 parent 7 60.4 6.495e+01 4.547441 4.2157 1.078684 -#> ds 5 parent 7 59.1 6.495e+01 5.847441 4.2157 1.387053 -#> ds 5 parent 14 56.5 4.626e+01 -10.241319 3.0380 -3.371047 -#> ds 5 parent 14 47.0 4.626e+01 -0.741319 3.0380 -0.244014 -#> ds 5 parent 28 30.2 3.026e+01 0.058478 2.0487 0.028544 -#> ds 5 parent 28 23.9 3.026e+01 6.358478 2.0487 3.103661 -#> ds 5 parent 60 17.0 1.792e+01 0.919046 1.3242 0.694024 -#> ds 5 parent 60 18.7 1.792e+01 -0.780954 1.3242 -0.589742 -#> ds 5 parent 90 11.3 1.187e+01 0.573917 1.0066 0.570144 -#> ds 5 parent 90 11.9 1.187e+01 -0.026083 1.0066 -0.025912 -#> ds 5 parent 120 9.0 7.898e+00 -1.102089 0.8307 -1.326622 -#> ds 5 parent 120 8.1 7.898e+00 -0.202089 0.8307 -0.243261 -#> ds 5 m1 0 0.7 -1.421e-14 -0.700000 0.6586 -1.062873 -#> ds 5 m1 1 3.0 3.144e+00 0.143526 0.6887 0.208390 -#> ds 5 m1 1 2.6 3.144e+00 0.543526 0.6887 0.789161 -#> ds 5 m1 3 5.1 8.390e+00 3.290265 0.8504 3.869277 -#> ds 5 m1 3 7.5 8.390e+00 0.890265 0.8504 1.046932 -#> ds 5 m1 7 16.5 1.566e+01 -0.841368 1.2007 -0.700751 -#> ds 5 m1 7 19.0 1.566e+01 -3.341368 1.2007 -2.782928 -#> ds 5 m1 14 22.9 2.188e+01 -1.017753 1.5498 -0.656687 -#> ds 5 m1 14 23.2 2.188e+01 -1.317753 1.5498 -0.850257 -#> ds 5 m1 28 22.2 2.386e+01 1.655914 1.6652 0.994399 -#> ds 5 m1 28 24.4 2.386e+01 -0.544086 1.6652 -0.326731 -#> ds 5 m1 60 15.5 1.859e+01 3.091124 1.3618 2.269915 -#> ds 5 m1 60 19.8 1.859e+01 -1.208876 1.3618 -0.887718 -#> ds 5 m1 90 14.9 1.372e+01 -1.176815 1.0990 -1.070784 -#> ds 5 m1 90 14.2 1.372e+01 -0.476815 1.0990 -0.433854 -#> ds 5 m1 120 10.9 9.961e+00 -0.938796 0.9174 -1.023332 -#> ds 5 m1 120 10.4 9.961e+00 -0.438796 0.9174 -0.478308</div><div class='input'><span class='co'># }</span> +#> ds name time observed predicted residual std standardized +#> ds 1 parent 0 89.8 9.838e+01 -8.584661 7.7094 -1.113536 +#> ds 1 parent 0 104.1 9.838e+01 5.715339 7.7094 0.741350 +#> ds 1 parent 1 88.7 9.388e+01 -5.182489 7.3611 -0.704041 +#> ds 1 parent 1 95.5 9.388e+01 1.617511 7.3611 0.219739 +#> ds 1 parent 3 81.8 8.563e+01 -3.825382 6.7229 -0.569010 +#> ds 1 parent 3 94.5 8.563e+01 8.874618 6.7229 1.320062 +#> ds 1 parent 7 71.5 7.169e+01 -0.188290 5.6482 -0.033336 +#> ds 1 parent 7 70.3 7.169e+01 -1.388290 5.6482 -0.245795 +#> ds 1 parent 14 54.2 5.361e+01 0.586595 4.2624 0.137621 +#> ds 1 parent 14 49.6 5.361e+01 -4.013405 4.2624 -0.941587 +#> ds 1 parent 28 31.5 3.219e+01 -0.688936 2.6496 -0.260011 +#> ds 1 parent 28 28.8 3.219e+01 -3.388936 2.6496 -1.279016 +#> ds 1 parent 60 12.1 1.278e+01 -0.678998 1.3145 -0.516562 +#> ds 1 parent 60 13.6 1.278e+01 0.821002 1.3145 0.624595 +#> ds 1 parent 90 6.2 6.157e+00 0.043461 0.9835 0.044188 +#> ds 1 parent 90 8.3 6.157e+00 2.143461 0.9835 2.179316 +#> ds 1 parent 120 2.2 3.076e+00 -0.876218 0.8916 -0.982775 +#> ds 1 parent 120 2.4 3.076e+00 -0.676218 0.8916 -0.758453 +#> ds 1 m1 1 0.3 1.134e+00 -0.833749 0.8633 -0.965750 +#> ds 1 m1 1 0.2 1.134e+00 -0.933749 0.8633 -1.081583 +#> ds 1 m1 3 2.2 3.157e+00 -0.957400 0.8933 -1.071763 +#> ds 1 m1 3 3.0 3.157e+00 -0.157400 0.8933 -0.176202 +#> ds 1 m1 7 6.5 6.369e+00 0.130995 0.9917 0.132090 +#> ds 1 m1 7 5.0 6.369e+00 -1.369005 0.9917 -1.380438 +#> ds 1 m1 14 10.2 9.971e+00 0.229362 1.1577 0.198112 +#> ds 1 m1 14 9.5 9.971e+00 -0.470638 1.1577 -0.406513 +#> ds 1 m1 28 12.2 1.265e+01 -0.447735 1.3067 -0.342637 +#> ds 1 m1 28 13.4 1.265e+01 0.752265 1.3067 0.575683 +#> ds 1 m1 60 11.8 1.097e+01 0.832027 1.2112 0.686945 +#> ds 1 m1 60 13.2 1.097e+01 2.232027 1.2112 1.842825 +#> ds 1 m1 90 6.6 7.876e+00 -1.275985 1.0553 -1.209109 +#> ds 1 m1 90 9.3 7.876e+00 1.424015 1.0553 1.349381 +#> ds 1 m1 120 3.5 5.336e+00 -1.835829 0.9540 -1.924292 +#> ds 1 m1 120 5.4 5.336e+00 0.064171 0.9540 0.067263 +#> ds 2 parent 0 118.0 1.092e+02 8.812058 8.5459 1.031142 +#> ds 2 parent 0 99.8 1.092e+02 -9.387942 8.5459 -1.098529 +#> ds 2 parent 1 90.2 1.023e+02 -12.114268 8.0135 -1.511724 +#> ds 2 parent 1 94.6 1.023e+02 -7.714268 8.0135 -0.962654 +#> ds 2 parent 3 96.1 9.066e+01 5.436165 7.1122 0.764344 +#> ds 2 parent 3 78.4 9.066e+01 -12.263835 7.1122 -1.724339 +#> ds 2 parent 7 77.9 7.365e+01 4.245773 5.7995 0.732090 +#> ds 2 parent 7 77.7 7.365e+01 4.045773 5.7995 0.697604 +#> ds 2 parent 14 56.0 5.593e+01 0.073803 4.4389 0.016626 +#> ds 2 parent 14 54.7 5.593e+01 -1.226197 4.4389 -0.276236 +#> ds 2 parent 28 36.6 3.892e+01 -2.320837 3.1502 -0.736737 +#> ds 2 parent 28 36.8 3.892e+01 -2.120837 3.1502 -0.673248 +#> ds 2 parent 60 22.1 2.136e+01 0.741020 1.8719 0.395868 +#> ds 2 parent 60 24.7 2.136e+01 3.341020 1.8719 1.784841 +#> ds 2 parent 90 12.4 1.251e+01 -0.113999 1.2989 -0.087765 +#> ds 2 parent 90 10.8 1.251e+01 -1.713999 1.2989 -1.319575 +#> ds 2 parent 120 6.8 7.338e+00 -0.537708 1.0315 -0.521281 +#> ds 2 parent 120 7.9 7.338e+00 0.562292 1.0315 0.545113 +#> ds 2 m1 1 1.3 1.576e+00 -0.276176 0.8675 -0.318352 +#> ds 2 m1 3 3.7 4.177e+00 -0.476741 0.9183 -0.519146 +#> ds 2 m1 3 4.7 4.177e+00 0.523259 0.9183 0.569801 +#> ds 2 m1 7 8.1 7.724e+00 0.376365 1.0485 0.358970 +#> ds 2 m1 7 7.9 7.724e+00 0.176365 1.0485 0.168214 +#> ds 2 m1 14 10.1 1.077e+01 -0.674433 1.2006 -0.561738 +#> ds 2 m1 14 10.3 1.077e+01 -0.474433 1.2006 -0.395158 +#> ds 2 m1 28 10.7 1.212e+01 -1.416179 1.2758 -1.110010 +#> ds 2 m1 28 12.2 1.212e+01 0.083821 1.2758 0.065699 +#> ds 2 m1 60 10.7 1.041e+01 0.294930 1.1807 0.249793 +#> ds 2 m1 60 12.5 1.041e+01 2.094930 1.1807 1.774316 +#> ds 2 m1 90 9.1 8.079e+00 1.020859 1.0646 0.958929 +#> ds 2 m1 90 7.4 8.079e+00 -0.679141 1.0646 -0.637941 +#> ds 2 m1 120 6.1 5.968e+00 0.131673 0.9765 0.134843 +#> ds 2 m1 120 4.5 5.968e+00 -1.468327 0.9765 -1.503683 +#> ds 3 parent 0 106.2 1.036e+02 2.638248 8.1101 0.325303 +#> ds 3 parent 0 106.9 1.036e+02 3.338248 8.1101 0.411614 +#> ds 3 parent 1 107.4 9.580e+01 11.600063 7.5094 1.544743 +#> ds 3 parent 1 96.1 9.580e+01 0.300063 7.5094 0.039958 +#> ds 3 parent 3 79.4 8.297e+01 -3.574516 6.5182 -0.548391 +#> ds 3 parent 3 82.6 8.297e+01 -0.374516 6.5182 -0.057457 +#> ds 3 parent 7 63.9 6.517e+01 -1.272397 5.1472 -0.247200 +#> ds 3 parent 7 62.4 6.517e+01 -2.772397 5.1472 -0.538618 +#> ds 3 parent 14 51.0 4.821e+01 2.790075 3.8512 0.724475 +#> ds 3 parent 14 47.1 4.821e+01 -1.109925 3.8512 -0.288205 +#> ds 3 parent 28 36.1 3.385e+01 2.250573 2.7723 0.811811 +#> ds 3 parent 28 36.6 3.385e+01 2.750573 2.7723 0.992168 +#> ds 3 parent 60 20.1 1.964e+01 0.455700 1.7543 0.259760 +#> ds 3 parent 60 19.8 1.964e+01 0.155700 1.7543 0.088753 +#> ds 3 parent 90 11.3 1.210e+01 -0.795458 1.2746 -0.624068 +#> ds 3 parent 90 10.7 1.210e+01 -1.395458 1.2746 -1.094792 +#> ds 3 parent 120 8.2 7.451e+00 0.749141 1.0364 0.722816 +#> ds 3 parent 120 7.3 7.451e+00 -0.150859 1.0364 -0.145558 +#> ds 3 m1 0 0.8 3.695e-13 0.800000 0.8588 0.931542 +#> ds 3 m1 1 1.8 1.740e+00 0.059741 0.8694 0.068714 +#> ds 3 m1 1 2.3 1.740e+00 0.559741 0.8694 0.643812 +#> ds 3 m1 3 4.2 4.531e+00 -0.331379 0.9285 -0.356913 +#> ds 3 m1 3 4.1 4.531e+00 -0.431379 0.9285 -0.464618 +#> ds 3 m1 7 6.8 8.113e+00 -1.312762 1.0661 -1.231333 +#> ds 3 m1 7 10.1 8.113e+00 1.987238 1.0661 1.863971 +#> ds 3 m1 14 11.4 1.079e+01 0.613266 1.2013 0.510507 +#> ds 3 m1 14 12.8 1.079e+01 2.013266 1.2013 1.675923 +#> ds 3 m1 28 11.5 1.133e+01 0.174252 1.2310 0.141553 +#> ds 3 m1 28 10.6 1.133e+01 -0.725748 1.2310 -0.589558 +#> ds 3 m1 60 7.5 8.948e+00 -1.448281 1.1059 -1.309561 +#> ds 3 m1 60 8.6 8.948e+00 -0.348281 1.1059 -0.314922 +#> ds 3 m1 90 7.3 6.665e+00 0.634932 1.0034 0.632752 +#> ds 3 m1 90 8.1 6.665e+00 1.434932 1.0034 1.430004 +#> ds 3 m1 120 5.3 4.795e+00 0.504936 0.9365 0.539199 +#> ds 3 m1 120 3.8 4.795e+00 -0.995064 0.9365 -1.062586 +#> ds 4 parent 0 104.7 9.985e+01 4.850494 7.8227 0.620050 +#> ds 4 parent 0 88.3 9.985e+01 -11.549506 7.8227 -1.476402 +#> ds 4 parent 1 94.2 9.676e+01 -2.556304 7.5834 -0.337093 +#> ds 4 parent 1 94.6 9.676e+01 -2.156304 7.5834 -0.284346 +#> ds 4 parent 3 78.1 9.092e+01 -12.817485 7.1318 -1.797230 +#> ds 4 parent 3 96.5 9.092e+01 5.582515 7.1318 0.782764 +#> ds 4 parent 7 76.2 8.050e+01 -4.297338 6.3270 -0.679204 +#> ds 4 parent 7 77.8 8.050e+01 -2.697338 6.3270 -0.426320 +#> ds 4 parent 14 70.8 6.562e+01 5.179989 5.1816 0.999687 +#> ds 4 parent 14 67.3 6.562e+01 1.679989 5.1816 0.324222 +#> ds 4 parent 28 43.1 4.499e+01 -1.886936 3.6069 -0.523140 +#> ds 4 parent 28 45.1 4.499e+01 0.113064 3.6069 0.031346 +#> ds 4 parent 60 21.3 2.151e+01 -0.214840 1.8827 -0.114114 +#> ds 4 parent 60 23.5 2.151e+01 1.985160 1.8827 1.054433 +#> ds 4 parent 90 11.8 1.190e+01 -0.098528 1.2633 -0.077990 +#> ds 4 parent 90 12.1 1.190e+01 0.201472 1.2633 0.159475 +#> ds 4 parent 120 7.0 6.886e+00 0.113832 1.0125 0.112431 +#> ds 4 parent 120 6.2 6.886e+00 -0.686168 1.0125 -0.677724 +#> ds 4 m1 0 1.6 4.263e-14 1.600000 0.8588 1.863085 +#> ds 4 m1 1 0.9 7.140e-01 0.185984 0.8606 0.216112 +#> ds 4 m1 3 3.7 2.022e+00 1.678243 0.8731 1.922160 +#> ds 4 m1 3 2.0 2.022e+00 -0.021757 0.8731 -0.024919 +#> ds 4 m1 7 3.6 4.207e+00 -0.607229 0.9192 -0.660633 +#> ds 4 m1 7 3.8 4.207e+00 -0.407229 0.9192 -0.443044 +#> ds 4 m1 14 7.1 6.912e+00 0.188339 1.0135 0.185828 +#> ds 4 m1 14 6.6 6.912e+00 -0.311661 1.0135 -0.307506 +#> ds 4 m1 28 9.5 9.449e+00 0.050714 1.1309 0.044843 +#> ds 4 m1 28 9.3 9.449e+00 -0.149286 1.1309 -0.132004 +#> ds 4 m1 60 8.3 8.997e+00 -0.697403 1.1083 -0.629230 +#> ds 4 m1 60 9.0 8.997e+00 0.002597 1.1083 0.002343 +#> ds 4 m1 90 6.6 6.697e+00 -0.096928 1.0047 -0.096472 +#> ds 4 m1 90 7.7 6.697e+00 1.003072 1.0047 0.998348 +#> ds 4 m1 120 3.7 4.622e+00 -0.921607 0.9312 -0.989749 +#> ds 4 m1 120 3.5 4.622e+00 -1.121607 0.9312 -1.204537 +#> ds 5 parent 0 110.4 1.045e+02 5.942426 8.1795 0.726502 +#> ds 5 parent 0 112.1 1.045e+02 7.642426 8.1795 0.934338 +#> ds 5 parent 1 93.5 9.739e+01 -3.893915 7.6327 -0.510162 +#> ds 5 parent 1 91.0 9.739e+01 -6.393915 7.6327 -0.837700 +#> ds 5 parent 3 71.0 8.519e+01 -14.188275 6.6891 -2.121098 +#> ds 5 parent 3 89.7 8.519e+01 4.511725 6.6891 0.674487 +#> ds 5 parent 7 60.4 6.684e+01 -6.439546 5.2753 -1.220701 +#> ds 5 parent 7 59.1 6.684e+01 -7.739546 5.2753 -1.467133 +#> ds 5 parent 14 56.5 4.736e+01 9.138979 3.7868 2.413407 +#> ds 5 parent 14 47.0 4.736e+01 -0.361021 3.7868 -0.095338 +#> ds 5 parent 28 30.2 3.033e+01 -0.131178 2.5132 -0.052195 +#> ds 5 parent 28 23.9 3.033e+01 -6.431178 2.5132 -2.558936 +#> ds 5 parent 60 17.0 1.771e+01 -0.705246 1.6243 -0.434177 +#> ds 5 parent 60 18.7 1.771e+01 0.994754 1.6243 0.612409 +#> ds 5 parent 90 11.3 1.180e+01 -0.504856 1.2580 -0.401315 +#> ds 5 parent 90 11.9 1.180e+01 0.095144 1.2580 0.075631 +#> ds 5 parent 120 9.0 7.917e+00 1.083499 1.0571 1.024928 +#> ds 5 parent 120 8.1 7.917e+00 0.183499 1.0571 0.173579 +#> ds 5 m1 0 0.7 3.553e-15 0.700000 0.8588 0.815100 +#> ds 5 m1 1 3.0 3.204e+00 -0.204414 0.8943 -0.228572 +#> ds 5 m1 1 2.6 3.204e+00 -0.604414 0.8943 -0.675845 +#> ds 5 m1 3 5.1 8.586e+00 -3.485889 1.0884 -3.202858 +#> ds 5 m1 3 7.5 8.586e+00 -1.085889 1.0884 -0.997722 +#> ds 5 m1 7 16.5 1.612e+01 0.376855 1.5211 0.247743 +#> ds 5 m1 7 19.0 1.612e+01 2.876855 1.5211 1.891237 +#> ds 5 m1 14 22.9 2.267e+01 0.228264 1.9633 0.116267 +#> ds 5 m1 14 23.2 2.267e+01 0.528264 1.9633 0.269072 +#> ds 5 m1 28 22.2 2.468e+01 -2.480178 2.1050 -1.178211 +#> ds 5 m1 28 24.4 2.468e+01 -0.280178 2.1050 -0.133099 +#> ds 5 m1 60 15.5 1.860e+01 -3.099615 1.6838 -1.840794 +#> ds 5 m1 60 19.8 1.860e+01 1.200385 1.6838 0.712883 +#> ds 5 m1 90 14.9 1.326e+01 1.636345 1.3433 1.218195 +#> ds 5 m1 90 14.2 1.326e+01 0.936345 1.3433 0.697072 +#> ds 5 m1 120 10.9 9.348e+00 1.551535 1.1258 1.378133 +#> ds 5 m1 120 10.4 9.348e+00 1.051535 1.1258 0.934014</div><div class='input'><span class='co'># }</span> </div></pre> </div> 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 351b21aa..89975db5 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 1edc7c1e..33a0ace2 100644 --- a/docs/dev/reference/synthetic_data_for_UBA_2014.html +++ b/docs/dev/reference/synthetic_data_for_UBA_2014.html @@ -87,7 +87,7 @@ 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-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -136,7 +136,7 @@ Compare also the code in the example section to see the degradation models." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -297,10 +297,10 @@ Compare also the code in the example section to see the degradation models." /> 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: 0.9.50.4 +</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 Nov 30 16:01:42 2020 -#> Date of summary: Mon Nov 30 16:01:42 2020 +#> 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 @@ -309,7 +309,7 @@ Compare also the code in the example section to see the degradation models." /> #> #> Model predictions using solution type deSolve #> -#> Fitted using 822 model solutions performed in 0.652 s +#> Fitted using 833 model solutions performed in 0.649 s #> #> Error model: Constant variance #> @@ -361,15 +361,15 @@ Compare also the code in the example section to see the degradation models." /> #> 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 -7.941e-08 -9.143e-09 -1.268e-08 5.947e-08 5.657e-08 +#> 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 -7.941e-08 -#> log_k_parent 4.102e-01 -9.143e-09 -#> log_k_M1 -8.109e-01 -1.268e-08 -#> log_k_M2 5.419e-01 5.947e-08 -#> f_parent_qlogis -8.605e-01 5.657e-08 -#> f_M1_qlogis 1.000e+00 -2.382e-10 -#> sigma -2.382e-10 1.000e+00 +#> 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. @@ -416,7 +416,7 @@ Compare also the code in the example section to see the degradation models." /> #> 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 9.532e-10 0.60000 +#> 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 @@ -427,7 +427,7 @@ Compare also the code in the example section to see the degradation models." /> #> 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 -9.671e-10 3.20000 +#> 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 diff --git a/docs/dev/reference/test_data_from_UBA_2014-1.png b/docs/dev/reference/test_data_from_UBA_2014-1.png Binary files differindex 9e0afad2..7bf0bd0f 100644 --- a/docs/dev/reference/test_data_from_UBA_2014-1.png +++ b/docs/dev/reference/test_data_from_UBA_2014-1.png diff --git a/docs/dev/reference/test_data_from_UBA_2014-2.png b/docs/dev/reference/test_data_from_UBA_2014-2.png Binary files differindex e889efde..fc1f77e0 100644 --- a/docs/dev/reference/test_data_from_UBA_2014-2.png +++ b/docs/dev/reference/test_data_from_UBA_2014-2.png diff --git a/docs/dev/reference/test_data_from_UBA_2014.html b/docs/dev/reference/test_data_from_UBA_2014.html index 89ec3480..539b8287 100644 --- a/docs/dev/reference/test_data_from_UBA_2014.html +++ b/docs/dev/reference/test_data_from_UBA_2014.html @@ -73,7 +73,7 @@ </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -122,7 +122,7 @@ <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -203,25 +203,25 @@ </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 76.55425649 0.859186399 89.1008710 1.113861e-26 74.755959406 +#> 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 0.84258614 0.806159820 1.0451850 1.545267e-01 0.113779670 -#> k_M2 0.04210880 0.017083035 2.4649483 1.170188e-02 0.018013857 -#> k_M3 0.01122918 0.007245855 1.5497385 6.885052e-02 0.002909431 -#> f_parent_to_M1 0.32240200 0.240783909 1.3389682 9.819073e-02 NA -#> f_parent_to_M2 0.16099855 0.033691953 4.7785463 6.531137e-05 NA -#> f_M1_to_M3 0.27921507 0.269423745 1.0363417 1.565266e-01 0.022978220 -#> f_M2_to_M3 0.55641253 0.595119954 0.9349586 1.807707e-01 0.008002509 +#> 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 78.35255357 +#> parent_0 78.35255358 #> k_parent 0.13084582 -#> k_M1 6.23970352 +#> 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 0.86450768 +#> 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 diff --git a/docs/dev/reference/tffm0.html b/docs/dev/reference/tffm0.html new file mode 100644 index 00000000..d993e8ff --- /dev/null +++ b/docs/dev/reference/tffm0.html @@ -0,0 +1,226 @@ +<!-- 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>Transform formation fractions as in the first published mkin version — tffm0 • 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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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="Transform formation fractions as in the first published mkin version — tffm0" /> +<meta property="og:description" content="The transformed fractions can be restricted between 0 and 1 in model +optimisations. Therefore this transformation was used originally in mkin. It +was later replaced by the ilr transformation because the ilr transformed +fractions can assumed to follow normal distribution. As the ilr +transformation is not available in RxODE and can therefore not be used in +the nlmixr modelling language, this transformation is currently used for +translating mkin models with formation fractions to more than one target +compartment for fitting with nlmixr in nlmixr_model. However, +this implementation cannot be used there, as it is not accessible +from RxODE." /> + + +<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">1.0.5</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" 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/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</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/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> + </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/"> + <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>Transform formation fractions as in the first published mkin version</h1> + <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/tffm0.R'><code>R/tffm0.R</code></a></small> + <div class="hidden name"><code>tffm0.Rd</code></div> + </div> + + <div class="ref-description"> + <p>The transformed fractions can be restricted between 0 and 1 in model +optimisations. Therefore this transformation was used originally in mkin. It +was later replaced by the <a href='ilr.html'>ilr</a> transformation because the ilr transformed +fractions can assumed to follow normal distribution. As the ilr +transformation is not available in RxODE and can therefore not be used in +the nlmixr modelling language, this transformation is currently used for +translating mkin models with formation fractions to more than one target +compartment for fitting with nlmixr in <a href='nlmixr.mmkin.html'>nlmixr_model</a>. However, +this implementation cannot be used there, as it is not accessible +from RxODE.</p> + </div> + + <pre class="usage"><span class='fu'>tffm0</span><span class='op'>(</span><span class='va'>ff</span><span class='op'>)</span> + +<span class='fu'>invtffm0</span><span class='op'>(</span><span class='va'>ff_trans</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>ff</th> + <td><p>Vector of untransformed formation fractions. The sum +must be smaller or equal to one</p></td> + </tr> + <tr> + <th>ff_trans</th> + <td><p>Vector of transformed formation fractions that can be +restricted to the interval from 0 to 1</p></td> + </tr> + </table> + + <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> + + <p>A vector of the transformed formation fractions</p> +<p>A vector of backtransformed formation fractions for natural use in degradation models</p> + + <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> + <pre class="examples"><div class='input'><span class='va'>ff_example</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'>0.10983681</span>, <span class='fl'>0.09035905</span>, <span class='fl'>0.08399383</span> +<span class='op'>)</span> +<span class='va'>ff_example_trans</span> <span class='op'><-</span> <span class='fu'>tffm0</span><span class='op'>(</span><span class='va'>ff_example</span><span class='op'>)</span> +<span class='fu'>invtffm0</span><span class='op'>(</span><span class='va'>ff_example_trans</span><span class='op'>)</span> +</div><div class='output co'>#> [1] 0.10983681 0.09035905 0.08399383</div></pre> + </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>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> +</div> + + </footer> + </div> + + + + + </body> +</html> + + diff --git a/docs/dev/reference/transform_odeparms.html b/docs/dev/reference/transform_odeparms.html index 46b66073..75d6a1f9 100644 --- a/docs/dev/reference/transform_odeparms.html +++ b/docs/dev/reference/transform_odeparms.html @@ -77,7 +77,7 @@ 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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -126,7 +126,7 @@ the ilr transformation is used." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> @@ -231,50 +231,64 @@ This is no problem for the internal use in <a href='mkinfit.html'>mkinfit</a>.</ <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><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'>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> -</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='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> + 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'>$</span><span class='va'>par</span>, <span class='fl'>3</span><span class='op'>)</span> -</div><div class='output co'>#> Estimate Std. Error Lower Upper -#> parent_0 99.5985 1.5702 96.404 102.79 -#> log_k_parent -2.3157 0.0409 -2.399 -2.23 -#> log_k_m1 -5.2475 0.1332 -5.518 -4.98 -#> f_parent_qlogis 0.0579 0.0893 -0.124 0.24 -#> sigma 3.1255 0.3585 2.396 3.85</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>fit.s</span><span class='op'>$</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'> +</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</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_2006_D</span>, transform_rates <span class='op'>=</span> <span class='cn'>FALSE</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='error'>Error in if (cost < cost.current) { assign("cost.current", cost, inherits = TRUE) if (!quiet) cat(ifelse(OLS, "Sum of squared residuals", "Negative log-likelihood"), " at call ", calls, ": ", signif(cost.current, 6), "\n", sep = "")}: missing value where TRUE/FALSE needed</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 0.006 0 0.005</span></div><div class='input'><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> -</div><div class='output co'>#> <span class='error'>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'fit.2' not found</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'>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'>#> <span class='error'>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'print': object 'fit.2.s' not found</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'>fit.2.s</span><span class='op'>$</span><span class='va'>bpar</span>, <span class='fl'>3</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='error'>Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'print': object 'fit.2.s' not found</span></div><div class='input'><span class='co'># }</span> +<span class='co'># Compare to the version without transforming rate parameters (does not work</span> +<span class='co'># with analytical solution, we get NA values for m1 in predictions)</span> +<span class='va'>fit.2</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>SFO_SFO</span>, <span class='va'>FOCUS_D</span>, transform_rates <span class='op'>=</span> <span class='cn'>FALSE</span>, + solution_type <span class='op'>=</span> <span class='st'>"deSolve"</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> +<span class='va'>fit.2.s</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/saemix/man/summary-methods.html'>summary</a></span><span class='op'>(</span><span class='va'>fit.2</span><span class='op'>)</span> +<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>fit.2.s</span><span class='op'>$</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 log_k_m1 f_parent_qlogis -#> 100.750000 -2.302585 -2.301586 0.000000 </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 k_m1 f_parent_to_m1 -#> 100.7500 0.1000 0.1001 0.5000 </div><div class='input'> +</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</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_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='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='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 @@ -299,8 +313,8 @@ This is no problem for the internal use in <a href='mkinfit.html'>mkinfit</a>.</ 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_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='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='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 diff --git a/docs/dev/reference/update.mkinfit-1.png b/docs/dev/reference/update.mkinfit-1.png Binary files differindex 7d2f1bdb..df8473c1 100644 --- a/docs/dev/reference/update.mkinfit-1.png +++ b/docs/dev/reference/update.mkinfit-1.png diff --git a/docs/dev/reference/update.mkinfit-2.png b/docs/dev/reference/update.mkinfit-2.png Binary files differindex 8dcabcdc..13c99b44 100644 --- a/docs/dev/reference/update.mkinfit-2.png +++ b/docs/dev/reference/update.mkinfit-2.png diff --git a/docs/dev/reference/update.mkinfit.html b/docs/dev/reference/update.mkinfit.html index 10a93373..83f45028 100644 --- a/docs/dev/reference/update.mkinfit.html +++ b/docs/dev/reference/update.mkinfit.html @@ -75,7 +75,7 @@ override these starting values." /> </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">0.9.50.4</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.3.9000</span> </span> </div> @@ -124,7 +124,7 @@ override these starting values." /> <ul class="nav navbar-nav navbar-right"> <li> <a href="https://github.com/jranke/mkin/"> - <span class="fab fa fab fa-github fa-lg"></span> + <span class="fab fa-github fa-lg"></span> </a> </li> diff --git a/docs/dev/sitemap.xml b/docs/dev/sitemap.xml index ecf68e50..150840e1 100644 --- a/docs/dev/sitemap.xml +++ b/docs/dev/sitemap.xml @@ -106,6 +106,9 @@ <loc>https://pkgdown.jrwb.de/mkin/reference/mccall81_245T.html</loc> </url> <url> + <loc>https://pkgdown.jrwb.de/mkin/reference/mean_degparms.html</loc> + </url> + <url> <loc>https://pkgdown.jrwb.de/mkin/reference/mixed.html</loc> </url> <url> @@ -157,6 +160,9 @@ <loc>https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html</loc> </url> <url> + <loc>https://pkgdown.jrwb.de/mkin/reference/nlmixr.mmkin.html</loc> + </url> + <url> <loc>https://pkgdown.jrwb.de/mkin/reference/nobs.mkinfit.html</loc> </url> <url> @@ -175,9 +181,6 @@ <loc>https://pkgdown.jrwb.de/mkin/reference/plot.nafta.html</loc> </url> <url> - <loc>https://pkgdown.jrwb.de/mkin/reference/print.mmkin.html</loc> - </url> - <url> <loc>https://pkgdown.jrwb.de/mkin/reference/reexports.html</loc> </url> <url> @@ -199,6 +202,9 @@ <loc>https://pkgdown.jrwb.de/mkin/reference/summary.nlme.mmkin.html</loc> </url> <url> + <loc>https://pkgdown.jrwb.de/mkin/reference/summary.nlmixr.mmkin.html</loc> + </url> + <url> <loc>https://pkgdown.jrwb.de/mkin/reference/summary.saem.mmkin.html</loc> </url> <url> @@ -208,6 +214,9 @@ <loc>https://pkgdown.jrwb.de/mkin/reference/test_data_from_UBA_2014.html</loc> </url> <url> + <loc>https://pkgdown.jrwb.de/mkin/reference/tffm0.html</loc> + </url> + <url> <loc>https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html</loc> </url> <url> diff --git a/man/dimethenamid_2018.Rd b/man/dimethenamid_2018.Rd index b6f761e8..a0e5bbea 100644 --- a/man/dimethenamid_2018.Rd +++ b/man/dimethenamid_2018.Rd @@ -11,7 +11,7 @@ An \link{mkindsg} object grouping eight datasets with some meta information 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 -\url{http://registerofquestions.efsa.europa.eu/roqFrontend/outputLoader?output=ON-5211} +\url{https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716} } \usage{ dimethenamid_2018 @@ -31,5 +31,56 @@ specific pieces of information in the comments. } \examples{ print(dimethenamid_2018) +dmta_ds <- lapply(1:8, 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$title) +dmta_ds[["Borstel"]] <- rbind(dmta_ds[["Borstel 1"]], dmta_ds[["Borstel 2"]]) +dmta_ds[["Borstel 1"]] <- NULL +dmta_ds[["Borstel 2"]] <- NULL +dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]]) +dmta_ds[["Elliot 1"]] <- NULL +dmta_ds[["Elliot 2"]] <- NULL +\dontrun{ +dfop_sfo3_plus <- mkinmod( + DMTA = mkinsub("DFOP", c("M23", "M27", "M31")), + M23 = mkinsub("SFO"), + M27 = mkinsub("SFO"), + M31 = mkinsub("SFO", "M27", sink = FALSE), + quiet = TRUE +) +f_dmta_mkin_tc <- mmkin( + list("DFOP-SFO3+" = dfop_sfo3_plus), + dmta_ds, quiet = TRUE, error_model = "tc") +nlmixr_model(f_dmta_mkin_tc) +# The focei fit takes about four minutes on my system +system.time( + f_dmta_nlmixr_focei <- nlmixr(f_dmta_mkin_tc, est = "focei", + control = nlmixr::foceiControl(print = 500)) +) +summary(f_dmta_nlmixr_focei) +plot(f_dmta_nlmixr_focei) +# Using saemix takes about 18 minutes +system.time( + f_dmta_saemix <- saem(f_dmta_mkin_tc, test_log_parms = TRUE) +) + +# nlmixr with est = "saem" is pretty fast with default iteration numbers, most +# of the time (about 2.5 minutes) is spent for calculating the log likelihood at the end +# The likelihood calculated for the nlmixr fit is much lower than that found by saemix +# Also, the trace plot and the plot of the individual predictions is not +# convincing for the parent. It seems we are fitting an overparameterised +# model, so the result we get strongly depends on starting parameters and control settings. +system.time( + f_dmta_nlmixr_saem <- nlmixr(f_dmta_mkin_tc, est = "saem", + control = nlmixr::saemControl(print = 500, logLik = TRUE, nmc = 9)) +) +traceplot(f_dmta_nlmixr_saem$nm) +summary(f_dmta_nlmixr_saem) +plot(f_dmta_nlmixr_saem) +} } \keyword{datasets} diff --git a/man/endpoints.Rd b/man/endpoints.Rd index 0b225e62..a37ff98d 100644 --- a/man/endpoints.Rd +++ b/man/endpoints.Rd @@ -8,8 +8,8 @@ with mkinfit} endpoints(fit) } \arguments{ -\item{fit}{An object of class \link{mkinfit} or \link{nlme.mmkin} -or another object that has list components +\item{fit}{An object of class \link{mkinfit}, \link{nlme.mmkin}, \link{saem.mmkin} or +\link{nlmixr.mmkin}. Or another object that has list components mkinmod containing an \link{mkinmod} degradation model, and two numeric vectors, bparms.optim and bparms.fixed, that contain parameter values for that model.} @@ -32,8 +32,8 @@ Additional DT50 values are calculated from the FOMC DT90 and k1 and k2 from HS and DFOP, as well as from Eigenvalues b1 and b2 of any SFORB models } \note{ -The function is used internally by \link{summary.mkinfit} -and \link{summary.nlme.mmkin} +The function is used internally by \link{summary.mkinfit}, +\link{summary.nlme.mmkin} and \link{summary.saem.mmkin}. } \examples{ diff --git a/man/mean_degparms.Rd b/man/mean_degparms.Rd new file mode 100644 index 00000000..5e2b4b0f --- /dev/null +++ b/man/mean_degparms.Rd @@ -0,0 +1,29 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/mean_degparms.R +\name{mean_degparms} +\alias{mean_degparms} +\title{Calculate mean degradation parameters for an mmkin row object} +\usage{ +mean_degparms(object, random = FALSE, test_log_parms = FALSE, conf.level = 0.6) +} +\arguments{ +\item{object}{An mmkin row object containing several fits of the same model to different datasets} + +\item{random}{Should a list with fixed and random effects be returned?} + +\item{test_log_parms}{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.} + +\item{conf.level}{Possibility to adjust the required confidence level +for parameter that are tested if requested by 'test_log_parms'.} +} +\value{ +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. +} +\description{ +Calculate mean degradation parameters for an mmkin row object +} diff --git a/man/mixed.Rd b/man/mixed.Rd index 8b00382d..95cae364 100644 --- a/man/mixed.Rd +++ b/man/mixed.Rd @@ -23,6 +23,10 @@ mixed(object, ...) \item{digits}{Number of digits to use for printing.} } +\value{ +An object of class 'mixed.mmkin' which has the observed data in a +single dataframe which is convenient for plotting +} \description{ Create a mixed effects model from an mmkin row object } diff --git a/man/mkinmod.Rd b/man/mkinmod.Rd index bf073634..87ce9016 100644 --- a/man/mkinmod.Rd +++ b/man/mkinmod.Rd @@ -118,9 +118,6 @@ variable, specifying the corresponding submodel as well as outgoing pathways Print mkinmod objects in a way that the user finds his way to get to its components. - -This is a convenience function to set up the lists used as arguments for -\code{\link{mkinmod}}. } \details{ For the definition of model types and their parameters, the equations given diff --git a/man/nlme.Rd b/man/nlme.Rd index 307cca82..e87b7a00 100644 --- a/man/nlme.Rd +++ b/man/nlme.Rd @@ -2,29 +2,19 @@ % Please edit documentation in R/nlme.R \name{nlme_function} \alias{nlme_function} -\alias{mean_degparms} \alias{nlme_data} \title{Helper functions to create nlme models from mmkin row objects} \usage{ nlme_function(object) -mean_degparms(object, random = FALSE) - nlme_data(object) } \arguments{ \item{object}{An mmkin row object containing several fits of the same model to different datasets} - -\item{random}{Should a list with fixed and random effects be returned?} } \value{ A function that can be used with nlme -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. - A \code{\link{groupedData}} object } \description{ @@ -60,7 +50,7 @@ grouped_data <- nlme_data(f) nlme_f <- nlme_function(f) # These assignments are necessary for these objects to be # visible to nlme and augPred when evaluation is done by -# pkgdown to generated the html docs. +# pkgdown to generate the html docs. assign("nlme_f", nlme_f, globalenv()) assign("grouped_data", grouped_data, globalenv()) diff --git a/man/nlme.mmkin.Rd b/man/nlme.mmkin.Rd index 2fb0488a..ed58d603 100644 --- a/man/nlme.mmkin.Rd +++ b/man/nlme.mmkin.Rd @@ -13,7 +13,7 @@ paste(el, 1, sep = "~")))), random = pdDiag(fixed), groups, - start = mean_degparms(model, random = TRUE), + start = mean_degparms(model, random = TRUE, test_log_parms = TRUE), correlation = NULL, weights = NULL, subset, @@ -36,10 +36,9 @@ \item{fixed}{Ignored, all degradation parameters fitted in the mmkin model are used as fixed parameters} -\item{random}{If not specified, correlated random effects are set up -for all optimised degradation model parameters using the log-Cholesky -parameterization \link[nlme:pdLogChol]{nlme::pdLogChol} that is also the default of -the generic \link{nlme} method.} +\item{random}{If not specified, no correlations between random effects are +set up for the optimised degradation model parameters. This is +achieved by using the \link[nlme:pdDiag]{nlme::pdDiag} method.} \item{groups}{See the documentation of nlme} diff --git a/man/nlmixr.mmkin.Rd b/man/nlmixr.mmkin.Rd new file mode 100644 index 00000000..0f4f41a2 --- /dev/null +++ b/man/nlmixr.mmkin.Rd @@ -0,0 +1,209 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/nlmixr.R +\name{nlmixr.mmkin} +\alias{nlmixr.mmkin} +\alias{print.nlmixr.mmkin} +\alias{nlmixr_model} +\alias{nlmixr_data} +\title{Fit nonlinear mixed models using nlmixr} +\usage{ +\method{nlmixr}{mmkin}( + object, + data = NULL, + est = NULL, + control = list(), + table = tableControl(), + error_model = object[[1]]$err_mod, + test_log_parms = TRUE, + conf.level = 0.6, + degparms_start = "auto", + eta_start = "auto", + ..., + save = NULL, + envir = parent.frame() +) + +\method{print}{nlmixr.mmkin}(x, digits = max(3, getOption("digits") - 3), ...) + +nlmixr_model( + object, + est = c("saem", "focei"), + degparms_start = "auto", + eta_start = "auto", + test_log_parms = TRUE, + conf.level = 0.6, + error_model = object[[1]]$err_mod, + add_attributes = FALSE +) + +nlmixr_data(object, ...) +} +\arguments{ +\item{object}{An \link{mmkin} row object containing several fits of the same +\link{mkinmod} model to different datasets} + +\item{data}{Not used, as the data are extracted from the mmkin row object} + +\item{est}{Estimation method passed to \link[nlmixr:nlmixr]{nlmixr::nlmixr}} + +\item{control}{Passed to \link[nlmixr:nlmixr]{nlmixr::nlmixr}} + +\item{table}{Passed to \link[nlmixr:nlmixr]{nlmixr::nlmixr}} + +\item{error_model}{Possibility to override the error model which is being +set based on the error model used in the mmkin row object.} + +\item{test_log_parms}{If TRUE, an attempt is made to use more robust starting +values for population parameters fitted as log parameters in mkin (like +rate constants) by only considering rate constants that pass the t-test +when calculating mean degradation parameters using \link{mean_degparms}.} + +\item{conf.level}{Possibility to adjust the required confidence level +for parameter that are tested if requested by 'test_log_parms'.} + +\item{degparms_start}{Parameter values given as a named numeric vector will +be used to override the starting values obtained from the 'mmkin' object.} + +\item{eta_start}{Standard deviations on the transformed scale given as a +named numeric vector will be used to override the starting values obtained +from the 'mmkin' object.} + +\item{\dots}{Passed to \link{nlmixr_model}} + +\item{save}{Passed to \link[nlmixr:nlmixr]{nlmixr::nlmixr}} + +\item{envir}{Passed to \link[nlmixr:nlmixr]{nlmixr::nlmixr}} + +\item{x}{An nlmixr.mmkin object to print} + +\item{digits}{Number of digits to use for printing} + +\item{add_attributes}{Should the starting values used for degradation model +parameters and their distribution and for the error model parameters +be returned as attributes?