diff options
| author | Johannes Ranke <jranke@uni-bremen.de> | 2021-09-27 20:10:01 +0200 |
|---|---|---|
| committer | Johannes Ranke <jranke@uni-bremen.de> | 2021-09-27 20:10:01 +0200 |
| commit | 5c15ef747568b3a9a9c094b6aa546dc80e3aa87a (patch) | |
| tree | 70f86619437fb8f1bc1f144b847fc4dacbcba128 | |
| parent | 047d048b89e167fb354b45cd7c6b719b9f4cdd28 (diff) | |
intervals() methods, more DFOP/tc variants
43 files changed, 1255 insertions, 250 deletions
@@ -8,6 +8,8 @@ S3method(aw,mmkin) S3method(confint,mkinfit) S3method(f_time_norm_focus,mkindsg) S3method(f_time_norm_focus,numeric) +S3method(intervals,nlmixr.mmkin) +S3method(intervals,saem.mmkin) S3method(loftest,mkinfit) S3method(logLik,mkinfit) S3method(lrtest,mkinfit) @@ -61,6 +63,7 @@ export(endpoints) export(f_time_norm_focus) export(get_deg_func) export(ilr) +export(intervals) export(invilr) export(invtffm0) export(loftest) @@ -112,6 +115,7 @@ importFrom(dplyr,"%>%") importFrom(grDevices,dev.cur) importFrom(lmtest,lrtest) importFrom(methods,signature) +importFrom(nlme,intervals) importFrom(nlmixr,nlmixr) importFrom(nlmixr,tableControl) importFrom(parallel,detectCores) @@ -12,6 +12,8 @@ - 'vignettes/web_only/dimethenamid_2018.rmd': Example evaluations of the dimethenamid data. +- 'intervals': Provide methods of this nlme function for 'nlmixr.mmkin' and 'saem.mmkin' objects. + # mkin 1.0.5 (2021-09-15) - 'dimethenamid_2018': Correct the data for the Borstel soil. The five observations from Staudenmaier (2013) that were previously stored as "Borstel 2" are actually just a subset of the 16 observations in "Borstel 1" which is now simply "Borstel" diff --git a/R/intervals.R b/R/intervals.R new file mode 100644 index 00000000..e2d342f0 --- /dev/null +++ b/R/intervals.R @@ -0,0 +1,179 @@ +#' @importFrom nlme intervals +#' @export +nlme::intervals + +#' Confidence intervals for parameters in saem.mmkin objects +#' +#' @param object The fitted saem.mmkin object +#' @param level The confidence level. Must be the default of 0.95 as this is what +#' is available in the saemix object +#' @param backtransform In case the model was fitted with mkin transformations, +#' should we backtransform the parameters where a one to one correlation +#' between transformed and backtransformed parameters exists? +#' @return An object with 'intervals.saem.mmkin' and 'intervals.lme' in the +#' class attribute +#' @export +intervals.saem.mmkin <- function(object, level = 0.95, backtransform = TRUE, ...) +{ + if (!identical(level, 0.95)) { + stop("Confidence intervals are only available for a level of 95%") + } + + mod_vars <- names(object$mkinmod$diffs) + + pnames <- names(object$mean_dp_start) + + # Confidence intervals are available in the SaemixObject, so + # we just need to extract them and put them into a list modelled + # after the result of nlme::intervals.lme + + conf.int <- object$so@results@conf.int + rownames(conf.int) <- conf.int$name + colnames(conf.int)[2] <- "est." + confint_trans <- as.matrix(conf.int[pnames, c("lower", "est.", "upper")]) + + # Fixed effects + # In case objects were produced by earlier versions of saem + if (is.null(object$transformations)) object$transformations <- "mkin" + + if (object$transformations == "mkin" & backtransform) { + bp <- backtransform_odeparms(confint_trans[, "est."], object$mkinmod, + object$transform_rates, object$transform_fractions) + bpnames <- names(bp) + + # Transform boundaries of CI for one parameter at a time, + # with the exception of sets of formation fractions (single fractions are OK). + f_names_skip <- character(0) + for (box in mod_vars) { # Figure out sets of fractions to skip + f_names <- grep(paste("^f", box, sep = "_"), pnames, value = TRUE) + n_paths <- length(f_names) + if (n_paths > 1) f_names_skip <- c(f_names_skip, f_names) + } + + confint_back <- matrix(NA, nrow = length(bp), ncol = 3, + dimnames = list(bpnames, colnames(confint_trans))) + confint_back[, "est."] <- bp + + for (pname in pnames) { + if (!pname %in% f_names_skip) { + par.lower <- confint_trans[pname, "lower"] + par.upper <- confint_trans[pname, "upper"] + names(par.lower) <- names(par.upper) <- pname + bpl <- backtransform_odeparms(par.lower, object$mkinmod, + object$transform_rates, + object$transform_fractions) + bpu <- backtransform_odeparms(par.upper, object$mkinmod, + object$transform_rates, + object$transform_fractions) + confint_back[names(bpl), "lower"] <- bpl + confint_back[names(bpu), "upper"] <- bpu + } + } + confint_ret <- confint_back + } else { + confint_ret <- confint_trans + } + attr(confint_ret, "label") <- "Fixed effects:" + + # Random effects + ranef_ret <- as.matrix(conf.int[paste0("SD.", pnames), c("lower", "est.", "upper")]) + rownames(ranef_ret) <- paste0(gsub("SD\\.", "sd(", rownames(ranef_ret)), ")") + attr(ranef_ret, "label") <- "Random effects:" + + + # Error model + enames <- if (object$err_mod == "const") "a.1" else c("a.1", "b.1") + err_ret <- as.matrix(conf.int[enames, c("lower", "est.", "upper")]) + + res <- list( + fixed = confint_ret, + random = ranef_ret, + errmod = err_ret + ) + class(res) <- c("intervals.saemix.mmkin", "intervals.lme") + attr(res, "level") <- level + return(res) +} + +#' Confidence intervals for parameters in nlmixr.mmkin objects +#' +#' @param object The fitted saem.mmkin object +#' @param level The confidence level. +#' @param backtransform Should we backtransform the parameters where a one to +#' one correlation between transformed and backtransformed parameters exists? +#' @importFrom nlme intervals +#' @return An object with 'intervals.saem.mmkin' and 'intervals.lme' in the +#' class attribute +#' @export +intervals.nlmixr.mmkin <- function(object, level = 0.95, backtransform = TRUE, ...) +{ + + # Fixed effects + mod_vars <- names(object$mkinmod$diffs) + + conf.int <- confint(object$nm) + dpnames <- setdiff(rownames(conf.int), names(object$mean_ep_start)) + ndp <- length(dpnames) + + confint_trans <- as.matrix(conf.int[dpnames, c(3, 1, 4)]) + colnames(confint_trans) <- c("lower", "est.", "upper") + + if (backtransform) { + bp <- backtransform_odeparms(confint_trans[, "est."], object$mkinmod, + object$transform_rates, object$transform_fractions) + bpnames <- names(bp) + + # Transform boundaries of CI for one parameter at a time, + # with the exception of sets of formation fractions (single fractions are OK). + f_names_skip <- character(0) + for (box in mod_vars) { # Figure out sets of fractions to skip + f_names <- grep(paste("^f", box, sep = "_"), dpnames, value = TRUE) + n_paths <- length(f_names) + if (n_paths > 1) f_names_skip <- c(f_names_skip, f_names) + } + + confint_back <- matrix(NA, nrow = length(bp), ncol = 3, + dimnames = list(bpnames, colnames(confint_trans))) + confint_back[, "est."] <- bp + + for (pname in dpnames) { + if (!pname %in% f_names_skip) { + par.lower <- confint_trans[pname, "lower"] + par.upper <- confint_trans[pname, "upper"] + names(par.lower) <- names(par.upper) <- pname + bpl <- backtransform_odeparms(par.lower, object$mkinmod, + object$transform_rates, + object$transform_fractions) + bpu <- backtransform_odeparms(par.upper, object$mkinmod, + object$transform_rates, + object$transform_fractions) + confint_back[names(bpl), "lower"] <- bpl + confint_back[names(bpu), "upper"] <- bpu + } + } + confint_ret <- confint_back + } else { + confint_ret <- confint_trans + } + attr(confint_ret, "label") <- "Fixed effects:" + + # Random effects + ranef_ret <- as.matrix(data.frame(lower = NA, + "est." = sqrt(diag(object$nm$omega)), upper = NA)) + rownames(ranef_ret) <- paste0(gsub("eta\\.", "sd(", rownames(ranef_ret)), ")") + attr(ranef_ret, "label") <- "Random effects:" + + # Error model + enames <- names(object$nm$sigma) + err_ret <- as.matrix(conf.int[enames, c(3, 1, 4)]) + colnames(err_ret) <- c("lower", "est.", "upper") + + res <- list( + fixed = confint_ret, + random = ranef_ret, + errmod = err_ret + ) + class(res) <- c("intervals.nlmixr.mmkin", "intervals.lme") + attr(res, "level") <- level + return(res) +} @@ -31,8 +31,8 @@ nlmixr::nlmixr #' for parameter that are tested if requested by 'test_log_parms'. #' @param data Not used, as the data are extracted from the mmkin row object #' @param table Passed to [nlmixr::nlmixr] -#' @param error_model Possibility to override the error model which is being -#' set based on the error model used in the mmkin row object. +#' @param error_model Optional argument to override the error model which is +#' being set based on the error model used in the mmkin row object. #' @param control Passed to [nlmixr::nlmixr] #' @param \dots Passed to [nlmixr_model] #' @param save Passed to [nlmixr::nlmixr] diff --git a/_pkgdown.yml b/_pkgdown.yml index f0a95468..ec9bfb1b 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -52,6 +52,9 @@ reference: - get_deg_func - saemix_model - mixed + - intervals + - intervals.saem.mmkin + - intervals.nlmixr.mmkin - title: Datasets and known results contents: - focus_soil_moisture diff --git a/docs/dev/articles/web_only/dimethenamid_2018.html b/docs/dev/articles/web_only/dimethenamid_2018.html index 26b352e1..aa84435d 100644 --- a/docs/dev/articles/web_only/dimethenamid_2018.html +++ b/docs/dev/articles/web_only/dimethenamid_2018.html @@ -101,7 +101,7 @@ <h1 data-toc-skip>Example evaluations of the dimethenamid data from 2018</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">Last change 17 September 2021, built on 17 Sep 2021</h4> + <h4 class="date">Last change 27 September 2021, built on 27 Sep 2021</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/master/vignettes/web_only/dimethenamid_2018.rmd"><code>vignettes/web_only/dimethenamid_2018.rmd</code></a></small> <div class="hidden name"><code>dimethenamid_2018.rmd</code></div> @@ -174,7 +174,7 @@ <div id="nlme" class="section level3"> <h3 class="hasAnchor"> <a href="#nlme" class="anchor"></a>nlme</h3> -<p>The nlme package was the first R extension providing facilities to fit nonlinear mixed-effects models. We would like to do model selection from all four combinations of degradation models and error models based on the AIC. However, fitting the DFOP model with constant variance and using default control parameters results in an error, signalling that the maximum number of 50 iterations was reached, potentially indicating overparameterisation. However, the algorithm converges when the two-component error model is used in combination with the DFOP model. This can be explained by the fact that the smaller residues observed at later sampling times get more weight when using the two-component error model which will counteract the tendency of the algorithm to try parameter combinations unsuitable for fitting these data.</p> +<p>The nlme package was the first R extension providing facilities to fit nonlinear mixed-effects models. We would like to do model selection from all four combinations of degradation models and error models based on the AIC. However, fitting the DFOP model with constant variance and using default control parameters results in an error, signalling that the maximum number of 50 iterations was reached, potentially indicating overparameterisation. Nevertheless, the algorithm converges when the two-component error model is used in combination with the DFOP model. This can be explained by the fact that the smaller residues observed at later sampling times get more weight when using the two-component error model which will counteract the tendency of the algorithm to try parameter combinations unsuitable for fitting these data.</p> <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://svn.r-project.org/R-packages/trunk/nlme/">nlme</a></span><span class="op">)</span> <span class="va">f_parent_nlme_sfo_const</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html">nlme</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span> @@ -182,7 +182,7 @@ <span class="va">f_parent_nlme_sfo_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html">nlme</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span> <span class="va">f_parent_nlme_dfop_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html">nlme</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span></code></pre></div> <p>Note that a certain degree of overparameterisation is also indicated by a warning obtained when fitting DFOP with the two-component error model (‘false convergence’ in the ‘LME step’ in iteration 3). However, as this warning does not occur in later iterations, and specifically not in the last of the 6 iterations, we can ignore this warning.</p> -<p>The model comparison function of the nlme package can directly be applied to these fits showing a much lower AIC for the DFOP model fitted with the two-component error model. Also, the likelihood ratio test indicates that this difference is significant. as the p-value is below 0.0001.</p> +<p>The model comparison function of the nlme package can directly be applied to these fits showing a much lower AIC for the DFOP model fitted with the two-component error model. Also, the likelihood ratio test indicates that this difference is significant as the p-value is below 0.0001.