diff options
| -rw-r--r-- | NAMESPACE | 1 | ||||
| -rw-r--r-- | NEWS.md | 4 | ||||
| -rw-r--r-- | R/confint.mkinfit.R | 86 | ||||
| -rw-r--r-- | R/mmkin.R | 5 | ||||
| -rw-r--r-- | R/nlme.R | 2 | ||||
| -rw-r--r-- | R/parms.mkinfit.R | 4 | ||||
| -rw-r--r-- | R/saemix.R | 31 | ||||
| -rw-r--r-- | build.log | 2 | ||||
| -rw-r--r-- | check.log | 2 | ||||
| -rw-r--r-- | docs/news/index.html | 2 | ||||
| -rw-r--r-- | docs/pkgdown.yml | 2 | ||||
| -rw-r--r-- | docs/reference/confint.mkinfit.html | 20 | ||||
| -rw-r--r-- | docs/reference/mmkin.html | 16 | ||||
| -rw-r--r-- | docs/reference/nlme.html | 4 | ||||
| -rw-r--r-- | docs/reference/parms.html | 6 | ||||
| -rw-r--r-- | docs/reference/saemix-1.png | bin | 37443 -> 31551 bytes | |||
| -rw-r--r-- | docs/reference/saemix-2.png | bin | 38557 -> 58815 bytes | |||
| -rw-r--r-- | docs/reference/saemix.html | 135 | ||||
| -rw-r--r-- | man/confint.mkinfit.Rd | 7 | ||||
| -rw-r--r-- | man/mmkin.Rd | 5 | ||||
| -rw-r--r-- | man/nlme.Rd | 2 | ||||
| -rw-r--r-- | man/parms.Rd | 4 | ||||
| -rw-r--r-- | man/saemix.Rd | 23 | ||||
| -rw-r--r-- | test.log | 20 | ||||
| -rw-r--r-- | tests/testthat/FOCUS_2006_D.csf | 2 | ||||
| -rw-r--r-- | vignettes/FOCUS_D.html | 10 | ||||
| -rw-r--r-- | vignettes/FOCUS_L.html | 453 | ||||
| -rw-r--r-- | vignettes/mkin.html | 4 | ||||
| -rw-r--r-- | vignettes/twa.html | 19 |
29 files changed, 466 insertions, 405 deletions
@@ -116,5 +116,6 @@ importFrom(stats,qt) importFrom(stats,residuals) importFrom(stats,rnorm) importFrom(stats,update) +importFrom(stats,var) importFrom(utils,getFromNamespace) importFrom(utils,write.table) @@ -4,6 +4,10 @@ - 'saemix_model', 'saemix_data': Helper functions to fit nonlinear mixed-effects models for mmkin row objects using the saemix package +- 'mmkin' and 'confint(method = 'profile'): Use all cores detected by parallel::detectCores() per default + +- 'confint(method = 'profile'): Choose accuracy based on 'rel_tol' argument, relative to the bounds obtained by the quadratic approximation + # mkin 0.9.50.2 (2020-05-12) - Increase tolerance for a platform specific test results on the Solaris test machine on CRAN diff --git a/R/confint.mkinfit.R b/R/confint.mkinfit.R index 78dda95d..53eb45ee 100644 --- a/R/confint.mkinfit.R +++ b/R/confint.mkinfit.R @@ -27,7 +27,10 @@ #' @param backtransform If we approximate the likelihood in terms of the #' transformed parameters, should we backtransform the parameters with #' their confidence intervals? -#' @param cores The number of cores to be used for multicore processing. +#' @param rel_tol If the method is 'profile', what should be the accuracy +#' of the lower and upper bounds, relative to the estimate obtained from +#' the quadratic method? +#' @param cores The number of cores to be used for multicore processing. #' On Windows machines, cores > 1 is currently not supported. #' @param quiet Should we suppress the message "Profiling the likelihood" #' @return A matrix with columns giving lower and upper confidence limits for @@ -121,7 +124,7 @@ confint.mkinfit <- function(object, parm, level = 0.95, alpha = 1 - level, cutoff, method = c("quadratic", "profile"), transformed = TRUE, backtransform = TRUE, - cores = round(detectCores()/2), quiet = FALSE, ...) + cores = parallel::detectCores(), rel_tol = 0.01, quiet = FALSE, ...) { tparms <- parms(object, transformed = TRUE) bparms <- parms(object, transformed = FALSE) @@ -140,50 +143,50 @@ confint.mkinfit <- function(object, parm, a <- c(alpha / 2, 1 - (alpha / 2)) - if (method == "quadratic") { + quantiles <- qt(a, object$df.residual) - quantiles <- qt(a, object$df.residual) - - covar_pnames <- if (missing(parm)) { - if (transformed) tpnames else bpnames - } else { - parm - } + covar_pnames <- if (missing(parm)) { + if (transformed) tpnames else bpnames + } else { + parm + } - return_parms <- if (backtransform) bparms[return_pnames] - else tparms[return_pnames] + return_parms <- if (backtransform) bparms[return_pnames] + else tparms[return_pnames] - covar_parms <- if (transformed) tparms[covar_pnames] - else bparms[covar_pnames] + covar_parms <- if (transformed) tparms[covar_pnames] + else bparms[covar_pnames] - if (transformed) { - covar <- try(solve(object$hessian), silent = TRUE) - } else { - covar <- try(solve(object$hessian_notrans), silent = TRUE) - } + if (transformed) { + covar <- try(solve(object$hessian), silent = TRUE) + } else { + covar <- try(solve(object$hessian_notrans), silent = TRUE) + } - # If inverting the covariance matrix failed or produced NA values - if (!is.numeric(covar) | is.na(covar[1])) { - ses <- lci <- uci <- rep(NA, p) - } else { - ses <- sqrt(diag(covar))[covar_pnames] - lci <- covar_parms + quantiles[1] * ses - uci <- covar_parms + quantiles[2] * ses - if (transformed & backtransform) { - lci_back <- backtransform_odeparms(lci, - object$mkinmod, object$transform_rates, object$transform_fractions) - uci_back <- backtransform_odeparms(uci, - object$mkinmod, object$transform_rates, object$transform_fractions) - - return_errparm_names <- intersect(names(object$errparms), return_pnames) - lci <- c(lci_back, lci[return_errparm_names]) - uci <- c(uci_back, uci[return_errparm_names]) - } + # If inverting the covariance matrix failed or produced NA values + if (!is.numeric(covar) | is.na(covar[1])) { + ses <- lci <- uci <- rep(NA, p) + } else { + ses <- sqrt(diag(covar))[covar_pnames] + lci <- covar_parms + quantiles[1] * ses + uci <- covar_parms + quantiles[2] * ses + if (transformed & backtransform) { + lci_back <- backtransform_odeparms(lci, + object$mkinmod, object$transform_rates, object$transform_fractions) + uci_back <- backtransform_odeparms(uci, + object$mkinmod, object$transform_rates, object$transform_fractions) + + return_errparm_names <- intersect(names(object$errparms), return_pnames) + lci <- c(lci_back, lci[return_errparm_names]) + uci <- c(uci_back, uci[return_errparm_names]) } - ci <- cbind(lower = lci, upper = uci) } + ci <- cbind(lower = lci, upper = uci) if (method == "profile") { + + ci_quadratic <- ci + if (!quiet) message("Profiling the likelihood") lci <- uci <- rep(NA, p) @@ -215,9 +218,14 @@ confint.mkinfit <- function(object, parm, (cutoff - (object$logLik - profile_ll(x)))^2 } - lci_pname <- optimize(cost, lower = 0, upper = all_parms[pname])$minimum + lower_quadratic <- ci_quadratic["lower"][pname] + upper_quadratic <- ci_quadratic["upper"][pname] + ltol <- if (!is.na(lower_quadratic)) rel_tol * lower_quadratic else .Machine$double.eps^0.25 + utol <- if (!is.na(upper_quadratic)) rel_tol * upper_quadratic else .Machine$double.eps^0.25 + lci_pname <- optimize(cost, lower = 0, upper = all_parms[pname], tol = ltol)$minimum uci_pname <- optimize(cost, lower = all_parms[pname], - upper = ifelse(grepl("^f_|^g$", pname), 1, 15 * all_parms[pname]))$minimum + upper = ifelse(grepl("^f_|^g$", pname), 1, 15 * all_parms[pname]), + tol = utol)$minimum return(c(lci_pname, uci_pname)) } ci <- t(parallel::mcmapply(get_ci, profile_pnames, mc.cores = cores)) @@ -12,7 +12,8 @@ #' @param cores The number of cores to be used for multicore processing. This #' is only used when the \code{cluster} argument is \code{NULL}. On Windows #' machines, cores > 1 is not supported, you need to use the \code{cluster} -#' argument to use multiple logical processors. +#' argument to use multiple logical processors. Per default, all cores +#' detected by [parallel::detectCores()] are used. #' @param cluster A cluster as returned by \code{\link{makeCluster}} to be used #' for parallel execution. #' @param \dots Further arguments that will be passed to \code{\link{mkinfit}}. @@ -62,7 +63,7 @@ #' #' @export mmkin mmkin <- function(models = c("SFO", "FOMC", "DFOP"), datasets, - cores = round(detectCores()/2), cluster = NULL, ...) + cores = detectCores(), cluster = NULL, ...) { parent_models_available = c("SFO", "FOMC", "DFOP", "HS", "SFORB", "IORE", "logistic") n.m <- length(models) @@ -125,7 +125,7 @@ nlme_function <- function(object) { #' @return If random is FALSE (default), a named vector containing mean values #' of the fitted degradation model parameters. If random is TRUE, a list with #' fixed and random effects, in the format required by the start argument of -#' nlme for the case of a single grouping variable ds? +#' nlme for the case of a single grouping variable ds. #' @param random Should a list with fixed and random effects be returned? #' @export mean_degparms <- function(object, random = FALSE) { diff --git a/R/parms.mkinfit.R b/R/parms.mkinfit.R index aae6fa52..a1f2d209 100644 --- a/R/parms.mkinfit.R +++ b/R/parms.mkinfit.R @@ -21,11 +21,13 @@ #' ds <- lapply(experimental_data_for_UBA_2019[6:10], #' function(x) subset(x$data[c("name", "time", "value")])) #' names(ds) <- paste("Dataset", 6:10) -#' fits <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE) +#' \dontrun{ +#' fits <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE, cores = 1) #' parms(fits["SFO", ]) #' parms(fits[, 2]) #' parms(fits) #' parms(fits, transformed = TRUE) +#' } #' @export parms <- function(object, ...) { @@ -5,25 +5,37 @@ #' list of mkinfit objects that have been obtained by fitting the same model to #' a list of datasets. #' +#' Starting values for the fixed effects (population mean parameters, argument psi0 of +#' [saemix::saemixModel()] are the mean values of the parameters found using +#' mmkin. Starting variances of the random effects (argument omega.init) are the +#' variances of the deviations of the parameters from these mean values. +#' #' @param object An mmkin row object containing several fits of the same model to different datasets +#' @param cores The number of cores to be used for multicore processing. +#' On Windows machines, cores > 1 is currently not supported. #' @rdname saemix #' @importFrom saemix saemixData saemixModel +#' @importFrom stats var #' @examples #' ds <- lapply(experimental_data_for_UBA_2019[6:10], #' function(x) subset(x$data[c("name", "time", "value")])) #' names(ds) <- paste("Dataset", 6:10) #' sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), #' A1 = mkinsub("SFO")) -#' f_mmkin <- mmkin(list("SFO-SFO" = sfo_sfo), ds, quiet = TRUE, cores = 5) +#' \dontrun{ +#' f_mmkin <- mmkin(list("SFO-SFO" = sfo_sfo), ds, quiet = TRUE) +#' library(saemix) #' m_saemix <- saemix_model(f_mmkin) #' d_saemix <- saemix_data(f_mmkin) -#' saemix_options <- list(seed = 123456, save = FALSE, save.graphs = FALSE) -#' \dontrun{ -#' saemix(m_saemix, d_saemix, saemix_options) +#' saemix_options <- list(seed = 123456, +#' save = FALSE, save.graphs = FALSE, displayProgress = FALSE, +#' nbiter.saemix = c(200, 80)) +#' f_saemix <- saemix(m_saemix, d_saemix, saemix_options) +#' plot(f_saemix, plot.type = "convergence") #' } #' @return An [saemix::SaemixModel] object. #' @export -saemix_model <- function(object) { +saemix_model <- function(object, cores = parallel::detectCores()) { if (nrow(object) > 1) stop("Only row objects allowed") mkin_model <- object[[1]]$mkinmod @@ -81,14 +93,19 @@ saemix_model <- function(object) { out_values <- out_wide[out_index] } return(out_values) - }, mc.cores = 15) + }, mc.cores = cores) res <- unlist(res_list) return(res) } + raneff_0 <- mean_degparms(object, random = TRUE)$random$ds + var_raneff_0 <- apply(raneff_0, 2, var) + res <- saemixModel(model_function, psi0, "Mixed model generated from mmkin object", - transform.par = rep(0, length(degparms_optim))) + transform.par = rep(0, length(degparms_optim)), + omega.init = diag(var_raneff_0) + ) return(res) } @@ -5,5 +5,5 @@ * creating vignettes ... OK * checking for LF line-endings in source and make files and shell scripts * checking for empty or unneeded directories -* building ‘mkin_0.9.50.2.tar.gz’ +* building ‘mkin_0.9.50.3.tar.gz’ @@ -5,7 +5,7 @@ * using options ‘--no-tests --as-cran’ * checking for file ‘mkin/DESCRIPTION’ ... OK * checking extension type ... Package -* this is package ‘mkin’ version ‘0.9.50.2’ +* this is package ‘mkin’ version ‘0.9.50.3’ * package encoding: UTF-8 * checking CRAN incoming feasibility ... Note_to_CRAN_maintainers Maintainer: ‘Johannes Ranke <jranke@uni-bremen.de>’ diff --git a/docs/news/index.html b/docs/news/index.html index 149fc98e..c26652e9 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -148,6 +148,8 @@ <ul> <li><p>‘parms’: Add a method for mmkin objects</p></li> <li><p>‘saemix_model’, ‘saemix_data’: Helper functions to fit nonlinear mixed-effects models for mmkin row objects using the saemix package</p></li> +<li><p>‘mmkin’ and ‘confint(method = ’profile’): Use all cores detected by parallel::detectCores() per default</p></li> +<li><p>‘confint(method = ’profile’): Choose accuracy based on ‘rel_tol’ argument, relative to the bounds obtained by the quadratic approximation</p></li> </ul> </div> <div id="mkin-0-9-50-2-2020-05-12" class="section level1"> diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 0bb01ef4..a8b168ce 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -10,7 +10,7 @@ articles: NAFTA_examples: web_only/NAFTA_examples.html benchmarks: web_only/benchmarks.html compiled_models: web_only/compiled_models.html -last_built: 2020-05-25T10:48Z +last_built: 2020-05-26T16:38Z urls: reference: https://pkgdown.jrwb.de/mkin/reference article: https://pkgdown.jrwb.de/mkin/articles diff --git a/docs/reference/confint.mkinfit.html b/docs/reference/confint.mkinfit.html index 190494bc..0686c7bb 100644 --- a/docs/reference/confint.mkinfit.html +++ b/docs/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-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.9.50.2</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.9.50.3</span> </span> </div> @@ -116,6 +116,9 @@ method of Venzon and Moolgavkar (1988)." /> <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> @@ -168,7 +171,8 @@ method of Venzon and Moolgavkar (1988).</p> <span class='kw'>method</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"quadratic"</span>, <span class='st'>"profile"</span>), <span class='kw'>transformed</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>backtransform</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, - <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/Round.html'>round</a></span>(<span class='fu'>detectCores</span>()/<span class='fl'>2</span>), + <span class='kw'>cores</span> <span class='kw'>=</span> <span class='kw pkg'>parallel</span><span class='kw ns'>::</span><span class='fu'><a href='https://rdrr.io/r/parallel/detectCores.html'>detectCores</a></span>(), + <span class='kw'>rel_tol</span> <span class='kw'>=</span> <span class='fl'>0.01</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, <span class='no'>...</span> )</pre> @@ -224,6 +228,12 @@ their confidence intervals?</p></td> On Windows machines, cores > 1 is currently not supported.</p></td> </tr> <tr> + <th>rel_tol</th> + <td><p>If the method is 'profile', what should be the accuracy +of the lower and upper bounds, relative to the estimate obtained from +the quadratic method?</p></td> + </tr> + <tr> <th>quiet</th> <td><p>Should we suppress the message "Profiling the likelihood"</p></td> </tr> @@ -270,13 +280,13 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, <span class='no'>SFO_SFO.ff</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>), <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) <span class='no'>f_d_1</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span>(<span class='no'>FOCUS_2006_D</span>, <span class='no'>value</span> <span class='kw'>!=</span> <span class='fl'>0</span>), <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) -<span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span>(<span class='no'>ci_profile</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f_d_1</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"profile"</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>))</div><div class='output co'>#> User System verstrichen -#> 3.410 0.000 3.412 </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='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span>(<span class='no'>ci_profile</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f_d_1</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"profile"</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>))</div><div class='output co'>#> user system elapsed +#> 3.689 0.991 3.361 </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='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span>(<span class='no'>ci_profile_no_parent_0</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f_d_1</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"profile"</span>, - <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></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='kw'>cores</span> <span class='kw'>=</span> <span class='no'>n_cores</span>))</div><div class='output co'>#> <span class='message'>Profiling the likelihood</span></div><div class='output co'>#> <span class='warning'>Warning: scheduled cores 1, 2, 3 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: Länge von 'dimnames' [2] ungleich der Arrayausdehnung</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 0.008 0.044 0.201</span></div><div class='input'><span class='no'>ci_profile</span></div><div class='output co'>#> 2.5% 97.5% + <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></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='kw'>cores</span> <span class='kw'>=</span> <span class='no'>n_cores</span>))</div><div class='output co'>#> <span class='message'>Profiling the likelihood</span></div><div class='output co'>#> <span class='warning'>Warning: scheduled cores 2, 1, 3 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: 0.007 0.042 0.193</span></div><div class='input'><span class='no'>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 0.003892605 6.702778e-03 diff --git a/docs/reference/mmkin.html b/docs/reference/mmkin.html index 3be3b4b9..9628c017 100644 --- a/docs/reference/mmkin.html +++ b/docs/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-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.9.50.2</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.9.50.3</span> </span> </div> @@ -112,6 +112,9 @@ datasets specified in its first two arguments." /> <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> @@ -152,7 +155,7 @@ datasets specified in its first two arguments.