} +} +\value{ +An S3 object of class 'nlmixr.mmkin', containing the fitted +\link[nlmixr:nlmixr]{nlmixr::nlmixr} object as a list component named 'nm'. The +object also inherits from 'mixed.mmkin'. + +An function defining a model suitable for fitting with \link[nlmixr:nlmixr]{nlmixr::nlmixr}. + +An dataframe suitable for use with \link[nlmixr:nlmixr]{nlmixr::nlmixr} +} +\description{ +This function uses \code{\link[nlmixr:nlmixr]{nlmixr::nlmixr()}} as a backend for fitting nonlinear mixed +effects models created from \link{mmkin} row objects using the Stochastic Approximation +Expectation Maximisation algorithm (SAEM). +} +\details{ +An mmkin row object is essentially a list of mkinfit objects that have been +obtained by fitting the same model to a list of datasets using \link{mkinfit}. +} +\examples{ +\dontrun{ +ds <- lapply(experimental_data_for_UBA_2019[6:10], + function(x) subset(x$data[c("name", "time", "value")])) +names(ds) <- paste("Dataset", 6:10) + +f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP", "HS"), ds, quiet = TRUE, cores = 1) +f_mmkin_parent_tc <- mmkin(c("SFO", "FOMC", "DFOP"), ds, error_model = "tc", + cores = 1, quiet = TRUE) + +f_nlmixr_sfo_saem <- nlmixr(f_mmkin_parent["SFO", ], est = "saem") +f_nlmixr_sfo_focei <- nlmixr(f_mmkin_parent["SFO", ], est = "focei") + +f_nlmixr_fomc_saem <- nlmixr(f_mmkin_parent["FOMC", ], est = "saem") +f_nlmixr_fomc_focei <- nlmixr(f_mmkin_parent["FOMC", ], est = "focei") + +f_nlmixr_dfop_saem <- nlmixr(f_mmkin_parent["DFOP", ], est = "saem") +f_nlmixr_dfop_focei <- nlmixr(f_mmkin_parent["DFOP", ], est = "focei") + +f_nlmixr_hs_saem <- nlmixr(f_mmkin_parent["HS", ], est = "saem") +f_nlmixr_hs_focei <- nlmixr(f_mmkin_parent["HS", ], est = "focei") + +f_nlmixr_fomc_saem_tc <- nlmixr(f_mmkin_parent_tc["FOMC", ], est = "saem") +f_nlmixr_fomc_focei_tc <- nlmixr(f_mmkin_parent_tc["FOMC", ], est = "focei") + +AIC( + f_nlmixr_sfo_saem$nm, f_nlmixr_sfo_focei$nm, + f_nlmixr_fomc_saem$nm, f_nlmixr_fomc_focei$nm, + f_nlmixr_dfop_saem$nm, f_nlmixr_dfop_focei$nm, + f_nlmixr_hs_saem$nm, f_nlmixr_hs_focei$nm, + f_nlmixr_fomc_saem_tc$nm, f_nlmixr_fomc_focei_tc$nm) + +AIC(nlme(f_mmkin_parent["FOMC", ])) +AIC(nlme(f_mmkin_parent["HS", ])) + +# nlme is comparable to nlmixr with focei, saem finds a better +# solution, the two-component error model does not improve it +plot(f_nlmixr_fomc_saem) + +sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), + A1 = mkinsub("SFO")) +fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"), + A1 = mkinsub("SFO")) +dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"), + A1 = mkinsub("SFO")) + +f_mmkin_const <- mmkin(list( + "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo), + ds, quiet = TRUE, error_model = "const") +f_mmkin_obs <- mmkin(list( + "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo), + ds, quiet = TRUE, error_model = "obs") +f_mmkin_tc <- mmkin(list( + "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo), + ds, quiet = TRUE, error_model = "tc") + +# A single constant variance is currently only possible with est = 'focei' in nlmixr +f_nlmixr_sfo_sfo_focei_const <- nlmixr(f_mmkin_const["SFO-SFO", ], est = "focei") +f_nlmixr_fomc_sfo_focei_const <- nlmixr(f_mmkin_const["FOMC-SFO", ], est = "focei") +f_nlmixr_dfop_sfo_focei_const <- nlmixr(f_mmkin_const["DFOP-SFO", ], est = "focei") + +# Variance by variable is supported by 'saem' and 'focei' +f_nlmixr_fomc_sfo_saem_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "saem") +f_nlmixr_fomc_sfo_focei_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "focei") +f_nlmixr_dfop_sfo_saem_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "saem") +f_nlmixr_dfop_sfo_focei_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "focei") + +# Identical two-component error for all variables is only possible with +# est = 'focei' in nlmixr +f_nlmixr_fomc_sfo_focei_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei") +f_nlmixr_dfop_sfo_focei_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei") + +# Two-component error by variable is possible with both estimation methods +# Variance by variable is supported by 'saem' and 'focei' +f_nlmixr_fomc_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "saem", + error_model = "obs_tc") +f_nlmixr_fomc_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei", + error_model = "obs_tc") +f_nlmixr_dfop_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "saem", + error_model = "obs_tc") +f_nlmixr_dfop_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei", + error_model = "obs_tc") + +AIC( + f_nlmixr_sfo_sfo_focei_const$nm, + f_nlmixr_fomc_sfo_focei_const$nm, + f_nlmixr_dfop_sfo_focei_const$nm, + f_nlmixr_fomc_sfo_saem_obs$nm, + f_nlmixr_fomc_sfo_focei_obs$nm, + f_nlmixr_dfop_sfo_saem_obs$nm, + f_nlmixr_dfop_sfo_focei_obs$nm, + f_nlmixr_fomc_sfo_focei_tc$nm, + f_nlmixr_dfop_sfo_focei_tc$nm, + f_nlmixr_fomc_sfo_saem_obs_tc$nm, + f_nlmixr_fomc_sfo_focei_obs_tc$nm, + f_nlmixr_dfop_sfo_saem_obs_tc$nm, + f_nlmixr_dfop_sfo_focei_obs_tc$nm +) +# Currently, FOMC-SFO with two-component error by variable fitted by focei gives the +# lowest AIC +plot(f_nlmixr_fomc_sfo_focei_obs_tc) +summary(f_nlmixr_fomc_sfo_focei_obs_tc) +} +} +\seealso{ +\link{summary.nlmixr.mmkin} \link{plot.mixed.mmkin} +} diff --git a/man/plot.mixed.mmkin.Rd b/man/plot.mixed.mmkin.Rd index 87a82286..d87ca22c 100644 --- a/man/plot.mixed.mmkin.Rd +++ b/man/plot.mixed.mmkin.Rd @@ -13,6 +13,8 @@ xlim = range(x$data$time), resplot = c("predicted", "time"), pred_over = NULL, + test_log_parms = FALSE, + conf.level = 0.6, ymax = "auto", maxabs = "auto", ncol.legend = ifelse(length(i) <= 3, length(i) + 1, ifelse(length(i) <= 8, 3, 4)), @@ -27,7 +29,7 @@ ) } \arguments{ -\item{x}{An object of class \link{mixed.mmkin}, \link{nlme.mmkin}} +\item{x}{An object of class \link{mixed.mmkin}, \link{saem.mmkin} or \link{nlme.mmkin}} \item{i}{A numeric index to select datasets for which to plot the individual predictions, in case plots get too large} @@ -49,6 +51,12 @@ predicted values?} \item{pred_over}{Named list of alternative predictions as obtained from \link{mkinpredict} with a compatible \link{mkinmod}.} +\item{test_log_parms}{Passed to \link{mean_degparms} in the case of an +\link{mixed.mmkin} object} + +\item{conf.level}{Passed to \link{mean_degparms} in the case of an +\link{mixed.mmkin} object} + \item{ymax}{Vector of maximum y axis values} \item{maxabs}{Maximum absolute value of the residuals. This is used for the @@ -91,9 +99,23 @@ plot(f[, 3:4], standardized = TRUE) # For this fit we need to increase pnlsMaxiter, and we increase the # tolerance in order to speed up the fit for this example evaluation +# It still takes 20 seconds to run f_nlme <- nlme(f, control = list(pnlsMaxIter = 120, tolerance = 1e-3)) plot(f_nlme) +f_saem <- saem(f, transformations = "saemix") +plot(f_saem) + +f_obs <- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE, error_model = "obs") +f_nlmix <- nlmix(f_obs) +plot(f_nlmix) + +# We can overlay the two variants if we generate predictions +pred_nlme <- mkinpredict(dfop_sfo, + f_nlme$bparms.optim[-1], + c(parent = f_nlme$bparms.optim[[1]], A1 = 0), + seq(0, 180, by = 0.2)) +plot(f_saem, pred_over = list(nlme = pred_nlme)) } } \author{ diff --git a/man/reexports.Rd b/man/reexports.Rd index ccba7567..d4fc6b96 100644 --- a/man/reexports.Rd +++ b/man/reexports.Rd @@ -1,10 +1,11 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/lrtest.mkinfit.R, R/nlme.mmkin.R +% Please edit documentation in R/lrtest.mkinfit.R, R/nlme.mmkin.R, R/nlmixr.R \docType{import} \name{reexports} \alias{reexports} \alias{lrtest} \alias{nlme} +\alias{nlmixr} \title{Objects exported from other packages} \keyword{internal} \description{ @@ -15,5 +16,7 @@ below to see their documentation. \item{lmtest}{\code{\link[lmtest]{lrtest}}} \item{nlme}{\code{\link[nlme]{nlme}}} + + \item{nlmixr}{\code{\link[nlmixr]{nlmixr}}} }} diff --git a/man/saem.Rd b/man/saem.Rd new file mode 100644 index 00000000..00e9aeda --- /dev/null +++ b/man/saem.Rd @@ -0,0 +1,174 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/saem.R +\name{saem} +\alias{saem} +\alias{saem.mmkin} +\alias{print.saem.mmkin} +\alias{saemix_model} +\alias{saemix_data} +\title{Fit nonlinear mixed models with SAEM} +\usage{ +saem(object, ...) + +\method{saem}{mmkin}( + object, + transformations = c("mkin", "saemix"), + degparms_start = numeric(), + test_log_parms = TRUE, + conf.level = 0.6, + solution_type = "auto", + nbiter.saemix = c(300, 100), + control = list(displayProgress = FALSE, print = FALSE, nbiter.saemix = nbiter.saemix, + save = FALSE, save.graphs = FALSE), + fail_with_errors = TRUE, + verbose = FALSE, + quiet = FALSE, + ... +) + +\method{print}{saem.mmkin}(x, digits = max(3, getOption("digits") - 3), ...) + +saemix_model( + object, + solution_type = "auto", + transformations = c("mkin", "saemix"), + degparms_start = numeric(), + test_log_parms = FALSE, + verbose = FALSE, + ... +) + +saemix_data(object, verbose = FALSE, ...) +} +\arguments{ +\item{object}{An \link{mmkin} row object containing several fits of the same +\link{mkinmod} model to different datasets} + +\item{\dots}{Further parameters passed to \link[saemix:saemixModel]{saemix::saemixModel}.} + +\item{transformations}{Per default, all parameter transformations are done +in mkin. If this argument is set to 'saemix', parameter transformations +are done in 'saemix' for the supported cases. Currently this is only +supported in cases where the initial concentration of the parent is not fixed, +SFO or DFOP is used for the parent and there is either no metabolite or one.} + +\item{degparms_start}{Parameter values given as a named numeric vector will +be used to override the starting values obtained from the 'mmkin' object.} + +\item{test_log_parms}{If TRUE, an attempt is made to use more robust starting +values for population parameters fitted as log parameters in mkin (like +rate constants) by only considering rate constants that pass the t-test +when calculating mean degradation parameters using \link{mean_degparms}.} + +\item{conf.level}{Possibility to adjust the required confidence level +for parameter that are tested if requested by 'test_log_parms'.} + +\item{solution_type}{Possibility to specify the solution type in case the +automatic choice is not desired} + +\item{nbiter.saemix}{Convenience option to increase the number of +iterations} + +\item{control}{Passed to \link[saemix:saemix]{saemix::saemix}.} + +\item{fail_with_errors}{Should a failure to compute standard errors +from the inverse of the Fisher Information Matrix be a failure?} + +\item{verbose}{Should we print information about created objects of +type \link[saemix:SaemixModel-class]{saemix::SaemixModel} and \link[saemix:SaemixData-class]{saemix::SaemixData}?} + +\item{quiet}{Should we suppress the messages saemix prints at the beginning +and the end of the optimisation process?} + +\item{x}{An saem.mmkin object to print} + +\item{digits}{Number of digits to use for printing} +} +\value{ +An S3 object of class 'saem.mmkin', containing the fitted +\link[saemix:SaemixObject-class]{saemix::SaemixObject} as a list component named 'so'. The +object also inherits from 'mixed.mmkin'. + +An \link[saemix:SaemixModel-class]{saemix::SaemixModel} object. + +An \link[saemix:SaemixData-class]{saemix::SaemixData} object. +} +\description{ +This function uses \code{\link[saemix:saemix]{saemix::saemix()}} as a backend for fitting nonlinear mixed +effects models created from \link{mmkin} row objects using the Stochastic Approximation +Expectation Maximisation algorithm (SAEM). +} +\details{ +An mmkin row object is essentially a list of mkinfit objects that have been +obtained by fitting the same model to a list of datasets using \link{mkinfit}. + +Starting values for the fixed effects (population mean parameters, argument +psi0 of \code{\link[saemix:saemixModel]{saemix::saemixModel()}} are the mean values of the parameters found +using \link{mmkin}. +} +\examples{ +\dontrun{ +ds <- lapply(experimental_data_for_UBA_2019[6:10], + function(x) subset(x$data[c("name", "time", "value")])) +names(ds) <- paste("Dataset", 6:10) +f_mmkin_parent_p0_fixed <- mmkin("FOMC", ds, + state.ini = c(parent = 100), fixed_initials = "parent", quiet = TRUE) +f_saem_p0_fixed <- saem(f_mmkin_parent_p0_fixed) + +f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE) +f_saem_sfo <- saem(f_mmkin_parent["SFO", ]) +f_saem_fomc <- saem(f_mmkin_parent["FOMC", ]) +f_saem_dfop <- saem(f_mmkin_parent["DFOP", ]) + +# The returned saem.mmkin object contains an SaemixObject, therefore we can use +# functions from saemix +library(saemix) +compare.saemix(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so) +plot(f_saem_fomc$so, plot.type = "convergence") +plot(f_saem_fomc$so, plot.type = "individual.fit") +plot(f_saem_fomc$so, plot.type = "npde") +plot(f_saem_fomc$so, plot.type = "vpc") + +f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc") +f_saem_fomc_tc <- saem(f_mmkin_parent_tc["FOMC", ]) +compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so) + +sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), + A1 = mkinsub("SFO")) +fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"), + A1 = mkinsub("SFO")) +dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"), + A1 = mkinsub("SFO")) +# The following fit uses analytical solutions for SFO-SFO and DFOP-SFO, +# and compiled ODEs for FOMC that are much slower +f_mmkin <- mmkin(list( + "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo), + ds, quiet = TRUE) +# saem fits of SFO-SFO and DFOP-SFO to these data take about five seconds +# each on this system, as we use analytical solutions written for saemix. +# When using the analytical solutions written for mkin this took around +# four minutes +f_saem_sfo_sfo <- saem(f_mmkin["SFO-SFO", ]) +f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ]) +# We can use print, plot and summary methods to check the results +print(f_saem_dfop_sfo) +plot(f_saem_dfop_sfo) +summary(f_saem_dfop_sfo, data = TRUE) + +# The following takes about 6 minutes +#f_saem_dfop_sfo_deSolve <- saem(f_mmkin["DFOP-SFO", ], solution_type = "deSolve", +# control = list(nbiter.saemix = c(200, 80), nbdisplay = 10)) + +#saemix::compare.saemix(list( +# f_saem_dfop_sfo$so, +# f_saem_dfop_sfo_deSolve$so)) + +# If the model supports it, we can also use eigenvalue based solutions, which +# take a similar amount of time +#f_saem_sfo_sfo_eigen <- saem(f_mmkin["SFO-SFO", ], solution_type = "eigen", +# control = list(nbiter.saemix = c(200, 80), nbdisplay = 10)) +} +} +\seealso{ +\link{summary.saem.mmkin} \link{plot.mixed.mmkin} +} diff --git a/man/summary.nlmixr.mmkin.Rd b/man/summary.nlmixr.mmkin.Rd new file mode 100644 index 00000000..ab8abd5d --- /dev/null +++ b/man/summary.nlmixr.mmkin.Rd @@ -0,0 +1,103 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/summary.nlmixr.mmkin.R +\name{summary.nlmixr.mmkin} +\alias{summary.nlmixr.mmkin} +\alias{print.summary.nlmixr.mmkin} +\title{Summary method for class "nlmixr.mmkin"} +\usage{ +\method{summary}{nlmixr.mmkin}(object, data = FALSE, verbose = FALSE, distimes = TRUE, ...) + +\method{print}{summary.nlmixr.mmkin}(x, digits = max(3, getOption("digits") - 3), verbose = x$verbose, ...) +} +\arguments{ +\item{object}{an object of class \link{nlmixr.mmkin}} + +\item{data}{logical, indicating whether the full data should be included in +the summary.} + +\item{verbose}{Should the summary be verbose?} + +\item{distimes}{logical, indicating whether DT50 and DT90 values should be +included.} + +\item{\dots}{optional arguments passed to methods like \code{print}.} + +\item{x}{an object of class \link{summary.nlmixr.mmkin}} + +\item{digits}{Number of digits to use for printing} +} +\value{ +The summary function returns a list obtained in the fit, with at +least the following additional components +\item{nlmixrversion, mkinversion, Rversion}{The nlmixr, mkin and R versions used} +\item{date.fit, date.summary}{The dates where the fit and the summary were +produced} +\item{diffs}{The differential equations used in the degradation model} +\item{use_of_ff}{Was maximum or minimum use made of formation fractions} +\item{data}{The data} +\item{confint_back}{Backtransformed parameters, with confidence intervals if available} +\item{ff}{The estimated formation fractions derived from the fitted +model.} +\item{distimes}{The DT50 and DT90 values for each observed variable.} +\item{SFORB}{If applicable, eigenvalues of SFORB components of the model.} +The print method is called for its side effect, i.e. printing the summary. +} +\description{ +Lists model equations, initial parameter values, optimised parameters +for fixed effects (population), random effects (deviations from the +population mean) and residual error model, as well as the resulting +endpoints such as formation fractions and DT50 values. Optionally +(default is FALSE), the data are listed in full. +} +\examples{ +# Generate five datasets following DFOP-SFO kinetics +sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) +dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "m1"), + m1 = mkinsub("SFO"), quiet = TRUE) +set.seed(1234) +k1_in <- rlnorm(5, log(0.1), 0.3) +k2_in <- rlnorm(5, log(0.02), 0.3) +g_in <- plogis(rnorm(5, qlogis(0.5), 0.3)) +f_parent_to_m1_in <- plogis(rnorm(5, qlogis(0.3), 0.3)) +k_m1_in <- rlnorm(5, log(0.02), 0.3) + +pred_dfop_sfo <- function(k1, k2, g, f_parent_to_m1, k_m1) { + mkinpredict(dfop_sfo, + c(k1 = k1, k2 = k2, g = g, f_parent_to_m1 = f_parent_to_m1, k_m1 = k_m1), + c(parent = 100, m1 = 0), + sampling_times) +} + +ds_mean_dfop_sfo <- lapply(1:5, function(i) { + mkinpredict(dfop_sfo, + c(k1 = k1_in[i], k2 = k2_in[i], g = g_in[i], + f_parent_to_m1 = f_parent_to_m1_in[i], k_m1 = k_m1_in[i]), + c(parent = 100, m1 = 0), + sampling_times) +}) +names(ds_mean_dfop_sfo) <- paste("ds", 1:5) + +ds_syn_dfop_sfo <- lapply(ds_mean_dfop_sfo, function(ds) { + add_err(ds, + sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2), + n = 1)[[1]] +}) + +\dontrun{ +# Evaluate using mmkin and nlmixr +f_mmkin_dfop_sfo <- mmkin(list(dfop_sfo), ds_syn_dfop_sfo, + quiet = TRUE, error_model = "tc", cores = 5) +f_saemix_dfop_sfo <- mkin::saem(f_mmkin_dfop_sfo) +f_nlme_dfop_sfo <- mkin::nlme(f_mmkin_dfop_sfo) +f_nlmixr_dfop_sfo_saem <- nlmixr(f_mmkin_dfop_sfo, est = "saem") +# The following takes a very long time but gives +f_nlmixr_dfop_sfo_focei <- nlmixr(f_mmkin_dfop_sfo, est = "focei") +AIC(f_nlmixr_dfop_sfo_saem$nm, f_nlmixr_dfop_sfo_focei$nm) +summary(f_nlmixr_dfop_sfo_sfo, data = TRUE) +} + +} +\author{ +Johannes Ranke for the mkin specific parts +nlmixr authors for the parts inherited from nlmixr. +} diff --git a/man/summary.saem.mmkin.Rd b/man/summary.saem.mmkin.Rd new file mode 100644 index 00000000..67cb3cbb --- /dev/null +++ b/man/summary.saem.mmkin.Rd @@ -0,0 +1,100 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/summary.saem.mmkin.R +\name{summary.saem.mmkin} +\alias{summary.saem.mmkin} +\alias{print.summary.saem.mmkin} +\title{Summary method for class "saem.mmkin"} +\usage{ +\method{summary}{saem.mmkin}(object, data = FALSE, verbose = FALSE, distimes = TRUE, ...) + +\method{print}{summary.saem.mmkin}(x, digits = max(3, getOption("digits") - 3), verbose = x$verbose, ...) +} +\arguments{ +\item{object}{an object of class \link{saem.mmkin}} + +\item{data}{logical, indicating whether the full data should be included in +the summary.} + +\item{verbose}{Should the summary be verbose?} + +\item{distimes}{logical, indicating whether DT50 and DT90 values should be +included.} + +\item{\dots}{optional arguments passed to methods like \code{print}.} + +\item{x}{an object of class \link{summary.saem.mmkin}} + +\item{digits}{Number of digits to use for printing} +} +\value{ +The summary function returns a list based on the \link[saemix:SaemixObject-class]{saemix::SaemixObject} +obtained in the fit, with at least the following additional components +\item{saemixversion, mkinversion, Rversion}{The saemix, mkin and R versions used} +\item{date.fit, date.summary}{The dates where the fit and the summary were +produced} +\item{diffs}{The differential equations used in the degradation model} +\item{use_of_ff}{Was maximum or minimum use made of formation fractions} +\item{data}{The data} +\item{confint_trans}{Transformed parameters as used in the optimisation, with confidence intervals} +\item{confint_back}{Backtransformed parameters, with confidence intervals if available} +\item{confint_errmod}{Error model parameters with confidence intervals} +\item{ff}{The estimated formation fractions derived from the fitted +model.} +\item{distimes}{The DT50 and DT90 values for each observed variable.} +\item{SFORB}{If applicable, eigenvalues of SFORB components of the model.} +The print method is called for its side effect, i.e. printing the summary. +} +\description{ +Lists model equations, initial parameter values, optimised parameters +for fixed effects (population), random effects (deviations from the +population mean) and residual error model, as well as the resulting +endpoints such as formation fractions and DT50 values. Optionally +(default is FALSE), the data are listed in full. +} +\examples{ +# Generate five datasets following DFOP-SFO kinetics +sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) +dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "m1"), + m1 = mkinsub("SFO"), quiet = TRUE) +set.seed(1234) +k1_in <- rlnorm(5, log(0.1), 0.3) +k2_in <- rlnorm(5, log(0.02), 0.3) +g_in <- plogis(rnorm(5, qlogis(0.5), 0.3)) +f_parent_to_m1_in <- plogis(rnorm(5, qlogis(0.3), 0.3)) +k_m1_in <- rlnorm(5, log(0.02), 0.3) + +pred_dfop_sfo <- function(k1, k2, g, f_parent_to_m1, k_m1) { + mkinpredict(dfop_sfo, + c(k1 = k1, k2 = k2, g = g, f_parent_to_m1 = f_parent_to_m1, k_m1 = k_m1), + c(parent = 100, m1 = 0), + sampling_times) +} + +ds_mean_dfop_sfo <- lapply(1:5, function(i) { + mkinpredict(dfop_sfo, + c(k1 = k1_in[i], k2 = k2_in[i], g = g_in[i], + f_parent_to_m1 = f_parent_to_m1_in[i], k_m1 = k_m1_in[i]), + c(parent = 100, m1 = 0), + sampling_times) +}) +names(ds_mean_dfop_sfo) <- paste("ds", 1:5) + +ds_syn_dfop_sfo <- lapply(ds_mean_dfop_sfo, function(ds) { + add_err(ds, + sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2), + n = 1)[[1]] +}) + +\dontrun{ +# Evaluate using mmkin and saem +f_mmkin_dfop_sfo <- mmkin(list(dfop_sfo), ds_syn_dfop_sfo, + quiet = TRUE, error_model = "tc", cores = 5) +f_saem_dfop_sfo <- saem(f_mmkin_dfop_sfo) +summary(f_saem_dfop_sfo, data = TRUE) +} + +} +\author{ +Johannes Ranke for the mkin specific parts +saemix authors for the parts inherited from saemix. +} diff --git a/man/tffm0.Rd b/man/tffm0.Rd new file mode 100644 index 00000000..46978d5e --- /dev/null +++ b/man/tffm0.Rd @@ -0,0 +1,42 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/tffm0.R +\name{tffm0} +\alias{tffm0} +\alias{invtffm0} +\title{Transform formation fractions as in the first published mkin version} +\usage{ +tffm0(ff) + +invtffm0(ff_trans) +} +\arguments{ +\item{ff}{Vector of untransformed formation fractions. The sum +must be smaller or equal to one} + +\item{ff_trans}{Vector of transformed formation fractions that can be +restricted to the interval from 0 to 1} +} +\value{ +A vector of the transformed formation fractions + +A vector of backtransformed formation fractions for natural use in degradation models +} +\description{ +The transformed fractions can be restricted between 0 and 1 in model +optimisations. Therefore this transformation was used originally in mkin. It +was later replaced by the \link{ilr} transformation because the ilr transformed +fractions can assumed to follow normal distribution. As the ilr +transformation is not available in \link{RxODE} and can therefore not be used in +the nlmixr modelling language, this transformation is currently used for +translating mkin models with formation fractions to more than one target +compartment for fitting with nlmixr in \link{nlmixr_model}. However, +this implementation cannot be used there, as it is not accessible +from RxODE. +} +\examples{ +ff_example <- c( + 0.10983681, 0.09035905, 0.08399383 +) +ff_example_trans <- tffm0(ff_example) +invtffm0(ff_example_trans) +} @@ -3,35 +3,56 @@ Loading required package: parallel ℹ Testing mkin ✔ | OK F W S | Context ✔ | 5 | AIC calculation -✔ | 5 | Analytical solutions for coupled models [3.1 s] +✔ | 5 | Analytical solutions for coupled models [3.4 s] ✔ | 5 | Calculation of Akaike weights ✔ | 2 | Export dataset for reading into CAKE -✔ | 12 | Confidence intervals and p-values [1.3 s] -✔ | 14 | Error model fitting [4.2 s] +✔ | 12 | Confidence intervals and p-values [1.1 s] +✔ | 14 | Error model fitting [4.8 s] ✔ | 5 | Time step normalisation -✔ | 4 | Calculation of FOCUS chi2 error levels [0.5 s] -✔ | 14 | Results for FOCUS D established in expertise for UBA (Ranke 2014) [0.7 s] +✔ | 4 | Calculation of FOCUS chi2 error levels [0.6 s] +✔ | 14 | Results for FOCUS D established in expertise for UBA (Ranke 2014) [0.8 s] ✔ | 4 | Test fitting the decline of metabolites from their maximum [0.3 s] ✔ | 1 | Fitting the logistic model [0.2 s] -✔ | 5 | Nonlinear mixed-effects models [0.1 s] +✔ | 35 1 | Nonlinear mixed-effects models [27.8 s] +──────────────────────────────────────────────────────────────────────────────── +Skip (test_mixed.R:161:3): saem results are reproducible for biphasic fits +Reason: Fitting with saemix takes around 10 minutes when using deSolve +──────────────────────────────────────────────────────────────────────────────── ✔ | 2 | Test dataset classes mkinds and mkindsg -✔ | 10 | Special cases of mkinfit calls [0.3 s] +✔ | 10 | Special cases of mkinfit calls [0.4 s] ✔ | 1 | mkinfit features [0.3 s] ✔ | 8 | mkinmod model generation and printing [0.2 s] ✔ | 3 | Model predictions with mkinpredict [0.3 s] -✔ | 16 | Evaluations according to 2015 NAFTA guidance [1.4 s] -✔ | 9 | Nonlinear mixed-effects models [7.9 s] -✔ | 14 | Plotting [1.1 s] +✔ | 16 | Evaluations according to 2015 NAFTA guidance [1.5 s] +✔ | 9 | Nonlinear mixed-effects models with nlme [8.2 s] +✖ | 15 1 2 | Plotting [1.3 s] +──────────────────────────────────────────────────────────────────────────────── +Warning (test_plot.R:38:5): Plotting mkinfit, mmkin and mixed model objects is reproducible +Adding new file snapshot: '_snaps/plot/mixed-model-fit-for-saem-object-with-saemix-transformations.svg' + +Failure (test_plot.R:44:3): 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(...) test_plot.R:44:2 + 3. testthat::expect_snapshot_file(...) + +Warning (test_plot.R:53:5): Plotting mkinfit, mmkin and mixed model objects is reproducible +Adding new file snapshot: '_snaps/plot/mixed-model-fit-for-saem-object-with-mkin-transformations.svg' +──────────────────────────────────────────────────────────────────────────────── ✔ | 4 | Residuals extracted from mkinfit models ✔ | 2 | Complex test case from Schaefer et al. (2007) Piacenza paper [1.5 s] -✔ | 7 | Fitting the SFORB model [3.7 s] +✔ | 7 | Fitting the SFORB model [3.9 s] ✔ | 1 | Summaries of old mkinfit objects ✔ | 4 | Summary [0.1 s] -✔ | 4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [2.2 s] -✔ | 9 | Hypothesis tests [8.0 s] -✔ | 4 | Calculation of maximum time weighted average concentrations (TWAs) [2.4 s] +✔ | 4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [2.3 s] +✔ | 9 | Hypothesis tests [8.6 s] +✔ | 4 | Calculation of maximum time weighted average concentrations (TWAs) [2.2 s] ══ Results ═════════════════════════════════════════════════════════════════════ -Duration: 40.3 s +Duration: 70.3 s + +── Skipped tests ────────────────────────────────────────────────────────────── +• Fitting with saemix takes around 10 minutes when using deSolve (1) -[ FAIL 0 | WARN 0 | SKIP 0 | PASS 174 ] +[ FAIL 1 | WARN 2 | SKIP 1 | PASS 205 ] diff --git 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f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) exp(-k2 * time))) * parent - k_m1 * m1 Data: -509 observations of 2 variable(s) grouped in 15 datasets +507 observations of 2 variable(s) grouped in 15 datasets <mmkin> object Status of individual fits: @@ -21,6 +21,6 @@ OK: No warnings Mean fitted parameters: parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 - 100.702 -5.347 -0.078 -2.681 -4.366 + 100.667 -5.378 -0.095 -2.743 -4.510 g_qlogis - -0.335 + -0.180 diff --git a/tests/testthat/print_nlme_biphasic.txt b/tests/testthat/print_nlme_biphasic.txt index f86bda76..f40d438d 100644 --- a/tests/testthat/print_nlme_biphasic.txt +++ b/tests/testthat/print_nlme_biphasic.txt @@ -9,21 +9,21 @@ d_m1/dt = + f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) exp(-k2 * time))) * parent - k_m1 * m1 Data: -509 observations of 2 variable(s) grouped in 15 datasets +507 observations of 2 variable(s) grouped in 15 datasets -Log-likelihood: -1329 +Log-likelihood: -1326 Fixed effects: list(parent_0 ~ 1, log_k_m1 ~ 1, f_parent_qlogis ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 - 100.43 -5.34 -0.08 -2.90 -4.34 + 100.7 -5.4 -0.1 -2.8 -4.5 g_qlogis - -0.19 + -0.1 Random effects: Formula: list(parent_0 ~ 1, log_k_m1 ~ 1, f_parent_qlogis ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) Level: ds Structure: Diagonal parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 g_qlogis Residual -StdDev: 1 0.1 0.3 0.6 0.5 0.3 3 +StdDev: 1 0.03 0.3 0.3 0.2 0.3 3 diff --git a/tests/testthat/print_sfo_saem_1.txt b/tests/testthat/print_sfo_saem_1.txt index d341e9e7..0c0e32ce 100644 --- a/tests/testthat/print_sfo_saem_1.txt +++ b/tests/testthat/print_sfo_saem_1.txt @@ -3,19 +3,19 @@ Structural model: d_parent/dt = - k_parent * parent Data: -264 observations of 1 variable(s) grouped in 15 datasets +262 observations of 1 variable(s) grouped in 15 datasets Likelihood computed by importance sampling AIC BIC logLik - 1320 1324 -654 + 1310 1315 -649 Fitted parameters: estimate lower upper -parent_0 1e+02 98.78 1e+02 +parent_0 1e+02 98.87 1e+02 k_parent 4e-02 0.03 4e-02 -Var.parent_0 8e-01 -1.76 3e+00 -Var.k_parent 9e-02 0.03 2e-01 -a.1 9e-01 0.70 1e+00 -b.1 4e-02 0.03 4e-02 -SD.parent_0 9e-01 -0.57 2e+00 +Var.parent_0 1e+00 -1.72 5e+00 +Var.k_parent 1e-01 0.03 2e-01 +a.1 9e-01 0.75 1e+00 +b.1 5e-02 0.04 5e-02 +SD.parent_0 1e+00 -0.12 3e+00 SD.k_parent 3e-01 0.20 4e-01 diff --git a/tests/testthat/setup_script.R b/tests/testthat/setup_script.R index 547b2d6c..96e865d4 100644 --- a/tests/testthat/setup_script.R +++ b/tests/testthat/setup_script.R @@ -106,6 +106,7 @@ 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) @@ -118,6 +119,7 @@ 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) @@ -131,6 +133,7 @@ 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) @@ -144,6 +147,7 @@ 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) @@ -171,6 +175,7 @@ ds_biphasic_mean <- lapply(1:n_biphasic, 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), @@ -178,6 +183,10 @@ ds_biphasic <- lapply(ds_biphasic_mean, function(ds) { }) # Mixed model fits +saemix_available <- FALSE +if (requireNamespace("saemix", quietly = TRUE)) { + if(packageVersion("saemix") >= "3.1.9000") saemix_available <- TRUE +} 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_biphasic <- mmkin(list("DFOP-SFO" = DFOP_SFO), ds_biphasic, quiet = TRUE, cores = n_cores) @@ -186,6 +195,26 @@ mmkin_biphasic_mixed <- mixed(mmkin_biphasic) dfop_nlme_1 <- nlme(mmkin_dfop_1) nlme_biphasic <- nlme(mmkin_biphasic) +if (saemix_available) { + sfo_saem_1 <- saem(mmkin_sfo_1, quiet = TRUE, transformations = "saemix") + + # With default control parameters, we do not get good results with mkin + # transformations here + dfop_saemix_1 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "mkin", + control = list( + displayProgress = FALSE, print = FALSE, save = FALSE, save.graphs = FALSE, + rw.init = 1, nbiter.saemix = c(600, 100)) + ) + dfop_saemix_2 <- saem(mmkin_dfop_1, quiet = TRUE, transformations = "saemix", + control = list( + displayProgress = FALSE, print = FALSE, save = FALSE, save.graphs = FALSE, + rw.init = 0.5, nbiter.saemix = c(600, 100)) + ) + + saem_biphasic_m <- saem(mmkin_biphasic, transformations = "mkin", quiet = TRUE) + saem_biphasic_s <- saem(mmkin_biphasic, transformations = "saemix", quiet = TRUE) +} + ds_uba <- lapply(experimental_data_for_UBA_2019[6:10], function(x) subset(x$data[c("name", "time", "value")])) names(ds_uba) <- paste("Dataset", 6:10) @@ -197,3 +226,7 @@ f_uba_mmkin <- mmkin(list("SFO-SFO" = sfo_sfo_uba, "DFOP-SFO" = dfop_sfo_uba), ds_uba, quiet = TRUE, cores = n_cores) f_uba_dfop_sfo_mixed <- mixed(f_uba_mmkin[2, ]) +if (saemix_available) { + f_uba_sfo_sfo_saem <- saem(f_uba_mmkin["SFO-SFO", ], quiet = TRUE, transformations = "saemix") + f_uba_dfop_sfo_saem <- saem(f_uba_mmkin["DFOP-SFO", ], quiet = TRUE, transformations = "saemix") +} diff --git a/tests/testthat/summary_nlme_biphasic_s.txt b/tests/testthat/summary_nlme_biphasic_s.txt index 65aead62..86049775 100644 --- a/tests/testthat/summary_nlme_biphasic_s.txt +++ b/tests/testthat/summary_nlme_biphasic_s.txt @@ -13,19 +13,19 @@ d_m1/dt = + f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) exp(-k2 * time))) * parent - k_m1 * m1 Data: -509 observations of 2 variable(s) grouped in 15 datasets +507 observations of 2 variable(s) grouped in 15 datasets Model predictions using solution type analytical -Fitted in test time 0 s using 3 iterations +Fitted in test time 0 s using 4 iterations Variance model: Constant variance Mean of starting values for individual parameters: parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 - 100.70 -5.35 -0.08 -2.68 -4.37 + 100.67 -5.38 -0.09 -2.74 -4.51 g_qlogis - -0.33 + -0.18 Fixed degradation parameter values: value type @@ -34,40 +34,40 @@ m1_0 0 state Results: AIC BIC logLik - 2683 2738 -1329 + 2678 2733 -1326 Optimised, transformed parameters with symmetric confidence intervals: - lower est. upper -parent_0 99.6 100.43 101.26 -log_k_m1 -5.5 -5.34 -5.18 -f_parent_qlogis -0.3 -0.08 0.09 -log_k1 -3.2 -2.90 -2.60 -log_k2 -4.6 -4.34 -4.07 -g_qlogis -0.5 -0.19 0.08 + lower est. upper +parent_0 99.8 100.7 101.62 +log_k_m1 -5.6 -5.4 -5.25 +f_parent_qlogis -0.3 -0.1 0.06 +log_k1 -3.0 -2.8 -2.57 +log_k2 -4.7 -4.5 -4.35 +g_qlogis -0.4 -0.1 0.17 Correlation: prnt_0 lg_k_1 f_prn_ log_k1 log_k2 -log_k_m1 -0.177 -f_parent_qlogis -0.164 0.385 -log_k1 0.108 -0.017 -0.025 -log_k2 0.036 0.054 0.008 0.096 -g_qlogis -0.068 -0.110 -0.030 -0.269 -0.267 +log_k_m1 -0.167 +f_parent_qlogis -0.145 0.380 +log_k1 0.170 0.005 -0.026 +log_k2 0.083 0.168 0.032 0.365 +g_qlogis -0.088 -0.170 -0.044 -0.472 -0.631 Random effects: Formula: list(parent_0 ~ 1, log_k_m1 ~ 1, f_parent_qlogis ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) Level: ds Structure: Diagonal parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 g_qlogis Residual -StdDev: 1 0.1 0.3 0.6 0.5 0.3 3 +StdDev: 1 0.03 0.3 0.3 0.2 0.3 3 Backtransformed parameters with asymmetric confidence intervals: lower est. upper parent_0 1e+02 1e+02 1e+02 -k_m1 4e-03 5e-03 6e-03 +k_m1 4e-03 4e-03 5e-03 f_parent_to_m1 4e-01 5e-01 5e-01 -k1 4e-02 6e-02 7e-02 -k2 1e-02 1e-02 2e-02 +k1 5e-02 6e-02 8e-02 +k2 9e-03 1e-02 1e-02 g 4e-01 5e-01 5e-01 Resulting formation fractions: @@ -77,5 +77,5 @@ parent_sink 0.5 Estimated disappearance times: DT50 DT90 DT50back DT50_k1 DT50_k2 -parent 26 131 39 13 53 -m1 144 479 NA NA NA +parent 25 150 45 11 63 +m1 154 512 NA NA NA diff --git a/tests/testthat/summary_saem_biphasic_s.txt b/tests/testthat/summary_saem_biphasic_s.txt new file mode 100644 index 00000000..8dfae367 --- /dev/null +++ b/tests/testthat/summary_saem_biphasic_s.txt @@ -0,0 +1,77 @@ +saemix version used for fitting: Dummy 0.0 for testing +mkin version used for pre-fitting: Dummy 0.0 for testing +R version used for fitting: Dummy R version for testing +Date of fit: Dummy date for testing +Date of summary: Dummy date for testing + +Equations: +d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * parent +d_m1/dt = + f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) + * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * + exp(-k2 * time))) * parent - k_m1 * m1 + +Data: +507 observations of 2 variable(s) grouped in 15 datasets + +Model predictions using solution type analytical + +Fitted in test time 0 s using 300, 100 iterations + +Variance model: Constant variance + +Mean of starting values for individual parameters: + parent_0 k_m1 f_parent_to_m1 k1 k2 + 1.0e+02 4.6e-03 4.8e-01 6.4e-02 1.1e-02 + g + 4.6e-01 + +Fixed degradation parameter values: +None + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2702 2711 -1338 + +Optimised parameters: + est. lower upper +parent_0 1.0e+02 1.0e+02 1.0e+02 +k_m1 4.7e-03 3.9e-03 5.6e-03 +f_parent_to_m1 4.8e-01 4.3e-01 5.2e-01 +k1 4.8e-02 3.1e-02 6.5e-02 +k2 1.3e-02 8.7e-03 1.7e-02 +g 5.0e-01 4.1e-01 5.8e-01 + +Correlation: + prnt_0 k_m1 f_p__1 k1 k2 +k_m1 -0.152 +f_parent_to_m1 -0.143 0.366 +k1 0.097 -0.014 -0.021 +k2 0.022 0.083 0.023 0.101 +g -0.084 -0.144 -0.044 -0.303 -0.364 + +Random effects: + est. lower upper +SD.parent_0 1.22 0.316 2.12 +SD.k_m1 0.15 -0.079 0.38 +SD.f_parent_to_m1 0.32 0.191 0.44 +SD.k1 0.66 0.416 0.90 +SD.k2 0.59 0.368 0.80 +SD.g 0.16 -0.373 0.70 + +Variance model: + est. lower upper +a.1 2.9 2.7 3 + +Resulting formation fractions: + ff +parent_m1 0.48 +parent_sink 0.52 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +parent 26 127 38 14 54 +m1 146 485 NA NA NA diff --git a/tests/testthat/test_mixed.R b/tests/testthat/test_mixed.R index 6f28d0c3..9c8a84d7 100644 --- a/tests/testthat/test_mixed.R +++ b/tests/testthat/test_mixed.R @@ -1,9 +1,104 @@ context("Nonlinear mixed-effects models") +test_that("Parent fits using saemix are correctly implemented", { + skip_if(!saemix_available) + + expect_error(saem(fits), "Only row objects") + # Some fits were done in the setup script + mmkin_sfo_2 <- update(mmkin_sfo_1, fixed_initials = c(parent = 100)) + expect_error(update(mmkin_sfo_1, models = c("SFOOO")), "Please supply models.