</p> <div class="sourceCode" id="cb8"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/stats/anova.html">anova</a></span><span class="op">(</span> <span class="va">f_parent_nlme_sfo_const</span>, <span class="va">f_parent_nlme_sfo_tc</span>, <span class="va">f_parent_nlme_dfop_tc</span> @@ -216,6 +216,8 @@ f_parent_nlme_dfop_tc 3 10 671.91 702.34 -325.96 2 vs 3 134.69 <.0001 <div class="sourceCode" id="cb12"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va">saemix</span><span class="op">)</span> <span class="va">saemix_control</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/saemixControl.html">saemixControl</a></span><span class="op">(</span>nbiter.saemix <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">800</span>, <span class="fl">300</span><span class="op">)</span>, nb.chains <span class="op">=</span> <span class="fl">15</span>, + print <span class="op">=</span> <span class="cn">FALSE</span>, save <span class="op">=</span> <span class="cn">FALSE</span>, save.graphs <span class="op">=</span> <span class="cn">FALSE</span>, displayProgress <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span> +<span class="va">saemix_control_10k</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/saemixControl.html">saemixControl</a></span><span class="op">(</span>nbiter.saemix <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">10000</span>, <span class="fl">1000</span><span class="op">)</span>, nb.chains <span class="op">=</span> <span class="fl">15</span>, print <span class="op">=</span> <span class="cn">FALSE</span>, save <span class="op">=</span> <span class="cn">FALSE</span>, save.graphs <span class="op">=</span> <span class="cn">FALSE</span>, displayProgress <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></code></pre></div> <p>The convergence plot for the SFO model using constant variance is shown below.</p> <div class="sourceCode" id="cb13"><pre class="downlit sourceCode r"> @@ -229,35 +231,65 @@ f_parent_nlme_dfop_tc 3 10 671.91 702.34 -325.96 2 vs 3 134.69 <.0001 control <span class="op">=</span> <span class="va">saemix_control</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="va">f_parent_saemix_sfo_tc</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_saemix_sfo_tc-1.png" width="700"></p> -<p>When fitting the DFOP model with constant variance, parameter convergence is not as unambiguous (see the failure of nlme with the default number of iterations above). Therefore, the number of iterations in the first phase of the algorithm was increased, leading to visually satisfying convergence.</p> +<p>When fitting the DFOP model with constant variance (see below), parameter convergence is not as unambiguous.</p> <div class="sourceCode" id="cb15"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">f_parent_saemix_dfop_const</span> <span class="op"><-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, control <span class="op">=</span> <span class="va">saemix_control</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_const</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_const-1.png" width="700"></p> -<p>The same applies in the case where the DFOP model is fitted with the two-component error model. Convergence of the variance of k2 is enhanced by using the two-component error, it remains more or less stable already after 200 iterations of the first phase.</p> +<p>This is improved when the DFOP model is fitted with the two-component error model. Convergence of the variance of k2 is enhanced, it remains more or less stable already after 200 iterations of the first phase.</p> <div class="sourceCode" id="cb16"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">f_parent_saemix_dfop_tc</span> <span class="op"><-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, control <span class="op">=</span> <span class="va">saemix_control</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></code></pre></div> -<p><img src="dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc-1.png" width="700"> The four combinations and including the variations of the DFOP/tc combination can be compared using the model comparison function from the saemix package:</p> +<p><img src="dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc-1.png" width="700"></p> +<p>We also check if using many more iterations (10 000 for the first and 1000 for the second phase) improve the result in a significant way. The AIC values obtained are compared further below.</p> <div class="sourceCode" id="cb17"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/compare.saemix.html">compare.saemix</a></span><span class="op">(</span> +<code class="sourceCode R"><span class="va">f_parent_saemix_dfop_tc_10k</span> <span class="op"><-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, + control <span class="op">=</span> <span class="va">saemix_control_10k</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span> +<span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc_10k</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></code></pre></div> +<p><img src="dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc_10k-1.png" width="700"></p> +<p>An alternative way to fit DFOP in combination with the two-component error model is to use the model formulation with transformed parameters as used per default in mkin.</p> +<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span class="va">f_parent_saemix_dfop_tc_mkin</span> <span class="op"><-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, + control <span class="op">=</span> <span class="va">saemix_control</span>, transformations <span class="op">=</span> <span class="st">"mkin"</span><span class="op">)</span> +<span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc_mkin</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></code></pre></div> +<p><img src="dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc_mkin-1.png" width="700"></p> +<p>As the convergence plots do not clearly indicate that the algorithm has converged, we again use a much larger number of iterations, which leads to satisfactory convergence (see below).</p> +<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span class="va">f_parent_saemix_dfop_tc_mkin_10k</span> <span class="op"><-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, + control <span class="op">=</span> <span class="va">saemix_control_10k</span>, transformations <span class="op">=</span> <span class="st">"mkin"</span><span class="op">)</span> +<span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc_mkin_10k</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></code></pre></div> +<p><img src="dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc_mkin_10k-1.png" width="700"></p> +<p>The four combinations (SFO/const, SFO/tc, DFOP/const and DFOP/tc), including the variations of the DFOP/tc combination can be compared using the model comparison function of the saemix package:</p> +<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span class="va">AIC_parent_saemix</span> <span class="op"><-</span> <span class="fu">saemix</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/compare.saemix.html">compare.saemix</a></span><span class="op">(</span> <span class="va">f_parent_saemix_sfo_const</span><span class="op">$</span><span class="va">so</span>, <span class="va">f_parent_saemix_sfo_tc</span><span class="op">$</span><span class="va">so</span>, <span class="va">f_parent_saemix_dfop_const</span><span class="op">$</span><span class="va">so</span>, - <span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span><span class="op">)</span></code></pre></div> + <span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span>, + <span class="va">f_parent_saemix_dfop_tc_10k</span><span class="op">$</span><span class="va">so</span>, + <span class="va">f_parent_saemix_dfop_tc_mkin</span><span class="op">$</span><span class="va">so</span>, + <span class="va">f_parent_saemix_dfop_tc_mkin_10k</span><span class="op">$</span><span class="va">so</span><span class="op">)</span></code></pre></div> <pre><code>Likelihoods calculated by importance sampling</code></pre> -<pre><code> AIC BIC -1 796.37 795.33 -2 798.37 797.13 -3 713.16 711.28 -4 666.10 664.01</code></pre> -<p>As in the case of nlme fits, the DFOP model fitted with two-component error (number 4) gives the lowest AIC. Using more iterations and/or more chains does not have a large influence on the final AIC (not shown).</p> +<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/colnames.html">rownames</a></span><span class="op">(</span><span class="va">AIC_parent_saemix</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span> + <span class="st">"SFO const"</span>, <span class="st">"SFO tc"</span>, <span class="st">"DFOP const"</span>, <span class="st">"DFOP tc"</span>, <span class="st">"DFOP tc more iterations"</span>, + <span class="st">"DFOP tc mkintrans"</span>, <span class="st">"DFOP tc mkintrans more iterations"</span><span class="op">)</span> +<span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span><span class="op">(</span><span class="va">AIC_parent_saemix</span><span class="op">)</span></code></pre></div> +<pre><code> AIC BIC +SFO const 796.37 795.33 +SFO tc 798.37 797.13 +DFOP const 713.16 711.28 +DFOP tc 666.10 664.01 +DFOP tc more iterations 666.15 664.06 +DFOP tc mkintrans 682.26 680.17 +DFOP tc mkintrans more iterations 666.12 664.04</code></pre> +<p>As in the case of nlme fits, the DFOP model fitted with two-component error (number 4) gives the lowest AIC. Using a much larger number of iterations does not improve the fit a lot. When the mkin transformations are used instead of the saemix transformations, this large number of iterations leads to a goodness of fit that is comparable to the result obtained with saemix transformations.</p> <p>In order to check the influence of the likelihood calculation algorithms implemented in saemix, the likelihood from Gaussian quadrature is added to the best fit, and the AIC values obtained from the three methods are compared.</p> -<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span> <span class="op"><-</span> - <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/llgq.saemix.html">llgq.saemix</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span><span class="op">)</span> + <span class="fu">saemix</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/llgq.saemix.html">llgq.saemix</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span><span class="op">)</span> <span class="va">AIC_parent_saemix_methods</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span> is <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html">AIC</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span>, method <span class="op">=</span> <span class="st">"is"</span><span class="op">)</span>, gq <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html">AIC</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span>, method <span class="op">=</span> <span class="st">"gq"</span><span class="op">)</span>, @@ -273,19 +305,19 @@ f_parent_nlme_dfop_tc 3 10 671.91 702.34 -325.96 2 vs 3 134.69 <.0001 <a href="#nlmixr" class="anchor"></a>nlmixr</h3> <p>In the last years, a lot of effort has been put into the nlmixr package which is designed for pharmacokinetics, where nonlinear mixed-effects models are routinely used, but which can also be used for related data like chemical degradation data. A current development branch of the mkin package provides an interface between mkin and nlmixr. Here, we check if we get equivalent results when using a refined version of the First Order Conditional Estimation (FOCE) algorithm used in nlme, namely the First Order Conditional Estimation with Interaction (FOCEI), and the SAEM algorithm as implemented in nlmixr.</p> <p>First, the focei algorithm is used for the four model combinations. A number of warnings are produced with unclear significance.</p> -<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/nlmixrdevelopment/nlmixr">nlmixr</a></span><span class="op">)</span> <span class="va">f_parent_nlmixr_focei_sfo_const</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html">nlmixr</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span><span class="op">)</span> <span class="va">f_parent_nlmixr_focei_sfo_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html">nlmixr</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span><span class="op">)</span> <span class="va">f_parent_nlmixr_focei_dfop_const</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html">nlmixr</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span><span class="op">)</span> <span class="va">f_parent_nlmixr_focei_dfop_tc</span><span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html">nlmixr</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"focei"</span><span class="op">)</span></code></pre></div> -<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">aic_nlmixr_focei</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">sapply</a></span><span class="op">(</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span><span class="op">(</span><span class="va">f_parent_nlmixr_focei_sfo_const</span><span class="op">$</span><span class="va">nm</span>, <span class="va">f_parent_nlmixr_focei_sfo_tc</span><span class="op">$</span><span class="va">nm</span>, <span class="va">f_parent_nlmixr_focei_dfop_const</span><span class="op">$</span><span class="va">nm</span>, <span class="va">f_parent_nlmixr_focei_dfop_tc</span><span class="op">$</span><span class="va">nm</span><span class="op">)</span>, <span class="va">AIC</span><span class="op">)</span></code></pre></div> <p>The AIC values are very close to the ones obtained with nlme which are repeated below for convenience.</p> -<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">aic_nlme</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">sapply</a></span><span class="op">(</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span><span class="op">(</span><span class="va">f_parent_nlme_sfo_const</span>, <span class="cn">NA</span>, <span class="va">f_parent_nlme_sfo_tc</span>, <span class="va">f_parent_nlme_dfop_tc</span><span class="op">)</span>, <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html">is.na</a></span><span class="op">(</span><span class="va">x</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span><span class="op">)</span><span class="op">)</span> <span class="cn">NA</span> <span class="kw">else</span> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html">AIC</a></span><span class="op">(</span><span class="va">x</span><span class="op">)</span><span class="op">)</span> @@ -297,48 +329,70 @@ f_parent_nlme_dfop_tc 3 10 671.91 702.34 -325.96 2 vs 3 134.69 <.0001 check.names <span class="op">=</span> <span class="cn">FALSE</span> <span class="op">)</span></code></pre></div> <p>Secondly, we use the SAEM estimation routine and check the convergence plots. The control parameters also used for the saemix fits are defined beforehand.