</p> <pre class="usage"><span class='fu'>mmkin</span>( <span class='kw'>models</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"FOMC"</span>, <span class='st'>"DFOP"</span>), <span class='no'>datasets</span>, - <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/Round.html'>round</a></span>(<span class='fu'>detectCores</span>()/<span class='fl'>2</span>), + <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fu'>detectCores</span>(), <span class='kw'>cluster</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='no'>...</span> )</pre> @@ -176,7 +179,8 @@ data for <code><a href='mkinfit.html'>mkinfit</a></code>.</p></td> <td><p>The number of cores to be used for multicore processing. This is only used when the <code>cluster</code> argument is <code>NULL</code>. On Windows machines, cores > 1 is not supported, you need to use the <code>cluster</code> -argument to use multiple logical processors.</p></td> +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> </tr> <tr> <th>cluster</th> @@ -215,9 +219,9 @@ plotting.</p></div> <span class='no'>time_default</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span>(<span class='no'>fits.0</span> <span class='kw'><-</span> <span class='fu'>mmkin</span>(<span class='no'>models</span>, <span class='no'>datasets</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)) <span class='no'>time_1</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span>(<span class='no'>fits.4</span> <span class='kw'><-</span> <span class='fu'>mmkin</span>(<span class='no'>models</span>, <span class='no'>datasets</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</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='no'>time_default</span></div><div class='output co'>#> User System verstrichen -#> 4.520 0.374 1.284 </div><div class='input'><span class='no'>time_1</span></div><div class='output co'>#> User System verstrichen -#> 5.076 0.004 5.083 </div><div class='input'> +<span class='no'>time_default</span></div><div class='output co'>#> user system elapsed +#> 4.457 0.561 1.328 </div><div class='input'><span class='no'>time_1</span></div><div class='output co'>#> user system elapsed +#> 5.031 0.004 5.038 </div><div class='input'> <span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fits.0</span><span class='kw'>[[</span><span class='st'>"SFO_lin"</span>, <span class='fl'>2</span>]])</div><div class='output co'>#> $ff #> parent_M1 parent_sink M1_M2 M1_sink #> 0.7340478 0.2659522 0.7505691 0.2494309 diff --git a/docs/reference/nlme.html b/docs/reference/nlme.html index b2d415dc..3462e52e 100644 --- a/docs/reference/nlme.html +++ b/docs/reference/nlme.html @@ -75,7 +75,7 @@ datasets. They are used internally by the nlme.mmkin() method." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.9.50.2</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.9.50.3</span> </span> </div> @@ -178,7 +178,7 @@ datasets. They are used internally by the <code><a href='nlme.mmkin.html'>nlme.m <p>If random is FALSE (default), a named vector containing mean values of the fitted degradation model parameters. If random is TRUE, a list with fixed and random effects, in the format required by the start argument of -nlme for the case of a single grouping variable ds?</p> +nlme for the case of a single grouping variable ds.</p> <p>A <code><a href='https://rdrr.io/pkg/nlme/man/groupedData.html'>groupedData</a></code> object</p> <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2> diff --git a/docs/reference/parms.html b/docs/reference/parms.html index 432bbc88..2fe91c26 100644 --- a/docs/reference/parms.html +++ b/docs/reference/parms.html @@ -195,7 +195,8 @@ such matrices is returned.</p> <span class='no'>ds</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span>(<span class='no'>experimental_data_for_UBA_2019</span>[<span class='fl'>6</span>:<span class='fl'>10</span>], <span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span>(<span class='no'>x</span>$<span class='no'>data</span>[<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"name"</span>, <span class='st'>"time"</span>, <span class='st'>"value"</span>)])) <span class='fu'><a href='https://rdrr.io/r/base/names.html'>names</a></span>(<span class='no'>ds</span>) <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste</a></span>(<span class='st'>"Dataset"</span>, <span class='fl'>6</span>:<span class='fl'>10</span>) -<span class='no'>fits</span> <span class='kw'><-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"FOMC"</span>, <span class='st'>"DFOP"</span>), <span class='no'>ds</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) +<span class='co'># \dontrun{</span> +<span class='no'>fits</span> <span class='kw'><-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"FOMC"</span>, <span class='st'>"DFOP"</span>), <span class='no'>ds</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>) <span class='fu'>parms</span>(<span class='no'>fits</span>[<span class='st'>"SFO"</span>, ])</div><div class='output co'>#> Dataset 6 Dataset 7 Dataset 8 Dataset 9 Dataset 10 #> parent_0 88.52275400 82.666781678 86.8547308 91.7779306 82.14809450 #> k_parent_sink 0.05794659 0.009647805 0.2102974 0.1232258 0.00720421 @@ -259,7 +260,8 @@ such matrices is returned.</p> #> log_k2 -3.5206791 -5.85402317 -2.5794240 -3.4233253 -5.676532 #> g_ilr -0.1463234 0.07627854 0.4719196 0.4477805 -0.460676 #> sigma 1.3569047 2.22130220 1.3416908 2.8715985 1.942068 -#> </div></pre> +#> </div><div class='input'># } +</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/reference/saemix-1.png b/docs/reference/saemix-1.png Binary files differindex 529588ce..0d79300d 100644 --- a/docs/reference/saemix-1.png +++ b/docs/reference/saemix-1.png diff --git a/docs/reference/saemix-2.png b/docs/reference/saemix-2.png Binary files differindex b85f878f..04de70b5 100644 --- a/docs/reference/saemix-2.png +++ b/docs/reference/saemix-2.png diff --git a/docs/reference/saemix.html b/docs/reference/saemix.html index 1737a21c..d3eb216c 100644 --- a/docs/reference/saemix.html +++ b/docs/reference/saemix.html @@ -153,7 +153,7 @@ list of mkinfit objects that have been obtained by fitting the same model to a list of datasets.</p> </div> - <pre class="usage"><span class='fu'>saemix_model</span>(<span class='no'>object</span>) + <pre class="usage"><span class='fu'>saemix_model</span>(<span class='no'>object</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='kw pkg'>parallel</span><span class='kw ns'>::</span><span class='fu'><a href='https://rdrr.io/r/parallel/detectCores.html'>detectCores</a></span>()) <span class='fu'>saemix_data</span>(<span class='no'>object</span>, <span class='no'>...</span>)</pre> @@ -165,6 +165,11 @@ a list of datasets.</p> <td><p>An mmkin row object containing several fits of the same model to different datasets</p></td> </tr> <tr> + <th>cores</th> + <td><p>The number of cores to be used for multicore processing. +On Windows machines, cores > 1 is currently not supported.</p></td> + </tr> + <tr> <th>...</th> <td><p>Further parameters passed to <a href='https://rdrr.io/pkg/saemix/man/saemixData.html'>saemix::saemixData</a></p></td> </tr> @@ -174,21 +179,22 @@ a list of datasets.</p> <p>An <a href='https://rdrr.io/pkg/saemix/man/SaemixModel-class.html'>saemix::SaemixModel</a> object.</p> <p>An <a href='https://rdrr.io/pkg/saemix/man/SaemixData-class.html'>saemix::SaemixData</a> object.</p> + <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2> + + <p>Starting values for the fixed effects (population mean parameters, argument psi0 of +<code><a href='https://rdrr.io/pkg/saemix/man/saemixModel.html'>saemix::saemixModel()</a></code> are the mean values of the parameters found using +mmkin. Starting variances of the random effects (argument omega.init) are the +variances of the deviations of the parameters from these mean values.</p> <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> <pre class="examples"><div class='input'><span class='no'>ds</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span>(<span class='no'>experimental_data_for_UBA_2019</span>[<span class='fl'>6</span>:<span class='fl'>10</span>], <span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span>(<span class='no'>x</span>$<span class='no'>data</span>[<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"name"</span>, <span class='st'>"time"</span>, <span class='st'>"value"</span>)])) <span class='fu'><a href='https://rdrr.io/r/base/names.html'>names</a></span>(<span class='no'>ds</span>) <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste</a></span>(<span class='st'>"Dataset"</span>, <span class='fl'>6</span>:<span class='fl'>10</span>) <span class='no'>sfo_sfo</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"A1"</span>), - <span class='kw'>A1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>))</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='no'>f_mmkin</span> <span class='kw'><-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='st'>"SFO-SFO"</span> <span class='kw'>=</span> <span class='no'>sfo_sfo</span>), <span class='no'>ds</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>5</span>) -<span class='co'># \dontrun{</span> -<span class='kw'>if</span> (<span class='fu'><a href='https://rdrr.io/r/base/library.html'>require</a></span>(<span class='no'>saemix</span>)) { - <span class='no'>m_saemix</span> <span class='kw'><-</span> <span class='fu'>saemix_model</span>(<span class='no'>f_mmkin</span>) - <span class='no'>d_saemix</span> <span class='kw'><-</span> <span class='fu'>saemix_data</span>(<span class='no'>f_mmkin</span>) - <span class='no'>saemix_options</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>seed</span> <span class='kw'>=</span> <span class='fl'>123456</span>, <span class='kw'>save</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, <span class='kw'>save.graphs</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>) - <span class='fu'><a href='https://rdrr.io/pkg/saemix/man/saemix.html'>saemix</a></span>(<span class='no'>m_saemix</span>, <span class='no'>d_saemix</span>, <span class='no'>saemix_options</span>) -}</div><div class='output co'>#> <span class='message'>Loading required package: saemix</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='output co'>#> + <span class='kw'>A1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>))</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='co'># \dontrun{</span> +<span class='no'>f_mmkin</span> <span class='kw'><-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='st'>"SFO-SFO"</span> <span class='kw'>=</span> <span class='no'>sfo_sfo</span>), <span class='no'>ds</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) +<span class='fu'><a href='https://rdrr.io/r/base/library.html'>library</a></span>(<span class='no'>saemix</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='no'>m_saemix</span> <span class='kw'><-</span> <span class='fu'>saemix_model</span>(<span class='no'>f_mmkin</span>)</div><div class='output co'>#> #> #> The following SaemixModel object was successfully created: #> @@ -224,12 +230,12 @@ a list of datasets.</p> #> out_values <- out_wide[out_index] #> } #> return(out_values) -#> }, mc.cores = 15) +#> }, mc.cores = cores) #> res <- unlist(res_list) #> return(res) #> } -#> <bytecode: 0x555559875398> -#> <environment: 0x55555973a248> +#> <bytecode: 0x555559668108> +#> <environment: 0x555559677c08> #> Nb of parameters: 4 #> parameter names: parent_0 log_k_parent log_k_A1 f_parent_ilr_1 #> distribution: @@ -248,8 +254,7 @@ a list of datasets.</p> #> No covariate in the model. #> Initial values #> parent_0 log_k_parent log_k_A1 f_parent_ilr_1 -#> Pop.CondInit 86.53449 -3.207005 -3.060308 -1.920449 -#> +#> Pop.CondInit 86.53449 -3.207005 -3.060308 -1.920449</div><div class='input'><span class='no'>d_saemix</span> <span class='kw'><-</span> <span class='fu'>saemix_data</span>(<span class='no'>f_mmkin</span>)</div><div class='output co'>#> #> #> The following SaemixData object was successfully created: #> @@ -257,12 +262,14 @@ a list of datasets.</p> #> longitudinal data for use with the SAEM algorithm #> Dataset ds_saemix #> Structured data: value ~ time + name | ds -#> X variable for graphs: time () -#> Running main SAEM algorithm -#> [1] "Mon May 25 12:48:51 2020" -#> .</div><div class='img'><img src='saemix-1.png' alt='' width='700' height='433' /></div><div class='output co'>#> .</div><div class='img'><img src='saemix-2.png' alt='' width='700' height='433' /></div><div class='output co'>#> .</div><div class='img'><img src='saemix-3.png' alt='' width='700' height='433' /></div><div class='output co'>#> .</div><div class='img'><img src='saemix-4.png' alt='' width='700' height='433' /></div><div class='output co'>#> +#> X variable for graphs: time () </div><div class='input'><span class='no'>saemix_options</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>seed</span> <span class='kw'>=</span> <span class='fl'>123456</span>, + <span class='kw'>save</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, <span class='kw'>save.graphs</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, <span class='kw'>displayProgress</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, + <span class='kw'>nbiter.saemix</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>200</span>, <span class='fl'>80</span>)) +<span class='no'>f_saemix</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/saemix/man/saemix.html'>saemix</a></span>(<span class='no'>m_saemix</span>, <span class='no'>d_saemix</span>, <span class='no'>saemix_options</span>)</div><div class='output co'>#> Running main SAEM algorithm +#> [1] "Tue May 26 18:25:16 2020" +#> .. #> Minimisation finished -#> [1] "Mon May 25 12:56:39 2020"</div><div class='output co'>#> Nonlinear mixed-effects model fit by the SAEM algorithm +#> [1] "Tue May 26 18:31:09 2020"</div><div class='img'><img src='saemix-1.png' alt='' width='700' height='433' /></div><div class='output co'>#> Nonlinear mixed-effects model fit by the SAEM algorithm #> ----------------------------------- #> ---- Data ---- #> ----------------------------------- @@ -322,12 +329,12 @@ a list of datasets.</p> #> out_values <- out_wide[out_index] #> } #> return(out_values) -#> }, mc.cores = 15) +#> }, mc.cores = cores) #> res <- unlist(res_list) #> return(res) #> } -#> <bytecode: 0x555559875398> -#> <environment: 0x55555973a248> +#> <bytecode: 0x555559668108> +#> <environment: 0x555559677c08> #> Nb of parameters: 4 #> parameter names: parent_0 log_k_parent log_k_A1 f_parent_ilr_1 #> distribution: @@ -353,7 +360,7 @@ a list of datasets.</p> #> Estimation of individual parameters (MAP) #> Estimation of standard errors and linearised log-likelihood #> Estimation of log-likelihood by importance sampling -#> Number of iterations: K1=300, K2=100 +#> Number of iterations: K1=200, K2=80 #> Number of chains: 10 #> Seed: 123456 #> Number of MCMC iterations for IS: 5000 @@ -369,19 +376,19 @@ a list of datasets.</p> #> ----------------- Fixed effects ------------------ #> ---------------------------------------------------- #> Parameter Estimate SE CV(%) -#> [1,] parent_0 86.21 1.51 1.7 +#> [1,] parent_0 86.14 1.61 1.9 #> [2,] log_k_parent -3.21 0.59 18.5 -#> [3,] log_k_A1 -4.64 0.29 6.3 -#> [4,] f_parent_ilr_1 -0.32 0.30 93.2 -#> [5,] a.1 4.69 0.27 5.8 +#> [3,] log_k_A1 -4.66 0.30 6.4 +#> [4,] f_parent_ilr_1 -0.33 0.30 91.7 +#> [5,] a.1 4.68 0.27 5.8 #> ---------------------------------------------------- #> ----------- Variance of random effects ----------- #> ---------------------------------------------------- #> Parameter Estimate SE CV(%) -#> parent_0 omega2.parent_0 6.07 7.08 117 -#> log_k_parent omega2.log_k_parent 1.75 1.11 63 +#> parent_0 omega2.parent_0 7.71 8.14 106 +#> log_k_parent omega2.log_k_parent 1.76 1.12 63 #> log_k_A1 omega2.log_k_A1 0.26 0.26 101 -#> f_parent_ilr_1 omega2.f_parent_ilr_1 0.38 0.27 71 +#> f_parent_ilr_1 omega2.f_parent_ilr_1 0.39 0.28 71 #> ---------------------------------------------------- #> ------ Correlation matrix of random effects ------ #> ---------------------------------------------------- @@ -399,66 +406,16 @@ a list of datasets.