*") + + sfo_saem_2 <- saem(mmkin_sfo_1, quiet = TRUE, transformations = "mkin") + sfo_saem_3 <- expect_error(saem(mmkin_sfo_2, quiet = TRUE), "at least two parameters") + s_sfo_s1 <- summary(sfo_saem_1) + s_sfo_s2 <- summary(sfo_saem_2) + + sfo_nlme_1 <- expect_warning(nlme(mmkin_sfo_1), "not converge") + s_sfo_n <- summary(sfo_nlme_1) + + # Compare with input + expect_equal(round(s_sfo_s2$confint_ranef["SD.log_k_parent", "est."], 1), 0.3) + # k_parent is a bit different from input 0.03 here + expect_equal(round(s_sfo_s1$confint_back["k_parent", "est."], 3), 0.035) + expect_equal(round(s_sfo_s2$confint_back["k_parent", "est."], 3), 0.035) + + # But the result is pretty unanimous between methods + expect_equal(round(s_sfo_s1$confint_back["k_parent", "est."], 3), + round(s_sfo_s2$confint_back["k_parent", "est."], 3)) + expect_equal(round(s_sfo_s1$confint_back["k_parent", "est."], 3), + round(s_sfo_n$confint_back["k_parent", "est."], 3)) + + mmkin_fomc_1 <- mmkin("FOMC", ds_fomc, quiet = TRUE, error_model = "tc", cores = n_cores) + fomc_saem_1 <- saem(mmkin_fomc_1, quiet = TRUE) + ci_fomc_s1 <- summary(fomc_saem_1)$confint_back + + fomc_pop <- as.numeric(fomc_pop) + expect_true(all(ci_fomc_s1[, "lower"] < fomc_pop)) + expect_true(all(ci_fomc_s1[, "upper"] > fomc_pop)) + + mmkin_fomc_2 <- update(mmkin_fomc_1, state.ini = 100, fixed_initials = "parent") + fomc_saem_2 <- saem(mmkin_fomc_2, quiet = TRUE, transformations = "mkin") + ci_fomc_s2 <- summary(fomc_saem_2)$confint_back + + expect_true(all(ci_fomc_s2[, "lower"] < fomc_pop[2:3])) + expect_true(all(ci_fomc_s2[, "upper"] > fomc_pop[2:3])) + + s_dfop_s1 <- summary(dfop_saemix_1) + s_dfop_s2 <- summary(dfop_saemix_2) + s_dfop_n <- summary(dfop_nlme_1) + + dfop_pop <- as.numeric(dfop_pop) + expect_true(all(s_dfop_s1$confint_back[, "lower"] < dfop_pop)) + expect_true(all(s_dfop_s1$confint_back[, "upper"] > dfop_pop)) + expect_true(all(s_dfop_s2$confint_back[, "lower"] < dfop_pop)) + expect_true(all(s_dfop_s2$confint_back[, "upper"] > dfop_pop)) + + dfop_mmkin_means_trans_tested <- mean_degparms(mmkin_dfop_1, test_log_parms = TRUE) + dfop_mmkin_means_trans <- apply(parms(mmkin_dfop_1, transformed = TRUE), 1, mean) + + dfop_mmkin_means_tested <- backtransform_odeparms(dfop_mmkin_means_trans_tested, mmkin_dfop_1$mkinmod) + dfop_mmkin_means <- backtransform_odeparms(dfop_mmkin_means_trans, mmkin_dfop_1$mkinmod) + + # We get < 20% deviations for parent_0 and k1 by averaging the transformed parameters + # If we average only parameters passing the t-test, the deviation for k2 is also < 20% + rel_diff_mmkin <- (dfop_mmkin_means - dfop_pop) / dfop_pop + rel_diff_mmkin_tested <- (dfop_mmkin_means_tested - dfop_pop) / dfop_pop + expect_true(all(rel_diff_mmkin[c("parent_0", "k1")] < 0.20)) + expect_true(all(rel_diff_mmkin_tested[c("parent_0", "k1", "k2")] < 0.20)) + + # We get < 30% deviations with transformations made in mkin + rel_diff_1 <- (s_dfop_s1$confint_back[, "est."] - dfop_pop) / dfop_pop + expect_true(all(rel_diff_1 < 0.5)) + + # We get < 20% deviations with transformations made in saemix + rel_diff_2 <- (s_dfop_s2$confint_back[, "est."] - dfop_pop) / dfop_pop + expect_true(all(rel_diff_2 < 0.2)) + + mmkin_hs_1 <- mmkin("HS", ds_hs, quiet = TRUE, error_model = "const", cores = n_cores) + hs_saem_1 <- saem(mmkin_hs_1, quiet = TRUE) + ci_hs_s1 <- summary(hs_saem_1)$confint_back + + hs_pop <- as.numeric(hs_pop) + # expect_true(all(ci_hs_s1[, "lower"] < hs_pop)) # k1 is overestimated + expect_true(all(ci_hs_s1[, "upper"] > hs_pop)) + + mmkin_hs_2 <- update(mmkin_hs_1, state.ini = 100, fixed_initials = "parent") + hs_saem_2 <- saem(mmkin_hs_2, quiet = TRUE) + ci_hs_s2 <- summary(hs_saem_2)$confint_back + + #expect_true(all(ci_hs_s2[, "lower"] < hs_pop[2:4])) # k1 again overestimated + expect_true(all(ci_hs_s2[, "upper"] > hs_pop[2:4])) + + # HS would likely benefit from implemenation of transformations = "saemix" +}) + test_that("Print methods work", { expect_known_output(print(fits[, 2:3], digits = 2), "print_mmkin_parent.txt") expect_known_output(print(mmkin_biphasic_mixed, digits = 2), "print_mmkin_biphasic_mixed.txt") expect_known_output(print(nlme_biphasic, digits = 1), "print_nlme_biphasic.txt") + + skip_if(!saemix_available) + expect_known_output(print(sfo_saem_1, digits = 1), "print_sfo_saem_1.txt") }) test_that("nlme results are reproducible to some degree", { @@ -18,8 +113,56 @@ test_that("nlme results are reproducible to some degree", { expect_known_output(print(test_summary, digits = 1), "summary_nlme_biphasic_s.txt") + # k1 just fails the first test (lower bound of the ci), so we need to exclude it + dfop_no_k1 <- c("parent_0", "k_m1", "f_parent_to_m1", "k2", "g") + dfop_sfo_pop_no_k1 <- as.numeric(dfop_sfo_pop[dfop_no_k1]) dfop_sfo_pop <- as.numeric(dfop_sfo_pop) + ci_dfop_sfo_n <- summary(nlme_biphasic)$confint_back - # expect_true(all(ci_dfop_sfo_n[, "lower"] < dfop_sfo_pop)) # k2 is overestimated + + expect_true(all(ci_dfop_sfo_n[dfop_no_k1, "lower"] < dfop_sfo_pop_no_k1)) expect_true(all(ci_dfop_sfo_n[, "upper"] > dfop_sfo_pop)) }) + +test_that("saem results are reproducible for biphasic fits", { + + skip_if(!saemix_available) + test_summary <- summary(saem_biphasic_s) + test_summary$saemixversion <- "Dummy 0.0 for testing" + test_summary$mkinversion <- "Dummy 0.0 for testing" + test_summary$Rversion <- "Dummy R version for testing" + test_summary$date.fit <- "Dummy date for testing" + test_summary$date.summary <- "Dummy date for testing" + test_summary$time <- c(elapsed = "test time 0") + + expect_known_output(print(test_summary, digits = 2), "summary_saem_biphasic_s.txt") + + dfop_sfo_pop <- as.numeric(dfop_sfo_pop) + no_k1 <- c(1, 2, 3, 5, 6) + no_k2 <- c(1, 2, 3, 4, 6) + no_k1_k2 <- c(1, 2, 3, 6) + + ci_dfop_sfo_s_s <- summary(saem_biphasic_s)$confint_back + # k1 and k2 are overestimated + expect_true(all(ci_dfop_sfo_s_s[no_k1_k2, "lower"] < dfop_sfo_pop[no_k1_k2])) + expect_true(all(ci_dfop_sfo_s_s[, "upper"] > dfop_sfo_pop)) + + # k1 and k2 are not fitted well + ci_dfop_sfo_s_m <- summary(saem_biphasic_m)$confint_back + expect_true(all(ci_dfop_sfo_s_m[no_k2, "lower"] < dfop_sfo_pop[no_k2])) + expect_true(all(ci_dfop_sfo_s_m[no_k1, "upper"] > dfop_sfo_pop[no_k1])) + + # I tried to only do few iterations in routine tests as this is so slow + # but then deSolve fails at some point (presumably at the switch between + # the two types of iterations) + #saem_biphasic_2 <- saem(mmkin_biphasic, solution_type = "deSolve", + # control = list(nbiter.saemix = c(10, 5), nbiter.burn = 5), quiet = TRUE) + + skip("Fitting with saemix takes around 10 minutes when using deSolve") + saem_biphasic_2 <- saem(mmkin_biphasic, solution_type = "deSolve", quiet = TRUE) + + # As with the analytical solution, k1 and k2 are not fitted well + ci_dfop_sfo_s_d <- summary(saem_biphasic_2)$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_nlme.R b/tests/testthat/test_nlme.R index 989914da..a3bc9413 100644 --- a/tests/testthat/test_nlme.R +++ b/tests/testthat/test_nlme.R @@ -1,4 +1,4 @@ -context("Nonlinear mixed-effects models") +context("Nonlinear mixed-effects models with nlme") library(nlme) diff --git a/tests/testthat/test_nlmixr.r b/tests/testthat/test_nlmixr.r new file mode 100644 index 00000000..dcbb50ac --- /dev/null +++ b/tests/testthat/test_nlmixr.r @@ -0,0 +1,99 @@ + + +# dmta_ds <- lapply(1:8, 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[["Borstel"]] <- rbind(dmta_ds[["Borstel 1"]], dmta_ds[["Borstel 2"]]) +# dmta_ds[["Borstel 1"]] <- NULL +# dmta_ds[["Borstel 2"]] <- NULL +# dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]]) +# dmta_ds[["Elliot 1"]] <- NULL +# dmta_ds[["Elliot 2"]] <- NULL +# dfop_sfo3_plus <- mkinmod( +# DMTA = mkinsub("DFOP", c("M23", "M27", "M31")), +# M23 = mkinsub("SFO"), +# M27 = mkinsub("SFO"), +# M31 = mkinsub("SFO", "M27", sink = FALSE), +# quiet = TRUE +# ) +# f_dmta_mkin_tc <- mmkin( +# list("DFOP-SFO3+" = dfop_sfo3_plus), +# dmta_ds, quiet = TRUE, error_model = "tc") +# +# d_dmta_nlmixr <- nlmixr_data(f_dmta_mkin_tc) +# m_dmta_nlmixr <- function () +# { +# ini({ +# DMTA_0 = 98.7697627680706 +# eta.DMTA_0 ~ 2.35171765917765 +# log_k_M23 = -3.92162409637283 +# eta.log_k_M23 ~ 0.549278519419884 +# log_k_M27 = -4.33774620773911 +# eta.log_k_M27 ~ 0.864474956685295 +# log_k_M31 = -4.24767627688461 +# eta.log_k_M31 ~ 0.750297149164171 +# f_DMTA_tffm0_1_qlogis = -2.092409 +# eta.f_DMTA_tffm0_1_qlogis ~ 0.3 +# f_DMTA_tffm0_2_qlogis = -2.180576 +# eta.f_DMTA_tffm0_2_qlogis ~ 0.3 +# f_DMTA_tffm0_3_qlogis = -2.142672 +# eta.f_DMTA_tffm0_3_qlogis ~ 0.3 +# log_k1 = -2.2341008812259 +# eta.log_k1 ~ 0.902976221565793 +# log_k2 = -3.7762779983269 +# eta.log_k2 ~ 1.57684519529298 +# g_qlogis = 0.450175725479389 +# eta.g_qlogis ~ 3.0851335687675 +# sigma_low_DMTA = 0.697933852349996 +# rsd_high_DMTA = 0.0257724286053519 +# sigma_low_M23 = 0.697933852349996 +# rsd_high_M23 = 0.0257724286053519 +# sigma_low_M27 = 0.697933852349996 +# rsd_high_M27 = 0.0257724286053519 +# sigma_low_M31 = 0.697933852349996 +# rsd_high_M31 = 0.0257724286053519 +# }) +# model({ +# DMTA_0_model = DMTA_0 + eta.DMTA_0 +# DMTA(0) = DMTA_0_model +# k_M23 = exp(log_k_M23 + eta.log_k_M23) +# k_M27 = exp(log_k_M27 + eta.log_k_M27) +# k_M31 = exp(log_k_M31 + eta.log_k_M31) +# k1 = exp(log_k1 + eta.log_k1) +# k2 = exp(log_k2 + eta.log_k2) +# g = expit(g_qlogis + eta.g_qlogis) +# f_DMTA_tffm0_1 = expit(f_DMTA_tffm0_1_qlogis + eta.f_DMTA_tffm0_1_qlogis) +# f_DMTA_tffm0_2 = expit(f_DMTA_tffm0_2_qlogis + eta.f_DMTA_tffm0_2_qlogis) +# f_DMTA_tffm0_3 = expit(f_DMTA_tffm0_3_qlogis + eta.f_DMTA_tffm0_3_qlogis) +# f_DMTA_to_M23 = f_DMTA_tffm0_1 +# f_DMTA_to_M27 = (1 - f_DMTA_tffm0_1) * f_DMTA_tffm0_2 +# f_DMTA_to_M31 = (1 - f_DMTA_tffm0_1) * (1 - f_DMTA_tffm0_2) * f_DMTA_tffm0_3 +# d/dt(DMTA) = -((k1 * g * exp(-k1 * time) + k2 * (1 - +# g) * exp(-k2 * time))/(g * exp(-k1 * time) + (1 - +# g) * exp(-k2 * time))) * DMTA +# d/dt(M23) = +f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + +# k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + +# (1 - g) * exp(-k2 * time))) * DMTA - k_M23 * M23 +# d/dt(M27) = +f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + +# k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + +# (1 - g) * exp(-k2 * time))) * DMTA - k_M27 * M27 + +# k_M31 * M31 +# d/dt(M31) = +f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + +# k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + +# (1 - g) * exp(-k2 * time))) * DMTA - k_M31 * M31 +# DMTA ~ add(sigma_low_DMTA) + prop(rsd_high_DMTA) +# M23 ~ add(sigma_low_M23) + prop(rsd_high_M23) +# M27 ~ add(sigma_low_M27) + prop(rsd_high_M27) +# M31 ~ add(sigma_low_M31) + prop(rsd_high_M31) +# }) +# } +# m_dmta_nlmixr_mkin <- nlmixr_model(f_dmta_mkin_tc, test_log_parms = TRUE) +# f_dmta_nlmixr_saem <- nlmixr(f_dmta_mkin_tc, est = "saem", control = saemControl(print = 250)) +# f_dmta_nlmixr_focei <- nlmixr(f_dmta_mkin_tc, est = "focei", control = foceiControl(print = 250)) +# plot(f_dmta_nlmixr_saem) +# plot(f_dmta_nlmixr_focei) +# diff --git a/tests/testthat/test_plot.R b/tests/testthat/test_plot.R index 28c537bb..e08fe277 100644 --- a/tests/testthat/test_plot.R +++ b/tests/testthat/test_plot.R @@ -33,6 +33,11 @@ test_that("Plotting mkinfit, mmkin and mixed model objects is reproducible", { plot_biphasic_mmkin <- function() plot(f_uba_dfop_sfo_mixed) vdiffr::expect_doppelganger("mixed model fit for mmkin object", plot_biphasic_mmkin) + if (saemix_available) { + 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) + } + skip_on_travis() plot_biphasic_nlme <- function() plot(dfop_nlme_1) @@ -41,6 +46,12 @@ test_that("Plotting mkinfit, mmkin and mixed model objects is reproducible", { #plot_biphasic_mmkin <- function() plot(mixed(mmkin_biphasic)) # Biphasic fits with lots of data and fits have lots of potential for differences plot_biphasic_nlme <- function() plot(nlme_biphasic) + if (saemix_available) { + #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) + } # 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/vignettes/FOCUS_D.html b/vignettes/FOCUS_D.html index c2599ee5..a1d57b02 100644 --- a/vignettes/FOCUS_D.html +++ b/vignettes/FOCUS_D.html @@ -363,7 +363,11 @@ pre code { <h1 class="title toc-ignore">Example evaluation of FOCUS Example Dataset D</h1> <h4 class="author">Johannes Ranke</h4> +<<<<<<< HEAD +<h4 class="date">Last change 31 January 2019 (rebuilt 2021-04-21)</h4> +======= <h4 class="date">Last change 31 January 2019 (rebuilt 2021-07-23)</h4> +>>>>>>> master </div> @@ -437,10 +441,17 @@ print(FOCUS_2006_D)</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> <pre class="r"><code>summary(fit)</code></pre> +<<<<<<< HEAD +<pre><code>## mkin version used for fitting: 1.0.4.9000 +## R version used for fitting: 4.0.5 +## Date of fit: Wed Apr 21 16:40:29 2021 +## Date of summary: Wed Apr 21 16:40:29 2021 +======= <pre><code>## mkin version used for fitting: 1.1.0 ## R version used for fitting: 4.1.0 ## Date of fit: Fri Jul 23 15:39:43 2021 ## Date of summary: Fri Jul 23 15:39:43 2021 +>>>>>>> master ## ## Equations: ## d_parent/dt = - k_parent * parent @@ -448,7 +459,11 @@ print(FOCUS_2006_D)</code></pre> ## ## Model predictions using solution type analytical ## +<<<<<<< HEAD +## Fitted using 401 model solutions performed in 0.144 s +======= ## Fitted using 401 model solutions performed in 0.152 s +>>>>>>> master ## ## Error model: Constant variance ## diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html index 77b64659..717a01be 100644 --- a/vignettes/FOCUS_L.html +++ b/vignettes/FOCUS_L.html @@ -1500,12 +1500,12 @@ div.tocify { <!-- setup 3col/9col grid for toc_float and main content --> <div class="row"> -<div class="col-sm-12 col-md-4 col-lg-3"> +<div class="col-xs-12 col-sm-4 col-md-3"> <div id="TOC" class="tocify"> </div> </div> -<div class="toc-content col-sm-12 col-md-8 col-lg-9"> +<div class="toc-content col-xs-12 col-sm-8 col-md-9"> @@ -1516,7 +1516,7 @@ div.tocify { <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 17 November 2016 (rebuilt 2021-03-31)</h4> +<h4 class="date">Last change 17 November 2016 (rebuilt 2021-07-23)</h4> </div> @@ -1536,9 +1536,9 @@ FOCUS_2006_L1_mkin <- mkin_wide_to_long(FOCUS_2006_L1)</code></pre> <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: 1.0.4 -## R version used for fitting: 4.0.4 -## Date of fit: Wed Mar 31 19:01:37 2021 -## Date of summary: Wed Mar 31 19:01:37 2021 +## R version used for fitting: 4.1.0 +## Date of fit: Fri Jul 23 18:03:18 2021 +## Date of summary: Fri Jul 23 18:03:18 2021 ## ## Equations: ## d_parent/dt = - k_parent * parent @@ -1637,16 +1637,16 @@ summary(m.L1.SFO)</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: 1.0.4 -## R version used for fitting: 4.0.4 -## Date of fit: Wed Mar 31 19:01:37 2021 -## Date of summary: Wed Mar 31 19:01:37 2021 +## R version used for fitting: 4.1.0 +## Date of fit: Fri Jul 23 18:03:19 2021 +## Date of summary: Fri Jul 23 18:03:19 2021 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 369 model solutions performed in 0.083 s +## Fitted using 369 model solutions performed in 0.082 s ## ## Error model: Constant variance ## @@ -1742,9 +1742,9 @@ plot(m.L2.FOMC, show_residuals = TRUE, <p><img src="data:image/png;base64,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" /><!-- --></p> <pre class="r"><code>summary(m.L2.FOMC, data = FALSE)</code></pre> <pre><code>## mkin version used for fitting: 1.0.4 -## R version used for fitting: 4.0.4 -## Date of fit: Wed Mar 31 19:01:37 2021 -## Date of summary: Wed Mar 31 19:01:37 2021 +## R version used for fitting: 4.1.0 +## Date of fit: Fri Jul 23 18:03:19 2021 +## Date of summary: Fri Jul 23 18:03:19 2021 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent @@ -1820,9 +1820,9 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, <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: 1.0.4 -## R version used for fitting: 4.0.4 -## Date of fit: Wed Mar 31 19:01:38 2021 -## Date of summary: Wed Mar 31 19:01:38 2021 +## R version used for fitting: 4.1.0 +## Date of fit: Fri Jul 23 18:03:19 2021 +## Date of summary: Fri Jul 23 18:03:19 2021 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -1831,7 +1831,7 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, ## ## Model predictions using solution type analytical ## -## Fitted using 581 model solutions performed in 0.134 s +## Fitted using 581 model solutions performed in 0.133 s ## ## Error model: Constant variance ## @@ -1920,9 +1920,9 @@ plot(mm.L3)</code></pre> <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: 1.0.4 -## R version used for fitting: 4.0.4 -## Date of fit: Wed Mar 31 19:01:38 2021 -## Date of summary: Wed Mar 31 19:01:38 2021 +## R version used for fitting: 4.1.0 +## Date of fit: Fri Jul 23 18:03:19 2021 +## Date of summary: Fri Jul 23 18:03:20 2021 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -2028,16 +2028,16 @@ plot(mm.L4)</code></pre> <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: 1.0.4 -## R version used for fitting: 4.0.4 -## Date of fit: Wed Mar 31 19:01:38 2021 -## Date of summary: Wed Mar 31 19:01:38 2021 +## R version used for fitting: 4.1.0 +## Date of fit: Fri Jul 23 18:03:20 2021 +## Date of summary: Fri Jul 23 18:03:20 2021 ## ## Equations: ## d_parent/dt = - k_parent * parent ## ## Model predictions using solution type analytical ## -## Fitted using 142 model solutions performed in 0.031 s +## Fitted using 142 model solutions performed in 0.03 s ## ## Error model: Constant variance ## @@ -2092,9 +2092,9 @@ plot(mm.L4)</code></pre> ## 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: 1.0.4 -## R version used for fitting: 4.0.4 -## Date of fit: Wed Mar 31 19:01:38 2021 -## Date of summary: Wed Mar 31 19:01:38 2021 +## R version used for fitting: 4.1.0 +## Date of fit: Fri Jul 23 18:03:20 2021 +## Date of summary: Fri Jul 23 18:03:20 2021 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent @@ -2208,6 +2208,9 @@ $(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/references.bib b/vignettes/references.bib index 18b93fd3..f7eb4692 100644 --- a/vignettes/references.bib +++ b/vignettes/references.bib @@ -1,6 +1,3 @@ -% This file was originally created with JabRef 2.7b. -% Encoding: ISO8859_1 - @BOOK{bates1988, title = {Nonlinear regression and its applications}, publisher = {Wiley-Interscience}, @@ -97,7 +94,7 @@ @Techreport{ranke2014, title = {{Prüfung und Validierung von Modellierungssoftware als Alternative zu ModelMaker 4.0}}, - author = {J. Ranke}, + author = {J. Ranke}, year = 2014, institution = {Umweltbundesamt}, volume = {Projektnummer 27452} @@ -146,3 +143,21 @@ 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} +} diff --git a/vignettes/web_only/.build.timestamp b/vignettes/web_only/.build.timestamp new file mode 100644 index 00000000..e69de29b --- /dev/null +++ b/vignettes/web_only/.build.timestamp diff --git a/vignettes/web_only/dimethenamid_2018.html b/vignettes/web_only/dimethenamid_2018.html new file mode 100644 index 00000000..e84a435c --- /dev/null +++ b/vignettes/web_only/dimethenamid_2018.html @@ -0,0 +1,1864 @@ +<!DOCTYPE html> + +<html> + +<head> + +<meta charset="utf-8" /> +<meta name="generator" content="pandoc" /> +<meta http-equiv="X-UA-Compatible" content="IE=EDGE" /> + + +<meta name="author" content="Johannes Ranke" /> + + +<title>Example evaluations of the dimethenamid data from 2018</title> + +<script>// Pandoc 2.9 adds attributes on both header and div. 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) 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this._mouseUp(t)}return(t.which||t.button)&&(this._mouseMoved=!0),this._mouseStarted?(this._mouseDrag(t),t.preventDefault()):(this._mouseDistanceMet(t)&&this._mouseDelayMet(t)&&(this._mouseStarted=this._mouseStart(this._mouseDownEvent,t)!==!1,this._mouseStarted?this._mouseDrag(t):this._mouseUp(t)),!this._mouseStarted)},_mouseUp:function(t){return this.document.unbind("mousemove."+this.widgetName,this._mouseMoveDelegate).unbind("mouseup."+this.widgetName,this._mouseUpDelegate),this._mouseStarted&&(this._mouseStarted=!1,t.target===this._mouseDownEvent.target&&e.data(t.target,this.widgetName+".preventClickEvent",!0),this._mouseStop(t)),o=!1,!1},_mouseDistanceMet:function(e){return Math.max(Math.abs(this._mouseDownEvent.pageX-e.pageX),Math.abs(this._mouseDownEvent.pageY-e.pageY))>=this.options.distance},_mouseDelayMet:function(){return this.mouseDelayMet},_mouseStart:function(){},_mouseDrag:function(){},_mouseStop:function(){},_mouseCapture:function(){return!0}}),function(){function t(e,t,i){return[parseFloat(e[0])*(p.test(e[0])?t/100:1),parseFloat(e[1])*(p.test(e[1])?i/100:1)]}function i(t,i){return parseInt(e.css(t,i),10)||0}function s(t){var i=t[0];return 9===i.nodeType?{width:t.width(),height:t.height(),offset:{top:0,left:0}}:e.isWindow(i)?{width:t.width(),height:t.height(),offset:{top:t.scrollTop(),left:t.scrollLeft()}}:i.preventDefault?{width:0,height:0,offset:{top:i.pageY,left:i.pageX}}:{width:t.outerWidth(),height:t.outerHeight(),offset:t.offset()}}e.ui=e.ui||{};var n,a,o=Math.max,r=Math.abs,h=Math.round,l=/left|center|right/,u=/top|center|bottom/,d=/[\+\-]\d+(\.[\d]+)?%?/,c=/^\w+/,p=/%$/,f=e.fn.position;e.position={scrollbarWidth:function(){if(void 0!==n)return n;var t,i,s=e("<div style='display:block;position:absolute;width:50px;height:50px;overflow:hidden;'><div style='height:100px;width:auto;'></div></div>"),a=s.children()[0];return e("body").append(s),t=a.offsetWidth,s.css("overflow","scroll"),i=a.offsetWidth,t===i&&(i=s[0].clientWidth),s.remove(),n=t-i},getScrollInfo:function(t){var i=t.isWindow||t.isDocument?"":t.element.css("overflow-x"),s=t.isWindow||t.isDocument?"":t.element.css("overflow-y"),n="scroll"===i||"auto"===i&&t.width<t.element[0].scrollWidth,a="scroll"===s||"auto"===s&&t.height<t.element[0].scrollHeight;return{width:a?e.position.scrollbarWidth():0,height:n?e.position.scrollbarWidth():0}},getWithinInfo:function(t){var i=e(t||window),s=e.isWindow(i[0]),n=!!i[0]&&9===i[0].nodeType;return{element:i,isWindow:s,isDocument:n,offset:i.offset()||{left:0,top:0},scrollLeft:i.scrollLeft(),scrollTop:i.scrollTop(),width:s||n?i.width():i.outerWidth(),height:s||n?i.height():i.outerHeight()}}},e.fn.position=function(n){if(!n||!n.of)return f.apply(this,arguments);n=e.extend({},n);var p,m,g,v,y,b,_=e(n.of),x=e.position.getWithinInfo(n.within),w=e.position.getScrollInfo(x),k=(n.collision||"flip").split(" "),T={};return b=s(_),_[0].preventDefault&&(n.at="left top"),m=b.width,g=b.height,v=b.offset,y=e.extend({},v),e.each(["my","at"],function(){var e,t,i=(n[this]||"").split(" ");1===i.length&&(i=l.test(i[0])?i.concat(["center"]):u.test(i[0])?["center"].concat(i):["center","center"]),i[0]=l.test(i[0])?i[0]:"center",i[1]=u.test(i[1])?i[1]:"center",e=d.exec(i[0]),t=d.exec(i[1]),T[this]=[e?e[0]:0,t?t[0]:0],n[this]=[c.exec(i[0])[0],c.exec(i[1])[0]]}),1===k.length&&(k[1]=k[0]),"right"===n.at[0]?y.left+=m:"center"===n.at[0]&&(y.left+=m/2),"bottom"===n.at[1]?y.top+=g:"center"===n.at[1]&&(y.top+=g/2),p=t(T.at,m,g),y.left+=p[0],y.top+=p[1],this.each(function(){var s,l,u=e(this),d=u.outerWidth(),c=u.outerHeight(),f=i(this,"marginLeft"),b=i(this,"marginTop"),D=d+f+i(this,"marginRight")+w.width,S=c+b+i(this,"marginBottom")+w.height,N=e.extend({},y),M=t(T.my,u.outerWidth(),u.outerHeight());"right"===n.my[0]?N.left-=d:"center"===n.my[0]&&(N.left-=d/2),"bottom"===n.my[1]?N.top-=c:"center"===n.my[1]&&(N.top-=c/2),N.left+=M[0],N.top+=M[1],a||(N.left=h(N.left),N.top=h(N.top)),s={marginLeft:f,marginTop:b},e.each(["left","top"],function(t,i){e.ui.position[k[t]]&&e.ui.position[k[t]][i](N,{targetWidth:m,targetHeight:g,elemWidth:d,elemHeight:c,collisionPosition:s,collisionWidth:D,collisionHeight:S,offset:[p[0]+M[0],p[1]+M[1]],my:n.my,at:n.at,within:x,elem:u})}),n.using&&(l=function(e){var t=v.left-N.left,i=t+m-d,s=v.top-N.top,a=s+g-c,h={target:{element:_,left:v.left,top:v.top,width:m,height:g},element:{element:u,left:N.left,top:N.top,width:d,height:c},horizontal:0>i?"left":t>0?"right":"center",vertical:0>a?"top":s>0?"bottom":"middle"};d>m&&m>r(t+i)&&(h.horizontal="center"),c>g&&g>r(s+a)&&(h.vertical="middle"),h.important=o(r(t),r(i))>o(r(s),r(a))?"horizontal":"vertical",n.using.call(this,e,h)}),u.offset(e.extend(N,{using:l}))})},e.ui.position={fit:{left:function(e,t){var i,s=t.within,n=s.isWindow?s.scrollLeft:s.offset.left,a=s.width,r=e.left-t.collisionPosition.marginLeft,h=n-r,l=r+t.collisionWidth-a-n;t.collisionWidth>a?h>0&&0>=l?(i=e.left+h+t.collisionWidth-a-n,e.left+=h-i):e.left=l>0&&0>=h?n:h>l?n+a-t.collisionWidth:n:h>0?e.left+=h:l>0?e.left-=l:e.left=o(e.left-r,e.left)},top:function(e,t){var i,s=t.within,n=s.isWindow?s.scrollTop:s.offset.top,a=t.within.height,r=e.top-t.collisionPosition.marginTop,h=n-r,l=r+t.collisionHeight-a-n;t.collisionHeight>a?h>0&&0>=l?(i=e.top+h+t.collisionHeight-a-n,e.top+=h-i):e.top=l>0&&0>=h?n:h>l?n+a-t.collisionHeight:n:h>0?e.top+=h:l>0?e.top-=l:e.top=o(e.top-r,e.top)}},flip:{left:function(e,t){var i,s,n=t.within,a=n.offset.left+n.scrollLeft,o=n.width,h=n.isWindow?n.scrollLeft:n.offset.left,l=e.left-t.collisionPosition.marginLeft,u=l-h,d=l+t.collisionWidth-o-h,c="left"===t.my[0]?-t.elemWidth:"right"===t.my[0]?t.elemWidth:0,p="left"===t.at[0]?t.targetWidth:"right"===t.at[0]?-t.targetWidth:0,f=-2*t.offset[0];0>u?(i=e.left+c+p+f+t.collisionWidth-o-a,(0>i||r(u)>i)&&(e.left+=c+p+f)):d>0&&(s=e.left-t.collisionPosition.marginLeft+c+p+f-h,(s>0||d>r(s))&&(e.left+=c+p+f))},top:function(e,t){var i,s,n=t.within,a=n.offset.top+n.scrollTop,o=n.height,h=n.isWindow?n.scrollTop:n.offset.top,l=e.top-t.collisionPosition.marginTop,u=l-h,d=l+t.collisionHeight-o-h,c="top"===t.my[1],p=c?-t.elemHeight:"bottom"===t.my[1]?t.elemHeight:0,f="top"===t.at[1]?t.targetHeight:"bottom"===t.at[1]?-t.targetHeight:0,m=-2*t.offset[1];0>u?(s=e.top+p+f+m+t.collisionHeight-o-a,(0>s||r(u)>s)&&(e.top+=p+f+m)):d>0&&(i=e.top-t.collisionPosition.marginTop+p+f+m-h,(i>0||d>r(i))&&(e.top+=p+f+m))}},flipfit:{left:function(){e.ui.position.flip.left.apply(this,arguments),e.ui.position.fit.left.apply(this,arguments)},top:function(){e.ui.position.flip.top.apply(this,arguments),e.ui.position.fit.top.apply(this,arguments)}}},function(){var t,i,s,n,o,r=document.getElementsByTagName("body")[0],h=document.createElement("div");t=document.createElement(r?"div":"body"),s={visibility:"hidden",width:0,height:0,border:0,margin:0,background:"none"},r&&e.extend(s,{position:"absolute",left:"-1000px",top:"-1000px"});for(o in s)t.style[o]=s[o];t.appendChild(h),i=r||document.documentElement,i.insertBefore(t,i.firstChild),h.style.cssText="position: absolute; left: 10.7432222px;",n=e(h).offset().left,a=n>10&&11>n,t.innerHTML="",i.removeChild(t)}()}(),e.ui.position,e.widget("ui.draggable",e.ui.mouse,{version:"1.11.4",widgetEventPrefix:"drag",options:{addClasses:!0,appendTo:"parent",axis:!1,connectToSortable:!1,containment:!1,cursor:"auto",cursorAt:!1,grid:!1,handle:!1,helper:"original",iframeFix:!1,opacity:!1,refreshPositions:!1,revert:!1,revertDuration:500,scope:"default",scroll:!0,scrollSensitivity:20,scrollSpeed:20,snap:!1,snapMode:"both",snapTolerance:20,stack:!1,zIndex:!1,drag:null,start:null,stop:null},_create:function(){"original"===this.options.helper&&this._setPositionRelative(),this.options.addClasses&&this.element.addClass("ui-draggable"),this.options.disabled&&this.element.addClass("ui-draggable-disabled"),this._setHandleClassName(),this._mouseInit()},_setOption:function(e,t){this._super(e,t),"handle"===e&&(this._removeHandleClassName(),this._setHandleClassName())},_destroy:function(){return(this.helper||this.element).is(".ui-draggable-dragging")?(this.destroyOnClear=!0,void 0):(this.element.removeClass("ui-draggable ui-draggable-dragging ui-draggable-disabled"),this._removeHandleClassName(),this._mouseDestroy(),void 0)},_mouseCapture:function(t){var i=this.options;return this._blurActiveElement(t),this.helper||i.disabled||e(t.target).closest(".ui-resizable-handle").length>0?!1:(this.handle=this._getHandle(t),this.handle?(this._blockFrames(i.iframeFix===!0?"iframe":i.iframeFix),!0):!1)},_blockFrames:function(t){this.iframeBlocks=this.document.find(t).map(function(){var t=e(this);return e("<div>").css("position","absolute").appendTo(t.parent()).outerWidth(t.outerWidth()).outerHeight(t.outerHeight()).offset(t.offset())[0]})},_unblockFrames:function(){this.iframeBlocks&&(this.iframeBlocks.remove(),delete this.iframeBlocks)},_blurActiveElement:function(t){var i=this.document[0];if(this.handleElement.is(t.target))try{i.activeElement&&"body"!==i.activeElement.nodeName.toLowerCase()&&e(i.activeElement).blur()}catch(s){}},_mouseStart:function(t){var i=this.options;return this.helper=this._createHelper(t),this.helper.addClass("ui-draggable-dragging"),this._cacheHelperProportions(),e.ui.ddmanager&&(e.ui.ddmanager.current=this),this._cacheMargins(),this.cssPosition=this.helper.css("position"),this.scrollParent=this.helper.scrollParent(!0),this.offsetParent=this.helper.offsetParent(),this.hasFixedAncestor=this.helper.parents().filter(function(){return"fixed"===e(this).css("position")}).length>0,this.positionAbs=this.element.offset(),this._refreshOffsets(t),this.originalPosition=this.position=this._generatePosition(t,!1),this.originalPageX=t.pageX,this.originalPageY=t.pageY,i.cursorAt&&this._adjustOffsetFromHelper(i.cursorAt),this._setContainment(),this._trigger("start",t)===!1?(this._clear(),!1):(this._cacheHelperProportions(),e.ui.ddmanager&&!i.dropBehaviour&&e.ui.ddmanager.prepareOffsets(this,t),this._normalizeRightBottom(),this._mouseDrag(t,!0),e.ui.ddmanager&&e.ui.ddmanager.dragStart(this,t),!0)},_refreshOffsets:function(e){this.offset={top:this.positionAbs.top-this.margins.top,left:this.positionAbs.left-this.margins.left,scroll:!1,parent:this._getParentOffset(),relative:this._getRelativeOffset()},this.offset.click={left:e.pageX-this.offset.left,top:e.pageY-this.offset.top}},_mouseDrag:function(t,i){if(this.hasFixedAncestor&&(this.offset.parent=this._getParentOffset()),this.position=this._generatePosition(t,!0),this.positionAbs=this._convertPositionTo("absolute"),!i){var s=this._uiHash();if(this._trigger("drag",t,s)===!1)return this._mouseUp({}),!1;this.position=s.position}return this.helper[0].style.left=this.position.left+"px",this.helper[0].style.top=this.position.top+"px",e.ui.ddmanager&&e.ui.ddmanager.drag(this,t),!1},_mouseStop:function(t){var i=this,s=!1;return e.ui.ddmanager&&!this.options.dropBehaviour&&(s=e.ui.ddmanager.drop(this,t)),this.dropped&&(s=this.dropped,this.dropped=!1),"invalid"===this.options.revert&&!s||"valid"===this.options.revert&&s||this.options.revert===!0||e.isFunction(this.options.revert)&&this.options.revert.call(this.element,s)?e(this.helper).animate(this.originalPosition,parseInt(this.options.revertDuration,10),function(){i._trigger("stop",t)!==!1&&i._clear()}):this._trigger("stop",t)!==!1&&this._clear(),!1},_mouseUp:function(t){return this._unblockFrames(),e.ui.ddmanager&&e.ui.ddmanager.dragStop(this,t),this.handleElement.is(t.target)&&this.element.focus(),e.ui.mouse.prototype._mouseUp.call(this,t)},cancel:function(){return this.helper.is(".ui-draggable-dragging")?this._mouseUp({}):this._clear(),this},_getHandle:function(t){return this.options.handle?!!e(t.target).closest(this.element.find(this.options.handle)).length:!0},_setHandleClassName:function(){this.handleElement=this.options.handle?this.element.find(this.options.handle):this.element,this.handleElement.addClass("ui-draggable-handle")},_removeHandleClassName:function(){this.handleElement.removeClass("ui-draggable-handle")},_createHelper:function(t){var i=this.options,s=e.isFunction(i.helper),n=s?e(i.helper.apply(this.element[0],[t])):"clone"===i.helper?this.element.clone().removeAttr("id"):this.element;return n.parents("body").length||n.appendTo("parent"===i.appendTo?this.element[0].parentNode:i.appendTo),s&&n[0]===this.element[0]&&this._setPositionRelative(),n[0]===this.element[0]||/(fixed|absolute)/.test(n.css("position"))||n.css("position","absolute"),n},_setPositionRelative:function(){/^(?:r|a|f)/.test(this.element.css("position"))||(this.element[0].style.position="relative")},_adjustOffsetFromHelper:function(t){"string"==typeof t&&(t=t.split(" ")),e.isArray(t)&&(t={left:+t[0],top:+t[1]||0}),"left"in t&&(this.offset.click.left=t.left+this.margins.left),"right"in t&&(this.offset.click.left=this.helperProportions.width-t.right+this.margins.left),"top"in t&&(this.offset.click.top=t.top+this.margins.top),"bottom"in t&&(this.offset.click.top=this.helperProportions.height-t.bottom+this.margins.top)},_isRootNode:function(e){return/(html|body)/i.test(e.tagName)||e===this.document[0]},_getParentOffset:function(){var t=this.offsetParent.offset(),i=this.document[0];return"absolute"===this.cssPosition&&this.scrollParent[0]!==i&&e.contains(this.scrollParent[0],this.offsetParent[0])&&(t.left+=this.scrollParent.scrollLeft(),t.top+=this.scrollParent.scrollTop()),this._isRootNode(this.offsetParent[0])&&(t={top:0,left:0}),{top:t.top+(parseInt(this.offsetParent.css("borderTopWidth"),10)||0),left:t.left+(parseInt(this.offsetParent.css("borderLeftWidth"),10)||0)}},_getRelativeOffset:function(){if("relative"!==this.cssPosition)return{top:0,left:0};var e=this.element.position(),t=this._isRootNode(this.scrollParent[0]);return{top:e.top-(parseInt(this.helper.css("top"),10)||0)+(t?0:this.scrollParent.scrollTop()),left:e.left-(parseInt(this.helper.css("left"),10)||0)+(t?0:this.scrollParent.scrollLeft())}},_cacheMargins:function(){this.margins={left:parseInt(this.element.css("marginLeft"),10)||0,top:parseInt(this.element.css("marginTop"),10)||0,right:parseInt(this.element.css("marginRight"),10)||0,bottom:parseInt(this.element.css("marginBottom"),10)||0}},_cacheHelperProportions:function(){this.helperProportions={width:this.helper.outerWidth(),height:this.helper.outerHeight()}},_setContainment:function(){var t,i,s,n=this.options,a=this.document[0];return this.relativeContainer=null,n.containment?"window"===n.containment?(this.containment=[e(window).scrollLeft()-this.offset.relative.left-this.offset.parent.left,e(window).scrollTop()-this.offset.relative.top-this.offset.parent.top,e(window).scrollLeft()+e(window).width()-this.helperProportions.width-this.margins.left,e(window).scrollTop()+(e(window).height()||a.body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top],void 0):"document"===n.containment?(this.containment=[0,0,e(a).width()-this.helperProportions.width-this.margins.left,(e(a).height()||a.body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top],void 0):n.containment.constructor===Array?(this.containment=n.containment,void 0):("parent"===n.containment&&(n.containment=this.helper[0].parentNode),i=e(n.containment),s=i[0],s&&(t=/(scroll|auto)/.test(i.css("overflow")),this.containment=[(parseInt(i.css("borderLeftWidth"),10)||0)+(parseInt(i.css("paddingLeft"),10)||0),(parseInt(i.css("borderTopWidth"),10)||0)+(parseInt(i.css("paddingTop"),10)||0),(t?Math.max(s.scrollWidth,s.offsetWidth):s.offsetWidth)-(parseInt(i.css("borderRightWidth"),10)||0)-(parseInt(i.css("paddingRight"),10)||0)-this.helperProportions.width-this.margins.left-this.margins.right,(t?Math.max(s.scrollHeight,s.offsetHeight):s.offsetHeight)-(parseInt(i.css("borderBottomWidth"),10)||0)-(parseInt(i.css("paddingBottom"),10)||0)-this.helperProportions.height-this.margins.top-this.margins.bottom],this.relativeContainer=i),void 0):(this.containment=null,void 0)},_convertPositionTo:function(e,t){t||(t=this.position);var i="absolute"===e?1:-1,s=this._isRootNode(this.scrollParent[0]);return{top:t.top+this.offset.relative.top*i+this.offset.parent.top*i-("fixed"===this.cssPosition?-this.offset.scroll.top:s?0:this.offset.scroll.top)*i,left:t.left+this.offset.relative.left*i+this.offset.parent.left*i-("fixed"===this.cssPosition?-this.offset.scroll.left:s?0:this.offset.scroll.left)*i}},_generatePosition:function(e,t){var i,s,n,a,o=this.options,r=this._isRootNode(this.scrollParent[0]),h=e.pageX,l=e.pageY;return r&&this.offset.scroll||(this.offset.scroll={top:this.scrollParent.scrollTop(),left:this.scrollParent.scrollLeft()}),t&&(this.containment&&(this.relativeContainer?(s=this.relativeContainer.offset(),i=[this.containment[0]+s.left,this.containment[1]+s.top,this.containment[2]+s.left,this.containment[3]+s.top]):i=this.containment,e.pageX-this.offset.click.left<i[0]&&(h=i[0]+this.offset.click.left),e.pageY-this.offset.click.top<i[1]&&(l=i[1]+this.offset.click.top),e.pageX-this.offset.click.left>i[2]&&(h=i[2]+this.offset.click.left),e.pageY-this.offset.click.top>i[3]&&(l=i[3]+this.offset.click.top)),o.grid&&(n=o.grid[1]?this.originalPageY+Math.round((l-this.originalPageY)/o.grid[1])*o.grid[1]:this.originalPageY,l=i?n-this.offset.click.top>=i[1]||n-this.offset.click.top>i[3]?n:n-this.offset.click.top>=i[1]?n-o.grid[1]:n+o.grid[1]:n,a=o.grid[0]?this.originalPageX+Math.round((h-this.originalPageX)/o.grid[0])*o.grid[0]:this.originalPageX,h=i?a-this.offset.click.left>=i[0]||a-this.offset.click.left>i[2]?a:a-this.offset.click.left>=i[0]?a-o.grid[0]:a+o.grid[0]:a),"y"===o.axis&&(h=this.originalPageX),"x"===o.axis&&(l=this.originalPageY)),{top:l-this.offset.click.top-this.offset.relative.top-this.offset.parent.top+("fixed"===this.cssPosition?-this.offset.scroll.top:r?0:this.offset.scroll.top),left:h-this.offset.click.left-this.offset.relative.left-this.offset.parent.left+("fixed"===this.cssPosition?-this.offset.scroll.left:r?0:this.offset.scroll.left)}},_clear:function(){this.helper.removeClass("ui-draggable-dragging"),this.helper[0]===this.element[0]||this.cancelHelperRemoval||this.helper.remove(),this.helper=null,this.cancelHelperRemoval=!1,this.destroyOnClear&&this.destroy()},_normalizeRightBottom:function(){"y"!==this.options.axis&&"auto"!==this.helper.css("right")&&(this.helper.width(this.helper.width()),this.helper.css("right","auto")),"x"!==this.options.axis&&"auto"!