</p> -<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">nlmixr_saem_control</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/saemControl.html">saemControl</a></span><span class="op">(</span>logLik <span class="op">=</span> <span class="cn">TRUE</span>, - nBurn <span class="op">=</span> <span class="fl">1000</span>, nEm <span class="op">=</span> <span class="fl">300</span>, nmc <span class="op">=</span> <span class="fl">15</span><span class="op">)</span></code></pre></div> +<div class="sourceCode" id="cb29"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span class="va">nlmixr_saem_control_800</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/saemControl.html">saemControl</a></span><span class="op">(</span>logLik <span class="op">=</span> <span class="cn">TRUE</span>, + nBurn <span class="op">=</span> <span class="fl">800</span>, nEm <span class="op">=</span> <span class="fl">300</span>, nmc <span class="op">=</span> <span class="fl">15</span><span class="op">)</span> +<span class="va">nlmixr_saem_control_1000</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/saemControl.html">saemControl</a></span><span class="op">(</span>logLik <span class="op">=</span> <span class="cn">TRUE</span>, + nBurn <span class="op">=</span> <span class="fl">1000</span>, nEm <span class="op">=</span> <span class="fl">300</span>, nmc <span class="op">=</span> <span class="fl">15</span><span class="op">)</span> +<span class="va">nlmixr_saem_control_10k</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/saemControl.html">saemControl</a></span><span class="op">(</span>logLik <span class="op">=</span> <span class="cn">TRUE</span>, + nBurn <span class="op">=</span> <span class="fl">10000</span>, nEm <span class="op">=</span> <span class="fl">1000</span>, nmc <span class="op">=</span> <span class="fl">15</span><span class="op">)</span></code></pre></div> <p>The we fit SFO with constant variance</p> -<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb30"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">f_parent_nlmixr_saem_sfo_const</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html">nlmixr</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span>, - control <span class="op">=</span> <span class="va">nlmixr_saem_control</span><span class="op">)</span> + control <span class="op">=</span> <span class="va">nlmixr_saem_control_800</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/traceplot.html">traceplot</a></span><span class="op">(</span><span class="va">f_parent_nlmixr_saem_sfo_const</span><span class="op">$</span><span class="va">nm</span><span class="op">)</span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_sfo_const-1.png" width="700"></p> <p>and SFO with two-component error.</p> -<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb31"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">f_parent_nlmixr_saem_sfo_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html">nlmixr</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span>, - control <span class="op">=</span> <span class="va">nlmixr_saem_control</span><span class="op">)</span> + control <span class="op">=</span> <span class="va">nlmixr_saem_control_800</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/traceplot.html">traceplot</a></span><span class="op">(</span><span class="va">f_parent_nlmixr_saem_sfo_tc</span><span class="op">$</span><span class="va">nm</span><span class="op">)</span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_sfo_tc-1.png" width="700"></p> <p>For DFOP with constant variance, the convergence plots show considerable instability of the fit, which indicates overparameterisation which was already observed earlier for this model combination.</p> -<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb32"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">f_parent_nlmixr_saem_dfop_const</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html">nlmixr</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span>, - control <span class="op">=</span> <span class="va">nlmixr_saem_control</span><span class="op">)</span> + control <span class="op">=</span> <span class="va">nlmixr_saem_control_800</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/traceplot.html">traceplot</a></span><span class="op">(</span><span class="va">f_parent_nlmixr_saem_dfop_const</span><span class="op">$</span><span class="va">nm</span><span class="op">)</span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_const-1.png" width="700"></p> <p>For DFOP with two-component error, a less erratic convergence is seen.</p> -<div class="sourceCode" id="cb29"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb33"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">f_parent_nlmixr_saem_dfop_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html">nlmixr</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span>, - control <span class="op">=</span> <span class="va">nlmixr_saem_control</span><span class="op">)</span> + control <span class="op">=</span> <span class="va">nlmixr_saem_control_800</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/traceplot.html">traceplot</a></span><span class="op">(</span><span class="va">f_parent_nlmixr_saem_dfop_tc</span><span class="op">$</span><span class="va">nm</span><span class="op">)</span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_tc-1.png" width="700"></p> -<p>The AIC values are internally calculated using Gaussian quadrature. For an unknown reason, the AIC value obtained for the DFOP fit using constant error is given as Infinity.</p> -<div class="sourceCode" id="cb30"><pre class="downlit sourceCode r"> +<p>To check if an increase in the number of iterations improves the fit, we repeat the fit with 1000 iterations for the burn in phase and 300 iterations for the second phase.</p> +<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span class="va">f_parent_nlmixr_saem_dfop_tc_1000</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html">nlmixr</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span>, + control <span class="op">=</span> <span class="va">nlmixr_saem_control_1000</span><span class="op">)</span> +<span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/traceplot.html">traceplot</a></span><span class="op">(</span><span class="va">f_parent_nlmixr_saem_dfop_tc_1000</span><span class="op">$</span><span class="va">nm</span><span class="op">)</span></code></pre></div> +<p><img src="dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_tc_1k-1.png" width="700"></p> +<p>Here the fit looks very similar, but we will see below that it shows a higher AIC than the fit with 800 iterations in the burn in phase. Next we choose 10 000 iterations for the burn in phase and 1000 iterations for the second phase for comparison with saemix.</p> +<div class="sourceCode" id="cb35"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span class="va">f_parent_nlmixr_saem_dfop_tc_10k</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html">nlmixr</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span>, + control <span class="op">=</span> <span class="va">nlmixr_saem_control_10k</span><span class="op">)</span> +<span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/traceplot.html">traceplot</a></span><span class="op">(</span><span class="va">f_parent_nlmixr_saem_dfop_tc_10k</span><span class="op">$</span><span class="va">nm</span><span class="op">)</span></code></pre></div> +<p><img src="dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_tc_10k-1.png" width="700"></p> +<p>In the above convergence plot, the time course of ‘eta.DMTA_0’ and ‘log_k2’ indicate a false convergence.</p> +<p>The AIC values are internally calculated using Gaussian quadrature.</p> +<div class="sourceCode" id="cb36"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html">AIC</a></span><span class="op">(</span><span class="va">f_parent_nlmixr_saem_sfo_const</span><span class="op">$</span><span class="va">nm</span>, <span class="va">f_parent_nlmixr_saem_sfo_tc</span><span class="op">$</span><span class="va">nm</span>, - <span class="va">f_parent_nlmixr_saem_dfop_const</span><span class="op">$</span><span class="va">nm</span>, <span class="va">f_parent_nlmixr_saem_dfop_tc</span><span class="op">$</span><span class="va">nm</span><span class="op">)</span></code></pre></div> -<pre><code> df AIC -f_parent_nlmixr_saem_sfo_const$nm 5 798.68 -f_parent_nlmixr_saem_sfo_tc$nm 6 808.88 -f_parent_nlmixr_saem_dfop_const$nm 9 815.95 -f_parent_nlmixr_saem_dfop_tc$nm 10 669.57</code></pre> + <span class="va">f_parent_nlmixr_saem_dfop_const</span><span class="op">$</span><span class="va">nm</span>, <span class="va">f_parent_nlmixr_saem_dfop_tc</span><span class="op">$</span><span class="va">nm</span>, + <span class="va">f_parent_nlmixr_saem_dfop_tc_1000</span><span class="op">$</span><span class="va">nm</span>, + <span class="va">f_parent_nlmixr_saem_dfop_tc_10k</span><span class="op">$</span><span class="va">nm</span><span class="op">)</span></code></pre></div> +<pre><code> df AIC +f_parent_nlmixr_saem_sfo_const$nm 5 798.69 +f_parent_nlmixr_saem_sfo_tc$nm 6 810.33 +f_parent_nlmixr_saem_dfop_const$nm 9 736.00 +f_parent_nlmixr_saem_dfop_tc$nm 10 664.85 +f_parent_nlmixr_saem_dfop_tc_1000$nm 10 669.57 +f_parent_nlmixr_saem_dfop_tc_10k$nm 10 Inf</code></pre> +<p>We can see that again, the DFOP/tc model shows the best goodness of fit. However, increasing the number of burn-in iterations from 800 to 1000 results in a higher AIC. If we further increase the number of iterations to 10 000 (burn-in) and 1000 (second phase), the AIC cannot be calculated for the nlmixr/saem fit, supporting that the fit did not converge properly.</p> </div> <div id="comparison" class="section level3"> <h3 class="hasAnchor"> <a href="#comparison" class="anchor"></a>Comparison</h3> -<p>The following table gives the AIC values obtained with the three packages.</p> -<div class="sourceCode" id="cb32"><pre class="downlit sourceCode r"> +<p>The following table gives the AIC values obtained with the three packages using the same control parameters (800 iterations burn-in, 300 iterations second phase, 15 chains).</p> +<div class="sourceCode" id="cb38"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">AIC_all</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span> check.names <span class="op">=</span> <span class="cn">FALSE</span>, <span class="st">"Degradation model"</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"SFO"</span>, <span class="st">"DFOP"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, @@ -368,7 +422,7 @@ f_parent_nlmixr_saem_dfop_tc$nm 10 669.57</code></pre> <td align="right">796.60</td> <td align="right">796.62</td> <td align="right">796.37</td> -<td align="right">798.68</td> +<td align="right">798.69</td> </tr> <tr class="even"> <td align="left">SFO</td> @@ -376,7 +430,7 @@ f_parent_nlmixr_saem_dfop_tc$nm 10 669.57</code></pre> <td align="right">798.60</td> <td align="right">798.61</td> <td align="right">798.37</td> -<td align="right">808.88</td> +<td align="right">810.33</td> </tr> <tr class="odd"> <td align="left">DFOP</td> @@ -384,7 +438,7 @@ f_parent_nlmixr_saem_dfop_tc$nm 10 669.57</code></pre> <td align="right">NA</td> <td align="right">750.91</td> <td align="right">713.16</td> -<td align="right">815.95</td> +<td align="right">736.00</td> </tr> <tr class="even"> <td align="left">DFOP</td> @@ -392,10 +446,142 @@ f_parent_nlmixr_saem_dfop_tc$nm 10 669.57</code></pre> <td align="right">671.91</td> <td align="right">666.60</td> <td align="right">666.10</td> -<td align="right">669.57</td> +<td align="right">664.85</td> </tr> </tbody> </table> +<div class="sourceCode" id="cb39"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/intervals.html">intervals</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">)</span></code></pre></div> +<pre><code>Approximate 95% confidence intervals + + Fixed effects: + lower est. upper +DMTA_0 96.2802274 98.2761977 100.272168 +k1 0.0339753 0.0645487 0.095122 +k2 0.0058977 0.0088887 0.011880 +g 0.9064373 0.9514417 0.996446 + + Random effects: + lower est. upper +sd(DMTA_0) 0.44404 2.102366 3.76069 +sd(k1) 0.25433 0.589731 0.92514 +sd(k2) -0.33139 0.099797 0.53099 +sd(g) 0.39606 1.092234 1.78841 + + + lower est. upper +a.1 0.863644 1.063021 1.262398 +b.1 0.022555 0.029599 0.036643</code></pre> +<div class="sourceCode" id="cb41"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/intervals.html">intervals</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">)</span></code></pre></div> +<pre><code>Approximate 95% confidence intervals + + Fixed effects: + lower est. upper +DMTA_0 96.2802274 98.2761977 100.272168 +k1 0.0339753 0.0645487 0.095122 +k2 0.0058977 0.0088887 0.011880 +g 0.9064373 0.9514417 0.996446 + + Random effects: + lower est. upper +sd(DMTA_0) 0.44404 2.102366 3.76069 +sd(k1) 0.25433 0.589731 0.92514 +sd(k2) -0.33139 0.099797 0.53099 +sd(g) 0.39606 1.092234 1.78841 + + + lower est. upper +a.1 0.863644 1.063021 1.262398 +b.1 0.022555 0.029599 0.036643</code></pre> +<div class="sourceCode" id="cb43"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/intervals.html">intervals</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc_10k</span><span class="op">)</span></code></pre></div> +<pre><code>Approximate 95% confidence intervals + + Fixed effects: + lower est. upper +DMTA_0 96.3027896 98.2641150 100.225440 +k1 0.0338214 0.0644055 0.094990 +k2 0.0058857 0.0087896 0.011693 +g 0.9086138 0.9521421 0.995670 + + Random effects: + lower est. upper +sd(DMTA_0) 0.41448 2.05327 3.69206 +sd(k1) 0.25507 0.59132 0.92758 +sd(k2) -0.36781 0.09016 0.54813 +sd(g) 0.38585 1.06994 1.75402 + + + lower est. upper +a.1 0.866273 1.066115 1.265957 +b.1 0.022501 0.029541 0.036581</code></pre> +<div class="sourceCode" id="cb45"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/intervals.html">intervals</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc_mkin_10k</span><span class="op">)</span></code></pre></div> +<pre><code>Approximate 95% confidence intervals + + Fixed effects: + lower est. upper +DMTA_0 96.3021306 98.2736091 100.245088 +k1 0.0401701 0.0645140 0.103611 +k2 0.0064706 0.0089398 0.012351 +g 0.8817692 0.9511605 0.980716 + + Random effects: + lower est. upper +sd(DMTA_0) 0.42392 2.068018 3.71212 +sd(log_k1) 0.25440 0.589877 0.92536 +sd(log_k2) -0.38431 0.084334 0.55298 +sd(g_qlogis) 0.39107 1.077303 1.76353 + + + lower est. upper +a.1 0.865291 1.064897 1.264504 +b.1 0.022491 0.029526 0.036561</code></pre> +<div class="sourceCode" id="cb47"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/intervals.html">intervals</a></span><span class="op">(</span><span class="va">f_parent_nlmixr_saem_dfop_tc</span><span class="op">)</span></code></pre></div> +<pre><code>Approximate 95% confidence intervals + + Fixed effects: + lower est. upper +DMTA_0 96.3059406 98.2990616 100.292183 +k1 0.0402306 0.0648255 0.104456 +k2 0.0067864 0.0093097 0.012771 +g 0.8769017 0.9505258 0.981067 + + Random effects: + lower est. upper +sd(DMTA_0) NA 1.724654 NA +sd(log_k1) NA 0.592808 NA +sd(log_k2) NA 0.010741 NA +sd(g_qlogis) NA 1.087349 NA + + + lower est. upper +sigma_low NA 1.081809 NA +rsd_high NA 0.032051 NA</code></pre> +<div class="sourceCode" id="cb49"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/intervals.html">intervals</a></span><span class="op">(</span><span class="va">f_parent_nlmixr_saem_dfop_tc_10k</span><span class="op">)</span></code></pre></div> +<pre><code>Approximate 95% confidence intervals + + Fixed effects: + lower est. upper +DMTA_0 96.426510 97.8987836 99.371057 +k1 0.040006 0.0644407 0.103799 +k2 0.006748 0.0092476 0.012673 +g 0.879251 0.9511399 0.981147 + + Random effects: + lower est. upper +sd(DMTA_0) NA 3.7049e-04 NA +sd(log_k1) NA 5.9221e-01 NA +sd(log_k2) NA 3.8628e-07 NA +sd(g_qlogis) NA 1.0694e+00 NA + + + lower est. upper +sigma_low NA 1.082343 NA +rsd_high NA 0.034895 NA</code></pre> </div> </div> </div> diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_const-1.png b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_const-1.png Binary files differindex db28e43d..af70163c 100644 --- a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_const-1.png +++ b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_const-1.png diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_tc-1.png b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_tc-1.png 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b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc_mkin_10k-1.png Binary files differnew file mode 100644 index 00000000..ae8c1555 --- /dev/null +++ b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc_mkin_10k-1.png diff --git a/docs/dev/news/index.html b/docs/dev/news/index.html index f3cc89d9..2a6b3d9d 100644 --- a/docs/dev/news/index.html +++ b/docs/dev/news/index.html @@ -153,6 +153,7 @@ <li><p>‘mean_degparms’: New argument ‘test_log_parms’ that makes the function only consider log-transformed parameters where the untransformed parameters pass the t-test for a certain confidence level. This can be used to obtain more plausible starting parameters for the different mixed-effects model backends</p></li> <li><p>‘plot.mixed.mmkin’: Gains arguments ‘test_log_parms’ and ‘conf.level’</p></li> <li><p>‘vignettes/web_only/dimethenamid_2018.rmd’: Example evaluations of the dimethenamid data.</p></li> +<li><p>‘intervals’: Provide methods of this nlme function for ‘nlmixr.mmkin’ and ‘saem.mmkin’ objects.</p></li> </ul> </div> </div> diff --git a/docs/dev/pkgdown.yml b/docs/dev/pkgdown.yml index 256387c1..e52da77d 100644 --- a/docs/dev/pkgdown.yml +++ b/docs/dev/pkgdown.yml @@ -11,7 +11,7 @@ articles: web_only/benchmarks: benchmarks.html web_only/compiled_models: compiled_models.html web_only/dimethenamid_2018: dimethenamid_2018.html -last_built: 2021-09-17T06:44Z +last_built: 2021-09-27T18:09Z urls: reference: https://pkgdown.jrwb.de/mkin/reference article: https://pkgdown.jrwb.de/mkin/articles diff --git a/docs/dev/reference/Rplot001.png b/docs/dev/reference/Rplot001.png Binary files differindex 17a35806..c26c864f 100644 --- a/docs/dev/reference/Rplot001.png +++ b/docs/dev/reference/Rplot001.png diff --git a/docs/dev/reference/index.html b/docs/dev/reference/index.html index d5ec387a..092e35c8 100644 --- a/docs/dev/reference/index.html +++ b/docs/dev/reference/index.html @@ -370,6 +370,24 @@ of an mmkin object</p></td> <p><code><a href="mixed.html">mixed()</a></code> <code><a href="mixed.html">print(<i><mixed.mmkin></i>)</a></code> </p> </td> <td><p>Create a mixed effects model from an mmkin row object</p></td> + </tr><tr> + + <td> + <p><code><a href="reexports.html">reexports</a></code> </p> + </td> + <td><p>Objects exported from other packages</p></td> + </tr><tr> + + <td> + <p><code><a href="intervals.saem.mmkin.html">intervals(<i><saem.mmkin></i>)</a></code> </p> + </td> + <td><p>Confidence intervals for parameters in saem.mmkin objects</p></td> + </tr><tr> + + <td> + <p><code><a href="intervals.nlmixr.mmkin.html">intervals(<i><nlmixr.mmkin></i>)</a></code> </p> + </td> + <td><p>Confidence intervals for parameters in nlmixr.mmkin objects</p></td> </tr> </tbody><tbody> <tr> diff --git a/docs/dev/reference/intervals.nlmixr.mmkin.html b/docs/dev/reference/intervals.nlmixr.mmkin.html new file mode 100644 index 00000000..ccc348b1 --- /dev/null +++ b/docs/dev/reference/intervals.nlmixr.mmkin.html @@ -0,0 +1,203 @@ +<!-- Generated by pkgdown: do not edit by hand --> +<!DOCTYPE html> +<html lang="en"> + <head> + <meta charset="utf-8"> +<meta http-equiv="X-UA-Compatible" content="IE=edge"> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> + +<title>Confidence intervals for parameters in nlmixr.mmkin objects — intervals.nlmixr.mmkin • mkin</title> + + +<!-- jquery --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script> +<!-- 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+ <h1>Confidence intervals for parameters in nlmixr.mmkin objects</h1> + <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/intervals.R'><code>R/intervals.R</code></a></small> + <div class="hidden name"><code>intervals.nlmixr.mmkin.Rd</code></div> + </div> + + <div class="ref-description"> + <p>Confidence intervals for parameters in nlmixr.mmkin objects</p> + </div> + + <pre class="usage"><span class='co'># S3 method for nlmixr.mmkin</span> +<span class='fu'><a href='https://rdrr.io/pkg/nlme/man/intervals.html'>intervals</a></span><span class='op'>(</span><span class='va'>object</span>, level <span class='op'>=</span> <span class='fl'>0.95</span>, backtransform <span class='op'>=</span> <span class='cn'>TRUE</span>, <span class='va'>...</span><span class='op'>)</span></pre> + + <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> + <table class="ref-arguments"> + <colgroup><col class="name" /><col class="desc" /></colgroup> + <tr> + <th>object</th> + <td><p>The fitted saem.mmkin object</p></td> + </tr> + <tr> + <th>level</th> + <td><p>The confidence level.</p></td> + </tr> + <tr> + <th>backtransform</th> + <td><p>Should we backtransform the parameters where a one to +one correlation between transformed and backtransformed parameters exists?</p></td> + </tr> + </table> + + <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> + + <p>An object with 'intervals.saem.mmkin' and 'intervals.lme' in the +class attribute</p> + + </div> + <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> + <nav id="toc" data-toggle="toc" class="sticky-top"> + <h2 data-toc-skip>Contents</h2> + </nav> + </div> +</div> + + + <footer> + <div class="copyright"> + <p>Developed by Johannes Ranke.</p> +</div> + +<div class="pkgdown"> + <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> +</div> + + </footer> + </div> + + + + + </body> +</html> + + diff --git 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href="../index.html">mkin</a> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span> + </span> + </div> + + <div id="navbar" class="navbar-collapse collapse"> + <ul class="nav navbar-nav"> + <li> + <a href="../reference/index.html">Functions and data</a> +</li> +<li class="dropdown"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + Articles + + <span class="caret"></span> + </a> + <ul class="dropdown-menu" role="menu"> + <li> + <a href="../articles/mkin.html">Introduction to mkin</a> + </li> + <li> + <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> + </li> + <li> + <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> + </li> + <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + </li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> + <li> + <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> + </li> + </ul> +</li> +<li> + <a href="../news/index.html">News</a> +</li> + </ul> + <ul class="nav navbar-nav navbar-right"> + <li> + <a href="https://github.com/jranke/mkin/"> + <span class="fab fa-github fa-lg"></span> + + </a> +</li> + </ul> + + </div><!--/.nav-collapse --> + </div><!--/.container --> +</div><!--/.navbar --> + + + + </header> + +<div class="row"> + <div class="col-md-9 contents"> + <div class="page-header"> + <h1>Confidence intervals for parameters in saem.mmkin objects</h1> + <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/intervals.R'><code>R/intervals.R</code></a></small> + <div class="hidden name"><code>intervals.saem.mmkin.Rd</code></div> + </div> + + <div class="ref-description"> + <p>Confidence intervals for parameters in saem.mmkin objects</p> + </div> + + <pre class="usage"><span class='co'># S3 method for saem.mmkin</span> +<span class='fu'><a href='https://rdrr.io/pkg/nlme/man/intervals.html'>intervals</a></span><span class='op'>(</span><span class='va'>object</span>, level <span class='op'>=</span> <span class='fl'>0.95</span>, backtransform <span class='op'>=</span> <span class='cn'>TRUE</span>, <span class='va'>...</span><span class='op'>)</span></pre> + + <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> + <table class="ref-arguments"> + <colgroup><col class="name" /><col class="desc" /></colgroup> + <tr> + <th>object</th> + <td><p>The fitted saem.mmkin object</p></td> + </tr> + <tr> + <th>level</th> + <td><p>The confidence level. Must be the default of 0.95 as this is what +is available in the saemix object</p></td> + </tr> + <tr> + <th>backtransform</th> + <td><p>In case the model was fitted with mkin transformations, +should we backtransform the parameters where a one to one correlation +between transformed and backtransformed parameters exists?</p></td> + </tr> + </table> + + <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> + + <p>An object with 'intervals.saem.mmkin' and 'intervals.lme' in the +class attribute</p> + + </div> + <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> + <nav id="toc" data-toggle="toc" class="sticky-top"> + <h2 data-toc-skip>Contents</h2> + </nav> + </div> +</div> + + + <footer> + <div class="copyright"> + <p>Developed by Johannes Ranke.</p> +</div> + +<div class="pkgdown"> + <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> +</div> + + </footer> + </div> + + + + + </body> +</html> + + diff --git a/docs/dev/reference/nlmixr.mmkin.html b/docs/dev/reference/nlmixr.mmkin.html index db114483..568078c6 100644 --- a/docs/dev/reference/nlmixr.mmkin.html +++ b/docs/dev/reference/nlmixr.mmkin.html @@ -210,8 +210,8 @@ Expectation Maximisation algorithm (SAEM).</p> </tr> <tr> <th>error_model</th> - <td><p>Possibility to override the error model which is being -set based on the error model used in the mmkin row object.</p></td> + <td><p>Optional argument to override the error model which is +being set based on the error model used in the mmkin row object.