</p> #> --------------- Statistical criteria ------------- #> ---------------------------------------------------- #> Likelihood computed by linearisation -#> -2LL= 1064.397 -#> AIC = 1082.397 -#> BIC = 1078.882 +#> -2LL= 1064.364 +#> AIC = 1082.364 +#> BIC = 1078.848 #> #> Likelihood computed by importance sampling -#> -2LL= 1063.161 -#> AIC = 1081.161 -#> BIC = 1077.646 -#> ----------------------------------------------------</div><div class='output co'>#> Nonlinear mixed-effects model fit by the SAEM algorithm -#> ----------------------------------------- -#> ---- Data and Model ---- -#> ----------------------------------------- -#> Data -#> Dataset ds_saemix -#> Longitudinal data: value ~ time + name | ds -#> -#> Model: -#> Mixed model generated from mmkin object -#> 4 parameters: parent_0 log_k_parent log_k_A1 f_parent_ilr_1 -#> error model: constant -#> No covariate -#> -#> Key options -#> Estimation of individual parameters (MAP) -#> Estimation of standard errors and linearised log-likelihood -#> Estimation of log-likelihood by importance sampling -#> Number of iterations: K1=300, K2=100 -#> Number of chains: 10 -#> Seed: 123456 -#> Number of MCMC iterations for IS: 5000 -#> Input/output -#> results not saved -#> no graphs -#> ---------------------------------------------------- -#> ---- Results ---- -#> Fixed effects -#> Parameter Estimate SE CV(%) -#> parent_0 86.214 1.506 1.75 -#> log_k_parent -3.210 0.593 18.47 -#> log_k_A1 -4.643 0.294 6.34 -#> f_parent_ilr_1 -0.322 0.300 93.24 -#> a.1 4.689 0.270 5.76 -#> -#> Variance of random effects -#> Parameter Estimate SE CV(%) -#> omega2.parent_0 6.068 7.078 116.7 -#> omega2.log_k_parent 1.752 1.111 63.4 -#> omega2.log_k_A1 0.256 0.257 100.5 -#> omega2.f_parent_ilr_1 0.385 0.273 70.8 -#> -#> Statistical criteria -#> Likelihood computed by linearisation -#> -2LL= 1064.397 -#> AIC= 1082.397 -#> BIC= 1078.882 -#> Likelihood computed by importance sampling -#> -2LL= 1063.161 -#> AIC= 1081.161 -#> BIC= 1077.646 </div><div class='input'># } -</div><div class='img'><img src='saemix-5.png' alt='' width='700' height='433' /></div></pre> +#> -2LL= 1063.462 +#> AIC = 1081.462 +#> BIC = 1077.947 +#> ----------------------------------------------------</div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html'>plot</a></span>(<span class='no'>f_saemix</span>, <span class='kw'>plot.type</span> <span class='kw'>=</span> <span class='st'>"convergence"</span>)</div><div class='output co'>#> Plotting convergence plots</div><div class='img'><img src='saemix-2.png' alt='' width='700' height='433' /></div><div class='input'># } +</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/man/confint.mkinfit.Rd b/man/confint.mkinfit.Rd index f295afc4..fd2890ff 100644 --- a/man/confint.mkinfit.Rd +++ b/man/confint.mkinfit.Rd @@ -13,7 +13,8 @@ method = c("quadratic", "profile"), transformed = TRUE, backtransform = TRUE, - cores = round(detectCores()/2), + cores = parallel::detectCores(), + rel_tol = 0.01, quiet = FALSE, ... ) @@ -48,6 +49,10 @@ their confidence intervals?} \item{cores}{The number of cores to be used for multicore processing. On Windows machines, cores > 1 is currently not supported.} +\item{rel_tol}{If the method is 'profile', what should be the accuracy +of the lower and upper bounds, relative to the estimate obtained from +the quadratic method?} + \item{quiet}{Should we suppress the message "Profiling the likelihood"} \item{\dots}{Not used} diff --git a/man/mmkin.Rd b/man/mmkin.Rd index eda0d837..9a74a9cd 100644 --- a/man/mmkin.Rd +++ b/man/mmkin.Rd @@ -8,7 +8,7 @@ more datasets} mmkin( models = c("SFO", "FOMC", "DFOP"), datasets, - cores = round(detectCores()/2), + cores = detectCores(), cluster = NULL, ... ) @@ -24,7 +24,8 @@ data for \code{\link{mkinfit}}.} \item{cores}{The number of cores to be used for multicore processing. This is only used when the \code{cluster} argument is \code{NULL}. On Windows machines, cores > 1 is not supported, you need to use the \code{cluster} -argument to use multiple logical processors.} +argument to use multiple logical processors. Per default, all cores +detected by \code{\link[parallel:detectCores]{parallel::detectCores()}} are used.} \item{cluster}{A cluster as returned by \code{\link{makeCluster}} to be used for parallel execution.} diff --git a/man/nlme.Rd b/man/nlme.Rd index 5e981a14..2ee2a20c 100644 --- a/man/nlme.Rd +++ b/man/nlme.Rd @@ -23,7 +23,7 @@ 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? +nlme for the case of a single grouping variable ds. A \code{\link{groupedData}} object } diff --git a/man/parms.Rd b/man/parms.Rd index d3917639..af92bd2a 100644 --- a/man/parms.Rd +++ b/man/parms.Rd @@ -42,9 +42,11 @@ parms(fit, transformed = TRUE) ds <- lapply(experimental_data_for_UBA_2019[6:10], function(x) subset(x$data[c("name", "time", "value")])) names(ds) <- paste("Dataset", 6:10) -fits <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE) +\dontrun{ +fits <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE, cores = 1) parms(fits["SFO", ]) parms(fits[, 2]) parms(fits) parms(fits, transformed = TRUE) } +} diff --git a/man/saemix.Rd b/man/saemix.Rd index 23b0a4ad..b41796ca 100644 --- a/man/saemix.Rd +++ b/man/saemix.Rd @@ -5,13 +5,16 @@ \alias{saemix_data} \title{Create saemix models from mmkin row objects} \usage{ -saemix_model(object) +saemix_model(object, cores = parallel::detectCores()) saemix_data(object, ...) } \arguments{ \item{object}{An mmkin row object containing several fits of the same model to different datasets} +\item{cores}{The number of cores to be used for multicore processing. +On Windows machines, cores > 1 is currently not supported.} + \item{\dots}{Further parameters passed to \link[saemix:saemixData]{saemix::saemixData}} } \value{ @@ -25,17 +28,27 @@ object for use with the saemix package. 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. } +\details{ +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 +mmkin. Starting variances of the random effects (argument omega.init) are the +variances of the deviations of the parameters from these mean values. +} \examples{ ds <- lapply(experimental_data_for_UBA_2019[6:10], function(x) subset(x$data[c("name", "time", "value")])) names(ds) <- paste("Dataset", 6:10) sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), A1 = mkinsub("SFO")) -f_mmkin <- mmkin(list("SFO-SFO" = sfo_sfo), ds, quiet = TRUE, cores = 5) +\dontrun{ +f_mmkin <- mmkin(list("SFO-SFO" = sfo_sfo), ds, quiet = TRUE) +library(saemix) m_saemix <- saemix_model(f_mmkin) d_saemix <- saemix_data(f_mmkin) -saemix_options <- list(seed = 123456, save = FALSE, save.graphs = FALSE) -\dontrun{ - saemix(m_saemix, d_saemix, saemix_options) +saemix_options <- list(seed = 123456, + save = FALSE, save.graphs = FALSE, displayProgress = FALSE, + nbiter.saemix = c(200, 80)) +f_saemix <- saemix(m_saemix, d_saemix, saemix_options) +plot(f_saemix, plot.type = "convergence") } } @@ -4,31 +4,31 @@ Testing mkin ✔ | 2 | Export dataset for reading into CAKE ✔ | 13 | Results for FOCUS D established in expertise for UBA (Ranke 2014) [1.1 s] ✔ | 4 | Calculation of FOCUS chi2 error levels [0.4 s] -✔ | 7 | Fitting the SFORB model [3.2 s] -✔ | 5 | Analytical solutions for coupled models [3.0 s] +✔ | 7 | Fitting the SFORB model [3.3 s] +✔ | 5 | Analytical solutions for coupled models [3.1 s] ✔ | 5 | Calculation of Akaike weights ✔ | 10 | Confidence intervals and p-values [1.0 s] -✔ | 14 | Error model fitting [3.7 s] -✔ | 4 | Test fitting the decline of metabolites from their maximum [0.2 s] +✔ | 14 | Error model fitting [3.9 s] +✔ | 4 | Test fitting the decline of metabolites from their maximum [0.3 s] ✔ | 1 | Fitting the logistic model [0.2 s] ✔ | 1 | Test dataset class mkinds used in gmkin ✔ | 12 | Special cases of mkinfit calls [0.6 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 [1.3 s] -✔ | 9 | Nonlinear mixed-effects models [7.5 s] +✔ | 3 | Model predictions with mkinpredict [0.4 s] +✔ | 16 | Evaluations according to 2015 NAFTA guidance [1.5 s] +✔ | 9 | Nonlinear mixed-effects models [7.7 s] ✔ | 4 | Calculation of maximum time weighted average concentrations (TWAs) [2.4 s] -✔ | 3 | Summary +✔ | 3 | Summary [0.1 s] ✔ | 14 | Plotting [1.4 s] ✔ | 4 | AIC calculation ✔ | 4 | Residuals extracted from mkinfit models ✔ | 2 | Complex test case from Schaefer et al. (2007) Piacenza paper [1.4 s] ✔ | 1 | Summaries of old mkinfit objects ✔ | 4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [2.3 s] -✔ | 9 | Hypothesis tests [6.6 s] +✔ | 9 | Hypothesis tests [6.5 s] ══ Results ═════════════════════════════════════════════════════════════════════ -Duration: 37.2 s +Duration: 38.2 s OK: 159 Failed: 0 diff --git a/tests/testthat/FOCUS_2006_D.csf b/tests/testthat/FOCUS_2006_D.csf index 6b23d445..5d946ecd 100644 --- a/tests/testthat/FOCUS_2006_D.csf +++ b/tests/testthat/FOCUS_2006_D.csf @@ -5,7 +5,7 @@ Description: MeasurementUnits: % AR TimeUnits: days Comments: Created using mkin::CAKE_export -Date: 2020-05-12 +Date: 2020-05-26 Optimiser: IRLS [Data] diff --git a/vignettes/FOCUS_D.html b/vignettes/FOCUS_D.html index 38c597b0..16bc2084 100644 --- a/vignettes/FOCUS_D.html +++ b/vignettes/FOCUS_D.html @@ -11,7 +11,7 @@ <meta name="author" content="Johannes Ranke" /> -<meta name="date" content="2020-05-11" /> +<meta name="date" content="2020-05-26" /> <title>Example evaluation of FOCUS Example Dataset D</title> @@ -365,7 +365,7 @@ summary { <h1 class="title toc-ignore">Example evaluation of FOCUS Example Dataset D</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">2020-05-11</h4> +<h4 class="date">2020-05-26</h4> </div> @@ -439,10 +439,10 @@ print(FOCUS_2006_D)</code></pre> <p><img 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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> -<pre><code>## mkin version used for fitting: 0.9.50 +<pre><code>## mkin version used for fitting: 0.9.50.3 ## R version used for fitting: 4.0.0 -## Date of fit: Mon May 11 04:41:12 2020 -## Date of summary: Mon May 11 04:41:12 2020 +## Date of fit: Tue May 26 17:01:07 2020 +## Date of summary: Tue May 26 17:01:07 2020 ## ## Equations: ## d_parent/dt = - k_parent * parent diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html index 968ebf0c..7573ef58 100644 --- a/vignettes/FOCUS_L.html +++ b/vignettes/FOCUS_L.html @@ -1,17 +1,17 @@ <!DOCTYPE html> -<html xmlns="http://www.w3.org/1999/xhtml"> +<html> <head> <meta charset="utf-8" /> -<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <meta name="generator" content="pandoc" /> +<meta http-equiv="X-UA-Compatible" content="IE=EDGE" /> <meta name="author" content="Johannes Ranke" /> -<meta name="date" content="2019-05-02" /> +<meta name="date" content="2020-05-26" /> <title>Example evaluation of FOCUS Laboratory Data L1 to L3</title> @@ -69,8 +69,6 @@ overflow: auto; margin-left: 2%; position: fixed; border: 1px solid #ccc; -webkit-border-radius: 6px; -moz-border-radius: 6px; border-radius: 6px; } @@ -98,10 +96,15 @@ font-size: 12px; .tocify-subheader .tocify-subheader { text-indent: 30px; } - .tocify-subheader .tocify-subheader .tocify-subheader { text-indent: 40px; } +.tocify-subheader .tocify-subheader .tocify-subheader .tocify-subheader { +text-indent: 50px; +} +.tocify-subheader .tocify-subheader .tocify-subheader .tocify-subheader .tocify-subheader { +text-indent: 60px; +} .tocify .tocify-item > a, .tocify .nav-list .nav-header { margin: 0px; @@ -504,13 +507,13 @@ float: none; item.append($("<a/>", { - "text": self.text() + "html": self.html() })); } else { - item.text(self.text()); + item.html(self.html()); } @@ -1341,7 +1344,6 @@ code { } img { max-width:100%; - height: auto; } .tabbed-pane { padding-top: 12px; @@ -1403,6 +1405,7 @@ summary { border: none; display: inline-block; border-radius: 4px; + background-color: transparent; } .tabset-dropdown > .nav-tabs.nav-tabs-open > li { @@ -1415,49 +1418,10 @@ summary { } </style> -<script> -$(document).ready(function () { - window.buildTabsets("TOC"); -}); - -$(document).ready(function () { - $('.tabset-dropdown > .nav-tabs > li').click(function () { - $(this).parent().toggleClass('nav-tabs-open') - }); -}); -</script> - <!-- code folding --> -<script> -$(document).ready(function () { - - // move toc-ignore selectors from section div to header - $('div.section.toc-ignore') - .removeClass('toc-ignore') - .children('h1,h2,h3,h4,h5').addClass('toc-ignore'); - - // establish options - var options = { - selectors: "h1,h2,h3", - theme: "bootstrap3", - context: '.toc-content', - hashGenerator: function (text) { - return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_').toLowerCase(); - }, - ignoreSelector: ".toc-ignore", - scrollTo: 0 - }; - options.showAndHide = false; - options.smoothScroll = true; - - // tocify - var toc = $("#TOC").tocify(options).data("toc-tocify"); -}); -</script> - <style type="text/css"> #TOC { @@ -1470,6 +1434,12 @@ $(document).ready(function () { } } +@media print { +.toc-content { + /* see https://github.com/w3c/csswg-drafts/issues/4434 */ + float: right; +} +} .toc-content { padding-left: 30px; @@ -1505,8 +1475,6 @@ div.tocify { .tocify-subheader .tocify-item { font-size: 0.90em; - padding-left: 25px; - text-indent: 0; } .tocify .list-group-item { @@ -1550,7 +1518,7 @@ 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">2019-05-02</h4> +<h4 class="date">2020-05-26</h4> </div> @@ -1569,36 +1537,40 @@ FOCUS_2006_L1_mkin <- mkin_wide_to_long(FOCUS_2006_L1)</code></pre> <p>Since mkin version 0.9-32 (July 2014), we can use shorthand notation like <code>"SFO"</code> for parent only degradation models. The following two lines fit the model and produce the summary report of the model fit. This covers the numerical analysis given in the FOCUS report.</p> <pre class="r"><code>m.L1.SFO <- mkinfit("SFO", FOCUS_2006_L1_mkin, quiet = TRUE) summary(m.L1.SFO)</code></pre> -<pre><code>## mkin version used for fitting: 0.9.49.4 -## R version used for fitting: 3.6.0 -## Date of fit: Thu May 2 18:43:50 2019 -## Date of summary: Thu May 2 18:43:50 2019 +<pre><code>## mkin version used for fitting: 0.9.50.3 +## R version used for fitting: 4.0.0 +## Date of fit: Tue May 26 17:01:08 2020 +## Date of summary: Tue May 26 17:01:08 2020 ## ## Equations: ## d_parent/dt = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted using 133 model solutions performed in 0.283 s +## Fitted using 133 model solutions performed in 0.031 s +## +## Error model: Constant variance ## -## Error model: -## Constant variance +## Error model algorithm: OLS ## ## Starting values for parameters to be optimised: -## value type -## parent_0 89.850000 state -## k_parent_sink 0.100000 deparm -## sigma 2.779827 error +## value type +## parent_0 89.85 state +## k_parent_sink 0.10 deparm ## ## Starting values for the transformed parameters actually optimised: ## value lower upper ## parent_0 89.850000 -Inf Inf ## log_k_parent_sink -2.302585 -Inf Inf -## sigma 2.779827 0 Inf ## ## Fixed parameter values: ## None ## +## Results: +## +## AIC BIC logLik +## 93.88778 96.5589 -43.94389 +## ## Optimised, transformed parameters with symmetric confidence intervals: ## Estimate Std. Error Lower Upper ## parent_0 92.470 1.28200 89.740 95.200 @@ -1607,9 +1579,9 @@ summary(m.L1.SFO)</code></pre> ## ## Parameter correlation: ## parent_0 log_k_parent_sink sigma -## parent_0 1.000e+00 6.186e-01 -1.712e-09 -## log_k_parent_sink 6.186e-01 1.000e+00 -3.237e-09 -## sigma -1.712e-09 -3.237e-09 1.000e+00 +## parent_0 1.000e+00 6.186e-01 -1.516e-09 +## log_k_parent_sink 6.186e-01 1.000e+00 -3.124e-09 +## sigma -1.516e-09 -3.124e-09 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -1625,10 +1597,6 @@ summary(m.L1.SFO)</code></pre> ## All data 3.424 2 7 ## parent 3.424 2 7 ## -## Resulting formation fractions: -## ff -## parent_sink 1 -## ## Estimated disappearance times: ## DT50 DT90 ## parent 7.249 24.08 @@ -1655,25 +1623,25 @@ summary(m.L1.SFO)</code></pre> ## 30 parent 4.0 5.251 -1.2513</code></pre> <p>A plot of the fit is obtained with the plot function for mkinfit objects.</p> <pre class="r"><code>plot(m.L1.SFO, show_errmin = TRUE, main = "FOCUS L1 - SFO")</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <p>The residual plot can be easily obtained by</p> <pre class="r"><code>mkinresplot(m.L1.SFO, ylab = "Observed", xlab = "Time")</code></pre> -<p><img src="data:image/png;base64,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" /><!-- --></p> +<p><img src="data:image/png;base64,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" /><!-- --></p> <p>For comparison, the FOMC model is fitted as well, and the <span class="math inline"><em>χ</em><sup>2</sup></span> error level is checked.