diff options
65 files changed, 818 insertions, 642 deletions
@@ -14,14 +14,7 @@ Maintainer: ‘Johannes Ranke <jranke@uni-bremen.de>’ * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for executable files ... OK -* checking for hidden files and directories ... NOTE -Found the following hidden files and directories: - vignettes/web_only/.build.timestamp -These were most likely included in error. See section ‘Package -structure’ in the ‘Writing R Extensions’ manual. - -CRAN-pack does not know about - vignettes/web_only/.build.timestamp +* checking for hidden files and directories ... OK * checking for portable file names ... OK * checking for sufficient/correct file permissions ... OK * checking serialization versions ... OK @@ -75,9 +68,5 @@ CRAN-pack does not know about * checking for detritus in the temp directory ... OK * DONE -Status: 1 NOTE -See - ‘/home/jranke/git/mkin/mkin.Rcheck/00check.log’ -for details. - +Status: OK diff --git a/docs/dev/404.html b/docs/dev/404.html index 98c0b1e0..38898979 100644 --- a/docs/dev/404.html +++ b/docs/dev/404.html @@ -71,7 +71,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="https://pkgdown.jrwb.de/mkin/index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span> </span> </div> diff --git a/docs/dev/articles/index.html b/docs/dev/articles/index.html index 99ce950b..c0338df8 100644 --- a/docs/dev/articles/index.html +++ b/docs/dev/articles/index.html @@ -71,7 +71,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span> </span> </div> diff --git a/docs/dev/articles/web_only/dimethenamid_2018.html b/docs/dev/articles/web_only/dimethenamid_2018.html index 9a6d8388..b35d8210 100644 --- a/docs/dev/articles/web_only/dimethenamid_2018.html +++ b/docs/dev/articles/web_only/dimethenamid_2018.html @@ -32,7 +32,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span> </span> </div> @@ -95,13 +95,13 @@ - </header><script src="dimethenamid_2018_files/header-attrs-2.9/header-attrs.js"></script><script src="dimethenamid_2018_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> + </header><script src="dimethenamid_2018_files/header-attrs-2.11/header-attrs.js"></script><script src="dimethenamid_2018_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> <h1 data-toc-skip>Example evaluations of the dimethenamid data from 2018</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">Last change 4 August 2021, built on 04 Aug 2021</h4> + <h4 class="date">Last change 16 September 2021, built on 16 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> @@ -110,31 +110,28 @@ -<p><a href="http://www.jrwb.de">Wissenschaftlicher Berater, Kronacher Str. 12, 79639 Grenzach-Wyhlen, Germany</a><br><a href="http://chem.uft.uni-bremen.de/ranke">Privatdozent at the University of Bremen</a></p> +<p><a href="http://www.jrwb.de">Wissenschaftlicher Berater, Kronacher Str. 12, 79639 Grenzach-Wyhlen, Germany</a></p> <div id="introduction" class="section level1"> <h1 class="hasAnchor"> <a href="#introduction" class="anchor"></a>Introduction</h1> -<p>During the preparation of the journal article on nonlinear mixed-effects models in degradation kinetics (submitted) and the analysis of the dimethenamid degradation data analysed therein, a need for a more detailed analysis using not only nlme and saemix, but also nlmixr for fitting the mixed-effects models was identified.</p> +<p>During the preparation of the journal article on nonlinear mixed-effects models in degradation kinetics <span class="citation">(Ranke et al. 2021)</span> and the analysis of the dimethenamid degradation data analysed therein, a need for a more detailed analysis using not only nlme and saemix, but also nlmixr for fitting the mixed-effects models was identified.</p> <p>This vignette is an attempt to satisfy this need.</p> </div> <div id="data" class="section level1"> <h1 class="hasAnchor"> <a href="#data" class="anchor"></a>Data</h1> -<p>Residue data forming the basis for the endpoints derived in the conclusion on the peer review of the pesticide risk assessment of dimethenamid-P published by the European Food Safety Authority (EFSA) in 2018 <span class="citation">(EFSA 2018)</span> were transcribed from the risk assessment report <span class="citation">(Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria 2018)</span> which can be downloaded from the <a href="https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716">EFSA register of questions</a>.</p> +<p>Residue data forming the basis for the endpoints derived in the conclusion on the peer review of the pesticide risk assessment of dimethenamid-P published by the European Food Safety Authority (EFSA) in 2018 <span class="citation">(EFSA 2018)</span> were transcribed from the risk assessment report <span class="citation">(Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria 2018)</span> which can be downloaded from the Open EFSA repository <a href="https://open.efsa.europa.eu">https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716</a>.</p> <p>The data are <a href="https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html">available in the mkin package</a>. The following code (hidden by default, please use the button to the right to show it) treats the data available for the racemic mixture dimethenamid (DMTA) and its enantiomer dimethenamid-P (DMTAP) in the same way, as no difference between their degradation behaviour was identified in the EU risk assessment. The observation times of each dataset are multiplied with the corresponding normalisation factor also available in the dataset, in order to make it possible to describe all datasets with a single set of parameters.</p> <p>Also, datasets observed in the same soil are merged, resulting in dimethenamid (DMTA) data from six soils.</p> <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span><span class="op">)</span> -<span class="va">dmta_ds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">8</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span> +<span class="va">dmta_ds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span> <span class="va">ds_i</span> <span class="op"><-</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span><span class="op">[[</span><span class="va">i</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span> <span class="va">ds_i</span><span class="op">[</span><span class="va">ds_i</span><span class="op">$</span><span class="va">name</span> <span class="op">==</span> <span class="st">"DMTAP"</span>, <span class="st">"name"</span><span class="op">]</span> <span class="op"><-</span> <span class="st">"DMTA"</span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op"><-</span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">*</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">f_time_norm</span><span class="op">[</span><span class="va">i</span><span class="op">]</span> <span class="va">ds_i</span> <span class="op">}</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">sapply</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="va">ds</span><span class="op">$</span><span class="va">title</span><span class="op">)</span> -<span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Borstel"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html">rbind</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Borstel 1"</span><span class="op">]</span><span class="op">]</span>, <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Borstel 2"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span> -<span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Borstel 1"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="cn">NULL</span> -<span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Borstel 2"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="cn">NULL</span> <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html">rbind</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span>, <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span> <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="cn">NULL</span> <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="cn">NULL</span></code></pre></div> @@ -154,20 +151,20 @@ error_model <span class="op">=</span> <span class="st">"tc"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> <p>The plot of the individual SFO fits shown below suggests that at least in some datasets the degradation slows down towards later time points, and that the scatter of the residuals error is smaller for smaller values (panel to the right):</p> <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_mkin_sfo_const-1.png" width="700"></p> <p>Using biexponential decline (DFOP) results in a slightly more random scatter of the residuals:</p> <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png" width="700"></p> <p>The population curve (bold line) in the above plot results from taking the mean of the individual transformed parameters, i.e. of log k1 and log k2, as well as of the logit of the g parameter of the DFOP model). Here, this procedure does not result in parameters that represent the degradation well, because in some datasets the fitted value for k2 is extremely close to zero, leading to a log k2 value that dominates the average. This is alleviated if only rate constants that pass the t-test for significant difference from zero (on the untransformed scale) are considered in the averaging:</p> <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const_test-1.png" width="700"></p> <p>While this is visually much more satisfactory, such an average procedure could introduce a bias, as not all results from the individual fits enter the population curve with the same weight. This is where nonlinear mixed-effects models can help out by treating all datasets with equally by fitting a parameter distribution model together with the degradation model and the error model (see below).</p> <p>The remaining trend of the residuals to be higher for higher predicted residues is reduced by using the two-component error model:</p> <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_tc_test-1.png" width="700"></p> </div> <div id="nonlinear-mixed-effects-models" class="section level2"> @@ -177,175 +174,184 @@ <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 use would like to do model selection from all four combinations of degradation models and error models based on the AIC. However, fitting the DFOP model with constant variance and using default control parameters results in an error, signalling that the maximum number of 50 iterations was reached, potentially indicating overparameterisation. However, the algorithm converges when the two-component error model is used in combination with the DFOP model. This can be explained by the fact that the smaller residues observed at later sampling times get more weight when using the two-component error model which will counteract the tendency of the algorithm to try parameter combinations unsuitable for fitting these data.</p> +<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> <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> -<span class="co">#f_parent_nlme_dfop_const <- nlme(f_parent_mkin_const["DFOP", ])</span> -<span class="co"># maxIter = 50 reached</span> +<span class="co"># f_parent_nlme_dfop_const <- nlme(f_parent_mkin_const["DFOP", ])</span> <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 overparameterisation is also indicated by warnings obtained when fitting SFO or DFOP with the two-component error model (‘false convergence’ in the ‘LME step’ in some iterations). In addition to these fits, attempts were also made to include correlations between random effects by using the log Cholesky parameterisation of the matrix specifying them. The code used for these attempts can be made visible below.