</p></td> </tr> <tr> <th>test_log_parms</th> @@ -290,7 +290,8 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit. cores <span class='op'>=</span> <span class='fl'>1</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> <span class='va'>f_nlmixr_sfo_saem</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_parent</span><span class='op'>[</span><span class='st'>"SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"saem"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'>→ generate SAEM model</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> 1: 86.5083 -3.1968 4.1673 1.7173 48.7028 +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'>→ generate SAEM model</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>RxODE 1.1.1 using 8 threads (see ?getRxThreads)</span> +#> <span class='message'> no cache: create with `rxCreateCache()`</span></div><div class='output co'>#> 1: 86.5083 -3.1968 4.1673 1.7173 48.7028 #> 2: 87.3628 -3.1468 3.9589 1.6315 45.1225 #> 3: 86.8866 -3.2249 3.7610 1.8212 43.0034 #> 4: 85.9210 -3.2427 3.5729 1.7302 39.4197 @@ -4502,7 +4503,7 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit. #> <span class='message'>k_A1=rx_expr_11;</span> #> <span class='message'>f_parent=1/(1+exp(-(ETA[4]+THETA[4])));</span> #> <span class='message'>tad=tad();</span> -#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 5.579 0.328 5.909</span></div><div class='input'><span class='va'>f_nlmixr_fomc_sfo_focei_const</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_const</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 5.546 0.379 5.924</span></div><div class='input'><span class='va'>f_nlmixr_fomc_sfo_focei_const</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_const</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> </div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Model:</span></div><div class='output co'>#> <span class='message'>cmt(parent);</span> #> <span class='message'>cmt(A1);</span> #> <span class='message'>rx_expr_6~ETA[1]+THETA[1];</span> @@ -4551,7 +4552,7 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit. #> <span class='message'>beta=exp(rx_expr_8);</span> #> <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span> #> <span class='message'>tad=tad();</span> -#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 7.125 0.34 7.462</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_const</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_const</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 6.842 0.338 7.177</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_const</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_const</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> </div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Model:</span></div><div class='output co'>#> <span class='message'>cmt(parent);</span> #> <span class='message'>cmt(A1);</span> #> <span class='message'>rx_expr_6~ETA[1]+THETA[1];</span> @@ -4608,10 +4609,10 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit. #> <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span> #> <span class='message'>g=1/(rx_expr_20);</span> #> <span class='message'>tad=tad();</span> -#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 15.03 0.407 15.43</span></div><div class='input'> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 15.14 0.971 16.13</span></div><div class='input'> <span class='co'># Variance by variable is supported by 'saem' and 'focei'</span> <span class='va'>f_nlmixr_fomc_sfo_saem_obs</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_obs</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"saem"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'>→ generate SAEM model</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='error'>Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 1.253 0.121 1.373</span></div><div class='input'><span class='va'>f_nlmixr_fomc_sfo_focei_obs</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_obs</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'>→ generate SAEM model</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='error'>Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 1.271 0.123 1.393</span></div><div class='input'><span class='va'>f_nlmixr_fomc_sfo_focei_obs</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_obs</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> </div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Model:</span></div><div class='output co'>#> <span class='message'>cmt(parent);</span> #> <span class='message'>cmt(A1);</span> #> <span class='message'>rx_expr_6~ETA[1]+THETA[1];</span> @@ -4660,8 +4661,8 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit. #> <span class='message'>beta=exp(rx_expr_8);</span> #> <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span> #> <span class='message'>tad=tad();</span> -#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 6.487 0.387 6.872</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_saem_obs</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_obs</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"saem"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'>→ generate SAEM model</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='error'>Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 1.385 0.1 1.485</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_obs</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_obs</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 6.699 0.341 7.038</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_saem_obs</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_obs</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"saem"</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'>→ generate SAEM model</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='error'>Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 1.384 0.092 1.477</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_obs</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_obs</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> </div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Model:</span></div><div class='output co'>#> <span class='message'>cmt(parent);</span> #> <span class='message'>cmt(A1);</span> #> <span class='message'>rx_expr_6~ETA[1]+THETA[1];</span> @@ -4718,7 +4719,7 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit. #> <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span> #> <span class='message'>g=1/(rx_expr_19);</span> #> <span class='message'>tad=tad();</span> -#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 15.06 0.434 15.49</span></div><div class='input'> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 14.86 1.172 16.03</span></div><div class='input'> <span class='co'># Identical two-component error for all variables is only possible with</span> <span class='co'># est = 'focei' in nlmixr</span> <span class='va'>f_nlmixr_fomc_sfo_focei_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> @@ -4772,7 +4773,7 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit. #> <span class='message'>beta=exp(rx_expr_8);</span> #> <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span> #> <span class='message'>tad=tad();</span> -#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 8.599 0.38 8.975</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 8.461 0.404 8.863</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> </div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Model:</span></div><div class='output co'>#> <span class='message'>cmt(parent);</span> #> <span class='message'>cmt(A1);</span> #> <span class='message'>rx_expr_6~ETA[1]+THETA[1];</span> @@ -4831,12 +4832,12 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit. #> <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span> #> <span class='message'>g=1/(rx_expr_21);</span> #> <span class='message'>tad=tad();</span> -#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 17.64 0.453 18.09</span></div><div class='input'> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 18.02 0.925 18.94</span></div><div class='input'> <span class='co'># Two-component error by variable is possible with both estimation methods</span> <span class='co'># Variance by variable is supported by 'saem' and 'focei'</span> <span class='va'>f_nlmixr_fomc_sfo_saem_obs_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"saem"</span>, error_model <span class='op'>=</span> <span class='st'>"obs_tc"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='error'>Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 0.772 0.04 0.813</span></div><div class='input'><span class='va'>f_nlmixr_fomc_sfo_focei_obs_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span>, +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='error'>Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 0.782 0.02 0.803</span></div><div class='input'><span class='va'>f_nlmixr_fomc_sfo_focei_obs_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span>, error_model <span class='op'>=</span> <span class='st'>"obs_tc"</span><span class='op'>)</span> </div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Model:</span></div><div class='output co'>#> <span class='message'>cmt(parent);</span> #> <span class='message'>cmt(A1);</span> @@ -4888,9 +4889,9 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit. #> <span class='message'>beta=exp(rx_expr_8);</span> #> <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span> #> <span class='message'>tad=tad();</span> -#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 8.33 0.419 8.745</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_saem_obs_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"saem"</span>, +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 8.173 0.405 8.575</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_saem_obs_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"saem"</span>, error_model <span class='op'>=</span> <span class='st'>"obs_tc"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='error'>Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 0.832 0.032 0.866</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_obs_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span>, +</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='error'>Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 0.824 0.024 0.851</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_obs_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span>, error_model <span class='op'>=</span> <span class='st'>"obs_tc"</span><span class='op'>)</span> </div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Model:</span></div><div class='output co'>#> <span class='message'>cmt(parent);</span> #> <span class='message'>cmt(A1);</span> @@ -4950,7 +4951,7 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit. #> <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span> #> <span class='message'>g=1/(rx_expr_19);</span> #> <span class='message'>tad=tad();</span> -#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 18.15 0.42 18.57</span></div><div class='input'> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 17.44 0.876 18.31</span></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span> <span class='va'>f_nlmixr_sfo_sfo_focei_const</span><span class='op'>$</span><span class='va'>nm</span>, <span class='va'>f_nlmixr_fomc_sfo_focei_const</span><span class='op'>$</span><span class='va'>nm</span>, diff --git a/docs/dev/reference/reexports.html b/docs/dev/reference/reexports.html index ac4fa4d9..4fe5164b 100644 --- a/docs/dev/reference/reexports.html +++ b/docs/dev/reference/reexports.html @@ -45,7 +45,7 @@ below to see their documentation. lmtestlrtest - nlmenlme + nlmeintervals, nlme nlmixrnlmixr @@ -148,7 +148,7 @@ below to see their documentation. <div class="col-md-9 contents"> <div class="page-header"> <h1>Objects exported from other packages</h1> - <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/lrtest.mkinfit.R'><code>R/lrtest.mkinfit.R</code></a>, <a href='https://github.com/jranke/mkin/blob/master/R/nlme.mmkin.R'><code>R/nlme.mmkin.R</code></a>, <a href='https://github.com/jranke/mkin/blob/master/R/nlmixr.R'><code>R/nlmixr.R</code></a></small> + <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/intervals.R'><code>R/intervals.R</code></a>, <a href='https://github.com/jranke/mkin/blob/master/R/lrtest.mkinfit.R'><code>R/lrtest.mkinfit.R</code></a>, <a href='https://github.com/jranke/mkin/blob/master/R/nlme.mmkin.R'><code>R/nlme.mmkin.R</code></a>, and 1 more</small> <div class="hidden name"><code>reexports.Rd</code></div> </div> @@ -158,7 +158,7 @@ below to see their documentation.</p> <dl> <dt>lmtest</dt><dd><p><code><a href='https://rdrr.io/pkg/lmtest/man/lrtest.html'>lrtest</a></code></p></dd> - <dt>nlme</dt><dd><p><code><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></code></p></dd> + <dt>nlme</dt><dd><p><code><a href='https://rdrr.io/pkg/nlme/man/intervals.html'>intervals</a></code>, <code><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></code></p></dd> <dt>nlmixr</dt><dd><p><code><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></code></p></dd> diff --git a/docs/dev/sitemap.xml b/docs/dev/sitemap.xml index b5e83f34..963d4200 100644 --- a/docs/dev/sitemap.xml +++ b/docs/dev/sitemap.xml @@ -88,6 +88,12 @@ <loc>https://pkgdown.jrwb.de/mkin/reference/ilr.html</loc> </url> <url> + <loc>https://pkgdown.jrwb.de/mkin/reference/intervals.nlmixr.mmkin.html</loc> + </url> + <url> + <loc>https://pkgdown.jrwb.de/mkin/reference/intervals.saem.mmkin.html</loc> + </url> + <url> <loc>https://pkgdown.jrwb.de/mkin/reference/loftest.html</loc> </url> <url> diff --git a/man/intervals.nlmixr.mmkin.Rd b/man/intervals.nlmixr.mmkin.Rd new file mode 100644 index 00000000..b1ab5da4 --- /dev/null +++ b/man/intervals.nlmixr.mmkin.Rd @@ -0,0 +1,23 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/intervals.R +\name{intervals.nlmixr.mmkin} +\alias{intervals.nlmixr.mmkin} +\title{Confidence intervals for parameters in nlmixr.mmkin objects} +\usage{ +\method{intervals}{nlmixr.mmkin}(object, level = 0.95, backtransform = TRUE, ...) +} +\arguments{ +\item{object}{The fitted saem.mmkin object} + +\item{level}{The confidence level.} + +\item{backtransform}{Should we backtransform the parameters where a one to +one correlation between transformed and backtransformed parameters exists?} +} +\value{ +An object with 'intervals.saem.mmkin' and 'intervals.lme' in the +class attribute +} +\description{ +Confidence intervals for parameters in nlmixr.mmkin objects +} diff --git a/man/intervals.saem.mmkin.Rd b/man/intervals.saem.mmkin.Rd new file mode 100644 index 00000000..4a41dd1b --- /dev/null +++ b/man/intervals.saem.mmkin.Rd @@ -0,0 +1,25 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/intervals.R +\name{intervals.saem.mmkin} +\alias{intervals.saem.mmkin} +\title{Confidence intervals for parameters in saem.mmkin objects} +\usage{ +\method{intervals}{saem.mmkin}(object, level = 0.95, backtransform = TRUE, ...) +} +\arguments{ +\item{object}{The fitted saem.mmkin object} + +\item{level}{The confidence level. Must be the default of 0.95 as this is what +is available in the saemix object} + +\item{backtransform}{In case the model was fitted with mkin transformations, +should we backtransform the parameters where a one to one correlation +between transformed and backtransformed parameters exists?