</p> <pre class="r"><code>m.L1.FOMC <- mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet=TRUE)</code></pre> <pre><code>## Warning in mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet = TRUE): Optimisation did not converge: ## false convergence (8)</code></pre> <pre class="r"><code>plot(m.L1.FOMC, show_errmin = TRUE, main = "FOCUS L1 - FOMC")</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>summary(m.L1.FOMC, data = FALSE)</code></pre> -<pre><code>## Warning in sqrt(diag(covar)): NaNs wurden erzeugt</code></pre> -<pre><code>## Warning in sqrt(1/diag(V)): NaNs wurden erzeugt</code></pre> -<pre><code>## Warning in cov2cor(ans$cov.unscaled): diag(.) had 0 or NA entries; non- -## finite result is doubtful</code></pre> -<pre><code>## mkin version used for fitting: 0.9.49.4 -## R version used for fitting: 3.6.0 -## Date of fit: Thu May 2 18:43:51 2019 -## Date of summary: Thu May 2 18:43:51 2019 +<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>## mkin version used for fitting: 0.9.50.3 +## R version used for fitting: 4.0.0 +## Date of fit: Tue May 26 17:01:09 2020 +## Date of summary: Tue May 26 17:01:09 2020 ## ## ## Warning: Optimisation did not converge: @@ -1685,51 +1653,55 @@ summary(m.L1.SFO)</code></pre> ## ## Model predictions using solution type analytical ## -## Fitted using 599 model solutions performed in 1.239 s +## Fitted using 380 model solutions performed in 0.08 s ## -## Error model: -## Constant variance +## Error model: Constant variance +## +## Error model algorithm: OLS ## ## Starting values for parameters to be optimised: -## value type -## parent_0 89.850000 state -## alpha 1.000000 deparm -## beta 10.000000 deparm -## sigma 2.779868 error +## value type +## parent_0 89.85 state +## alpha 1.00 deparm +## beta 10.00 deparm ## ## Starting values for the transformed parameters actually optimised: ## value lower upper ## parent_0 89.850000 -Inf Inf ## log_alpha 0.000000 -Inf Inf ## log_beta 2.302585 -Inf Inf -## sigma 2.779868 0 Inf ## ## Fixed parameter values: ## None ## +## Results: +## +## AIC BIC logLik +## 95.88778 99.44927 -43.94389 +## ## Optimised, transformed parameters with symmetric confidence intervals: ## Estimate Std. Error Lower Upper -## parent_0 92.47 1.2810 89.720 95.220 -## log_alpha 10.66 NaN NaN NaN -## log_beta 13.01 NaN NaN NaN -## sigma 2.78 0.4599 1.794 3.766 +## 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 ## ## Parameter correlation: ## parent_0 log_alpha log_beta sigma -## parent_0 1.000000 NaN NaN 0.003475 +## parent_0 1.000000 NaN NaN 0.002218 ## log_alpha NaN 1 NaN NaN ## log_beta NaN NaN 1 NaN -## sigma 0.003475 NaN NaN 1.000000 +## sigma 0.002218 NaN NaN 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 92.47 72.13000 1.052e-19 89.720 95.220 -## alpha 42700.00 0.02298 4.910e-01 NA NA -## beta 446600.00 0.02298 4.910e-01 NA NA -## sigma 2.78 6.00000 1.628e-05 1.794 3.766 +## 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 ## ## FOCUS Chi2 error levels in percent: ## err.min n.optim df @@ -1737,8 +1709,8 @@ summary(m.L1.SFO)</code></pre> ## parent 3.619 3 6 ## ## Estimated disappearance times: -## DT50 DT90 DT50back -## parent 7.249 24.08 7.25</code></pre> +## DT50 DT90 DT50back +## parent 7.25 24.08 7.25</code></pre> <p>We get a warning that the default optimisation algorithm <code>Port</code> did not converge, which is an indication that the model is overparameterised, <em>i.e.</em> contains too many parameters that are ill-defined as a consequence.</p> <p>And in fact, due to the higher number of parameters, and the lower number of degrees of freedom of the fit, the <span class="math inline"><em>χ</em><sup>2</sup></span> error level is actually higher for the FOMC model (3.6%) than for the SFO model (3.4%). Additionally, the parameters <code>log_alpha</code> and <code>log_beta</code> internally fitted in the model have excessive confidence intervals, that span more than 25 orders of magnitude (!) when backtransformed to the scale of <code>alpha</code> and <code>beta</code>. Also, the t-test for significant difference from zero does not indicate such a significant difference, with p-values greater than 0.1, and finally, the parameter correlation of <code>log_alpha</code> and <code>log_beta</code> is 1.000, clearly indicating that the model is overparameterised.</p> <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error levels reported in Appendix 3 and Appendix 7 to the FOCUS kinetics report are rounded to integer percentages and partly deviate by one percentage point from the results calculated by mkin. The reason for this is not known. However, mkin gives the same <span class="math inline"><em>χ</em><sup>2</sup></span> error levels as the kinfit package and the calculation routines of the kinfit package have been extensively compared to the results obtained by the KinGUI software, as documented in the kinfit package vignette. KinGUI was the first widely used standard package in this field. Also, the calculation of <span class="math inline"><em>χ</em><sup>2</sup></span> error levels was compared with KinGUII, CAKE and DegKin manager in a project sponsored by the German Umweltbundesamt <span class="citation">(Ranke 2014)</span>.</p> @@ -1758,7 +1730,7 @@ FOCUS_2006_L2_mkin <- mkin_wide_to_long(FOCUS_2006_L2)</code></pre> <pre class="r"><code>m.L2.SFO <- mkinfit("SFO", FOCUS_2006_L2_mkin, quiet=TRUE) plot(m.L2.SFO, show_residuals = TRUE, show_errmin = TRUE, main = "FOCUS L2 - SFO")</code></pre> -<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAJACAMAAABlpiR1AAADAFBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7////isF19AAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nO2dB3gU1deHbyCBFAgJJYReAoYuEJAmTaqgUhQbKCpFAQUhYkBAQER6EzCUv6CigHSVHmkihCp8oCI1AjaKoUQgkGTvNzO7Gzabnd2Znbnsndnf+zxM2b1zzmHzPrM7d8olFACOIb4uAAB3QFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EleF1YmeHsGZZ81R0UIlOqy3WN29PaxQZWuvVk+JyPCErxflKQgYJs6/igvKEVn/+UHagCYQkZq/89ESZgvXG33GV0HFDt8n9CwgqQw5HbrS3Lbe7Lr53prJ1Le9M6izoF/atptgDOQq6Na/0Xt303PlybOguuZ8BQWUQHOmXKPEnpZ0JCe0yvEsoIY9lUvpfFUIKdx/VJQ8J2OwsaBlCnlz0Wf9gkvcnWyAHQS2VScjspC6EfJQ7X44N3ST3NyCoDIIj39mXNxBS8Rdh/mtFQj6ldBohNS9SScnHnQS9Skgj8Zv4S0Im2zZ2EPRPQoZSeqsgaZUrXc4N3ST3NyCoDI6OPELIFmlhEyH1KW1IyHZp9fGYmhk5Bb1JSBtxJW306G9sGzsI+gMhXwqzaqRSrnQ5N3ST3N+AoDIIjry5UORnmhlMyltftJQjwZmWSFLM4Xgl51d8Y0LaL/rLMZCDoPeuXBF+fd4II61z58uxoXxyPf+LhgCCypB9nDKbnhTcsb3ahpBTlwl5xKFhTkHP1Be3KdV14TX7+zmO4gXSn7NtkJMcG8on1/O/aAggqAwOjvwm/daUeJyQY5cIaejQ0KmbKXPnsGYFhc2i99redxL0n0bWdiLfDhCYY1tx3FA+OYv/KtdAUBkcfgZm5CcxtsWKJPCuJYKUcGjoJKhI5vY+hMTaVnIKergMyTPZ/gNhhNR55BDLvqF8ch3+Z8YCgsrgeJxSh5Cd0sJOQmpR2oCQ/dJq53IVbtKPCFkoriwkZBw90LOndSthb3fDunEOQZcEk8JbstccBM25oZvk/gYElcHRkTmEVDkjzM9UIWQWpZMJaSz+Utybl9SmdDMhHYRdoqUDIRvpz4Q8miG8lVWfhNkOaBwF3RBAKp9zmS7nhm6S+xsQVAZHR+4JvxsLPv/+88JPxEbCl2xaJULKD57WK5iQRZTeLCf8Jh05oiEhZW/QzKqE1Ju1akErQrrZNhYE7S4dkS/8L1No+pa4tCpXupwbuknub0BQGRwdoVeb2Q5aml8VV0+Wt62+I67tL2BdKZAsvlXU9laVVNu2E7JPW17ca1+qnjtfjg3dJfczIKgMORyhWUs7RAVGdViWZV1Nm1CvYGiNF2xnMy+/ExcRERd/WVq5ObFJqXzlWyzMPt3uIOhnbgTNsaHb5P4FBAVcA0EB10BQwDUQFHANBAVcA0EB10BQwDUQ1BtOt3ryY1/X4CdAUG8Ytos+6usa/AQI6g03sy50ct9ih4v3Xb0GPABBveKbjn+4b6BA0Cs1pNmk7rb17IUJW7UVZyogqDdsHprjvHjWFeu/+2sKBJ1es5w4Oxhu81JcON2w5iR6+Tm9yzUyEFQdX5c8fK/i5tc6PC1pNbN8zJDM5C61E8R/lnEVysdLa1SSUXqz7RpK43Zal3MKunVhOWF6s94Eq6DSwpDvaaNbg/zvvg43QFB1zP9p8KKW9ls2ttf8J+25KcmhJ6j4b9PD1++0+kxcoqKg1jcX9aBnKmRZlyVBy0mXM4nXJl8sJ0xeXrbMKqi0cLpRnYkn3/DF/4tbIKg60umImAP2lZHlmjev/mxyC0rFf0OnU7q8p7hERUGtb16Lujt+jK2h09e+KOhXr1CroNkLtMefD+r/YgggqFrGPJu9OGEspZn3kjsKggr/3plB6Yoe4hIVBbW+SZ/a/PBZW0MXgnaKLlc0VLxrM3th9/uWYbVanH9w/x/OgaAquV3my+zlQ1X+vdd1jl3Q7+reTG/7abag1jfp0uYt7A1dfcVTcceZdeKedYFSS6ebF1vTVWMf9H+LWyCoSiY+N/D+ytzKpfpm70EtYypWeDsjW1DrmzQtZLG9oYs9KJW8vENSqE3Qr+dR+m6d5jmeTeLXQFB1/Fvkty6/zPV1FX4EBFXHkJ7067AVvq7Cj4Cg6rgl/Fr0y5vXfAUEBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANToLOiQOAAU8+o9vBG24+BAAnqn8fz4SNFnfeMCk1IKggGc4EvTolFGrMvTNAgwPN4JmvVXmnQ9a1TijbxpgdLgRdH6TNGE62w8HOgfu4EbQJkni1FLxV33zAIPDjaDlU6RZ2y25mgJ/hhtBG+2QZpWP65sHGBxuBJ3d8o4w/bS2xVVr4DOydiWxx83YotwImvlqzJiZnWLwE5QzjhVszZxK78nn50ZQSn98f9DidBdNgS85Upt9jgnD5N/jR9C709s1egPdoLwBQW1cr/3Mpr0fRa3XNw3QCgS1May3OE0ueU/fPEAjENTGw4ekWd39+uYBGoGgNiqco3+sobQNRlbhCwhq4/HlNLk+TY+6qG8eoBEIamNz+Z9vFPjvtW76pgFagaB2lpRoFlys50190wCtQNBs0nbV/lzfJEA7ENSBQdP0TQK0A0EdmNdb3yRAOxDUgV1N9E0CtANBHbgaoW8SoB2Ggna3DwdlGEFpsb/1zQI0k0tQy5p3Bi9XO36J5YSL69QMKGjzbfpmAZpxFvRa08ZTpreod0l2gxRyvFuR2A8Fha/3KZGvwgcWSkt99RhJoYvqhlZfJDQotXRc9fCnr9IGhJBr0ibGEbTfbH2zAM04C9qnv3jPQ8JzshukkIcWXF4dLjj3crFJ3wwhywQlY9t/dntm4Ij1A8gnwlrczPSTxd+gV7u2+9u6JzaOoB/31zcL0IwkaHqqnX8LnhJnfxT4O/slpy/vFNJHmE4PuUY7i93atYYKSta10LRIcdzbPqWFtVbCwuuNDPkV/31LfbMAzUiCdoi0ExFgnecplP1S45wbpJA1wvQk2StMLRc+C4oXlHyP0n1kz5UrV5aRO7TU+8I78Q0MKeifxfXNAjTj/BVf+A9xei1c9uacFLJbmKaSVfSntkULtY0SBZ1G6XJi5RQtNYPyIWjGJTcXH8s8PCzSzf19wBc4Czr4BeGvmtm3l+wGKUQcQvcYSb4W/NzeLFpfFFRQchuxX6fGh6BHehUPIAHFXz0q876MoI12q0sDWOMs6O2uVRKG12x/Q3aDFCJekTY09PpWUcmb4TZBLwXNE16e0c3Ch6A/BMcmJC5NHFEreI/rBjKC9pqvKg1gTu6O+t0TP9ruZoMUkn/A+oSAEfRsnn57vmsY1viEVcmhBaZsGZ13ssMe9JVqB61PM/SBoI92sabO6t3MdQMZQae+rSoNYI7qM0nCQVKHQpXHZ1H6RUxogzXb42ZYlcyaUi2kylyLTdBR/SjdXr7AdWkTHwhacLVt4cdw1w1kBN3YTlUawBwvBD2oNocPBK39jm1hcl3XDWQETSmjKg1gjkkF/ZS8sOrQqcPregV85rqBjKCWgvK/voEvMKmg9IsaUq9XjSUy78s9oz4Otx3zhWkvt7OcT96QfN7pWXVp2c8zq/SD681eWqwyD2CLaQUVsZxOzfnCgewHmgXK3N3x0bte5AHsMKugazp1TbI8TUgvmVNiBRa6fn3dE+ryAMaYVNClpFHLkL4PrZxbcKrrBnKCnoxRlQewxqSCPvwmpWPIHiF3DdcN5ATNDL2lKhFgjEkFDdkgXmB1m9ItIa4byAlKax5RlQgwxqSCxkyi9H/kN0oXynxlywr63FeqEgHGmFTQccHxwwvWa/f7oQoDXTeQFXTMSFWJAGNMKui9d6NKTvuvKiF1r7huICvoiq6qEgHGmFRQSi0WStO/3XRH5m1ZQX+OVZkIMOXnfBWZU3iUfH5f3fIhK2hGGB5wxxUpZ9njpueGO0HpI7ioHtyHP0HfmKVvJmBo+BN0QU99MwFDw5+gh2rqmwkYGv4EvYuTneA+/AlK6+zTNxUwMhwK2vsTfVMBI8OhoHPxIHCQDYeCJsvcCAr8EQ4FvROKUeOBHQ4FpTUP6ZsLGBgeBe25QN9cwMDwKOisfvrmAgaGR0F3P6JvLmBgeBQ0LdTNk2+Bf8GjoLSK0qKA6eFS0BcX65sMGBcuBZ36lr7JgHHhUtDtGFQW2OBS0OsFMvXNBgwLl4LSSr/qmw0YFj4FfVbuubfA31ApaJH7dPC0hduBvNwLOmGIwqqA2VEpaGJi4oywcvHT340p7f7uYE8DebkXdGsLhVUBs6P+K/71lneFaUa7vu5aexzIy72gVwpZ3L0N/Af1gpZcKc3WlnDX2uNAXu4FpTE/KywLmBz1gpaYKc1muR3RyONAXh4EfXmewrKAyVEv6Gvh6y3Usr6Q2694jwN5eRB0YQ+FZQGTo17QG01JZPVI0tztM748DuTlQdAT5RSWBUyOF/2glqQJgybv8HAU42kgLw+CWqIuKKwLmBuvOurvnKYej7JdDuR1vK+doBnuN++yVGFdwNx4IegnpQmh3aYo6Aj678TdnC9cWTjfRnCi+02n9VdYFzA36gX9lPRZRuj0PG6f/5E1vQu980YAyf9hlusGHr7i6QE8QgyIqBe06pv0irAyqrq71hPIEDoydMKmUUEyd2h6EjQj/F+FhQFTo17QkA2SoJtlBkCyUnYopRUnCAvv13LdwJOgtNV3CgsDpka9oLVHSYJOkhlCzkrEakoLrRcWNoa5buBR0DEJCgsDpka9oP8LHLuXXJgXJjMIp5W23S207RhhYaR3HfWUft9YYWHA1KgX1DIjkhCS712Zox8r+0PbLf+68NQdY4Nkzll6FPRWGJ5jC7zrB03bt3zbJQ/N93fKI3bUl5kt875HQekjuxRWBsyMekH77VF2Kdy1Y5t3nMqQe9ezoPEfKkoDzI0Xl9uR8iNPaM7rWdC17TUnAcZHvaBZe4aUJ3HT/9KW17OgVwrh1k7g3bl4y6HhD+VpqymvZ0Fp1Z80ZQCmwLu7Oq9//WK+PJryKhC0z8eaMgBT4IWg5+e0DQpsM9/Tcbx7FAj6eTdNGYAp8OJMEsnXcZHmE+UKBD0XrTUJMD6qBU2vvfC6DnkVCEpLn9YhETA2qgX9lXyhR14lgvbEiF5AtaCWxx7X4551JYIuf1KHRMDYqP8N+lVs3HvTZghoyqtE0NRCGDDJ71EvaCk7mvIqEZQ2TtKUA5gAPp9uZ2McniHm9zC7q9MDigQ9VFVjFmB4mN7V6QZFglpKnNWUBBgfRnd1ekSRoOhoAozu6vSIMkHR0eT3MLqr0yPKBEVHk9/D6K5OjygTFB1Nfg+juzo9olBQdDT5O4zu6vSIQkHR0eTvsLqr0xMKBUVHk7/j5ZmkU+sva8urUFB0NPk76gU93+ZNujEviTigKa9SQdHR5OeoF7RT9ArapMmZDh7vCtYwkNd90NHk56gXNHIevRawlC4r6ra5toG8bNzePG9ro60KCwSmRL2ghZbSr8k/dIXMY+usaBzIy8rOii1fb16ks8ICgSlRL2ib5nviGtMbT9Zx11rrQF4if0VtE6af5flTYYXAjKgX9GhxErKT1ghc46611oG8RKa8Ic2i8LR6f8aLbqZbBy9RutL9iO5aB/IS6WftYepSTVF9wJx40w965YdVB9Lct9Y6kJfIiPel2YCQ2wpLBCZEvaAZg/IRQiImur9gWeNAXiIHy06uF1V/askGqz02BaZFvaCjyNDjqcfiySz37V0O5LWdZDPJY0bLQ8GDvhyQv9aCZxWWCEyIekFj4qVZvJILltfKnrJXsAddH7e3f8c391VfF+Hh9wQwMV501H8jzb6NULKR7OWcCgSNt+5kx47o+JWCVMCceHGqc6A0e6ODu9bdrZDHund33UCBoAOsz7efEr8E5+P9F5WCHjlyZGuRF9cmr+kW7bafqTEp20CAVGnQwHUDBYIu6CrNHv/yZiGMOue3qBT0/kEOaeKu9b34CLEjX9NX/K1KH92j6aOrpdNn/qewSGA6VAp6+j4X3bdfF/H2XW2C0vOdIutEPPMHpSvbKCwSmA7Vv0GzlvV5tNaLc+66bSxyLu6R37UJSmnqT9fE2Z3C/yhpDUyIWkF/a0jKtOkUS6oe9rhFev9IrYLa6SE3HhgwOyoFvVW1wndi5/u+mhVuet5m1aDf5N5SJ2hSbTWtgYlQKeioMJtyFyOGa8qrTlBLrMxlpcDsqBS02Rv29bebasqrTlA6U6Y7FZgdlYKGL7CvLy6kKa9KQa9H4jDJP1EpaMXx9vXJFTXlVSko7TPecxtgQlQK2q2p7fokS7sumvKqFfRoWYzc6ZeoFHRbwIdWQxeQDZryqhWUNl6nKR8wKGr7QYeTpsuPnljbhfTWlle1oF+205YQGBO1glpWlxVPxEcu0PbsMPWC3o0+qS0jMCTqL7fLPLV2+THPZzo9oFpQOhxPYvRHuB6GJgfni/6nbwnACBhHUPoULrrzQwwk6JaaegwSCoyFgQSlDVbpWwMwAEYSdEN1jV0HwHgYSVDsQv0QQwm6EbtQv8NQgtImK/WtAnCPsQTdhF2ov2EsQemjK/QtA/COwQTFLtTfMJig2IX6G0YTdEu1DH0LAXzDUFBdxknKxePTc65b1sQP/BJX25sWVoLqMk6SK04X+8Nx9UbzRlNntXn4Ly+jAd5hJKgu4yS5ZsTzjmv9+4pXkIzGYEpmhZGgeoyTJMPtit87rBWRRlG6E46HMJsURoLqMU6SHN9Uvj98570g67zCOa/DAa5hJKge4yTJ8uSE+8vRkplp4be8Dwd4hpGgeoyTJMv5oinZywlP/zBlQlL/l7yPBriG1VG8DuMkyTO2a/bi9bJB9RuFFPldQzTAM8z6QV2Ok3R54XwbwYkq4zmSXmW5fTHhmR8mf/T9AOxBzQozQW//LvWe3/rb8cXjfe0EzVAZLwfHou0HRfgNanIYCXr3rbyk0l5hYaHMdpq+4imdVd96Zz6O4s0OI0En5hv7RdMCKcwEtXQaZl1AP6jJYSRo7Fjh2z2uMzNBaWr5rdJ8QG/xZ+772h61B/iFkaDBm4XJoYD9zASlu0pIv25vtGg49ePWtXEu3qwwErTyR+K0Z43bzASlI9tJXQSWtfEDv8LVTKaFkaDj8r+3m9Kr0e3fZiZoRtMRWkMA/mEkaHpCcKww+60aYSYovRo7R3MMwDvM+kEzL4jTrB/muX5bB0Hp2ZJ46rLpMdotHzk4WCxZhyiAZwwtKF0ffUqPMIBfjC0onRt7VZc4gFcMLih972GM8GVqjC4onVjxrE6RAI8YXlA6pzyG/zAxxheULikld28zMD4mEJSuLI7Buk2LGQSlm6NkTgcAw2MKQenpWj1wSb05MYeg9M6rtXEwb0pMIiils4qv1zki4AHTCEp/rPBKqt4xgc8xj6D0VkLJz3UPCnyMiQSldG+1Jy4yCAt8iKkEpemjio7H4bypMJeglP7et/hEzYPZA34wm6CUHmz10EoMBWIazCcopVvrxyZ68UU/unZMJ9y+zBtmFJTSQy8VHqjycCmtREiX3pXyyj2ND/gIcwpK6ek3C3f7Ts2INR2jxJ+u7+TDKDd8YVZBKb2+4NHibx9R3DxsrjTLj4HC+MK8ggqceb9C7Ls/KnvsSOBuaVbkI5YFueXH9wctSvfczM8wtaCUWg6PrlPs1ZWXPbeM+ECcZgRuZV2SDJmvVBo7o3PMLz5Kzy0mF1Tk/JwnImoOXOvh9s/XQ09RmtW60IOpKTcfPybuPRc/bPHY0r/wA0EFMg9Mfjy8cveZe+/It2maJ7Z+WMEDD66onDTa9umz7RP+rHzcVwVwin8IKpL1y2f964VUfXbcmjOuf5Vuf+mJSb7r4S9Xr83yTcOi6m3xWQW+5tj0MWty/2UMN5isNu4dWzrsiQrBsU8M/mTjL1ydtS/zmDj9PtBf96CWfgWKhBeplOuyc8MNJqsH6b+sndK3XdWQonWf6jd24XcHznOgapla4lPMPw496OtCfMTU/P1/vvhlREXn1403mKyOXDr4zSfv9+oYVyY4tGy9ts/3GzFl/orNe39OSXWz62dFhZ5l3vmwVY1HfdWL4GtKPCtOf83rvEcz3mCyTEg7t3/z0rnj4vt0a9uwWrnIoKDIsjFxDVs/2e2lvm8lDJs4cdb8+V+tWLEpKWn7oUOHfjor8GeqiH7H3G3WHJ08YuXtEim6RTQWQZukWaHpTq8/2MFkD7S2EzhNVbwHzb3U388c2pv07YrP58+aOCEhYWDfvi9069audeuWcXFxtSsKlIwUCZDG0yMFI22UqJiDh+M80Dj7A3k4tEnr1q3KFm3tp+Sp0bq1IGeo847rwQ4mm5Zkp9IPquJxzs1UG3+dzcHRQx7Yk/2BJL0V0bBVdJM1SX5KxbKbks7S+XmdnwXnq8FkG+LRs85c+XaJvx7CC+wJKT1+TsfQ55xf99VgshAU5CSpbESx0IRcd0M82MFk7wNBgROWcz+5ONHnqzNJEBQoAoICroGggGt8Jehj2V2H9wkjAcxgGJolBi07T+4/rrcEKh3HRWdB76TmZmmrc8wos4tZ6JGvMQt9KpBZ6HM9PmAW+vtKLv66XnJdqVE6C+qKb59kF7vCOWahpw9mFjojkFlo2n8us9AnqjALLQ8ElQOCOgNB1QNBnYGgqoGgzkBQ5UBQOSCoMxBUPRDUGQiqGgjqDARVDgSVA4I6Y1ZBTzMcjmt0GrPQu9cyC215x3Mbb1m5j1no6+OYhZbnAQgKgPdAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAdewF3RR3fBmu5lEXiU9yKwXg8iD4qUZi9KtoRmUnv5hbEjVSeJTunQvOzs0u09cFuaCLiGDV3cOVj52oQomRiUK7NA9btbGUMkiBqXbQzMoPSH/+E2j873Fouzs0Kw+cTewFtRS83lKM2J7s4jduw2LqHRNAUJEixiUbg/NoPTM4OHCdGzgbf3Lzg7N6hN3B2tBz5NVwjQhmkXsFgP+PZyqf9jU48dLixYxKN0emkHpF2LFb/UvyO/6l50dmtUn7g7WgiYTcQCWxAAWw2GXjg0ipIuCATtVEyNaxKZ0KTSr0m81jclk9ImLoRl+4rKwFnQDEZ8StZRc0j/0neC2v95YV/gp/SNbLWJTuhSaUemH4wrsYVS2FJrhJy4L+z3oYWE6LyDXM6D1Yga5pn9Q2x6URenWPaiEzqVffSWgcwqbsm2hrTD5xGVh/xt0nTAdUZxZgo1E6YP8VBBj/Q3KonQHQfUt/WSJ6vvFOYOy7aGtMPnEZWF+FF+9F6VZtV5lEDopf5IwHRnuegxZTUgWsSldCs2g9Kxq7W5LC/qXnR2a4ScuC/N+0C8Cpu7pEyw3tqcWMuuWmrTxvSAW94Fbd3NMSpdCMyh9N3l7ocgt/cvODs3wE5eF/ZmkxbULNpUZ2VMjf78SHVb/a/2GLbyP7XuYRenW0PqXPs86OB75W/+y74dm94nLgnPxgGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAanQVdNxEABUy55RtBG76UAIBniv2fjwRN1jceMCm1ICjgGQgKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrjC/ojg9Hb3igw0mAB4nRBb3dqfqIMfWbXtUnGuCOByuoJdVOnE6Cxr+YIYQd/Lw+0QB3PFhBd0baCZipRzxKo86L0//ClV7zAgyGr77iCyzUJcy9IOu8wjldwgHuMLigtOhFcXo7PE2fcIA3jC7ooJ5ZwnRYN32iAe4wuqBpbet8NLlZ/Uv6RAPcYXRBKd04fOiqLL2CAd4wvqDA1EBQwDUQFHANBAVcA0EB10BQwDXGF3T/tEk79IoFuMPogqa/GDPk3ZrtrusTDXCH0QUd9nQ6pZn9XtYnGuAOLwU9tf6ytrx6CRotXcZ0M/y2PuEAb6gX9HybN+nGvCTigKa8uNwOKEK9oJ2iV9AmTc50aK8pr1570MJ/idP08Jv6hAO8oV7QyHn0WsBSuqyoprx6Cdr/dfGGuTGd9YkGuEO9oIWW0q/JP3RFmKa8egl6vWnjGXPb1vxTn2iAO9QL2qb5nrjG9MaTdTTl1a0f1LJqUP8lGToFA9yhXtCjxUnITlojcI3nba6dypR7C2eSgCK86Ga6dfASpSt/ddu61w5KLz9JSPhcmWcqQFCgCEYd9SSR0s6FZ2waGbTCdYPcgmYueKZ1/B+qsgDzo1LQRAfctk6k/5JvhYXhj7hukEvQtEbtVyaNiPpeYTnAT1ApaBEH3LZOpIeIeCvwhhwH+ztINpOctni/h9SgLI53gCPsvuKvBp4QFiaWdN0g1x609n5p9vBBVXmA2fFa0F9nuG0d1WbAQ+0oTSr1musGuQQtbz1Z2WarwnoYcGYXdt/coV5Qy9Kh8QKN6rlrvWpi7xalgynNH/eP6wa5BG27Wpzei/5dYT26szAsIDCgw11fpQeuUS/o2IBaQVH1C4bt8LjJHWEPKtcRmkvQbyqfofTum08pLEd3luYZSunuyDhf5QeuUS9o+Xi6uB292WCtpry5u5kSox5/sdzTqZqiaqC09ATHcwGnfFUAcIl6QfN9Qy9EZNFvGmrK66Kj/t+NX/2iKaYmgqw/fiOn+q4E4AL1gkZPppYi++gePi4W0Y1866RZwTk+rgPkRL2gvaM+p22fv/hGZU15uRO0WjNxujPgis8q+PnjD76RvXbBb6dmcsIAAArbSURBVFEv6PWXO9OfgkneJZrycifogbzN9v0zMshnzxK3xJd6a1TTOim+ys8rXvaDXtt6Vlte7gSlRx7KGxA+3mfp/9fghjCdpu2XvQkx+l2dpqHpFnFqKX/C14VwhnpBO9oYqikvBHWifIo0a7vFt2Vwh3pBewq83L7QQ59qygtBnWiwS5rFHvNxHbzh7Vf89ZbzNeWFoE7MaHNPmC6piUHzcuL1b9DtNTXlhaBOZLxYsXPnpuWwA3XCa0HXm6yj3tdYhhWpU6di3Hlf18Eb6gVdIjGzTHNNeSGoE4vrXROmE5v4ug7eUC9ofomQhu7vmvMEBHWi2WZxain3m68L8Rn/N2306twX5PLUD3rhZz++GtPfu5ksg0oPGdWyZq7zP/wIur1qmeqFRt/TN41xeOQHaVZF6d/DbCyo26VQSK1Xc91j6f1Ncx08bZFxyY1tuQTdX0L4jvvz8f4KyzEd09qLX2/Lq/lrN1O9wlOvZ+ysXtj5l6P6245nhJWLn/5uTOndbpsf6VU8gAQUf/WozPu5BO0qvXAjUuNzRw3LvW41pnzavcwRX9fhKwpIZyYvBq1zel39V/zrLcVfihnt+rpr/UNwbELi0sQRtYL3yNTjLGjMGWn2mP/eGL8l/rXZ//m6CJ8R8pnj7D7qBS25UpqtLeGu9aNdrMdjWb2buW6QS9Bq1i7qhu53zMCsFGuYLkw/C9vm9Lp6QUvMlGazyrhrXXC1beHHcMeXd0TaCQiJjCxygdKekbZ5/mBx/lv+CKfXMfePeb7APKEfdC0XlOvvr1rQ18LXW6hlfSG3X/G137EtTK6b4/VrqTYKzEpNFS+AvJtqm58oPeDQ38vKLkh1eh1z/5gnFxs85K2x9d9zfr2GakFvNCWR1SNJc7cP3f6UvLDq0KnD63oFOP+msJG7m+nqwIei2+ML3m852jKsWKl5uQZW96If1JI0YdDkHR66Q76oIT2BqYbcjSE4kwScueOqB4dZR73lfPKG5POyGkNQoAi1gvacPcOOprwQFChCraDk6VJ2NOWFoEARagVN02lINwgKFGH0oRCByTH4UIjA7Bh9KERgcow+FCIwOUYfChGYHMMPhQjMDdOhEN0AQYEiGA2F6BEIChThVT/ondNU660zEBQowgtBPylNCO02RZuiEBQoQr2gn5I+ywidnucTTXkhKFCEekGrvkmvCCujqmvKC0GBItQLGrJBEnRziKa8EBQoQr2gtUdJgk6qoSkvBAWKUC/o/wLH7iUX5oVpG/EKggJFeDGY7IxIQki+d3Pd3qQKCAoU4U0/aNq+5dsuacwLQYEiHuzT7XZWtJNnkoLmu3o91nuvHnmBYVEr6M0tiw9aKL11fGtHL7JlnTtrI0zBHjQhJnHbnPKjvcgDTINKQX8pI/z+fOP/YgOEmaa8Cr7ikytcF6b/lpF7QB7wB1QK+kThr44tK1qy4bIt+9I05VUg6PAx0mzYB5oSAWOjUtCi44TJBJKiOa8CQftZT6ZOH6w5GTAuKgUly4XJSqL9McAKBJ3eW5r1SNScDBgXtYKKDwddq8MRvQJBLxX/TpiuKpGqPRswLBwLSvfGNny5fvWD2pMB48KzoPTezs935x45B/gTagUNzJ8/fyCRxvLSlBdnkoAiVAo6wgFNeSEoUAQ/A3kB4AIICriGb0GvHLiqb1pgNBgKqm4oRBekdCxSP7LzBdWJgYlgJajqoRBz81/FKRn07rjYO+oyA1PBSFD1QyHmZv4z0qyj3EAhwB9gJKj6oRBzM2C2NJsaryozMBeMBJUZCvE+CgSNnyjNxoxUlRmYC0aCyg2FmI0CQTfWEQ+y0qvtVJUZmAtGgnoxFGIuLF1a7vp7+6MvqkoMTAaro3g9hkLMmt8wuvEibbc3A4ODoRAB1/B9Jgn4PRAUcM2DFfTHODt5p+kRD5geRoLG38fx5XuHD9moLnOGCYAcMBJ0UAgpHWPFdYOGyariAX+F1Vf8enLc7fsQFCiClaBZoRAU6ACzg6TV7m9nh6BAEb7qZoKgQBFMBU2Qf8wtBAWKYCooOSH7FgQFioCggGsgKOAapoIm/Sf7FgQFisBRPOAanwk6bH4uxrfrwYzHujML3fkJZqF7tGQXumNXZqFf6Jz7j+stpXwk6MK+uWlRoCozgioxC128MLPQVQKYha4aGc0sdMUQF39dLxl4wzeCuuLbJ9nFrnCOWWiGD9HPCGQWmvafyyz0iSrMQssDQeWAoM5AUPVAUGcgqGogqDMQVDkQVA4I6gwEVQ8EdQaCqgaCOgNBlQNB5YCgzphV0E1d2MWuzO5BzbOHMgudGcYsNB24gFno0zWYhZbnAQiaeY1dbIbPur8jf0mMZhiWnZbOLrYvRhZ4AIIC4D0QFHANBAVcA0EB10BQwDUQFHANBAVcA0EB10BQwDUQFHANBAVcA0EB10BQwDXsBV1UN7zZbiaRV0mj3vViEHmQdYwIFqVbQzMoPf3D2JCqk+5SBmVnh2b3icvCXNAlZPDqzsFHWISeGJUosEP3uFkbQyWLGJRuD82g9IT84zeNzvcWi7KzQ7P6xN3AWlBLzecpzYjtzSJ27zYsotI1BQgRLWJQuj00g9Izg4cL07GBt/UvOzs0q0/cHawFPU9WCdOEaBaxWwz497D7R+Z7Rerx46VFixiUbg/NoPQLseK3+hfkd/3Lzg7N6hN3B2tBk8lBYZoYkMEgdunYIEK6XGYQOUa0iE3pUmhWpd9qGpPJ6BMXQzP8xGVhLegGckqYLiXyj7X3mjvBbX+9sa7wU/pHtlrEpnQpNKPSD8cV2MOobCk0w09cFvZ70MPCdF7AXVYJZhAGtzzZ9qAsSo+5P4ikzqVffSWgcwqbsm2hrTD5xGVh/xt0nTAdUZxZgo3SDkNnYqy/QVmU7iCovqWfLFF9vzhnULY9tBUmn7gszI/iq/eiNKvWqwxCJ+VPEqYjwzP1Dy1ZxKZ0KTSD0rOqtbstLehfdnZohp+4LMz7Qb8ImLqnT/BRBpEz65aatPG9IBYPKrDu5piULoVmUPpu8vZCkVv6l50dmuEnLgv7M0mLaxdsymaQ7r9fiQ6r/7WFQWTb9zCL0q2h9S99HrHyt/5l3w/N7hOXBefiAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EFRvnrY9KIaM83UlpgCC6s3elStXlm4sTH5NIz19XYzxgaAsqP6cOL399GxfF2J8ICgLrILSUjMoLfd5/zIV5vzRoUjZpVQcwii0+iLf1mYwICgLHAUttyFzLKmyP6NH8G06M3DE+gHkEx9XZyggKAscBX2F0j/Ih5RuIyfTIscKr/Yp7dvijAUEZYGjoBMpzRCHLjpOTuwje65cubKM3PFxeUYCgrLAUdApoqBrJUGX2zqgHuQgBEYHgrJARtBt5KKPCzMeEJQFMoJeCponvDqj2wN9yLvBgaAskBGUDi0wZcvovJN9XJ2hgKAscClozT9o1pRqIVXmYgeqAggKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGggKuOb/AWOM1j7HJ62VAAAAAElFTkSuQmCC" 