</p> +<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> <div class="sourceCode" id="cb8"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">f_parent_nlme_sfo_const_logchol</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>, - random <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/pdLogChol.html">pdLogChol</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">DMTA_0</span> <span class="op">~</span> <span class="fl">1</span>, <span class="va">log_k_DMTA</span> <span class="op">~</span> <span class="fl">1</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> -<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_const_logchol</span><span class="op">)</span> <span class="co"># not better</span> -<span class="co">#f_parent_nlme_dfop_tc_logchol <- update(f_parent_nlme_dfop_tc,</span> -<span class="co"># random = pdLogChol(list(DMTA_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)))</span> -<span class="co"># using log Cholesky parameterisation for random effects (nlme default) does</span> -<span class="co"># not converge here and gives lots of warnings about the LME step not converging</span></code></pre></div> -<p>The model comparison function of the nlme package can directly be applied to these fits showing a similar goodness-of-fit of the SFO model, but a much lower AIC for the DFOP model fitted with the two-component error model. Also, the likelihood ratio test indicates that this difference is significant. as the p-value is below 0.0001.</p> -<div class="sourceCode" id="cb9"><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> <span class="op">)</span></code></pre></div> <pre><code> Model df AIC BIC logLik Test L.Ratio p-value -f_parent_nlme_sfo_const 1 5 818.63 834.00 -404.31 -f_parent_nlme_sfo_tc 2 6 820.61 839.06 -404.31 1 vs 2 0.014 0.9049 -f_parent_nlme_dfop_tc 3 10 687.84 718.59 -333.92 2 vs 3 140.771 <.0001</code></pre> +f_parent_nlme_sfo_const 1 5 796.60 811.82 -393.30 +f_parent_nlme_sfo_tc 2 6 798.60 816.86 -393.30 1 vs 2 0.00 0.998 +f_parent_nlme_dfop_tc 3 10 671.91 702.34 -325.96 2 vs 3 134.69 <.0001</code></pre> +<p>In addition to these fits, attempts were also made to include correlations between random effects by using the log Cholesky parameterisation of the matrix specifying them. The code used for these attempts can be made visible below.</p> +<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span class="va">f_parent_nlme_sfo_const_logchol</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>, + random <span class="op">=</span> <span class="fu">nlme</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/pdLogChol.html">pdLogChol</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">DMTA_0</span> <span class="op">~</span> <span class="fl">1</span>, <span class="va">log_k_DMTA</span> <span class="op">~</span> <span class="fl">1</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> +<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_const_logchol</span><span class="op">)</span> +<span class="va">f_parent_nlme_sfo_tc_logchol</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>, + random <span class="op">=</span> <span class="fu">nlme</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/pdLogChol.html">pdLogChol</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">DMTA_0</span> <span class="op">~</span> <span class="fl">1</span>, <span class="va">log_k_DMTA</span> <span class="op">~</span> <span class="fl">1</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> +<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_tc</span>, <span class="va">f_parent_nlme_sfo_tc_logchol</span><span class="op">)</span> +<span class="va">f_parent_nlme_dfop_tc_logchol</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">"DFOP"</span>, <span class="op">]</span>, + random <span class="op">=</span> <span class="fu">nlme</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/pdLogChol.html">pdLogChol</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">DMTA_0</span> <span class="op">~</span> <span class="fl">1</span>, <span class="va">log_k1</span> <span class="op">~</span> <span class="fl">1</span>, <span class="va">log_k2</span> <span class="op">~</span> <span class="fl">1</span>, <span class="va">g_qlogis</span> <span class="op">~</span> <span class="fl">1</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> +<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_dfop_tc</span>, <span class="va">f_parent_nlme_dfop_tc_logchol</span><span class="op">)</span></code></pre></div> +<p>While the SFO variants converge fast, the additional parameters introduced by this lead to convergence warnings for the DFOP model. The model comparison clearly show that adding correlations between random effects does not improve the fits.</p> <p>The selected model (DFOP with two-component error) fitted to the data assuming no correlations between random effects is shown below.</p> <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="va">f_parent_nlme_dfop_tc</span><span class="op">)</span></code></pre></div> +<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html">plot</a></span><span class="op">(</span><span class="va">f_parent_nlme_dfop_tc</span><span class="op">)</span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png" width="700"></p> </div> <div id="saemix" class="section level3"> <h3 class="hasAnchor"> <a href="#saemix" class="anchor"></a>saemix</h3> -<p>The saemix package provided the first Open Source implementation of the Stochastic Approximation to the Expectation Maximisation (SAEM) algorithm. SAEM fits of degradation models can be performed using an interface to the saemix package available in current development versions of the mkin package.</p> -<p>The corresponding SAEM fits of the four combinations of degradation and error models are fitted below. As there is no convergence criterion implemented in the saemix package, the convergence plots need to be manually checked for every fit.</p> -<p>The convergence plot for the SFO model using constant variance is shown below.</p> +<p>The saemix package provided the first Open Source implementation of the Stochastic Approximation to the Expectation Maximisation (SAEM) algorithm. SAEM fits of degradation models can be conveniently performed using an interface to the saemix package available in current development versions of the mkin package.</p> +<p>The corresponding SAEM fits of the four combinations of degradation and error models are fitted below. As there is no convergence criterion implemented in the saemix package, the convergence plots need to be manually checked for every fit. 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> <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">f_parent_saemix_sfo_const</span> <span class="op"><-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, - transformations <span class="op">=</span> <span class="st">"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></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"> +<code class="sourceCode R"><span class="va">f_parent_saemix_sfo_const</span> <span class="op"><-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, + 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_const</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_saemix_sfo_const-1.png" width="700"></p> <p>Obviously the default number of iterations is sufficient to reach convergence. This can also be said for the SFO fit using the two-component error model.</p> -<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">f_parent_saemix_sfo_tc</span> <span class="op"><-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, - transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span> + control <span class="op">=</span> <span class="va">saemix_control</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html">plot</a></span><span class="op">(</span><span class="va">f_parent_saemix_sfo_tc</span><span class="op">$</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> -<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r"> +<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="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">200</span><span class="op">)</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>, - transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</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 to the case where the DFOP model is fitted with the two-component error model. Convergence of the variance of k2 is enhanced by using the two-component error, it remains more or less stable already after 200 iterations of the first phase.</p> -<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">f_parent_saemix_dfop_tc_moreiter</span> <span class="op"><-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, - control <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">200</span><span class="op">)</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>, - 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_moreiter</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_moreiter-1.png" width="700"></p> -<p>The four combinations can be compared using the model comparison function from the saemix package:</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> <div class="sourceCode" id="cb16"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/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">$</span><span class="va">so</span>, <span class="va">f_parent_saemix_dfop_tc_moreiter</span><span class="op">$</span><span class="va">so</span><span class="op">)</span></code></pre></div> +<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">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></code></pre></div> +<p><img src="dimethenamid_2018_files/figure-html/f_parent_saemix_dfop_tc-1.png" width="700"> The four combinations and including the variations of the DFOP/tc combination can be compared using the model comparison function from the saemix package:</p> +<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/compare.saemix.html">compare.saemix</a></span><span class="op">(</span> + <span class="va">f_parent_saemix_sfo_const</span><span class="op">$</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">$</span><span class="va">so</span><span class="op">)</span></code></pre></div> <pre><code>Likelihoods calculated by importance sampling</code></pre> <pre><code> AIC BIC -1 818.37 817.33 -2 820.38 819.14 -3 725.91 724.04 -4 683.64 681.55</code></pre> -<p>As in the case of nlme fits, the DFOP model fitted with two-component error (number 4) gives the lowest AIC. The numeric values are reasonably close to the ones obtained using nlme, considering that the algorithms for fitting the model and for the likelihood calculation are quite different.</p> +1 796.37 795.33 +2 798.37 797.13 +3 713.16 711.28 +4 666.10 664.01</code></pre> +<p>As in the case of nlme fits, the DFOP model fitted with two-component error (number 4) gives the lowest AIC. Using more iterations and/or more chains does not have a large influence on the final AIC (not shown).</p> <p>In order to check the influence of the likelihood calculation algorithms implemented in saemix, the likelihood from Gaussian quadrature is added to the best fit, and the AIC values obtained from the three methods are compared.</p> -<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="va">f_parent_saemix_dfop_tc_moreiter</span><span class="op">$</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_moreiter</span><span class="op">$</span><span class="va">so</span><span class="op">)</span> -<span class="fu"><a href="https://rdrr.io/r/stats/AIC.html">AIC</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc_moreiter</span><span class="op">$</span><span class="va">so</span><span class="op">)</span></code></pre></div> -<pre><code>[1] 683.64</code></pre> -<div class="sourceCode" id="cb21"><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_saemix_dfop_tc_moreiter</span><span class="op">$</span><span class="va">so</span>, method <span class="op">=</span> <span class="st">"gq"</span><span class="op">)</span></code></pre></div> -<pre><code>[1] 683.7</code></pre> -<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html">AIC</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc_moreiter</span><span class="op">$</span><span class="va">so</span>, method <span class="op">=</span> <span class="st">"lin"</span><span class="op">)</span></code></pre></div> -<pre><code>[1] 683.17</code></pre> -<p>The AIC values based on importance sampling and Gaussian quadrature are quite similar. Using linearisation is less accurate, but still gives a similar value.