} +} +\value{ +An object with 'intervals.saem.mmkin' and 'intervals.lme' in the +class attribute +} +\description{ +Confidence intervals for parameters in saem.mmkin objects +} diff --git a/man/nlmixr.mmkin.Rd b/man/nlmixr.mmkin.Rd index 0f4f41a2..173b0d39 100644 --- a/man/nlmixr.mmkin.Rd +++ b/man/nlmixr.mmkin.Rd @@ -50,8 +50,8 @@ nlmixr_data(object, ...) \item{table}{Passed to \link[nlmixr:nlmixr]{nlmixr::nlmixr}} -\item{error_model}{Possibility to override the error model which is being -set based on the error model used in the mmkin row object.} +\item{error_model}{Optional argument to override the error model which is +being set based on the error model used in the mmkin row object.} \item{test_log_parms}{If TRUE, an attempt is made to use more robust starting values for population parameters fitted as log parameters in mkin (like diff --git a/man/reexports.Rd b/man/reexports.Rd index d4fc6b96..dfbb76a7 100644 --- a/man/reexports.Rd +++ b/man/reexports.Rd @@ -1,8 +1,10 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/lrtest.mkinfit.R, R/nlme.mmkin.R, R/nlmixr.R +% Please edit documentation in R/intervals.R, R/lrtest.mkinfit.R, +% R/nlme.mmkin.R, R/nlmixr.R \docType{import} \name{reexports} \alias{reexports} +\alias{intervals} \alias{lrtest} \alias{nlme} \alias{nlmixr} @@ -15,7 +17,7 @@ below to see their documentation. \describe{ \item{lmtest}{\code{\link[lmtest]{lrtest}}} - \item{nlme}{\code{\link[nlme]{nlme}}} + \item{nlme}{\code{\link[nlme]{intervals}}, \code{\link[nlme]{nlme}}} \item{nlmixr}{\code{\link[nlmixr]{nlmixr}}} }} diff --git a/vignettes/web_only/dimethenamid_2018.R b/vignettes/web_only/dimethenamid_2018.R deleted file mode 100644 index 2c01bc14..00000000 --- a/vignettes/web_only/dimethenamid_2018.R +++ /dev/null @@ -1,116 +0,0 @@ -## ---- include = FALSE--------------------------------------------------------- -require(knitr) -options(digits = 5) -opts_chunk$set( - comment = "", - tidy = FALSE, - cache = TRUE -) - -## ----saemix_control----------------------------------------------------------- -library(saemix) -saemix_control <- saemixControl(nbiter.saemix = c(800, 200), nb.chains = 15, - print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE) - -## ----f_parent_saemix_sfo_const, results = 'hide', dependson = "saemix_control"---- -f_parent_saemix_sfo_const <- mkin::saem(f_parent_mkin_const["SFO", ], quiet = TRUE, - control = saemix_control, transformations = "saemix") -plot(f_parent_saemix_sfo_const$so, plot.type = "convergence") - -## ----f_parent_saemix_sfo_tc, results = 'hide', dependson = "saemix_control"---- -f_parent_saemix_sfo_tc <- mkin::saem(f_parent_mkin_tc["SFO", ], quiet = TRUE, - control = saemix_control, transformations = "saemix") -plot(f_parent_saemix_sfo_tc$so, plot.type = "convergence") - -## ----f_parent_saemix_dfop_const, results = 'hide', dependson = "saemix_control"---- -f_parent_saemix_dfop_const <- mkin::saem(f_parent_mkin_const["DFOP", ], quiet = TRUE, - control = saemix_control, transformations = "saemix") -plot(f_parent_saemix_dfop_const$so, plot.type = "convergence") - -## ----f_parent_saemix_dfop_tc, results = 'hide', dependson = "saemix_control"---- -f_parent_saemix_dfop_tc <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE, - control = saemix_control, transformations = "saemix") -plot(f_parent_saemix_dfop_tc$so, plot.type = "convergence") - -## ----AIC_parent_saemix-------------------------------------------------------- -compare.saemix( - f_parent_saemix_sfo_const$so, - f_parent_saemix_sfo_tc$so, - f_parent_saemix_dfop_const$so, - f_parent_saemix_dfop_tc$so) - -## ----AIC_parent_saemix_methods------------------------------------------------ -f_parent_saemix_dfop_tc$so <- - llgq.saemix(f_parent_saemix_dfop_tc$so) -AIC(f_parent_saemix_dfop_tc$so) -AIC(f_parent_saemix_dfop_tc$so, method = "gq") -AIC(f_parent_saemix_dfop_tc$so, method = "lin") - -## ----f_parent_nlmixr_focei, results = "hide", message = FALSE, warning = FALSE---- -library(nlmixr) -f_parent_nlmixr_focei_sfo_const <- nlmixr(f_parent_mkin_const["SFO", ], est = "focei") -f_parent_nlmixr_focei_sfo_tc <- nlmixr(f_parent_mkin_tc["SFO", ], est = "focei") -f_parent_nlmixr_focei_dfop_const <- nlmixr(f_parent_mkin_const["DFOP", ], est = "focei") -f_parent_nlmixr_focei_dfop_tc<- nlmixr(f_parent_mkin_tc["DFOP", ], est = "focei") - -## ----AIC_parent_nlmixr_focei-------------------------------------------------- -aic_nlmixr_focei <- sapply( - list(f_parent_nlmixr_focei_sfo_const$nm, f_parent_nlmixr_focei_sfo_tc$nm, - f_parent_nlmixr_focei_dfop_const$nm, f_parent_nlmixr_focei_dfop_tc$nm), - AIC) - -## ----AIC_parent_nlme_rep------------------------------------------------------ -aic_nlme <- sapply( - list(f_parent_nlme_sfo_const, NA, f_parent_nlme_sfo_tc, f_parent_nlme_dfop_tc), - function(x) if (is.na(x[1])) NA else AIC(x)) -aic_nlme_nlmixr_focei <- data.frame( - "Degradation model" = c("SFO", "SFO", "DFOP", "DFOP"), - "Error model" = rep(c("constant variance", "two-component"), 2), - "AIC (nlme)" = aic_nlme, - "AIC (nlmixr with FOCEI)" = aic_nlmixr_focei, - check.names = FALSE -) - -## ----nlmixr_saem_control------------------------------------------------------ -nlmixr_saem_control <- saemControl(logLik = TRUE, - nBurn = 800, nEm = 200, nmc = 15) - -## ----f_parent_nlmixr_saem_sfo_const, results = "hide", warning = FALSE, message = FALSE, dependson = "nlmixr_saem_control"---- -f_parent_nlmixr_saem_sfo_const <- nlmixr(f_parent_mkin_const["SFO", ], est = "saem", - control = nlmixr_saem_control) -traceplot(f_parent_nlmixr_saem_sfo_const$nm) - -## ----f_parent_nlmixr_saem_sfo_tc, results = "hide", warning = FALSE, message = FALSE, dependson = "nlmixr_saem_control"---- -f_parent_nlmixr_saem_sfo_tc <- nlmixr(f_parent_mkin_tc["SFO", ], est = "saem", - control = nlmixr_saem_control) -traceplot(f_parent_nlmixr_saem_sfo_tc$nm) - -## ----f_parent_nlmixr_saem_dfop_const, results = "hide", warning = FALSE, message = FALSE, dependson = "nlmixr_saem_control"---- -f_parent_nlmixr_saem_dfop_const <- nlmixr(f_parent_mkin_const["DFOP", ], est = "saem", - control = nlmixr_saem_control) -traceplot(f_parent_nlmixr_saem_dfop_const$nm) - -## ----f_parent_nlmixr_saem_dfop_tc, results = "hide", warning = FALSE, message = FALSE, dependson = "nlmixr_saem_control"---- -f_parent_nlmixr_saem_dfop_tc <- nlmixr(f_parent_mkin_tc["DFOP", ], est = "saem", - control = nlmixr_saem_control) -traceplot(f_parent_nlmixr_saem_dfop_tc$nm) - -## ----AIC_parent_nlmixr_saem--------------------------------------------------- -AIC(f_parent_nlmixr_saem_sfo_const$nm, f_parent_nlmixr_saem_sfo_tc$nm, - f_parent_nlmixr_saem_dfop_const$nm, f_parent_nlmixr_saem_dfop_tc$nm) - -## ----AIC_all------------------------------------------------------------------ -AIC_all <- data.frame( - check.names = FALSE, - "Degradation model" = c("SFO", "SFO", "DFOP", "DFOP"), - "Error model" = c("const", "tc", "const", "tc"), - nlme = c(AIC(f_parent_nlme_sfo_const), AIC(f_parent_nlme_sfo_tc), NA, AIC(f_parent_nlme_dfop_tc)), - nlmixr_focei = sapply(list(f_parent_nlmixr_focei_sfo_const$nm, f_parent_nlmixr_focei_sfo_tc$nm, - f_parent_nlmixr_focei_dfop_const$nm, f_parent_nlmixr_focei_dfop_tc$nm), AIC), - saemix = sapply(list(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so, - f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc$so), AIC), - nlmixr_saem = sapply(list(f_parent_nlmixr_saem_sfo_const$nm, f_parent_nlmixr_saem_sfo_tc$nm, - f_parent_nlmixr_saem_dfop_const$nm, f_parent_nlmixr_saem_dfop_tc$nm), AIC) -) -kable(AIC_all) - diff --git a/vignettes/web_only/dimethenamid_2018.html b/vignettes/web_only/dimethenamid_2018.html index a92a720d..95e41c71 100644 --- a/vignettes/web_only/dimethenamid_2018.html +++ b/vignettes/web_only/dimethenamid_2018.html @@ -1591,7 +1591,7 @@ div.tocify { <h1 class="title toc-ignore">Example evaluations of the dimethenamid data from 2018</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">Last change 17 September 2021, built on 17 Sep 2021</h4> +<h4 class="date">Last change 27 September 2021, built on 27 Sep 2021</h4> </div> @@ -1648,14 +1648,14 @@ f_parent_mkin_tc <- mmkin(c("SFO", "DFOP"), dmta_ds, <p>Instead of taking a model selection decision for each of the individual fits, we fit nonlinear mixed-effects models (using different fitting algorithms as implemented in different packages) and do model selection using all available data at the same time. In order to make sure that these decisions are not unduly influenced by the type of algorithm used, by implementation details or by the use of wrong control parameters, we compare the model selection results obtained with different R packages, with different algorithms and checking control parameters.</p> <div id="nlme" class="section level3"> <h3>nlme</h3> -<p>The nlme package was the first R extension providing facilities to fit nonlinear mixed-effects models. We would like to do model selection from all four combinations of degradation models and error models based on the AIC. However, fitting the DFOP model with constant variance and using default control parameters results in an error, signalling that the maximum number of 50 iterations was reached, potentially indicating overparameterisation. However, the algorithm converges when the two-component error model is used in combination with the DFOP model. This can be explained by the fact that the smaller residues observed at later sampling times get more weight when using the two-component error model which will counteract the tendency of the algorithm to try parameter combinations unsuitable for fitting these data.</p> +<p>The nlme package was the first R extension providing facilities to fit nonlinear mixed-effects models. We would like to do model selection from all four combinations of degradation models and error models based on the AIC. However, fitting the DFOP model with constant variance and using default control parameters results in an error, signalling that the maximum number of 50 iterations was reached, potentially indicating overparameterisation. Nevertheless, the algorithm converges when the two-component error model is used in combination with the DFOP model. This can be explained by the fact that the smaller residues observed at later sampling times get more weight when using the two-component error model which will counteract the tendency of the algorithm to try parameter combinations unsuitable for fitting these data.</p> <pre class="r"><code>library(nlme) f_parent_nlme_sfo_const <- nlme(f_parent_mkin_const["SFO", ]) # f_parent_nlme_dfop_const <- nlme(f_parent_mkin_const["DFOP", ]) f_parent_nlme_sfo_tc <- nlme(f_parent_mkin_tc["SFO", ]) f_parent_nlme_dfop_tc <- nlme(f_parent_mkin_tc["DFOP", ])</code></pre> <p>Note that a certain degree of overparameterisation is also indicated by a warning obtained when fitting DFOP with the two-component error model (‘false convergence’ in the ‘LME step’ in iteration 3). However, as this warning does not occur in later iterations, and specifically not in the last of the 6 iterations, we can ignore this warning.</p> -<p>The model comparison function of the nlme package can directly be applied to these fits showing a much lower AIC for the DFOP model fitted with the two-component error model. Also, the likelihood ratio test indicates that this difference is significant. as the p-value is below 0.0001.</p> +<p>The model comparison function of the nlme package can directly be applied to these fits showing a much lower AIC for the DFOP model fitted with the two-component error model. Also, the likelihood ratio test indicates that this difference is significant as the p-value is below 0.0001.</p> <pre class="r"><code>anova( f_parent_nlme_sfo_const, f_parent_nlme_sfo_tc, f_parent_nlme_dfop_tc )</code></pre> @@ -1684,6 +1684,8 @@ anova(f_parent_nlme_dfop_tc, f_parent_nlme_dfop_tc_logchol)</code></pre> <p>The corresponding SAEM fits of the four combinations of degradation and error models are fitted below. As there is no convergence criterion implemented in the saemix package, the convergence plots need to be manually checked for every fit. As we will compare the SAEM implementation of saemix to the results obtained using the nlmixr package later, we define control settings that work well for all the parent data fits shown in this vignette.</p> <pre class="r"><code>library(saemix) saemix_control <- saemixControl(nbiter.saemix = c(800, 300), nb.chains = 15, + print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE) +saemix_control_10k <- saemixControl(nbiter.saemix = c(10000, 1000), nb.chains = 15, print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE)</code></pre> <p>The convergence plot for the SFO model using constant variance is shown below.</p> <pre class="r"><code>f_parent_saemix_sfo_const <- mkin::saem(f_parent_mkin_const["SFO", ], quiet = TRUE, @@ -1695,31 +1697,57 @@ plot(f_parent_saemix_sfo_const$so, plot.type = "convergence")</code></ control = saemix_control, transformations = "saemix") plot(f_parent_saemix_sfo_tc$so, plot.type = "convergence")</code></pre> <p><img 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" /><!-- --></p> -<p>When fitting the DFOP model with constant variance, parameter convergence is not as unambiguous (see the failure of nlme with the default number of iterations above). Therefore, the number of iterations in the first phase of the algorithm was increased, leading to visually satisfying convergence.</p> +<p>When fitting the DFOP model with constant variance (see below), parameter convergence is not as unambiguous.</p> <pre class="r"><code>f_parent_saemix_dfop_const <- mkin::saem(f_parent_mkin_const["DFOP", ], quiet = TRUE, control = saemix_control, transformations = "saemix") plot(f_parent_saemix_dfop_const$so, plot.type = "convergence")</code></pre> <p><img src="data:image/png;base64,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" /><!-- --></p> -<p>The same applies in the case where the DFOP model is fitted with the two-component error model. Convergence of the variance of k2 is enhanced by using the two-component error, it remains more or less stable already after 200 iterations of the first phase.