==this.helper.css("bottom")&&(this.helper.height(this.helper.height()),this.helper.css("bottom","auto"))},_trigger:function(t,i,s){return s=s||this._uiHash(),e.ui.plugin.call(this,t,[i,s,this],!0),/^(drag|start|stop)/.test(t)&&(this.positionAbs=this._convertPositionTo("absolute"),s.offset=this.positionAbs),e.Widget.prototype._trigger.call(this,t,i,s)},plugins:{},_uiHash:function(){return{helper:this.helper,position:this.position,originalPosition:this.originalPosition,offset:this.positionAbs}}}),e.ui.plugin.add("draggable","connectToSortable",{start:function(t,i,s){var n=e.extend({},i,{item:s.element});s.sortables=[],e(s.options.connectToSortable).each(function(){var i=e(this).sortable("instance");i&&!i.options.disabled&&(s.sortables.push(i),i.refreshPositions(),i._trigger("activate",t,n))})},stop:function(t,i,s){var n=e.extend({},i,{item:s.element});s.cancelHelperRemoval=!1,e.each(s.sortables,function(){var e=this;e.isOver?(e.isOver=0,s.cancelHelperRemoval=!0,e.cancelHelperRemoval=!1,e._storedCSS={position:e.placeholder.css("position"),top:e.placeholder.css("top"),left:e.placeholder.css("left")},e._mouseStop(t),e.options.helper=e.options._helper):(e.cancelHelperRemoval=!0,e._trigger("deactivate",t,n))})},drag:function(t,i,s){e.each(s.sortables,function(){var n=!1,a=this;a.positionAbs=s.positionAbs,a.helperProportions=s.helperProportions,a.offset.click=s.offset.click,a._intersectsWith(a.containerCache)&&(n=!0,e.each(s.sortables,function(){return this.positionAbs=s.positionAbs,this.helperProportions=s.helperProportions,this.offset.click=s.offset.click,this!==a&&this._intersectsWith(this.containerCache)&&e.contains(a.element[0],this.element[0])&&(n=!1),n +})),n?(a.isOver||(a.isOver=1,s._parent=i.helper.parent(),a.currentItem=i.helper.appendTo(a.element).data("ui-sortable-item",!0),a.options._helper=a.options.helper,a.options.helper=function(){return i.helper[0]},t.target=a.currentItem[0],a._mouseCapture(t,!0),a._mouseStart(t,!0,!0),a.offset.click.top=s.offset.click.top,a.offset.click.left=s.offset.click.left,a.offset.parent.left-=s.offset.parent.left-a.offset.parent.left,a.offset.parent.top-=s.offset.parent.top-a.offset.parent.top,s._trigger("toSortable",t),s.dropped=a.element,e.each(s.sortables,function(){this.refreshPositions()}),s.currentItem=s.element,a.fromOutside=s),a.currentItem&&(a._mouseDrag(t),i.position=a.position)):a.isOver&&(a.isOver=0,a.cancelHelperRemoval=!0,a.options._revert=a.options.revert,a.options.revert=!1,a._trigger("out",t,a._uiHash(a)),a._mouseStop(t,!0),a.options.revert=a.options._revert,a.options.helper=a.options._helper,a.placeholder&&a.placeholder.remove(),i.helper.appendTo(s._parent),s._refreshOffsets(t),i.position=s._generatePosition(t,!0),s._trigger("fromSortable",t),s.dropped=!1,e.each(s.sortables,function(){this.refreshPositions()}))})}}),e.ui.plugin.add("draggable","cursor",{start:function(t,i,s){var n=e("body"),a=s.options;n.css("cursor")&&(a._cursor=n.css("cursor")),n.css("cursor",a.cursor)},stop:function(t,i,s){var n=s.options;n._cursor&&e("body").css("cursor",n._cursor)}}),e.ui.plugin.add("draggable","opacity",{start:function(t,i,s){var n=e(i.helper),a=s.options;n.css("opacity")&&(a._opacity=n.css("opacity")),n.css("opacity",a.opacity)},stop:function(t,i,s){var n=s.options;n._opacity&&e(i.helper).css("opacity",n._opacity)}}),e.ui.plugin.add("draggable","scroll",{start:function(e,t,i){i.scrollParentNotHidden||(i.scrollParentNotHidden=i.helper.scrollParent(!1)),i.scrollParentNotHidden[0]!==i.document[0]&&"HTML"!==i.scrollParentNotHidden[0].tagName&&(i.overflowOffset=i.scrollParentNotHidden.offset())},drag:function(t,i,s){var n=s.options,a=!1,o=s.scrollParentNotHidden[0],r=s.document[0];o!==r&&"HTML"!==o.tagName?(n.axis&&"x"===n.axis||(s.overflowOffset.top+o.offsetHeight-t.pageY<n.scrollSensitivity?o.scrollTop=a=o.scrollTop+n.scrollSpeed:t.pageY-s.overflowOffset.top<n.scrollSensitivity&&(o.scrollTop=a=o.scrollTop-n.scrollSpeed)),n.axis&&"y"===n.axis||(s.overflowOffset.left+o.offsetWidth-t.pageX<n.scrollSensitivity?o.scrollLeft=a=o.scrollLeft+n.scrollSpeed:t.pageX-s.overflowOffset.left<n.scrollSensitivity&&(o.scrollLeft=a=o.scrollLeft-n.scrollSpeed))):(n.axis&&"x"===n.axis||(t.pageY-e(r).scrollTop()<n.scrollSensitivity?a=e(r).scrollTop(e(r).scrollTop()-n.scrollSpeed):e(window).height()-(t.pageY-e(r).scrollTop())<n.scrollSensitivity&&(a=e(r).scrollTop(e(r).scrollTop()+n.scrollSpeed))),n.axis&&"y"===n.axis||(t.pageX-e(r).scrollLeft()<n.scrollSensitivity?a=e(r).scrollLeft(e(r).scrollLeft()-n.scrollSpeed):e(window).width()-(t.pageX-e(r).scrollLeft())<n.scrollSensitivity&&(a=e(r).scrollLeft(e(r).scrollLeft()+n.scrollSpeed)))),a!==!1&&e.ui.ddmanager&&!n.dropBehaviour&&e.ui.ddmanager.prepareOffsets(s,t)}}),e.ui.plugin.add("draggable","snap",{start:function(t,i,s){var n=s.options;s.snapElements=[],e(n.snap.constructor!==String?n.snap.items||":data(ui-draggable)":n.snap).each(function(){var t=e(this),i=t.offset();this!==s.element[0]&&s.snapElements.push({item:this,width:t.outerWidth(),height:t.outerHeight(),top:i.top,left:i.left})})},drag:function(t,i,s){var n,a,o,r,h,l,u,d,c,p,f=s.options,m=f.snapTolerance,g=i.offset.left,v=g+s.helperProportions.width,y=i.offset.top,b=y+s.helperProportions.height;for(c=s.snapElements.length-1;c>=0;c--)h=s.snapElements[c].left-s.margins.left,l=h+s.snapElements[c].width,u=s.snapElements[c].top-s.margins.top,d=u+s.snapElements[c].height,h-m>v||g>l+m||u-m>b||y>d+m||!e.contains(s.snapElements[c].item.ownerDocument,s.snapElements[c].item)?(s.snapElements[c].snapping&&s.options.snap.release&&s.options.snap.release.call(s.element,t,e.extend(s._uiHash(),{snapItem:s.snapElements[c].item})),s.snapElements[c].snapping=!1):("inner"!==f.snapMode&&(n=m>=Math.abs(u-b),a=m>=Math.abs(d-y),o=m>=Math.abs(h-v),r=m>=Math.abs(l-g),n&&(i.position.top=s._convertPositionTo("relative",{top:u-s.helperProportions.height,left:0}).top),a&&(i.position.top=s._convertPositionTo("relative",{top:d,left:0}).top),o&&(i.position.left=s._convertPositionTo("relative",{top:0,left:h-s.helperProportions.width}).left),r&&(i.position.left=s._convertPositionTo("relative",{top:0,left:l}).left)),p=n||a||o||r,"outer"!==f.snapMode&&(n=m>=Math.abs(u-y),a=m>=Math.abs(d-b),o=m>=Math.abs(h-g),r=m>=Math.abs(l-v),n&&(i.position.top=s._convertPositionTo("relative",{top:u,left:0}).top),a&&(i.position.top=s._convertPositionTo("relative",{top:d-s.helperProportions.height,left:0}).top),o&&(i.position.left=s._convertPositionTo("relative",{top:0,left:h}).left),r&&(i.position.left=s._convertPositionTo("relative",{top:0,left:l-s.helperProportions.width}).left)),!s.snapElements[c].snapping&&(n||a||o||r||p)&&s.options.snap.snap&&s.options.snap.snap.call(s.element,t,e.extend(s._uiHash(),{snapItem:s.snapElements[c].item})),s.snapElements[c].snapping=n||a||o||r||p)}}),e.ui.plugin.add("draggable","stack",{start:function(t,i,s){var n,a=s.options,o=e.makeArray(e(a.stack)).sort(function(t,i){return(parseInt(e(t).css("zIndex"),10)||0)-(parseInt(e(i).css("zIndex"),10)||0)});o.length&&(n=parseInt(e(o[0]).css("zIndex"),10)||0,e(o).each(function(t){e(this).css("zIndex",n+t)}),this.css("zIndex",n+o.length))}}),e.ui.plugin.add("draggable","zIndex",{start:function(t,i,s){var n=e(i.helper),a=s.options;n.css("zIndex")&&(a._zIndex=n.css("zIndex")),n.css("zIndex",a.zIndex)},stop:function(t,i,s){var n=s.options;n._zIndex&&e(i.helper).css("zIndex",n._zIndex)}}),e.ui.draggable,e.widget("ui.droppable",{version:"1.11.4",widgetEventPrefix:"drop",options:{accept:"*",activeClass:!1,addClasses:!0,greedy:!1,hoverClass:!1,scope:"default",tolerance:"intersect",activate:null,deactivate:null,drop:null,out:null,over:null},_create:function(){var t,i=this.options,s=i.accept;this.isover=!1,this.isout=!0,this.accept=e.isFunction(s)?s:function(e){return e.is(s)},this.proportions=function(){return arguments.length?(t=arguments[0],void 0):t?t:t={width:this.element[0].offsetWidth,height:this.element[0].offsetHeight}},this._addToManager(i.scope),i.addClasses&&this.element.addClass("ui-droppable")},_addToManager:function(t){e.ui.ddmanager.droppables[t]=e.ui.ddmanager.droppables[t]||[],e.ui.ddmanager.droppables[t].push(this)},_splice:function(e){for(var t=0;e.length>t;t++)e[t]===this&&e.splice(t,1)},_destroy:function(){var t=e.ui.ddmanager.droppables[this.options.scope];this._splice(t),this.element.removeClass("ui-droppable ui-droppable-disabled")},_setOption:function(t,i){if("accept"===t)this.accept=e.isFunction(i)?i:function(e){return e.is(i)};else if("scope"===t){var s=e.ui.ddmanager.droppables[this.options.scope];this._splice(s),this._addToManager(i)}this._super(t,i)},_activate:function(t){var i=e.ui.ddmanager.current;this.options.activeClass&&this.element.addClass(this.options.activeClass),i&&this._trigger("activate",t,this.ui(i))},_deactivate:function(t){var i=e.ui.ddmanager.current;this.options.activeClass&&this.element.removeClass(this.options.activeClass),i&&this._trigger("deactivate",t,this.ui(i))},_over:function(t){var i=e.ui.ddmanager.current;i&&(i.currentItem||i.element)[0]!==this.element[0]&&this.accept.call(this.element[0],i.currentItem||i.element)&&(this.options.hoverClass&&this.element.addClass(this.options.hoverClass),this._trigger("over",t,this.ui(i)))},_out:function(t){var i=e.ui.ddmanager.current;i&&(i.currentItem||i.element)[0]!==this.element[0]&&this.accept.call(this.element[0],i.currentItem||i.element)&&(this.options.hoverClass&&this.element.removeClass(this.options.hoverClass),this._trigger("out",t,this.ui(i)))},_drop:function(t,i){var s=i||e.ui.ddmanager.current,n=!1;return s&&(s.currentItem||s.element)[0]!==this.element[0]?(this.element.find(":data(ui-droppable)").not(".ui-draggable-dragging").each(function(){var i=e(this).droppable("instance");return i.options.greedy&&!i.options.disabled&&i.options.scope===s.options.scope&&i.accept.call(i.element[0],s.currentItem||s.element)&&e.ui.intersect(s,e.extend(i,{offset:i.element.offset()}),i.options.tolerance,t)?(n=!0,!1):void 0}),n?!1:this.accept.call(this.element[0],s.currentItem||s.element)?(this.options.activeClass&&this.element.removeClass(this.options.activeClass),this.options.hoverClass&&this.element.removeClass(this.options.hoverClass),this._trigger("drop",t,this.ui(s)),this.element):!1):!1},ui:function(e){return{draggable:e.currentItem||e.element,helper:e.helper,position:e.position,offset:e.positionAbs}}}),e.ui.intersect=function(){function e(e,t,i){return e>=t&&t+i>e}return function(t,i,s,n){if(!i.offset)return!1;var a=(t.positionAbs||t.position.absolute).left+t.margins.left,o=(t.positionAbs||t.position.absolute).top+t.margins.top,r=a+t.helperProportions.width,h=o+t.helperProportions.height,l=i.offset.left,u=i.offset.top,d=l+i.proportions().width,c=u+i.proportions().height;switch(s){case"fit":return a>=l&&d>=r&&o>=u&&c>=h;case"intersect":return a+t.helperProportions.width/2>l&&d>r-t.helperProportions.width/2&&o+t.helperProportions.height/2>u&&c>h-t.helperProportions.height/2;case"pointer":return e(n.pageY,u,i.proportions().height)&&e(n.pageX,l,i.proportions().width);case"touch":return(o>=u&&c>=o||h>=u&&c>=h||u>o&&h>c)&&(a>=l&&d>=a||r>=l&&d>=r||l>a&&r>d);default:return!1}}}(),e.ui.ddmanager={current:null,droppables:{"default":[]},prepareOffsets:function(t,i){var s,n,a=e.ui.ddmanager.droppables[t.options.scope]||[],o=i?i.type:null,r=(t.currentItem||t.element).find(":data(ui-droppable)").addBack();e:for(s=0;a.length>s;s++)if(!(a[s].options.disabled||t&&!a[s].accept.call(a[s].element[0],t.currentItem||t.element))){for(n=0;r.length>n;n++)if(r[n]===a[s].element[0]){a[s].proportions().height=0;continue e}a[s].visible="none"!==a[s].element.css("display"),a[s].visible&&("mousedown"===o&&a[s]._activate.call(a[s],i),a[s].offset=a[s].element.offset(),a[s].proportions({width:a[s].element[0].offsetWidth,height:a[s].element[0].offsetHeight}))}},drop:function(t,i){var s=!1;return e.each((e.ui.ddmanager.droppables[t.options.scope]||[]).slice(),function(){this.options&&(!this.options.disabled&&this.visible&&e.ui.intersect(t,this,this.options.tolerance,i)&&(s=this._drop.call(this,i)||s),!this.options.disabled&&this.visible&&this.accept.call(this.element[0],t.currentItem||t.element)&&(this.isout=!0,this.isover=!1,this._deactivate.call(this,i)))}),s},dragStart:function(t,i){t.element.parentsUntil("body").bind("scroll.droppable",function(){t.options.refreshPositions||e.ui.ddmanager.prepareOffsets(t,i)})},drag:function(t,i){t.options.refreshPositions&&e.ui.ddmanager.prepareOffsets(t,i),e.each(e.ui.ddmanager.droppables[t.options.scope]||[],function(){if(!this.options.disabled&&!this.greedyChild&&this.visible){var s,n,a,o=e.ui.intersect(t,this,this.options.tolerance,i),r=!o&&this.isover?"isout":o&&!this.isover?"isover":null;r&&(this.options.greedy&&(n=this.options.scope,a=this.element.parents(":data(ui-droppable)").filter(function(){return e(this).droppable("instance").options.scope===n}),a.length&&(s=e(a[0]).droppable("instance"),s.greedyChild="isover"===r)),s&&"isover"===r&&(s.isover=!1,s.isout=!0,s._out.call(s,i)),this[r]=!0,this["isout"===r?"isover":"isout"]=!1,this["isover"===r?"_over":"_out"].call(this,i),s&&"isout"===r&&(s.isout=!1,s.isover=!0,s._over.call(s,i)))}})},dragStop:function(t,i){t.element.parentsUntil("body").unbind("scroll.droppable"),t.options.refreshPositions||e.ui.ddmanager.prepareOffsets(t,i)}},e.ui.droppable,e.widget("ui.resizable",e.ui.mouse,{version:"1.11.4",widgetEventPrefix:"resize",options:{alsoResize:!1,animate:!1,animateDuration:"slow",animateEasing:"swing",aspectRatio:!1,autoHide:!1,containment:!1,ghost:!1,grid:!1,handles:"e,s,se",helper:!1,maxHeight:null,maxWidth:null,minHeight:10,minWidth:10,zIndex:90,resize:null,start:null,stop:null},_num:function(e){return parseInt(e,10)||0},_isNumber:function(e){return!isNaN(parseInt(e,10))},_hasScroll:function(t,i){if("hidden"===e(t).css("overflow"))return!1;var s=i&&"left"===i?"scrollLeft":"scrollTop",n=!1;return t[s]>0?!0:(t[s]=1,n=t[s]>0,t[s]=0,n)},_create:function(){var t,i,s,n,a,o=this,r=this.options;if(this.element.addClass("ui-resizable"),e.extend(this,{_aspectRatio:!!r.aspectRatio,aspectRatio:r.aspectRatio,originalElement:this.element,_proportionallyResizeElements:[],_helper:r.helper||r.ghost||r.animate?r.helper||"ui-resizable-helper":null}),this.element[0].nodeName.match(/^(canvas|textarea|input|select|button|img)$/i)&&(this.element.wrap(e("<div class='ui-wrapper' style='overflow: hidden;'></div>").css({position:this.element.css("position"),width:this.element.outerWidth(),height:this.element.outerHeight(),top:this.element.css("top"),left:this.element.css("left")})),this.element=this.element.parent().data("ui-resizable",this.element.resizable("instance")),this.elementIsWrapper=!0,this.element.css({marginLeft:this.originalElement.css("marginLeft"),marginTop:this.originalElement.css("marginTop"),marginRight:this.originalElement.css("marginRight"),marginBottom:this.originalElement.css("marginBottom")}),this.originalElement.css({marginLeft:0,marginTop:0,marginRight:0,marginBottom:0}),this.originalResizeStyle=this.originalElement.css("resize"),this.originalElement.css("resize","none"),this._proportionallyResizeElements.push(this.originalElement.css({position:"static",zoom:1,display:"block"})),this.originalElement.css({margin:this.originalElement.css("margin")}),this._proportionallyResize()),this.handles=r.handles||(e(".ui-resizable-handle",this.element).length?{n:".ui-resizable-n",e:".ui-resizable-e",s:".ui-resizable-s",w:".ui-resizable-w",se:".ui-resizable-se",sw:".ui-resizable-sw",ne:".ui-resizable-ne",nw:".ui-resizable-nw"}:"e,s,se"),this._handles=e(),this.handles.constructor===String)for("all"===this.handles&&(this.handles="n,e,s,w,se,sw,ne,nw"),t=this.handles.split(","),this.handles={},i=0;t.length>i;i++)s=e.trim(t[i]),a="ui-resizable-"+s,n=e("<div class='ui-resizable-handle "+a+"'></div>"),n.css({zIndex:r.zIndex}),"se"===s&&n.addClass("ui-icon ui-icon-gripsmall-diagonal-se"),this.handles[s]=".ui-resizable-"+s,this.element.append(n);this._renderAxis=function(t){var i,s,n,a;t=t||this.element;for(i in this.handles)this.handles[i].constructor===String?this.handles[i]=this.element.children(this.handles[i]).first().show():(this.handles[i].jquery||this.handles[i].nodeType)&&(this.handles[i]=e(this.handles[i]),this._on(this.handles[i],{mousedown:o._mouseDown})),this.elementIsWrapper&&this.originalElement[0].nodeName.match(/^(textarea|input|select|button)$/i)&&(s=e(this.handles[i],this.element),a=/sw|ne|nw|se|n|s/.test(i)?s.outerHeight():s.outerWidth(),n=["padding",/ne|nw|n/.test(i)?"Top":/se|sw|s/.test(i)?"Bottom":/^e$/.test(i)?"Right":"Left"].join(""),t.css(n,a),this._proportionallyResize()),this._handles=this._handles.add(this.handles[i])},this._renderAxis(this.element),this._handles=this._handles.add(this.element.find(".ui-resizable-handle")),this._handles.disableSelection(),this._handles.mouseover(function(){o.resizing||(this.className&&(n=this.className.match(/ui-resizable-(se|sw|ne|nw|n|e|s|w)/i)),o.axis=n&&n[1]?n[1]:"se")}),r.autoHide&&(this._handles.hide(),e(this.element).addClass("ui-resizable-autohide").mouseenter(function(){r.disabled||(e(this).removeClass("ui-resizable-autohide"),o._handles.show())}).mouseleave(function(){r.disabled||o.resizing||(e(this).addClass("ui-resizable-autohide"),o._handles.hide())})),this._mouseInit()},_destroy:function(){this._mouseDestroy();var t,i=function(t){e(t).removeClass("ui-resizable ui-resizable-disabled ui-resizable-resizing").removeData("resizable").removeData("ui-resizable").unbind(".resizable").find(".ui-resizable-handle").remove()};return this.elementIsWrapper&&(i(this.element),t=this.element,this.originalElement.css({position:t.css("position"),width:t.outerWidth(),height:t.outerHeight(),top:t.css("top"),left:t.css("left")}).insertAfter(t),t.remove()),this.originalElement.css("resize",this.originalResizeStyle),i(this.originalElement),this},_mouseCapture:function(t){var i,s,n=!1;for(i in this.handles)s=e(this.handles[i])[0],(s===t.target||e.contains(s,t.target))&&(n=!0);return!this.options.disabled&&n},_mouseStart:function(t){var i,s,n,a=this.options,o=this.element;return this.resizing=!0,this._renderProxy(),i=this._num(this.helper.css("left")),s=this._num(this.helper.css("top")),a.containment&&(i+=e(a.containment).scrollLeft()||0,s+=e(a.containment).scrollTop()||0),this.offset=this.helper.offset(),this.position={left:i,top:s},this.size=this._helper?{width:this.helper.width(),height:this.helper.height()}:{width:o.width(),height:o.height()},this.originalSize=this._helper?{width:o.outerWidth(),height:o.outerHeight()}:{width:o.width(),height:o.height()},this.sizeDiff={width:o.outerWidth()-o.width(),height:o.outerHeight()-o.height()},this.originalPosition={left:i,top:s},this.originalMousePosition={left:t.pageX,top:t.pageY},this.aspectRatio="number"==typeof a.aspectRatio?a.aspectRatio:this.originalSize.width/this.originalSize.height||1,n=e(".ui-resizable-"+this.axis).css("cursor"),e("body").css("cursor","auto"===n?this.axis+"-resize":n),o.addClass("ui-resizable-resizing"),this._propagate("start",t),!0},_mouseDrag:function(t){var i,s,n=this.originalMousePosition,a=this.axis,o=t.pageX-n.left||0,r=t.pageY-n.top||0,h=this._change[a];return this._updatePrevProperties(),h?(i=h.apply(this,[t,o,r]),this._updateVirtualBoundaries(t.shiftKey),(this._aspectRatio||t.shiftKey)&&(i=this._updateRatio(i,t)),i=this._respectSize(i,t),this._updateCache(i),this._propagate("resize",t),s=this._applyChanges(),!this._helper&&this._proportionallyResizeElements.length&&this._proportionallyResize(),e.isEmptyObject(s)||(this._updatePrevProperties(),this._trigger("resize",t,this.ui()),this._applyChanges()),!1):!1},_mouseStop:function(t){this.resizing=!1;var i,s,n,a,o,r,h,l=this.options,u=this;return this._helper&&(i=this._proportionallyResizeElements,s=i.length&&/textarea/i.test(i[0].nodeName),n=s&&this._hasScroll(i[0],"left")?0:u.sizeDiff.height,a=s?0:u.sizeDiff.width,o={width:u.helper.width()-a,height:u.helper.height()-n},r=parseInt(u.element.css("left"),10)+(u.position.left-u.originalPosition.left)||null,h=parseInt(u.element.css("top"),10)+(u.position.top-u.originalPosition.top)||null,l.animate||this.element.css(e.extend(o,{top:h,left:r})),u.helper.height(u.size.height),u.helper.width(u.size.width),this._helper&&!l.animate&&this._proportionallyResize()),e("body").css("cursor","auto"),this.element.removeClass("ui-resizable-resizing"),this._propagate("stop",t),this._helper&&this.helper.remove(),!1},_updatePrevProperties:function(){this.prevPosition={top:this.position.top,left:this.position.left},this.prevSize={width:this.size.width,height:this.size.height}},_applyChanges:function(){var e={};return this.position.top!==this.prevPosition.top&&(e.top=this.position.top+"px"),this.position.left!==this.prevPosition.left&&(e.left=this.position.left+"px"),this.size.width!==this.prevSize.width&&(e.width=this.size.width+"px"),this.size.height!==this.prevSize.height&&(e.height=this.size.height+"px"),this.helper.css(e),e},_updateVirtualBoundaries:function(e){var t,i,s,n,a,o=this.options;a={minWidth:this._isNumber(o.minWidth)?o.minWidth:0,maxWidth:this._isNumber(o.maxWidth)?o.maxWidth:1/0,minHeight:this._isNumber(o.minHeight)?o.minHeight:0,maxHeight:this._isNumber(o.maxHeight)?o.maxHeight:1/0},(this._aspectRatio||e)&&(t=a.minHeight*this.aspectRatio,s=a.minWidth/this.aspectRatio,i=a.maxHeight*this.aspectRatio,n=a.maxWidth/this.aspectRatio,t>a.minWidth&&(a.minWidth=t),s>a.minHeight&&(a.minHeight=s),a.maxWidth>i&&(a.maxWidth=i),a.maxHeight>n&&(a.maxHeight=n)),this._vBoundaries=a},_updateCache:function(e){this.offset=this.helper.offset(),this._isNumber(e.left)&&(this.position.left=e.left),this._isNumber(e.top)&&(this.position.top=e.top),this._isNumber(e.height)&&(this.size.height=e.height),this._isNumber(e.width)&&(this.size.width=e.width)},_updateRatio:function(e){var t=this.position,i=this.size,s=this.axis;return this._isNumber(e.height)?e.width=e.height*this.aspectRatio:this._isNumber(e.width)&&(e.height=e.width/this.aspectRatio),"sw"===s&&(e.left=t.left+(i.width-e.width),e.top=null),"nw"===s&&(e.top=t.top+(i.height-e.height),e.left=t.left+(i.width-e.width)),e},_respectSize:function(e){var t=this._vBoundaries,i=this.axis,s=this._isNumber(e.width)&&t.maxWidth&&t.maxWidth<e.width,n=this._isNumber(e.height)&&t.maxHeight&&t.maxHeight<e.height,a=this._isNumber(e.width)&&t.minWidth&&t.minWidth>e.width,o=this._isNumber(e.height)&&t.minHeight&&t.minHeight>e.height,r=this.originalPosition.left+this.originalSize.width,h=this.position.top+this.size.height,l=/sw|nw|w/.test(i),u=/nw|ne|n/.test(i);return a&&(e.width=t.minWidth),o&&(e.height=t.minHeight),s&&(e.width=t.maxWidth),n&&(e.height=t.maxHeight),a&&l&&(e.left=r-t.minWidth),s&&l&&(e.left=r-t.maxWidth),o&&u&&(e.top=h-t.minHeight),n&&u&&(e.top=h-t.maxHeight),e.width||e.height||e.left||!e.top?e.width||e.height||e.top||!e.left||(e.left=null):e.top=null,e},_getPaddingPlusBorderDimensions:function(e){for(var t=0,i=[],s=[e.css("borderTopWidth"),e.css("borderRightWidth"),e.css("borderBottomWidth"),e.css("borderLeftWidth")],n=[e.css("paddingTop"),e.css("paddingRight"),e.css("paddingBottom"),e.css("paddingLeft")];4>t;t++)i[t]=parseInt(s[t],10)||0,i[t]+=parseInt(n[t],10)||0;return{height:i[0]+i[2],width:i[1]+i[3]}},_proportionallyResize:function(){if(this._proportionallyResizeElements.length)for(var e,t=0,i=this.helper||this.element;this._proportionallyResizeElements.length>t;t++)e=this._proportionallyResizeElements[t],this.outerDimensions||(this.outerDimensions=this._getPaddingPlusBorderDimensions(e)),e.css({height:i.height()-this.outerDimensions.height||0,width:i.width()-this.outerDimensions.width||0})},_renderProxy:function(){var t=this.element,i=this.options;this.elementOffset=t.offset(),this._helper?(this.helper=this.helper||e("<div style='overflow:hidden;'></div>"),this.helper.addClass(this._helper).css({width:this.element.outerWidth()-1,height:this.element.outerHeight()-1,position:"absolute",left:this.elementOffset.left+"px",top:this.elementOffset.top+"px",zIndex:++i.zIndex}),this.helper.appendTo("body").disableSelection()):this.helper=this.element},_change:{e:function(e,t){return{width:this.originalSize.width+t}},w:function(e,t){var i=this.originalSize,s=this.originalPosition;return{left:s.left+t,width:i.width-t}},n:function(e,t,i){var s=this.originalSize,n=this.originalPosition;return{top:n.top+i,height:s.height-i}},s:function(e,t,i){return{height:this.originalSize.height+i}},se:function(t,i,s){return e.extend(this._change.s.apply(this,arguments),this._change.e.apply(this,[t,i,s]))},sw:function(t,i,s){return e.extend(this._change.s.apply(this,arguments),this._change.w.apply(this,[t,i,s]))},ne:function(t,i,s){return e.extend(this._change.n.apply(this,arguments),this._change.e.apply(this,[t,i,s]))},nw:function(t,i,s){return e.extend(this._change.n.apply(this,arguments),this._change.w.apply(this,[t,i,s]))}},_propagate:function(t,i){e.ui.plugin.call(this,t,[i,this.ui()]),"resize"!==t&&this._trigger(t,i,this.ui())},plugins:{},ui:function(){return{originalElement:this.originalElement,element:this.element,helper:this.helper,position:this.position,size:this.size,originalSize:this.originalSize,originalPosition:this.originalPosition}}}),e.ui.plugin.add("resizable","animate",{stop:function(t){var i=e(this).resizable("instance"),s=i.options,n=i._proportionallyResizeElements,a=n.length&&/textarea/i.test(n[0].nodeName),o=a&&i._hasScroll(n[0],"left")?0:i.sizeDiff.height,r=a?0:i.sizeDiff.width,h={width:i.size.width-r,height:i.size.height-o},l=parseInt(i.element.css("left"),10)+(i.position.left-i.originalPosition.left)||null,u=parseInt(i.element.css("top"),10)+(i.position.top-i.originalPosition.top)||null;i.element.animate(e.extend(h,u&&l?{top:u,left:l}:{}),{duration:s.animateDuration,easing:s.animateEasing,step:function(){var s={width:parseInt(i.element.css("width"),10),height:parseInt(i.element.css("height"),10),top:parseInt(i.element.css("top"),10),left:parseInt(i.element.css("left"),10)};n&&n.length&&e(n[0]).css({width:s.width,height:s.height}),i._updateCache(s),i._propagate("resize",t)}})}}),e.ui.plugin.add("resizable","containment",{start:function(){var t,i,s,n,a,o,r,h=e(this).resizable("instance"),l=h.options,u=h.element,d=l.containment,c=d instanceof e?d.get(0):/parent/.test(d)?u.parent().get(0):d;c&&(h.containerElement=e(c),/document/.test(d)||d===document?(h.containerOffset={left:0,top:0},h.containerPosition={left:0,top:0},h.parentData={element:e(document),left:0,top:0,width:e(document).width(),height:e(document).height()||document.body.parentNode.scrollHeight}):(t=e(c),i=[],e(["Top","Right","Left","Bottom"]).each(function(e,s){i[e]=h._num(t.css("padding"+s))}),h.containerOffset=t.offset(),h.containerPosition=t.position(),h.containerSize={height:t.innerHeight()-i[3],width:t.innerWidth()-i[1]},s=h.containerOffset,n=h.containerSize.height,a=h.containerSize.width,o=h._hasScroll(c,"left")?c.scrollWidth:a,r=h._hasScroll(c)?c.scrollHeight:n,h.parentData={element:c,left:s.left,top:s.top,width:o,height:r}))},resize:function(t){var i,s,n,a,o=e(this).resizable("instance"),r=o.options,h=o.containerOffset,l=o.position,u=o._aspectRatio||t.shiftKey,d={top:0,left:0},c=o.containerElement,p=!0;c[0]!==document&&/static/.test(c.css("position"))&&(d=h),l.left<(o._helper?h.left:0)&&(o.size.width=o.size.width+(o._helper?o.position.left-h.left:o.position.left-d.left),u&&(o.size.height=o.size.width/o.aspectRatio,p=!1),o.position.left=r.helper?h.left:0),l.top<(o._helper?h.top:0)&&(o.size.height=o.size.height+(o._helper?o.position.top-h.top:o.position.top),u&&(o.size.width=o.size.height*o.aspectRatio,p=!1),o.position.top=o._helper?h.top:0),n=o.containerElement.get(0)===o.element.parent().get(0),a=/relative|absolute/.test(o.containerElement.css("position")),n&&a?(o.offset.left=o.parentData.left+o.position.left,o.offset.top=o.parentData.top+o.position.top):(o.offset.left=o.element.offset().left,o.offset.top=o.element.offset().top),i=Math.abs(o.sizeDiff.width+(o._helper?o.offset.left-d.left:o.offset.left-h.left)),s=Math.abs(o.sizeDiff.height+(o._helper?o.offset.top-d.top:o.offset.top-h.top)),i+o.size.width>=o.parentData.width&&(o.size.width=o.parentData.width-i,u&&(o.size.height=o.size.width/o.aspectRatio,p=!1)),s+o.size.height>=o.parentData.height&&(o.size.height=o.parentData.height-s,u&&(o.size.width=o.size.height*o.aspectRatio,p=!1)),p||(o.position.left=o.prevPosition.left,o.position.top=o.prevPosition.top,o.size.width=o.prevSize.width,o.size.height=o.prevSize.height)},stop:function(){var t=e(this).resizable("instance"),i=t.options,s=t.containerOffset,n=t.containerPosition,a=t.containerElement,o=e(t.helper),r=o.offset(),h=o.outerWidth()-t.sizeDiff.width,l=o.outerHeight()-t.sizeDiff.height;t._helper&&!i.animate&&/relative/.test(a.css("position"))&&e(this).css({left:r.left-n.left-s.left,width:h,height:l}),t._helper&&!i.animate&&/static/.test(a.css("position"))&&e(this).css({left:r.left-n.left-s.left,width:h,height:l})}}),e.ui.plugin.add("resizable","alsoResize",{start:function(){var t=e(this).resizable("instance"),i=t.options;e(i.alsoResize).each(function(){var t=e(this);t.data("ui-resizable-alsoresize",{width:parseInt(t.width(),10),height:parseInt(t.height(),10),left:parseInt(t.css("left"),10),top:parseInt(t.css("top"),10)})})},resize:function(t,i){var s=e(this).resizable("instance"),n=s.options,a=s.originalSize,o=s.originalPosition,r={height:s.size.height-a.height||0,width:s.size.width-a.width||0,top:s.position.top-o.top||0,left:s.position.left-o.left||0};e(n.alsoResize).each(function(){var t=e(this),s=e(this).data("ui-resizable-alsoresize"),n={},a=t.parents(i.originalElement[0]).length?["width","height"]:["width","height","top","left"];e.each(a,function(e,t){var i=(s[t]||0)+(r[t]||0);i&&i>=0&&(n[t]=i||null)}),t.css(n)})},stop:function(){e(this).removeData("resizable-alsoresize")}}),e.ui.plugin.add("resizable","ghost",{start:function(){var t=e(this).resizable("instance"),i=t.options,s=t.size;t.ghost=t.originalElement.clone(),t.ghost.css({opacity:.25,display:"block",position:"relative",height:s.height,width:s.width,margin:0,left:0,top:0}).addClass("ui-resizable-ghost").addClass("string"==typeof i.ghost?i.ghost:""),t.ghost.appendTo(t.helper)},resize:function(){var t=e(this).resizable("instance");t.ghost&&t.ghost.css({position:"relative",height:t.size.height,width:t.size.width})},stop:function(){var t=e(this).resizable("instance");t.ghost&&t.helper&&t.helper.get(0).removeChild(t.ghost.get(0))}}),e.ui.plugin.add("resizable","grid",{resize:function(){var t,i=e(this).resizable("instance"),s=i.options,n=i.size,a=i.originalSize,o=i.originalPosition,r=i.axis,h="number"==typeof s.grid?[s.grid,s.grid]:s.grid,l=h[0]||1,u=h[1]||1,d=Math.round((n.width-a.width)/l)*l,c=Math.round((n.height-a.height)/u)*u,p=a.width+d,f=a.height+c,m=s.maxWidth&&p>s.maxWidth,g=s.maxHeight&&f>s.maxHeight,v=s.minWidth&&s.minWidth>p,y=s.minHeight&&s.minHeight>f;s.grid=h,v&&(p+=l),y&&(f+=u),m&&(p-=l),g&&(f-=u),/^(se|s|e)$/.test(r)?(i.size.width=p,i.size.height=f):/^(ne)$/.test(r)?(i.size.width=p,i.size.height=f,i.position.top=o.top-c):/^(sw)$/.test(r)?(i.size.width=p,i.size.height=f,i.position.left=o.left-d):((0>=f-u||0>=p-l)&&(t=i._getPaddingPlusBorderDimensions(this)),f-u>0?(i.size.height=f,i.position.top=o.top-c):(f=u-t.height,i.size.height=f,i.position.top=o.top+a.height-f),p-l>0?(i.size.width=p,i.position.left=o.left-d):(p=l-t.width,i.size.width=p,i.position.left=o.left+a.width-p))}}),e.ui.resizable,e.widget("ui.selectable",e.ui.mouse,{version:"1.11.4",options:{appendTo:"body",autoRefresh:!0,distance:0,filter:"*",tolerance:"touch",selected:null,selecting:null,start:null,stop:null,unselected:null,unselecting:null},_create:function(){var t,i=this;this.element.addClass("ui-selectable"),this.dragged=!1,this.refresh=function(){t=e(i.options.filter,i.element[0]),t.addClass("ui-selectee"),t.each(function(){var t=e(this),i=t.offset();e.data(this,"selectable-item",{element:this,$element:t,left:i.left,top:i.top,right:i.left+t.outerWidth(),bottom:i.top+t.outerHeight(),startselected:!1,selected:t.hasClass("ui-selected"),selecting:t.hasClass("ui-selecting"),unselecting:t.hasClass("ui-unselecting")})})},this.refresh(),this.selectees=t.addClass("ui-selectee"),this._mouseInit(),this.helper=e("<div class='ui-selectable-helper'></div>")},_destroy:function(){this.selectees.removeClass("ui-selectee").removeData("selectable-item"),this.element.removeClass("ui-selectable ui-selectable-disabled"),this._mouseDestroy()},_mouseStart:function(t){var i=this,s=this.options;this.opos=[t.pageX,t.pageY],this.options.disabled||(this.selectees=e(s.filter,this.element[0]),this._trigger("start",t),e(s.appendTo).append(this.helper),this.helper.css({left:t.pageX,top:t.pageY,width:0,height:0}),s.autoRefresh&&this.refresh(),this.selectees.filter(".ui-selected").each(function(){var s=e.data(this,"selectable-item");s.startselected=!0,t.metaKey||t.ctrlKey||(s.$element.removeClass("ui-selected"),s.selected=!1,s.$element.addClass("ui-unselecting"),s.unselecting=!0,i._trigger("unselecting",t,{unselecting:s.element}))}),e(t.target).parents().addBack().each(function(){var s,n=e.data(this,"selectable-item");return n?(s=!t.metaKey&&!t.ctrlKey||!n.$element.hasClass("ui-selected"),n.$element.removeClass(s?"ui-unselecting":"ui-selected").addClass(s?"ui-selecting":"ui-unselecting"),n.unselecting=!s,n.selecting=s,n.selected=s,s?i._trigger("selecting",t,{selecting:n.element}):i._trigger("unselecting",t,{unselecting:n.element}),!1):void 0}))},_mouseDrag:function(t){if(this.dragged=!0,!this.options.disabled){var i,s=this,n=this.options,a=this.opos[0],o=this.opos[1],r=t.pageX,h=t.pageY;return a>r&&(i=r,r=a,a=i),o>h&&(i=h,h=o,o=i),this.helper.css({left:a,top:o,width:r-a,height:h-o}),this.selectees.each(function(){var i=e.data(this,"selectable-item"),l=!1; +i&&i.element!==s.element[0]&&("touch"===n.tolerance?l=!(i.left>r||a>i.right||i.top>h||o>i.bottom):"fit"===n.tolerance&&(l=i.left>a&&r>i.right&&i.top>o&&h>i.bottom),l?(i.selected&&(i.$element.removeClass("ui-selected"),i.selected=!1),i.unselecting&&(i.$element.removeClass("ui-unselecting"),i.unselecting=!1),i.selecting||(i.$element.addClass("ui-selecting"),i.selecting=!0,s._trigger("selecting",t,{selecting:i.element}))):(i.selecting&&((t.metaKey||t.ctrlKey)&&i.startselected?(i.$element.removeClass("ui-selecting"),i.selecting=!1,i.$element.addClass("ui-selected"),i.selected=!0):(i.$element.removeClass("ui-selecting"),i.selecting=!1,i.startselected&&(i.$element.addClass("ui-unselecting"),i.unselecting=!0),s._trigger("unselecting",t,{unselecting:i.element}))),i.selected&&(t.metaKey||t.ctrlKey||i.startselected||(i.$element.removeClass("ui-selected"),i.selected=!1,i.$element.addClass("ui-unselecting"),i.unselecting=!0,s._trigger("unselecting",t,{unselecting:i.element})))))}),!1}},_mouseStop:function(t){var i=this;return this.dragged=!1,e(".ui-unselecting",this.element[0]).each(function(){var s=e.data(this,"selectable-item");s.$element.removeClass("ui-unselecting"),s.unselecting=!1,s.startselected=!1,i._trigger("unselected",t,{unselected:s.element})}),e(".ui-selecting",this.element[0]).each(function(){var s=e.data(this,"selectable-item");s.$element.removeClass("ui-selecting").addClass("ui-selected"),s.selecting=!1,s.selected=!0,s.startselected=!0,i._trigger("selected",t,{selected:s.element})}),this._trigger("stop",t),this.helper.remove(),!1}}),e.widget("ui.sortable",e.ui.mouse,{version:"1.11.4",widgetEventPrefix:"sort",ready:!1,options:{appendTo:"parent",axis:!1,connectWith:!1,containment:!1,cursor:"auto",cursorAt:!1,dropOnEmpty:!0,forcePlaceholderSize:!1,forceHelperSize:!1,grid:!1,handle:!1,helper:"original",items:"> *",opacity:!1,placeholder:!1,revert:!1,scroll:!0,scrollSensitivity:20,scrollSpeed:20,scope:"default",tolerance:"intersect",zIndex:1e3,activate:null,beforeStop:null,change:null,deactivate:null,out:null,over:null,receive:null,remove:null,sort:null,start:null,stop:null,update:null},_isOverAxis:function(e,t,i){return e>=t&&t+i>e},_isFloating:function(e){return/left|right/.test(e.css("float"))||/inline|table-cell/.test(e.css("display"))},_create:function(){this.containerCache={},this.element.addClass("ui-sortable"),this.refresh(),this.offset=this.element.offset(),this._mouseInit(),this._setHandleClassName(),this.ready=!0},_setOption:function(e,t){this._super(e,t),"handle"===e&&this._setHandleClassName()},_setHandleClassName:function(){this.element.find(".ui-sortable-handle").removeClass("ui-sortable-handle"),e.each(this.items,function(){(this.instance.options.handle?this.item.find(this.instance.options.handle):this.item).addClass("ui-sortable-handle")})},_destroy:function(){this.element.removeClass("ui-sortable ui-sortable-disabled").find(".ui-sortable-handle").removeClass("ui-sortable-handle"),this._mouseDestroy();for(var e=this.items.length-1;e>=0;e--)this.items[e].item.removeData(this.widgetName+"-item");return this},_mouseCapture:function(t,i){var s=null,n=!1,a=this;return this.reverting?!1:this.options.disabled||"static"===this.options.type?!1:(this._refreshItems(t),e(t.target).parents().each(function(){return e.data(this,a.widgetName+"-item")===a?(s=e(this),!1):void 0}),e.data(t.target,a.widgetName+"-item")===a&&(s=e(t.target)),s?!this.options.handle||i||(e(this.options.handle,s).find("*").addBack().each(function(){this===t.target&&(n=!0)}),n)?(this.currentItem=s,this._removeCurrentsFromItems(),!0):!1:!1)},_mouseStart:function(t,i,s){var n,a,o=this.options;if(this.currentContainer=this,this.refreshPositions(),this.helper=this._createHelper(t),this._cacheHelperProportions(),this._cacheMargins(),this.scrollParent=this.helper.scrollParent(),this.offset=this.currentItem.offset(),this.offset={top:this.offset.top-this.margins.top,left:this.offset.left-this.margins.left},e.extend(this.offset,{click:{left:t.pageX-this.offset.left,top:t.pageY-this.offset.top},parent:this._getParentOffset(),relative:this._getRelativeOffset()}),this.helper.css("position","absolute"),this.cssPosition=this.helper.css("position"),this.originalPosition=this._generatePosition(t),this.originalPageX=t.pageX,this.originalPageY=t.pageY,o.cursorAt&&this._adjustOffsetFromHelper(o.cursorAt),this.domPosition={prev:this.currentItem.prev()[0],parent:this.currentItem.parent()[0]},this.helper[0]!==this.currentItem[0]&&this.currentItem.hide(),this._createPlaceholder(),o.containment&&this._setContainment(),o.cursor&&"auto"!