</p> +<div class="sourceCode" id="cb20"><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"><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">$</span><span class="va">so</span><span class="op">)</span></code></pre></div> +<pre><code>[1] 666.1</code></pre> +<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html">AIC</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span>, method <span class="op">=</span> <span class="st">"gq"</span><span class="op">)</span></code></pre></div> +<pre><code>[1] 666.03</code></pre> +<div class="sourceCode" id="cb24"><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_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span>, method <span class="op">=</span> <span class="st">"lin"</span><span class="op">)</span></code></pre></div> +<pre><code>[1] 665.48</code></pre> +<p>The AIC values based on importance sampling and Gaussian quadrature are very similar. Using linearisation is known to be less accurate, but still gives a similar value.</p> </div> <div id="nlmixr" class="section level3"> <h3 class="hasAnchor"> <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 First Order Conditional Estimation with Interaction (FOCEI), and the SAEM algorithm as implemented in nlmixr.</p> -<p>First, the focei algorithm is used for the four model combinations and the goodness of fit of the results is compared.</p> -<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r"> +<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="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="cb26"><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_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">$</span><span class="va">nm</span><span class="op">)</span></code></pre></div> -<pre><code> df AIC -f_parent_nlmixr_focei_sfo_const$nm 5 818.63 -f_parent_nlmixr_focei_sfo_tc$nm 6 820.61 -f_parent_nlmixr_focei_dfop_const$nm 9 728.11 -f_parent_nlmixr_focei_dfop_tc$nm 10 687.82</code></pre> +<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span class="va">aic_nlmixr_focei</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">sapply</a></span><span class="op">(</span> + <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span><span class="op">(</span><span class="va">f_parent_nlmixr_focei_sfo_const</span><span class="op">$</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="cb28"><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_nlme_sfo_const</span>, <span class="va">f_parent_nlme_sfo_tc</span>, <span class="va">f_parent_nlme_dfop_tc</span> +<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> +<span class="va">aic_nlme_nlmixr_focei</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> + <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>, + <span class="st">"Error model"</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html">rep</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"constant variance"</span>, <span class="st">"two-component"</span><span class="op">)</span>, <span class="fl">2</span><span class="op">)</span>, + <span class="st">"AIC (nlme)"</span> <span class="op">=</span> <span class="va">aic_nlme</span>, + <span class="st">"AIC (nlmixr with FOCEI)"</span> <span class="op">=</span> <span class="va">aic_nlmixr_focei</span>, + check.names <span class="op">=</span> <span class="cn">FALSE</span> <span class="op">)</span></code></pre></div> -<pre><code> df AIC -f_parent_nlme_sfo_const 5 818.63 -f_parent_nlme_sfo_tc 6 820.61 -f_parent_nlme_dfop_tc 10 687.84</code></pre> -<p>Secondly, we use the SAEM estimation routine and check the convergence plots for SFO with constant variance</p> +<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="cb29"><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> +<p>The we fit SFO with constant variance</p> <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="fu">nlmixr</span><span class="fu">::</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><span class="op">)</span><span class="op">)</span> + control <span class="op">=</span> <span class="va">nlmixr_saem_control</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>for SFO with two-component error</p> +<p>and SFO with two-component error.</p> <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="fu">nlmixr</span><span class="fu">::</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><span class="op">)</span><span class="op">)</span> + control <span class="op">=</span> <span class="va">nlmixr_saem_control</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">$</span><span class="va">nm</span><span class="op">)</span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_sfo_tc-1.png" width="700"></p> -<p>For DFOP with constant variance, the convergence plots show considerable instability of the fit, which can be alleviated by increasing the number of iterations and the number of parallel chains for the first phase of algorithm.</p> +<p>For DFOP with constant variance, the convergence plots show considerable instability of the fit, which indicates overparameterisation which was already observed earlier for this model combination.</p> <div class="sourceCode" id="cb32"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="va">f_parent_nlmixr_saem_dfop_const</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html">nlmixr</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, est <span class="op">=</span> <span class="st">"saem"</span>, - control <span class="op">=</span> <span class="fu">nlmixr</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/saemControl.html">saemControl</a></span><span class="op">(</span>logLik <span class="op">=</span> <span class="cn">TRUE</span>, nBurn <span class="op">=</span> <span class="fl">1000</span><span class="op">)</span>, nmc <span class="op">=</span> <span class="fl">15</span><span class="op">)</span> + control <span class="op">=</span> <span class="va">nlmixr_saem_control</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/traceplot.html">traceplot</a></span><span class="op">(</span><span class="va">f_parent_nlmixr_saem_dfop_const</span><span class="op">$</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, the same increase in iterations and parallel chains was used, but using the two-component error appears to lead to a less erratic convergence, so this may not be necessary to this degree.</p> +<p>For DFOP with two-component error, a less erratic convergence is seen.</p> <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="fu">nlmixr</span><span class="fu">::</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>, nmc <span class="op">=</span> <span class="fl">15</span><span class="op">)</span><span class="op">)</span> + control <span class="op">=</span> <span class="va">nlmixr_saem_control</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">$</span><span class="va">nm</span><span class="op">)</span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_nlmixr_saem_dfop_tc-1.png" width="700"></p> -<p>The AIC values are internally calculated using Gaussian quadrature. For an unknown reason, the AIC value obtained for the DFOP fit using the two-component error model is given as Infinity.</p> +<p>The AIC values are internally calculated using Gaussian quadrature. For an unknown reason, the AIC value obtained for the DFOP fit using constant error is given as Infinity.</p> <div class="sourceCode" id="cb34"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html">AIC</a></span><span class="op">(</span><span class="va">f_parent_nlmixr_saem_sfo_const</span><span class="op">$</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 5 820.54 -f_parent_nlmixr_saem_sfo_tc$nm 6 835.26 -f_parent_nlmixr_saem_dfop_const$nm 9 842.84 -f_parent_nlmixr_saem_dfop_tc$nm 10 684.51</code></pre> +f_parent_nlmixr_saem_sfo_const$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> <p>The following table gives the AIC values obtained with the three packages.</p> <div class="sourceCode" id="cb36"><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>, <span class="st">"Error 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">"const"</span>, <span class="st">"tc"</span>, <span class="st">"const"</span>, <span class="st">"tc"</span><span class="op">)</span>, nlme <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/AIC.html">AIC</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/r/stats/AIC.html">AIC</a></span><span class="op">(</span><span class="va">f_parent_nlme_sfo_tc</span><span class="op">)</span>, <span class="cn">NA</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_nlme_dfop_tc</span><span class="op">)</span><span class="op">)</span>, nlmixr_focei <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">$</span><span class="va">nm</span><span class="op">)</span>, <span class="va">AIC</span><span class="op">)</span>, saemix <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">sapply</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span><span class="op">(</span><span class="va">f_parent_saemix_sfo_const</span><span class="op">$</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_moreiter</span><span class="op">$</span><span class="va">so</span><span class="op">)</span>, <span class="va">AIC</span><span class="op">)</span>, + <span class="va">f_parent_saemix_dfop_const</span><span class="op">$</span><span class="va">so</span>, <span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span><span class="op">)</span>, <span class="va">AIC</span><span class="op">)</span>, nlmixr_saem <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html">sapply</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span><span class="op">(</span><span class="va">f_parent_nlmixr_saem_sfo_const</span><span class="op">$</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">$</span><span class="va">nm</span><span class="op">)</span>, <span class="va">AIC</span><span class="op">)</span> <span class="op">)</span> <span class="fu">kable</span><span class="op">(</span><span class="va">AIC_all</span><span class="op">)</span></code></pre></div> <table class="table"> <thead><tr class="header"> -<th align="left">Degradation.model</th> -<th align="left">Error.model</th> +<th align="left">Degradation model</th> +<th align="left">Error model</th> <th align="right">nlme</th> <th align="right">nlmixr_focei</th> <th align="right">saemix</th> @@ -355,34 +361,34 @@ f_parent_nlmixr_saem_dfop_tc$nm 10 684.51</code></pre> <tr class="odd"> <td align="left">SFO</td> <td align="left">const</td> -<td align="right">818.63</td> -<td align="right">818.63</td> -<td align="right">818.37</td> -<td align="right">820.54</td> +<td align="right">796.60</td> +<td align="right">796.62</td> +<td align="right">796.37</td> +<td align="right">798.68</td> </tr> <tr class="even"> <td align="left">SFO</td> <td align="left">tc</td> -<td align="right">820.61</td> -<td align="right">820.61</td> -<td align="right">820.38</td> -<td align="right">835.26</td> +<td align="right">798.60</td> +<td align="right">798.61</td> +<td align="right">798.37</td> +<td align="right">808.88</td> </tr> <tr class="odd"> <td align="left">DFOP</td> <td align="left">const</td> <td align="right">NA</td> -<td align="right">728.11</td> -<td align="right">725.91</td> -<td align="right">842.84</td> +<td align="right">750.91</td> +<td align="right">713.16</td> +<td align="right">815.95</td> </tr> <tr class="even"> <td align="left">DFOP</td> <td align="left">tc</td> -<td align="right">687.84</td> -<td align="right">687.82</td> -<td align="right">683.64</td> -<td align="right">684.51</td> +<td align="right">671.91</td> +<td align="right">666.60</td> +<td align="right">666.10</td> +<td align="right">669.57</td> </tr> </tbody> </table> @@ -397,6 +403,9 @@ f_parent_nlmixr_saem_dfop_tc$nm 10 684.51</code></pre> <div id="ref-efsa_2018_dimethenamid"> <p>EFSA. 2018. “Peer Review of the Pesticide Risk Assessment of the Active Substance Dimethenamid-P.” <em>EFSA Journal</em> 16 (4): 5211.