</p> +<p>This is improved when the DFOP model is fitted with the two-component error model. Convergence of the variance of k2 is enhanced, it remains more or less stable already after 200 iterations of the first phase.</p> <pre class="r"><code>f_parent_saemix_dfop_tc <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE, control = saemix_control, transformations = "saemix") plot(f_parent_saemix_dfop_tc$so, plot.type = "convergence")</code></pre> -<p><img 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" /><!-- --> The four combinations and including the variations of the DFOP/tc combination can be compared using the model comparison function from the saemix package:</p> -<pre class="r"><code>compare.saemix( +<p><img src="data:image/png;base64,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" /><!-- --></p> +<p>We also check if using many more iterations (10 000 for the first and 1000 for the second phase) improve the result in a significant way. The AIC values obtained are compared further below.</p> +<pre class="r"><code>f_parent_saemix_dfop_tc_10k <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE, + control = saemix_control_10k, transformations = "saemix") +plot(f_parent_saemix_dfop_tc_10k$so, plot.type = "convergence")</code></pre> +<p><img 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" /><!-- --></p> +<p>An alternative way to fit DFOP in combination with the two-component error model is to use the model formulation with transformed parameters as used per default in mkin.</p> +<pre class="r"><code>f_parent_saemix_dfop_tc_mkin <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE, + control = saemix_control, transformations = "mkin") +plot(f_parent_saemix_dfop_tc_mkin$so, plot.type = "convergence")</code></pre> +<p><img src="data:image/png;base64,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" /><!-- --></p> +<p>As the convergence plots do not clearly indicate that the algorithm has converged, we again use a much larger number of iterations, which leads to satisfactory convergence (see below).</p> +<pre class="r"><code>f_parent_saemix_dfop_tc_mkin_10k <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE, + control = saemix_control_10k, transformations = "mkin") +plot(f_parent_saemix_dfop_tc_mkin_10k$so, plot.type = "convergence")</code></pre> +<p><img src="data:image/png;base64,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" /><!-- --></p> +<p>The four combinations (SFO/const, SFO/tc, DFOP/const and DFOP/tc), including the variations of the DFOP/tc combination can be compared using the model comparison function of the saemix package:</p> +<pre class="r"><code>AIC_parent_saemix <- saemix::compare.saemix( f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so, f_parent_saemix_dfop_const$so, - f_parent_saemix_dfop_tc$so)</code></pre> + f_parent_saemix_dfop_tc$so, + f_parent_saemix_dfop_tc_10k$so, + f_parent_saemix_dfop_tc_mkin$so, + f_parent_saemix_dfop_tc_mkin_10k$so)</code></pre> <pre><code>Likelihoods calculated by importance sampling</code></pre> -<pre><code> AIC BIC -1 796.37 795.33 -2 798.37 797.13 -3 713.16 711.28 -4 666.10 664.01</code></pre> -<p>As in the case of nlme fits, the DFOP model fitted with two-component error (number 4) gives the lowest AIC. Using more iterations and/or more chains does not have a large influence on the final AIC (not shown).</p> +<pre class="r"><code>rownames(AIC_parent_saemix) <- c( + "SFO const", "SFO tc", "DFOP const", "DFOP tc", "DFOP tc more iterations", + "DFOP tc mkintrans", "DFOP tc mkintrans more iterations") +print(AIC_parent_saemix)</code></pre> +<pre><code> AIC BIC +SFO const 796.37 795.33 +SFO tc 798.37 797.13 +DFOP const 713.16 711.28 +DFOP tc 666.10 664.01 +DFOP tc more iterations 666.15 664.06 +DFOP tc mkintrans 682.26 680.17 +DFOP tc mkintrans more iterations 666.12 664.04</code></pre> +<p>As in the case of nlme fits, the DFOP model fitted with two-component error (number 4) gives the lowest AIC. Using a much larger number of iterations does not improve the fit a lot. When the mkin transformations are used instead of the saemix transformations, this large number of iterations leads to a goodness of fit that is comparable to the result obtained with saemix transformations.</p> <p>In order to check the influence of the likelihood calculation algorithms implemented in saemix, the likelihood from Gaussian quadrature is added to the best fit, and the AIC values obtained from the three methods are compared.</p> <pre class="r"><code>f_parent_saemix_dfop_tc$so <- - llgq.saemix(f_parent_saemix_dfop_tc$so) + saemix::llgq.saemix(f_parent_saemix_dfop_tc$so) AIC_parent_saemix_methods <- c( is = AIC(f_parent_saemix_dfop_tc$so, method = "is"), gq = AIC(f_parent_saemix_dfop_tc$so, method = "gq"), @@ -1755,40 +1783,60 @@ aic_nlme_nlmixr_focei <- data.frame( check.names = FALSE )</code></pre> <p>Secondly, we use the SAEM estimation routine and check the convergence plots. The control parameters also used for the saemix fits are defined beforehand.</p> -<pre class="r"><code>nlmixr_saem_control <- saemControl(logLik = TRUE, - nBurn = 1000, nEm = 300, nmc = 15)</code></pre> +<pre class="r"><code>nlmixr_saem_control_800 <- saemControl(logLik = TRUE, + nBurn = 800, nEm = 300, nmc = 15) +nlmixr_saem_control_1000 <- saemControl(logLik = TRUE, + nBurn = 1000, nEm = 300, nmc = 15) +nlmixr_saem_control_10k <- saemControl(logLik = TRUE, + nBurn = 10000, nEm = 1000, nmc = 15)</code></pre> <p>The we fit SFO with constant variance</p> <pre class="r"><code>f_parent_nlmixr_saem_sfo_const <- nlmixr(f_parent_mkin_const["SFO", ], est = "saem", control = nlmixr_saem_control) traceplot(f_parent_nlmixr_saem_sfo_const$nm)</code></pre> -<p><img 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" 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" /><!-- --></p> <p>and SFO with two-component error.</p> <pre class="r"><code>f_parent_nlmixr_saem_sfo_tc <- nlmixr(f_parent_mkin_tc["SFO", ], est = "saem", - control = nlmixr_saem_control) + control = nlmixr_saem_control_800) traceplot(f_parent_nlmixr_saem_sfo_tc$nm)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <p>For DFOP with constant variance, the convergence plots show considerable instability of the fit, which indicates overparameterisation which was already observed earlier for this model combination.</p> <pre class="r"><code>f_parent_nlmixr_saem_dfop_const <- nlmixr(f_parent_mkin_const["DFOP", ], est = "saem", - control = nlmixr_saem_control) + control = nlmixr_saem_control_800) traceplot(f_parent_nlmixr_saem_dfop_const$nm)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <p>For DFOP with two-component error, a less erratic convergence is seen.</p> <pre class="r"><code>f_parent_nlmixr_saem_dfop_tc <- nlmixr(f_parent_mkin_tc["DFOP", ], est = "saem", - control = nlmixr_saem_control) + control = nlmixr_saem_control_800) traceplot(f_parent_nlmixr_saem_dfop_tc$nm)</code></pre> +<p><img src="data:image/png;base64,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" /><!-- --></p> +<p>To check if an increase in the number of iterations improves the fit, we repeat the fit with 1000 iterations for the burn in phase and 300 iterations for the second phase.</p> +<pre class="r"><code>f_parent_nlmixr_saem_dfop_tc_1000 <- nlmixr(f_parent_mkin_tc["DFOP", ], est = "saem", + control = nlmixr_saem_control_1000) +traceplot(f_parent_nlmixr_saem_dfop_tc_1000$nm)</code></pre> <p><img src="data:image/png;base64,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" /><!-- --></p> -<p>The AIC values are internally calculated using Gaussian quadrature. For an unknown reason, the AIC value obtained for the DFOP fit using constant error is given as Infinity.</p> +<p>Here the fit looks very similar, but we will see below that it shows a higher AIC than the fit with 800 iterations in the burn in phase. Next we choose 10 000 iterations for the burn in phase and 1000 iterations for the second phase for comparison with saemix.</p> +<pre class="r"><code>f_parent_nlmixr_saem_dfop_tc_10k <- nlmixr(f_parent_mkin_tc["DFOP", ], est = "saem", + control = nlmixr_saem_control_10k) +traceplot(f_parent_nlmixr_saem_dfop_tc_10k$nm)</code></pre> +<p><img src="data:image/png;base64,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" /><!-- --></p> +<p>In the above convergence plot, the time course of ‘eta.DMTA_0’ and ‘log_k2’ indicate a false convergence.</p> +<p>The AIC values are internally calculated using Gaussian quadrature.</p> <pre class="r"><code>AIC(f_parent_nlmixr_saem_sfo_const$nm, f_parent_nlmixr_saem_sfo_tc$nm, - f_parent_nlmixr_saem_dfop_const$nm, f_parent_nlmixr_saem_dfop_tc$nm)</code></pre> -<pre><code> df AIC -f_parent_nlmixr_saem_sfo_const$nm 5 798.68 -f_parent_nlmixr_saem_sfo_tc$nm 6 808.88 -f_parent_nlmixr_saem_dfop_const$nm 9 815.95 -f_parent_nlmixr_saem_dfop_tc$nm 10 669.57</code></pre> + f_parent_nlmixr_saem_dfop_const$nm, f_parent_nlmixr_saem_dfop_tc$nm, + f_parent_nlmixr_saem_dfop_tc_1000$nm, + f_parent_nlmixr_saem_dfop_tc_10k$nm)</code></pre> +<pre><code> df AIC +f_parent_nlmixr_saem_sfo_const$nm 5 798.69 +f_parent_nlmixr_saem_sfo_tc$nm 6 810.33 +f_parent_nlmixr_saem_dfop_const$nm 9 736.00 +f_parent_nlmixr_saem_dfop_tc$nm 10 664.85 +f_parent_nlmixr_saem_dfop_tc_1000$nm 10 669.57 +f_parent_nlmixr_saem_dfop_tc_10k$nm 10 Inf</code></pre> +<p>We can see that again, the DFOP/tc model shows the best goodness of fit. However, increasing the number of burn-in iterations from 800 to 1000 results in a higher AIC. If we further increase the number of iterations to 10 000 (burn-in) and 1000 (second phase), the AIC cannot be calculated for the nlmixr/saem fit, supporting that the fit did not converge properly.</p> </div> <div id="comparison" class="section level3"> <h3>Comparison</h3> -<p>The following table gives the AIC values obtained with the three packages.</p> +<p>The following table gives the AIC values obtained with the three packages using the same control parameters (800 iterations burn-in, 300 iterations second phase, 15 chains).</p> <pre class="r"><code>AIC_all <- data.frame( check.names = FALSE, "Degradation model" = c("SFO", "SFO", "DFOP", "DFOP"), @@ -1820,7 +1868,7 @@ kable(AIC_all)</code></pre> <td align="right">796.60</td> <td align="right">796.62</td> <td align="right">796.37</td> -<td align="right">798.68</td> +<td align="right">798.69</td> </tr> <tr class="even"> <td align="left">SFO</td> @@ -1828,7 +1876,7 @@ kable(AIC_all)</code></pre> <td align="right">798.60</td> <td align="right">798.61</td> <td align="right">798.37</td> -<td align="right">808.88</td> +<td align="right">810.33</td> </tr> <tr class="odd"> <td align="left">DFOP</td> @@ -1836,7 +1884,7 @@ kable(AIC_all)</code></pre> <td align="right">NA</td> <td align="right">750.91</td> <td align="right">713.16</td> -<td align="right">815.95</td> +<td align="right">736.00</td> </tr> <tr class="even"> <td align="left">DFOP</td> @@ -1844,10 +1892,136 @@ kable(AIC_all)</code></pre> <td align="right">671.91</td> <td align="right">666.60</td> <td align="right">666.10</td> -<td align="right">669.57</td> +<td align="right">664.85</td> </tr> </tbody> </table> +<pre class="r"><code>intervals(f_parent_saemix_dfop_tc)</code></pre> +<pre><code>Approximate 95% confidence intervals + + Fixed effects: + lower est. upper +DMTA_0 96.2802274 98.2761977 100.272168 +k1 0.0339753 0.0645487 0.095122 +k2 0.0058977 0.0088887 0.011880 +g 0.9064373 0.9514417 0.996446 + + Random effects: + lower est. upper +sd(DMTA_0) 0.44404 2.102366 3.76069 +sd(k1) 0.25433 0.589731 0.92514 +sd(k2) -0.33139 0.099797 0.53099 +sd(g) 0.39606 1.092234 1.78841 + + + lower est. upper +a.1 0.863644 1.063021 1.262398 +b.1 0.022555 0.029599 0.036643</code></pre> +<pre class="r"><code>intervals(f_parent_saemix_dfop_tc)</code></pre> +<pre><code>Approximate 95% confidence intervals + + Fixed effects: + lower est. upper +DMTA_0 96.2802274 98.2761977 100.272168 +k1 0.0339753 0.0645487 0.095122 +k2 0.0058977 0.0088887 0.011880 +g 0.9064373 0.9514417 0.996446 + + Random effects: + lower est. upper +sd(DMTA_0) 0.44404 2.102366 3.76069 +sd(k1) 0.25433 0.589731 0.92514 +sd(k2) -0.33139 0.099797 0.53099 +sd(g) 0.39606 1.092234 1.78841 + + + lower est. upper +a.1 0.863644 1.063021 1.262398 +b.1 0.022555 0.029599 0.036643</code></pre> +<pre class="r"><code>intervals(f_parent_saemix_dfop_tc_10k)</code></pre> +<pre><code>Approximate 95% confidence intervals + + Fixed effects: + lower est. upper +DMTA_0 96.3027896 98.2641150 100.225440 +k1 0.0338214 0.0644055 0.094990 +k2 0.0058857 0.0087896 0.011693 +g 0.9086138 0.9521421 0.995670 + + Random effects: + lower est. upper +sd(DMTA_0) 0.41448 2.05327 3.69206 +sd(k1) 0.25507 0.59132 0.92758 +sd(k2) -0.36781 0.09016 0.54813 +sd(g) 0.38585 1.06994 1.75402 + + + lower est. upper +a.1 0.866273 1.066115 1.265957 +b.1 0.022501 0.029541 0.036581</code></pre> +<pre class="r"><code>intervals(f_parent_saemix_dfop_tc_mkin_10k)</code></pre> +<pre><code>Approximate 95% confidence intervals + + Fixed effects: + lower est. upper +DMTA_0 96.3021306 98.2736091 100.245088 +k1 0.0401701 0.0645140 0.103611 +k2 0.0064706 0.0089398 0.012351 +g 0.8817692 0.9511605 0.980716 + + Random effects: + lower est. upper +sd(DMTA_0) 0.42392 2.068018 3.71212 +sd(log_k1) 0.25440 0.589877 0.92536 +sd(log_k2) -0.38431 0.084334 0.55298 +sd(g_qlogis) 0.39107 1.077303 1.76353 + + + lower est. upper +a.1 0.865291 1.064897 1.264504 +b.1 0.022491 0.029526 0.036561</code></pre> +<pre class="r"><code>intervals(f_parent_nlmixr_saem_dfop_tc)</code></pre> +<pre><code>Approximate 95% confidence intervals + + Fixed effects: + lower est. upper +DMTA_0 96.3059406 98.2990616 100.292183 +k1 0.0402306 0.0648255 0.104456 +k2 0.0067864 0.0093097 0.012771 +g 0.8769017 0.9505258 0.981067 + + Random effects: + lower