==o.cursor&&(a=this.document.find("body"),this.storedCursor=a.css("cursor"),a.css("cursor",o.cursor),this.storedStylesheet=e("<style>*{ cursor: "+o.cursor+" !important; }</style>").appendTo(a)),o.opacity&&(this.helper.css("opacity")&&(this._storedOpacity=this.helper.css("opacity")),this.helper.css("opacity",o.opacity)),o.zIndex&&(this.helper.css("zIndex")&&(this._storedZIndex=this.helper.css("zIndex")),this.helper.css("zIndex",o.zIndex)),this.scrollParent[0]!==this.document[0]&&"HTML"!==this.scrollParent[0].tagName&&(this.overflowOffset=this.scrollParent.offset()),this._trigger("start",t,this._uiHash()),this._preserveHelperProportions||this._cacheHelperProportions(),!s)for(n=this.containers.length-1;n>=0;n--)this.containers[n]._trigger("activate",t,this._uiHash(this));return e.ui.ddmanager&&(e.ui.ddmanager.current=this),e.ui.ddmanager&&!o.dropBehaviour&&e.ui.ddmanager.prepareOffsets(this,t),this.dragging=!0,this.helper.addClass("ui-sortable-helper"),this._mouseDrag(t),!0},_mouseDrag:function(t){var i,s,n,a,o=this.options,r=!1;for(this.position=this._generatePosition(t),this.positionAbs=this._convertPositionTo("absolute"),this.lastPositionAbs||(this.lastPositionAbs=this.positionAbs),this.options.scroll&&(this.scrollParent[0]!==this.document[0]&&"HTML"!==this.scrollParent[0].tagName?(this.overflowOffset.top+this.scrollParent[0].offsetHeight-t.pageY<o.scrollSensitivity?this.scrollParent[0].scrollTop=r=this.scrollParent[0].scrollTop+o.scrollSpeed:t.pageY-this.overflowOffset.top<o.scrollSensitivity&&(this.scrollParent[0].scrollTop=r=this.scrollParent[0].scrollTop-o.scrollSpeed),this.overflowOffset.left+this.scrollParent[0].offsetWidth-t.pageX<o.scrollSensitivity?this.scrollParent[0].scrollLeft=r=this.scrollParent[0].scrollLeft+o.scrollSpeed:t.pageX-this.overflowOffset.left<o.scrollSensitivity&&(this.scrollParent[0].scrollLeft=r=this.scrollParent[0].scrollLeft-o.scrollSpeed)):(t.pageY-this.document.scrollTop()<o.scrollSensitivity?r=this.document.scrollTop(this.document.scrollTop()-o.scrollSpeed):this.window.height()-(t.pageY-this.document.scrollTop())<o.scrollSensitivity&&(r=this.document.scrollTop(this.document.scrollTop()+o.scrollSpeed)),t.pageX-this.document.scrollLeft()<o.scrollSensitivity?r=this.document.scrollLeft(this.document.scrollLeft()-o.scrollSpeed):this.window.width()-(t.pageX-this.document.scrollLeft())<o.scrollSensitivity&&(r=this.document.scrollLeft(this.document.scrollLeft()+o.scrollSpeed))),r!==!1&&e.ui.ddmanager&&!o.dropBehaviour&&e.ui.ddmanager.prepareOffsets(this,t)),this.positionAbs=this._convertPositionTo("absolute"),this.options.axis&&"y"===this.options.axis||(this.helper[0].style.left=this.position.left+"px"),this.options.axis&&"x"===this.options.axis||(this.helper[0].style.top=this.position.top+"px"),i=this.items.length-1;i>=0;i--)if(s=this.items[i],n=s.item[0],a=this._intersectsWithPointer(s),a&&s.instance===this.currentContainer&&n!==this.currentItem[0]&&this.placeholder[1===a?"next":"prev"]()[0]!==n&&!e.contains(this.placeholder[0],n)&&("semi-dynamic"===this.options.type?!e.contains(this.element[0],n):!0)){if(this.direction=1===a?"down":"up","pointer"!==this.options.tolerance&&!this._intersectsWithSides(s))break;this._rearrange(t,s),this._trigger("change",t,this._uiHash());break}return this._contactContainers(t),e.ui.ddmanager&&e.ui.ddmanager.drag(this,t),this._trigger("sort",t,this._uiHash()),this.lastPositionAbs=this.positionAbs,!1},_mouseStop:function(t,i){if(t){if(e.ui.ddmanager&&!this.options.dropBehaviour&&e.ui.ddmanager.drop(this,t),this.options.revert){var s=this,n=this.placeholder.offset(),a=this.options.axis,o={};a&&"x"!==a||(o.left=n.left-this.offset.parent.left-this.margins.left+(this.offsetParent[0]===this.document[0].body?0:this.offsetParent[0].scrollLeft)),a&&"y"!==a||(o.top=n.top-this.offset.parent.top-this.margins.top+(this.offsetParent[0]===this.document[0].body?0:this.offsetParent[0].scrollTop)),this.reverting=!0,e(this.helper).animate(o,parseInt(this.options.revert,10)||500,function(){s._clear(t)})}else this._clear(t,i);return!1}},cancel:function(){if(this.dragging){this._mouseUp({target:null}),"original"===this.options.helper?this.currentItem.css(this._storedCSS).removeClass("ui-sortable-helper"):this.currentItem.show();for(var t=this.containers.length-1;t>=0;t--)this.containers[t]._trigger("deactivate",null,this._uiHash(this)),this.containers[t].containerCache.over&&(this.containers[t]._trigger("out",null,this._uiHash(this)),this.containers[t].containerCache.over=0)}return this.placeholder&&(this.placeholder[0].parentNode&&this.placeholder[0].parentNode.removeChild(this.placeholder[0]),"original"!==this.options.helper&&this.helper&&this.helper[0].parentNode&&this.helper.remove(),e.extend(this,{helper:null,dragging:!1,reverting:!1,_noFinalSort:null}),this.domPosition.prev?e(this.domPosition.prev).after(this.currentItem):e(this.domPosition.parent).prepend(this.currentItem)),this},serialize:function(t){var i=this._getItemsAsjQuery(t&&t.connected),s=[];return t=t||{},e(i).each(function(){var i=(e(t.item||this).attr(t.attribute||"id")||"").match(t.expression||/(.+)[\-=_](.+)/);i&&s.push((t.key||i[1]+"[]")+"="+(t.key&&t.expression?i[1]:i[2]))}),!s.length&&t.key&&s.push(t.key+"="),s.join("&")},toArray:function(t){var i=this._getItemsAsjQuery(t&&t.connected),s=[];return t=t||{},i.each(function(){s.push(e(t.item||this).attr(t.attribute||"id")||"")}),s},_intersectsWith:function(e){var t=this.positionAbs.left,i=t+this.helperProportions.width,s=this.positionAbs.top,n=s+this.helperProportions.height,a=e.left,o=a+e.width,r=e.top,h=r+e.height,l=this.offset.click.top,u=this.offset.click.left,d="x"===this.options.axis||s+l>r&&h>s+l,c="y"===this.options.axis||t+u>a&&o>t+u,p=d&&c;return"pointer"===this.options.tolerance||this.options.forcePointerForContainers||"pointer"!==this.options.tolerance&&this.helperProportions[this.floating?"width":"height"]>e[this.floating?"width":"height"]?p:t+this.helperProportions.width/2>a&&o>i-this.helperProportions.width/2&&s+this.helperProportions.height/2>r&&h>n-this.helperProportions.height/2},_intersectsWithPointer:function(e){var t="x"===this.options.axis||this._isOverAxis(this.positionAbs.top+this.offset.click.top,e.top,e.height),i="y"===this.options.axis||this._isOverAxis(this.positionAbs.left+this.offset.click.left,e.left,e.width),s=t&&i,n=this._getDragVerticalDirection(),a=this._getDragHorizontalDirection();return s?this.floating?a&&"right"===a||"down"===n?2:1:n&&("down"===n?2:1):!1},_intersectsWithSides:function(e){var t=this._isOverAxis(this.positionAbs.top+this.offset.click.top,e.top+e.height/2,e.height),i=this._isOverAxis(this.positionAbs.left+this.offset.click.left,e.left+e.width/2,e.width),s=this._getDragVerticalDirection(),n=this._getDragHorizontalDirection();return this.floating&&n?"right"===n&&i||"left"===n&&!i:s&&("down"===s&&t||"up"===s&&!t)},_getDragVerticalDirection:function(){var e=this.positionAbs.top-this.lastPositionAbs.top;return 0!==e&&(e>0?"down":"up")},_getDragHorizontalDirection:function(){var e=this.positionAbs.left-this.lastPositionAbs.left;return 0!==e&&(e>0?"right":"left")},refresh:function(e){return this._refreshItems(e),this._setHandleClassName(),this.refreshPositions(),this},_connectWith:function(){var e=this.options;return e.connectWith.constructor===String?[e.connectWith]:e.connectWith},_getItemsAsjQuery:function(t){function i(){r.push(this)}var s,n,a,o,r=[],h=[],l=this._connectWith();if(l&&t)for(s=l.length-1;s>=0;s--)for(a=e(l[s],this.document[0]),n=a.length-1;n>=0;n--)o=e.data(a[n],this.widgetFullName),o&&o!==this&&!o.options.disabled&&h.push([e.isFunction(o.options.items)?o.options.items.call(o.element):e(o.options.items,o.element).not(".ui-sortable-helper").not(".ui-sortable-placeholder"),o]);for(h.push([e.isFunction(this.options.items)?this.options.items.call(this.element,null,{options:this.options,item:this.currentItem}):e(this.options.items,this.element).not(".ui-sortable-helper").not(".ui-sortable-placeholder"),this]),s=h.length-1;s>=0;s--)h[s][0].each(i);return e(r)},_removeCurrentsFromItems:function(){var t=this.currentItem.find(":data("+this.widgetName+"-item)");this.items=e.grep(this.items,function(e){for(var i=0;t.length>i;i++)if(t[i]===e.item[0])return!1;return!0})},_refreshItems:function(t){this.items=[],this.containers=[this];var i,s,n,a,o,r,h,l,u=this.items,d=[[e.isFunction(this.options.items)?this.options.items.call(this.element[0],t,{item:this.currentItem}):e(this.options.items,this.element),this]],c=this._connectWith();if(c&&this.ready)for(i=c.length-1;i>=0;i--)for(n=e(c[i],this.document[0]),s=n.length-1;s>=0;s--)a=e.data(n[s],this.widgetFullName),a&&a!==this&&!a.options.disabled&&(d.push([e.isFunction(a.options.items)?a.options.items.call(a.element[0],t,{item:this.currentItem}):e(a.options.items,a.element),a]),this.containers.push(a));for(i=d.length-1;i>=0;i--)for(o=d[i][1],r=d[i][0],s=0,l=r.length;l>s;s++)h=e(r[s]),h.data(this.widgetName+"-item",o),u.push({item:h,instance:o,width:0,height:0,left:0,top:0})},refreshPositions:function(t){this.floating=this.items.length?"x"===this.options.axis||this._isFloating(this.items[0].item):!1,this.offsetParent&&this.helper&&(this.offset.parent=this._getParentOffset());var i,s,n,a;for(i=this.items.length-1;i>=0;i--)s=this.items[i],s.instance!==this.currentContainer&&this.currentContainer&&s.item[0]!==this.currentItem[0]||(n=this.options.toleranceElement?e(this.options.toleranceElement,s.item):s.item,t||(s.width=n.outerWidth(),s.height=n.outerHeight()),a=n.offset(),s.left=a.left,s.top=a.top);if(this.options.custom&&this.options.custom.refreshContainers)this.options.custom.refreshContainers.call(this);else for(i=this.containers.length-1;i>=0;i--)a=this.containers[i].element.offset(),this.containers[i].containerCache.left=a.left,this.containers[i].containerCache.top=a.top,this.containers[i].containerCache.width=this.containers[i].element.outerWidth(),this.containers[i].containerCache.height=this.containers[i].element.outerHeight();return this},_createPlaceholder:function(t){t=t||this;var i,s=t.options;s.placeholder&&s.placeholder.constructor!==String||(i=s.placeholder,s.placeholder={element:function(){var s=t.currentItem[0].nodeName.toLowerCase(),n=e("<"+s+">",t.document[0]).addClass(i||t.currentItem[0].className+" ui-sortable-placeholder").removeClass("ui-sortable-helper");return"tbody"===s?t._createTrPlaceholder(t.currentItem.find("tr").eq(0),e("<tr>",t.document[0]).appendTo(n)):"tr"===s?t._createTrPlaceholder(t.currentItem,n):"img"===s&&n.attr("src",t.currentItem.attr("src")),i||n.css("visibility","hidden"),n},update:function(e,n){(!i||s.forcePlaceholderSize)&&(n.height()||n.height(t.currentItem.innerHeight()-parseInt(t.currentItem.css("paddingTop")||0,10)-parseInt(t.currentItem.css("paddingBottom")||0,10)),n.width()||n.width(t.currentItem.innerWidth()-parseInt(t.currentItem.css("paddingLeft")||0,10)-parseInt(t.currentItem.css("paddingRight")||0,10)))}}),t.placeholder=e(s.placeholder.element.call(t.element,t.currentItem)),t.currentItem.after(t.placeholder),s.placeholder.update(t,t.placeholder)},_createTrPlaceholder:function(t,i){var s=this;t.children().each(function(){e("<td> </td>",s.document[0]).attr("colspan",e(this).attr("colspan")||1).appendTo(i)})},_contactContainers:function(t){var i,s,n,a,o,r,h,l,u,d,c=null,p=null;for(i=this.containers.length-1;i>=0;i--)if(!e.contains(this.currentItem[0],this.containers[i].element[0]))if(this._intersectsWith(this.containers[i].containerCache)){if(c&&e.contains(this.containers[i].element[0],c.element[0]))continue;c=this.containers[i],p=i}else this.containers[i].containerCache.over&&(this.containers[i]._trigger("out",t,this._uiHash(this)),this.containers[i].containerCache.over=0);if(c)if(1===this.containers.length)this.containers[p].containerCache.over||(this.containers[p]._trigger("over",t,this._uiHash(this)),this.containers[p].containerCache.over=1);else{for(n=1e4,a=null,u=c.floating||this._isFloating(this.currentItem),o=u?"left":"top",r=u?"width":"height",d=u?"clientX":"clientY",s=this.items.length-1;s>=0;s--)e.contains(this.containers[p].element[0],this.items[s].item[0])&&this.items[s].item[0]!==this.currentItem[0]&&(h=this.items[s].item.offset()[o],l=!1,t[d]-h>this.items[s][r]/2&&(l=!0),n>Math.abs(t[d]-h)&&(n=Math.abs(t[d]-h),a=this.items[s],this.direction=l?"up":"down"));if(!a&&!this.options.dropOnEmpty)return;if(this.currentContainer===this.containers[p])return this.currentContainer.containerCache.over||(this.containers[p]._trigger("over",t,this._uiHash()),this.currentContainer.containerCache.over=1),void 0;a?this._rearrange(t,a,null,!0):this._rearrange(t,null,this.containers[p].element,!0),this._trigger("change",t,this._uiHash()),this.containers[p]._trigger("change",t,this._uiHash(this)),this.currentContainer=this.containers[p],this.options.placeholder.update(this.currentContainer,this.placeholder),this.containers[p]._trigger("over",t,this._uiHash(this)),this.containers[p].containerCache.over=1}},_createHelper:function(t){var i=this.options,s=e.isFunction(i.helper)?e(i.helper.apply(this.element[0],[t,this.currentItem])):"clone"===i.helper?this.currentItem.clone():this.currentItem;return s.parents("body").length||e("parent"!==i.appendTo?i.appendTo:this.currentItem[0].parentNode)[0].appendChild(s[0]),s[0]===this.currentItem[0]&&(this._storedCSS={width:this.currentItem[0].style.width,height:this.currentItem[0].style.height,position:this.currentItem.css("position"),top:this.currentItem.css("top"),left:this.currentItem.css("left")}),(!s[0].style.width||i.forceHelperSize)&&s.width(this.currentItem.width()),(!s[0].style.height||i.forceHelperSize)&&s.height(this.currentItem.height()),s},_adjustOffsetFromHelper:function(t){"string"==typeof t&&(t=t.split(" ")),e.isArray(t)&&(t={left:+t[0],top:+t[1]||0}),"left"in t&&(this.offset.click.left=t.left+this.margins.left),"right"in t&&(this.offset.click.left=this.helperProportions.width-t.right+this.margins.left),"top"in t&&(this.offset.click.top=t.top+this.margins.top),"bottom"in t&&(this.offset.click.top=this.helperProportions.height-t.bottom+this.margins.top)},_getParentOffset:function(){this.offsetParent=this.helper.offsetParent();var t=this.offsetParent.offset();return"absolute"===this.cssPosition&&this.scrollParent[0]!==this.document[0]&&e.contains(this.scrollParent[0],this.offsetParent[0])&&(t.left+=this.scrollParent.scrollLeft(),t.top+=this.scrollParent.scrollTop()),(this.offsetParent[0]===this.document[0].body||this.offsetParent[0].tagName&&"html"===this.offsetParent[0].tagName.toLowerCase()&&e.ui.ie)&&(t={top:0,left:0}),{top:t.top+(parseInt(this.offsetParent.css("borderTopWidth"),10)||0),left:t.left+(parseInt(this.offsetParent.css("borderLeftWidth"),10)||0)}},_getRelativeOffset:function(){if("relative"===this.cssPosition){var e=this.currentItem.position();return{top:e.top-(parseInt(this.helper.css("top"),10)||0)+this.scrollParent.scrollTop(),left:e.left-(parseInt(this.helper.css("left"),10)||0)+this.scrollParent.scrollLeft()}}return{top:0,left:0}},_cacheMargins:function(){this.margins={left:parseInt(this.currentItem.css("marginLeft"),10)||0,top:parseInt(this.currentItem.css("marginTop"),10)||0}},_cacheHelperProportions:function(){this.helperProportions={width:this.helper.outerWidth(),height:this.helper.outerHeight()}},_setContainment:function(){var t,i,s,n=this.options;"parent"===n.containment&&(n.containment=this.helper[0].parentNode),("document"===n.containment||"window"===n.containment)&&(this.containment=[0-this.offset.relative.left-this.offset.parent.left,0-this.offset.relative.top-this.offset.parent.top,"document"===n.containment?this.document.width():this.window.width()-this.helperProportions.width-this.margins.left,("document"===n.containment?this.document.width():this.window.height()||this.document[0].body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top]),/^(document|window|parent)$/.test(n.containment)||(t=e(n.containment)[0],i=e(n.containment).offset(),s="hidden"!==e(t).css("overflow"),this.containment=[i.left+(parseInt(e(t).css("borderLeftWidth"),10)||0)+(parseInt(e(t).css("paddingLeft"),10)||0)-this.margins.left,i.top+(parseInt(e(t).css("borderTopWidth"),10)||0)+(parseInt(e(t).css("paddingTop"),10)||0)-this.margins.top,i.left+(s?Math.max(t.scrollWidth,t.offsetWidth):t.offsetWidth)-(parseInt(e(t).css("borderLeftWidth"),10)||0)-(parseInt(e(t).css("paddingRight"),10)||0)-this.helperProportions.width-this.margins.left,i.top+(s?Math.max(t.scrollHeight,t.offsetHeight):t.offsetHeight)-(parseInt(e(t).css("borderTopWidth"),10)||0)-(parseInt(e(t).css("paddingBottom"),10)||0)-this.helperProportions.height-this.margins.top])},_convertPositionTo:function(t,i){i||(i=this.position);var s="absolute"===t?1:-1,n="absolute"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&e.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,a=/(html|body)/i.test(n[0].tagName);return{top:i.top+this.offset.relative.top*s+this.offset.parent.top*s-("fixed"===this.cssPosition?-this.scrollParent.scrollTop():a?0:n.scrollTop())*s,left:i.left+this.offset.relative.left*s+this.offset.parent.left*s-("fixed"===this.cssPosition?-this.scrollParent.scrollLeft():a?0:n.scrollLeft())*s}},_generatePosition:function(t){var i,s,n=this.options,a=t.pageX,o=t.pageY,r="absolute"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&e.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,h=/(html|body)/i.test(r[0].tagName);return"relative"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&this.scrollParent[0]!==this.offsetParent[0]||(this.offset.relative=this._getRelativeOffset()),this.originalPosition&&(this.containment&&(t.pageX-this.offset.click.left<this.containment[0]&&(a=this.containment[0]+this.offset.click.left),t.pageY-this.offset.click.top<this.containment[1]&&(o=this.containment[1]+this.offset.click.top),t.pageX-this.offset.click.left>this.containment[2]&&(a=this.containment[2]+this.offset.click.left),t.pageY-this.offset.click.top>this.containment[3]&&(o=this.containment[3]+this.offset.click.top)),n.grid&&(i=this.originalPageY+Math.round((o-this.originalPageY)/n.grid[1])*n.grid[1],o=this.containment?i-this.offset.click.top>=this.containment[1]&&i-this.offset.click.top<=this.containment[3]?i:i-this.offset.click.top>=this.containment[1]?i-n.grid[1]:i+n.grid[1]:i,s=this.originalPageX+Math.round((a-this.originalPageX)/n.grid[0])*n.grid[0],a=this.containment?s-this.offset.click.left>=this.containment[0]&&s-this.offset.click.left<=this.containment[2]?s:s-this.offset.click.left>=this.containment[0]?s-n.grid[0]:s+n.grid[0]:s)),{top:o-this.offset.click.top-this.offset.relative.top-this.offset.parent.top+("fixed"===this.cssPosition?-this.scrollParent.scrollTop():h?0:r.scrollTop()),left:a-this.offset.click.left-this.offset.relative.left-this.offset.parent.left+("fixed"===this.cssPosition?-this.scrollParent.scrollLeft():h?0:r.scrollLeft())}},_rearrange:function(e,t,i,s){i?i[0].appendChild(this.placeholder[0]):t.item[0].parentNode.insertBefore(this.placeholder[0],"down"===this.direction?t.item[0]:t.item[0].nextSibling),this.counter=this.counter?++this.counter:1;var n=this.counter;this._delay(function(){n===this.counter&&this.refreshPositions(!s)})},_clear:function(e,t){function i(e,t,i){return function(s){i._trigger(e,s,t._uiHash(t))}}this.reverting=!1;var s,n=[];if(!this._noFinalSort&&this.currentItem.parent().length&&this.placeholder.before(this.currentItem),this._noFinalSort=null,this.helper[0]===this.currentItem[0]){for(s in this._storedCSS)("auto"===this._storedCSS[s]||"static"===this._storedCSS[s])&&(this._storedCSS[s]="");this.currentItem.css(this._storedCSS).removeClass("ui-sortable-helper")}else this.currentItem.show();for(this.fromOutside&&!t&&n.push(function(e){this._trigger("receive",e,this._uiHash(this.fromOutside))}),!this.fromOutside&&this.domPosition.prev===this.currentItem.prev().not(".ui-sortable-helper")[0]&&this.domPosition.parent===this.currentItem.parent()[0]||t||n.push(function(e){this._trigger("update",e,this._uiHash())}),this!==this.currentContainer&&(t||(n.push(function(e){this._trigger("remove",e,this._uiHash())}),n.push(function(e){return function(t){e._trigger("receive",t,this._uiHash(this))}}.call(this,this.currentContainer)),n.push(function(e){return function(t){e._trigger("update",t,this._uiHash(this))}}.call(this,this.currentContainer)))),s=this.containers.length-1;s>=0;s--)t||n.push(i("deactivate",this,this.containers[s])),this.containers[s].containerCache.over&&(n.push(i("out",this,this.containers[s])),this.containers[s].containerCache.over=0);if(this.storedCursor&&(this.document.find("body").css("cursor",this.storedCursor),this.storedStylesheet.remove()),this._storedOpacity&&this.helper.css("opacity",this._storedOpacity),this._storedZIndex&&this.helper.css("zIndex","auto"===this._storedZIndex?"":this._storedZIndex),this.dragging=!1,t||this._trigger("beforeStop",e,this._uiHash()),this.placeholder[0].parentNode.removeChild(this.placeholder[0]),this.cancelHelperRemoval||(this.helper[0]!==this.currentItem[0]&&this.helper.remove(),this.helper=null),!t){for(s=0;n.length>s;s++)n[s].call(this,e);this._trigger("stop",e,this._uiHash())}return this.fromOutside=!1,!this.cancelHelperRemoval},_trigger:function(){e.Widget.prototype._trigger.apply(this,arguments)===!1&&this.cancel()},_uiHash:function(t){var i=t||this;return{helper:i.helper,placeholder:i.placeholder||e([]),position:i.position,originalPosition:i.originalPosition,offset:i.positionAbs,item:i.currentItem,sender:t?t.element:null}}}),e.widget("ui.accordion",{version:"1.11.4",options:{active:0,animate:{},collapsible:!1,event:"click",header:"> li > :first-child,> :not(li):even",heightStyle:"auto",icons:{activeHeader:"ui-icon-triangle-1-s",header:"ui-icon-triangle-1-e"},activate:null,beforeActivate:null},hideProps:{borderTopWidth:"hide",borderBottomWidth:"hide",paddingTop:"hide",paddingBottom:"hide",height:"hide"},showProps:{borderTopWidth:"show",borderBottomWidth:"show",paddingTop:"show",paddingBottom:"show",height:"show"},_create:function(){var t=this.options;this.prevShow=this.prevHide=e(),this.element.addClass("ui-accordion ui-widget ui-helper-reset").attr("role","tablist"),t.collapsible||t.active!==!1&&null!=t.active||(t.active=0),this._processPanels(),0>t.active&&(t.active+=this.headers.length),this._refresh()},_getCreateEventData:function(){return{header:this.active,panel:this.active.length?this.active.next():e()}},_createIcons:function(){var t=this.options.icons;t&&(e("<span>").addClass("ui-accordion-header-icon ui-icon "+t.header).prependTo(this.headers),this.active.children(".ui-accordion-header-icon").removeClass(t.header).addClass(t.activeHeader),this.headers.addClass("ui-accordion-icons"))},_destroyIcons:function(){this.headers.removeClass("ui-accordion-icons").children(".ui-accordion-header-icon").remove()},_destroy:function(){var e;this.element.removeClass("ui-accordion ui-widget ui-helper-reset").removeAttr("role"),this.headers.removeClass("ui-accordion-header ui-accordion-header-active ui-state-default ui-corner-all ui-state-active ui-state-disabled ui-corner-top").removeAttr("role").removeAttr("aria-expanded").removeAttr("aria-selected").removeAttr("aria-controls").removeAttr("tabIndex").removeUniqueId(),this._destroyIcons(),e=this.headers.next().removeClass("ui-helper-reset ui-widget-content ui-corner-bottom ui-accordion-content ui-accordion-content-active ui-state-disabled").css("display","").removeAttr("role").removeAttr("aria-hidden").removeAttr("aria-labelledby").removeUniqueId(),"content"!==this.options.heightStyle&&e.css("height","")},_setOption:function(e,t){return"active"===e?(this._activate(t),void 0):("event"===e&&(this.options.event&&this._off(this.headers,this.options.event),this._setupEvents(t)),this._super(e,t),"collapsible"!==e||t||this.options.active!==!1||this._activate(0),"icons"===e&&(this._destroyIcons(),t&&this._createIcons()),"disabled"===e&&(this.element.toggleClass("ui-state-disabled",!!t).attr("aria-disabled",t),this.headers.add(this.headers.next()).toggleClass("ui-state-disabled",!!t)),void 0)},_keydown:function(t){if(!t.altKey&&!t.ctrlKey){var i=e.ui.keyCode,s=this.headers.length,n=this.headers.index(t.target),a=!1;switch(t.keyCode){case i.RIGHT:case i.DOWN:a=this.headers[(n+1)%s];break;case i.LEFT:case i.UP:a=this.headers[(n-1+s)%s];break;case i.SPACE:case i.ENTER:this._eventHandler(t);break;case i.HOME:a=this.headers[0];break;case i.END:a=this.headers[s-1]}a&&(e(t.target).attr("tabIndex",-1),e(a).attr("tabIndex",0),a.focus(),t.preventDefault())}},_panelKeyDown:function(t){t.keyCode===e.ui.keyCode.UP&&t.ctrlKey&&e(t.currentTarget).prev().focus()},refresh:function(){var t=this.options;this._processPanels(),t.active===!1&&t.collapsible===!0||!this.headers.length?(t.active=!1,this.active=e()):t.active===!1?this._activate(0):this.active.length&&!e.contains(this.element[0],this.active[0])?this.headers.length===this.headers.find(".ui-state-disabled").length?(t.active=!1,this.active=e()):this._activate(Math.max(0,t.active-1)):t.active=this.headers.index(this.active),this._destroyIcons(),this._refresh()},_processPanels:function(){var e=this.headers,t=this.panels;this.headers=this.element.find(this.options.header).addClass("ui-accordion-header ui-state-default ui-corner-all"),this.panels=this.headers.next().addClass("ui-accordion-content ui-helper-reset ui-widget-content ui-corner-bottom").filter(":not(.ui-accordion-content-active)").hide(),t&&(this._off(e.not(this.headers)),this._off(t.not(this.panels)))},_refresh:function(){var t,i=this.options,s=i.heightStyle,n=this.element.parent();this.active=this._findActive(i.active).addClass("ui-accordion-header-active ui-state-active ui-corner-top").removeClass("ui-corner-all"),this.active.next().addClass("ui-accordion-content-active").show(),this.headers.attr("role","tab").each(function(){var t=e(this),i=t.uniqueId().attr("id"),s=t.next(),n=s.uniqueId().attr("id");t.attr("aria-controls",n),s.attr("aria-labelledby",i)}).next().attr("role","tabpanel"),this.headers.not(this.active).attr({"aria-selected":"false","aria-expanded":"false",tabIndex:-1}).next().attr({"aria-hidden":"true"}).hide(),this.active.length?this.active.attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0}).next().attr({"aria-hidden":"false"}):this.headers.eq(0).attr("tabIndex",0),this._createIcons(),this._setupEvents(i.event),"fill"===s?(t=n.height(),this.element.siblings(":visible").each(function(){var i=e(this),s=i.css("position");"absolute"!==s&&"fixed"!==s&&(t-=i.outerHeight(!0))}),this.headers.each(function(){t-=e(this).outerHeight(!0)}),this.headers.next().each(function(){e(this).height(Math.max(0,t-e(this).innerHeight()+e(this).height()))}).css("overflow","auto")):"auto"===s&&(t=0,this.headers.next().each(function(){t=Math.max(t,e(this).css("height","").height())}).height(t))},_activate:function(t){var i=this._findActive(t)[0];i!==this.active[0]&&(i=i||this.active[0],this._eventHandler({target:i,currentTarget:i,preventDefault:e.noop}))},_findActive:function(t){return"number"==typeof t?this.headers.eq(t):e()},_setupEvents:function(t){var i={keydown:"_keydown"};t&&e.each(t.split(" "),function(e,t){i[t]="_eventHandler"}),this._off(this.headers.add(this.headers.next())),this._on(this.headers,i),this._on(this.headers.next(),{keydown:"_panelKeyDown"}),this._hoverable(this.headers),this._focusable(this.headers)},_eventHandler:function(t){var i=this.options,s=this.active,n=e(t.currentTarget),a=n[0]===s[0],o=a&&i.collapsible,r=o?e():n.next(),h=s.next(),l={oldHeader:s,oldPanel:h,newHeader:o?e():n,newPanel:r}; +t.preventDefault(),a&&!i.collapsible||this._trigger("beforeActivate",t,l)===!1||(i.active=o?!1:this.headers.index(n),this.active=a?e():n,this._toggle(l),s.removeClass("ui-accordion-header-active ui-state-active"),i.icons&&s.children(".ui-accordion-header-icon").removeClass(i.icons.activeHeader).addClass(i.icons.header),a||(n.removeClass("ui-corner-all").addClass("ui-accordion-header-active ui-state-active ui-corner-top"),i.icons&&n.children(".ui-accordion-header-icon").removeClass(i.icons.header).addClass(i.icons.activeHeader),n.next().addClass("ui-accordion-content-active")))},_toggle:function(t){var i=t.newPanel,s=this.prevShow.length?this.prevShow:t.oldPanel;this.prevShow.add(this.prevHide).stop(!0,!0),this.prevShow=i,this.prevHide=s,this.options.animate?this._animate(i,s,t):(s.hide(),i.show(),this._toggleComplete(t)),s.attr({"aria-hidden":"true"}),s.prev().attr({"aria-selected":"false","aria-expanded":"false"}),i.length&&s.length?s.prev().attr({tabIndex:-1,"aria-expanded":"false"}):i.length&&this.headers.filter(function(){return 0===parseInt(e(this).attr("tabIndex"),10)}).attr("tabIndex",-1),i.attr("aria-hidden","false").prev().attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0})},_animate:function(e,t,i){var s,n,a,o=this,r=0,h=e.css("box-sizing"),l=e.length&&(!t.length||e.index()<t.index()),u=this.options.animate||{},d=l&&u.down||u,c=function(){o._toggleComplete(i)};return"number"==typeof d&&(a=d),"string"==typeof d&&(n=d),n=n||d.easing||u.easing,a=a||d.duration||u.duration,t.length?e.length?(s=e.show().outerHeight(),t.animate(this.hideProps,{duration:a,easing:n,step:function(e,t){t.now=Math.round(e)}}),e.hide().animate(this.showProps,{duration:a,easing:n,complete:c,step:function(e,i){i.now=Math.round(e),"height"!==i.prop?"content-box"===h&&(r+=i.now):"content"!==o.options.heightStyle&&(i.now=Math.round(s-t.outerHeight()-r),r=0)}}),void 0):t.animate(this.hideProps,a,n,c):e.animate(this.showProps,a,n,c)},_toggleComplete:function(e){var t=e.oldPanel;t.removeClass("ui-accordion-content-active").prev().removeClass("ui-corner-top").addClass("ui-corner-all"),t.length&&(t.parent()[0].className=t.parent()[0].className),this._trigger("activate",null,e)}}),e.widget("ui.menu",{version:"1.11.4",defaultElement:"<ul>",delay:300,options:{icons:{submenu:"ui-icon-carat-1-e"},items:"> *",menus:"ul",position:{my:"left-1 top",at:"right top"},role:"menu",blur:null,focus:null,select:null},_create:function(){this.activeMenu=this.element,this.mouseHandled=!1,this.element.uniqueId().addClass("ui-menu ui-widget ui-widget-content").toggleClass("ui-menu-icons",!!this.element.find(".ui-icon").length).attr({role:this.options.role,tabIndex:0}),this.options.disabled&&this.element.addClass("ui-state-disabled").attr("aria-disabled","true"),this._on({"mousedown .ui-menu-item":function(e){e.preventDefault()},"click .ui-menu-item":function(t){var i=e(t.target);!this.mouseHandled&&i.not(".ui-state-disabled").length&&(this.select(t),t.isPropagationStopped()||(this.mouseHandled=!0),i.has(".ui-menu").length?this.expand(t):!this.element.is(":focus")&&e(this.document[0].activeElement).closest(".ui-menu").length&&(this.element.trigger("focus",[!0]),this.active&&1===this.active.parents(".ui-menu").length&&clearTimeout(this.timer)))},"mouseenter .ui-menu-item":function(t){if(!this.previousFilter){var i=e(t.currentTarget);i.siblings(".ui-state-active").removeClass("ui-state-active"),this.focus(t,i)}},mouseleave:"collapseAll","mouseleave .ui-menu":"collapseAll",focus:function(e,t){var i=this.active||this.element.find(this.options.items).eq(0);t||this.focus(e,i)},blur:function(t){this._delay(function(){e.contains(this.element[0],this.document[0].activeElement)||this.collapseAll(t)})},keydown:"_keydown"}),this.refresh(),this._on(this.document,{click:function(e){this._closeOnDocumentClick(e)&&this.collapseAll(e),this.mouseHandled=!1}})},_destroy:function(){this.element.removeAttr("aria-activedescendant").find(".ui-menu").addBack().removeClass("ui-menu ui-widget ui-widget-content ui-menu-icons ui-front").removeAttr("role").removeAttr("tabIndex").removeAttr("aria-labelledby").removeAttr("aria-expanded").removeAttr("aria-hidden").removeAttr("aria-disabled").removeUniqueId().show(),this.element.find(".ui-menu-item").removeClass("ui-menu-item").removeAttr("role").removeAttr("aria-disabled").removeUniqueId().removeClass("ui-state-hover").removeAttr("tabIndex").removeAttr("role").removeAttr("aria-haspopup").children().each(function(){var t=e(this);t.data("ui-menu-submenu-carat")&&t.remove()}),this.element.find(".ui-menu-divider").removeClass("ui-menu-divider ui-widget-content")},_keydown:function(t){var i,s,n,a,o=!0;switch(t.keyCode){case e.ui.keyCode.PAGE_UP:this.previousPage(t);break;case e.ui.keyCode.PAGE_DOWN:this.nextPage(t);break;case e.ui.keyCode.HOME:this._move("first","first",t);break;case e.ui.keyCode.END:this._move("last","last",t);break;case e.ui.keyCode.UP:this.previous(t);break;case e.ui.keyCode.DOWN:this.next(t);break;case e.ui.keyCode.LEFT:this.collapse(t);break;case e.ui.keyCode.RIGHT:this.active&&!this.active.is(".ui-state-disabled")&&this.expand(t);break;case e.ui.keyCode.ENTER:case e.ui.keyCode.SPACE:this._activate(t);break;case e.ui.keyCode.ESCAPE:this.collapse(t);break;default:o=!1,s=this.previousFilter||"",n=String.fromCharCode(t.keyCode),a=!1,clearTimeout(this.filterTimer),n===s?a=!0:n=s+n,i=this._filterMenuItems(n),i=a&&-1!==i.index(this.active.next())?this.active.nextAll(".ui-menu-item"):i,i.length||(n=String.fromCharCode(t.keyCode),i=this._filterMenuItems(n)),i.length?(this.focus(t,i),this.previousFilter=n,this.filterTimer=this._delay(function(){delete this.previousFilter},1e3)):delete this.previousFilter}o&&t.preventDefault()},_activate:function(e){this.active.is(".ui-state-disabled")||(this.active.is("[aria-haspopup='true']")?this.expand(e):this.select(e))},refresh:function(){var t,i,s=this,n=this.options.icons.submenu,a=this.element.find(this.options.menus);this.element.toggleClass("ui-menu-icons",!!this.element.find(".ui-icon").length),a.filter(":not(.ui-menu)").addClass("ui-menu ui-widget ui-widget-content ui-front").hide().attr({role:this.options.role,"aria-hidden":"true","aria-expanded":"false"}).each(function(){var t=e(this),i=t.parent(),s=e("<span>").addClass("ui-menu-icon ui-icon "+n).data("ui-menu-submenu-carat",!0);i.attr("aria-haspopup","true").prepend(s),t.attr("aria-labelledby",i.attr("id"))}),t=a.add(this.element),i=t.find(this.options.items),i.not(".ui-menu-item").each(function(){var t=e(this);s._isDivider(t)&&t.addClass("ui-widget-content ui-menu-divider")}),i.not(".ui-menu-item, .ui-menu-divider").addClass("ui-menu-item").uniqueId().attr({tabIndex:-1,role:this._itemRole()}),i.filter(".ui-state-disabled").attr("aria-disabled","true"),this.active&&!e.contains(this.element[0],this.active[0])&&this.blur()},_itemRole:function(){return{menu:"menuitem",listbox:"option"}[this.options.role]},_setOption:function(e,t){"icons"===e&&this.element.find(".ui-menu-icon").removeClass(this.options.icons.submenu).addClass(t.submenu),"disabled"===e&&this.element.toggleClass("ui-state-disabled",!!t).attr("aria-disabled",t),this._super(e,t)},focus:function(e,t){var i,s;this.blur(e,e&&"focus"===e.type),this._scrollIntoView(t),this.active=t.first(),s=this.active.addClass("ui-state-focus").removeClass("ui-state-active"),this.options.role&&this.element.attr("aria-activedescendant",s.attr("id")),this.active.parent().closest(".ui-menu-item").addClass("ui-state-active"),e&&"keydown"===e.type?this._close():this.timer=this._delay(function(){this._close()},this.delay),i=t.children(".ui-menu"),i.length&&e&&/^mouse/.test(e.type)&&this._startOpening(i),this.activeMenu=t.parent(),this._trigger("focus",e,{item:t})},_scrollIntoView:function(t){var i,s,n,a,o,r;this._hasScroll()&&(i=parseFloat(e.css(this.activeMenu[0],"borderTopWidth"))||0,s=parseFloat(e.css(this.activeMenu[0],"paddingTop"))||0,n=t.offset().top-this.activeMenu.offset().top-i-s,a=this.activeMenu.scrollTop(),o=this.activeMenu.height(),r=t.outerHeight(),0>n?this.activeMenu.scrollTop(a+n):n+r>o&&this.activeMenu.scrollTop(a+n-o+r))},blur:function(e,t){t||clearTimeout(this.timer),this.active&&(this.active.removeClass("ui-state-focus"),this.active=null,this._trigger("blur",e,{item:this.active}))},_startOpening:function(e){clearTimeout(this.timer),"true"===e.attr("aria-hidden")&&(this.timer=this._delay(function(){this._close(),this._open(e)},this.delay))},_open:function(t){var i=e.extend({of:this.active},this.options.position);clearTimeout(this.timer),this.element.find(".ui-menu").not(t.parents(".ui-menu")).hide().attr("aria-hidden","true"),t.show().removeAttr("aria-hidden").attr("aria-expanded","true").position(i)},collapseAll:function(t,i){clearTimeout(this.timer),this.timer=this._delay(function(){var s=i?this.element:e(t&&t.target).closest(this.element.find(".ui-menu"));s.length||(s=this.element),this._close(s),this.blur(t),this.activeMenu=s},this.delay)},_close:function(e){e||(e=this.active?this.active.parent():this.element),e.find(".ui-menu").hide().attr("aria-hidden","true").attr("aria-expanded","false").end().find(".ui-state-active").not(".ui-state-focus").removeClass("ui-state-active")},_closeOnDocumentClick:function(t){return!e(t.target).closest(".ui-menu").length},_isDivider:function(e){return!/[^\-\u2014\u2013\s]/.test(e.text())},collapse:function(e){var t=this.active&&this.active.parent().closest(".ui-menu-item",this.element);t&&t.length&&(this._close(),this.focus(e,t))},expand:function(e){var t=this.active&&this.active.children(".ui-menu ").find(this.options.items).first();t&&t.length&&(this._open(t.parent()),this._delay(function(){this.focus(e,t)}))},next:function(e){this._move("next","first",e)},previous:function(e){this._move("prev","last",e)},isFirstItem:function(){return this.active&&!this.active.prevAll(".ui-menu-item").length},isLastItem:function(){return this.active&&!this.active.nextAll(".ui-menu-item").length},_move:function(e,t,i){var s;this.active&&(s="first"===e||"last"===e?this.active["first"===e?"prevAll":"nextAll"](".ui-menu-item").eq(-1):this.active[e+"All"](".ui-menu-item").eq(0)),s&&s.length&&this.active||(s=this.activeMenu.find(this.options.items)[t]()),this.focus(i,s)},nextPage:function(t){var i,s,n;return this.active?(this.isLastItem()||(this._hasScroll()?(s=this.active.offset().top,n=this.element.height(),this.active.nextAll(".ui-menu-item").each(function(){return i=e(this),0>i.offset().top-s-n}),this.focus(t,i)):this.focus(t,this.activeMenu.find(this.options.items)[this.active?"last":"first"]())),void 0):(this.next(t),void 0)},previousPage:function(t){var i,s,n;return this.active?