</p> </div> +<div id="ref-ranke2021"> +<p>Ranke, Johannes, Janina Wöltjen, Jana Schmidt, and Emmanuelle Comets. 2021. “Taking Kinetic Evaluations of Degradation Data to the Next Level with Nonlinear Mixed-Effects Models.” <em>Environments</em> 8 (8). <a href="https://doi.org/10.3390/environments8080071">https://doi.org/10.3390/environments8080071</a>.</p> +</div> <div id="ref-dimethenamid_rar_2018_b8"> <p>Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria. 2018. “Renewal Assessment Report Dimethenamid-P Volume 3 - B.8 Environmental fate and behaviour, Rev. 2 - November 2017.” <a href="https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716">https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716</a>.</p> </div> diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png Binary files differindex c51afe54..8eb464d1 100644 --- a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png +++ b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const_test-1.png b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const_test-1.png Binary 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diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png Binary files differindex 6edeb794..ed978664 100644 --- a/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png +++ b/docs/dev/articles/web_only/dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/header-attrs-2.11/header-attrs.js b/docs/dev/articles/web_only/dimethenamid_2018_files/header-attrs-2.11/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/dev/articles/web_only/dimethenamid_2018_files/header-attrs-2.11/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/dev/authors.html b/docs/dev/authors.html index 4208dc24..943cba1b 100644 --- a/docs/dev/authors.html +++ b/docs/dev/authors.html @@ -71,7 +71,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span> </span> </div> diff --git a/docs/dev/index.html b/docs/dev/index.html index 8b0b2386..452d9bdb 100644 --- a/docs/dev/index.html +++ b/docs/dev/index.html @@ -43,7 +43,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span> </span> </div> @@ -264,7 +264,7 @@ Ranke J, Wöltjen J, Meinecke S (2018) Comparison of software tools for kinetic <h2>Dev status</h2> <ul class="list-unstyled"> <li><a href="https://cran.r-project.org/package=mkin"><img src="https://www.r-pkg.org/badges/version/mkin"></a></li> -<li><a href="https://travis-ci.com/jranke/mkin"><img src="https://travis-ci.com/jranke/mkin.svg?branch=master" alt="Build Status"></a></li> +<li><a href="https://app.travis-ci.com/github/jranke/mkin"><img src="https://travis-ci.com/jranke/mkin.svg?branch=master" alt="Build Status"></a></li> <li><a href="https://codecov.io/github/jranke/mkin"><img src="https://codecov.io/github/jranke/mkin/branch/master/graphs/badge.svg" alt="codecov"></a></li> </ul> </div> diff --git a/docs/dev/news/index.html b/docs/dev/news/index.html index 5b153f37..f3cc89d9 100644 --- a/docs/dev/news/index.html +++ b/docs/dev/news/index.html @@ -71,7 +71,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span> </span> </div> @@ -141,9 +141,9 @@ <small>Source: <a href='https://github.com/jranke/mkin/blob/master/NEWS.md'><code>NEWS.md</code></a></small> </div> - <div id="mkin-105-unreleased" class="section level1"> -<h1 class="page-header" data-toc-text="1.0.5"> -<a href="#mkin-105-unreleased" class="anchor"></a>mkin 1.0.5 (unreleased)</h1> + <div id="mkin-110-unreleased" class="section level1"> +<h1 class="page-header" data-toc-text="1.1.0"> +<a href="#mkin-110-unreleased" class="anchor"></a>mkin 1.1.0 (unreleased)</h1> <div id="mixed-effects-models" class="section level2"> <h2 class="hasAnchor"> <a href="#mixed-effects-models" class="anchor"></a>Mixed-effects models</h2> @@ -156,6 +156,13 @@ </ul> </div> </div> + <div id="mkin-105-2021-09-15" class="section level1"> +<h1 class="page-header" data-toc-text="1.0.5"> +<a href="#mkin-105-2021-09-15" class="anchor"></a>mkin 1.0.5 (2021-09-15)</h1> +<ul> +<li>‘dimethenamid_2018’: Correct the data for the Borstel soil. The five observations from Staudenmaier (2013) that were previously stored as “Borstel 2” are actually just a subset of the 16 observations in “Borstel 1” which is now simply “Borstel”</li> +</ul> +</div> <div id="mkin-104-2021-04-20" class="section level1"> <h1 class="page-header" data-toc-text="1.0.4"> <a href="#mkin-104-2021-04-20" class="anchor"></a>mkin 1.0.4 (2021-04-20)</h1> diff --git a/docs/dev/pkgdown.yml b/docs/dev/pkgdown.yml index f184b7a5..e932afd0 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: 2021-08-04T13:49Z +last_built: 2021-09-16T15:10Z urls: reference: https://pkgdown.jrwb.de/mkin/reference article: https://pkgdown.jrwb.de/mkin/articles diff --git a/docs/dev/reference/dimethenamid_2018-1.png b/docs/dev/reference/dimethenamid_2018-1.png Binary files differindex 52b8a2be..b8c5355f 100644 --- a/docs/dev/reference/dimethenamid_2018-1.png +++ b/docs/dev/reference/dimethenamid_2018-1.png diff --git a/docs/dev/reference/dimethenamid_2018-2.png b/docs/dev/reference/dimethenamid_2018-2.png Binary files differindex a81b2aaf..3b8a123b 100644 --- a/docs/dev/reference/dimethenamid_2018-2.png +++ b/docs/dev/reference/dimethenamid_2018-2.png diff --git a/docs/dev/reference/dimethenamid_2018.html b/docs/dev/reference/dimethenamid_2018.html index a77cf0f4..919e9363 100644 --- a/docs/dev/reference/dimethenamid_2018.html +++ b/docs/dev/reference/dimethenamid_2018.html @@ -77,7 +77,7 @@ constrained by data protection regulations." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span> </span> </div> @@ -162,7 +162,7 @@ constrained by data protection regulations.</p> <h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2> - <p>An <a href='mkindsg.html'>mkindsg</a> object grouping eight datasets with some meta information</p> + <p>An <a href='mkindsg.html'>mkindsg</a> object grouping seven datasets with some meta information</p> <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2> <p>Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria (2018) @@ -177,42 +177,36 @@ specific pieces of information in the comments.</p> <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> <pre class="examples"><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>dimethenamid_2018</span><span class='op'>)</span> -</div><div class='output co'>#> <mkindsg> holding 8 mkinds objects +</div><div class='output co'>#> <mkindsg> holding 7 mkinds objects #> Title $title: Aerobic soil degradation data on dimethenamid-P from the EU assessment in 2018 #> Occurrence of observed compounds $observed_n: #> DMTAP M23 M27 M31 DMTA -#> 4 7 7 7 4 +#> 3 7 7 7 4 #> Time normalisation factors $f_time_norm: -#> [1] 1.0000000 0.9706477 0.9706477 1.2284784 1.2284784 0.6233856 0.7678922 -#> [8] 0.6733938 +#> [1] 1.0000000 0.9706477 1.2284784 1.2284784 0.6233856 0.7678922 0.6733938 #> Meta information $meta: -#> study usda_soil_type study_moisture_ref_type -#> Calke Unsworth 2014 Sandy loam pF2 -#> Borstel 1 Staudenmaier 2013 Sand pF1 -#> Borstel 2 Staudenmaier 2009 Sand pF1 -#> Elliot 1 Wendt 1997 Clay loam pF2.5 -#> Elliot 2 Wendt 1997 Clay loam pF2.5 -#> Flaach König 1996 Sandy clay loam pF1 -#> BBA 2.2 König 1995 Loamy sand pF1 -#> BBA 2.3 König 1995 Sandy loam pF1 -#> rel_moisture study_ref_moisture temperature -#> Calke 1.00 NA 20 -#> Borstel 1 0.50 23.00 20 -#> Borstel 2 0.50 23.00 20 -#> Elliot 1 0.75 33.37 23 -#> Elliot 2 0.75 33.37 23 -#> Flaach 0.40 NA 20 -#> BBA 2.2 0.40 NA 20 -#> BBA 2.3 0.40 NA 20</div><div class='input'><span class='va'>dmta_ds</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span><span class='op'>(</span><span class='fl'>1</span><span class='op'>:</span><span class='fl'>8</span>, <span class='kw'>function</span><span class='op'>(</span><span class='va'>i</span><span class='op'>)</span> <span class='op'>{</span> +#> study usda_soil_type study_moisture_ref_type rel_moisture +#> Calke Unsworth 2014 Sandy loam pF2 1.00 +#> Borstel Staudenmaier 2009 Sand pF1 0.50 +#> Elliot 1 Wendt 1997 Clay loam pF2.5 0.75 +#> Elliot 2 Wendt 1997 Clay loam pF2.5 0.75 +#> Flaach König 1996 Sandy clay loam pF1 0.40 +#> BBA 2.2 König 1995 Loamy sand pF1 0.40 +#> BBA 2.3 König 1995 Sandy loam pF1 0.40 +#> study_ref_moisture temperature +#> Calke NA 20 +#> Borstel 23.00 20 +#> Elliot 1 33.37 23 +#> Elliot 2 33.37 23 +#> Flaach NA 20 +#> BBA 2.2 NA 20 +#> BBA 2.3 NA 20</div><div class='input'><span class='va'>dmta_ds</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span><span class='op'>(</span><span class='fl'>1</span><span class='op'>:</span><span class='fl'>7</span>, <span class='kw'>function</span><span class='op'>(</span><span class='va'>i</span><span class='op'>)</span> <span class='op'>{</span> <span class='va'>ds_i</span> <span class='op'><-</span> <span class='va'>dimethenamid_2018</span><span class='op'>$</span><span class='va'>ds</span><span class='op'>[[</span><span class='va'>i</span><span class='op'>]</span><span class='op'>]</span><span class='op'>$</span><span class='va'>data</span> <span class='va'>ds_i</span><span class='op'>[</span><span class='va'>ds_i</span><span class='op'>$</span><span class='va'>name</span> <span class='op'>==</span> <span class='st'>"DMTAP"</span>, <span class='st'>"name"</span><span class='op'>]</span> <span class='op'><-</span> <span class='st'>"DMTA"</span> <span class='va'>ds_i</span><span class='op'>$</span><span class='va'>time</span> <span class='op'><-</span> <span class='va'>ds_i</span><span class='op'>$</span><span class='va'>time</span> <span class='op'>*</span> <span class='va'>dimethenamid_2018</span><span class='op'>$</span><span class='va'>f_time_norm</span><span class='op'>[</span><span class='va'>i</span><span class='op'>]</span> <span class='va'>ds_i</span> <span class='op'>}</span><span class='op'>)</span> <span class='fu'><a href='https://rdrr.io/r/base/names.html'>names</a></span><span class='op'>(</span><span class='va'>dmta_ds</span><span class='op'>)</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>sapply</a></span><span class='op'>(</span><span class='va'>dimethenamid_2018</span><span class='op'>$</span><span class='va'>ds</span>, <span class='kw'>function</span><span class='op'>(</span><span class='va'>ds</span><span class='op'>)</span> <span class='va'>ds</span><span class='op'>$</span><span class='va'>title</span><span class='op'>)</span> -<span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Borstel"</span><span class='op'>]</span><span class='op'>]</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Borstel 1"</span><span class='op'>]</span><span class='op'>]</span>, <span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Borstel 2"</span><span class='op'>]</span><span class='op'>]</span><span class='op'>)</span> -<span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Borstel 1"</span><span class='op'>]</span><span class='op'>]</span> <span class='op'><-</span> <span class='cn'>NULL</span> -<span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Borstel 2"</span><span class='op'>]</span><span class='op'>]</span> <span class='op'><-</span> <span class='cn'>NULL</span> <span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Elliot"</span><span class='op'>]</span><span class='op'>]</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span><span class='op'>(</span><span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Elliot 1"</span><span class='op'>]</span><span class='op'>]</span>, <span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Elliot 2"</span><span class='op'>]</span><span class='op'>]</span><span class='op'>)</span> <span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Elliot 1"</span><span class='op'>]</span><span class='op'>]</span> <span class='op'><-</span> <span class='cn'>NULL</span> <span class='va'>dmta_ds</span><span class='op'>[[</span><span class='st'>"Elliot 2"</span><span class='op'>]</span><span class='op'>]</span> <span class='op'><-</span> <span class='cn'>NULL</span> @@ -231,33 +225,33 @@ specific pieces of information in the comments.