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Ar6r090JQKcsaigrwWPHh/epNOZ76Jfct1AVdCpE3UlApyxqKB3xpYuP+9GLULiVC7wVBV0VU9diQBnLCqonfQvt6hdeK0q6JEaXiQC3DhSKJo7xSep5/fVLR+qgt4JxQPuhOK3k/xx03MjnKC0KS6qB3cRT9DnF7LNBEyNeIK+159tJmBqxBP0oMq5J+CXiCdoOk52gruIJyhtuI9tKmBmBBR04DtsUwEzI6Cgi/AgcJCNgILubcQ2FTAzAgp6KwSjxoMsBBSU1tV93yqwLCIK2v89trmAiRFR0IVD2OYCJkZEQb9uyjYXMDEiCpoacpttMmBeRBSUxmotClgeIQV9YinbZMC8CCno3OFskwHzIqSgOzCoLHAgpKBXwjLYZgOmRUhBaczPbLMB0yKmoL2Xs80GTItOQe+5SxdPW7gdyMu9oK+/rLEqYHV0CpqQkLAgrPLo+WNjova4be5pIC/3gm5ro7EqYHX0H+Kff0A+z5PR6Tl3rT0O5OVe0AsRNo1lAYujX9Dya5TZhnLuWnscyMu9oLTqEY1lAYujX9By9gcrvFnRXWuPA3l5EPSpBI1lAYujX9BnIzZK043F3B7iPQ7k5UHQxX01lgUsjn5Br7UixesUJ21S3bX2OJCXB0GPVtJYFrA43vSDbo8fMWeXh+aeBvLyIKitVO4hloB/4lVHfdoJDRu4Gsjrx8FZBM13v3WPTzXWBayNF4K+GxVAaO+5Gra58UuuK48vLE50UORd95u+MVRjXcDa6Bf0g4DBKwmdX8CtYbb5PWnakEBSZIZKh6aHQzw9gEeIARn9gtYaTi9KK5PruGsdHzCKTgqN3zK5kIqIngS9E35JY2HA0ugXNGSTIuhWlQGQ7FQaQ2nVeGlhSn3XDTwJStt9qbEwYGn0C9pwsiLobLfH4Mh1lBaT+0s3h7lu4FHQqWM1FgYsjX5B3w+avpecSwxTGYTTTse+0p9p0sIk7zrqKd3eQmNhwNJ48St+QXFCSOGxbi/nOBDa6bNVJd7YNb1QousGHgW9EYrn2ALv+kGv7/9sxz8emh/oXkDuqK/4tsr7HgWlTXdrrAxYGf2CDlW5gC43V37cuuu46r1FngUd9Zq2PMDSeHG5Haky6RfDeT0Lur6T4STA/OgX1JY8qgppPP9PY3k9C3ohArd2Ai9vmvtufI0CHQ3l9SworXnIUAZgCbwT9OqqJwsXMJRXg6CD3jSUAVgCLwQ9u6hjoaAO73n6He8eDYIue8xQBmAJvDiTRAp3XZpiNK8GQU+VMZoEmB/dgqY3ev8qg7waBKUVjjNIBMyNbkGPEiZP/dAiaH+M6AX0H+LbPcQirxZBV3ZjkQmYGv2CfhrbeMK8BRKG8moRNCUCAyb5PfoFrZCFobxaBKUtkgzlABZAzKfbOfg3niHm9/C7q9M9mgQ9WNNwHmByuN7V6QZNgtrKnjSWBZgeTnd1ekSToOhoApzu6vSINkHR0eT3cLqr0yPaBEVHk9/D6a5Oj2gTFB1Nfg+nuzo9olFQdDT5O5zu6vSIRkHR0eTv8Lqr0xMaBUVHk7/j5Zmk4xsvGMurUVB0NPk7+gU922E43VyQRH5rKK9WQdHR5OfoF7R72dX0vvtOdunsaQsDA3ndBR1Nfo5+QYsn0iuBK+jKkm6bGxvIy8HNLQnbmm/TWCCwJPoFLbaCrgr4m65WeWydHYMDednZFd32udYlumssEFgS/YJ2aJPcuCW99nAjd62NDuQl80ep/0jTDwN/11ghsCL6Bf1fGRKym9YNWu+utdGBvGTmPK/MSuFp9f6MF91MNw/+Q+mao25bGx3IS2aIvYepRy1N9QFr4k0/6MWv13573X1rowN5yUyYrMyGBd/UWCKwIPoFzRhRmBASOct9c4MDecl8W3F241JN5pa7d63GEoEF0S/o5IAxRy7/ODrA05OTXA3ktYNk40FwCVv1Ii99PKxwvfd6aywRWBD9gsaMVmajtVywvEH1lL2GPehXcclDu7ywr/aGYm6HBQWWxouO+i+U2ZeRWjbarvaOBkFH2Xey0yZ0+URDKmBNvDjV+ZIyG9LFXeu+dki7virjamsQdNhbymzOqOU4H++/6BT08OHDSfc8uWHf+t5l3fYztSSVmkmQms2auW6gQdD3eiqzzh9fi8Coc36LTkHv/sgh97lrfWd0pNyRb+gQfyNm5m2aNqVWGu21RGORwHLoFPTEXc67b/955MjbxgSlZx6JbFisl5Rn9YMaiwSWQ/d3UNvKwffXf3LRbbeNZX5rfO8ZY4JSmnLosjy7VfwvLa2BBdEr6K/NScUO3WMDanke4SB9WHGjgmbR9y09rYGF0CnozVrRX8nz/fWiNXROrh3xq9pb+gRNaqCnNbAQOgWdHOZQ7nzkeEN59Qlqq/GNoWzAtOgUtPWQrPWRrQzl1ScoXfCkoWzAtOgUNCLbqw+LGcqrU9ArkfiZ5J/oFLTqzKz1OVUN5dUpKB00w1A6YFZ0Cto7+8DeqaehvHoFPVwRI3f6JToF3RHo2JMtJpsM5dUrKG2xwVA+YFL09oOOJ60++98vG3qSQcby6hb0Y2OD1wKTovtM0rpK8on44ouNPTtMv6DpZVT7VIGF0X+5XebxDZ/96PlMpwd0C0pfxZMY/RGhh6HJwZl7PNyoB6yIeQSlD+OiOz/ERIJurWvwey8wISYSlN67hm0NwASYSdCNtTPZFgHEx0yCYhfqh5hK0E3YhfodphKUtlzNtgogPOYSdDN2of6GuQSl961iWwYQHZMJil2ov2EyQbEL9TfMJujWWm7GtgHWg6OgTMZJykPneTnXbetGvfgxrra3LLwEZTJOkiuOl8zxzJ2rrZvPXfhg/T+8jAZEh5OgTMZJcs2Ex53Xhg6WryCZgsGUrAonQVmMk6TCzWjn5+mUUEZRuhWOhzBbFE6CshgnSY3Pq90dvvN2kH1e5ZTX4YDQcBKUxThJqnR7/e5yGcXM1PAb3ocDIsNJUBbjJKly5p7fspfHPvrfOa8nDe3nfTQgNLx+xTMYJ0mdaXcfGnGlYlCT5sElThuIBkSGXz+oq3GS/lmc6KDIu3rjOZEWuzJrcWyv/86euX0Y9qBWhZugt84oZ81v5njo14+DswiarzNeDn4ok/WjCN9BLQ4nQW+/WJBU2ystLFHZztAhntKFTdLtefAr3uJwEnRW4enLWxU9zU1Q2yPj7AvoB7U4nASNnS4d3Rv34CYoTam8TZkPGyifSZrcw1g0ICycBA3eKk2+CzzATVC6u+yf8uxqm2Zz32zfAOfirQonQasrfekD6t7iJiid2FF5joNt/agXP8HVTJaFk6CvFZmwh9JLZTuP5CbonfsnGA0BxIeToOnjgmOl2a+1CTdB6cUabxuOAUSHWz9o5jl5avs60fXbDASlJ8vhqcuWx2y3fOTg25J7GUQBImNqQelXZY6xCAPExdyC0kU1LjKJA0TF5ILS8fUxwpelMbugND76JKNIQERMLyh9uzKG/7Aw5heULi9/mFksIBoWEJSuLo3Bui2LFQSlW0olMIwGRMISgtLj9friknprYg1B6a2nG+DHvCWxiKCULiz9FeOIQAQsIyjdU2VACuuYwOdYR1B6Y2w5ladEAPNiIUEpTa7V9RyHsMCHWEpQmjbpnhn4OW8prCUopacHl45P5xMa+AKrCUrpt+2qr8ZQIJbBeoJSuq1JjXe9ONBPaVD1Edy+LBpWFJTSg/2Kv6jz51Jq2eAeA2MKLOdTEPAWawpK6fEXij/2pZ4Ra7qUkr+6ji6EUW7EwqqCUnrlvftKj/hec/PQRcqsMAYKEwvrCipxYnKVGmP2aHvsSMGvlVmJmTwLcsueyS99kOa5mZ9haUEptX03pWHJp1f/47llseny9E7BbbxLUiFjQMy0+d2r/uSj9MJicUFlzrzdtVjdF9d7uP3zuZBjlGa2VxmUhD9vPiDvPZfWt/mqAEHxA0ElMg7M7hxe7ckFybfU29wfWKNJaNED+VdUTpr/5/3encb+Xu1HXxUgKP4hqEzmTx8ObRxcs/e/151w/a10R7+us3zXw1+p8YMrN48r1XirzyrwNT/Mm7ou77+M6QaTNcbtHz4d17VKkRpdR76z6SehztpHPSBPtxf01z2obUhYifASMXkuOzfdYLIsSPtp/ZzBHWsG39Po4SHTFn954IwAqkbVk59i/mbIt74uxEfMLTz0yLmPi0Xnft18g8ky5O9vP39n8rNd4qKKhFRs3OHxIRPmJK7aknzkt5Tb+V9Llf5Ro19rV+c+X/Ui+JqyveXpzwVy30JuvsFkuZB6av+WTxf9e9Sgxzo0q1UpMigosmLVuGbtuz3Wb/DwsePi4xcmJn6yatXmpKQdBw8ePHRS4vcUGXa/uR9cd3j2hNU3y/7GLKK5CNqszCLm5Xo9fweTPdA+i4Jv6IqX39xOOX3iYHLSF6uWJS6Mf33s2BcHD+7z2GMd27dvGxcX1yBaolykTIAynh4pGumgbHQO6sd5oEX2B1I/pGX79u0q3tPeTwms0769JGdI7h1X/g4mm5qURcx/dcUTnGspDv44mYPDBz3wTfYHkjS8WLN2ZVquS/JToituTjpJEwvkfhacrwaTbYZHz+bmwhfL/fUnvMQ3wRVmvN0l5F+5X/fVYLIQFOQkqWKxkiFj89wNkb+Dyd4FgoJc2E4dcnGiz1dnkiAo0AQEBUIDQYHQ+ErQB7K7Du8SSgK4wTE0T0xadmDef1xvKah1HBfGgt5Kycun7U5xI2o3t9ATn+EW+lhBbqFP9Z3OLfT2GBf/ul5yRatRjAV1xRfd+MWucopb6HkjuYW+U5BbaDp0EbfQR2O5hVYHgqoBQXMDQfUDQXMDQXUDQXMDQbUDQdWAoLmBoPqBoLmBoLqBoLmBoNqBoGpA0NxYVdDjHIfjmpLKLfTX67mFto3mFpqu3sct9JV/cwutTj4ICoD3QFAgNBAUCA0EBUIDQYHQQFAgNBAUCA0EBUIDQYHQQFAgNBAUCA0EBUIDQYHQ8Bd0aVxE6z1cIq9VHmQ2kEPkEfbLjXiUbg/NofT0GbEhtWbLD5FmXnZ2aH6fuCrcBf2YvLyuR3DuJ0AzYVbpBIldzOPaNocqFnEoPSs0h9LHFZm5ZWrhF3mUnR2a1yfuBu6C1utDaUbsIB6hB3XgEZWuL0qIYhH70rNDsy89M3i8NJ0edIt92XdDc/rE3cFb0LNkrTQdV5ZH7LYvpBy6zD7s5SNHomSLOJSeFZpD6edi5aP6cnKGfdnZoXl94u7gLeg+clCaJgRqG/pVH1GxhQjpeYFD5BjZIj6lK6F5lX6zVUwmp09cDs3xE1eFt6CbyHFpuoJoGPdVL2nBHY9e+7zEI+wj2y3iU7oSmlPphxoXTeZUthKa4yeuCv896CFpmhjIbYiiBUTzc6i049iD8ig95u79SIxLv/R0YI/TfMp2hLbD5RNXhf930M+l6cQy3BJsVnYYjImxfwflUbqToGxLP1aujjJkLoeys0Lb4fKJq8L9V3ydgZTa6j/DIfL2Itul6aQIDiPF2i3iUroSmkPpttqdHI+CZ152dmiOn7gq3AVdHvhG8uBgrc/T1UNmXIXZmycUeodDaLugXEpXQnMofQ8ZuUTmJvuys0Nz/MRV4X8m6cOG4a1URvY0yF9Plw1ruopHZMdxmEfp9tDsS0+0D45H/mJf9t3Q/D5xVXAuHggNBAVCA0GB0EBQIDQQFAgNBAVCA0GB0EBQIDQQFAgNBAVCA0GB0EBQIDQQFAgNBAVCA0GB0EBQIDQQFAgNBAVCA0GB0EBQIDQQFAgNBAVCA0GB0EBQIDQQFAgNBAVCw1jQDfEAaGDODd8I2qzfWAA8U1Lrw81YC7qXbTxgUepBUCAyEBQIDQQFQgNBgdBAUCA0EBQIDQQFQgNBgdCYWtBdMyZ/ZWMQB4iLiQW92bPW+Gn3tszXEUtBfpO/gtpSsogzLuiYf92RIr7Si0FdQFjyV9BdkVkELDAcrOwpeXoz/JrhSEBcfHWID1tsNEJGAfvXz2rHDBcDxMW8gtLSyrjPaRFXDUcC4mJiQV/uJw+mO7GH8WKAuJhY0OudGsyc0zruLwbVAGExsaCUbn71lVX5OSQ5yH9MLSiwPhAUCA0EBUIDQYHQQFAgNBAUCI2pBT3wRvwOBmGAwJhY0PS+0S+Prdc+hUE1QFhMLOiE7mmUZg5/gkE1QFhMLGj54/L0eniq4UhAXMwrKC638wvMKygteU6epkdcNhwJiIuJBR0+UN6FvtbVeDFAXEws6NXWzee/06n2OQbVAGHRKWiCE4bysugHta0dMWTZHeNxgMDoFPQeJzxvc+W46tWaOJMENMHpED9wF6UXHiYk4h2VBhAUaMJrQY+6vW+YSF8AepRYsGVSodWuG+QVNOO9Xu1HnddYDfAXvBB0xZjREi2auG2dQFPIl9LC+HtdN8gjaGrzTquTJpTarrEc4CfoF3R6YP1CpZuGh+1y2zqBfkeuSwubwpxf3kmymZVri8l9lQYV8aMHOKNf0Cqj6YedaGqzDW5bJ9BLQb9IC7PKu26QZw/aYL8yq/+txnqAf6Bf0MJf0HORNvpFc7etS3d4oUYnSrdXeNZ1gzyCVlYeZEMf3KaxHkakbr2UvwmBPvQLWnYOpffsp8lh7lqvnTWobVQwpUUa/+26QR5BO6yVp7fLnNZYDxNO1CRBARX25WdKoA/9gg4q/RHt2Of8kOoeN0mT9qBqHaF5BP282glK0194WGM5TEgPq/cHTW0XhGc/iIt+Qa/270G/DyYFPzaUN28307ulOj9R6dF8vfx4YlFlVvGx/EwKdOFlP+iVpFPG8rroqL+06ZOfjAXVS5tWyuyZavmbFujAxBeLGOfBZsqsT6yP6wDq6Be0q4MxhvIKIeiiQvLV+JmRw/I37baZcw/kb0YTo1/QARL9Oxer8YGhvEIISitGfnh5Q4Xw9PzMeemBRq+Oin4yX3OaGG8P8VcfeM9QXjEEvdW7CCnULn97QnuPzKQ07ZFJ+ZrUvHj9HXRnPUN5xRDUB1wLvyHPfq3o60JMgteCbnTbUe8RvxX0V3v/sa0AHmyqCf2CfqywsGIbQ38N17UAAArqSURBVHn9VtCUCOXb5+kyvi7EJOgXtIhCSPOjhvL6raD0oRnSJLP/SF/XYRL8uh/UJ5yr07R799rt8LgJbUDQ/Obyg1ENG5UacNvXdZgE72+a62Ior/8K2md4BqU3u0z1dR0mQf9txwvCKo+ePzYmao+hvH4r6LVw+UYDerSSj+vIB258NHGRwUs2vDnEP/+AfHTK6PScobyuBD17xA9Orzi6mTKt382UXKnn1CGl5hmMol/Q8muU2YZyhvLmFXRHzajaEVMs/9UsJUL5K54p7etCeHO9/NCYwiWfitptLIx+QcstVGZvejwVkvGPmxvg8gi6v+wWSn/vPFRjOealo3y7oO3Zl3xdB2/WV2h7OPPPl0s+ZSyMfkGfjdgoTTcWc3+IPzywTCAJLPOMWvg8gvZUXrga+Y/GekzLmdhuCW82b2X5QcTHh92UZw8avNhWv6DXWpHidYqTNm478r4Ojh2XsCJhYv3gZNcN8gha9YQye8D6N8bffn/w8DU2X1fBneejlNnQKGNhvOkH3R4/Yo7bu+Ipvb9nhjK3DWrtukEeQWv9oMyafa2xHiA4C4Llfp5L5ZsaC8Opoz58nWPhmwjnl3dGZhEQHBlZ4iyl/SMd88JF5PkvhYvleh1zk87DAwMfXzQhpECwwTg6BR3w9oIs3LVu+IpjYU5cjtcvpzgIW5iSclV6IT3FMT9aYdjBP1dUfC8l1+uYm3XevcO0oVOGVThpLE4dnYKSXhWycNf6A/LE2u+OH/p8YOAy1w3ydjNdfLF6mU44wFuHW9Miy4T2PmMwit5D/PVb2povr6s8gamu2s3Jfnsmya/40/iTtrz8Dnp84wVPG5zdt2nfWdV3ISjQhH5Bz3YYTjcXJJHGnvIFQYEm9Avavexqet99J7t0NpQXggJN6Be0eCK9EriCrixpKC8EBZrQL2ixFXRVwN90NW6aA/mAfkE7tElu3JJee7iRobwQFGhCv6D/K0NCdtO6QesN5YWgQBNedDPdPPgPpWuM3dQJQYE2vOoHTTthOC8EBZrwQtB3owII7T3XWF4ICjShX9APAgavJHR+gXcN5YWgQBP6Ba01nF6UVibXMZQXggJN6Bc0ZJMi6NYQQ3khKNCEfkEbTlYEnV3XUF4ICjShX9D3g6bvJecSw94wlBeCAk148St+QXFCSOGxxm77gqBAE970g17f/9kOo3cHQ1CgCTzdDgiNXkFTt314UJrdPJLU1VBeCAo0oVPQnytK3z+H/BAbSEiAobwQFGhCp6DdSnz648qS5Zuv3Lb/uqG8EBRoQqegJV+TJvHE2zGzd0VnETjLY+M/xnZ87L0MLzMBi6BTUPKZNFnj9Q+mzFMnHYR63IPuKj12y6qO9+JZ7v6NXkHlh4NuYPCL3uMhPrNKkjx7aqLxXMDECCvo97WV2SFjZ1SB2RFW0J32gcLOlzeeC5gYvYIGFSlSJIgoY3kZyutR0HMllQfWf/GAoTTA7OgUdKIThvJ67mbqPlQy9FSNdZ7aAUsj7qnOy70qPdGl1Fts0wKzIa6glP70yVcX2WYFpkNkQQGAoEBsICgQGggKhEZkQW99f9L6wwkB93AUVN9QiHm4PalYg+hqW3SnBZaCl6C6h0LMw7CH/qR0Rzljo34Ds8NJUP1DIebmQrEr8mxZN115gdXgJKj+oRBzs7uVMjvjcVBlYGnydyjEu3gUdL99iLqfq+vKC6wGJ0HVhkLMxqOg6aV+lGevWn8AeeAOToJ6MRRibj4pv+zskRGV/tSVF1gNXr/iGQyFuL9bVO2Rl/SlBVaDXz8ohkIEDBD5TBIAEBSITf4KuicuiwLGHi8K/AVOgo6+i/PLt7876KD2N7riAX+Fk6AjQkhUjB3XDZrt1RUP+Cu8DvEbyRG370NQoAlegtpCIShgALcfSesuu30bggJN+KqbCYICTXAVdJz6UAsQFGiCq6DkF9W3ICjQBAQFQgNBgdBwFXT7DdW3ICjQBH7FA6HxmaDjEvMwo2Nf1vRpzzzkk22Zh+zLIWS7PsxDduzNPGSX8Xk1yEl5Hwm6eHBe2oTVZE2VIsxDVi/APGTNwBrMQxaOZh4yLIp5yMg4Fx7k4MWrvhHUFV+wvwf+cH3mIVMimYekRa8xD1nb/almb+i8iXnI5xKYhYKgDiAoQyAoBGUHBIWg7ICg7IGgDIGg7IGgDIGg7IGgDIGg7IGgDIGg7Nncg3nIIyrPLjPAtVLMQ9Li6hcteEt99Qt1vKVbEvOQw95nFiofBM1wf5eIV3AYCcxvQ6ZkMg95LZ1ZqHwQFADvgaBAaCAoEBoICoQGggKhgaBAaCAoEBoICoQGggKhgaBAaCAoEBoICoQGggKh4S/o0riI1mxHh1+rjHY3kF3AEfYhIZhWao/JrtT0GbEhtWbfpgzLzA7JrsqrL1QObbhCXmJVJXdBPyYvr+sRfJhlyFmlEyR2sQpn2xyqyMSy0qyY7EodV2TmlqmFX2RZZnZIdlU+Vvq9LQPJRoZVche0Xh9KM2IHsQw5qAPLaOuLEqLIxLDS7JjMSs0MHi9NpwfdYlfm3ZDMqrxEPpSmdfoy/DB5C3qWrJWm48qyjNn2hZRD7C6CvnzkSJQsE8tKs2KyK/VcrHy8XE7OsCszOyS7Kn8bcFqatnmK4YfJW9B95KA0TQjMYBgzKrYQIT0vsAsYI8vEuFIlJuNSb7aKyWRcphySaZV/75ledCfDD5O3oJvIcWm6gqg/1l43acEdj177vMQj7CIqMjGuVInJttRDjYsmMy5TCcm0yvjgkk/dYFgl/z3oIWmaGHibdeAF5AqzWI49KNNKY+4OFsmm1EtPB/Y4zbZMR0g7zD5QW59HGFbJ/zvo59J0YhnmgTcr/0fZEGP/Dsq0UidBmZR6rFydA/KcYZlZIe2wqHJTP3m6hGSyq5L7r/g6A6X/U/WfYRhxe5Ht0nRSBLubEe0ysa1UicmuVFvtTrfsS8zKzA7JrsokZcf5fDmGVXIXdHngG8mDg7U+T1cLmXEVZm+eUOgddhHtgrKtVInJrtQ9ZOQSmZvsyswOya7K2/VjPto6psDbDD9M/meSPmwY3iqZacS/ni4b1nQVw4COwzHTSu0xmZWaSOz8xa7MuyHZfaDnnigX2ugjeYlVlTgXD4QGggKhgaBAaCAoEBoICoQGggKhgaBAaCAoEBoICoQGggKhgaBAaCAoEBoICoQGggKhgaBAaCAoEBoICoQGggKhgaBAaCAoEBoICoQGggKhgaBAaCAoEBoICoQGggKhgaCs6eV4ogx5zdeVWAIIypq9a9asiWopTY5eJwN8XYz5gaA8qPO4PL3V621fF2J+ICgP7ILSCgsorfzRsIrRi37vck8lefigpXGhdZb6tDSzAUF54Cxo5U2Z0wNqHsjoF3yLLgyauPGFgHd9XJ2pgKA8cBb0aUp/JzMo3UGOXS8+XXp1cJRvizMXEJQHzoLOojRDHjXoCPllP0m+ePHiyoA0H5dnJiAoD5wFnSsLukER9DNHBxS74R+sDwTlgYqgO8h5HxdmPiAoD1QE/adQovTqgt6+Lc5cQFAeqAhKxxSdu21qwTk+rs5UQFAeuBS03u/UNrd2SE2G4+f4ARAUCA0EBUIDQYHQQFAgNBAUCA0EBUIDQYHQQFAgNBAUCA0EBUIDQYHQQFAgNBAUCA0EBUIDQYHQQFAgNBAUCA0EBUIDQYHQQFAgNBAUCA0EBUIDQYHQQFAgNBAUCM3/AWXpqbelV6EuAAAAAElFTkSuQmCC" /><!-- --></p> <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 14% suggests that the model does not fit very well. This is also obvious from the plots of the fit, in which we have included the residual plot.</p> <p>In the FOCUS kinetics report, it is stated that there is no apparent systematic error observed from the residual plot up to the measured DT90 (approximately at day 5), and there is an underestimation beyond that point.</p> <p>We may add that it is difficult to judge the random nature of the residuals just from the three samplings at days 0, 1 and 3. Also, it is not clear <em>a priori</em> why a consistent underestimation after the approximate DT90 should be irrelevant. However, this can be rationalised by the fact that the FOCUS fate models generally only implement SFO kinetics.</p> @@ -1769,40 +1741,44 @@ plot(m.L2.SFO, show_residuals = TRUE, show_errmin = TRUE, <pre class="r"><code>m.L2.FOMC <- mkinfit("FOMC", FOCUS_2006_L2_mkin, quiet = TRUE) plot(m.L2.FOMC, show_residuals = TRUE, main = "FOCUS L2 - FOMC")</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>summary(m.L2.FOMC, data = FALSE)</code></pre> -<pre><code>## mkin version used for fitting: 0.9.49.4 -## R version used for fitting: 3.6.0 -## Date of fit: Thu May 2 18:43:52 2019 -## Date of summary: Thu May 2 18:43:52 2019 +<pre><code>## mkin version used for fitting: 0.9.50.3 +## R version used for fitting: 4.0.0 +## Date of fit: Tue May 26 17:01:09 2020 +## Date of summary: Tue May 26 17:01:09 2020 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 240 model solutions performed in 0.483 s +## Fitted using 239 model solutions performed in 0.047 s +## +## Error model: Constant variance ## -## Error model: -## Constant variance +## Error model algorithm: OLS ## ## Starting values for parameters to be optimised: -## value type -## parent_0 93.950000 state -## alpha 1.000000 deparm -## beta 10.000000 deparm -## sigma 2.275722 error +## value type +## parent_0 93.95 state +## alpha 1.00 deparm +## beta 10.00 deparm ## ## Starting values for the transformed parameters actually optimised: ## value lower upper ## parent_0 93.950000 -Inf Inf ## log_alpha 0.000000 -Inf Inf ## log_beta 2.302585 -Inf Inf -## sigma 2.275722 0 Inf ## ## Fixed parameter values: ## None ## +## Results: +## +## AIC BIC logLik +## 61.78966 63.72928 -26.89483 +## ## Optimised, transformed parameters with symmetric confidence intervals: ## Estimate Std. Error Lower Upper ## parent_0 93.7700 1.6130 90.05000 97.4900 @@ -1812,10 +1788,10 @@ plot(m.L2.FOMC, show_residuals = TRUE, ## ## Parameter correlation: ## parent_0 log_alpha log_beta sigma -## parent_0 1.000e+00 -1.151e-01 -2.085e-01 1.606e-08 -## log_alpha -1.151e-01 1.000e+00 9.741e-01 -1.168e-07 -## log_beta -2.085e-01 9.741e-01 1.000e+00 -1.029e-07 -## sigma 1.606e-08 -1.168e-07 -1.029e-07 1.000e+00 +## 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 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -1843,32 +1819,32 @@ plot(m.L2.FOMC, show_residuals = TRUE, <pre class="r"><code>m.L2.DFOP <- mkinfit("DFOP", FOCUS_2006_L2_mkin, quiet = TRUE) plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, main = "FOCUS L2 - DFOP")</code></pre> -<p><img src="data:image/png;base64,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" 