(this.isFirstItem()||(this._hasScroll()?(s=this.active.offset().top,n=this.element.height(),this.active.prevAll(".ui-menu-item").each(function(){return i=e(this),i.offset().top-s+n>0}),this.focus(t,i)):this.focus(t,this.activeMenu.find(this.options.items).first())),void 0):(this.next(t),void 0)},_hasScroll:function(){return this.element.outerHeight()<this.element.prop("scrollHeight")},select:function(t){this.active=this.active||e(t.target).closest(".ui-menu-item");var i={item:this.active};this.active.has(".ui-menu").length||this.collapseAll(t,!0),this._trigger("select",t,i)},_filterMenuItems:function(t){var i=t.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g,"\\$&"),s=RegExp("^"+i,"i");return this.activeMenu.find(this.options.items).filter(".ui-menu-item").filter(function(){return s.test(e.trim(e(this).text()))})}}),e.widget("ui.autocomplete",{version:"1.11.4",defaultElement:"<input>",options:{appendTo:null,autoFocus:!1,delay:300,minLength:1,position:{my:"left top",at:"left bottom",collision:"none"},source:null,change:null,close:null,focus:null,open:null,response:null,search:null,select:null},requestIndex:0,pending:0,_create:function(){var t,i,s,n=this.element[0].nodeName.toLowerCase(),a="textarea"===n,o="input"===n;this.isMultiLine=a?!0:o?!1:this.element.prop("isContentEditable"),this.valueMethod=this.element[a||o?"val":"text"],this.isNewMenu=!0,this.element.addClass("ui-autocomplete-input").attr("autocomplete","off"),this._on(this.element,{keydown:function(n){if(this.element.prop("readOnly"))return t=!0,s=!0,i=!0,void 0;t=!1,s=!1,i=!1;var a=e.ui.keyCode;switch(n.keyCode){case a.PAGE_UP:t=!0,this._move("previousPage",n);break;case a.PAGE_DOWN:t=!0,this._move("nextPage",n);break;case a.UP:t=!0,this._keyEvent("previous",n);break;case a.DOWN:t=!0,this._keyEvent("next",n);break;case a.ENTER:this.menu.active&&(t=!0,n.preventDefault(),this.menu.select(n));break;case a.TAB:this.menu.active&&this.menu.select(n);break;case a.ESCAPE:this.menu.element.is(":visible")&&(this.isMultiLine||this._value(this.term),this.close(n),n.preventDefault());break;default:i=!0,this._searchTimeout(n)}},keypress:function(s){if(t)return t=!1,(!this.isMultiLine||this.menu.element.is(":visible"))&&s.preventDefault(),void 0;if(!i){var n=e.ui.keyCode;switch(s.keyCode){case n.PAGE_UP:this._move("previousPage",s);break;case n.PAGE_DOWN:this._move("nextPage",s);break;case n.UP:this._keyEvent("previous",s);break;case n.DOWN:this._keyEvent("next",s)}}},input:function(e){return s?(s=!1,e.preventDefault(),void 0):(this._searchTimeout(e),void 0)},focus:function(){this.selectedItem=null,this.previous=this._value()},blur:function(e){return this.cancelBlur?(delete this.cancelBlur,void 0):(clearTimeout(this.searching),this.close(e),this._change(e),void 0)}}),this._initSource(),this.menu=e("<ul>").addClass("ui-autocomplete ui-front").appendTo(this._appendTo()).menu({role:null}).hide().menu("instance"),this._on(this.menu.element,{mousedown:function(t){t.preventDefault(),this.cancelBlur=!0,this._delay(function(){delete this.cancelBlur});var i=this.menu.element[0];e(t.target).closest(".ui-menu-item").length||this._delay(function(){var t=this;this.document.one("mousedown",function(s){s.target===t.element[0]||s.target===i||e.contains(i,s.target)||t.close()})})},menufocus:function(t,i){var s,n;return this.isNewMenu&&(this.isNewMenu=!1,t.originalEvent&&/^mouse/.test(t.originalEvent.type))?(this.menu.blur(),this.document.one("mousemove",function(){e(t.target).trigger(t.originalEvent)}),void 0):(n=i.item.data("ui-autocomplete-item"),!1!==this._trigger("focus",t,{item:n})&&t.originalEvent&&/^key/.test(t.originalEvent.type)&&this._value(n.value),s=i.item.attr("aria-label")||n.value,s&&e.trim(s).length&&(this.liveRegion.children().hide(),e("<div>").text(s).appendTo(this.liveRegion)),void 0)},menuselect:function(e,t){var i=t.item.data("ui-autocomplete-item"),s=this.previous;this.element[0]!==this.document[0].activeElement&&(this.element.focus(),this.previous=s,this._delay(function(){this.previous=s,this.selectedItem=i})),!1!==this._trigger("select",e,{item:i})&&this._value(i.value),this.term=this._value(),this.close(e),this.selectedItem=i}}),this.liveRegion=e("<span>",{role:"status","aria-live":"assertive","aria-relevant":"additions"}).addClass("ui-helper-hidden-accessible").appendTo(this.document[0].body),this._on(this.window,{beforeunload:function(){this.element.removeAttr("autocomplete")}})},_destroy:function(){clearTimeout(this.searching),this.element.removeClass("ui-autocomplete-input").removeAttr("autocomplete"),this.menu.element.remove(),this.liveRegion.remove()},_setOption:function(e,t){this._super(e,t),"source"===e&&this._initSource(),"appendTo"===e&&this.menu.element.appendTo(this._appendTo()),"disabled"===e&&t&&this.xhr&&this.xhr.abort()},_appendTo:function(){var t=this.options.appendTo;return t&&(t=t.jquery||t.nodeType?e(t):this.document.find(t).eq(0)),t&&t[0]||(t=this.element.closest(".ui-front")),t.length||(t=this.document[0].body),t},_initSource:function(){var t,i,s=this;e.isArray(this.options.source)?(t=this.options.source,this.source=function(i,s){s(e.ui.autocomplete.filter(t,i.term))}):"string"==typeof this.options.source?(i=this.options.source,this.source=function(t,n){s.xhr&&s.xhr.abort(),s.xhr=e.ajax({url:i,data:t,dataType:"json",success:function(e){n(e)},error:function(){n([])}})}):this.source=this.options.source},_searchTimeout:function(e){clearTimeout(this.searching),this.searching=this._delay(function(){var t=this.term===this._value(),i=this.menu.element.is(":visible"),s=e.altKey||e.ctrlKey||e.metaKey||e.shiftKey;(!t||t&&!i&&!s)&&(this.selectedItem=null,this.search(null,e))},this.options.delay)},search:function(e,t){return e=null!=e?e:this._value(),this.term=this._value(),e.length<this.options.minLength?this.close(t):this._trigger("search",t)!==!1?this._search(e):void 0},_search:function(e){this.pending++,this.element.addClass("ui-autocomplete-loading"),this.cancelSearch=!1,this.source({term:e},this._response())},_response:function(){var t=++this.requestIndex;return e.proxy(function(e){t===this.requestIndex&&this.__response(e),this.pending--,this.pending||this.element.removeClass("ui-autocomplete-loading")},this)},__response:function(e){e&&(e=this._normalize(e)),this._trigger("response",null,{content:e}),!this.options.disabled&&e&&e.length&&!this.cancelSearch?(this._suggest(e),this._trigger("open")):this._close()},close:function(e){this.cancelSearch=!0,this._close(e)},_close:function(e){this.menu.element.is(":visible")&&(this.menu.element.hide(),this.menu.blur(),this.isNewMenu=!0,this._trigger("close",e))},_change:function(e){this.previous!==this._value()&&this._trigger("change",e,{item:this.selectedItem})},_normalize:function(t){return t.length&&t[0].label&&t[0].value?t:e.map(t,function(t){return"string"==typeof t?{label:t,value:t}:e.extend({},t,{label:t.label||t.value,value:t.value||t.label})})},_suggest:function(t){var i=this.menu.element.empty();this._renderMenu(i,t),this.isNewMenu=!0,this.menu.refresh(),i.show(),this._resizeMenu(),i.position(e.extend({of:this.element},this.options.position)),this.options.autoFocus&&this.menu.next()},_resizeMenu:function(){var e=this.menu.element;e.outerWidth(Math.max(e.width("").outerWidth()+1,this.element.outerWidth()))},_renderMenu:function(t,i){var s=this;e.each(i,function(e,i){s._renderItemData(t,i)})},_renderItemData:function(e,t){return this._renderItem(e,t).data("ui-autocomplete-item",t)},_renderItem:function(t,i){return e("<li>").text(i.label).appendTo(t)},_move:function(e,t){return this.menu.element.is(":visible")?this.menu.isFirstItem()&&/^previous/.test(e)||this.menu.isLastItem()&&/^next/.test(e)?(this.isMultiLine||this._value(this.term),this.menu.blur(),void 0):(this.menu[e](t),void 0):(this.search(null,t),void 0)},widget:function(){return this.menu.element},_value:function(){return this.valueMethod.apply(this.element,arguments)},_keyEvent:function(e,t){(!this.isMultiLine||this.menu.element.is(":visible"))&&(this._move(e,t),t.preventDefault())}}),e.extend(e.ui.autocomplete,{escapeRegex:function(e){return e.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g,"\\$&")},filter:function(t,i){var s=RegExp(e.ui.autocomplete.escapeRegex(i),"i");return e.grep(t,function(e){return s.test(e.label||e.value||e)})}}),e.widget("ui.autocomplete",e.ui.autocomplete,{options:{messages:{noResults:"No search results.",results:function(e){return e+(e>1?" results are":" result is")+" available, use up and down arrow keys to navigate."}}},__response:function(t){var i;this._superApply(arguments),this.options.disabled||this.cancelSearch||(i=t&&t.length?this.options.messages.results(t.length):this.options.messages.noResults,this.liveRegion.children().hide(),e("<div>").text(i).appendTo(this.liveRegion))}}),e.ui.autocomplete;var r,h="ui-button ui-widget ui-state-default ui-corner-all",l="ui-button-icons-only ui-button-icon-only ui-button-text-icons ui-button-text-icon-primary ui-button-text-icon-secondary ui-button-text-only",u=function(){var t=e(this);setTimeout(function(){t.find(":ui-button").button("refresh")},1)},d=function(t){var i=t.name,s=t.form,n=e([]);return i&&(i=i.replace(/'/g,"\\'"),n=s?e(s).find("[name='"+i+"'][type=radio]"):e("[name='"+i+"'][type=radio]",t.ownerDocument).filter(function(){return!this.form})),n};e.widget("ui.button",{version:"1.11.4",defaultElement:"<button>",options:{disabled:null,text:!0,label:null,icons:{primary:null,secondary:null}},_create:function(){this.element.closest("form").unbind("reset"+this.eventNamespace).bind("reset"+this.eventNamespace,u),"boolean"!=typeof this.options.disabled?this.options.disabled=!!this.element.prop("disabled"):this.element.prop("disabled",this.options.disabled),this._determineButtonType(),this.hasTitle=!!this.buttonElement.attr("title");var t=this,i=this.options,s="checkbox"===this.type||"radio"===this.type,n=s?"":"ui-state-active";null===i.label&&(i.label="input"===this.type?this.buttonElement.val():this.buttonElement.html()),this._hoverable(this.buttonElement),this.buttonElement.addClass(h).attr("role","button").bind("mouseenter"+this.eventNamespace,function(){i.disabled||this===r&&e(this).addClass("ui-state-active")}).bind("mouseleave"+this.eventNamespace,function(){i.disabled||e(this).removeClass(n)}).bind("click"+this.eventNamespace,function(e){i.disabled&&(e.preventDefault(),e.stopImmediatePropagation())}),this._on({focus:function(){this.buttonElement.addClass("ui-state-focus")},blur:function(){this.buttonElement.removeClass("ui-state-focus")}}),s&&this.element.bind("change"+this.eventNamespace,function(){t.refresh()}),"checkbox"===this.type?this.buttonElement.bind("click"+this.eventNamespace,function(){return i.disabled?!1:void 0}):"radio"===this.type?this.buttonElement.bind("click"+this.eventNamespace,function(){if(i.disabled)return!1;e(this).addClass("ui-state-active"),t.buttonElement.attr("aria-pressed","true");var s=t.element[0];d(s).not(s).map(function(){return e(this).button("widget")[0]}).removeClass("ui-state-active").attr("aria-pressed","false")}):(this.buttonElement.bind("mousedown"+this.eventNamespace,function(){return i.disabled?!1:(e(this).addClass("ui-state-active"),r=this,t.document.one("mouseup",function(){r=null}),void 0)}).bind("mouseup"+this.eventNamespace,function(){return i.disabled?!1:(e(this).removeClass("ui-state-active"),void 0)}).bind("keydown"+this.eventNamespace,function(t){return i.disabled?!1:((t.keyCode===e.ui.keyCode.SPACE||t.keyCode===e.ui.keyCode.ENTER)&&e(this).addClass("ui-state-active"),void 0)}).bind("keyup"+this.eventNamespace+" blur"+this.eventNamespace,function(){e(this).removeClass("ui-state-active")}),this.buttonElement.is("a")&&this.buttonElement.keyup(function(t){t.keyCode===e.ui.keyCode.SPACE&&e(this).click()})),this._setOption("disabled",i.disabled),this._resetButton()},_determineButtonType:function(){var e,t,i;this.type=this.element.is("[type=checkbox]")?"checkbox":this.element.is("[type=radio]")?"radio":this.element.is("input")?"input":"button","checkbox"===this.type||"radio"===this.type?(e=this.element.parents().last(),t="label[for='"+this.element.attr("id")+"']",this.buttonElement=e.find(t),this.buttonElement.length||(e=e.length?e.siblings():this.element.siblings(),this.buttonElement=e.filter(t),this.buttonElement.length||(this.buttonElement=e.find(t))),this.element.addClass("ui-helper-hidden-accessible"),i=this.element.is(":checked"),i&&this.buttonElement.addClass("ui-state-active"),this.buttonElement.prop("aria-pressed",i)):this.buttonElement=this.element},widget:function(){return this.buttonElement},_destroy:function(){this.element.removeClass("ui-helper-hidden-accessible"),this.buttonElement.removeClass(h+" ui-state-active "+l).removeAttr("role").removeAttr("aria-pressed").html(this.buttonElement.find(".ui-button-text").html()),this.hasTitle||this.buttonElement.removeAttr("title")},_setOption:function(e,t){return this._super(e,t),"disabled"===e?(this.widget().toggleClass("ui-state-disabled",!!t),this.element.prop("disabled",!!t),t&&("checkbox"===this.type||"radio"===this.type?this.buttonElement.removeClass("ui-state-focus"):this.buttonElement.removeClass("ui-state-focus ui-state-active")),void 0):(this._resetButton(),void 0)},refresh:function(){var t=this.element.is("input, button")?this.element.is(":disabled"):this.element.hasClass("ui-button-disabled");t!==this.options.disabled&&this._setOption("disabled",t),"radio"===this.type?d(this.element[0]).each(function(){e(this).is(":checked")?e(this).button("widget").addClass("ui-state-active").attr("aria-pressed","true"):e(this).button("widget").removeClass("ui-state-active").attr("aria-pressed","false")}):"checkbox"===this.type&&(this.element.is(":checked")?this.buttonElement.addClass("ui-state-active").attr("aria-pressed","true"):this.buttonElement.removeClass("ui-state-active").attr("aria-pressed","false"))},_resetButton:function(){if("input"===this.type)return this.options.label&&this.element.val(this.options.label),void 0;var t=this.buttonElement.removeClass(l),i=e("<span></span>",this.document[0]).addClass("ui-button-text").html(this.options.label).appendTo(t.empty()).text(),s=this.options.icons,n=s.primary&&s.secondary,a=[];s.primary||s.secondary?(this.options.text&&a.push("ui-button-text-icon"+(n?"s":s.primary?"-primary":"-secondary")),s.primary&&t.prepend("<span class='ui-button-icon-primary ui-icon "+s.primary+"'></span>"),s.secondary&&t.append("<span class='ui-button-icon-secondary ui-icon "+s.secondary+"'></span>"),this.options.text||(a.push(n?"ui-button-icons-only":"ui-button-icon-only"),this.hasTitle||t.attr("title",e.trim(i)))):a.push("ui-button-text-only"),t.addClass(a.join(" "))}}),e.widget("ui.buttonset",{version:"1.11.4",options:{items:"button, input[type=button], input[type=submit], input[type=reset], input[type=checkbox], input[type=radio], a, :data(ui-button)"},_create:function(){this.element.addClass("ui-buttonset")},_init:function(){this.refresh()},_setOption:function(e,t){"disabled"===e&&this.buttons.button("option",e,t),this._super(e,t)},refresh:function(){var t="rtl"===this.element.css("direction"),i=this.element.find(this.options.items),s=i.filter(":ui-button");i.not(":ui-button").button(),s.button("refresh"),this.buttons=i.map(function(){return e(this).button("widget")[0]}).removeClass("ui-corner-all ui-corner-left ui-corner-right").filter(":first").addClass(t?"ui-corner-right":"ui-corner-left").end().filter(":last").addClass(t?"ui-corner-left":"ui-corner-right").end().end()},_destroy:function(){this.element.removeClass("ui-buttonset"),this.buttons.map(function(){return e(this).button("widget")[0]}).removeClass("ui-corner-left ui-corner-right").end().button("destroy")}}),e.ui.button,e.widget("ui.dialog",{version:"1.11.4",options:{appendTo:"body",autoOpen:!0,buttons:[],closeOnEscape:!0,closeText:"Close",dialogClass:"",draggable:!0,hide:null,height:"auto",maxHeight:null,maxWidth:null,minHeight:150,minWidth:150,modal:!1,position:{my:"center",at:"center",of:window,collision:"fit",using:function(t){var i=e(this).css(t).offset().top;0>i&&e(this).css("top",t.top-i)}},resizable:!0,show:null,title:null,width:300,beforeClose:null,close:null,drag:null,dragStart:null,dragStop:null,focus:null,open:null,resize:null,resizeStart:null,resizeStop:null},sizeRelatedOptions:{buttons:!0,height:!0,maxHeight:!0,maxWidth:!0,minHeight:!0,minWidth:!0,width:!0},resizableRelatedOptions:{maxHeight:!0,maxWidth:!0,minHeight:!0,minWidth:!0},_create:function(){this.originalCss={display:this.element[0].style.display,width:this.element[0].style.width,minHeight:this.element[0].style.minHeight,maxHeight:this.element[0].style.maxHeight,height:this.element[0].style.height},this.originalPosition={parent:this.element.parent(),index:this.element.parent().children().index(this.element)},this.originalTitle=this.element.attr("title"),this.options.title=this.options.title||this.originalTitle,this._createWrapper(),this.element.show().removeAttr("title").addClass("ui-dialog-content ui-widget-content").appendTo(this.uiDialog),this._createTitlebar(),this._createButtonPane(),this.options.draggable&&e.fn.draggable&&this._makeDraggable(),this.options.resizable&&e.fn.resizable&&this._makeResizable(),this._isOpen=!1,this._trackFocus()},_init:function(){this.options.autoOpen&&this.open()},_appendTo:function(){var t=this.options.appendTo;return t&&(t.jquery||t.nodeType)?e(t):this.document.find(t||"body").eq(0)},_destroy:function(){var e,t=this.originalPosition;this._untrackInstance(),this._destroyOverlay(),this.element.removeUniqueId().removeClass("ui-dialog-content ui-widget-content").css(this.originalCss).detach(),this.uiDialog.stop(!0,!0).remove(),this.originalTitle&&this.element.attr("title",this.originalTitle),e=t.parent.children().eq(t.index),e.length&&e[0]!==this.element[0]?e.before(this.element):t.parent.append(this.element)},widget:function(){return this.uiDialog},disable:e.noop,enable:e.noop,close:function(t){var i,s=this;if(this._isOpen&&this._trigger("beforeClose",t)!==!1){if(this._isOpen=!1,this._focusedElement=null,this._destroyOverlay(),this._untrackInstance(),!this.opener.filter(":focusable").focus().length)try{i=this.document[0].activeElement,i&&"body"!==i.nodeName.toLowerCase()&&e(i).blur()}catch(n){}this._hide(this.uiDialog,this.options.hide,function(){s._trigger("close",t)})}},isOpen:function(){return this._isOpen},moveToTop:function(){this._moveToTop()},_moveToTop:function(t,i){var s=!1,n=this.uiDialog.siblings(".ui-front:visible").map(function(){return+e(this).css("z-index")}).get(),a=Math.max.apply(null,n);return a>=+this.uiDialog.css("z-index")&&(this.uiDialog.css("z-index",a+1),s=!0),s&&!i&&this._trigger("focus",t),s},open:function(){var t=this;return this._isOpen?(this._moveToTop()&&this._focusTabbable(),void 0):(this._isOpen=!0,this.opener=e(this.document[0].activeElement),this._size(),this._position(),this._createOverlay(),this._moveToTop(null,!0),this.overlay&&this.overlay.css("z-index",this.uiDialog.css("z-index")-1),this._show(this.uiDialog,this.options.show,function(){t._focusTabbable(),t._trigger("focus")}),this._makeFocusTarget(),this._trigger("open"),void 0)},_focusTabbable:function(){var e=this._focusedElement;e||(e=this.element.find("[autofocus]")),e.length||(e=this.element.find(":tabbable")),e.length||(e=this.uiDialogButtonPane.find(":tabbable")),e.length||(e=this.uiDialogTitlebarClose.filter(":tabbable")),e.length||(e=this.uiDialog),e.eq(0).focus()},_keepFocus:function(t){function i(){var t=this.document[0].activeElement,i=this.uiDialog[0]===t||e.contains(this.uiDialog[0],t);i||this._focusTabbable()}t.preventDefault(),i.call(this),this._delay(i)},_createWrapper:function(){this.uiDialog=e("<div>").addClass("ui-dialog ui-widget ui-widget-content ui-corner-all ui-front "+this.options.dialogClass).hide().attr({tabIndex:-1,role:"dialog"}).appendTo(this._appendTo()),this._on(this.uiDialog,{keydown:function(t){if(this.options.closeOnEscape&&!t.isDefaultPrevented()&&t.keyCode&&t.keyCode===e.ui.keyCode.ESCAPE)return t.preventDefault(),this.close(t),void 0;if(t.keyCode===e.ui.keyCode.TAB&&!t.isDefaultPrevented()){var i=this.uiDialog.find(":tabbable"),s=i.filter(":first"),n=i.filter(":last");t.target!==n[0]&&t.target!==this.uiDialog[0]||t.shiftKey?t.target!==s[0]&&t.target!==this.uiDialog[0]||!t.shiftKey||(this._delay(function(){n.focus()}),t.preventDefault()):(this._delay(function(){s.focus()}),t.preventDefault())}},mousedown:function(e){this._moveToTop(e)&&this._focusTabbable()}}),this.element.find("[aria-describedby]").length||this.uiDialog.attr({"aria-describedby":this.element.uniqueId().attr("id")})},_createTitlebar:function(){var t;this.uiDialogTitlebar=e("<div>").addClass("ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix").prependTo(this.uiDialog),this._on(this.uiDialogTitlebar,{mousedown:function(t){e(t.target).closest(".ui-dialog-titlebar-close")||this.uiDialog.focus()}}),this.uiDialogTitlebarClose=e("<button type='button'></button>").button({label:this.options.closeText,icons:{primary:"ui-icon-closethick"},text:!1}).addClass("ui-dialog-titlebar-close").appendTo(this.uiDialogTitlebar),this._on(this.uiDialogTitlebarClose,{click:function(e){e.preventDefault(),this.close(e)}}),t=e("<span>").uniqueId().addClass("ui-dialog-title").prependTo(this.uiDialogTitlebar),this._title(t),this.uiDialog.attr({"aria-labelledby":t.attr("id")})},_title:function(e){this.options.title||e.html(" "),e.text(this.options.title) +},_createButtonPane:function(){this.uiDialogButtonPane=e("<div>").addClass("ui-dialog-buttonpane ui-widget-content ui-helper-clearfix"),this.uiButtonSet=e("<div>").addClass("ui-dialog-buttonset").appendTo(this.uiDialogButtonPane),this._createButtons()},_createButtons:function(){var t=this,i=this.options.buttons;return this.uiDialogButtonPane.remove(),this.uiButtonSet.empty(),e.isEmptyObject(i)||e.isArray(i)&&!i.length?(this.uiDialog.removeClass("ui-dialog-buttons"),void 0):(e.each(i,function(i,s){var n,a;s=e.isFunction(s)?{click:s,text:i}:s,s=e.extend({type:"button"},s),n=s.click,s.click=function(){n.apply(t.element[0],arguments)},a={icons:s.icons,text:s.showText},delete s.icons,delete s.showText,e("<button></button>",s).button(a).appendTo(t.uiButtonSet)}),this.uiDialog.addClass("ui-dialog-buttons"),this.uiDialogButtonPane.appendTo(this.uiDialog),void 0)},_makeDraggable:function(){function t(e){return{position:e.position,offset:e.offset}}var i=this,s=this.options;this.uiDialog.draggable({cancel:".ui-dialog-content, .ui-dialog-titlebar-close",handle:".ui-dialog-titlebar",containment:"document",start:function(s,n){e(this).addClass("ui-dialog-dragging"),i._blockFrames(),i._trigger("dragStart",s,t(n))},drag:function(e,s){i._trigger("drag",e,t(s))},stop:function(n,a){var o=a.offset.left-i.document.scrollLeft(),r=a.offset.top-i.document.scrollTop();s.position={my:"left top",at:"left"+(o>=0?"+":"")+o+" "+"top"+(r>=0?"+":"")+r,of:i.window},e(this).removeClass("ui-dialog-dragging"),i._unblockFrames(),i._trigger("dragStop",n,t(a))}})},_makeResizable:function(){function t(e){return{originalPosition:e.originalPosition,originalSize:e.originalSize,position:e.position,size:e.size}}var i=this,s=this.options,n=s.resizable,a=this.uiDialog.css("position"),o="string"==typeof n?n:"n,e,s,w,se,sw,ne,nw";this.uiDialog.resizable({cancel:".ui-dialog-content",containment:"document",alsoResize:this.element,maxWidth:s.maxWidth,maxHeight:s.maxHeight,minWidth:s.minWidth,minHeight:this._minHeight(),handles:o,start:function(s,n){e(this).addClass("ui-dialog-resizing"),i._blockFrames(),i._trigger("resizeStart",s,t(n))},resize:function(e,s){i._trigger("resize",e,t(s))},stop:function(n,a){var o=i.uiDialog.offset(),r=o.left-i.document.scrollLeft(),h=o.top-i.document.scrollTop();s.height=i.uiDialog.height(),s.width=i.uiDialog.width(),s.position={my:"left top",at:"left"+(r>=0?"+":"")+r+" "+"top"+(h>=0?"+":"")+h,of:i.window},e(this).removeClass("ui-dialog-resizing"),i._unblockFrames(),i._trigger("resizeStop",n,t(a))}}).css("position",a)},_trackFocus:function(){this._on(this.widget(),{focusin:function(t){this._makeFocusTarget(),this._focusedElement=e(t.target)}})},_makeFocusTarget:function(){this._untrackInstance(),this._trackingInstances().unshift(this)},_untrackInstance:function(){var t=this._trackingInstances(),i=e.inArray(this,t);-1!==i&&t.splice(i,1)},_trackingInstances:function(){var e=this.document.data("ui-dialog-instances");return e||(e=[],this.document.data("ui-dialog-instances",e)),e},_minHeight:function(){var e=this.options;return"auto"===e.height?e.minHeight:Math.min(e.minHeight,e.height)},_position:function(){var e=this.uiDialog.is(":visible");e||this.uiDialog.show(),this.uiDialog.position(this.options.position),e||this.uiDialog.hide()},_setOptions:function(t){var i=this,s=!1,n={};e.each(t,function(e,t){i._setOption(e,t),e in i.sizeRelatedOptions&&(s=!0),e in i.resizableRelatedOptions&&(n[e]=t)}),s&&(this._size(),this._position()),this.uiDialog.is(":data(ui-resizable)")&&this.uiDialog.resizable("option",n)},_setOption:function(e,t){var i,s,n=this.uiDialog;"dialogClass"===e&&n.removeClass(this.options.dialogClass).addClass(t),"disabled"!==e&&(this._super(e,t),"appendTo"===e&&this.uiDialog.appendTo(this._appendTo()),"buttons"===e&&this._createButtons(),"closeText"===e&&this.uiDialogTitlebarClose.button({label:""+t}),"draggable"===e&&(i=n.is(":data(ui-draggable)"),i&&!t&&n.draggable("destroy"),!i&&t&&this._makeDraggable()),"position"===e&&this._position(),"resizable"===e&&(s=n.is(":data(ui-resizable)"),s&&!t&&n.resizable("destroy"),s&&"string"==typeof t&&n.resizable("option","handles",t),s||t===!1||this._makeResizable()),"title"===e&&this._title(this.uiDialogTitlebar.find(".ui-dialog-title")))},_size:function(){var e,t,i,s=this.options;this.element.show().css({width:"auto",minHeight:0,maxHeight:"none",height:0}),s.minWidth>s.width&&(s.width=s.minWidth),e=this.uiDialog.css({height:"auto",width:s.width}).outerHeight(),t=Math.max(0,s.minHeight-e),i="number"==typeof s.maxHeight?Math.max(0,s.maxHeight-e):"none","auto"===s.height?this.element.css({minHeight:t,maxHeight:i,height:"auto"}):this.element.height(Math.max(0,s.height-e)),this.uiDialog.is(":data(ui-resizable)")&&this.uiDialog.resizable("option","minHeight",this._minHeight())},_blockFrames:function(){this.iframeBlocks=this.document.find("iframe").map(function(){var t=e(this);return e("<div>").css({position:"absolute",width:t.outerWidth(),height:t.outerHeight()}).appendTo(t.parent()).offset(t.offset())[0]})},_unblockFrames:function(){this.iframeBlocks&&(this.iframeBlocks.remove(),delete this.iframeBlocks)},_allowInteraction:function(t){return e(t.target).closest(".ui-dialog").length?!0:!!e(t.target).closest(".ui-datepicker").length},_createOverlay:function(){if(this.options.modal){var t=!0;this._delay(function(){t=!1}),this.document.data("ui-dialog-overlays")||this._on(this.document,{focusin:function(e){t||this._allowInteraction(e)||(e.preventDefault(),this._trackingInstances()[0]._focusTabbable())}}),this.overlay=e("<div>").addClass("ui-widget-overlay ui-front").appendTo(this._appendTo()),this._on(this.overlay,{mousedown:"_keepFocus"}),this.document.data("ui-dialog-overlays",(this.document.data("ui-dialog-overlays")||0)+1)}},_destroyOverlay:function(){if(this.options.modal&&this.overlay){var e=this.document.data("ui-dialog-overlays")-1;e?this.document.data("ui-dialog-overlays",e):this.document.unbind("focusin").removeData("ui-dialog-overlays"),this.overlay.remove(),this.overlay=null}}}),e.widget("ui.progressbar",{version:"1.11.4",options:{max:100,value:0,change:null,complete:null},min:0,_create:function(){this.oldValue=this.options.value=this._constrainedValue(),this.element.addClass("ui-progressbar ui-widget ui-widget-content ui-corner-all").attr({role:"progressbar","aria-valuemin":this.min}),this.valueDiv=e("<div class='ui-progressbar-value ui-widget-header ui-corner-left'></div>").appendTo(this.element),this._refreshValue()},_destroy:function(){this.element.removeClass("ui-progressbar ui-widget ui-widget-content ui-corner-all").removeAttr("role").removeAttr("aria-valuemin").removeAttr("aria-valuemax").removeAttr("aria-valuenow"),this.valueDiv.remove()},value:function(e){return void 0===e?this.options.value:(this.options.value=this._constrainedValue(e),this._refreshValue(),void 0)},_constrainedValue:function(e){return void 0===e&&(e=this.options.value),this.indeterminate=e===!1,"number"!=typeof e&&(e=0),this.indeterminate?!1:Math.min(this.options.max,Math.max(this.min,e))},_setOptions:function(e){var t=e.value;delete e.value,this._super(e),this.options.value=this._constrainedValue(t),this._refreshValue()},_setOption:function(e,t){"max"===e&&(t=Math.max(this.min,t)),"disabled"===e&&this.element.toggleClass("ui-state-disabled",!!t).attr("aria-disabled",t),this._super(e,t)},_percentage:function(){return this.indeterminate?100:100*(this.options.value-this.min)/(this.options.max-this.min)},_refreshValue:function(){var t=this.options.value,i=this._percentage();this.valueDiv.toggle(this.indeterminate||t>this.min).toggleClass("ui-corner-right",t===this.options.max).width(i.toFixed(0)+"%"),this.element.toggleClass("ui-progressbar-indeterminate",this.indeterminate),this.indeterminate?(this.element.removeAttr("aria-valuenow"),this.overlayDiv||(this.overlayDiv=e("<div class='ui-progressbar-overlay'></div>").appendTo(this.valueDiv))):(this.element.attr({"aria-valuemax":this.options.max,"aria-valuenow":t}),this.overlayDiv&&(this.overlayDiv.remove(),this.overlayDiv=null)),this.oldValue!==t&&(this.oldValue=t,this._trigger("change")),t===this.options.max&&this._trigger("complete")}}),e.widget("ui.selectmenu",{version:"1.11.4",defaultElement:"<select>",options:{appendTo:null,disabled:null,icons:{button:"ui-icon-triangle-1-s"},position:{my:"left top",at:"left bottom",collision:"none"},width:null,change:null,close:null,focus:null,open:null,select:null},_create:function(){var e=this.element.uniqueId().attr("id");this.ids={element:e,button:e+"-button",menu:e+"-menu"},this._drawButton(),this._drawMenu(),this.options.disabled&&this.disable()},_drawButton:function(){var t=this;this.label=e("label[for='"+this.ids.element+"']").attr("for",this.ids.button),this._on(this.label,{click:function(e){this.button.focus(),e.preventDefault()}}),this.element.hide(),this.button=e("<span>",{"class":"ui-selectmenu-button ui-widget ui-state-default ui-corner-all",tabindex:this.options.disabled?-1:0,id:this.ids.button,role:"combobox","aria-expanded":"false","aria-autocomplete":"list","aria-owns":this.ids.menu,"aria-haspopup":"true"}).insertAfter(this.element),e("<span>",{"class":"ui-icon "+this.options.icons.button}).prependTo(this.button),this.buttonText=e("<span>",{"class":"ui-selectmenu-text"}).appendTo(this.button),this._setText(this.buttonText,this.element.find("option:selected").text()),this._resizeButton(),this._on(this.button,this._buttonEvents),this.button.one("focusin",function(){t.menuItems||t._refreshMenu()}),this._hoverable(this.button),this._focusable(this.button)},_drawMenu:function(){var t=this;this.menu=e("<ul>",{"aria-hidden":"true","aria-labelledby":this.ids.button,id:this.ids.menu}),this.menuWrap=e("<div>",{"class":"ui-selectmenu-menu ui-front"}).append(this.menu).appendTo(this._appendTo()),this.menuInstance=this.menu.menu({role:"listbox",select:function(e,i){e.preventDefault(),t._setSelection(),t._select(i.item.data("ui-selectmenu-item"),e)},focus:function(e,i){var s=i.item.data("ui-selectmenu-item");null!=t.focusIndex&&s.index!==t.focusIndex&&(t._trigger("focus",e,{item:s}),t.isOpen||t._select(s,e)),t.focusIndex=s.index,t.button.attr("aria-activedescendant",t.menuItems.eq(s.index).attr("id"))}}).menu("instance"),this.menu.addClass("ui-corner-bottom").removeClass("ui-corner-all"),this.menuInstance._off(this.menu,"mouseleave"),this.menuInstance._closeOnDocumentClick=function(){return!1},this.menuInstance._isDivider=function(){return!1}},refresh:function(){this._refreshMenu(),this._setText(this.buttonText,this._getSelectedItem().text()),this.options.width||this._resizeButton()},_refreshMenu:function(){this.menu.empty();var e,t=this.element.find("option");t.length&&(this._parseOptions(t),this._renderMenu(this.menu,this.items),this.menuInstance.refresh(),this.menuItems=this.menu.find("li").not(".ui-selectmenu-optgroup"),e=this._getSelectedItem(),this.menuInstance.focus(null,e),this._setAria(e.data("ui-selectmenu-item")),this._setOption("disabled",this.element.prop("disabled")))},open:function(e){this.options.disabled||(this.menuItems?(this.menu.find(".ui-state-focus").removeClass("ui-state-focus"),this.menuInstance.focus(null,this._getSelectedItem())):this._refreshMenu(),this.isOpen=!0,this._toggleAttr(),this._resizeMenu(),this._position(),this._on(this.document,this._documentClick),this._trigger("open",e))},_position:function(){this.menuWrap.position(e.extend({of:this.button},this.options.position))},close:function(e){this.isOpen&&(this.isOpen=!1,this._toggleAttr(),this.range=null,this._off(this.document),this._trigger("close",e))},widget:function(){return this.button},menuWidget:function(){return this.menu},_renderMenu:function(t,i){var s=this,n="";e.each(i,function(i,a){a.optgroup!==n&&(e("<li>",{"class":"ui-selectmenu-optgroup ui-menu-divider"+(a.element.parent("optgroup").prop("disabled")?" ui-state-disabled":""),text:a.optgroup}).appendTo(t),n=a.optgroup),s._renderItemData(t,a)})},_renderItemData:function(e,t){return this._renderItem(e,t).data("ui-selectmenu-item",t)},_renderItem:function(t,i){var s=e("<li>");return i.disabled&&s.addClass("ui-state-disabled"),this._setText(s,i.label),s.appendTo(t)},_setText:function(e,t){t?e.text(t):e.html(" ")},_move:function(e,t){var i,s,n=".ui-menu-item";this.isOpen?i=this.menuItems.eq(this.focusIndex):(i=this.menuItems.eq(this.element[0].selectedIndex),n+=":not(.ui-state-disabled)"),s="first"===e||"last"===e?i["first"===e?"prevAll":"nextAll"](n).eq(-1):i[e+"All"](n).eq(0),s.length&&this.menuInstance.focus(t,s)},_getSelectedItem:function(){return this.menuItems.eq(this.element[0].selectedIndex)},_toggle:function(e){this[this.isOpen?"close":"open"](e)},_setSelection:function(){var e;this.range&&(window.getSelection?(e=window.getSelection(),e.removeAllRanges(),e.addRange(this.range)):this.range.select(),this.button.focus())},_documentClick:{mousedown:function(t){this.isOpen&&(e(t.target).closest(".ui-selectmenu-menu, #"+this.ids.button).length||this.close(t))}},_buttonEvents:{mousedown:function(){var e;window.getSelection?(e=window.getSelection(),e.rangeCount&&(this.range=e.getRangeAt(0))):this.range=document.selection.createRange()},click:function(e){this._setSelection(),this._toggle(e)},keydown:function(t){var i=!0;switch(t.keyCode){case e.ui.keyCode.TAB:case e.ui.keyCode.ESCAPE:this.close(t),i=!1;break;case e.ui.keyCode.ENTER:this.isOpen&&this._selectFocusedItem(t);break;case e.ui.keyCode.UP:t.altKey?this._toggle(t):this._move("prev",t);break;case e.ui.keyCode.DOWN:t.altKey?this._toggle(t):this._move("next",t);break;case e.ui.keyCode.SPACE:this.isOpen?this._selectFocusedItem(t):this._toggle(t);break;case e.ui.keyCode.LEFT:this._move("prev",t);break;case e.ui.keyCode.RIGHT:this._move("next",t);break;case e.ui.keyCode.HOME:case e.ui.keyCode.PAGE_UP:this._move("first",t);break;case e.ui.keyCode.END:case e.ui.keyCode.PAGE_DOWN:this._move("last",t);break;default:this.menu.trigger(t),i=!1}i&&t.preventDefault()}},_selectFocusedItem:function(e){var t=this.menuItems.eq(this.focusIndex);t.hasClass("ui-state-disabled")||this._select(t.data("ui-selectmenu-item"),e)},_select:function(e,t){var i=this.element[0].selectedIndex;this.element[0].selectedIndex=e.index,this._setText(this.buttonText,e.label),this._setAria(e),this._trigger("select",t,{item:e}),e.index!==i&&this._trigger("change",t,{item:e}),this.close(t)},_setAria:function(e){var t=this.menuItems.eq(e.index).attr("id");this.button.attr({"aria-labelledby":t,"aria-activedescendant":t}),this.menu.attr("aria-activedescendant",t)},_setOption:function(e,t){"icons"===e&&this.button.find("span.ui-icon").removeClass(this.options.icons.button).addClass(t.button),this._super(e,t),"appendTo"===e&&this.menuWrap.appendTo(this._appendTo()),"disabled"===e&&(this.menuInstance.option("disabled",t),this.button.toggleClass("ui-state-disabled",t).attr("aria-disabled",t),this.element.prop("disabled",t),t?(this.button.attr("tabindex",-1),this.close()):this.button.attr("tabindex",0)),"width"===e&&this._resizeButton()},_appendTo:function(){var t=this.options.appendTo;return t&&(t=t.jquery||t.nodeType?e(t):this.document.find(t).eq(0)),t&&t[0]||(t=this.element.closest(".ui-front")),t.length||(t=this.document[0].body),t},_toggleAttr:function(){this.button.toggleClass("ui-corner-top",this.isOpen).toggleClass("ui-corner-all",!this.isOpen).attr("aria-expanded",this.isOpen),this.menuWrap.toggleClass("ui-selectmenu-open",this.isOpen),this.menu.attr("aria-hidden",!this.isOpen)},_resizeButton:function(){var e=this.options.width;e||(e=this.element.show().outerWidth(),this.element.hide()),this.button.outerWidth(e)},_resizeMenu:function(){this.menu.outerWidth(Math.max(this.button.outerWidth(),this.menu.width("").outerWidth()+1))},_getCreateOptions:function(){return{disabled:this.element.prop("disabled")}},_parseOptions:function(t){var i=[];t.each(function(t,s){var n=e(s),a=n.parent("optgroup");i.push({element:n,index:t,value:n.val(),label:n.text(),optgroup:a.attr("label")||"",disabled:a.prop("disabled")||n.prop("disabled")})}),this.items=i},_destroy:function(){this.menuWrap.remove(),this.button.remove(),this.element.show(),this.element.removeUniqueId(),this.label.attr("for",this.ids.element)}}),e.widget("ui.slider",e.ui.mouse,{version:"1.11.4",widgetEventPrefix:"slide",options:{animate:!1,distance:0,max:100,min:0,orientation:"horizontal",range:!1,step:1,value:0,values:null,change:null,slide:null,start:null,stop:null},numPages:5,_create:function(){this._keySliding=!1,this._mouseSliding=!1,this._animateOff=!0,this._handleIndex=null,this._detectOrientation(),this._mouseInit(),this._calculateNewMax(),this.element.addClass("ui-slider ui-slider-"+this.orientation+" ui-widget"+" ui-widget-content"+" ui-corner-all"),this._refresh(),this._setOption("disabled",this.options.disabled),this._animateOff=!1},_refresh:function(){this._createRange(),this._createHandles(),this._setupEvents(),this._refreshValue()},_createHandles:function(){var t,i,s=this.options,n=this.element.find(".ui-slider-handle").addClass("ui-state-default ui-corner-all"),a="<span class='ui-slider-handle ui-state-default ui-corner-all' tabindex='0'></span>",o=[];for(i=s.values&&s.values.length||1,n.length>i&&(n.slice(i).remove(),n=n.slice(0,i)),t=n.length;i>t;t++)o.push(a);this.handles=n.add(e(o.join("")).appendTo(this.element)),this.handle=this.handles.eq(0),this.handles.each(function(t){e(this).data("ui-slider-handle-index",t)})},_createRange:function(){var t=this.options,i="";t.range?