</p> </div><div class='output co'>#> <span class='message'>With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)</span></div><div class='output co'>#> function () #> { #> ini({ -#> DMTA_0 = 98.7697627680706 -#> eta.DMTA_0 ~ 2.35171765917765 +#> DMTA_0 = 98.7132391714013 +#> eta.DMTA_0 ~ 2.32692496033921 #> log_k_M23 = -3.92162409637283 #> eta.log_k_M23 ~ 0.549278519419884 -#> log_k_M27 = -4.33774620773911 -#> eta.log_k_M27 ~ 0.864474956685295 -#> log_k_M31 = -4.24767627688461 -#> eta.log_k_M31 ~ 0.750297149164171 -#> log_k1 = -2.2341008812259 -#> eta.log_k1 ~ 0.902976221565793 -#> log_k2 = -3.7762779983269 -#> eta.log_k2 ~ 1.57684519529298 -#> g_qlogis = 0.450175725479389 -#> eta.g_qlogis ~ 3.0851335687675 -#> f_DMTA_tffm0_1_qlogis = -2.09240906629456 +#> log_k_M27 = -4.33057580082049 +#> eta.log_k_M27 ~ 0.855184233768426 +#> log_k_M31 = -4.24415516780733 +#> eta.log_k_M31 ~ 0.745746058085877 +#> log_k1 = -2.23515804885306 +#> eta.log_k1 ~ 0.901033446532357 +#> log_k2 = -3.77581484944379 +#> eta.log_k2 ~ 1.57682329638124 +#> g_qlogis = 0.436302910942805 +#> eta.g_qlogis ~ 3.10190528862808 +#> f_DMTA_tffm0_1_qlogis = -2.0914852208395 #> eta.f_DMTA_tffm0_1_qlogis ~ 0.3 -#> f_DMTA_tffm0_2_qlogis = -2.18057573598794 +#> f_DMTA_tffm0_2_qlogis = -2.17879574608926 #> eta.f_DMTA_tffm0_2_qlogis ~ 0.3 -#> f_DMTA_tffm0_3_qlogis = -2.14267187609763 +#> f_DMTA_tffm0_3_qlogis = -2.14036526460782 #> eta.f_DMTA_tffm0_3_qlogis ~ 0.3 -#> sigma_low_DMTA = 0.697933852349996 +#> sigma_low_DMTA = 0.700117227383809 #> rsd_high_DMTA = 0.0257724286053519 -#> sigma_low_M23 = 0.697933852349996 +#> sigma_low_M23 = 0.700117227383809 #> rsd_high_M23 = 0.0257724286053519 -#> sigma_low_M27 = 0.697933852349996 +#> sigma_low_M27 = 0.700117227383809 #> rsd_high_M27 = 0.0257724286053519 -#> sigma_low_M31 = 0.697933852349996 +#> sigma_low_M31 = 0.700117227383809 #> rsd_high_M31 = 0.0257724286053519 #> }) #> model({ @@ -295,7 +289,7 @@ specific pieces of information in the comments.</p> #> M31 ~ add(sigma_low_M31) + prop(rsd_high_M31) #> }) #> } -#> <environment: 0x555559ac3820></div><div class='input'><span class='co'># The focei fit takes about four minutes on my system</span> +#> <environment: 0x555559e97ac0></div><div class='input'><span class='co'># The focei fit takes about four minutes on my system</span> <span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span><span class='op'>(</span> <span class='va'>f_dmta_nlmixr_focei</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_dmta_mkin_tc</span>, est <span class='op'>=</span> <span class='st'>"focei"</span>, control <span class='op'>=</span> <span class='fu'>nlmixr</span><span class='fu'>::</span><span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/foceiControl.html'>foceiControl</a></span><span class='op'>(</span>print <span class='op'>=</span> <span class='fl'>500</span><span class='op'>)</span><span class='op'>)</span> @@ -308,7 +302,7 @@ specific pieces of information in the comments.</p> #> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:07 #> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 -#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> [1] "CMT"</div><div class='output co'>#> <span class='message'>RxODE 1.1.0 using 8 threads (see ?getRxThreads)</span> +#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> [1] "CMT"</div><div class='output co'>#> <span class='message'>RxODE 1.1.1 using 8 threads (see ?getRxThreads)</span> #> <span class='message'> no cache: create with `rxCreateCache()`</span></div><div class='output co'>#> <span style='font-weight: bold;'>Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation #> F: Forward difference gradient approximation #> C: Central difference gradient approximation @@ -324,12 +318,12 @@ specific pieces of information in the comments.</p> #> <span style='text-decoration: underline;'>|.....................| o9 | o10 |...........|...........|</span> #> calculating covariance matrix #> done</div><div class='output co'>#> <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#> <span class='message'>done</span></div><div class='output co'>#> <span class='warning'>Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span></div><div class='output co'>#> <span class='warning'>Warning: last objective function was not at minimum, possible problems in optimization</span></div><div class='output co'>#> <span class='warning'>Warning: S matrix non-positive definite</span></div><div class='output co'>#> <span class='warning'>Warning: using R matrix to calculate covariance</span></div><div class='output co'>#> <span class='warning'>Warning: gradient problems with initial estimate and covariance; see $scaleInfo</span></div><div class='output co'>#> user system elapsed -#> 232.621 14.126 246.850 </div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_focei</span><span class='op'>)</span> -</div><div class='output co'>#> nlmixr version used for fitting: 2.0.4 -#> mkin version used for pre-fitting: 1.0.5 -#> R version used for fitting: 4.1.0 -#> Date of fit: Wed Aug 4 15:53:54 2021 -#> Date of summary: Wed Aug 4 15:53:54 2021 +#> 230.015 8.962 238.957 </div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_focei</span><span class='op'>)</span> +</div><div class='output co'>#> nlmixr version used for fitting: 2.0.5 +#> mkin version used for pre-fitting: 1.1.0 +#> R version used for fitting: 4.1.1 +#> Date of fit: Thu Sep 16 14:06:55 2021 +#> Date of summary: Thu Sep 16 14:06:55 2021 #> #> Equations: #> d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -346,23 +340,23 @@ specific pieces of information in the comments.</p> #> exp(-k2 * time))) * DMTA - k_M31 * M31 #> #> Data: -#> 568 observations of 4 variable(s) grouped in 6 datasets +#> 563 observations of 4 variable(s) grouped in 6 datasets #> #> Degradation model predictions using RxODE #> -#> Fitted in 246.669 s +#> Fitted in 238.792 s #> #> Variance model: Two-component variance function #> #> Mean of starting values for individual parameters: #> DMTA_0 log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1 f_DMTA_ilr_2 -#> 98.7698 -3.9216 -4.3377 -4.2477 0.1380 0.1393 +#> 98.7132 -3.9216 -4.3306 -4.2442 0.1376 0.1388 #> f_DMTA_ilr_3 log_k1 log_k2 g_qlogis -#> -1.7571 -2.2341 -3.7763 0.4502 +#> -1.7554 -2.2352 -3.7758 0.4363 #> #> Mean of starting values for error model parameters: #> sigma_low rsd_high -#> 0.69793 0.02577 +#> 0.70012 0.02577 #> #> Fixed degradation parameter values: #> None @@ -371,20 +365,20 @@ specific pieces of information in the comments.</p> #> #> Likelihood calculated by focei #> AIC BIC logLik -#> 1936 2031 -945.9 +#> 1918 2014 -937.2 #> #> Optimised parameters: #> est. lower upper -#> DMTA_0 98.7698 98.7356 98.8039 -#> log_k_M23 -3.9216 -3.9235 -3.9197 -#> log_k_M27 -4.3377 -4.3398 -4.3357 -#> log_k_M31 -4.2477 -4.2497 -4.2457 -#> log_k1 -2.2341 -2.2353 -2.2329 -#> log_k2 -3.7763 -3.7781 -3.7744 -#> g_qlogis 0.4502 0.4496 0.4507 -#> f_DMTA_tffm0_1_qlogis -2.0924 -2.0936 -2.0912 -#> f_DMTA_tffm0_2_qlogis -2.1806 -2.1818 -2.1794 -#> f_DMTA_tffm0_3_qlogis -2.1427 -2.1439 -2.1415 +#> DMTA_0 98.7132 98.6801 98.7464 +#> log_k_M23 -3.9216 -3.9235 -3.9198 +#> log_k_M27 -4.3306 -4.3326 -4.3286 +#> log_k_M31 -4.2442 -4.2461 -4.2422 +#> log_k1 -2.2352 -2.2364 -2.2340 +#> log_k2 -3.7758 -3.7776 -3.7740 +#> g_qlogis 0.4363 0.4358 0.4368 +#> f_DMTA_tffm0_1_qlogis -2.0915 -2.0926 -2.0903 +#> f_DMTA_tffm0_2_qlogis -2.1788 -2.1800 -2.1776 +#> f_DMTA_tffm0_3_qlogis -2.1404 -2.1415 -2.1392 #> #> Correlation: #> DMTA_0 l__M23 l__M27 l__M31 log_k1 log_k2 g_qlgs @@ -410,10 +404,10 @@ specific pieces of information in the comments.</p> #> #> Random effects (omega): #> eta.DMTA_0 eta.log_k_M23 eta.log_k_M27 eta.log_k_M31 -#> eta.DMTA_0 2.352 0.0000 0.0000 0.0000 +#> eta.DMTA_0 2.327 0.0000 0.0000 0.0000 #> eta.log_k_M23 0.000 0.5493 0.0000 0.0000 -#> eta.log_k_M27 0.000 0.0000 0.8645 0.0000 -#> eta.log_k_M31 0.000 0.0000 0.0000 0.7503 +#> eta.log_k_M27 0.000 0.0000 0.8552 0.0000 +#> eta.log_k_M31 0.000 0.0000 0.0000 0.7457 #> eta.log_k1 0.000 0.0000 0.0000 0.0000 #> eta.log_k2 0.000 0.0000 0.0000 0.0000 #> eta.g_qlogis 0.000 0.0000 0.0000 0.0000 @@ -425,9 +419,9 @@ specific pieces of information in the comments.</p> #> eta.log_k_M23 0.000 0.000 0.000 #> eta.log_k_M27 0.000 0.000 0.000 #> eta.log_k_M31 0.000 0.000 0.000 -#> eta.log_k1 0.903 0.000 0.000 +#> eta.log_k1 0.901 0.000 0.000 #> eta.log_k2 0.000 1.577 0.000 -#> eta.g_qlogis 0.000 0.000 3.085 +#> eta.g_qlogis 0.000 0.000 3.102 #> eta.f_DMTA_tffm0_1_qlogis 0.000 0.000 0.000 #> eta.f_DMTA_tffm0_2_qlogis 0.000 0.000 0.000 #> eta.f_DMTA_tffm0_3_qlogis 0.000 0.000 0.000 @@ -456,44 +450,44 @@ specific pieces of information in the comments.</p> #> #> Variance model: #> sigma_low rsd_high -#> 0.69793 0.02577 +#> 0.70012 0.02577 #> #> Backtransformed parameters: #> est. lower upper -#> DMTA_0 98.76976 98.73563 98.80390 +#> DMTA_0 98.71324 98.68012 98.74636 #> k_M23 0.01981 0.01977 0.01985 -#> k_M27 0.01307 0.01304 0.01309 -#> k_M31 0.01430 0.01427 0.01433 -#> f_DMTA_to_M23 0.10984 NA NA -#> f_DMTA_to_M27 0.09036 NA NA -#> f_DMTA_to_M31 0.08399 NA NA -#> k1 0.10709 0.10696 0.10722 -#> k2 0.02291 0.02287 0.02295 -#> g 0.61068 0.61055 0.61081 +#> k_M27 0.01316 0.01313 0.01319 +#> k_M31 0.01435 0.01432 0.01438 +#> f_DMTA_to_M23 0.10993 NA NA +#> f_DMTA_to_M27 0.09049 NA NA +#> f_DMTA_to_M31 0.08414 NA NA +#> k1 0.10698 0.10685 0.10710 +#> k2 0.02292 0.02288 0.02296 +#> g 0.60738 0.60725 0.60751 #> #> Resulting formation fractions: #> ff -#> DMTA_M23 0.10984 -#> DMTA_M27 0.09036 -#> DMTA_M31 0.08399 -#> DMTA_sink 0.71581 +#> DMTA_M23 0.10993 +#> DMTA_M27 0.09049 +#> DMTA_M31 0.08414 +#> DMTA_sink 0.71543 #> #> Estimated disappearance times: -#> DT50 DT90 DT50back DT50_k1 DT50_k2 -#> DMTA 10.66 59.78 18 6.473 30.26 -#> M23 34.99 116.24 NA NA NA -#> M27 53.05 176.23 NA NA NA -#> M31 48.48 161.05 NA NA NA</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_focei</span><span class='op'>)</span> +#> DT50 DT90 DT50back DT50_k1 DT50_k2 +#> DMTA 10.72 60.1 18.09 6.48 30.24 +#> M23 34.99 116.2 NA NA NA +#> M27 52.67 175.0 NA NA NA +#> M31 48.31 160.5 NA NA NA</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_focei</span><span class='op'>)</span> </div><div class='img'><img src='dimethenamid_2018-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># Using saemix takes about 18 minutes</span> <span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span><span class='op'>(</span> <span class='va'>f_dmta_saemix</span> <span class='op'><-</span> <span class='fu'><a href='saem.html'>saem</a></span><span class='op'>(</span><span class='va'>f_dmta_mkin_tc</span>, test_log_parms <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> <span class='op'>)</span> </div><div class='output co'>#> Running main SAEM algorithm -#> [1] "Wed Aug 4 15:53:55 2021" +#> [1] "Thu Sep 16 14:06:56 2021" #> .... #> Minimisation finished -#> [1] "Wed Aug 4 16:12:40 2021"</div><div class='output co'>#> user system elapsed -#> 1192.021 0.064 1192.182 </div><div class='input'> +#> [1] "Thu Sep 16 14:25:28 2021"</div><div class='output co'>#> user system elapsed +#> 1176.278 0.021 1176.388 </div><div class='input'> <span class='co'># nlmixr with est = "saem" is pretty fast with default iteration numbers, most</span> <span class='co'># of the time (about 2.5 minutes) is spent for calculating the log likelihood at the end</span> <span class='co'># The likelihood calculated for the nlmixr fit is much lower than that found by saemix</span> @@ -504,15 +498,15 @@ specific pieces of information in the comments.