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Hnwx95eX1tvYF3Q+jK/WIHLcFNB6aKa0lmvmt/KvG9d0FYpyrIAzXHf2+3O7V2/95zFa2k5/ZlF/Gxtm5cXK80CNMZ9BRX5+2bu9X05HZp5f2ytfXySM1mAhriroKs6vLSFdiYFelu5D1AkwOrN8+9NUZYFaI6bCrqYNHzO/60nl39W1Oo3pZygE2x0FAlcgpsKWnsgpR8Q4Zh8ck3rDawL+unbirIA7XFTQf3XizdYpVO6yd96A+uCLnpNURagPW4qaMRUSr8ixymdF2G9gXVB17RTlAVoj5sKOsEvYWTReq3PHgh/x3oD64LueEZRFqA9biroo+Fln5hxrzohMTIDOFgX9FAtRVmA9ripoEYe/rRB7sZr64L+U9GJLEBLjhQK15wSNh43d9UjH9YFvSlz7xNwHf+c0p578un5EjSroOIeAYB7w5egNFD+UQLgkXAmaOhZtmmA3uFM0Kj/sU0D9A5ngjbayTYN0DucCfo8RpoDueBM0Ffl7sAHHgpngvabyzYN0DucCTpiIts0QO9wJuik4WzTAL3DmaCf9WWbBugdzgT9vhvbNEDvcCboujZs0wC9w5mguxpafRl4LJwJergG2zRA73Am6LkQtmmA3uFM0DsBbNMAvcOZoAZvG/3aAw+EM0Fp8VS2eYDO4U3QSqfZ5gE6hzdBo39nmwfoHN4EbbKdbR6gcxQKWuoxbe1tYXMgLzlBX/jRwXqAZ6BQ0KSkpFkBlRJmDo8I3WWzub2BvOQE7SHTpz3wUJTv4vs+lyFMM1u/Zau13YG85AQd8ImD9QDPQLmgTyyXZqvL22ptdyAvOUFHfehgPcAzUC5o+dnS7JMKtlrbHchLTtCpCQ7WAzwD5YK+WWydMF1X3OYu3u5AXnKCJvdxsB7gGSgX9E4TUiKqBIlLs9Xa7kBecoIu7epgPcAzcOY86JbJg6fZO11pbyAvOUE3tHSwHuAZOHWi/sHfDmxgbSCvw/HZ+My0vtWepx2sB3gGTgj6eWgBQrtOd2Cbe8cycr9w7ctkE76fW9/kr0gH6wGegXJB5xeIX0LozIIyhhkxzOxEH/TzIr4fGaw3kNvFXwx2sB7gGSgXtPpAel1YGRtlq/XkAkPpmCKTN4wtJCOinKD3/BysB3gGygX1Xy8JulFmACQjFYdRWnmysDAu2noDOUGpj8zYicAzUS5onbGSoFNlhpAzErSS0uLi+dIUmWc4ZAUtec3BgoBHoFzQr3wS95DzyQEyg3AaadVd+PeBsDBG4Yl6WvmkgwUBj8CJo/hZJQghhYfLHP0Y2Vek9dJlJT/enlgo2XoDWUHr7newIOAROHMe9O6vS7detdN8X4eC4on6CnNk3pcVtOl/HSwIeATKBe0vcwOdJbcOb9x+MlPuXVlBO6yUeQN4JE7cbkfCxhxTnVdW0J7zVccGboRyQQ27h4aR2Jn/qssrK+g7MtdAgWfi3ENzB0Y+WbCVqryygo4dryoucDOcE/T2stcKF1SVV1bQj4eoigvcDCcEPTe3VSGfll/YO463jayg83qpigvcDCeuJJHC7Rao7qBGVtDlL6kNDdwJxYI+rPsViwFfZQXd3IxBdOA2KBb0KFnEIq+soPtiWYQH7oLyXXyz51nklRX0REUW4YG7oFzQ7yNjR82YJaAqr6ygWdG4lAQeo1zQkGxU5ZUVlKY8iT5sQQ689W4n0EzmBijgiWj3VKdtbAj6+xN3VYcH7oKmT3XawIag9OUJ6mIDN0KjpzrtYkvQ0yWvqIoN3AiNnuq0iy1B6YB3VMUGboRGT3XaxaagV0up/5EL3AONnuq0i01B6QevqgoO3AeNnuq0i21B057Yoyo6cBs0eqrTLrYFpd/WlX2aCXgUWj3VaQ87gtKmco+DAs/CyStJJ9ep7ADEnqB/lrqkLgFwD5QLeq7lQJriTYJ+U5XXnqA0oaeq+MBNUC5oh+AfaOPGp9q2sbeFUwN55XAnBGPOAWcELZFMb3ktpktK22zu7EBej1kS9ej+hqRNDxysD7gnygUtvpguK3CF/iDTbZ0RpwfyMqP1wPCmbz0b8YuDBQK3RLmgLeN2xzaid16sa6u10wN5mfGz1zJhuq4s+mP0ZJQL+kc54r+D1vRZZau10wN5mTEtpr04e32ugxUCd8SJ00z391+ldPlRm62dHsjLjH6f1PxOmM1ATw6ejDPnQa/vXPGbnXuKnR7Iy4xRYw+W+5fSYbg71JNRLmjm4MKEkKAptps7O5CXGb9VmBpcuN708ocdrBC4I8oFHVtg2JGbhxMK2Bs329pAXltJDnYEFzBU9R0Q0rJwLQcLBG6JckEjjOMRJzhyw/Jq2Uv2DnyDro3Z3b+x//oa6HLZk3HiRP0aafZTkCMbbZF7xwFBh4pfssPbfzDKgUTAXXHiUqfxeYx+bW217m6ENOve3XoDBwR9+1Nhkh7ZfaiDFQJ3RKGghw4d2lzqtdV7V3UNtnmeqRGpWF+AVKtf33oDBwT9opM4PVAIXS57MgoFfXyQQxrbav0oIUg8ka9qF38vYmIGfTCubL0Mu02B26JQ0L8fc8F2+x+DhmSoE5SebR9Up3jn8y+MdrBE4IYo/g1qWBL/TPRrc+1/q/0T+/RZdYJSmnrwJqVXQ2SjALdHqaDHG5AKLTtEFqh+0O4WD98uoVZQI9vLX3a8MXAvFAp6v3r4WnH+a63wNPvbrBh8XO4tJYLSEW3UPaEH9ItCQccGmJS7EDRSVV5FgmbUn6EqGdAvCgV9tl/2+pAmqvIqEpSeCd6qKhvQLQoFLZbj1cLiqvIqE5TuLHdKVTqgVxQKWnli9vq0yqryKhSUzoy+pyof0CkKBe2as2Nv3UlVXqWC0l7orskjUSjoVq+PjKtfkvWq8ioWNL2eyi5zgS5Reh50JGmy9I9jqzuRPuryKhaUng3erC4l0COKryStrCheiC/xpcozk8oFpdvL/qkuJ9Ahym+3yzq5eulh1fdvOCEoXRxqeYc+cHs4HIZGnolRN9lWAbhHV4LSgU0fsi0D8I6+BM3s+CquynsW+hKU3ntqDNs6AOfoTFB69UmcDvUo9CYovVB5GtNCAN/oTlB6LlzdGHdAV+hPUHqm0hcMCwF8o0NB6YmQ79gVAvhGj4LSI8FyfZIBd0OXgtKjFdCbg4egT0HpmSrDGRUC+EangtLLtQfhmpInoKGg6sZJskdqg3hpnAbDyqGDvsXAnm6LVoKqHyfJHndbvphG6e1nG0yf3SIa4ya6KxoJymKcJHs86l/rLO0fL+7px3VQHQ3wiUaCshgnyT6zQn4reVFcSC/qQD8nQI9oJCiLcZIcYH25gsaFsNMswgH+0EhQFuMkOcL+gsPEXXxaUTw176ZoJCiLcZIcon/JxomTNvfvwSYa4A6tjuIZjJPkELcqeBWq6VfyDJtogDu0Ow9qbZykq18mm/BldM/c8M4/vx7w7tv4BnVXNBM0/WyWOLufq+/Zw/HZ+DC6mF5OODr6q9p/AvEb1E3RSNCMQd6kyh5hYZ7Mdox28Rk+4vRuL58VTMIB7tBI0CmFExc1CTyjuaDUdB7Uv8xwjAXilmgkaGSisHeP7ai9oG/3Fk8zje14sWX9v9lEBFyhkaB+G4XJAa99mgt6O67+9E+a175EDbNLfYx7RtwPjQStOkmc9qyZrrWg1LBq6KDvJDNPtaj9G6OggBs0EnSC76hdlN4IbjNEa0HNWVZu0F0NwgIXopGgD0f4RQqz4zVIfgpKL79cZa0WcYHL0Ow8aNZ5cWrYmWz9bW0EpTQlss0xbSIDl6DXRz5kyZhVatAtjWKD/MftBKX0Su/gOeil0V1wQ0Ep/f35sG+yNIwP8g+3FJTSnxtF/ahpApBPuKmglP4UHbMS36L6x20FpXRz/Rpf23jwGegCNxZUUDSu8mc4c69v3FpQSn95qdSws/mSCWiDmwtK6T9DS3bdmU+5AHvcXlBK0z6tVn3mjXxLB5jiAYIK/Nyj+GvbcEyvRzxDUEpTZ0dXHImxPvWHpwgqcHh4aN0ZOGLSGR4kKKVZW98s9fQ09JKjJzxKUIFHm+JLxyQeROe3esHTBBXI3PpulZC3fsKT9LrAAwUVOT69aUDTSftxYM89HiqoQNraQdVKdZlzGHt7rvFcQUUufPNmlVKdZu2VOn0YV7tye3QlzhueLajIxe/6Rwc0fu/7Mn4de0cUXOTqckBuIKhI2n8/LONVpu24n94qhBv0+AKCmigy9/yqUa3LFaj57tcH7ru6GJADBDXhbbzlKeiNyd2i/SI6jFy0L7+fDd019p35D/I5J/9AUBPFE8XpI+9N4vTY8sRX6hYJjoufsvxQPo0fktkz4oOZHSrjdgELIKiJt/xPUJrV3HxQknObPx/aIcqvbP1XRiRv+FPjW/M/eU789lwQjbNeuYGg2Tzj9WS9IoH78r5x6ZfvJvRuEelXola7vh/MTzl8TZP0Df77VdfWwy9WOaxJdP0CQXPY2qPdFFuXlq79vuazMT1b1ShVKOSpF3qNmvXdlj8usvvNWDG2xZKUEWViNzKLqDf+N2P8yrwdaOp2MFkX8vD83jVffDioW1xUsE9g+FOtXx04bvbXa3YevnBHRdDQ58TpFm9P/QY19AsoWbRkxCnL1/U7mCwf3D65d/2i2eMG9mjXqEb5AK+g8DrPtnslftiHM5KXrt26/9iZVEd7Jg+tJR6NfeLvqV2cTi/c/8j5b4uHW76u48FkOSTzxqkD2376Pnny6CHxXZ6Pi3myQpAPCSobXivmuZZdXo1/e/joydOSk5ctS9m8Y//+U6fOpabmdCIV9npowoRmUY03ubJ+FxLcVZz+VfCQxeu6HkxWFxhS/z31x/4tG5d9m/zp5A+HD42P79KldfNnYmLCw0OCggoREhAUFBoeHlC5emhYred8n+/S5T/x8fEDhgtMnCyQJA4rtWCZyMrNEjv3G/nzlIlLqTmo+ZXhUnxSpFmxGRav5+9gsvuaZ+P9saJ47kxaauq5U6c+qbDm180pHesJHn4tGPmp6Ob7oqVvicNKvd5FpKPxs2scY6RauIngoBwCiDk+QXkIDpcnMkYdTzd3Hq+o5s0FJ/wtv7jydzDZtM3ZRPysKJ4HMKdMu+5hHdg+Hp2RmodLp+Q5ul8dezc7T3iFlM3naXLByxZ/gqsGk62/R1E8T+DamkWeeggv8ItfyEdz2vq/bPm6qwaThaAgN5srFC/tPzxPz8P5O5jsYyAosMBw+mB63ldddSUJggKHgKCAayAo4BpXCfpcYN5TdEVIAc3QMLSW6LRsr7z/cZ3F+4RrBE3Pe4Yu9ftmpzUjdIdmoUf30iz0CW/NQp/unqhZ6C0RVv7rOonDzzUwFtQaa17QLnaYdj0yzRiiWehH3pqFpv3nahb6aKRmoeWBoHJAUEsgqHIgqCUQVDEQ1BII6jgQVA4IagkEVQ4EtQSCKgaCWgJBHQeCygFBLXFXQU8maRd7nHYdhOxcpVloQ4JmoekPezULfetDzULLkw+CAuA8EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA12gu6IKbYs7s0ibxC6sistwaRBxtvN9KidGNoDUp/+FGkf/WpYtfkzMvOCa3dJy6L5oJ+S95d2dHPsgdoJkwpmySwnXlcQ0oRySINSs8OrUHpI3wnbhhfeJAWZeeE1uoTt4HmgtbqRmlmZB8tQvdpqUVUuiqQEMki9qXnhGZfepbfSGGa6JPOvuzHoTX6xG2htaDnyAphOiJYi9hNB6QevMk+7M0jR0JFizQoPTu0BqWfjxT36ovIWfZl54TW6hO3hdaC7iX7hWmSV96hnNQTGlmIkE5ajBAXIVqkTelSaK1Kv98kIkujT1wMreEnLovWgq4nJ4XpYnKVfegHfq2O3vmxZHv2kY0WaVO6FFqj0g/GBu7WqGwptIafuCzaf4MeFKbJXo4OfKWYWUSD8bVN36BalB7x+HkkxqXfeMOr4xltyjaFNqLJJy6L9r9BfxSmo8tpliBF+sJgTITxN6gWpZsJyrb0E+WjpCFzNSg7O7QRTT5xWTQ/io/qTakhupcGkbf4bhGmY4rZGinWSYwWaVK6FFqD0g01Wpu6gmdedk5oDT9xWTQXdJHXx7vj/RztT1cJWTEhU1NGFfpMg9BGQTUpXQqtQem7yJB5IvfZl50TWsNPXBbtryQtrFO0iczIniq5/EZwwFPLtIhs2g9rUboxNPvSk02j0F1mX/bj0Np94rLgWjzgGggKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGggKuAaCAq6BoIBrICjgGggKuIaxoKsnA+AA0+65RtD6PYYDYJ/SjnZuxlrQPWzjATelFgQFPANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDUeCHpwyaukjtlmA7uFG0Ky3Kwyb0KJ6fo5N5iourNt03dU16AZuBE1qdFeYzollm4ZDMt4p83yLMlNcXYZe0EzQ9LPSOHj3L1t/O4+gjcTB86gh/E+FeXTHkBdvUnouJtnVdegEjQTNGORNqogOzpPZLo+glf6RZi03KsqjP9KLScNf/1rd1YXoBI0EnVI4cVGTwDMKBG2wTZpVOawoj/44UUWaZfrk50CrOkYjQSMThb17bEcFgn4aJw6UO6+2QVEe/XG5pGTm1eKuLkQnaCSon7inPuC1z0LQ4zn3oQYtt9gis1flcTPbR/ylJMvFVctOKSqLB+otFqfje7i6Dp2gkaBVJ4nTnjXTcwt6YUr2nfyF5+TZ5pfxgxc+UJDDMKZMhy7B8Uo24YEDZYdt29Qz/IKr69AJGgk6wXfULkpvBLcZIrNdwJeK4lljZqMrlN55aZDqQPnMlYS4FhPuuroKvaCRoA9H+EUKs+M1iHaCVv5dnF4vlq46EuAXzc6DZp0Xp4adMuf71Aua6W08nqriCRefPBdXXUli8A1a7Jo4zQy6pjoS4BcdC9pjtDid31h1IMAxOhb0UtVuq1L6lT/CoBrALToWlKZPb99m/E31cXgaEpYAAAzuSURBVADH6FlQ4AFAUMA1EBRwDQQFXMOzoI+Oy9ztDDwHfgXNnFS8atma29mmBXqDX0HfbXaW0rXl9rHNC3SGQkGTzFCV166gN4qnirN5HVSlAXpHoaClzFCV166gPz8jzc5UVJUG6B1ud/F76kmzYxFs8wKd4bSgR2epymtX0PSSx8TZuD6q0gC944Sgi4clCDSspyqv/YOkryr+kHp61BPnVaUBeke5oIle0YXKPlU0QN0JIAfOg257Ligs/l9VWYDuUS5oWAJd2Jqm1V9tq3XCY6w3wJUk4BDKBS28hp4PMtA1DWy1HuxPQiOMmL+8t3k2Bac7USzwPJQLGjyN0lK/0t0BNpuvI9ZuJL63ZbMJvy8UFAk8F+WC9in7DW3V7UK/qjabG4rYvtMdu3jgEMoFvf16R/q7H/H+1nb7lbZvdfdkQY8sWPyPq2vQDU6eB721+bS6vJ4r6L3XKvR8pdx76DvMMbi9kuS29OmRTumNJh+7uo784LL6Lt2VC9rOxDBVeT1W0HtFb4mzQ+5/CTf9g6Cy/l3PqoyiXNCeAq+3Kf7kfFV5PVbQ48b+QbO83X4f36XDOXr/owoqu+N3dhd/+zl154k8VtBrQZni7JK6m8F0wMGwDHHW9wN1YZz+Dbqtlqq8HisobSL1VvWu298Ek2T8C39qqy6M04Kus32i3h6eK+jR0DeWf9+ultsPRPPFm9Js9QvqwigX9FuJ2RXiVOX1XEFp2uSXuiVluLoKzTkScl+cva5ywB3lgvpK+Dc4qiqvBwvqKfSO27Jt95Aqd9RFwXlQoBGXYgp6FSyZojIKBAXakNUwIf0hXVlG5SVH5x+aU3d0BkHdne3RUg/Yo2RuCHYU5Y8dzwqolDBzeEToLlV5Iai7M7efNEtprS6M8l183+fEI9DM1m+pygtB3Z0Fr0mzpZ3UhVEu6BPGIbhWl1eVF4K6O2dLXxGmhtYqR81VLmj52dLskwr2tsi8auNeFgjq9nwUseTEtrZNVJ7xVS7om8XWCdN1xW3v4g/1LudFvMr1kgsPQd2fLe2qNJmt9oqEckHvNCElokqQuDRbrXf6RY5IWpw0Otpvt/UGEBQ4hDPnQbdMHjzNzlPxz3SS7tmhhj7PWm8AQYFDaHSivuhK08Ivxcxf3haUTQG/oKCS5yh9PQhzzG3NFQrac86sbGy1rvOeaWFaTK7Xb6aaCJidmnpbeOFhKuaY25hHKRSUdA7Jxlbr+eTVFQdOHvyxt9fX1htgFw8cQuku/q6DYwsvqklEaso9nAxBgUM4+Rv05Dq7Q7ie27t+7znZdyEocAjlgp5rOZCmeJOg31TlhaDAIZQL2iH4B9q48am2bVTlhaDAIZQLWiKZ3vJaTJeUVpUXggKHUC5o8cV0WYEr9Ac8NAfyAeWCtozbHduI3nmxrqq8EBQ4hHJB/yhH/HfQmj6rVOWFoMAhnDjNdH//VUqXq3uoE4ICx3DqPOiDv1Xn5UXQe9tvu7oEYAsnBP08tAChXVX2Mc+HoKdrEB9SYb+rywDyKBd0foH4JYTOLPi5qrxcCPowMOoCvRnnc8XVhQBZlAtafSC9LqyMjVKVlwtBxxpPlVXo6uI6gDzKBfVfLwm60V9VXi4EbdpEmvWq4uI6gDzKBa0zVhJ0ak1VebkQtEV9adYt0sV1AHmUC/qVT+Iecj45QF0n61wIOqfQPWGaVaKfqwsBsjhxFD+rBCGk8HCDqrxcCEpDSn6Xtq5CoIP3uAIX4Mx50Lu/Lt16VWVePgRN71SY+MTZvbUVuA70bge4RqmgaZsWiue17x/Z3M6JbI9OZVMEggJHUCjoXxWE35/9/hfpRUgBJ7LtDM/GS2XP0CInOpcNarVPfRzAMQoFfaHk94eXlH6iwZJNv95VlZfBLv5AmdmXU78J/kl1IMAxCgUtPUGYTCZnVOdlIGiLheL058qqAwGOUSgoWSpMljM4YFIvqMH3njQPPq+6GMAvSgUVOwddzYWgWYWMHadVUtkJOuAa/QpKY6QBJI6XyVQdCfALx4Le/6L/iP/aeP+nitsp/aPWHPXFAH5RKqiPr6+vD5HG8lKV176gR8I6z51U7WUbHaD+VKVcxeCvVJUBeEehoKPNUJXXvqDRYrdjGa2n2WpzSf3pBMA33F7q/Ctcmv0SyzYv0BncCrq1qTS7oG4wEaB3uBX0ZGiWONvagG1eoDO4FZQ2Fq/W32mgcpgdoHP4FfRs3cZj3nlisLr7ooHe4VdQmvVj4qzDbLMC3cGxoABoKGj6Wekg5/5l629DUOAQGgmaMcibVNkjLMyT2Q6CAofQSNAphRMXNQk8A0GBSjQSNDJR2LvHdoSgQCUaCeq3UZgc8NoHQYE6NBK06iRx2rNmOgQFqtBI0Am+o3ZReiO4zZBc211flo1v3t4bj3720bosRVmA+6ORoA9H+Ik9ch2vQXJt90eXbAK+s9jCMDy438iGsbh/DuRCs/OgWdKzbIadMtfS6++xeGF+vZvCdEpDhWmAm6PplaQR8j045RG0yQZxaqh4zIk8wH3RVFAib1seQSv9I81abnQiD3BfuBH0qZ+lWaSj9QDPgBtBZ7R6JEwX18DtdcAcTQXdck/2rTyCZnStMXXeq6GHnEgD3BhX3W6XR1BKNyW8OUddj2TA/eBIUADyAkEB10BQwDUQFHANBAVcA0EB10BQwDUQFHANBAVc4zJBRyTn4aNW3VnTrTnzkK81ZR6yuwYhm3VjHrJVV+Yh247Mq0FunnCRoF/G5yUuoBprwnyZh6xakHnIal5PMg9ZOJx5yIBQ5iGDYqx4kItBt10jqDXWvMA85KFo5iFTg5iHpIF3mIescYR5yDbrmYd8K4lZKAhqAoIyBIJCUHZAUAjKDgjKHgjKEAjKHgjKEAjKHgjKEAjKHgjKEAjKnpSOzEMeiWEe8k4Z5iFpCfmnC50lmn0PGC9sZh7ybXYjWeaDoJk32ce8jpDMSGXftdudh8xC5YOgADgPBAVcA0EB10BQwDUQFHANBAVcA0EB10BQwDUQFHANBAVcA0EB10BQwDUQFHCN9oIuiCn27C6mEVcQkd7sAg5OkGZMKzXGZFfqw48i/atPzaAMy8wJya7K2wMqFamzWFxiVaXmgn5L3l3Z0Y/pIApTyiYJbGcVzpBSRJKJZaXZMdmVOsJ34obxhQexLDMnJLsqu5T9YkNvso5hlZoLWqsbpZmRfViG7NOSZbRVgYRIMjGsNCcms1Kz/EYK00SfdHZlPg7JrMobZKEwjerO8MPUWtBzZIUwHRHMMmbTAakH2d0EffPIkVBRJpaVZsdkV+r5SHF/uYicZVdmTkh2Vf7TUxw1OO4/DD9MrQXdS/YL0ySvTIYxQyMLEdLpGruAEaJMjCuVYjIu9X6TiCzGZYohmVZ5ZVdi4DaGH6bWgq4nJ4XpYiI//qxiHvi1Onrnx5Lt2UWUZGJcqRSTbakHYwN3My5TCsm0ysl+pf9zj2GV2n+DHhSmyV4ZrAPPIreYxTJ9gzKt1PgNKsGm1BtveHU8w7ZMU0gjzD5QQ7f2DKvU/jfoj8J0dDnmgVOk/0fZEGH8Dcq0UjNBmZR6onzUPnHOsMzskEZYVLm+hzidR7LYVan5UXxUb+H/qeheDCNu8d0iTMcUY/cwolEmtpVKMdmVaqjROt24xKzMnJDsqtwsfXH2Lc+wSs0FXeT18e54P5ajcWfFhExNGVXoM3YRjYKyrVSKya7UXWTIPJH77MrMCcmuyozoiG82Dis4h+GHqf2VpIV1ijbZzTTi5TeCA55axjCgaXfMtFJjTGalJhMjl9mV+Tgkuw/0/Kvli9T9RlxiVSWuxQOugaCAayAo4BoICrgGggKugaCAayAo4BoICrgGggKugaCAayAo4BoICrgGggKugaCAayAo4BoICrgGggKugaCAayAo4BoICrgGggKugaCAayAo4BoICrgGggKugaCAayAoazqbepQhE1xdiVsAQVmzZ/ny5aGNhMnRu6Snq4vRPxBUC6JeEafpnee4uhD9A0G1wCgoDZlFaaVv3q4QPvdi21IVxeGDFsQUiVrg0tL0BgTVAnNBK63PSixQbV9mD790Ottn9LoBBT53cXW6AoJqgbmgb1B6kXxE6VZy4m6JROHV+FDXFqcvIKgWmAs6hdJMcdSgI+TYr2T39evXlxR44OLy9AQE1QJzQaeLgq6WBF1qOgHFbvgH9weCaoGMoFvJBRcXpj8gqBbICHq1ULLw6qyuri1OX0BQLZARlA4LnL5pvPc0F1enKyCoFlgVtNZFaphew78aw/FzPAAICrgGggKugaCAayAo4BoICrgGggKugaCAayAo4BoICrgGggKugaCAayAo4BoICrgGggKugaCAayAo4BoICrgGggKugaCAayAo4BoICrgGggKugaCAayAo4BoICrjm/0aWRU4pXWwPAAAAAElFTkSuQmCC" /><!-- --></p> <pre class="r"><code>summary(m.L2.DFOP, data = FALSE)</code></pre> -<pre><code>## mkin version used for fitting: 0.9.49.4 -## R version used for fitting: 3.6.0 -## Date of fit: Thu May 2 18:43:54 2019 -## Date of summary: Thu May 2 18:43:54 2019 +<pre><code>## mkin version used for fitting: 0.9.50.3 +## R version used for fitting: 4.0.0 +## Date of fit: Tue May 26 17:01:09 2020 +## Date of summary: Tue May 26 17:01:09 2020 ## ## 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_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * +## time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) +## * parent ## ## Model predictions using solution type analytical ## -## Fitted using 587 model solutions performed in 1.211 s +## Fitted using 572 model solutions performed in 0.13 s +## +## Error model: Constant variance ## -## Error model: -## Constant variance +## Error model algorithm: OLS ## ## Starting values for parameters to be optimised: -## value type -## parent_0 93.950000 state -## k1 0.100000 deparm -## k2 0.010000 deparm -## g 0.500000 deparm -## sigma 1.413899 error +## value type +## parent_0 93.95 state +## k1 0.10 deparm +## k2 0.01 deparm +## g 0.50 deparm ## ## Starting values for the transformed parameters actually optimised: ## value lower upper @@ -1876,26 +1852,30 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, ## log_k1 -2.302585 -Inf Inf ## log_k2 -4.605170 -Inf Inf ## g_ilr 0.000000 -Inf Inf -## sigma 1.413899 0 Inf ## ## Fixed parameter values: ## None ## +## Results: +## +## AIC BIC logLik +## 52.36695 54.79148 -21.18347 +## ## Optimised, transformed parameters with symmetric confidence intervals: ## Estimate Std. Error Lower Upper ## parent_0 93.9500 9.998e-01 91.5900 96.3100 -## log_k1 3.1330 2.265e+03 -5354.0000 5360.0000 +## log_k1 3.1370 2.376e+03 -5615.0000 5622.0000 ## log_k2 -1.0880 6.285e-02 -1.2370 -0.9394 ## g_ilr -0.2821 7.033e-02 -0.4484 -0.1158 ## sigma 1.4140 2.886e-01 0.7314 2.0960 ## ## Parameter correlation: ## parent_0 log_k1 log_k2 g_ilr sigma -## parent_0 1.000e+00 5.434e-07 -9.989e-11 2.665e-01 -3.978e-10 -## log_k1 5.434e-07 1.000e+00 8.888e-05 -1.748e-04 -8.207e-06 -## log_k2 -9.989e-11 8.888e-05 1.000e+00 -7.903e-01 5.751e-10 -## g_ilr 2.665e-01 -1.748e-04 -7.903e-01 1.000e+00 -7.109e-10 -## sigma -3.978e-10 -8.207e-06 5.751e-10 -7.109e-10 1.000e+00 +## parent_0 1.000e+00 5.157e-07 2.376e-09 2.665e-01 -6.837e-09 +## log_k1 5.157e-07 1.000e+00 8.434e-05 -1.659e-04 -7.786e-06 +## log_k2 2.376e-09 8.434e-05 1.000e+00 -7.903e-01 -1.263e-08 +## g_ilr 2.665e-01 -1.659e-04 -7.903e-01 1.000e+00 3.248e-08 +## sigma -6.837e-09 -7.786e-06 -1.263e-08 3.248e-08 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -1903,7 +1883,7 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, ## 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 22.9300 4.514e-04 4.998e-01 0.0000 Inf +## k1 23.0400 4.303e-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 @@ -1915,7 +1895,7 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, ## ## Estimated disappearance times: ## DT50 DT90 DT50_k1 DT50_k2 -## parent 0.5335 5.311 0.03023 2.058</code></pre> +## parent 0.5335 5.311 0.03009 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> @@ -1933,7 +1913,7 @@ FOCUS_2006_L3_mkin <- mkin_wide_to_long(FOCUS_2006_L3)</code></pre> mm.L3 <- mmkin(c("SFO", "FOMC", "DFOP"), cores = 1, list("FOCUS L3" = FOCUS_2006_L3_mkin), quiet = TRUE) plot(mm.L3)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 21% as well as the plot suggest that the SFO model does not fit very well. The FOMC model performs better, with an error level at which the <span class="math inline"><em>χ</em><sup>2</sup></span> test passes of 7%. Fitting the four parameter DFOP model further reduces the <span class="math inline"><em>χ</em><sup>2</sup></span> error level considerably.</p> </div> <div id="accessing-mmkin-objects" class="section level2"> @@ -1941,30 +1921,30 @@ plot(mm.L3)</code></pre> <p>The objects returned by mmkin are arranged like a matrix, with models as a row index and datasets as a column index.</p> <p>We can extract the summary and plot for <em>e.g.</em> the DFOP fit, using square brackets for indexing which will result in the use of the summary and plot functions working on mkinfit objects.</p> <pre class="r"><code>summary(mm.L3[["DFOP", 1]])</code></pre> -<pre><code>## mkin version used for fitting: 0.9.49.4 -## R version used for fitting: 3.6.0 -## Date of fit: Thu May 2 18:43:55 2019 -## Date of summary: Thu May 2 18:43:56 2019 +<pre><code>## mkin version used for fitting: 0.9.50.3 +## R version used for fitting: 4.0.0 +## Date of fit: Tue May 26 17:01:10 2020 +## Date of summary: Tue May 26 17:01:10 2020 ## ## 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_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * +## time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) +## * parent ## ## Model predictions using solution type analytical ## -## Fitted using 372 model solutions performed in 0.761 s +## Fitted using 373 model solutions performed in 0.083 s ## -## Error model: -## Constant variance +## Error model: Constant variance +## +## Error model algorithm: OLS ## ## Starting values for parameters to be optimised: -## value type -## parent_0 97.800000 state -## k1 0.100000 deparm -## k2 0.010000 deparm -## g 0.500000 deparm -## sigma 1.017292 error +## value type +## parent_0 97.80 state +## k1 0.10 deparm +## k2 0.01 deparm +## g 0.50 deparm ## ## Starting values for the transformed parameters actually optimised: ## value lower upper @@ -1972,11 +1952,15 @@ plot(mm.L3)</code></pre> ## log_k1 -2.302585 -Inf Inf ## log_k2 -4.605170 -Inf Inf ## g_ilr 0.000000 -Inf Inf -## sigma 1.017292 0 Inf ## ## Fixed parameter values: ## None ## +## Results: +## +## AIC BIC logLik +## 32.97732 33.37453 -11.48866 +## ## Optimised, transformed parameters with symmetric confidence intervals: ## Estimate Std. Error Lower Upper ## parent_0 97.7500 1.01900 94.5000 101.000000 @@ -1986,12 +1970,12 @@ plot(mm.L3)</code></pre> ## sigma 1.0170 0.25430 0.2079 1.827000 ## ## Parameter correlation: -## parent_0 log_k1 log_k2 g_ilr sigma -## parent_0 1.000e+00 1.732e-01 2.282e-02 4.009e-01 1.660e-07 -## log_k1 1.732e-01 1.000e+00 4.945e-01 -5.809e-01 6.635e-08 -## log_k2 2.282e-02 4.945e-01 1.000e+00 -6.812e-01 3.880e-07 -## g_ilr 4.009e-01 -5.809e-01 -6.812e-01 1.000e+00 -3.822e-07 -## sigma 1.660e-07 6.635e-08 3.880e-07 -3.822e-07 1.000e+00 +## parent_0 log_k1 log_k2 g_ilr sigma +## parent_0 1.000e+00 1.732e-01 2.282e-02 4.009e-01 -6.868e-07 +## log_k1 1.732e-01 1.000e+00 4.945e-01 -5.809e-01 3.175e-07 +## log_k2 2.282e-02 4.945e-01 1.000e+00 -6.812e-01 7.631e-07 +## g_ilr 4.009e-01 -5.809e-01 -6.812e-01 1.000e+00 -8.694e-07 +## sigma -6.868e-07 3.175e-07 7.631e-07 -8.694e-07 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -2024,7 +2008,7 @@ plot(mm.L3)</code></pre> ## 91 parent 15.0 15.18 -0.18181 ## 120 parent 12.0 10.19 1.81395</code></pre> <pre class="r"><code>plot(mm.L3[["DFOP", 1]], show_errmin = TRUE)</code></pre> -<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAMAAABNUi8GAAADAFBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7////isF19AAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nO3dB1wTZwPH8aN1AMpSC7hAREUR0eLb14V7Va2DWvVV3Li1daDirFoHTtRWxVFH0aJWUeuoq2qrIq1iUWsdaIuorbU460Iged5LAphALuS458hz5P/9fBoCOZ7nML+ScHe5cASAYZylVwDAFAQKTEOgwDQECkxDoMA0BApMQ6DANAQKTEOgwDQECkxDoMA0BApMQ6DANAQKTEOgwDQECkxDoMA0BApMQ6DANAQKTEOgwDQECkxDoMA0BApMQ6DANAQKTEOgwDQECkxDoMA0BApMQ6DANAQKTEOgwDQECkxDoMA0BApMQ6DANAQKTEOgwDQECkxDoMA0BApMQ6DANAQKTEOgwDQECkxDoMA0BApMQ6DANAQKTEOgwDQECkxDoPlxo2XHzy29DlYCgebHpB9JoKXXwUog0Pz4V3W7s+klThi53djXIA8INF++7XDX9AJ5BxpRyb3v8+wr6rHuZUerdLeEH6GzkoUCAs2PQxNU+p+qUnT/vfks70ATSqU8aTcj+8o+v4dPfPfdqF9rAfmnhzwrrUwIVJzt5c6nVT40sH3XYM1nyyp5j8uIC6oTpvlPPdurUqj2M6KNUXtjm12E1P1Bd90g0G+nErIuOPvKhVMk7f3vxn1PGrwYfckiPxmjEKg4a34Zu6G5OvOT47X+ftZjUZz9VaL572DtJ69abtJcI5pAdTdu6E1ueql017WBenIa6Zpl/gmI0Y2jvTK/aJD6RoN3518fZomfi1kIVJxUMtX7bNYn0zybNq3ZPa4ZIZr/JkQQsq2f5hrRBKq78bHr67kzMxc0fNhXb6y00eBKfK0Nmg+9/yyYn0QhEKhYM7tnXw2fRUhGWlwHPlD+v/FLCfmmt+Ya0QSqu5F0OlT798wFDQLNCO74p96VbScI+XwA/+mpT9WT/JslF9zPwzgEKtLLiluyr8dXf5j24YqsQPcF/JvaZn12oLobSXTTZlkLGjzEx7TUPFFQXU3TXYlsmva4y+f8r9PO/95pRXbOssjPxiIEKtL8Hp+8+WRl1fJDsn+DqmdW9hqTnh2o7kbyzG5j1oIGv0HHvF28ePHer7gk3ZW0AZXKjUzj/whbTcjEd5v+VZA/EtMQqDgPS18L+m2lpdfCiiBQccb1I9tLfGPptbAiCFScF/zDsCrvxYAWBApMQ6DANAQKTBMfaPr9NBnWA8AokYEmhLjZcDZuAy7IszYAOYgL9KStT1hkdORUf9tYmdYHwIC4QAODtIfhENWgJnKsDEBO4gJ1yDw+jJx2pL8qALmJC7TO+MwrCwPorwpAbuICXc/13BmfeH5PiM0mmdYHwIDIv+Kj/LRHi/ltlmdtAHIQux1UnRx3IC5ZLXTzuLoAZgj8W6ZA89hQX39jPEDeql6UJ9C8NtTXjxM3Hlgpf3kCzXNDPQIFs8gUaJ4b6hEomEWmQAU21P9YOcvbq0WNB9ZKpkAFNtRn/J6lxDpR44G1kinQPDfUlzQS6EtztyiA9ZAp0Dw31OcONKFJCVf3lXgZDxiQK9C8NtTnCvSyW5SKXGkwReQ0UMjJFmgecgXac7nmMsX5Ed15QOGYCbTKdfLkZ0KaHac7DygcM4H6XCFHWhES+CPdeUDhZAo09A3jC+QKdOAckuBPbpd6JmoeKOxkCnS0HVfBW8f4ArkC/cN9wW9lj/kuETUNFHpyPcTv5341eXvuzUxJ/3Phss4kDMx4NHyI/L4Xnl+uQFX2YgPlOeFPeOYkVFwju6BJwvPL9kdSjOnYjAZa9brISUB2CXXknyPcEoHmwWigDU/RnQSkQ6B6Ou+iOwlIh0D1DFpDdxKQDoHqmTKb7iQgHQLVs/QTI18Ei0Kgerb0pDsJSIdA9RxuRXcSkA6B6knwpzsJSIdA9dwtS3cSkA6B6nldTPAIfLAQBKoPO+OZg0D1VcHOeNYgUH0NT9OdBSSTMdDgrPfMVU6g2BnPnFyBXu5U2qXdeZGjqK+m5v6iAgPFznjm5Aw01m31g0cb3Y8IfkMS92u30j5zVIQ8GVy2mNdn/N+95b9uwSWRDQH2NTfwC5SPnl3TsesDUo/juMfab1FOoJPn0J0FJMsZaCPtOzgf9hP8hiSu2tp/Yhz55vq+s+DbcdxWPkmf9ze9XFZk6v6R3Cr+s7rLUq+7DSMPPmx7T3eeDuUEGjGa7iwgmTbQA9kHv68qskrzYbV9RPaXcjwtS+IG85cRdo9Jl6/4K/4T+CQD1OSZyyz+s8EV+M9a8leGNlDkQzx2xjNHG+ji7FcPhbyt+2jXJ/tL4YbfkMRpir3OneEv1bc3FQ3lk5xCyE9cbEpKylbuFSn/KX9LaD1FBoqd8czJ+RBf8wftVz0Ed6kkcZrXRTzidpJf2pRxauOqCXQJIds4nURSfilRbKC/1KY7C0iWM9AYL76/s9U3Cn5DEqd5lnqJi3ts2+OMirynCZRP8hh3J3MBJQd6txzdWUCyXJuZ9ni7l/eIFv6GJK4bfznB/skRTZL/OmYGer+o5pTFS7upFR0odsYzx8iG+nt3TX1DEld85P4wm6nk97eGx+6rX6LhVV2SE0ouOjzj7YV6v0H7+57TnSheOYESp8d0pwGpRO9J4v9Iau9Uda6KkChv+3q7jtddqktStcjXrvpKdWag04cTcrxSySfab1FQoNgZz5p8BHpO7BwKCrQBdsYzBoEa6LSb7jQgFQI1gJ3xrMHhdgawM541CNQAdsazBoEa2NyL7jQgFQI1cLg13WlAKgRqADvjWYNADdzBznjGIFAD2BnPGgRqyBE749mCQA15J9KdByRKqBYvu1GWCTT9fprwjUKBYmc8Y/6qV1d+W4TnlyvQhBA3G87GbcAFgduFAsXOeDAgU6AnbX3CIqMjp/rbxhpfQCjQkLWi5oHCTqZAA4N0x0qrBjUxvoBQoNgZDwZkCtQh6z0NTzsaX0AoUOyMBwMyBVpnfOaVhQHGFxAKFDvjwYBMga7neu6MTzy/J8Rmk/EFhAI9hJ3xoE+uv+Kj/LQv0/fbLHC7UKC/FMB2YVAQ2baDqpPjDsQl59hx+Vf2CX2KrzT+bdgZDwZk3ZOkvpHjlN7Xhmad0KfoMuPfkoqd8aBPrkB3df7wqLorx4UYOXGphtBDPHbGgwGZAo3mGjS3G1Jtx0qHxcYXEAwUO+NBn0yB1h5FyEwulpBwgTOdCgZaX2DXE1gnmQK1O6A5I+RLQg7bGV9AMFDsjAd9MgXqvYCQL7lrhKzzNr6AYKDYGQ/6ZAp0tm3oZIf/tL0V7yXwDtuCgU6aK2oiKORkCjRtomu5Jc9rcFxAivEFBANdMkbURFDIybehXk1I6t6DrwRuFgx0c7DIiaBQY+0lH9gZDwaYC/Q8dsaDHuYC/cuV7kSgbMwFin2doI+9QOv+THcmUDT2Au0ZRXcmUDT2Ap0xje5MoGjsBRrdne5MoGjsBRqPMzDCG+wF+q+9iu5UoGTsBUrK3aY7FSgZg4E2O0p3KlAyBgMdKvCCT7BGDAa6BGe/gWwMBrrvfbpTgZIxGGiiF92pQMkYDDTdVugoZ7A+DAZKfC7TnQsUjMVAO8UI3wZWhsVAx4fTnQsUjMVA1w6gOxcoGIuB/tiQ7lygYCwGeq8M3blAwVgMlDg/oDsZKBeTgb53hu5koFxMBhos8M4LYH2YDHTWFLqTgXIxGei2rnQnA+ViMtCEWnQnA+ViMtDndhl0ZwPFYjJQUiGJ7mygWGwG2vIw3dlAsdgMdPjndGcDxWIz0KWj6M4GisVmoN+1oTsbKBabgd70pDsbKBabgWbYP6M7HSgVm4GSZgfoTgdKxWigs8fRnQ6UitFA4/zpTgdKJWOg6ffThG/MI9CMUvdEzweFkVyBJoS42XA2bgMuCNyeR6CkS7S4+aCQkinQk7Y+YZHRkVP9bQXe/j2vQL8IETUfFFYyBRoYlK79qBrUxPgCeQV61UPUfFBYyRSoQ9bJQU47Gl8gr0APOX2MA0ZAtkDrjM+8sjDA+AKmA33aKqBOhzqtn4qaEwojmQJdz/XcGZ94fk+IjcDr30wHGjJUtbmravAQUXNCYSQy0NJvtDe5eJQfp+G3WeB2k4GmOz4kf7tkPHDCgfVWT2SgkZGRS0t4hkZM9K5wyvTy6uS4A3HJasMvnuCyLTDxvSmaU4vUPEtK4wQOVk/8Q/zQ5q/5y/S2Zjz+Pr/6Wugm079BHR4TMnreI8d0M1cOCi3xgZbbof2wu6yppVURQeTVMBuu+ByBd+Uy/Ry0/yg12dtyBLaFgvhAyy7Tflhe0dTS4dw4Ms0+/OD0omuNL2A60EdN6oXPfLsx3jkexAc60HG/mqj3O5l8iPeYQEhlzXloPxU46iOP7aDqb8Mm1cCGUMhHoE8bcy41Xbim/5pa2jmGEKf9/JXvShhfIK8N9bwZYWauGhRi+dgOqj4aPnrhCbWJZQlpE6wmbWbyV6bla0O91sn/mLlqUIjla0P9qxvEdJ/kZ/u227aXWnxiVtHVxhcwI9A0J2xlgnwEuqoCx5Fui0wn+nPntzRbOyt+IXC7GYGS/+HV8SA+0PXc4K0ciXhrVR7f8PjSoROJghsyzQn0rBf2JFk98YHWGEVS+E+m15Q0rzmBkoY7Jc0BhYD4QO0OaAM9ZCdpXrMCjXlP0hxQCIgPtM50baAL/CTNa1agqio4Wb21Ex/ol0VmneFury6xWNK8ZgVKvvhI0iSgfOIDVS914f88LzZRYCe7mcwL9IXrTUmzgOLlZzvos5+2HbsvcV7zAiWTPpE4Dyic+ECHx+axkd4sZgb6pws21lu3fBxux1WadlXyvGYGSvrOlzwVKJn4QFWx4ypxdSP+kjavuYFeKi94zDNYg3zti1fHT672lrRzzJobKGkVKWkeULj8varzyfZexd6SNK/ZgV5555qkiUDZ8hFo8oo2RYu0XiPt73izAyWr6uJB3orlY08SV6zDhodS5zU/UNJ5Mn+RvqZPj4gXUmcFxREdaGqddU8ozCsi0JTyx8g//h9EbevteYPCzKAoogO9wkXRmFdEoORIxQcDJ2iurGhJY2pQEtGBqlu0K8AN9TpjP3TWPuNNd35EYWpQEvHPQb/2qTtlyVKepHlFBZpa+y3d/xRV8BhvbcQHWj6LpHlFBUpuFJmm+fDY6aWkSUF5GH0ThZwm2vF/yr8KHkp3JYB9Mr2qM08iA80YXKTmIM9e2M5kdeR6VWdeRAZKSHzl5pckzQiKJN+rOk0THSh53KA/foFaH6Zf1WnoeZ8aCZLmBAVi+lWdOe1wnSHthSagOEy/qjOX5KYt7kiaFpSG7Vd15pIx13U5TrtsTdh+VacR1ztUw1t1WxHGX9VpzFHfVr9JnB0UI597khL3/yNtXgmBktcLygy/LW16UArxgSa3HkW+e5tzPitpXimBEvJgRpkh+GvJKogPtLP7N6RRo5vt35c0r7RACUmZWPpj/Ba1AuIDdVlNHttEk61lJM0rNVBC7k8o3fdXqYMA68QH6hRNtnN/k28E3h3BTNIDJeTfZRUa7ZU+DLBMfKCtm8bWbUiednxX0rw0AiUkdZ3Pf7bgRZ+FmfhAL7hxdj8QvyK7JM1LJ1DNW4584B52l85YwKB8bGZ6ce4+ITuuSJuXVqC834a4BI/8sMcK/CYtjPKzHTTl5M6zzyTOSzFQQuKcHaoO71RT6s4DYJD4QNNHF+M4znl+AR+wbErjdar97VzrdaM4JDBCfKDTuQm/ProUyi2XNC/NQFNcNG9Xc3OETZsYHEhS2IgP1DtU+yE0zwOW0++nCd9IM9AbVbQf1EWiGpebfoveuMCAfGyo/1b7Ya+zycUTQtxsOBu3ARcEbqcZ6EtH7fkcLnoTcvmT0u12mfj/ApQmH7s6daeNH9be1NInbX3CIqMjp/rbxhpfgOpz0GE9XxDyoMkSzfVXm5uUnYy3Xig0RAaakJBwpHSv3XG7urmb3M4UGKR7Nqga1MT4AlQDfdm3Yp8ebmFZR6heDXVrvuUVxfHBckQGyr3RyNTSDjGZV047Gl+AaqB8k1HbkvU+fb2zXemR8VRnAMsQGeiNN0we7lZnfOaVhfl/v3hp7nzm5b80ReZJQHain4Oqtg4O9O+V126b9VzPnfGJ5/eE2GwyvoDsgfJ/1R/v4/zhPmx4UjaxgV6rz1Vs3dmHq3He9OJRftrnAX6bBW4vgEB5T9c2dA/FCUmUTGSgL2p47dPsQvqplte/ppdXJ8cdiEvOsbvpRliWYp+LXtX8uT61Yt3P8VCvWCIDnV4i8z037jhPNuObdufYPX57fpbiK8xeRalUR/s4d4pJLbD5gCaRgTYZlvX5mMbmfNNRoVsK5iE+y7NNLcrQeQtHKGAiA3Vcm/X5RidTSwfrcC2Cg40vULCB8m7P8/WegRM0K47IQCvPzfp8YWVTSzfkPOrxuOr16hlfoMAD5Z0f417/cxyTpywiA+3WOPNxUt02yNTSaaHOmiPuWXmIz5LxXW/ndpulHssKBUhkoMds5ugKXcvlcQKaPc5jXjMXKO9F9AdOPffi6HulELsddDLXeNuFq7uDuEF5fcMfdf97i8FAeQ8im5QefAInclQEsYGqYzw0G+Bd1uZ9/6aOcGEzUN7tRe+WGx2HP+vZJ/5wu4zE3dsumfcQuXO04DsVWzhQ3rVZNSqF4ZTNrFPI29DI4+Jk72rTcHYSpll1oLxzEzx9Z2rP5pixvvv7YX9aen0gB2sPlH9WHTfOo+aMy88btd52cJLrcUuvDhhCoDz1T6GeZfw0L5867plh6ZUBAwhUR12tt1eVsHOk9jlLrwkYQKCZvP4g5ydX8/SIwPZRpiDQTK21r6K64ODrPvQw9jOxA4Fm2lPtD0Jef9KR/L6wYaleO7C/nhEINMtK1w7BlYIeaq7eW93WseOXOO6JBQg0W8q+LW822j/Z2sMlcBGOH7U4BCro9aFh5WuEncEfTRaFQE1Rn5te23VgDJ6QWg4Czcutz1s7vL8yOe8FQQ4I1AxPd/QtU3tyLHYyWQACNU/GmSm1y/TZ9sjS62F1EKj5bkd2dGwcbu4/GFCBQEV5dehj7wqDd+VxVhWgB4GKdn1ZW8fmC/CLtGAg0Px4cWBUlXIDtj+09HpYAQSaXzdXdnSq/2ksTu8oLwQqwevjkwJcuq7+w9LrUZghUInub+lXtsqI3Y8tvR6FFQKl4NKStg71p/2Aw0hlgEDpSD0++T3HdosTcC4IyhAoPY9iRlR7p8caHKNHEwKl6/bGPuU9+kfhHexpQaD0XYvs9k7VIVvvWXo9CgUEKgv1peVdXHxHbMfLRqRCoLJR/RLRSRMpfpNKgUBllXE+olOp6kO+xnPS/EKgslNdXN7VtXK/DYmWXhFFQqAF48qa3hXduy1PwFH5IiHQgpMUNbiGU7s5P7609IooCQItWP/sDq1v3yA051vwgRAEWvBe/DC7vXPVfmsv4yX3eUOglqH6dXXfqs7tPvserx4xDYFa0P09EwNL+g/76rqlV4RhCNTC0n5e3tOzdPvPjjyx9JqwCYGy4N7usKYO1Zr3jcYLSHKSMdD0+2nCNyLQnBaXCqharEj90G1Jll4TpsgVaEKImw1n4zbggsDtCDSHHb53+P+nR78XHlTetcPMA/9Yen1YIVOgJ219wiKjI6f628YaXwCB5tByj+YyvdwfhNzdPaW1s1f3hSeeWnqlGCBToIFBumdTqkFNjC+AQHOolKT90Oaw7lP19S1jAh1q9Fl2+rnl1okFMgXqEJN55bSj8QUQaA4BcdoPteL1vpZxacPIeiX8+i07Zb1nKJUp0DrjM68sDDC+AALN4bPumtfbHa6c62iS9IQvh//XvkbwkhNWuSFKpkDXcz13xiee3xNis8n4Agg0h5fNmkTtHet20vit6Rc2jGpU0rtb+MG/C3a1LE6uv+Kj/DRvK8/5bRa4HYHmpNrcp9N0k8eQZFz5enxLl3Idpu64aT2vbpZtO6g6Oe5AXHKOf0jVoywlEGg+Je3+tJOnY+CodedeWXpVCoJsgb68pX029cLgFTk/lHbJZLNA5Hig79GxiH517Gr2WnCosL+BuEyBvv74ba7KGf7KOoHvw0O8dK8TNo1r9c47LcduPF94f5nKFOj8YrOiGpdMQqDy++vQgt7+dr7d53z7e2F8ZipToD6z+Ef3ul0QaAFJu/h1WHsPh3qDl39fyHaSyhSo7SH+It7mZwRakB6fWj2iaSnXFh+vOV1oTgcpU6BV52ku+/m9RKAF7s8jESH1HCq0GbcurhBs2pcp0NnFp5wi5IH7+2MQqGXc+m7hgPccKrQes+aUok+lL1OgqWG2PvyHa74cArWkWwcXhzRwdm0+/IvvFXpyE9m2g2bc1lyqTq42fjMCLUB/fv/F8ObuTvX6h++6ZuIgcibhJR9W49GZ9RM7VbXz6RK2/swDS6+M2RColXn9W8y8fvWcyjQcOH/Xb6k5b1Vd2n+Fra2pCNQ6/f3D2vEdq9lWbvvxisN/ZB/id6Fu9Q6VG1+z5IrlhECtWXri/iXDWnrY+nYZv+b4bXVK2a8JUa/yYukgfgQK5OXFnfNDmpa1Les5Yc2xW6ouX1l6hfQgUMjy/MOh4SFNK9i+4z3mi++uM/KuTwgUso3VHgP5cmDw4uFtqxT3bDEofHv8IwuvEwKFbEdqag7be+r5i+aT9N+Prp7Y9V0nl4CPJq4+nGipX6gIFN7o/+6Oi19XH2/wtQdnt80b3LJycY+m/Wd+dfJOQZ8yEoGCnp2d/bseMnpLetLx9dN6NypXvEqrQXO2nL5bUKEiUBAl9frhNZN7NijLhzpw1lc/3qJ2ujP1jsHd5uQ+rgWBQr6kXju8dlqfQI/iFQODJ0fuvyz1RLyv2tZf+80I9zM5v45AQZL0Wyej5gxp51vSudYHw+dtPpmUz6NR5gVpdrHur5LzqQMCBToeXti7IqxXoEexcvU/GhPxTexdcQ/+dXVnmfP7JcfXESjQlXE39puIMR81KF+sfINuY5ZsO33LrC1UOU6elg2BgkzS78RuXzq2eyOPYu4BHYfP3nT4N1OvQGm+T3OZUeFmjq8jUJCb+q/4b1dO7dfa19Hep0lw6NJtp27mfi+zrX73CFFNbZXz6wgUCs7zaz9sXjS6e2BlW5earftOWr7j9O/ZqYa79hrp1yLXudEQKFjCw8uHv5r3SddGle2cajQLHrt48/eXL3694nTuBREoWNbjK8e3LBnXu4Vv6eLjjdyMQIEVqblegUIQKDAOgQLTECgwDYEC0xAoMA2BAtMQKDANgQLTECgwDYEC0xAoMA2BAtMQKDANgQLTECgwDYEC0xAoMA2BAtMQKDANgQLTECgwDYEC0xAoME3GQNPvmzhVJAIFs8gVaEKImw1n4zbggsDtCBTMIlOgJ219wiKjI6f628YaXwCBgllkCjQwSHd+XdWgJsYXQKBgFpkCdYjJvHLaUf/LF7tlKbpU1HhgrWQKtE7WicoWBuh/+eE3WTyOixoPrJVMga7neu6MTzy/J8Rmk/EF6seJGg+slVx/xUf5cRp+mwVuR6BgFtm2g6qT4w7EJauFbkagYBZL7UlCoGAWBApMQ6DANAQKTEOgwDQECkyzWKCT1uQyt21vGTSXY9B2XWUYtLu1//zBuZtYU95Cga4bkluzkjVk8JaPDIM6lZNh0IrW/vOXMRLFJ08tE6gxezvKMapd7jcula7vVzIMip9fyncjUH0M3kFCrOXnR6D6GLyDhFjLz49A9TF4Bwmxlp8fgepj8A4SYi0/PwLVx+AdJMRafn4Eqo/BO0iItfz8CFQfg3eQEGv5+Qsg0INBcozqlCrDoAOjZRgUP7+U7y6AQDMeyzHqAzkGfWripCn5hp9fyncXQKAA+YdAgWkIFJiGQIFpCBSYhkCBaQgUmIZAgWkIFJiGQIFpCBSYhkCBaQgUmCZ/oBsCHJucojhe6hwfuxoLXlMfOa1hMKE86Nm2zpUWqCkPmja3in0d7WFx1EYdHUoMxqMzsG5UqXeX7IFu5sbGdLFNoDdgWPG5B2cU+5j6yKGcJlCag54t0XPneG4p5TWdWHz+vpHcbnqjqr6z16aUPR6VgbNGlXp3yR2outb/CEn3GURtwAzbyfzlrCIvKY+8x9kjmPLqdmnD//YcF0R3ULXrOP6yQWdqo+4qyXGalLLHozJw1qiS7y65A03mdvKXYe7UBrzto3mYiOJu0R35D+dd9YLpru6Tt7bqrlBdU3XZKfxl8w+pjfro118raFLKHo/KwFmjSr675A40jjvHX0bapFMd9UVj7wyqI6fWHUM0gdIc9BK3va2T96eplP8NFjus/2mm3RGao3prUsoej9bA3qFZ16TcXXIHeoBL5C+jufs0Bz1ft2Qs3ZFH/Pe1NlCagx7iSs3YP9d+OOV/gxfvcRw3VE1zVG1K2ePRGjg7UEl3l/y/Qc/zl6ttXtMb8kF/my5JdEfe6XKLZP4GpTfoCW4RfznP5jnVf4PUGq1/e3Gqajc1xVEzf4Nmjkdr4MxAJd5d8j8H3cNfTnWjN+L1sjV/pj3yaE5nN81BE7kT/OVB7irVf4O9HP//EtnB3aA4qrfuOWjmeLQG1gUq9e6S/a/4miGEqPwHUBtQ5dv2JfWRrx3lVW9x9D7NQdMrfsZfflr8NdV/g4NcPH+5intEcVRtStnj0RpYO6rku0v27aBRNotjB9sKvb+8eKe4Mes0XlAfWfMQT3XQL4rOODij6Gy6g74K8PryyHyHgTRH1f2uyx6P0sDaUSXfXfLvSdpYx6GxwLvL58fqzEfje9RH1gZKddDIOvZ+kSrKg6aM8rL1XZhKc9TMZ4vZ49EZWDuq5LsL++KBaQgUmIZAgWkIFJiGQIFpCBSYhkCBaQgUmIZAgWkIFJiGQIFpCBSYhkCBaQgUmIZAgWkIFJiGQIFpCBSYhkCBaQgUmIZAgWkIFJiGQIFpCBSYhkCBaQgUmIZAaeuaebIXbrXdtYMAAAFUSURBVLal16RQQKC0ndmxY0eFhvzFlWdcP0uvjPIhUDnU7KG5fNn1C0uviPIhUDnoAiXllxLi+dWIil4r7rYv7aF5b6MNAfY1N1h23RQGgcpBP1DPAxmzuOo/p/e2fUmWFZm6fyS3ysJrpygIVA76gfYn5C43h5Bj3PVnLrP4rw6uYNmVUxYEKgf9QOcTkq55j6Bfuas/cbEpKSlbuVcWXj0lQaBy0A90kSbQ3dpAt2VugEq08OopCQKVg0Cgx7g7Fl4x5UGgchAI9H7R1fxXl3ZTW3btFAWBykEgUDKh5KLDM95eaOG1UxQEKgejgda6S1SLfO2qr8QvUBEQKDANgQLTECgwDYEC0xAoMA2BAtMQKDANgQLTECgwDYEC0xAoMA2BAtMQKDANgQLTECgwDYEC0xAoMA2BAtMQKDANgQLTECgwDYEC0xAoMA2BAtMQKDDt/1OZD+CTIV9pAAAAAElFTkSuQmCC" 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" /><!-- --></p> <p>Here, a look to the model plot, the confidence intervals of the parameters and the correlation matrix suggest that the parameter estimates are reliable, and the DFOP model can be used as the best-fit model based on the <span class="math inline"><em>χ</em><sup>2</sup></span> error level criterion for laboratory data L3.</p> <p>This is also an example where the standard t-test for the parameter <code>g_ilr</code> is misleading, as it tests for a significant difference from zero. In this case, zero appears to be the correct value for this parameter, and the confidence interval for the backtransformed parameter <code>g</code> is quite narrow.</p> </div> @@ -2042,39 +2026,43 @@ mm.L4 <- mmkin(c("SFO", "FOMC"), cores = 1, list("FOCUS L4" = FOCUS_2006_L4_mkin), quiet = TRUE) plot(mm.L4)</code></pre> -<p><img src="data:image/png;base64,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" 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N6hElRmhmUHC/rPK97jXX3VJwrKO1SCytwnyeGCnhvmPfGag7/rGCgo71AJKnOfJIqCnhniM9mViqKgvMPdDMunB/u87jpF+/mG0+Nb/ht8FJQtTGdYLhxunPe+fhBVl04P8p6YrRzGhPy/WFBQLiO1oF2sWlBQOZQELd20Bdb0mFY2gbJhjfHOIa3CKTt1eoj3eK3eHoxG0GSRasnJkmYUVA4lQUfGPtL1yS39pOeoMSjo2eE1X7X7DRi30Ag6zytt/fpa68s+F+c3Nn6EeMCPRcc0CpWgAfklXv+D3EBJM5O/+AujvYefYZDH1VAN8Qdarofy81WfNX6CSGpA2ykNQyVoUHZelYNwpY6kmdGQdDnV+4U/mGRyJXSfQfP6P289lR0O8XIoHqgPCH4tNq2D9HQKZgW9Pt2371FGuVwF7U7SuiSrJhRUDsW9+F+OwXfj10hPN2VY0Ly5AU//wCybK8DDTGzh5GQRKZ/0STHfo7FwSVgc+/9z54GCsoVPQedEffRB+FrzC/2HTaM38DBPDhEoKFv4FNT3NMAPzcteln7Rtt6yQtqkrgEFZQuXghZXFd4wL/uWb9r7tP8b1pdccggKyhYuBYWHPwKY2+3Otl+f9x6lgQOjKChb+BT0SGib6MZWNl4Y5/Pcz7SpnQ0KyhY+BYXC7/frbTTnzK/z2Fe83WvpTlBQtnAqqCwlHzZvku72GXPsgIKyRWuCCnzTxX86v+c6oaBs0aCgAL8NrjGIt9sxWEBB2aJJQQGuzgx4nM8PoygoWzQqKEDxmpiG7+W5YEUqQUHZollBBb7v4T3mL9esihwUlC1aFhTgzDifxG/4GulRULZoW1CAgvebNl7C00iPgrJF64IK7O7u/cr/XLpGe6CgbPEAQQHOTvR7/DNOTshDQdniEYICFK1tHTKbi9uAoqBs8RBBBX5+sUZfd84mbsZVgv6+ZPUN5mviEM8RFODfdyKaLnb3/5qLBP3Yf2iv2tq75lU9niQoQOmuXjVePOCutRtxjaCltX4DeLcX81Xxh2cJKnBpdljM+268jzKNoAVTkzcL/6RImm3U84I4WcZvEY6vSjN4nKAAhu3P1BjstrdRGkFTuqTHrAaoIm22rqfhodMAK7s6virN4IGCClx8I7zFEvd8GqUR1D8XroZdvC1oydtpItEh1qFLQ6a87HvE8VVpBs8UVHiL+bpXjf7u2KmnETTsGkBG19uCFk8ZL9IkuHyQ/qTxZNj90+e5464TLoebmyiw58pbEY3m2bq7oVOhEfRtn8kAfTtVljTfUc99oQ1rPmfn5queBkc3UXAC+wbW6PaFrYubnAfVXvzhz4X3/k2jJK3l66mvsw0Ku6ZRrENjcHUTBTbccXZT3gdtA8adcOHanXyY6VhT4eHrTszXwS2Kgqaakb5NirC/iQI9n9a/u1nmHS0nxgW0We6yPSYnC3q+9i2AtRXhAKgZRUHT09NfbJw2t+mbNpY74yYKlByuvd+wzf/CnY36rd0fem6Ha25N7+wD9QnP/bCxzrfM18EtJEN8i38BcqJtBUhuolCQGC8SUJtlD9UxS9jNgJQ1Vu3Zi2KDUl0x1Dtb0LyJrZ/6mvkq+IVE0MBCgKJQG8v1K/NzJyWlFVtel+7eKRJfl20f1TB7gvDQ9yNbi46Pq/2fxU6/fYiSoEkWyFPys9PpBkgEHf7Yps2dRtpY3i++sGf3tQlDJM3uLOjxgF0FGwNkDi7d2v7cQ0mbnDvtg5KgmRbIU6KgcpgF1S9/tu8KWwdr/IvANx/ynDRHvWN81bzaI1nyi3MzHvUe8r0TL2MiGOKL92zf/kUseUoUVA6zoIb5vfXv2TphPfooxJyGK9JzFjgv6Lk5TUMn/eqs7ASCvtDSJylkPnlKzuvpXEgEfbVPpKH7UBvL94f0Smw8osEqSTP/BT0yNqj5vHNOSU0gaD39G/svJJKn5L+eToRE0Lr5caCXjuNGirbMn7LklLRVCwU17HqhZtxyJ9wUlEBQ/8Ltb0ET8pRaqKfTIBE05EocXGtInlMjBS3a0svr6XX5jLMSCDo6LjtiZAx5So3U0zmQCLqwWej0iKXkObVT0Nw1Xbx6b2G6W09yHPQE7J99mjylYj0/jg561jkfWNwP0Xfxu15PUzOzsXYEFch+v2PN/l+ZTw/S/3aKNh+BoOkin5CnVKrn9vA956a04OSya9aQCJqp8qiMpgQV+Ofdtj4vfq0X9p3qRwR2yqFLRiBoamrqiLAZ5CmV6tlP3EuN9NCzl0kEjQ2ffkpNTq0JKnD27Va1Bu+MXAe3ho6gy0T4Vee1p8hTKtWz10bh4eH95Am1BNEQ/+fs5h1Xk+fUoKACp+fH3DVoR8lxFbvXtiAUVK/iDsZK9VzT8h9YF8rzvOgUkJ0PWrIjrip5Tm0KClBc7Y1W3p0j6f6nCQTtI9C4P3lKxXpOe6B6DPe3P3EQEkG3pPh1/VjF4RitCgrDHt+1zKdRjeQtNx3PQSDodoHMYsWwMpTraWB9sMxRrjG/GIVE0M4Z6k731aygJfM7JnwGF5c85tVznaP7SkqC2jsBXAbN1PNQ05rVxzE+z4FA0Gzp/eCV0ExBZcn+IOHBLssdmoxMSVB7J4DLoJV6loRuhOxHlrNNSiCoocVJdTm1UlC75G7o/VC7t9TPME4wxMufAG4bSz1/6dq4Nz8ToVrzSwvh4b8qTnQlgWSI71a9Y0JCAnlOjxBUoOirwf5Rrx8kGLP2xAV3/c30lEBQ2RPAZTDX86z/B78uDHbC6QOs+F994eHDvmyTkgiaZYQ8p6cIKmD4YXzDOsO+VtijOeG/+czi4H+NzwkElT0B3P48A+8MFx56rVNM7zYMrV/785swxpe8UJ0PKoMHCSpyck7bGj0/tPfONWui8NB1s/E5gaCyJ4Dbn2fAeLnVkGUkXXYTlwbWbaXiK1wiqM4HlcHDBAXx4sBnvNrPlb3mbuJs4eE504VQioJG/pFgxMYiq3kGLvwlkhQuPNyErNDv/9rhe1R4w/jb+Lpi/BtPdT6obTxPUIHCL4fWqTtqp83B/vvwv+AHf9NUSYqCfpoj+5FJOs9AfqNwkQfuER7eBnjlrsp3+wr/fhtuel0h/m1Ucc8HVc8vs/7zUI+VNq7IW+rtE/KV6SnhV53/2mpUmmeg5KyHnrJkh4p9Pqh6Lq/qVSN26n7pJBCGq5ZnBIJuGQBtqy+ytUQyz4AZT66nIhX+fFD16HePbeKb8rHMFfYEgobsy+x2LZx8hR5eT/uQTh5mc0iSoQIU9PSyrl6tplu9kQKRoAGlqSsgkHxlFaCe8hCdLCI7JNmmYhS0+JvXIn36rpF+IiUQNLFHcM6rbcnXVDHqKQPRRXM4JMlwLr1HzRapu8vv2hMImrv0CMw8S76WilNPG5AIikOSHW7tm9LKK+Hdsi/tCQSt4F98qINEUBySFLj+yaCyGTsJBMUvPlRAIigOSSogEBS/+FABiaA3xrVqP9PmSeZc30TBPZAcZsIvPsghEbRH3wMHnxtgYzn3N1FwAwSC4hcfKiCawFb4QH/L1vmLfN5Ewb2QfNWJX3yQQyJoh4sA51vbWM7jTRTcjaKgp34sBMg5+S55SqynHOa7fDzrP2SI70Qbyzm8iYLbURJ04V0hvl8Fe9XpQJ4S6ymHKOgyM+ttBUhObshvEyPiHcC4k1pCSdDav8Ae3VpVlz6ioHKYL5o7teuEnbu1dSn3/MhBkS71mXRNmygJWkP8UXdHHBRUDqOgn4bXbhMWYH1jF4FkkWrJyZJmLKgdfIQff3UpsZ5yiIJuC90tPB5s+LmN5fO80tavr7VeOvpjQe1QMzs721f4UXE/HKynHKKgbU33tP6pja2AAy3Xg/XxJyyoHapYIE+J9ZRDFLSW6fNSqe1hKa//89Z7RFhQtmA95RAFjTxjfPqP3KyE66xnksCCsgXrKYcoaNrjecJj0TMMZwT2aFBQtigKWjrEb9DM4cF97BxokoIFZQvWUw7TcdBj741doGp+aSyogxRMTRanJ0mRNGM95dDZWWYHLKiDpHRJj1kt7OhLm7GeMqCg6qER1D8XroZdREHLg4IyhkbQsGsAGV1R0PKgoIyhEfRtn8kAfTtVtrwu6BIvEoAn38iAgqqHai/+8OcAhk2jLC9Lv9spEl+PQb+0CgrKGNrDTH9aN2E95UBB1UMraKx1E9ZTDhRUPSgoW1BQxtAKauMuLlhPOVBQ9eBXnWxBQRmDgrIFBWUMCsoWFJQxKChbUFDGoKBsQUEZg4KyBQVlDArKFhSUMSgoW1BQxqCgbEFBGYOCsgUFZQwKyhYUlDEoKFtQUMagoGyhExRvomAFCsoWKkHxJgrWoKBsoRIUb6JgDQrKFipB8SYK1qCgbKESFG+iYA0Kyha6nSTpTRQiwkUe8GPRM41CLWgXqxYUVA4lQa0muzr3l0hSAxY90yg0guKc/9ZQCYqTXVlDIyjO+W8NlaA42ZU1VEM8zvlvBd1hJpzsygq6z6B3zvlvWPu+SKtw2k5pGCpBpZNdmUFBKSg/5//NYYNF6gfRpdQ0dHvxksmuzKCgFODcTHeCJ4swhrepb7LHtE85RvH77gYFZQxnghZHj9zzrr+Nt2WtgIIyhrO5mfa0Fh4mTXE8gbtBQRnD2Vedm7oLD0uGMeuLy0FBGcOZoOf9TkDOwxvZdcbVoKCM4UxQWOcb7TOaWVdcDwrKGN4EhbyfrygH8QsKyhjuBNU4KChjUFC2oKCMQUHZgoIyBgVlCwrKGBSULSgoYzxD0Gvb9xtcvlKboKCM8QhBt/p1jmyV4+q12gQFZYwnCKr3OwClw15z8Vptg4IyxhMEPREhPOx9xMVrtQ0KyhhPEPTGQ3kAH/R28Vptg4IyxhMEhWHtPprn+5Or12oTFJQxHiGoIaPvcE5Ow0dBGeMRgnIECsoYFJQtKChjUFC2oKCMoRFUvzI/d1JSWrGkGespBwqqHhpB+8UX9uy+NmGIpBnrKQcKqh4aQf2LwDcf8upImrGecqCg6qERNPooxJyGKxGSZqynHCioemgE3R/SK7HxiAarLK/zq+uM+LPomEZBQRlDtRdftGX+lCWnpK3LB1Gk1Dp4nyTGOGHyMBRUDrxPknqcMDcTCiqH2vsk5QfUELkPPzM5Dgp6J2zvk5R3XWQhFtRxbEwehoLK4eB9krCgbMF6yqH2PklmsKBswXrK4eBhJiwoW7CecqCg6kFB2YKCMgYFZYszBM3wiTERXbkqEXdrP+xe8ybHPPitY0VjVc9771MMuZ8gzT1VlNPco5ym8v2KIVW8Yuxht54OClp80Mze+z4kYUUVlmHLqxKFLatGFLa0OlHYEi/LNh9hPyOHqno+NkAxZL6fcproVxVDJjVSThM+QzFk2GMH7WG3ng4KWkZBVaKwvOpEYbkPEIXd8CIKu16DKCzbmyjsSi2iMEpI6vnSEsWQP+opp0n8TDEks4NymtgDiiEf9VVOIwcKioLKg4JKQEFRUAkoKAoqDwoqAQVFQSXQCloSRxRW3JEs7FGisKLHiMJuxhOFFXQiCsvvTBRGCUk95yqbdbmbcppX9yuG/DpYOU3K34ohu15XTiMHraAI4lRQUIRrUFCEa1BQhGtQUIRrUFCEa1BQhGtQUIRrUFCEaygF3dgwfL5SzIKwgP4FJJFzkwkS7o/x739LOWxeUO2RpUph2U3BskZ7kcYw4q2gQjE/UTeUC0lQReUKEpSPvnR0gl7xO5PT0GpenDs57J2d02UaQeRBr2TlhEUhR4riMxTDDgdk58duUghbEBVq2QR7kcYw4q2gQnm7SLqhXEiCKipXkKB8DEpHJ2hGCsC0yfZjPheWpycrR+a1TEtWTrilF0BhgWLY0eC8kkc2K4TtTA+1bIK9SGMY6VbQoZifpBsEhSSoonIFCcrHoHR0gs6aBLByoGLY1ZjNypH9169PVk44v2usX69c5Wxj7vfqqpjtfKhlE+xGimGkW0EHSX7FbhAUkqSKyhUkKB996