(t.range===!0&&(t.values?t.values.length&&2!==t.values.length?t.values=[t.values[0],t.values[0]]:e.isArray(t.values)&&(t.values=t.values.slice(0)):t.values=[this._valueMin(),this._valueMin()]),this.range&&this.range.length?this.range.removeClass("ui-slider-range-min ui-slider-range-max").css({left:"",bottom:""}):(this.range=e("<div></div>").appendTo(this.element),i="ui-slider-range ui-widget-header ui-corner-all"),this.range.addClass(i+("min"===t.range||"max"===t.range?" ui-slider-range-"+t.range:""))):(this.range&&this.range.remove(),this.range=null)},_setupEvents:function(){this._off(this.handles),this._on(this.handles,this._handleEvents),this._hoverable(this.handles),this._focusable(this.handles)},_destroy:function(){this.handles.remove(),this.range&&this.range.remove(),this.element.removeClass("ui-slider ui-slider-horizontal ui-slider-vertical ui-widget ui-widget-content ui-corner-all"),this._mouseDestroy()},_mouseCapture:function(t){var i,s,n,a,o,r,h,l,u=this,d=this.options;return d.disabled?!1:(this.elementSize={width:this.element.outerWidth(),height:this.element.outerHeight()},this.elementOffset=this.element.offset(),i={x:t.pageX,y:t.pageY},s=this._normValueFromMouse(i),n=this._valueMax()-this._valueMin()+1,this.handles.each(function(t){var i=Math.abs(s-u.values(t));(n>i||n===i&&(t===u._lastChangedValue||u.values(t)===d.min))&&(n=i,a=e(this),o=t)}),r=this._start(t,o),r===!1?!1:(this._mouseSliding=!0,this._handleIndex=o,a.addClass("ui-state-active").focus(),h=a.offset(),l=!e(t.target).parents().addBack().is(".ui-slider-handle"),this._clickOffset=l?{left:0,top:0}:{left:t.pageX-h.left-a.width()/2,top:t.pageY-h.top-a.height()/2-(parseInt(a.css("borderTopWidth"),10)||0)-(parseInt(a.css("borderBottomWidth"),10)||0)+(parseInt(a.css("marginTop"),10)||0)},this.handles.hasClass("ui-state-hover")||this._slide(t,o,s),this._animateOff=!0,!0))},_mouseStart:function(){return!0},_mouseDrag:function(e){var t={x:e.pageX,y:e.pageY},i=this._normValueFromMouse(t);return this._slide(e,this._handleIndex,i),!1},_mouseStop:function(e){return this.handles.removeClass("ui-state-active"),this._mouseSliding=!1,this._stop(e,this._handleIndex),this._change(e,this._handleIndex),this._handleIndex=null,this._clickOffset=null,this._animateOff=!1,!1},_detectOrientation:function(){this.orientation="vertical"===this.options.orientation?"vertical":"horizontal"},_normValueFromMouse:function(e){var t,i,s,n,a;return"horizontal"===this.orientation?(t=this.elementSize.width,i=e.x-this.elementOffset.left-(this._clickOffset?this._clickOffset.left:0)):(t=this.elementSize.height,i=e.y-this.elementOffset.top-(this._clickOffset?this._clickOffset.top:0)),s=i/t,s>1&&(s=1),0>s&&(s=0),"vertical"===this.orientation&&(s=1-s),n=this._valueMax()-this._valueMin(),a=this._valueMin()+s*n,this._trimAlignValue(a)},_start:function(e,t){var i={handle:this.handles[t],value:this.value()};return this.options.values&&this.options.values.length&&(i.value=this.values(t),i.values=this.values()),this._trigger("start",e,i)},_slide:function(e,t,i){var s,n,a;this.options.values&&this.options.values.length?(s=this.values(t?0:1),2===this.options.values.length&&this.options.range===!0&&(0===t&&i>s||1===t&&s>i)&&(i=s),i!==this.values(t)&&(n=this.values(),n[t]=i,a=this._trigger("slide",e,{handle:this.handles[t],value:i,values:n}),s=this.values(t?0:1),a!==!1&&this.values(t,i))):i!==this.value()&&(a=this._trigger("slide",e,{handle:this.handles[t],value:i}),a!==!1&&this.value(i))},_stop:function(e,t){var i={handle:this.handles[t],value:this.value()};this.options.values&&this.options.values.length&&(i.value=this.values(t),i.values=this.values()),this._trigger("stop",e,i)},_change:function(e,t){if(!this._keySliding&&!this._mouseSliding){var i={handle:this.handles[t],value:this.value()};this.options.values&&this.options.values.length&&(i.value=this.values(t),i.values=this.values()),this._lastChangedValue=t,this._trigger("change",e,i)}},value:function(e){return arguments.length?(this.options.value=this._trimAlignValue(e),this._refreshValue(),this._change(null,0),void 0):this._value()},values:function(t,i){var s,n,a;if(arguments.length>1)return this.options.values[t]=this._trimAlignValue(i),this._refreshValue(),this._change(null,t),void 0;if(!arguments.length)return this._values();if(!e.isArray(arguments[0]))return this.options.values&&this.options.values.length?this._values(t):this.value();for(s=this.options.values,n=arguments[0],a=0;s.length>a;a+=1)s[a]=this._trimAlignValue(n[a]),this._change(null,a);this._refreshValue()},_setOption:function(t,i){var s,n=0;switch("range"===t&&this.options.range===!0&&("min"===i?(this.options.value=this._values(0),this.options.values=null):"max"===i&&(this.options.value=this._values(this.options.values.length-1),this.options.values=null)),e.isArray(this.options.values)&&(n=this.options.values.length),"disabled"===t&&this.element.toggleClass("ui-state-disabled",!!i),this._super(t,i),t){case"orientation":this._detectOrientation(),this.element.removeClass("ui-slider-horizontal ui-slider-vertical").addClass("ui-slider-"+this.orientation),this._refreshValue(),this.handles.css("horizontal"===i?"bottom":"left","");break;case"value":this._animateOff=!0,this._refreshValue(),this._change(null,0),this._animateOff=!1;break;case"values":for(this._animateOff=!0,this._refreshValue(),s=0;n>s;s+=1)this._change(null,s);this._animateOff=!1;break;case"step":case"min":case"max":this._animateOff=!0,this._calculateNewMax(),this._refreshValue(),this._animateOff=!1;break;case"range":this._animateOff=!0,this._refresh(),this._animateOff=!1}},_value:function(){var e=this.options.value;return e=this._trimAlignValue(e)},_values:function(e){var t,i,s;if(arguments.length)return t=this.options.values[e],t=this._trimAlignValue(t);if(this.options.values&&this.options.values.length){for(i=this.options.values.slice(),s=0;i.length>s;s+=1)i[s]=this._trimAlignValue(i[s]);return i}return[]},_trimAlignValue:function(e){if(this._valueMin()>=e)return this._valueMin();if(e>=this._valueMax())return this._valueMax();var t=this.options.step>0?this.options.step:1,i=(e-this._valueMin())%t,s=e-i;return 2*Math.abs(i)>=t&&(s+=i>0?t:-t),parseFloat(s.toFixed(5))},_calculateNewMax:function(){var e=this.options.max,t=this._valueMin(),i=this.options.step,s=Math.floor(+(e-t).toFixed(this._precision())/i)*i;e=s+t,this.max=parseFloat(e.toFixed(this._precision()))},_precision:function(){var e=this._precisionOf(this.options.step);return null!==this.options.min&&(e=Math.max(e,this._precisionOf(this.options.min))),e},_precisionOf:function(e){var t=""+e,i=t.indexOf(".");return-1===i?0:t.length-i-1},_valueMin:function(){return this.options.min},_valueMax:function(){return this.max},_refreshValue:function(){var t,i,s,n,a,o=this.options.range,r=this.options,h=this,l=this._animateOff?!1:r.animate,u={};this.options.values&&this.options.values.length?this.handles.each(function(s){i=100*((h.values(s)-h._valueMin())/(h._valueMax()-h._valueMin())),u["horizontal"===h.orientation?"left":"bottom"]=i+"%",e(this).stop(1,1)[l?"animate":"css"](u,r.animate),h.options.range===!0&&("horizontal"===h.orientation?(0===s&&h.range.stop(1,1)[l?"animate":"css"]({left:i+"%"},r.animate),1===s&&h.range[l?"animate":"css"]({width:i-t+"%"},{queue:!1,duration:r.animate})):(0===s&&h.range.stop(1,1)[l?"animate":"css"]({bottom:i+"%"},r.animate),1===s&&h.range[l?"animate":"css"]({height:i-t+"%"},{queue:!1,duration:r.animate}))),t=i}):(s=this.value(),n=this._valueMin(),a=this._valueMax(),i=a!==n?100*((s-n)/(a-n)):0,u["horizontal"===this.orientation?"left":"bottom"]=i+"%",this.handle.stop(1,1)[l?"animate":"css"](u,r.animate),"min"===o&&"horizontal"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({width:i+"%"},r.animate),"max"===o&&"horizontal"===this.orientation&&this.range[l?"animate":"css"]({width:100-i+"%"},{queue:!1,duration:r.animate}),"min"===o&&"vertical"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({height:i+"%"},r.animate),"max"===o&&"vertical"===this.orientation&&this.range[l?"animate":"css"]({height:100-i+"%"},{queue:!1,duration:r.animate}))},_handleEvents:{keydown:function(t){var i,s,n,a,o=e(t.target).data("ui-slider-handle-index");switch(t.keyCode){case e.ui.keyCode.HOME:case e.ui.keyCode.END:case e.ui.keyCode.PAGE_UP:case e.ui.keyCode.PAGE_DOWN:case e.ui.keyCode.UP:case e.ui.keyCode.RIGHT:case e.ui.keyCode.DOWN:case e.ui.keyCode.LEFT:if(t.preventDefault(),!this._keySliding&&(this._keySliding=!0,e(t.target).addClass("ui-state-active"),i=this._start(t,o),i===!1))return}switch(a=this.options.step,s=n=this.options.values&&this.options.values.length?this.values(o):this.value(),t.keyCode){case e.ui.keyCode.HOME:n=this._valueMin();break;case e.ui.keyCode.END:n=this._valueMax();break;case e.ui.keyCode.PAGE_UP:n=this._trimAlignValue(s+(this._valueMax()-this._valueMin())/this.numPages);break;case e.ui.keyCode.PAGE_DOWN:n=this._trimAlignValue(s-(this._valueMax()-this._valueMin())/this.numPages);break;case e.ui.keyCode.UP:case e.ui.keyCode.RIGHT:if(s===this._valueMax())return;n=this._trimAlignValue(s+a);break;case e.ui.keyCode.DOWN:case e.ui.keyCode.LEFT:if(s===this._valueMin())return;n=this._trimAlignValue(s-a)}this._slide(t,o,n)},keyup:function(t){var i=e(t.target).data("ui-slider-handle-index");this._keySliding&&(this._keySliding=!1,this._stop(t,i),this._change(t,i),e(t.target).removeClass("ui-state-active"))}}}),e.widget("ui.spinner",{version:"1.11.4",defaultElement:"<input>",widgetEventPrefix:"spin",options:{culture:null,icons:{down:"ui-icon-triangle-1-s",up:"ui-icon-triangle-1-n"},incremental:!0,max:null,min:null,numberFormat:null,page:10,step:1,change:null,spin:null,start:null,stop:null},_create:function(){this._setOption("max",this.options.max),this._setOption("min",this.options.min),this._setOption("step",this.options.step),""!==this.value()&&this._value(this.element.val(),!0),this._draw(),this._on(this._events),this._refresh(),this._on(this.window,{beforeunload:function(){this.element.removeAttr("autocomplete")}})},_getCreateOptions:function(){var t={},i=this.element;return e.each(["min","max","step"],function(e,s){var n=i.attr(s);void 0!==n&&n.length&&(t[s]=n)}),t},_events:{keydown:function(e){this._start(e)&&this._keydown(e)&&e.preventDefault()},keyup:"_stop",focus:function(){this.previous=this.element.val()},blur:function(e){return this.cancelBlur?(delete this.cancelBlur,void 0):(this._stop(),this._refresh(),this.previous!==this.element.val()&&this._trigger("change",e),void 0)},mousewheel:function(e,t){if(t){if(!this.spinning&&!this._start(e))return!1;this._spin((t>0?1:-1)*this.options.step,e),clearTimeout(this.mousewheelTimer),this.mousewheelTimer=this._delay(function(){this.spinning&&this._stop(e)},100),e.preventDefault()}},"mousedown .ui-spinner-button":function(t){function i(){var e=this.element[0]===this.document[0].activeElement;e||(this.element.focus(),this.previous=s,this._delay(function(){this.previous=s}))}var s;s=this.element[0]===this.document[0].activeElement?this.previous:this.element.val(),t.preventDefault(),i.call(this),this.cancelBlur=!0,this._delay(function(){delete this.cancelBlur,i.call(this)}),this._start(t)!==!1&&this._repeat(null,e(t.currentTarget).hasClass("ui-spinner-up")?1:-1,t)},"mouseup .ui-spinner-button":"_stop","mouseenter .ui-spinner-button":function(t){return e(t.currentTarget).hasClass("ui-state-active")?this._start(t)===!1?!1:(this._repeat(null,e(t.currentTarget).hasClass("ui-spinner-up")?1:-1,t),void 0):void 0},"mouseleave .ui-spinner-button":"_stop"},_draw:function(){var e=this.uiSpinner=this.element.addClass("ui-spinner-input").attr("autocomplete","off").wrap(this._uiSpinnerHtml()).parent().append(this._buttonHtml());this.element.attr("role","spinbutton"),this.buttons=e.find(".ui-spinner-button").attr("tabIndex",-1).button().removeClass("ui-corner-all"),this.buttons.height()>Math.ceil(.5*e.height())&&e.height()>0&&e.height(e.height()),this.options.disabled&&this.disable()},_keydown:function(t){var i=this.options,s=e.ui.keyCode;switch(t.keyCode){case s.UP:return this._repeat(null,1,t),!0;case s.DOWN:return this._repeat(null,-1,t),!0;case s.PAGE_UP:return this._repeat(null,i.page,t),!0;case s.PAGE_DOWN:return this._repeat(null,-i.page,t),!0}return!1},_uiSpinnerHtml:function(){return"<span class='ui-spinner ui-widget ui-widget-content ui-corner-all'></span>"},_buttonHtml:function(){return"<a class='ui-spinner-button ui-spinner-up ui-corner-tr'><span class='ui-icon "+this.options.icons.up+"'>▲</span>"+"</a>"+"<a class='ui-spinner-button ui-spinner-down ui-corner-br'>"+"<span class='ui-icon "+this.options.icons.down+"'>▼</span>"+"</a>"},_start:function(e){return this.spinning||this._trigger("start",e)!==!1?(this.counter||(this.counter=1),this.spinning=!0,!0):!1},_repeat:function(e,t,i){e=e||500,clearTimeout(this.timer),this.timer=this._delay(function(){this._repeat(40,t,i)},e),this._spin(t*this.options.step,i)},_spin:function(e,t){var i=this.value()||0;this.counter||(this.counter=1),i=this._adjustValue(i+e*this._increment(this.counter)),this.spinning&&this._trigger("spin",t,{value:i})===!1||(this._value(i),this.counter++)},_increment:function(t){var i=this.options.incremental;return i?e.isFunction(i)?i(t):Math.floor(t*t*t/5e4-t*t/500+17*t/200+1):1},_precision:function(){var e=this._precisionOf(this.options.step);return null!==this.options.min&&(e=Math.max(e,this._precisionOf(this.options.min))),e},_precisionOf:function(e){var t=""+e,i=t.indexOf(".");return-1===i?0:t.length-i-1},_adjustValue:function(e){var t,i,s=this.options;return t=null!==s.min?s.min:0,i=e-t,i=Math.round(i/s.step)*s.step,e=t+i,e=parseFloat(e.toFixed(this._precision())),null!==s.max&&e>s.max?s.max:null!==s.min&&s.min>e?s.min:e},_stop:function(e){this.spinning&&(clearTimeout(this.timer),clearTimeout(this.mousewheelTimer),this.counter=0,this.spinning=!1,this._trigger("stop",e))},_setOption:function(e,t){if("culture"===e||"numberFormat"===e){var i=this._parse(this.element.val());return this.options[e]=t,this.element.val(this._format(i)),void 0}("max"===e||"min"===e||"step"===e)&&"string"==typeof t&&(t=this._parse(t)),"icons"===e&&(this.buttons.first().find(".ui-icon").removeClass(this.options.icons.up).addClass(t.up),this.buttons.last().find(".ui-icon").removeClass(this.options.icons.down).addClass(t.down)),this._super(e,t),"disabled"===e&&(this.widget().toggleClass("ui-state-disabled",!!t),this.element.prop("disabled",!!t),this.buttons.button(t?"disable":"enable"))},_setOptions:s(function(e){this._super(e)}),_parse:function(e){return"string"==typeof e&&""!==e&&(e=window.Globalize&&this.options.numberFormat?Globalize.parseFloat(e,10,this.options.culture):+e),""===e||isNaN(e)?null:e +},_format:function(e){return""===e?"":window.Globalize&&this.options.numberFormat?Globalize.format(e,this.options.numberFormat,this.options.culture):e},_refresh:function(){this.element.attr({"aria-valuemin":this.options.min,"aria-valuemax":this.options.max,"aria-valuenow":this._parse(this.element.val())})},isValid:function(){var e=this.value();return null===e?!1:e===this._adjustValue(e)},_value:function(e,t){var i;""!==e&&(i=this._parse(e),null!==i&&(t||(i=this._adjustValue(i)),e=this._format(i))),this.element.val(e),this._refresh()},_destroy:function(){this.element.removeClass("ui-spinner-input").prop("disabled",!1).removeAttr("autocomplete").removeAttr("role").removeAttr("aria-valuemin").removeAttr("aria-valuemax").removeAttr("aria-valuenow"),this.uiSpinner.replaceWith(this.element)},stepUp:s(function(e){this._stepUp(e)}),_stepUp:function(e){this._start()&&(this._spin((e||1)*this.options.step),this._stop())},stepDown:s(function(e){this._stepDown(e)}),_stepDown:function(e){this._start()&&(this._spin((e||1)*-this.options.step),this._stop())},pageUp:s(function(e){this._stepUp((e||1)*this.options.page)}),pageDown:s(function(e){this._stepDown((e||1)*this.options.page)}),value:function(e){return arguments.length?(s(this._value).call(this,e),void 0):this._parse(this.element.val())},widget:function(){return this.uiSpinner}}),e.widget("ui.tabs",{version:"1.11.4",delay:300,options:{active:null,collapsible:!1,event:"click",heightStyle:"content",hide:null,show:null,activate:null,beforeActivate:null,beforeLoad:null,load:null},_isLocal:function(){var e=/#.*$/;return function(t){var i,s;t=t.cloneNode(!1),i=t.href.replace(e,""),s=location.href.replace(e,"");try{i=decodeURIComponent(i)}catch(n){}try{s=decodeURIComponent(s)}catch(n){}return t.hash.length>1&&i===s}}(),_create:function(){var t=this,i=this.options;this.running=!1,this.element.addClass("ui-tabs ui-widget ui-widget-content ui-corner-all").toggleClass("ui-tabs-collapsible",i.collapsible),this._processTabs(),i.active=this._initialActive(),e.isArray(i.disabled)&&(i.disabled=e.unique(i.disabled.concat(e.map(this.tabs.filter(".ui-state-disabled"),function(e){return t.tabs.index(e)}))).sort()),this.active=this.options.active!==!1&&this.anchors.length?this._findActive(i.active):e(),this._refresh(),this.active.length&&this.load(i.active)},_initialActive:function(){var t=this.options.active,i=this.options.collapsible,s=location.hash.substring(1);return null===t&&(s&&this.tabs.each(function(i,n){return e(n).attr("aria-controls")===s?(t=i,!1):void 0}),null===t&&(t=this.tabs.index(this.tabs.filter(".ui-tabs-active"))),(null===t||-1===t)&&(t=this.tabs.length?0:!1)),t!==!1&&(t=this.tabs.index(this.tabs.eq(t)),-1===t&&(t=i?!1:0)),!i&&t===!1&&this.anchors.length&&(t=0),t},_getCreateEventData:function(){return{tab:this.active,panel:this.active.length?this._getPanelForTab(this.active):e()}},_tabKeydown:function(t){var i=e(this.document[0].activeElement).closest("li"),s=this.tabs.index(i),n=!0;if(!this._handlePageNav(t)){switch(t.keyCode){case e.ui.keyCode.RIGHT:case e.ui.keyCode.DOWN:s++;break;case e.ui.keyCode.UP:case e.ui.keyCode.LEFT:n=!1,s--;break;case e.ui.keyCode.END:s=this.anchors.length-1;break;case e.ui.keyCode.HOME:s=0;break;case e.ui.keyCode.SPACE:return t.preventDefault(),clearTimeout(this.activating),this._activate(s),void 0;case e.ui.keyCode.ENTER:return t.preventDefault(),clearTimeout(this.activating),this._activate(s===this.options.active?!1:s),void 0;default:return}t.preventDefault(),clearTimeout(this.activating),s=this._focusNextTab(s,n),t.ctrlKey||t.metaKey||(i.attr("aria-selected","false"),this.tabs.eq(s).attr("aria-selected","true"),this.activating=this._delay(function(){this.option("active",s)},this.delay))}},_panelKeydown:function(t){this._handlePageNav(t)||t.ctrlKey&&t.keyCode===e.ui.keyCode.UP&&(t.preventDefault(),this.active.focus())},_handlePageNav:function(t){return t.altKey&&t.keyCode===e.ui.keyCode.PAGE_UP?(this._activate(this._focusNextTab(this.options.active-1,!1)),!0):t.altKey&&t.keyCode===e.ui.keyCode.PAGE_DOWN?(this._activate(this._focusNextTab(this.options.active+1,!0)),!0):void 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.tocify-subheader .tocify-subheader { +text-indent: 60px; +} + +.tocify .tocify-item > a, .tocify .nav-list .nav-header { +margin: 0px; +} + +.tocify .tocify-item a, .tocify .list-group-item { +padding: 5px; +} +.tocify .nav-pills > li { +float: none; +} + + +</style> +<script>/* jquery Tocify - v1.9.1 - 2013-10-22 + * http://www.gregfranko.com/jquery.tocify.js/ + * Copyright (c) 2013 Greg Franko; Licensed MIT */ + +// Immediately-Invoked Function Expression (IIFE) [Ben Alman Blog Post](http://benalman.com/news/2010/11/immediately-invoked-function-expression/) that calls another IIFE that contains all of the plugin logic. I used this pattern so that anyone viewing this code would not have to scroll to the bottom of the page to view the local parameters that were passed to the main IIFE. +(function(tocify) { + + // ECMAScript 5 Strict Mode: [John Resig Blog Post](http://ejohn.org/blog/ecmascript-5-strict-mode-json-and-more/) + "use strict"; + + // Calls the second IIFE and locally passes in the global jQuery, window, and document objects + tocify(window.jQuery, window, document); + + } + + // Locally passes in `jQuery`, the `window` object, the `document` object, and an `undefined` variable. The `jQuery`, `window` and `document` objects are passed in locally, to improve performance, since javascript first searches for a variable match within the local variables set before searching the global variables set. All of the global variables are also passed in locally to be minifier friendly. `undefined` can be passed in locally, because it is not a reserved word in JavaScript. + (function($, window, document, undefined) { + + // ECMAScript 5 Strict Mode: [John Resig Blog Post](http://ejohn.org/blog/ecmascript-5-strict-mode-json-and-more/) + "use strict"; + + var tocClassName = "tocify", + tocClass = "." + tocClassName, + tocFocusClassName = "tocify-focus", + tocHoverClassName = "tocify-hover", + hideTocClassName = "tocify-hide", + hideTocClass = "." + hideTocClassName, + headerClassName = "tocify-header", + headerClass = "." + headerClassName, + subheaderClassName = "tocify-subheader", + subheaderClass = "." + subheaderClassName, + itemClassName = "tocify-item", + itemClass = "." + itemClassName, + extendPageClassName = "tocify-extend-page", + extendPageClass = "." + extendPageClassName; + + // Calling the jQueryUI Widget Factory Method + $.widget("toc.tocify", { + + //Plugin version + version: "1.9.1", + + // These options will be used as defaults + options: { + + // **context**: Accepts String: Any jQuery selector + // The container element that holds all of the elements used to generate the table of contents + context: "body", + + // **ignoreSelector**: Accepts String: Any jQuery selector + // A selector to any element that would be matched by selectors that you wish to be ignored + ignoreSelector: null, + + // **selectors**: Accepts an Array of Strings: Any jQuery selectors + // The element's used to generate the table of contents. The order is very important since it will determine the table of content's nesting structure + selectors: "h1, h2, h3", + + // **showAndHide**: Accepts a boolean: true or false + // Used to determine if elements should be shown and hidden + showAndHide: true, + + // **showEffect**: Accepts String: "none", "fadeIn", "show", or "slideDown" + // Used to display any of the table of contents nested items + showEffect: "slideDown", + + // **showEffectSpeed**: Accepts Number (milliseconds) or String: "slow", "medium", or "fast" + // The time duration of the show animation + showEffectSpeed: "medium", + + // **hideEffect**: Accepts String: "none", "fadeOut", "hide", or "slideUp" + // Used to hide any of the table of contents nested items + hideEffect: "slideUp", + + // **hideEffectSpeed**: Accepts Number (milliseconds) or String: "slow", "medium", or "fast" + // The time duration of the hide animation + hideEffectSpeed: "medium", + + // **smoothScroll**: Accepts a boolean: true or false + // Determines if a jQuery animation should be used to scroll to specific table of contents items on the page + smoothScroll: true, + + // **smoothScrollSpeed**: Accepts Number (milliseconds) or String: "slow", "medium", or "fast" + // The time duration of the smoothScroll animation + smoothScrollSpeed: "medium", + + // **scrollTo**: Accepts Number (pixels) + // The amount of space between the top of page and the selected table of contents item after the page has been scrolled + scrollTo: 0, + + // **showAndHideOnScroll**: Accepts a boolean: true or false + // Determines if table of contents nested items should be shown and hidden while scrolling + showAndHideOnScroll: true, + + // **highlightOnScroll**: Accepts a boolean: true or false + // Determines if table of contents nested items should be highlighted (set to a different color) while scrolling + highlightOnScroll: true, + + // **highlightOffset**: Accepts a number + // The offset distance in pixels to trigger the next active table of contents item + highlightOffset: 40, + + // **theme**: Accepts a string: "bootstrap", "jqueryui", or "none" + // Determines if Twitter Bootstrap, jQueryUI, or Tocify classes should be added to the table of contents + theme: "bootstrap", + + // **extendPage**: Accepts a boolean: true or false + // If a user scrolls to the bottom of the page and the page is not tall enough to scroll to the last table of contents item, then the page height is increased + extendPage: true, + + // **extendPageOffset**: Accepts a number: pixels + // How close to the bottom of the page a user must scroll before the page is extended + extendPageOffset: 100, + + // **history**: Accepts a boolean: true or false + // Adds a hash to the page url to maintain history + history: true, + + // **scrollHistory**: Accepts a boolean: true or false + // Adds a hash to the page url, to maintain history, when scrolling to a TOC item + scrollHistory: false, + + // **hashGenerator**: How the hash value (the anchor segment of the URL, following the + // # character) will be generated. + // + // "compact" (default) - #CompressesEverythingTogether + // "pretty" - #looks-like-a-nice-url-and-is-easily-readable + // function(text, element){} - Your own hash generation function that accepts the text as an + // argument, and returns the hash value. + hashGenerator: "compact", + + // **highlightDefault**: Accepts a boolean: true or false + // Set's the first TOC item as active if no other TOC item is active. + highlightDefault: true + + }, + + // _Create + // ------- + // Constructs the plugin. Only called once. + _create: function() { + + var self = this; + + self.extendPageScroll = true; + + // Internal array that keeps track of all TOC items (Helps to recognize if there are duplicate TOC item strings) + self.items = []; + + // Generates the HTML for the dynamic table of contents + self._generateToc(); + + // Adds CSS classes to the newly generated table of contents HTML + self._addCSSClasses(); + + self.webkit = (function() { + + for (var prop in window) { + + if (prop) { + + if (prop.toLowerCase().indexOf("webkit") !== -1) { + + return true; + + } + + } + + } + + return false; + + }()); + + // Adds jQuery event handlers to the newly generated table of contents + self._setEventHandlers(); + + // Binding to the Window load event to make sure the correct scrollTop is calculated + $(window).on("load", function() { + + // Sets the active TOC item + self._setActiveElement(true); + + // Once all animations on the page are complete, this callback function will be called + $("html, body").promise().done(function() { + + setTimeout(function() { + + self.extendPageScroll = false; + + }, 0); + + }); + + }); + + }, + + // _generateToc + // ------------ + // Generates the HTML for the dynamic table of contents + _generateToc: function() { + + // _Local variables_ + + // Stores the plugin context in the self variable + var self = this, + + // All of the HTML tags found within the context provided (i.e. body) that match the top level jQuery selector above + firstElem, + + // Instantiated variable that will store the top level newly created unordered list DOM element + ul, + ignoreSelector = self.options.ignoreSelector; + + + // Determine the element to start the toc with + // get all the top level selectors + firstElem = []; + var selectors = this.options.selectors.replace(/ /g, "").split(","); + // find the first set that have at least one non-ignored element + for(var i = 0; i < selectors.length; i++) { + var foundSelectors = $(this.options.context).find(selectors[i]); + for (var s = 0; s < foundSelectors.length; s++) { + if (!$(foundSelectors[s]).is(ignoreSelector)) { + firstElem = foundSelectors; + break; + } + } + if (firstElem.length> 0) + break; + } + + if (!firstElem.length) { + + self.element.addClass(hideTocClassName); + + return; + + } + + self.element.addClass(tocClassName); + + // Loops through each top level selector + firstElem.each(function(index) { + + //If the element matches the ignoreSelector then we skip it + if ($(this).is(ignoreSelector)) { + return; + } + + // Creates an unordered list HTML element and adds a dynamic ID and standard class name + ul = $("<ul/>", { + "id": headerClassName + index, + "class": headerClassName + }). + + // Appends a top level list item HTML element to the previously created HTML header + append(self._nestElements($(this), index)); + + // Add the created unordered list element to the HTML element calling the plugin + self.element.append(ul); + + // Finds all of the HTML tags between the header and subheader elements + $(this).nextUntil(this.nodeName.toLowerCase()).each(function() { + + // If there are no nested subheader elemements + if ($(this).find(self.options.selectors).length === 0) { + + // Loops through all of the subheader elements + $(this).filter(self.options.selectors).each(function() { + + //If the element matches the ignoreSelector then we skip it + if ($(this).is(ignoreSelector)) { + return; + } + + self._appendSubheaders.call(this, self, ul); + + }); + + } + + // If there are nested subheader elements + else { + + // Loops through all of the subheader elements + $(this).find(self.options.selectors).each(function() { + + //If the element matches the ignoreSelector then we skip it + if ($(this).is(ignoreSelector)) { + return; + } + + self._appendSubheaders.call(this, self, ul); + + }); + + } + + }); + + }); + + }, + + _setActiveElement: function(pageload) { + + var self = this, + + hash = window.location.hash.substring(1), + + elem = self.element.find('li[data-unique="' + hash + '"]'); + + if (hash.length) { + + // Removes highlighting from all of the list item's + self.element.find("." + self.focusClass).removeClass(self.focusClass); + + // Highlights the current list item that was clicked + elem.addClass(self.focusClass); + + // Triggers the click event on the currently focused TOC item + elem.click(); + + } else { + + // Removes highlighting from all of the list item's + self.element.find("." + self.focusClass).removeClass(self.focusClass); + + if (!hash.length && pageload && self.options.highlightDefault) { + + // Highlights the first TOC item if no other items are highlighted + self.element.find(itemClass).first().addClass(self.focusClass); + + } + + } + + return self; + + }, + + // _nestElements + // ------------- + // Helps create the table of contents list by appending nested list items + _nestElements: function(self, index) { + + var arr, item, hashValue; + + arr = $.grep(this.items, function(item) { + + return item === self.text(); + + }); + + // If there is already a duplicate TOC item + if (arr.length) { + + // Adds the current TOC item text and index (for slight randomization) to the internal array + this.items.push(self.text() + index); + + } + + // If there not a duplicate TOC item + else { + + // Adds the current TOC item text to the internal array + this.items.push(self.text()); + + } + + hashValue = this._generateHashValue(arr, self, index); + + // Appends a list item HTML element to the last unordered list HTML element found within the HTML element calling the plugin + item = $("<li/>", { + + // Sets a common class name to the list item + "class": itemClassName, + + "data-unique": hashValue + + }); + + if (this.options.theme !== "bootstrap3") { + + item.append($("<a/>", { + + "html": self.html() + + })); + + } else { + + item.html(self.html()); + + } + + // Adds an HTML anchor tag before the currently traversed HTML element + self.before($("<div/>", { + + // Sets a name attribute on the anchor tag to the text of the currently traversed HTML element (also making sure that all whitespace is replaced with an underscore) + "name": hashValue, + + "data-unique": hashValue + + })); + + return item; + + }, + + // _generateHashValue + // ------------------ + // Generates the hash value that will be used to refer to each item. + _generateHashValue: function(arr, self, index) { + + var hashValue = "", + hashGeneratorOption = this.options.hashGenerator; + + if (hashGeneratorOption === "pretty") { + + // prettify the text + hashValue = self.text().toLowerCase().replace(/\s/g, "-"); + + // fix double hyphens + while (hashValue.indexOf("--") > -1) { + hashValue = hashValue.replace(/--/g, "-"); + } + + // fix colon-space instances + while (hashValue.indexOf(":-") > -1) { + hashValue = hashValue.replace(/:-/g, "-"); + } + + } else if (typeof hashGeneratorOption === "function") { + + // call the function + hashValue = hashGeneratorOption(self.text(), self); + + } else { + + // compact - the default + hashValue = self.text().replace(/\s/g, ""); + + } + + // add the index if we need to + if (arr.length) { + hashValue += "" + index; + } + + // return the value + return hashValue; + + }, + + // _appendElements + // --------------- + // Helps create the table of contents list by appending subheader elements + + _appendSubheaders: function(self, ul) { + + // The current element index + var index = $(this).index(self.options.selectors), + + // Finds the previous header DOM element + previousHeader = $(self.options.selectors).eq(index - 1), + + currentTagName = +$(this).prop("tagName").charAt(1), + + previousTagName = +previousHeader.prop("tagName").charAt(1), + + lastSubheader; + + // If the current header DOM element is smaller than the previous header DOM element or the first subheader + if (currentTagName < previousTagName) { + + // Selects the last unordered list HTML found within the HTML element calling the plugin + self.element.find(subheaderClass + "[data-tag=" + currentTagName + "]").last().append(self._nestElements($(this), index)); + + } + + // If the current header DOM element is the same type of header(eg. h4) as the previous header DOM element + else if (currentTagName === previousTagName) { + + ul.find(itemClass).last().after(self._nestElements($(this), index)); + + } else { + + // Selects the last unordered list HTML found within the HTML element calling the plugin + ul.find(itemClass).last(). + + // Appends an unorderedList HTML element to the dynamic `unorderedList` variable and sets a common class name + after($("<ul/>", { + + "class": subheaderClassName, + + "data-tag": currentTagName + + })).next(subheaderClass). + + // Appends a list item HTML element to the last unordered list HTML element found within the HTML element calling the plugin + append(self._nestElements($(this), index)); + } + + }, + + // _setEventHandlers + // ---------------- + // Adds jQuery event handlers to the newly generated table of contents + _setEventHandlers: function() { + + // _Local variables_ + + // Stores the plugin context in the self variable + var self = this, + + // Instantiates a new variable that will be used to hold a specific element's context + $self, + + // Instantiates a new variable that will be used to determine the smoothScroll animation time duration + duration; + + // Event delegation that looks for any clicks on list item elements inside of the HTML element calling the plugin + this.element.on("click.tocify", "li", function(event) { + + if (self.options.history) { + + window.location.hash = $(this).attr("data-unique"); + + } + + // Removes highlighting from all of the list item's + self.element.find("." + self.focusClass).removeClass(self.focusClass); + + // Highlights the current list item that was clicked + $(this).addClass(self.focusClass); + + // If the showAndHide option is true + if (self.options.showAndHide) { + + var elem = $('li[data-unique="' + $(this).attr("data-unique") + '"]'); + + self._triggerShow(elem); + + } + + self._scrollTo($(this)); + + }); + + // Mouseenter and Mouseleave event handlers for the list item's within the HTML element calling the plugin + this.element.find("li").on({ + + // Mouseenter event handler + "mouseenter.tocify": function() { + + // Adds a hover CSS class to the current list item + $(this).addClass(self.hoverClass); + + // Makes sure the cursor is set to the pointer icon + $(this).css("cursor", "pointer"); + + }, + + // Mouseleave event handler + "mouseleave.tocify": function() { + + if (self.options.theme !== "bootstrap") { + + // Removes the hover CSS class from the current list item + $(this).removeClass(self.hoverClass); + + } + + } + }); + + // only attach handler if needed (expensive in IE) + if (self.options.extendPage || self.options.highlightOnScroll || self.options.scrollHistory || self.options.showAndHideOnScroll) { + // Window scroll event handler + $(window).on("scroll.tocify", function() { + + // Once all animations on the page are complete, this callback function will be called + $("html, body").promise().done(function() { + + // Local variables + + // Stores how far the user has scrolled + var winScrollTop = $(window).scrollTop(), + + // Stores the height of the window + winHeight = $(window).height(), + + // Stores the height of the document + docHeight = $(document).height(), + + scrollHeight = $("body")[0].scrollHeight, + + // Instantiates a variable that will be used to hold a selected HTML element + elem, + + lastElem, + + lastElemOffset, + + currentElem; + + if (self.options.extendPage) { + + // If the user has scrolled to the bottom of the page and the last toc item is not focused + if ((self.webkit && winScrollTop >= scrollHeight - winHeight - self.options.extendPageOffset) || (!self.webkit && winHeight + winScrollTop > docHeight - self.options.extendPageOffset)) { + + if (!