est. upper +sd(DMTA_0) NA 1.724654 NA +sd(log_k1) NA 0.592808 NA +sd(log_k2) NA 0.010741 NA +sd(g_qlogis) NA 1.087349 NA + + + lower est. upper +sigma_low NA 1.081809 NA +rsd_high NA 0.032051 NA</code></pre> +<pre class="r"><code>intervals(f_parent_nlmixr_saem_dfop_tc_10k)</code></pre> +<pre><code>Approximate 95% confidence intervals + + Fixed effects: + lower est. upper +DMTA_0 96.426510 97.8987836 99.371057 +k1 0.040006 0.0644407 0.103799 +k2 0.006748 0.0092476 0.012673 +g 0.879251 0.9511399 0.981147 + + Random effects: + lower est. upper +sd(DMTA_0) NA 3.7049e-04 NA +sd(log_k1) NA 5.9221e-01 NA +sd(log_k2) NA 3.8628e-07 NA +sd(g_qlogis) NA 1.0694e+00 NA + + + lower est. upper +sigma_low NA 1.082343 NA +rsd_high NA 0.034895 NA</code></pre> </div> </div> </div> diff --git a/vignettes/web_only/dimethenamid_2018.rmd b/vignettes/web_only/dimethenamid_2018.rmd index 7679edc4..ae93984d 100644 --- a/vignettes/web_only/dimethenamid_2018.rmd +++ b/vignettes/web_only/dimethenamid_2018.rmd @@ -1,7 +1,7 @@ --- title: Example evaluations of the dimethenamid data from 2018 author: Johannes Ranke -date: Last change 17 September 2021, built on `r format(Sys.Date(), format = "%d %b %Y")` +date: Last change 27 September 2021, built on `r format(Sys.Date(), format = "%d %b %Y")` output: html_document: toc: true @@ -18,6 +18,8 @@ vignette: > ```{r, include = FALSE} require(knitr) +require(mkin) +require(nlme) options(digits = 5) opts_chunk$set( comment = "", @@ -153,7 +155,7 @@ combinations of degradation models and error models based on the AIC. However, fitting the DFOP model with constant variance and using default control parameters results in an error, signalling that the maximum number of 50 iterations was reached, potentially indicating overparameterisation. -However, the algorithm converges when the two-component error model is +Nevertheless, the algorithm converges when the two-component error model is used in combination with the DFOP model. This can be explained by the fact that the smaller residues observed at later sampling times get more weight when using the two-component error model which will counteract the @@ -167,6 +169,7 @@ f_parent_nlme_sfo_const <- nlme(f_parent_mkin_const["SFO", ]) f_parent_nlme_sfo_tc <- nlme(f_parent_mkin_tc["SFO", ]) f_parent_nlme_dfop_tc <- nlme(f_parent_mkin_tc["DFOP", ]) ``` + Note that a certain degree of overparameterisation is also indicated by a warning obtained when fitting DFOP with the two-component error model ('false convergence' in the 'LME step' in iteration 3). However, as this warning does @@ -176,7 +179,7 @@ not occur in later iterations, and specifically not in the last of the The model comparison function of the nlme package can directly be applied to these fits showing a much lower AIC for the DFOP model fitted with the two-component error model. Also, the likelihood ratio test indicates that this -difference is significant. as the p-value is below 0.0001. +difference is significant as the p-value is below 0.0001. ```{r AIC_parent_nlme} anova( @@ -231,6 +234,8 @@ work well for all the parent data fits shown in this vignette. library(saemix) saemix_control <- saemixControl(nbiter.saemix = c(800, 300), nb.chains = 15, print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE) +saemix_control_10k <- saemixControl(nbiter.saemix = c(10000, 1000), nb.chains = 15, + print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE) ``` The convergence plot for the SFO model using constant variance is shown below. @@ -250,11 +255,8 @@ f_parent_saemix_sfo_tc <- mkin::saem(f_parent_mkin_tc["SFO", ], quiet = TRUE, plot(f_parent_saemix_sfo_tc$so, plot.type = "convergence") ``` -When fitting the DFOP model with constant variance, parameter convergence -is not as unambiguous (see the failure of nlme with the default number of -iterations above). Therefore, the number of iterations in the first -phase of the algorithm was increased, leading to visually satisfying -convergence. +When fitting the DFOP model with constant variance (see below), parameter +convergence is not as unambiguous. ```{r f_parent_saemix_dfop_const, results = 'hide', dependson = "saemix_control"} f_parent_saemix_dfop_const <- mkin::saem(f_parent_mkin_const["DFOP", ], quiet = TRUE, @@ -262,30 +264,71 @@ f_parent_saemix_dfop_const <- mkin::saem(f_parent_mkin_const["DFOP", ], quiet = plot(f_parent_saemix_dfop_const$so, plot.type = "convergence") ``` -The same applies in the case where the DFOP model is fitted with the -two-component error model. Convergence of the variance of k2 is enhanced by -using the two-component error, it remains more or less stable already after -200 iterations of the first phase. +This is improved when the DFOP model is fitted with the two-component error +model. Convergence of the variance of k2 is enhanced, it remains more or less +stable already after 200 iterations of the first phase. ```{r f_parent_saemix_dfop_tc, results = 'hide', dependson = "saemix_control"} f_parent_saemix_dfop_tc <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE, control = saemix_control, transformations = "saemix") plot(f_parent_saemix_dfop_tc$so, plot.type = "convergence") ``` -The four combinations and including the variations of the DFOP/tc combination -can be compared using the model comparison function from the saemix package: + +We also check if using many more iterations (10 000 for the first and 1000 for +the second phase) improve the result in a significant way. The AIC values +obtained are compared further below. + +```{r f_parent_saemix_dfop_tc_10k, results = 'hide', dependson = "saemix_control"} +f_parent_saemix_dfop_tc_10k <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE, + control = saemix_control_10k, transformations = "saemix") +plot(f_parent_saemix_dfop_tc_10k$so, plot.type = "convergence") +``` + +An alternative way to fit DFOP in combination with the two-component error model +is to use the model formulation with transformed parameters as used per default +in mkin. + +```{r f_parent_saemix_dfop_tc_mkin, results = 'hide', dependson = "saemix_control"} +f_parent_saemix_dfop_tc_mkin <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE, + control = saemix_control, transformations = "mkin") +plot(f_parent_saemix_dfop_tc_mkin$so, plot.type = "convergence") +``` + +As the convergence plots do not clearly indicate that the algorithm has converged, we +again use a much larger number of iterations, which leads to satisfactory +convergence (see below). + +```{r f_parent_saemix_dfop_tc_mkin_10k, results = 'hide', dependson = "saemix_control"} +f_parent_saemix_dfop_tc_mkin_10k <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE, + control = saemix_control_10k, transformations = "mkin") +plot(f_parent_saemix_dfop_tc_mkin_10k$so, plot.type = "convergence") +``` + +The four combinations (SFO/const, SFO/tc, DFOP/const and DFOP/tc), including +the variations of the DFOP/tc combination can be compared using the model +comparison function of the saemix package: ```{r AIC_parent_saemix, cache = FALSE} -compare.saemix( +AIC_parent_saemix <- saemix::compare.saemix( f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so, f_parent_saemix_dfop_const$so, - f_parent_saemix_dfop_tc$so) + f_parent_saemix_dfop_tc$so, + f_parent_saemix_dfop_tc_10k$so, + f_parent_saemix_dfop_tc_mkin$so, + f_parent_saemix_dfop_tc_mkin_10k$so) +rownames(AIC_parent_saemix) <- c( + "SFO const", "SFO tc", "DFOP const", "DFOP tc", "DFOP tc more iterations", + "DFOP tc mkintrans", "DFOP tc mkintrans more iterations") +print(AIC_parent_saemix) ``` As in the case of nlme fits, the DFOP model fitted with two-component error -(number 4) gives the lowest AIC. Using more iterations and/or more chains -does not have a large influence on the final AIC (not shown). +(number 4) gives the lowest AIC. Using a much larger number of iterations +does not improve the fit a lot. When the mkin transformations are used +instead of the saemix transformations, this large number of iterations leads +to a goodness of fit that is comparable to the result obtained with saemix +transformations. In order to check the influence of the likelihood calculation algorithms implemented in saemix, the likelihood from Gaussian quadrature is added @@ -294,7 +337,7 @@ are compared. ```{r AIC_parent_saemix_methods, cache = FALSE} f_parent_saemix_dfop_tc$so <- - llgq.saemix(f_parent_saemix_dfop_tc$so) + saemix::llgq.saemix(f_parent_saemix_dfop_tc$so) AIC_parent_saemix_methods <- c( is = AIC(f_parent_saemix_dfop_tc$so, method = "is"), gq = AIC(f_parent_saemix_dfop_tc$so, method = "gq"), @@ -302,6 +345,7 @@ AIC_parent_saemix_methods <- c( ) print(AIC_parent_saemix_methods) ``` + The AIC values based on importance sampling and Gaussian quadrature are very similar. Using linearisation is known to be less accurate, but still gives a similar value. @@ -355,15 +399,19 @@ Secondly, we use the SAEM estimation routine and check the convergence plots. Th control parameters also used for the saemix fits are defined beforehand. ```{r nlmixr_saem_control} -nlmixr_saem_control <- saemControl(logLik = TRUE, +nlmixr_saem_control_800 <- saemControl(logLik = TRUE, + nBurn = 800, nEm = 300, nmc = 15) +nlmixr_saem_control_1000 <- saemControl(logLik = TRUE, nBurn = 1000, nEm = 300, nmc = 15) +nlmixr_saem_control_10k <- saemControl(logLik = TRUE, + nBurn = 10000, nEm = 1000, nmc = 15) ``` The we fit SFO with constant variance ```{r f_parent_nlmixr_saem_sfo_const, results = "hide", warning = FALSE, message = FALSE, dependson = "nlmixr_saem_control"} f_parent_nlmixr_saem_sfo_const <- nlmixr(f_parent_mkin_const["SFO", ], est = "saem", - control = nlmixr_saem_control) + control = nlmixr_saem_control_800) traceplot(f_parent_nlmixr_saem_sfo_const$nm) ``` @@ -371,7 +419,7 @@ and SFO with two-component error. ```{r f_parent_nlmixr_saem_sfo_tc, results = "hide", warning = FALSE, message = FALSE, dependson = "nlmixr_saem_control"} f_parent_nlmixr_saem_sfo_tc <- nlmixr(f_parent_mkin_tc["SFO", ], est = "saem", - control = nlmixr_saem_control) + control = nlmixr_saem_control_800) traceplot(f_parent_nlmixr_saem_sfo_tc$nm) ``` @@ -381,7 +429,7 @@ observed earlier for this model combination. ```{r f_parent_nlmixr_saem_dfop_const, results = "hide", warning = FALSE, message = FALSE, dependson = "nlmixr_saem_control"} f_parent_nlmixr_saem_dfop_const <- nlmixr(f_parent_mkin_const["DFOP", ], est = "saem", - control = nlmixr_saem_control) + control = nlmixr_saem_control_800) traceplot(f_parent_nlmixr_saem_dfop_const$nm) ``` @@ -389,22 +437,54 @@ For DFOP with two-component error, a less erratic convergence is seen. ```{r f_parent_nlmixr_saem_dfop_tc, results = "hide", warning = FALSE, message = FALSE, dependson = "nlmixr_saem_control"} f_parent_nlmixr_saem_dfop_tc <- nlmixr(f_parent_mkin_tc["DFOP", ], est = "saem", - control = nlmixr_saem_control) + control = nlmixr_saem_control_800) traceplot(f_parent_nlmixr_saem_dfop_tc$nm) ``` -The AIC values are internally calculated using Gaussian quadrature. For an -unknown reason, the AIC value obtained for the DFOP fit using constant error -is given as Infinity. +To check if an increase in the number of iterations improves the fit, we repeat +the fit with 1000 iterations for the burn in phase and 300 iterations for the +second phase. + +```{r f_parent_nlmixr_saem_dfop_tc_1k, results = "hide", warning = FALSE, message = FALSE, dependson = "nlmixr_saem_control"} +f_parent_nlmixr_saem_dfop_tc_1000 <- nlmixr(f_parent_mkin_tc["DFOP", ], est = "saem", + control = nlmixr_saem_control_1000) +traceplot(f_parent_nlmixr_saem_dfop_tc_1000$nm) +``` + +Here the fit looks very similar, but we will see below that it shows a higher AIC +than the fit with 800 iterations in the burn in phase. Next we choose +10 000 iterations for the burn in phase and 1000 iterations for the second +phase for comparison with saemix. + +```{r f_parent_nlmixr_saem_dfop_tc_10k, results = "hide", warning = FALSE, message = FALSE, dependson = "nlmixr_saem_control"} +f_parent_nlmixr_saem_dfop_tc_10k <- nlmixr(f_parent_mkin_tc["DFOP", ], est = "saem", + control = nlmixr_saem_control_10k) +traceplot(f_parent_nlmixr_saem_dfop_tc_10k$nm) +``` + +In the above convergence plot, the time course of 'eta.DMTA_0' and +'log_k2' indicate a false convergence. + +The AIC values are internally calculated using Gaussian quadrature. ```{r AIC_parent_nlmixr_saem, cache = FALSE} AIC(f_parent_nlmixr_saem_sfo_const$nm, f_parent_nlmixr_saem_sfo_tc$nm, - f_parent_nlmixr_saem_dfop_const$nm, f_parent_nlmixr_saem_dfop_tc$nm) + f_parent_nlmixr_saem_dfop_const$nm, f_parent_nlmixr_saem_dfop_tc$nm, + f_parent_nlmixr_saem_dfop_tc_1000$nm, + f_parent_nlmixr_saem_dfop_tc_10k$nm) ``` +We can see that again, the DFOP/tc model shows the best goodness of fit. +However, increasing the number of burn-in iterations from 800 to 1000 results +in a higher AIC. If we further increase the number of iterations to 10 000 +(burn-in) and 1000 (second phase), the AIC cannot be calculated for the +nlmixr/saem fit, supporting that the fit did not converge properly. + ### Comparison -The following table gives the AIC values obtained with the three packages. +The following table gives the AIC values obtained with the three packages using +the same control parameters (800 iterations burn-in, 300 iterations second +phase, 15 chains). ```{r AIC_all, cache = FALSE} AIC_all <- data.frame( @@ -422,6 +502,15 @@ AIC_all <- data.frame( kable(AIC_all) ``` +```{r parms_all, cache = FALSE} +intervals(f_parent_saemix_dfop_tc) +intervals(f_parent_saemix_dfop_tc) +intervals(f_parent_saemix_dfop_tc_10k) +intervals(f_parent_saemix_dfop_tc_mkin_10k) +intervals(f_parent_nlmixr_saem_dfop_tc) +intervals(f_parent_nlmixr_saem_dfop_tc_10k) +``` + # References diff --git a/vignettes/web_only/dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_const-1.png 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