</p> <span class='va'>f_dmta_nlmixr_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_dmta_mkin_tc</span>, est <span class='op'>=</span> <span class='st'>"saem"</span>, control <span class='op'>=</span> <span class='fu'>nlmixr</span><span class='fu'>::</span><span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/saemControl.html'>saemControl</a></span><span class='op'>(</span>print <span class='op'>=</span> <span class='fl'>500</span>, logLik <span class='op'>=</span> <span class='cn'>TRUE</span>, nmc <span class='op'>=</span> <span class='fl'>9</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'>→ generate SAEM model</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> 1: 98.3427 -3.5148 -3.3187 -3.7728 -2.1163 -2.8457 0.9482 -2.8064 -2.7412 -2.8745 2.7912 0.6805 0.8213 0.8055 0.8578 1.4980 2.9309 0.2850 0.2854 0.2850 4.0990 0.3821 3.5349 0.6537 5.4143 0.0002 4.5093 0.1905 -#> 500: 97.8277 -4.3506 -4.0318 -4.1520 -3.0553 -3.5843 1.1326 -2.0873 -2.0421 -2.0751 0.2960 1.2515 0.2531 0.3807 0.7928 0.8863 6.5211 0.1433 0.1082 0.3353 0.8960 0.0470 0.7501 0.0475 0.9527 0.0281 0.7321 0.0594</div><div class='output co'>#> <span class='message'>Calculating covariance matrix</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> [1] "CMT"</div><div class='output co'>#> <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#> <span class='message'>done</span></div><div class='output co'>#> user system elapsed -#> 813.299 3.736 151.935 </div><div class='input'><span class='fu'>traceplot</span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_saem</span><span class='op'>$</span><span class='va'>nm</span><span class='op'>)</span> +</div><div class='output co'>#> <span class='message'>With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'>→ generate SAEM model</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> 1: 98.3400 -3.5096 -3.3392 -3.7596 -2.2055 -2.7755 1.0281 -2.7872 -2.7223 -2.8341 2.6422 0.7027 0.8124 0.7085 0.8560 1.4980 3.2777 0.3063 0.2850 0.2850 4.1120 0.3716 4.4582 0.3994 4.4820 0.4025 3.7803 0.5780 +#> 500: 97.8212 -4.4030 -4.0872 -4.1289 -2.8278 -4.3505 2.6614 -2.1252 -2.1308 -2.0749 2.9463 1.2933 0.2802 0.3467 0.4814 0.7877 3.0743 0.1508 0.1523 0.3155 0.9557 0.0333 0.4787 0.1073 0.6826 0.0707 0.7849 0.0356</div><div class='output co'>#> <span class='message'>Calculating covariance matrix</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> [1] "CMT"</div><div class='output co'>#> <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#> <span class='message'>done</span></div><div class='output co'>#> user system elapsed +#> 800.784 3.715 149.687 </div><div class='input'><span class='fu'>traceplot</span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_saem</span><span class='op'>$</span><span class='va'>nm</span><span class='op'>)</span> </div><div class='output co'>#> <span class='error'>Error in traceplot(f_dmta_nlmixr_saem$nm): could not find function "traceplot"</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_saem</span><span class='op'>)</span> -</div><div class='output co'>#> nlmixr version used for fitting: 2.0.4 -#> mkin version used for pre-fitting: 1.0.5 -#> R version used for fitting: 4.1.0 -#> Date of fit: Wed Aug 4 16:16:18 2021 -#> Date of summary: Wed Aug 4 16:16:18 2021 +</div><div class='output co'>#> nlmixr version used for fitting: 2.0.5 +#> mkin version used for pre-fitting: 1.1.0 +#> R version used for fitting: 4.1.1 +#> Date of fit: Thu Sep 16 14:29:02 2021 +#> Date of summary: Thu Sep 16 14:29:02 2021 #> #> Equations: #> d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -529,25 +523,25 @@ specific pieces of information in the comments.</p> #> exp(-k2 * time))) * DMTA - k_M31 * M31 #> #> Data: -#> 568 observations of 4 variable(s) grouped in 6 datasets +#> 563 observations of 4 variable(s) grouped in 6 datasets #> #> Degradation model predictions using RxODE #> -#> Fitted in 151.67 s +#> Fitted in 149.421 s #> #> Variance model: Two-component variance function #> #> Mean of starting values for individual parameters: #> DMTA_0 log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1 f_DMTA_ilr_2 -#> 98.7698 -3.9216 -4.3377 -4.2477 0.1380 0.1393 +#> 98.7132 -3.9216 -4.3306 -4.2442 0.1376 0.1388 #> f_DMTA_ilr_3 log_k1 log_k2 g_qlogis -#> -1.7571 -2.2341 -3.7763 0.4502 +#> -1.7554 -2.2352 -3.7758 0.4363 #> #> Mean of starting values for error model parameters: #> sigma_low_DMTA rsd_high_DMTA sigma_low_M23 rsd_high_M23 sigma_low_M27 -#> 0.69793 0.02577 0.69793 0.02577 0.69793 +#> 0.70012 0.02577 0.70012 0.02577 0.70012 #> rsd_high_M27 sigma_low_M31 rsd_high_M31 -#> 0.02577 0.69793 0.02577 +#> 0.02577 0.70012 0.02577 #> #> Fixed degradation parameter values: #> None @@ -556,32 +550,32 @@ specific pieces of information in the comments.</p> #> #> Likelihood calculated by focei #> AIC BIC logLik -#> 2036 2157 -989.8 +#> 1953 2074 -948.3 #> #> Optimised parameters: #> est. lower upper -#> DMTA_0 97.828 96.121 99.535 -#> log_k_M23 -4.351 -5.300 -3.401 -#> log_k_M27 -4.032 -4.470 -3.594 -#> log_k_M31 -4.152 -4.689 -3.615 -#> log_k1 -3.055 -3.785 -2.325 -#> log_k2 -3.584 -4.517 -2.651 -#> g_qlogis 1.133 -2.165 4.430 -#> f_DMTA_tffm0_1_qlogis -2.087 -2.407 -1.768 -#> f_DMTA_tffm0_2_qlogis -2.042 -2.336 -1.748 -#> f_DMTA_tffm0_3_qlogis -2.075 -2.557 -1.593 +#> DMTA_0 97.821 95.862 99.780 +#> log_k_M23 -4.403 -5.376 -3.430 +#> log_k_M27 -4.087 -4.545 -3.629 +#> log_k_M31 -4.129 -4.639 -3.618 +#> log_k1 -2.828 -3.389 -2.266 +#> log_k2 -4.351 -5.472 -3.229 +#> g_qlogis 2.661 0.824 4.499 +#> f_DMTA_tffm0_1_qlogis -2.125 -2.449 -1.801 +#> f_DMTA_tffm0_2_qlogis -2.131 -2.468 -1.794 +#> f_DMTA_tffm0_3_qlogis -2.075 -2.540 -1.610 #> #> Correlation: #> DMTA_0 l__M23 l__M27 l__M31 log_k1 log_k2 g_qlgs -#> log_k_M23 -0.031 -#> log_k_M27 -0.050 0.004 -#> log_k_M31 -0.032 0.003 0.078 -#> log_k1 0.014 -0.002 -0.002 -0.001 -#> log_k2 0.059 0.006 -0.001 0.002 -0.037 -#> g_qlogis -0.077 0.005 0.009 0.004 0.035 -0.201 -#> f_DMTA_tffm0_1_qlogis -0.104 0.066 0.009 0.006 0.000 -0.011 0.014 -#> f_DMTA_tffm0_2_qlogis -0.120 0.013 0.081 -0.033 -0.002 -0.013 0.017 -#> f_DMTA_tffm0_3_qlogis -0.086 0.010 0.060 0.078 -0.002 -0.005 0.010 +#> log_k_M23 -0.019 +#> log_k_M27 -0.028 0.004 +#> log_k_M31 -0.019 0.003 0.075 +#> log_k1 0.038 -0.004 -0.006 -0.003 +#> log_k2 0.046 0.011 0.008 0.009 0.068 +#> g_qlogis -0.067 0.004 0.006 0.001 -0.076 -0.409 +#> f_DMTA_tffm0_1_qlogis -0.062 0.055 0.006 0.004 -0.008 -0.004 0.012 +#> f_DMTA_tffm0_2_qlogis -0.062 0.010 0.058 -0.034 -0.008 -0.007 0.014 +#> f_DMTA_tffm0_3_qlogis -0.052 0.009 0.056 0.071 -0.006 -0.001 0.008 #> f_DMTA_0_1 f_DMTA_0_2 #> log_k_M23 #> log_k_M27 @@ -590,15 +584,15 @@ specific pieces of information in the comments.</p> #> log_k2 #> g_qlogis #> f_DMTA_tffm0_1_qlogis -#> f_DMTA_tffm0_2_qlogis 0.026 -#> f_DMTA_tffm0_3_qlogis 0.019 0.002 +#> f_DMTA_tffm0_2_qlogis 0.017 +#> f_DMTA_tffm0_3_qlogis 0.014 -0.005 #> #> Random effects (omega): #> eta.DMTA_0 eta.log_k_M23 eta.log_k_M27 eta.log_k_M31 -#> eta.DMTA_0 0.296 0.000 0.0000 0.0000 -#> eta.log_k_M23 0.000 1.252 0.0000 0.0000 -#> eta.log_k_M27 0.000 0.000 0.2531 0.0000 -#> eta.log_k_M31 0.000 0.000 0.0000 0.3807 +#> eta.DMTA_0 2.946 0.000 0.0000 0.0000 +#> eta.log_k_M23 0.000 1.293 0.0000 0.0000 +#> eta.log_k_M27 0.000 0.000 0.2802 0.0000 +#> eta.log_k_M31 0.000 0.000 0.0000 0.3467 #> eta.log_k1 0.000 0.000 0.0000 0.0000 #> eta.log_k2 0.000 0.000 0.0000 0.0000 #> eta.g_qlogis 0.000 0.000 0.0000 0.0000 @@ -610,9 +604,9 @@ specific pieces of information in the comments.</p> #> eta.log_k_M23 0.0000 0.0000 0.000 #> eta.log_k_M27 0.0000 0.0000 0.000 #> eta.log_k_M31 0.0000 0.0000 0.000 -#> eta.log_k1 0.7928 0.0000 0.000 -#> eta.log_k2 0.0000 0.8863 0.000 -#> eta.g_qlogis 0.0000 0.0000 6.521 +#> eta.log_k1 0.4814 0.0000 0.000 +#> eta.log_k2 0.0000 0.7877 0.000 +#> eta.g_qlogis 0.0000 0.0000 3.074 #> eta.f_DMTA_tffm0_1_qlogis 0.0000 0.0000 0.000 #> eta.f_DMTA_tffm0_2_qlogis 0.0000 0.0000 0.000 #> eta.f_DMTA_tffm0_3_qlogis 0.0000 0.0000 0.000 @@ -624,8 +618,8 @@ specific pieces of information in the comments.</p> #> eta.log_k1 0.0000 0.0000 #> eta.log_k2 0.0000 0.0000 #> eta.g_qlogis 0.0000 0.0000 -#> eta.f_DMTA_tffm0_1_qlogis 0.1433 0.0000 -#> eta.f_DMTA_tffm0_2_qlogis 0.0000 0.1082 +#> eta.f_DMTA_tffm0_1_qlogis 0.1508 0.0000 +#> eta.f_DMTA_tffm0_2_qlogis 0.0000 0.1523 #> eta.f_DMTA_tffm0_3_qlogis 0.0000 0.0000 #> eta.f_DMTA_tffm0_3_qlogis #> eta.DMTA_0 0.0000 @@ -637,40 +631,40 @@ specific pieces of information in the comments.</p> #> eta.g_qlogis 0.0000 #> eta.f_DMTA_tffm0_1_qlogis 0.0000 #> eta.f_DMTA_tffm0_2_qlogis 0.0000 -#> eta.f_DMTA_tffm0_3_qlogis 0.3353 +#> eta.f_DMTA_tffm0_3_qlogis 0.3155 #> #> Variance model: #> sigma_low_DMTA rsd_high_DMTA sigma_low_M23 rsd_high_M23 sigma_low_M27 -#> 0.89603 0.04704 0.75015 0.04753 0.95265 +#> 0.95572 0.03325 0.47871 0.10733 0.68264 #> rsd_high_M27 sigma_low_M31 rsd_high_M31 -#> 0.02810 0.73212 0.05942 +#> 0.07072 0.78486 0.03557 #> #> Backtransformed parameters: #> est. lower upper -#> DMTA_0 97.82774 96.120503 99.53498 -#> k_M23 0.01290 0.004991 0.03334 -#> k_M27 0.01774 0.011451 0.02749 -#> k_M31 0.01573 0.009195 0.02692 -#> f_DMTA_to_M23 0.11033 NA NA -#> f_DMTA_to_M27 0.10218 NA NA -#> f_DMTA_to_M31 0.08784 NA NA -#> k1 0.04711 0.022707 0.09773 -#> k2 0.02775 0.010918 0.07056 -#> g 0.75632 0.102960 0.98823 +#> DMTA_0 97.82122 95.862233 99.78020 +#> k_M23 0.01224 0.004625 0.03239 +#> k_M27 0.01679 0.010615 0.02654 +#> k_M31 0.01610 0.009664 0.02683 +#> f_DMTA_to_M23 0.10668 NA NA +#> f_DMTA_to_M27 0.09481 NA NA +#> f_DMTA_to_M31 0.08908 NA NA +#> k1 0.05914 0.033731 0.10370 +#> k2 0.01290 0.004204 0.03958 +#> g 0.93471 0.695081 0.98900 #> #> Resulting formation fractions: #> ff -#> DMTA_M23 0.11033 -#> DMTA_M27 0.10218 -#> DMTA_M31 0.08784 -#> DMTA_sink 0.69965 +#> DMTA_M23 0.10668 +#> DMTA_M27 0.09481 +#> DMTA_M31 0.08908 +#> DMTA_sink 0.70943 #> #> Estimated disappearance times: #> DT50 DT90 DT50back DT50_k1 DT50_k2 -#> DMTA 16.59 57.44 17.29 14.71 24.97 -#> M23 53.74 178.51 NA NA NA -#> M27 39.07 129.78 NA NA NA -#> M31 44.06 146.36 NA NA NA</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_saem</span><span class='op'>)</span> +#> DMTA 12.57 45.43 13.67 11.72 53.73 +#> M23 56.63 188.11 NA NA NA +#> M27 41.29 137.18 NA NA NA +#> M31 43.05 143.01 NA NA NA</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_dmta_nlmixr_saem</span><span class='op'>)</span> </div><div class='img'><img src='dimethenamid_2018-2.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># }</span> </div></pre> </div> diff --git a/docs/dev/reference/endpoints.html b/docs/dev/reference/endpoints.html index dc1d1f17..aa5bd773 100644 --- a/docs/dev/reference/endpoints.html +++ b/docs/dev/reference/endpoints.html @@ -78,7 +78,7 @@ advantage that the SFORB model can also be used for metabolites." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span> </span> </div> diff --git a/docs/dev/reference/index.html b/docs/dev/reference/index.html index bb030605..d5ec387a 100644 --- a/docs/dev/reference/index.html +++ b/docs/dev/reference/index.html @@ -71,7 +71,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span> </span> </div> diff --git a/docs/dev/reference/mean_degparms.html b/docs/dev/reference/mean_degparms.html index f63dbc31..5981c625 100644 --- a/docs/dev/reference/mean_degparms.html +++ b/docs/dev/reference/mean_degparms.html @@ -72,7 +72,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span> </span> </div> diff --git a/docs/dev/reference/mkinmod.html b/docs/dev/reference/mkinmod.html index ac7c2daa..6478cda4 100644 --- a/docs/dev/reference/mkinmod.html +++ b/docs/dev/reference/mkinmod.html @@ -76,7 +76,7 @@ components." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span> </span> </div> @@ -344,7 +344,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p> parent <span class='op'>=</span> <span class='fu'>mkinsub</span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"m1"</span>, full_name <span class='op'>=</span> <span class='st'>"Test compound"</span><span class='op'>)</span>, m1 <span class='op'>=</span> <span class='fu'>mkinsub</span><span class='op'>(</span><span class='st'>"SFO"</span>, full_name <span class='op'>=</span> <span class='st'>"Metabolite M1"</span><span class='op'>)</span>, name <span class='op'>=</span> <span class='st'>"SFO_SFO"</span>, dll_dir <span class='op'>=</span> <span class='va'>DLL_dir</span>, unload <span class='op'>=</span> <span class='cn'>TRUE</span>, overwrite <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Copied DLL from /tmp/RtmpKZJMFk/file179ba717d15c81.so to /home/jranke/.local/share/mkin/SFO_SFO.so</span></div><div class='input'><span class='co'># Now we can save the model and restore it in a new session</span> +</div><div class='output co'>#> <span class='message'>Copied DLL from /tmp/RtmpTzKqT5/file7993c266a7f90.so to /home/jranke/.local/share/mkin/SFO_SFO.so</span></div><div class='input'><span class='co'># Now we can save the model and restore it in a new session</span> <span class='fu'><a href='https://rdrr.io/r/base/readRDS.html'>saveRDS</a></span><span class='op'>(</span><span class='va'>SFO_SFO.2</span>, file <span class='op'>=</span> <span class='st'>"~/SFO_SFO.rds"</span><span class='op'>)</span> <span class='co'># Terminate the R session here if you would like to check, and then do</span> <span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://pkgdown.jrwb.de/mkin/'>mkin</a></span><span class='op'>)</span> @@ -393,7 +393,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p> #> }) #> return(predicted) #> } -#> <environment: 0x555559c54f78></div><div class='input'> +#> <environment: 0x555559365690></div><div class='input'> <span class='co'># If we have several parallel metabolites</span> <span class='co'># (compare tests/testthat/test_synthetic_data_for_UBA_2014.R)</span> <span class='va'>m_synth_DFOP_par</span> <span class='op'><-</span> <span class='fu'>mkinmod</span><span class='op'>(</span> diff --git a/docs/dev/reference/nlme-1.png b/docs/dev/reference/nlme-1.png Binary files differindex 365aaef0..e4bc2fde 100644 --- a/docs/dev/reference/nlme-1.png +++ b/docs/dev/reference/nlme-1.png diff --git a/docs/dev/reference/nlme-2.png b/docs/dev/reference/nlme-2.png Binary files differindex 40841404..31910ce4 100644 --- a/docs/dev/reference/nlme-2.png +++ b/docs/dev/reference/nlme-2.png diff --git a/docs/dev/reference/nlme.html b/docs/dev/reference/nlme.html index 55a94443..500ac391 100644 --- a/docs/dev/reference/nlme.html +++ b/docs/dev/reference/nlme.html @@ -75,7 +75,7 @@ datasets. They are used internally by the nlme.mmkin() method." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span> </span> </div> @@ -216,28 +216,28 @@ datasets. They are used internally by the <code><a href='nlme.mmkin.html'>nlme.m #> Model: value ~ nlme_f(name, time, parent_0, log_k_parent_sink) #> Data: grouped_data #> AIC BIC logLik -#> 300.6824 310.2426 -145.3412 +#> 289.8295 299.4886 -139.9148 #> #> Random effects: #> Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1) #> Level: ds #> Structure: Diagonal #> parent_0 log_k_parent_sink Residual -#> StdDev: 1.697361 0.6801209 3.666073 +#> StdDev: 1.839278 0.6988919 3.059894 #> #> Fixed effects: parent_0 + log_k_parent_sink ~ 1 #> Value Std.Error DF t-value p-value -#> parent_0 100.99378 1.3890416 46 72.70753 0 -#> log_k_parent_sink -3.07521 0.4018589 46 -7.65246 0 +#> parent_0 100.52780 1.3507449 47 74.42397 0 +#> log_k_parent_sink -3.08477 0.4124053 47 -7.47995 0 #> Correlation: #> prnt_0 -#> log_k_parent_sink 0.027 +#> log_k_parent_sink 0.019 #> #> Standardized Within-Group Residuals: -#> Min Q1 Med Q3 Max -#> -1.9942823 -0.5622565 0.1791579 0.7165038 2.0704781 +#> Min Q1 Med Q3 Max +#> -2.22350411 -0.51546184 0.04803417 0.55987705 3.49178405 #> -#> Number of Observations: 50 +#> Number of Observations: 51 #> Number of Groups: 3 </div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/pkg/nlme/man/augPred.html'>augPred</a></span><span class='op'>(</span><span class='va'>m_nlme</span>, level <span class='op'>=</span> <span class='fl'>0</span><span class='op'>:</span><span class='fl'>1</span><span class='op'>)</span>, layout <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fl'>1</span><span class='op'>)</span><span class='op'>)</span> </div><div class='img'><img src='nlme-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># augPred does not work on fits with more than one state</span> <span class='co'># variable</span> diff --git a/docs/dev/reference/nlme.mmkin.html b/docs/dev/reference/nlme.mmkin.html index db863392..866091ca 100644 --- a/docs/dev/reference/nlme.mmkin.html +++ b/docs/dev/reference/nlme.mmkin.html @@ -74,7 +74,7 @@ have been obtained by fitting the same model to a list of datasets." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span> </span> </div> diff --git a/docs/dev/reference/nlmixr.mmkin.html b/docs/dev/reference/nlmixr.mmkin.html index 99a7ad14..db114483 100644 --- a/docs/dev/reference/nlmixr.mmkin.html +++ b/docs/dev/reference/nlmixr.mmkin.html @@ -74,7 +74,7 @@ Expectation Maximisation algorithm (SAEM)." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span> </span> </div> @@ -4416,7 +4416,8 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit. #> |.....................| 5.960e-07 | 0.6941 | 1.222 | 1.493 | #> | X|<span style='font-weight: bold;'> 288.66432</span> | 94.40 | 0.8038 | 7.793 | 2.283 | #> |.....................| 5.960e-07 | 0.6941 | 1.222 | 1.493 | -#> done</div><div class='output co'>#> <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#> <span class='message'>done</span></div><div class='output co'>#> <span class='warning'>Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span></div><div class='output co'>#> <span class='warning'>Warning: ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))</span></div><div class='output co'>#> <span class='warning'>Warning: last objective function was not at minimum, possible problems in optimization</span></div><div class='output co'>#> <span class='warning'>Warning: parameter estimate near boundary; covariance not calculated</span> +#> done</div><div class='output co'>#> <span class='message'>Calculating residuals/tables</span></div><div class='output co'>#> <span class='message'>done</span></div><div class='output co'>#> <span class='warning'>Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span></div><div class='output co'>#> <span class='warning'>Warning: ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))</span></div><div class='output co'>#> <span class='warning'>Warning: last objective function was not at minimum, possible problems in optimization</span></div><div class='output co'>#> <span class='warning'>Warning: parameter estimate near boundary; covariance not calculated:</span> +#> <span class='warning'> "rsd_high" </span> #> <span class='warning'> use 'getVarCov' to calculate anyway</span></div><div class='output co'>#> <span class='warning'>Warning: gradient problems with initial estimate; see $scaleInfo</span></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span> <span class='va'>f_nlmixr_sfo_saem</span><span class='op'>$</span><span class='va'>nm</span>, <span class='va'>f_nlmixr_sfo_focei</span><span class='op'>$</span><span class='va'>nm</span>, @@ -4501,7 +4502,7 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit. #> <span class='message'>k_A1=rx_expr_11;</span> #> <span class='message'>f_parent=1/(1+exp(-(ETA[4]+THETA[4])));</span> #> <span class='message'>tad=tad();</span> -#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$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$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 5.723 0.414 6.136</span></div><div class='input'><span class='va'>f_nlmixr_fomc_sfo_focei_const</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_const</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$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: 5.579 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> </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> @@ -4550,7 +4551,7 @@ 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$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 6.874 0.399 7.27</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_const</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_const</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$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: 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> </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> @@ -4607,10 +4608,10 @@ 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$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 14.83 0.382 15.21</span></div><div class='input'> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$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: 15.03 0.407 15.43</span></div><div class='input'> <span class='co'># Variance by variable is supported by 'saem' and 'focei'</span> <span class='va'>f_nlmixr_fomc_sfo_saem_obs</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_obs</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"saem"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'>→ generate SAEM model</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='error'>Error in configsaem(model = model, data = dat, inits = inits, mcmc = .mcmc, ODEopt = .ODEopt, seed = .seed, distribution = .dist, DEBUG = .DEBUG, addProp = .addProp, tol = .tol, itmax = .itmax, type = .type, powRange = .powRange, lambdaRange = .lambdaRange): covariate(s) not found: f_parent_to_A1</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 1.22 0.146 1.365</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: 1.253 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'>→ 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> @@ -4659,8 +4660,8 @@ 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: 6.6 0.416 7.016</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: 1.551 0.126 1.673</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: 6.487 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> </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> @@ -4717,7 +4718,7 @@ 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$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 15.24 0.429 15.67</span></div><div class='input'> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$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$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 15.06 0.434 15.49</span></div><div class='input'> <span class='co'># Identical two-component error for all variables is only possible with</span> <span class='co'># est = 'focei' in nlmixr</span> <span class='va'>f_nlmixr_fomc_sfo_focei_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> @@ -4771,7 +4772,7 @@ obtained by fitting the same model to a list of datasets using <a href='mkinfit. #> <span class='message'>beta=exp(rx_expr_8);</span> #> <span class='message'>f_parent=1/(1+exp(-(ETA[3]+THETA[3])));</span> #> <span class='message'>tad=tad();</span> -#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$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$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 8.644 0.416 9.058</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_focei_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$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: 8.599 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> </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> @@ -4830,12 +4831,12 @@ 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$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 18.76 0.426 19.18</span></div><div class='input'> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$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: 17.64 0.453 18.09</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: 0.757 0.072 0.829</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: 0.772 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>, 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> @@ -4887,9 +4888,9 @@ 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$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 8.417 0.388 8.803</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_saem_obs_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_tc</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span>, est <span class='op'>=</span> <span class='st'>"saem"</span>, +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$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: 8.33 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>, 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: 0.82 0.035 0.857</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: 0.832 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>, 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> @@ -4949,7 +4950,7 @@ 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$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 17.61 0.452 18.06</span></div><div class='input'> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_A1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$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$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 18.15 0.42 18.57</span></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span> <span class='va'>f_nlmixr_sfo_sfo_focei_const</span><span class='op'>$</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/plot.mixed.mmkin.html b/docs/dev/reference/plot.mixed.mmkin.html index 746a8640..9f0eb965 100644 --- a/docs/dev/reference/plot.mixed.mmkin.html +++ b/docs/dev/reference/plot.mixed.mmkin.html @@ -72,7 +72,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span> </span> </div> @@ -296,10 +296,10 @@ corresponding model prediction lines for the different datasets.