$(extendPageClass).length) { + + lastElem = $('div[data-unique="' + $(itemClass).last().attr("data-unique") + '"]'); + + if (!lastElem.length) return; + + // Gets the top offset of the page header that is linked to the last toc item + lastElemOffset = lastElem.offset().top; + + // Appends a div to the bottom of the page and sets the height to the difference of the window scrollTop and the last element's position top offset + $(self.options.context).append($("<div/>", { + + "class": extendPageClassName, + + "height": Math.abs(lastElemOffset - winScrollTop) + "px", + + "data-unique": extendPageClassName + + })); + + if (self.extendPageScroll) { + + currentElem = self.element.find('li.' + self.focusClass); + + self._scrollTo($('div[data-unique="' + currentElem.attr("data-unique") + '"]')); + + } + + } + + } + + } + + // The zero timeout ensures the following code is run after the scroll events + setTimeout(function() { + + // _Local variables_ + + // Stores the distance to the closest anchor + var closestAnchorDistance = null, + + // Stores the index of the closest anchor + closestAnchorIdx = null, + + // Keeps a reference to all anchors + anchors = $(self.options.context).find("div[data-unique]"), + + anchorText; + + // Determines the index of the closest anchor + anchors.each(function(idx) { + var distance = Math.abs(($(this).next().length ? $(this).next() : $(this)).offset().top - winScrollTop - self.options.highlightOffset); + if (closestAnchorDistance == null || distance < closestAnchorDistance) { + closestAnchorDistance = distance; + closestAnchorIdx = idx; + } else { + return false; + } + }); + + anchorText = $(anchors[closestAnchorIdx]).attr("data-unique"); + + // Stores the list item HTML element that corresponds to the currently traversed anchor tag + elem = $('li[data-unique="' + anchorText + '"]'); + + // If the `highlightOnScroll` option is true and a next element is found + if (self.options.highlightOnScroll && elem.length) { + + // Removes highlighting from all of the list item's + self.element.find("." + self.focusClass).removeClass(self.focusClass); + + // Highlights the corresponding list item + elem.addClass(self.focusClass); + + } + + if (self.options.scrollHistory) { + + if (window.location.hash !== "#" + anchorText) { + + window.location.replace("#" + anchorText); + + } + } + + // If the `showAndHideOnScroll` option is true + if (self.options.showAndHideOnScroll && self.options.showAndHide) { + + self._triggerShow(elem, true); + + } + + }, 0); + + }); + + }); + } + + }, + + // Show + // ---- + // Opens the current sub-header + show: function(elem, scroll) { + + // Stores the plugin context in the `self` variable + var self = this, + element = elem; + + // If the sub-header is not already visible + if (!elem.is(":visible")) { + + // If the current element does not have any nested subheaders, is not a header, and its parent is not visible + if (!elem.find(subheaderClass).length && !elem.parent().is(headerClass) && !elem.parent().is(":visible")) { + + // Sets the current element to all of the subheaders within the current header + elem = elem.parents(subheaderClass).add(elem); + + } + + // If the current element does not have any nested subheaders and is not a header + else if (!elem.children(subheaderClass).length && !elem.parent().is(headerClass)) { + + // Sets the current element to the closest subheader + elem = elem.closest(subheaderClass); + + } + + //Determines what jQuery effect to use + switch (self.options.showEffect) { + + //Uses `no effect` + case "none": + + elem.show(); + + break; + + //Uses the jQuery `show` special effect + case "show": + + elem.show(self.options.showEffectSpeed); + + break; + + //Uses the jQuery `slideDown` special effect + case "slideDown": + + elem.slideDown(self.options.showEffectSpeed); + + break; + + //Uses the jQuery `fadeIn` special effect + case "fadeIn": + + elem.fadeIn(self.options.showEffectSpeed); + + break; + + //If none of the above options were passed, then a `jQueryUI show effect` is expected + default: + + elem.show(); + + break; + + } + + } + + // If the current subheader parent element is a header + if (elem.parent().is(headerClass)) { + + // Hides all non-active sub-headers + self.hide($(subheaderClass).not(elem)); + + } + + // If the current subheader parent element is not a header + else { + + // Hides all non-active sub-headers + self.hide($(subheaderClass).not(elem.closest(headerClass).find(subheaderClass).not(elem.siblings()))); + + } + + // Maintains chainablity + return self; + + }, + + // Hide + // ---- + // Closes the current sub-header + hide: function(elem) { + + // Stores the plugin context in the `self` variable + var self = this; + + //Determines what jQuery effect to use + switch (self.options.hideEffect) { + + // Uses `no effect` + case "none": + + elem.hide(); + + break; + + // Uses the jQuery `hide` special effect + case "hide": + + elem.hide(self.options.hideEffectSpeed); + + break; + + // Uses the jQuery `slideUp` special effect + case "slideUp": + + elem.slideUp(self.options.hideEffectSpeed); + + break; + + // Uses the jQuery `fadeOut` special effect + case "fadeOut": + + elem.fadeOut(self.options.hideEffectSpeed); + + break; + + // If none of the above options were passed, then a `jqueryUI hide effect` is expected + default: + + elem.hide(); + + break; + + } + + // Maintains chainablity + return self; + }, + + // _triggerShow + // ------------ + // Determines what elements get shown on scroll and click + _triggerShow: function(elem, scroll) { + + var self = this; + + // If the current element's parent is a header element or the next element is a nested subheader element + if (elem.parent().is(headerClass) || elem.next().is(subheaderClass)) { + + // Shows the next sub-header element + self.show(elem.next(subheaderClass), scroll); + + } + + // If the current element's parent is a subheader element + else if (elem.parent().is(subheaderClass)) { + + // Shows the parent sub-header element + self.show(elem.parent(), scroll); + + } + + // Maintains chainability + return self; + + }, + + // _addCSSClasses + // -------------- + // Adds CSS classes to the newly generated table of contents HTML + _addCSSClasses: function() { + + // If the user wants a jqueryUI theme + if (this.options.theme === "jqueryui") { + + this.focusClass = "ui-state-default"; + + this.hoverClass = "ui-state-hover"; + + //Adds the default styling to the dropdown list + this.element.addClass("ui-widget").find(".toc-title").addClass("ui-widget-header").end().find("li").addClass("ui-widget-content"); + + } + + // If the user wants a twitterBootstrap theme + else if (this.options.theme === "bootstrap") { + + this.element.find(headerClass + "," + subheaderClass).addClass("nav nav-list"); + + this.focusClass = "active"; + + } + + // If the user wants a twitterBootstrap theme + else if (this.options.theme === "bootstrap3") { + + this.element.find(headerClass + "," + subheaderClass).addClass("list-group"); + + this.element.find(itemClass).addClass("list-group-item"); + + this.focusClass = "active"; + + } + + // If a user does not want a prebuilt theme + else { + + // Adds more neutral classes (instead of jqueryui) + + this.focusClass = tocFocusClassName; + + this.hoverClass = tocHoverClassName; + + } + + //Maintains chainability + return this; + + }, + + // setOption + // --------- + // Sets a single Tocify option after the plugin is invoked + setOption: function() { + + // Calls the jQueryUI Widget Factory setOption method + $.Widget.prototype._setOption.apply(this, arguments); + + }, + + // setOptions + // ---------- + // Sets a single or multiple Tocify options after the plugin is invoked + setOptions: function() { + + // Calls the jQueryUI Widget Factory setOptions method + $.Widget.prototype._setOptions.apply(this, arguments); + + }, + + // _scrollTo + // --------- + // Scrolls to a specific element + _scrollTo: function(elem) { + + var self = this, + duration = self.options.smoothScroll || 0, + scrollTo = self.options.scrollTo, + currentDiv = $('div[data-unique="' + elem.attr("data-unique") + '"]'); + + if (!currentDiv.length) { + + return self; + + } + + // Once all animations on the page are complete, this callback function will be called + $("html, body").promise().done(function() { + + // Animates the html and body element scrolltops + $("html, body").animate({ + + // Sets the jQuery `scrollTop` to the top offset of the HTML div tag that matches the current list item's `data-unique` tag + "scrollTop": currentDiv.offset().top - ($.isFunction(scrollTo) ? scrollTo.call() : scrollTo) + "px" + + }, { + + // Sets the smoothScroll animation time duration to the smoothScrollSpeed option + "duration": duration + + }); + + }); + + // Maintains chainability + return self; + + } + + }); + + })); //end of plugin +</script> +<script> + +/** + * jQuery Plugin: Sticky Tabs + * + * @author Aidan Lister <aidan@php.net> + * adapted by Ruben Arslan to activate parent tabs too + * http://www.aidanlister.com/2014/03/persisting-the-tab-state-in-bootstrap/ + */ +(function($) { + "use strict"; + $.fn.rmarkdownStickyTabs = function() { + var context = this; + // Show the tab corresponding with the hash in the URL, or the first tab + var showStuffFromHash = function() { + var hash = window.location.hash; + var selector = hash ? 'a[href="' + hash + '"]' : 'li.active > a'; + var $selector = $(selector, context); + if($selector.data('toggle') === "tab") { + $selector.tab('show'); + // walk up the ancestors of this element, show any hidden tabs + $selector.parents('.section.tabset').each(function(i, elm) { + var link = $('a[href="#' + $(elm).attr('id') + '"]'); + if(link.data('toggle') === "tab") { + link.tab("show"); + } + }); + } + }; + + + // Set the correct tab when the page loads + showStuffFromHash(context); + + // Set the correct tab when a user uses their back/forward button + $(window).on('hashchange', function() { + showStuffFromHash(context); + }); + + // Change the URL when tabs are clicked + $('a', context).on('click', function(e) { + history.pushState(null, null, this.href); + showStuffFromHash(context); + }); + + return this; + }; +}(jQuery)); + +window.buildTabsets = function(tocID) { + + // build a tabset from a section div with the .tabset class + function buildTabset(tabset) { + + // check for fade and pills options + var fade = tabset.hasClass("tabset-fade"); + var pills = tabset.hasClass("tabset-pills"); + var navClass = pills ? "nav-pills" : "nav-tabs"; + + // determine the heading level of the tabset and tabs + var match = tabset.attr('class').match(/level(\d) /); + if (match === null) + return; + var tabsetLevel = Number(match[1]); + var tabLevel = tabsetLevel + 1; + + // find all subheadings immediately below + var tabs = tabset.find("div.section.level" + tabLevel); + if (!tabs.length) + return; + + // create tablist and tab-content elements + var tabList = $('<ul class="nav ' + navClass + '" role="tablist"></ul>'); + $(tabs[0]).before(tabList); + var tabContent = $('<div class="tab-content"></div>'); + $(tabs[0]).before(tabContent); + + // build the tabset + var activeTab = 0; + tabs.each(function(i) { + + // get the tab div + var tab = $(tabs[i]); + + // get the id then sanitize it for use with bootstrap tabs + var id = tab.attr('id'); + + // see if this is marked as the active tab + if (tab.hasClass('active')) + activeTab = i; + + // remove any table of contents entries associated with + // this ID (since we'll be removing the heading element) + $("div#" + tocID + " li a[href='#" + id + "']").parent().remove(); + + // sanitize the id for use with bootstrap tabs + id = id.replace(/[.\/?&!#<>]/g, '').replace(/\s/g, '_'); + tab.attr('id', id); + + // get the heading element within it, grab it's text, then remove it + var heading = tab.find('h' + tabLevel + ':first'); + var headingText = heading.html(); + heading.remove(); + + // build and append the tab list item + var a = $('<a role="tab" data-toggle="tab">' + headingText + '</a>'); + a.attr('href', '#' + id); + a.attr('aria-controls', id); + var li = $('<li role="presentation"></li>'); + li.append(a); + tabList.append(li); + + // set it's attributes + tab.attr('role', 'tabpanel'); + tab.addClass('tab-pane'); + tab.addClass('tabbed-pane'); + if (fade) + tab.addClass('fade'); + + // move it into the tab content div + tab.detach().appendTo(tabContent); + }); + + // set active tab + $(tabList.children('li')[activeTab]).addClass('active'); + var active = $(tabContent.children('div.section')[activeTab]); + active.addClass('active'); + if (fade) + active.addClass('in'); + + if (tabset.hasClass("tabset-sticky")) + tabset.rmarkdownStickyTabs(); + } + + // convert section divs with the .tabset class to tabsets + var tabsets = $("div.section.tabset"); + tabsets.each(function(i) { + buildTabset($(tabsets[i])); + }); +}; + +</script> +<script> +window.initializeCodeFolding = function(show) { + + // handlers for show-all and hide all + $("#rmd-show-all-code").click(function() { + $('div.r-code-collapse').each(function() { + $(this).collapse('show'); + }); + }); + $("#rmd-hide-all-code").click(function() { + $('div.r-code-collapse').each(function() { + $(this).collapse('hide'); + }); + }); + + // index for unique code element ids + var currentIndex = 1; + + // 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() { + + // 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'); + var id = 'rcode-643E0F36' + currentIndex++; + div.attr('id', id); + $(this).before(div); + $(this).detach().appendTo(div); + + // add a show code button right above + var showCodeText = $('<span>' + (showThis ? 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+</div> + +<div class="toc-content col-xs-12 col-sm-8 col-md-9"> + + + + +<div id="header"> + +<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" 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> +</div> + + + +<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 23 June 2021, built on 25 Jun 2021</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> +<div id="introduction" class="section level1"> +<h1>Introduction</h1> +<p>During the preparation of the journal article on nonlinear mixed-effects models in degradation kinetics (submitted) and the analysis of the dimethenamid degradation data analysed therein, a need for a more detailed analysis using not only nlme and saemix, but also nlmixr for fitting the mixed-effects models was identified.</p> +<p>This vignette is an attempt to satisfy this need.</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 <a href="https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716">EFSA register of questions</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) +dmta_ds <- lapply(1:8, 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[["Borstel"]] <- rbind(dmta_ds[["Borstel 1"]], dmta_ds[["Borstel 2"]]) +dmta_ds[["Borstel 1"]] <- NULL +dmta_ds[["Borstel 2"]] <- NULL +dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]]) +dmta_ds[["Elliot 1"]] <- NULL +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> +<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> +<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> +<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> +<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> +<pre class="r"><code>plot(mixed(f_parent_mkin_const["DFOP", ]), test_log_parms = TRUE)</code></pre> +<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> +</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> +<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 use 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. However, 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>f_parent_nlme_sfo_const <- nlme(f_parent_mkin_const["SFO", ]) +#f_parent_nlme_dfop_const <- nlme(f_parent_mkin_const["DFOP", ]) # error +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 overparameterisation is also indicated by warnings obtained when fitting SFO or DFOP with the two-component error model (‘false convergence’ in the ‘LME step’ in some iterations). 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 = pdLogChol(list(DMTA_0 ~ 1, log_k_DMTA ~ 1))) +anova(f_parent_nlme_sfo_const, f_parent_nlme_sfo_const_logchol) # not better +f_parent_nlme_dfop_tc_logchol <- update(f_parent_nlme_dfop_tc, + random = pdLogChol(list(DMTA_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1))) +# using log Cholesky parameterisation for random effects (nlme default) does +# not converge and gives lots of warnings about the LME step not converging</code></pre> +<p>The model comparison function of the nlme package can directly be applied to these fits showing a similar goodness-of-fit of the SFO model, but 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> +<pre><code> Model df AIC BIC logLik Test L.Ratio p-value +f_parent_nlme_sfo_const 1 5 818.63 834.00 -404.31 +f_parent_nlme_sfo_tc 2 6 820.61 839.06 -404.31 1 vs 2 0.014 0.9049 +f_parent_nlme_dfop_tc 3 10 687.84 718.59 -333.92 2 vs 3 140.771 <.0001</code></pre> +<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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" /><!-- --></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 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.</p> +<p>The convergence plot for the SFO model using constant variance is shown below.</p> +<pre class="r"><code>library(saemix) +f_parent_saemix_sfo_const <- saem(f_parent_mkin_const["SFO", ], quiet = TRUE, + transformations = "saemix") +plot(f_parent_saemix_sfo_const$so, plot.type = "convergence")</code></pre> +<p><img 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" /><!-- --></p> +<p>Obviously the default 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 <- saem(f_parent_mkin_tc["SFO", ], quiet = TRUE, + transformations = "saemix") +plot(f_parent_saemix_sfo_tc$so, plot.type = "convergence")</code></pre> +<p><img src="data:image/png;base64,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" /><!-- --></p> +<p>When fitting the DFOP model with constant variance, parameter convergence is not as unambiguous. Therefore, the number of iterations in the first phase of the algorithm was increased, leading to visually satisfying convergence.</p> +<pre class="r"><code>f_parent_saemix_dfop_const <- saem(f_parent_mkin_const["DFOP", ], quiet = TRUE, + control = saemixControl(nbiter.saemix = c(800, 200), print = FALSE, + save = FALSE, save.graphs = FALSE, displayProgress = FALSE), + transformations = "saemix") +plot(f_parent_saemix_dfop_const$so, plot.type = "convergence")</code></pre> +<p><img src="data:image/png;base64,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" /><!-- --></p> +<p>The same applies to the case where the DFOP model is fitted with the two-component error model.</p> +<pre class="r"><code>f_parent_saemix_dfop_tc_moreiter <- saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE, + control = saemixControl(nbiter.saemix = c(800, 200), print = FALSE, + save = FALSE, save.graphs = FALSE, displayProgress = FALSE), + transformations = "saemix") +plot(f_parent_saemix_dfop_tc_moreiter$so, plot.type = "convergence")</code></pre> +<p><img src="data:image/png;base64,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" /><!-- --></p> +<p>The four combinations can be compared using the model comparison function from the saemix package:</p> +<pre class="r"><code>compare.saemix(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so, + f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc$so)</code></pre> +<pre><code>Likelihoods calculated by importance sampling</code></pre> +<pre><code> AIC BIC +1 818.37 817.33 +2 820.38 819.14 +3 725.91 724.04 +4 688.09 686.01</code></pre> +<p>As in the case of nlme fits, the DFOP model fitted with two-component error (number 4) gives the lowest AIC. The numeric values are reasonably close to the ones obtained using nlme, considering that the algorithms for fitting the model and for the likelihood calculation are quite different.</p> +</div> +<div id="nlmixr" class="section level3"> +<h3>nlmixr</h3> +<p>In the last years, a lot of effort has been put into the nlmixr package which is designed for pharmacokinetics, where nonlinear mixed-effects models are routinely used, but which can also be used for related data like chemical degradation data. A current development branch of the mkin package provides an interface between mkin and nlmixr. Here, we check if we get equivalent results when using a refined version of the First Order Conditional Estimation (FOCE) algorithm used in nlme, namely First Order Conditional Estimation with Interaction (FOCEI), and the SAEM algorithm as implemented in nlmixr.</p> +<p>First, the focei algorithm is used for the four model combinations and the goodness of fit of the results is compared.</p> +<pre class="r"><code>f_parent_nlmixr_focei_sfo_const <- nlmixr(f_parent_mkin_const["SFO", ], est = "focei") +f_parent_nlmixr_focei_sfo_tc <- nlmixr(f_parent_mkin_tc["SFO", ], est = "focei") +f_parent_nlmixr_focei_dfop_const <- nlmixr(f_parent_mkin_const["DFOP", ], est = "focei") +f_parent_nlmixr_focei_dfop_tc<- nlmixr(f_parent_mkin_tc["DFOP", ], est = "focei")</code></pre> +<pre class="r"><code>AIC(f_parent_nlmixr_focei_sfo_const$nm, f_parent_nlmixr_focei_sfo_tc$nm, + f_parent_nlmixr_focei_dfop_const$nm, f_parent_nlmixr_focei_dfop_tc$nm)</code></pre> +<pre><code> df AIC +f_parent_nlmixr_focei_sfo_const$nm 5 818.63 +f_parent_nlmixr_focei_sfo_tc$nm 6 820.61 +f_parent_nlmixr_focei_dfop_const$nm 9 728.11 +f_parent_nlmixr_focei_dfop_tc$nm 10 687.82</code></pre> +<p>The AIC values are very close to the ones obtained with nlme.</p> +<p>Secondly, we use the SAEM estimation routine and check the convergence plots for SFO with constant variance</p> +<pre class="r"><code>f_parent_nlmixr_saem_sfo_const <- nlmixr(f_parent_mkin_const["SFO", ], est = "saem", + control = nlmixr::saemControl(logLik = TRUE)) +traceplot(f_parent_nlmixr_saem_sfo_const$nm)</code></pre> +<p><img 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" /><!-- --></p> +<p>for SFO with two-component error</p> +<pre class="r"><code>f_parent_nlmixr_saem_sfo_tc <- nlmixr(f_parent_mkin_tc["SFO", ], est = "saem", + control = nlmixr::saemControl(logLik = TRUE)) +nlmixr::traceplot(f_parent_nlmixr_saem_sfo_tc$nm)</code></pre> +<p><img src="data:image/png;base64,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" /><!-- --></p> +<p>For DFOP with constant variance, the convergence plots show considerable instability of the fit, which can be alleviated by increasing the number of iterations and the number of parallel chains for the first phase of algorithm.</p> +<pre class="r"><code>f_parent_nlmixr_saem_dfop_const <- nlmixr(f_parent_mkin_const["DFOP", ], est = "saem", + control = nlmixr::saemControl(logLik = TRUE, nBurn = 1000), nmc = 15) +nlmixr::traceplot(f_parent_nlmixr_saem_dfop_const$nm)</code></pre> +<p><img src="data:image/png;base64,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" /><!-- --></p> +<p>For DFOP with two-component error, the same increase in iterations and parallel chains was used, but using the two-component error appears to lead to a less erratic convergence, so this may not be necessary to this degree.</p> +<pre class="r"><code>f_parent_nlmixr_saem_dfop_tc <- nlmixr(f_parent_mkin_tc["DFOP", ], est = "saem", + control = nlmixr::saemControl(logLik = TRUE, nBurn = 1000, nmc = 15)) +nlmixr::traceplot(f_parent_nlmixr_saem_dfop_tc$nm)</code></pre> +<p><img src="data:image/png;base64,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" /><!-- --></p> +<p>The AIC values are internally calculated using Gaussian quadrature. For an unknown reason, the AIC value obtained for the DFOP fit using the two-component error model is given as Infinity.</p> +<pre class="r"><code>AIC(f_parent_nlmixr_saem_sfo_const$nm, f_parent_nlmixr_saem_sfo_tc$nm, + f_parent_nlmixr_saem_dfop_const$nm, f_parent_nlmixr_saem_dfop_tc$nm)</code></pre> +<pre><code> df AIC +f_parent_nlmixr_saem_sfo_const$nm 5 820.54 +f_parent_nlmixr_saem_sfo_tc$nm 6 835.26 +f_parent_nlmixr_saem_dfop_const$nm 9 850.72 +f_parent_nlmixr_saem_dfop_tc$nm 10 Inf</code></pre> +</div> +</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-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> +<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> +</div> +</div> + + + +</div> +</div> + +</div> + +<script> + +// add bootstrap table styles to pandoc tables +function bootstrapStylePandocTables() { + $('tr.odd').parent('tbody').parent('table').addClass('table table-condensed'); 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+ }, + ignoreSelector: ".toc-ignore", + scrollTo: 0 + }; + options.showAndHide = true; + options.smoothScroll = true; + + // tocify + var toc = $("#TOC").tocify(options).data("toc-tocify"); +}); +</script> + +<!-- dynamically load mathjax for compatibility with self-contained --> +<script> + (function () { + var script = document.createElement("script"); + script.type = "text/javascript"; + script.src = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"; + document.getElementsByTagName("head")[0].appendChild(script); + })(); +</script> + +</body> +</html> diff --git a/vignettes/web_only/dimethenamid_2018.rmd b/vignettes/web_only/dimethenamid_2018.rmd new file mode 100644 index 00000000..d3541a34 --- /dev/null +++ b/vignettes/web_only/dimethenamid_2018.rmd @@ -0,0 +1,374 @@ +--- +title: Example evaluations of the dimethenamid data from 2018 +author: Johannes Ranke +date: Last change 23 June 2021, built on `r format(Sys.Date(), format = "%d %b %Y")` +output: + html_document: + toc: true + toc_float: true + code_folding: hide + fig_retina: null +bibliography: ../references.bib +vignette: > + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +[Wissenschaftlicher Berater, Kronacher Str. 12, 79639 Grenzach-Wyhlen, Germany](http://www.jrwb.de)<br /> +[Privatdozent at the University of Bremen](http://chem.uft.uni-bremen.de/ranke) + +```{r, include = FALSE} +require(knitr) +options(digits = 5) +opts_chunk$set( + comment = "", + tidy = FALSE, + cache = TRUE +) +``` + +# Introduction + +During the preparation of the journal article on nonlinear mixed-effects models in +degradation kinetics (submitted) and the analysis of the dimethenamid degradation +data analysed therein, a need for a more detailed analysis using not only nlme and saemix, +but also nlmixr for fitting the mixed-effects models was identified. + +This vignette is an attempt to satisfy this need. + +# Data + +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 [@efsa_2018_dimethenamid] +were transcribed from the risk assessment report [@dimethenamid_rar_2018_b8] +which can be downloaded from the +[EFSA register of questions](https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716). + +The data are [available in the mkin +package](https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html). 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. + +Also, datasets observed in the same soil are merged, resulting in dimethenamid +(DMTA) data from six soils. + +```{r dimethenamid_data} +library(mkin) +dmta_ds <- lapply(1:8, 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[["Borstel"]] <- rbind(dmta_ds[["Borstel 1"]], dmta_ds[["Borstel 2"]]) +dmta_ds[["Borstel 1"]] <- NULL +dmta_ds[["Borstel 2"]] <- NULL +dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]]) +dmta_ds[["Elliot 1"]] <- NULL +dmta_ds[["Elliot 2"]] <- NULL +``` + +# Parent degradation + +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. + +## Separate evaluations + +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: + +```{r 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) +``` + +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): + +```{r f_parent_mkin_sfo_const} +plot(mixed(f_parent_mkin_const["SFO", ])) +``` + +Using biexponential decline (DFOP) results in a slightly more random +scatter of the residuals: + +```{r f_parent_mkin_dfop_const} +plot(mixed(f_parent_mkin_const["DFOP", ])) +``` + +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: + +```{r f_parent_mkin_dfop_const_test} +plot(mixed(f_parent_mkin_const["DFOP", ]), test_log_parms = TRUE) +``` + +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). + +The remaining trend of the residuals to be higher for higher predicted residues +is reduced by using the two-component error model: + +```{r f_parent_mkin_dfop_tc_test} +plot(mixed(f_parent_mkin_tc["DFOP", ]), test_log_parms = TRUE) +``` + +## Nonlinear mixed-effects models + +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. + +### nlme + +The nlme package was the first R extension providing facilities to fit nonlinear +mixed-effects models. We use 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. +However, 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. + +```{r f_parent_nlme, warning = FALSE} +f_parent_nlme_sfo_const <- nlme(f_parent_mkin_const["SFO", ]) +#f_parent_nlme_dfop_const <- nlme(f_parent_mkin_const["DFOP", ]) # error +f_parent_nlme_sfo_tc <- nlme(f_parent_mkin_tc["SFO", ]) +f_parent_nlme_dfop_tc <- nlme(f_parent_mkin_tc["DFOP", ]) +``` + +Note that overparameterisation is also indicated by warnings obtained when +fitting SFO or DFOP with the two-component error model ('false convergence' in +the 'LME step' in some iterations). 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. + +```{r f_parent_nlme_logchol, warning = FALSE, eval = FALSE} +f_parent_nlme_sfo_const_logchol <- nlme(f_parent_mkin_const["SFO", ], + random = pdLogChol(list(DMTA_0 ~ 1, log_k_DMTA ~ 1))) +anova(f_parent_nlme_sfo_const, f_parent_nlme_sfo_const_logchol) # not better +f_parent_nlme_dfop_tc_logchol <- update(f_parent_nlme_dfop_tc, + random = pdLogChol(list(DMTA_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1))) +# using log Cholesky parameterisation for random effects (nlme default) does +# not converge and gives lots of warnings about the LME step not converging +``` + +The model comparison function of the nlme package can directly be applied +to these fits showing a similar goodness-of-fit of the SFO model, but 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. + +```{r AIC_parent_nlme} +anova( + f_parent_nlme_sfo_const, f_parent_nlme_sfo_tc, f_parent_nlme_dfop_tc +) +``` + +The selected model (DFOP with two-component error) fitted to the data assuming +no correlations between random effects is shown below. + +```{r plot_parent_nlme} +plot(f_parent_nlme_dfop_tc) +``` + +### saemix + +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 performed using an interface to the +saemix package available in current development versions of the mkin package. + +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. + +The convergence plot for the SFO model using constant variance is shown below. + +```{r f_parent_saemix_sfo_const, results = 'hide'} +library(saemix) +f_parent_saemix_sfo_const <- saem(f_parent_mkin_const["SFO", ], quiet = TRUE, + transformations = "saemix") +plot(f_parent_saemix_sfo_const$so, plot.type = "convergence") +``` + +Obviously the default number of iterations is sufficient to reach convergence. +This can also be said for the SFO fit using the two-component error model. + +```{r f_parent_saemix_sfo_tc, results = 'hide'} +f_parent_saemix_sfo_tc <- saem(f_parent_mkin_tc["SFO", ], quiet = TRUE, + transformations = "saemix") +plot(f_parent_saemix_sfo_tc$so, plot.type = "convergence") +``` + +When fitting the DFOP model with constant variance, parameter convergence +is not as unambiguous. Therefore, the number of iterations in the first +phase of the algorithm was increased, leading to visually satisfying +convergence. + +```{r f_parent_saemix_dfop_const, results = 'hide'} +f_parent_saemix_dfop_const <- saem(f_parent_mkin_const["DFOP", ], quiet = TRUE, + control = saemixControl(nbiter.saemix = c(800, 200), print = FALSE, + save = FALSE, save.graphs = FALSE, displayProgress = FALSE), + transformations = "saemix") +plot(f_parent_saemix_dfop_const$so, plot.type = "convergence") +``` + +The same applies to the case where the DFOP model is fitted with the +two-component error model. Convergence of the variance of k2 is enhanced +by using the two-component error, it remains pretty stable already after 200 +iterations of the first phase. + +```{r f_parent_saemix_dfop_tc_moreiter, results = 'hide'} +f_parent_saemix_dfop_tc_moreiter <- saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE, + control = saemixControl(nbiter.saemix = c(800, 200), print = FALSE, + save = FALSE, save.graphs = FALSE, displayProgress = FALSE), + transformations = "saemix") +plot(f_parent_saemix_dfop_tc_moreiter$so, plot.type = "convergence") +``` + +The four combinations can be compared using the model comparison function from the +saemix package: + +```{r AIC_parent_saemix} +compare.saemix(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so, + f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc_moreiter$so) +``` + +As in the case of nlme fits, the DFOP model fitted with two-component error +(number 4) gives the lowest AIC. The numeric values are reasonably close to +the ones obtained using nlme, considering that the algorithms for fitting the +model and for the likelihood calculation are quite different. + +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. + +```{r AIC_parent_saemix_methods} +f_parent_saemix_dfop_tc_moreiter$so <- + llgq.saemix(f_parent_saemix_dfop_tc_moreiter$so) +AIC(f_parent_saemix_dfop_tc_moreiter$so) +AIC(f_parent_saemix_dfop_tc_moreiter$so, method = "gq") +AIC(f_parent_saemix_dfop_tc_moreiter$so, method = "lin") +``` + +The AIC values based on importance sampling and Gaussian quadrature are quite +similar. Using linearisation is less accurate, but still gives a similar value. + + +### nlmixr + +In the last years, a lot of effort has been put into the nlmixr package which +is designed for pharmacokinetics, where nonlinear mixed-effects models are +routinely used, but which can also be used for related data like chemical +degradation data. A current development branch of the mkin package provides +an interface between mkin and nlmixr. Here, we check if we get equivalent +results when using a refined version of the First Order Conditional Estimation +(FOCE) algorithm used in nlme, namely First Order Conditional Estimation with +Interaction (FOCEI), and the SAEM algorithm as implemented in nlmixr. + +First, the focei algorithm is used for the four model combinations and the +goodness of fit of the results is compared. + +```{r f_parent_nlmixr_focei, results = "hide", message = FALSE, warning = FALSE} +f_parent_nlmixr_focei_sfo_const <- nlmixr(f_parent_mkin_const["SFO", ], est = "focei") +f_parent_nlmixr_focei_sfo_tc <- nlmixr(f_parent_mkin_tc["SFO", ], est = "focei") +f_parent_nlmixr_focei_dfop_const <- nlmixr(f_parent_mkin_const["DFOP", ], est = "focei") +f_parent_nlmixr_focei_dfop_tc<- nlmixr(f_parent_mkin_tc["DFOP", ], est = "focei") +``` + +```{r AIC_parent_nlmixr_focei} +AIC(f_parent_nlmixr_focei_sfo_const$nm, f_parent_nlmixr_focei_sfo_tc$nm, + f_parent_nlmixr_focei_dfop_const$nm, f_parent_nlmixr_focei_dfop_tc$nm) +``` + +The AIC values are very close to the ones obtained with nlme. + +Secondly, we use the SAEM estimation routine and check the convergence plots for +SFO with constant variance + +```{r f_parent_nlmixr_saem_sfo_const, results = "hide", warning = FALSE, message = FALSE} +f_parent_nlmixr_saem_sfo_const <- nlmixr(f_parent_mkin_const["SFO", ], est = "saem", + control = nlmixr::saemControl(logLik = TRUE)) +traceplot(f_parent_nlmixr_saem_sfo_const$nm) +``` + +for SFO with two-component error + +```{r f_parent_nlmixr_saem_sfo_tc, results = "hide", warning = FALSE, message = FALSE} +f_parent_nlmixr_saem_sfo_tc <- nlmixr(f_parent_mkin_tc["SFO", ], est = "saem", + control = nlmixr::saemControl(logLik = TRUE)) +nlmixr::traceplot(f_parent_nlmixr_saem_sfo_tc$nm) +``` + +For DFOP with constant variance, the convergence plots show considerable instability +of the fit, which can be alleviated by increasing the number of iterations and +the number of parallel chains for the first phase of algorithm. + +```{r f_parent_nlmixr_saem_dfop_const, results = "hide", warning = FALSE, message = FALSE} +f_parent_nlmixr_saem_dfop_const <- nlmixr(f_parent_mkin_const["DFOP", ], est = "saem", + control = nlmixr::saemControl(logLik = TRUE, nBurn = 1000), nmc = 15) +nlmixr::traceplot(f_parent_nlmixr_saem_dfop_const$nm) +``` + +For DFOP with two-component error, the same increase in iterations and parallel +chains was used, but using the two-component error appears to lead to a less +erratic convergence, so this may not be necessary to this degree. + + +```{r f_parent_nlmixr_saem_dfop_tc, results = "hide", warning = FALSE, message = FALSE} +f_parent_nlmixr_saem_dfop_tc <- nlmixr(f_parent_mkin_tc["DFOP", ], est = "saem", + control = nlmixr::saemControl(logLik = TRUE, nBurn = 1000, nmc = 15)) +nlmixr::traceplot(f_parent_nlmixr_saem_dfop_tc$nm) +``` + +The AIC values are internally calculated using Gaussian quadrature. For an +unknown reason, the AIC value obtained for the DFOP fit using the two-component +error model is given as Infinity. + +```{r AIC_parent_nlmixr_saem} +AIC(f_parent_nlmixr_saem_sfo_const$nm, f_parent_nlmixr_saem_sfo_tc$nm, + f_parent_nlmixr_saem_dfop_const$nm, f_parent_nlmixr_saem_dfop_tc$nm) +``` + + + + +# References + +<!-- vim: set foldmethod=syntax: --> 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 differnew file mode 100644 index 00000000..de699f30 --- /dev/null +++ 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 differnew file mode 100644 index 00000000..5f752168 --- /dev/null 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