egEnSmutb9S1KqwVcqR654Hoa6KYVMjTuV3n6wY9l3sgZNPfKgUJpbPFGM30lRloq2ghCC/YjdICklQRYIKEpSPvnR0goprnDHRfowhOfEiQWRSQGital0Uw5aMAFibpBg2fqYwjvVQChPLZ4qxGymGEW4FJYr5CbpBUkiCKhJUkKB89KWjE/Ry4JXCSIVPvpvjSSOFP3zFsFMN/85LSFMMW9Piam6/aUphYvlMMXYjxTDyraBBMT9ZNxQLSVBFggoSlI++dJSHmdZHRSodOxhduUqVKikkkUJdlcMywgIHFyuGlY4PrP38TaUw4wBkirEXKYaRbwUVSvnJuqFcSOUqElSQoHz0pcMD9QjXoKAI16CgCNegoAjXoKAI16CgCNegoAjXoKAI16CgCNegoAjXoKAI16CgCNegoAjXoKAI16CgCNegoAjXoKAI16CgCNegoAjXoKAI16CgCNegoAjXcCvon/GJi9zdB8T9cCvohO+gnbv7gLgfbgXNKz2X5O4+IO7HpYLqvH18fN6C0rlRtdrtADiue0dovHLXZCh9o6lP3NE7gz9PuKCYMNOGw7baEM3iWkHzjf8M7XTasCdkExyvJt7BM6PaZBj19OXS9JDC8rE7xpUqJyQS1DTBL1imAX41oPYoc+q0nao3AXExbhD0uF+e8LirruF4s/rXAbp1m3zB+4bQkvR9+dgXnuqRfOdvl2abfm6/IhLUNMEvWCYD3tr0ek6TrX+2jpoLV/uw2CiuUTVmlSc9tfwrN45KbhD0vUHGF7VPH28+6kMoqjd78qdP2f21d8LqjTFkPdMiVfyBWeFhrxlfgbFwxoWdtwDE7jE9lxbTNMEvWCYDPrIX9E9uG/MttLk56pgzNpIr1IxZZj6NFB8rqKDC33M4jJ1pfNFm1/Hmu3vAV4PSJi98sXzUsLgSeHbZ7de7oy7n95mfVe0kiD/bm+cUxa8Wn4FYONNCwb2/wktNz43FHNNVZLY5w9WYzXB7+uI59z4Df7aJnvP7S67YZvdCNGbdHpZEcv4QHyuooMZyLRhufBF+4njzWyFFL32RNnldV7Hh+HFT1MEL/y2PS5kAABkkSURBVN3dutwH0NdD4+Iie2cJf/viz7gFABsGZJnuP5+ZZFp4w69k9nRzoHUxxQl+odz0xYeiMsTXKRedtJkcQTBmGQcj49iTlxQQ+l/IStC/HNTixdTtwgeg1HQYE+DbuzgzybTMHVvg0pUZy7U/uEh4PBSgP94cUr5scDNt8v98xL/wJ1Za4jY0K//pKG0GgEGflSDUUvgZuxDgkxTxGYiCmhZC1x3N/zYHSgU1TfALljl+N2QCLBLnSt87FSY063jWiZvLAQRjljgYmcaepT1K9w0Qirz8scLrEWZBT8QV6lvuyUwyLXPHFrh0ZaZPRH26X4IDDVeDIOin/+kKaZMhpcc1+NT3qiXulZfL/9ahiOv67u9ZBN0ak1fceWWZoKaF8HFcR0ugdIg3TfBbelJvmuN3WZz+RjfxO6qkvPOdYNMM12y5uyAYs8TByDT2HKkzYY9Y5Gc3AbxpeQfN3vGGz/bMJNMyd2yBS1dmElQ/LcKv9WcgCppX5QNRUP34Bn6P/GQJ+6n5yjt+bUmDoCFl76AwvW746FtlgpoWQn7VVZZA6TuoaYLfIt1p0xy/+oFhgSP0ABvfBxgfHfePUzfY7RCMWWIpzePQjRXd2gqv+wqf2OcZBR2Rvr/x/KwkQVDTMndsgTtWah99ix+O7PnZ3b3wDAjGLFFQ09gzcwxcr/5vVsKKTkW5UamZTfTXw9Jnj4SLoV9kJpmWuWML3LBOBdKGAgx92t298AwIxizjYGQce860rh36rriTNCIkevZbxf0bdJn+yanoJk/Pi81MMi1zxxa4Y6UIQgoKinANCopwDQqKcA0KinCNoqAHJg/sP8mJd/9DEHsoCTqv4bSVK2dEvuWSziCIFCVBw3LFx8JISXPOzgrMN0XM/xuwnnIoCVrfeDbFlWaS5vTA+IqL99dUMtoC6ymHkqDr/VMmTeofuEHSvHwQ5f+Ilum8nXlKrKccijtJVzJmzlp5RdqKBWUL1lMOB/fisaBswXrK4eBePBaULVhPOdTuxRc8ZfxcG9aYRc80CgrKFipBpXvxpXuMRwbi61kaKsCFPVJQULZQCSqzF5/SyPykNDjd8Z5pFBSULc7Yiy8TtOi7sHcc7ZhWQUHZQieobSyCvvtQWA3feZemD1tjcCyRFqERtHTrNsgYuFgvaUZB5aAT9Mewc3DSL7z66Pc6POdYIi1CI+jIJk90j1rU7WVJMwoqh5KgTUNNSJrNgs6cLDwMfdR/uKEo5CRFF7UFjaD+ufp7DsGNOmUN+4w7nS/3YNAvrUIlaHZU+nkRSbNZ0HeHCQ8926yM611sXEtpiePd1A5Ugv4LcwD0YZbXBU8YD9sFBLDomEahG+LftjlFoVnQcwHvH5sfMr3vzWfa1/oHiodXr5pYAQ470Qg6uulegMy4gZLmsp3Oiogzd5KOdI9M/qPwkdhnqwZegNcTrxelPu5YQi1BtRe/9QjAhmXSnSQUVA5KQU0Ydn/01+zQX6OOAtzyynMso4ZwwmEmFFQOJoIaWetX/yBAyYM3HcuoIVBQtrhIUNjzQIOTF1+oALujKChbXCUonPDx8h/sjvl7XAwKyhaXCQrXHkvIcSyfpkBB2eI6QUE/POJ3xxJqCRSULS4UFGCF31eOZdQQKChbXCoo7AuaYZ5fvvAXD51fGwVli2sFhYuPJFwX/90Z2Ny/l0d+9YmCssXFgkLJqPCDwvtnwPdQnDTXsfR8g4KyxdWCAnxSawkcihGefNXFsfR8g4KyxfWCwu8teh4P0gMs7+dYer5xh6Dnpw5fR3DzUk3iBkGhaER4u967Pgj40bH0fOMGQf/2H7u4zYv2YzSLOwQF2OIb37H3Pseyc44bBB05C+BmwBnm6+UC9wgK5zrGVcSCOoZSPRM/Ex7au+dWWk7HTYKCYY7vR45l5xw3CDrtBYAzPteYr5cL3CUowM9NemUrR2kONwia9/DDff3eZ75aPnCfoFA4JvBzx1bAM+7Yi7/1zUenmK+VE9woKMD39fpdd2wV/ILHQdniVkGh4JXamx1bB7egoGxxxvygagq6r1EPz7rU01WCFh084anH5u/AGfODqvqLL5zku9yTKu0iQX8KiQlrXwEuUGA7P6gZlUPS0VZtj6v6Ba6hEbRgarL4iSdF0myrng0+A8PLwxxflWZgOj+oGbWfmQxLao3PV/cr/EIjaEqX9JjVAFWkzdb1vOQvPByTvjN4Ik6dH5SYS8khm9T+DqfQzc0EV8Mu3hbUsOZ9kVbhVpFFDwiD15ePOr4qzeDU+UFVkBn5uGdMMEYjaNg1gIyutwUtHD5YpH6QdejLHbd+GPKZ46vSDFSClm7aAmt6TCubjSG/us5ILQc6ol9Y69UbDvweb9AI+rbPZIC+nSpLmm39wesXP9nT5sRYngaVoCNjH+n65JZ+jCa7ujzIf/kth36TJ6j24g9/Lozrm0ZJWvE4qBxKggbkl3j9D3IDJc0OF/TnuCjNvyvggXq2UAkalJ1X5SBcqSNppijo5vpPHnP4l7kABWUL3YH6gODXYtM6pEqaaQpa8o7vwHOO/7r7QUHZQrcX/8sx+G78GukXQXQFvTHBe7yGz21EQdni3pNFbHNhSK2Jqz7R6C49CsoWHgUF+KhKlcgAbX7/iYKyhU9Bm3x9tFuNRkW0adwBCqqS7NN2zxXiUtDiKkKfd90b9G4hZSI3gIKq4mb3msFN7I2VXAoKQb8CfP3wwcTAtwtoU7kaFFQV45OLIaOpnQA+Bc0ImTPTfxvA4R5+b2hsZwkFVUXMIeHB/x/5AD4Fhb2vpR41Pvmtn3eqne7zBwqqikd3ABR52TnZklNBy3FqRM0hGpqZGQVVxccNtx14RnqGdnn4FxTgylTfZzQzTw4Kqo6PO8ZOs3dzIi0IClCwuF6rjdIbsPEJCsoWbQgKYNjcPmSuFi6iR0HZohVBBQ6l1BjM/6lOKChbNCQowKXptTt+yvlIj4KyRVOCApSsbx84TXp/eq5AQdmiMUEFjg2r2W27wZlroAIFZYv2BAXIWx4bOovXt1EUlC1aFFTg0NCaT2/h8j5LKChbNCooQMGqdn5jODxlFAVli2YFFfh9YlDLxbzN0oyCskXLggLc2vHcQ0mbuTqxGQVli7YFFcjJ6Og9KJOfvXoaQZuGmrC8Lp46XqRJMIuOaRTNCypwdk5UnbE/u3adstAImh2Vfl7E8rrkrTSR6BAG/dIqniCowPGJ4Q2n/uby1dqAaoh/2+bEKjjEy6EdQQX2v1qn6Uz3z5CHn0HZoihoqhnpPN9G6OeoZ0np3lcCm05z86EnFJQtioKmp6e/2DhtbtM3bSxnMUc9Wwx7RwU3mnjAjZPeo6BsIRniW/wr7CxH21jOZo56xpT+lNoweOS37jrpCQVlC4mggYUARaE2ljOao54peW8PX1b86+yW3imf5Lpj/SgoW0gEHf7Yps2dRtpYzmyOenbkRwxYnNjhFsD5pU88+PiiUy7vgJKgSRbIU6KgcpgF1S9/tu8Km0OmZI760sydIvH1WPZQHe/3EbrR2rRReZ8O8G06/jvXztqsJGimBfKUKKgcZkEN83vr37P136xfmZ87KSmt2PK6ICFeJKA22z6qYYK4Mzd0qeWl4cfJ0TV7r77kug4QDPHFe7Zv/yKWPCUKKodZ0Ff7RBq6D7WxvF98Yc/uaxOGSJrdWdCN7W5BQf395ZsuftCjRszE71y010Qg6AstfZJC5pOnREHlMAtaNz8O9NJ5vkX8i8A3H/IYTgFOjaF7k35ho6Wt+u8nxT6UtOQPF3SAQNB6+jf2X0gkT4mCymEWNORKHFxraGN59FGIOQ1XIiTN7i3ovtWHbbZfXTcgMGzQRmefnkcgqH/h9regCXlKFFQOs6ALm4VOj1hqY/n+kF6JjUc0WCVp5regxxcmPBg9drsz77tIIOjouOyIkTHkKfmtpwsg+i5+1+tptk8WKtoyf8qSU9JWrguq3zu9ffVHpux21tSjJMdBT8D+2afJU3JdT2dDImimym8OuS9owdcTWlXvOC3TGZISCJou8gl5Su7r6UxIBI0Nn35KTU5NFDTvq/GtqrefspP1cE8gaGpq6oiwGeQpNVFPZ0E0xP85u3nH1eQ5NVPQvB2T21VvOXpL2VcNhr+oj5gSftV57SnylHfW8+yGnZxPrsIUsvNBS3bEVSXPqRlBRQq/e+NJr0YDV4pnkv7aJMzn6Ty6fISC6huQp7yjnmv9erVu9q+6PmkZEkG3pPh1/VjFUKgpQUVuHV7cN6RW4pz6y6BkoPRWriohELSPQOP+5CnL1/Om9/8ARr6mvl9ahUTQzhnq5onXnKBGzm8cWrlqy5FzVLy12YJA0O0CmcWKYWWUr+cvLYSH3R3V90urEAiaLb0XpxLaFBSg6IFre+e3v8/36Zk7Hb9zg5Kgdq9QsE35el6rmQvwzvOO9U2LEAhqaKHySh+tCgqDEvZtbbj2/KaxHR5o1G9RlkNHoZQEtXeFggx31POVlksn+nJxeaBrIBniu1XvmJCQQJ5Ts4IWvtE63nRy662jHwyNrho9ZMXPaieAIhjiZa9QkOGOepZuHDpJxUF+zUMiaJYR8pyaFVRCYdbiAZHVHh664pCKD4wEgspeoSCDp9TTIajOB5XBowpasPedfpH3R7/43g/2jmMUn7XUh0BQ2SsUOLtKVjXXR0U/zvqKF6rzQWXQTEGJufnjkkExVSP6pO2wfSB//kN1/DeZnhIIKnuFAn9XyaqitOOQQ5uCdrNNSnU+qAxaKahKSo6sfvXRmgGdx390TGLXrgYX4Bf/M8bnioJG/pFgxMYi6VWyN5N7ioRUEx42Ahzqyfe/n4n9fCmFbd5mNOeDyuChgpo4+8Wsng3vj+4//+uLZW3G60ySPzQ+VxT00xzZz/TSq2QN/90o0t5fePgdIHsj3//uChf+XZbINm87mvNBZfBoQY0U/LTilUd9fB59+f0fcoSXs8cJD099ZlxE+FWnzS8rObxKVg1FQdsgL34x26R054PaRisFpeWfnW+/0LJayJNj53gv/zWtrukyfAJBtwyAttUX2VoiuUrWjGbquTc8zOslxjNhkk4epub0BM0UlAWlf32eltLo7mopf5teEwgasi+z27Vw8lVop56Gv3NYpyQ6WUT2L9422ikoMwxlx6AIBA0oTV0BgeTJK2A9b0N00ZzH/sU7AQJBE3sE57zaljwl1lMOs6D4F68CAkFzlx6BmWfJU2I95TALin/xKiAQtEJ/M6cWEkHxL14FBILiN3MqIBH0xrhW7WfeJM+JBbUPfjOnAhJBe/Q9cPC5AeQ5saD2wW/mVEA0ga3weemWzdPDNH72jTMgEBS/mVMBiaAdLgKcb21jucbPvnEKJF914jdz5JDc5eNZ/yFDfCfaWM7lHPVuRlHQUz8WAuScfJc8JdZTDlHQZWbW21jO4xz17kZJ0IV3hfh+FexVpwN5SqynHOaL5k7tOmF7LguNn33jFJQErf0L7NGtVTXbFdZTDqOgn4bXbhMWsMZmgOTsm6JJppufqjiE4nEoCVpD/FF3xg8KKoco6LbQ3cLjwYafywV1uf1Uv8h081MVF4R5HEqC+gg//upSoqByiIK2/d749Kc2NpYni1RLTpY0Y0HtUDM7O9tX+FEx0zPWUw5R0Fqm4ajU1l/9PK+09etrrZfuP2FB7VDFAnlKrKccoqCRpmvB/rE5p/qBluvBejzHgrIF6ymHKGja4+J8hEXP2J5wNa//8wFWjVhQtmA95RAFLR3iN2jm8OA+cpOmrrO+px8WlC1YTzlMx0GPvTd2wX47UVZgQdmC9ZRDZ2eZHbCgbKGqZ+GC5yZcVA7jFhSUMTSCNg01IWmmqafhsR5rxwX+43gCd4OCMoZG0Oyo9PMikmaaev7Q3AAwapbjCdwNCsoYqiH+7Z13vMxvHSPirfKbp/J80lN4WCa93a+GQEEZw/Qz6NGDIl3qO57hr4B/QB+fwaxHLgcFZQytoH9aN1HVc4FfYt2ejKejcSUoKGNoBY21bqKr59nPjtD8urtBQRnDnaAaBwVlDK2gy62bsJ5yoKDq4e1AvdZBQRmDgrIFBWUMCsoWFJQxKChbUFDGoKBsQUEZg4KyBQVlDArKFhSUMSgoW1BQxqCgbEFBGYOCsgUFZQwKyhYUlDEoKFtQUMagoGxBQRmDgrKFTlCco94KFJQtVILiHPXWoKBsoRIU56i3BgVlC5WgOEe9NSgoW6gExTnqraERtGBq8mbhnxRJM9ZTDsWdJMkc9fmNw0Ue8GPRM41CI2hKl/SY1QDSuW1RUDmUBLX6iz/7l0hSAxY90yg0gvrnwtWwiyhoeagExb94a2gEDbsGkNEV61keKkHxL94aGkHf9pkM0LdTZcvr/IAaIvfWYtExjUJ3mAn/4q2g2os//DmAYdOoste510V6Yz1lUBJU+hdvBgWlgPXcTFqHbi9e+hdvAgtKAU59cyd4sghjUFC2oKCMwbmZ2IKCMga/6mQLCsoYFJQtKChjUFC2oKCMQUHZgoIyBgVlCwrKGBSULSgoY5wp6Ikv/mKenHdQUMY4UdCXghMCXmeenXNQUMY4T9AvowvhRt2fmKfnGxSUMc4TdOp04WHEIubpbXF23bYSl6xIERSUMc4TdHlf4aHTFubpbfCx37PtG191xZoUQUEZ4zxBc+q+sm5giyLm6a0p9j4BMO5lF6xJGRSUMU7cScqe8uzsXObZbfBbY+FhXxtXrEoRFJQxnnAcNM/rOsB7z7l4rbZBQRnjCYJCavNFE32PuXqtNkFBGeMRgsJnIyafcvlKbYKCMsYzBOUHFJQxNIKWbt0GGQMX6yXNWE85UFD10Ag6sskT3aMWdZMe38F6yoGCqodu6hv9PYfgRh1JM9ZTDhRUPVSC/gtzAPRhkmaspxwoqHpoBB3ddC9AZtzAsoYc08wiDRn0S6ugoIyh2ovfegRgw7KynaT8QOPcTPdJx/yKBArKGCdMfbN8EF1KTYN3+WCME2YWQUHlwLt8qAcFZQvbu3ycwxmWnTD1DQoqh9q7fOQ3Ms5R/2Awi55pFCd81YmCyuHgXT6woGzBesqh9i4fZrCgbMF6yuHgYSYsKFuwnnKgoOpBQdmCgjIGBWWLUwTtd93Mhb8rCpcsm/yoEwT17Hpeu24Pu/V0UNAtNSzoKhHhAWGVyraZ/dQfquvJYJtI1sQozd017GKvng4KWkZBVaKwvOpEYbkPEIXd8CIKu16DKCzbmyjsikvutUVSz5eWKIb8UU85TeJniiGZHZTTxB5QDPmor3IaOVBQFFQeFFQCCoqCSkBBUVB5UFAJKCgKKgEFRUHl8QBBDWOJwm6NYxmmH08UVpJKFFY8gWUYJST13JilGJI7TTnNst8VQ84tUE4z57JiyNFVymnkoBUUQZwKCopwDQqKcA0KinANCopwDQqKcA0KinANCopwDQqKcA2loBsbhs9XilkQFtC/gCRybjJBwv0x/v1vKYfNC6o9slQpLLspWNZoL9IYRrwVVCjmJ+qGciEJqqhcQYLy0ZeOTtArfmdyGlpN3HQnh72zc7pMI4g86JWsnLAo5EhRfIZi2OGA7PzYTQphC6JCLZtgL9IYRrwVVChvF0k3lAtJUEXlChKUj0Hp6ATNSAGYNtl+zOfC8vRk5ci8lmnJygm39AIoLFAMOxqcV/LIZoWwnemhlk2wF2kMI90KOhTzk3SDoJAEVVSuIEH5GJSOTtBZkwBWDlQMuxqzWTmy//r1ycoJ53eN9euVq5xtzP1eXRWznQ+1bILdSDGMdCvoIMmv2A2CQpJUUbmCBOWjLx2doDPFtfZXiloVtko5ct3zINRVMWxqxKn87pMVw76LPXDyiQ+VwsTymWLsRpqqTLQVlBDkV+wGSSEJqkhQQYLy0ZeOTlBxjTMm2o8xJCdeJIhMCgitVa2LYtiSEQBrkxTDxs8UxrEeSmFi+UwxdiPFMMKtoEQxP0E3SApJUEWCChKUj750dIJeDrxSGKnwyXdzPGmk8IevGHaq4d95CWmKYWtaXM3tN00pTCyfKcZupBhGvhU0KOYn64ZiIQmqSFBBgvLRl47yMNP6qEilYwejK1epUiWFJFKoq3JYRljg4GLFsNLxgbWfv6kUZhyATDH2IsUw8q2gQik/WTeUC6lcRYIKEpSPvnR4oB7hGhQU4RoUFOEaFBThGhQU4RoUFOEaFBThGhQU4RoUFOEaFBThGhQU4RoUFOEaFBThGhQU4RoUFOEaFBThGhQU4RoUFOEa7Qj6lE9NnY/PUHd3w2PQSD21IyhAtnirkE8j3d0Nj0ET9dScoDl/uLsbHoMm6qk5QbMSshIGths6s2vTi/BOWL0xBnf3Srtoop5aFPTBvFs+6TD23d1Rl/P7OPUqYM9GE/XUoqCdAJqfgcXTXg+Ni4vs7e5eaRdN1FOLgj4hFPS8UNC0GQAGvbt7pV00UU8tC3oo4rq++3vu7pV20UQ9tSwoLGkQNIS3v3gNoYl6aknQOynlrZQah9N6aldQpEKAgiJcg4IiXIOCIlyDgiJcg4IiXIOCIlyDgiJcg4IiXIOCIlyDgiJcg4IiXIOCIlyDgiJcg4IiXIOCIlzzf7AuXhMGmiKYAAAAAElFTkSuQmCC" /><!-- --></p> <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 3.3% as well as the plot suggest that the SFO model fits very well. The error level at which the <span class="math inline"><em>χ</em><sup>2</sup></span> test passes is slightly lower for the FOMC model. However, the difference appears negligible.</p> <pre class="r"><code>summary(mm.L4[["SFO", 1]], data = FALSE)</code></pre> -<pre><code>## mkin version used for fitting: 0.9.49.4 -## R version used for fitting: 3.6.0 -## Date of fit: Thu May 2 18:43:56 2019 -## Date of summary: Thu May 2 18:43:57 2019 +<pre><code>## mkin version used for fitting: 0.9.50.3 +## R version used for fitting: 4.0.0 +## Date of fit: Tue May 26 17:01:10 2020 +## Date of summary: Tue May 26 17:01:10 2020 ## ## Equations: ## d_parent/dt = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted using 146 model solutions performed in 0.291 s +## Fitted using 142 model solutions performed in 0.029 s +## +## Error model: Constant variance ## -## Error model: -## Constant variance +## Error model algorithm: OLS ## ## Starting values for parameters to be optimised: -## value type -## parent_0 96.60000 state -## k_parent_sink 0.10000 deparm -## sigma 3.16181 error +## value type +## parent_0 96.6 state +## k_parent_sink 0.1 deparm ## ## Starting values for the transformed parameters actually optimised: ## value lower upper ## parent_0 96.600000 -Inf Inf ## log_k_parent_sink -2.302585 -Inf Inf -## sigma 3.161810 0 Inf ## ## Fixed parameter values: ## None ## +## Results: +## +## AIC BIC logLik +## 47.12133 47.35966 -20.56067 +## ## Optimised, transformed parameters with symmetric confidence intervals: ## Estimate Std. Error Lower Upper ## parent_0 96.440 1.69900 92.070 100.800 @@ -2082,10 +2070,10 @@ plot(mm.L4)</code></pre> ## sigma 3.162 0.79050 1.130 5.194 ## ## Parameter correlation: -## parent_0 log_k_parent_sink sigma -## parent_0 1.000e+00 5.938e-01 4.256e-10 -## log_k_parent_sink 5.938e-01 1.000e+00 -7.280e-10 -## sigma 4.256e-10 -7.280e-10 1.000e+00 +## parent_0 log_k_parent_sink sigma +## parent_0 1.000e+00 5.938e-01 3.387e-07 +## log_k_parent_sink 5.938e-01 1.000e+00 5.830e-07 +## sigma 3.387e-07 5.830e-07 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -2101,46 +2089,46 @@ plot(mm.L4)</code></pre> ## All data 3.287 2 6 ## parent 3.287 2 6 ## -## Resulting formation fractions: -## ff -## parent_sink 1 -## ## Estimated disappearance times: ## DT50 DT90 ## parent 106 352</code></pre> <pre class="r"><code>summary(mm.L4[["FOMC", 1]], data = FALSE)</code></pre> -<pre><code>## mkin version used for fitting: 0.9.49.4 -## R version used for fitting: 3.6.0 -## Date of fit: Thu May 2 18:43:56 2019 -## Date of summary: Thu May 2 18:43:57 2019 +<pre><code>## mkin version used for fitting: 0.9.50.3 +## R version used for fitting: 4.0.0 +## Date of fit: Tue May 26 17:01:10 2020 +## Date of summary: Tue May 26 17:01:10 2020 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 224 model solutions performed in 0.451 s +## Fitted using 224 model solutions performed in 0.044 s +## +## Error model: Constant variance ## -## Error model: -## Constant variance +## Error model algorithm: OLS ## ## Starting values for parameters to be optimised: -## value type -## parent_0 96.600000 state -## alpha 1.000000 deparm -## beta 10.000000 deparm -## sigma 1.830055 error +## value type +## parent_0 96.6 state +## alpha 1.0 deparm +## beta 10.0 deparm ## ## Starting values for the transformed parameters actually optimised: ## value lower upper ## parent_0 96.600000 -Inf Inf ## log_alpha 0.000000 -Inf Inf ## log_beta 2.302585 -Inf Inf -## sigma 1.830055 0 Inf ## ## Fixed parameter values: ## None ## +## Results: +## +## AIC BIC logLik +## 40.37255 40.69032 -16.18628 +## ## Optimised, transformed parameters with symmetric confidence intervals: ## Estimate Std. Error Lower Upper ## parent_0 99.1400 1.2670 95.6300 102.7000 @@ -2150,10 +2138,10 @@ plot(mm.L4)</code></pre> ## ## Parameter correlation: ## parent_0 log_alpha log_beta sigma -## parent_0 1.000e+00 -4.696e-01 -5.543e-01 -2.473e-07 -## log_alpha -4.696e-01 1.000e+00 9.889e-01 2.429e-08 -## log_beta -5.543e-01 9.889e-01 1.000e+00 5.183e-08 -## sigma -2.473e-07 2.429e-08 5.183e-08 1.000e+00 +## 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 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -2203,6 +2191,49 @@ $(document).ready(function () { </script> +<!-- tabsets --> + +<script> +$(document).ready(function () { + window.buildTabsets("TOC"); +}); + +$(document).ready(function () { + $('.tabset-dropdown > .nav-tabs > li').click(function () { + $(this).parent().toggleClass('nav-tabs-open') + }); +}); +</script> + +<!-- code folding --> + +<script> +$(document).ready(function () { + + // move toc-ignore selectors from section div to header + $('div.section.toc-ignore') + .removeClass('toc-ignore') + .children('h1,h2,h3,h4,h5').addClass('toc-ignore'); + + // establish options + var options = { + selectors: "h1,h2,h3", + theme: "bootstrap3", + context: '.toc-content', + hashGenerator: function (text) { + return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_').toLowerCase(); + }, + ignoreSelector: ".toc-ignore", + scrollTo: 0 + }; + options.showAndHide = false; + options.smoothScroll = true; + + // tocify + var toc = $("#TOC").tocify(options).data("toc-tocify"); +}); +</script> + </body> </html> diff --git a/vignettes/mkin.html b/vignettes/mkin.html index 28b3fa16..e14cb374 100644 --- a/vignettes/mkin.html +++ b/vignettes/mkin.html @@ -11,7 +11,7 @@ <meta name="author" content="Johannes Ranke" /> -<meta name="date" content="2020-05-12" /> +<meta name="date" content="2020-05-26" /> <title>Introduction to mkin</title> @@ -1583,7 +1583,7 @@ div.tocify { <h1 class="title toc-ignore">Introduction to mkin</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">2020-05-12</h4> +<h4 class="date">2020-05-26</h4> </div> diff --git a/vignettes/twa.html b/vignettes/twa.html index 41989b5d..80272eef 100644 --- a/vignettes/twa.html +++ b/vignettes/twa.html @@ -1,18 +1,18 @@ <!DOCTYPE html> -<html xmlns="http://www.w3.org/1999/xhtml"> +<html> <head> <meta charset="utf-8" /> -<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <meta name="generator" content="pandoc" /> +<meta http-equiv="X-UA-Compatible" content="IE=EDGE" /> -<meta name="viewport" content="width=device-width, initial-scale=1"> +<meta name="viewport" content="width=device-width, initial-scale=1" /> <meta name="author" content="Johannes Ranke" /> -<meta name="date" content="2019-09-18" /> +<meta name="date" content="2020-05-26" /> <title>Calculation of time weighted average concentrations with mkin</title> @@ -32,9 +32,6 @@ font-family: "Open Sans", "Helvetica Neue", Helvetica, Arial, sans-serif; font-size: 14px; line-height: 1.35; } -#header { -text-align: center; -} #TOC { clear: both; margin: 0 0 10px 10px; @@ -202,7 +199,8 @@ code > span.st { color: #d14; } code > span.co { color: #888888; font-style: italic; } code > span.ot { color: #007020; } code > span.al { color: #ff0000; font-weight: bold; } -code > span.fu { color: #900; font-weight: bold; } code > span.er { color: #a61717; background-color: #e3d2d2; } +code > span.fu { color: #900; font-weight: bold; } +code > span.er { color: #a61717; background-color: #e3d2d2; } </style> @@ -217,7 +215,7 @@ code > span.fu { color: #900; font-weight: bold; } code > span.er { color: #a61 <h1 class="title toc-ignore">Calculation of time weighted average concentrations with mkin</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">2019-09-18</h4> +<h4 class="date">2020-05-26</h4> @@ -261,6 +259,9 @@ code > span.fu { color: #900; font-weight: bold; } code > span.er { color: #a61 +<!-- code folding --> + + <!-- dynamically load mathjax for compatibility with self-contained --> <script> (function () { |