</p></td> </div><div class='img'><img src='plot.mixed.mmkin-2.png' alt='' width='700' height='433' /></div><div class='input'> <span class='va'>f_saem</span> <span class='op'><-</span> <span class='fu'><a href='saem.html'>saem</a></span><span class='op'>(</span><span class='va'>f</span>, transformations <span class='op'>=</span> <span class='st'>"saemix"</span><span class='op'>)</span> </div><div class='output co'>#> Running main SAEM algorithm -#> [1] "Wed Aug 4 16:21:52 2021" +#> [1] "Thu Sep 16 14:34:31 2021" #> .... #> Minimisation finished -#> [1] "Wed Aug 4 16:22:00 2021"</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_saem</span><span class='op'>)</span> +#> [1] "Thu Sep 16 14:34:38 2021"</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_saem</span><span class='op'>)</span> </div><div class='img'><img src='plot.mixed.mmkin-3.png' alt='' width='700' height='433' /></div><div class='input'> <span class='va'>f_obs</span> <span class='op'><-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='st'>"DFOP-SFO"</span> <span class='op'>=</span> <span class='va'>dfop_sfo</span><span class='op'>)</span>, <span class='va'>ds</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span>, error_model <span class='op'>=</span> <span class='st'>"obs"</span><span class='op'>)</span> <span class='va'>f_nlmix</span> <span class='op'><-</span> <span class='fu'>nlmix</span><span class='op'>(</span><span class='va'>f_obs</span><span class='op'>)</span> diff --git a/docs/dev/reference/reexports.html b/docs/dev/reference/reexports.html index f5ace044..ac4fa4d9 100644 --- a/docs/dev/reference/reexports.html +++ b/docs/dev/reference/reexports.html @@ -81,7 +81,7 @@ below to see their documentation. </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span> </span> </div> diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html index 620173b2..8d986126 100644 --- a/docs/dev/reference/saem.html +++ b/docs/dev/reference/saem.html @@ -74,7 +74,7 @@ Expectation Maximisation algorithm (SAEM)." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span> </span> </div> @@ -288,27 +288,27 @@ using <a href='mmkin.html'>mmkin</a>.</p> state.ini <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fl'>100</span><span class='op'>)</span>, fixed_initials <span class='op'>=</span> <span class='st'>"parent"</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> <span class='va'>f_saem_p0_fixed</span> <span class='op'><-</span> <span class='fu'>saem</span><span class='op'>(</span><span class='va'>f_mmkin_parent_p0_fixed</span><span class='op'>)</span> </div><div class='output co'>#> Running main SAEM algorithm -#> [1] "Wed Aug 4 16:22:05 2021" +#> [1] "Thu Sep 16 14:34:42 2021" #> .... #> Minimisation finished -#> [1] "Wed Aug 4 16:22:06 2021"</div><div class='input'> +#> [1] "Thu Sep 16 14:34:43 2021"</div><div class='input'> <span class='va'>f_mmkin_parent</span> <span class='op'><-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"FOMC"</span>, <span class='st'>"DFOP"</span><span class='op'>)</span>, <span class='va'>ds</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> <span class='va'>f_saem_sfo</span> <span class='op'><-</span> <span class='fu'>saem</span><span class='op'>(</span><span class='va'>f_mmkin_parent</span><span class='op'>[</span><span class='st'>"SFO"</span>, <span class='op'>]</span><span class='op'>)</span> </div><div class='output co'>#> Running main SAEM algorithm -#> [1] "Wed Aug 4 16:22:08 2021" +#> [1] "Thu Sep 16 14:34:45 2021" #> .... #> Minimisation finished -#> [1] "Wed Aug 4 16:22:10 2021"</div><div class='input'><span class='va'>f_saem_fomc</span> <span class='op'><-</span> <span class='fu'>saem</span><span class='op'>(</span><span class='va'>f_mmkin_parent</span><span class='op'>[</span><span class='st'>"FOMC"</span>, <span class='op'>]</span><span class='op'>)</span> +#> [1] "Thu Sep 16 14:34:47 2021"</div><div class='input'><span class='va'>f_saem_fomc</span> <span class='op'><-</span> <span class='fu'>saem</span><span class='op'>(</span><span class='va'>f_mmkin_parent</span><span class='op'>[</span><span class='st'>"FOMC"</span>, <span class='op'>]</span><span class='op'>)</span> </div><div class='output co'>#> Running main SAEM algorithm -#> [1] "Wed Aug 4 16:22:10 2021" +#> [1] "Thu Sep 16 14:34:47 2021" #> .... #> Minimisation finished -#> [1] "Wed Aug 4 16:22:12 2021"</div><div class='input'><span class='va'>f_saem_dfop</span> <span class='op'><-</span> <span class='fu'>saem</span><span class='op'>(</span><span class='va'>f_mmkin_parent</span><span class='op'>[</span><span class='st'>"DFOP"</span>, <span class='op'>]</span><span class='op'>)</span> +#> [1] "Thu Sep 16 14:34:49 2021"</div><div class='input'><span class='va'>f_saem_dfop</span> <span class='op'><-</span> <span class='fu'>saem</span><span class='op'>(</span><span class='va'>f_mmkin_parent</span><span class='op'>[</span><span class='st'>"DFOP"</span>, <span class='op'>]</span><span class='op'>)</span> </div><div class='output co'>#> Running main SAEM algorithm -#> [1] "Wed Aug 4 16:22:12 2021" +#> [1] "Thu Sep 16 14:34:49 2021" #> .... #> Minimisation finished -#> [1] "Wed Aug 4 16:22:16 2021"</div><div class='input'> +#> [1] "Thu Sep 16 14:34:52 2021"</div><div class='input'> <span class='co'># The returned saem.mmkin object contains an SaemixObject, therefore we can use</span> <span class='co'># functions from saemix</span> <span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'>saemix</span><span class='op'>)</span> @@ -357,10 +357,10 @@ using <a href='mmkin.html'>mmkin</a>.</p> <span class='va'>f_mmkin_parent_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/update.html'>update</a></span><span class='op'>(</span><span class='va'>f_mmkin_parent</span>, error_model <span class='op'>=</span> <span class='st'>"tc"</span><span class='op'>)</span> <span class='va'>f_saem_fomc_tc</span> <span class='op'><-</span> <span class='fu'>saem</span><span class='op'>(</span><span class='va'>f_mmkin_parent_tc</span><span class='op'>[</span><span class='st'>"FOMC"</span>, <span class='op'>]</span><span class='op'>)</span> </div><div class='output co'>#> Running main SAEM algorithm -#> [1] "Wed Aug 4 16:22:19 2021" +#> [1] "Thu Sep 16 14:34:55 2021" #> .... #> Minimisation finished -#> [1] "Wed Aug 4 16:22:24 2021"</div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/compare.saemix.html'>compare.saemix</a></span><span class='op'>(</span><span class='va'>f_saem_fomc</span><span class='op'>$</span><span class='va'>so</span>, <span class='va'>f_saem_fomc_tc</span><span class='op'>$</span><span class='va'>so</span><span class='op'>)</span> +#> [1] "Thu Sep 16 14:35:00 2021"</div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/compare.saemix.html'>compare.saemix</a></span><span class='op'>(</span><span class='va'>f_saem_fomc</span><span class='op'>$</span><span class='va'>so</span>, <span class='va'>f_saem_fomc_tc</span><span class='op'>$</span><span class='va'>so</span><span class='op'>)</span> </div><div class='output co'>#> <span class='message'>Likelihoods calculated by importance sampling</span></div><div class='output co'>#> AIC BIC #> 1 467.7096 464.9757 #> 2 469.6831 466.5586</div><div class='input'> @@ -381,15 +381,15 @@ using <a href='mmkin.html'>mmkin</a>.</p> <span class='co'># four minutes</span> <span class='va'>f_saem_sfo_sfo</span> <span class='op'><-</span> <span class='fu'>saem</span><span class='op'>(</span><span class='va'>f_mmkin</span><span class='op'>[</span><span class='st'>"SFO-SFO"</span>, <span class='op'>]</span><span class='op'>)</span> </div><div class='output co'>#> Running main SAEM algorithm -#> [1] "Wed Aug 4 16:22:27 2021" +#> [1] "Thu Sep 16 14:35:03 2021" #> .... #> Minimisation finished -#> [1] "Wed Aug 4 16:22:32 2021"</div><div class='input'><span class='va'>f_saem_dfop_sfo</span> <span class='op'><-</span> <span class='fu'>saem</span><span class='op'>(</span><span class='va'>f_mmkin</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span><span class='op'>)</span> +#> [1] "Thu Sep 16 14:35:08 2021"</div><div class='input'><span class='va'>f_saem_dfop_sfo</span> <span class='op'><-</span> <span class='fu'>saem</span><span class='op'>(</span><span class='va'>f_mmkin</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span><span class='op'>)</span> </div><div class='output co'>#> Running main SAEM algorithm -#> [1] "Wed Aug 4 16:22:33 2021" +#> [1] "Thu Sep 16 14:35:08 2021" #> .... #> Minimisation finished -#> [1] "Wed Aug 4 16:22:42 2021"</div><div class='input'><span class='co'># We can use print, plot and summary methods to check the results</span> +#> [1] "Thu Sep 16 14:35:17 2021"</div><div class='input'><span class='co'># We can use print, plot and summary methods to check the results</span> <span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>f_saem_dfop_sfo</span><span class='op'>)</span> </div><div class='output co'>#> Kinetic nonlinear mixed-effects model fit by SAEM #> Structural model: @@ -430,10 +430,10 @@ using <a href='mmkin.html'>mmkin</a>.</p> #> SD.g_qlogis 0.44816 -1.25437 2.1507</div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html'>plot</a></span><span class='op'>(</span><span class='va'>f_saem_dfop_sfo</span><span class='op'>)</span> </div><div class='img'><img src='saem-5.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/summary-methods.html'>summary</a></span><span class='op'>(</span><span class='va'>f_saem_dfop_sfo</span>, data <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> </div><div class='output co'>#> saemix version used for fitting: 3.1.9000 -#> mkin version used for pre-fitting: 1.0.5 -#> R version used for fitting: 4.1.0 -#> Date of fit: Wed Aug 4 16:22:43 2021 -#> Date of summary: Wed Aug 4 16:22:43 2021 +#> mkin version used for pre-fitting: 1.1.0 +#> R version used for fitting: 4.1.1 +#> Date of fit: Thu Sep 16 14:35:18 2021 +#> Date of summary: Thu Sep 16 14:35:18 2021 #> #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -448,7 +448,7 @@ using <a href='mmkin.html'>mmkin</a>.</p> #> #> Model predictions using solution type analytical #> -#> Fitted in 10.143 s using 300, 100 iterations +#> Fitted in 9.349 s using 300, 100 iterations #> #> Variance model: Constant variance #> diff --git a/docs/dev/reference/summary.nlmixr.mmkin.html b/docs/dev/reference/summary.nlmixr.mmkin.html index 70a71683..4831bbdf 100644 --- a/docs/dev/reference/summary.nlmixr.mmkin.html +++ b/docs/dev/reference/summary.nlmixr.mmkin.html @@ -76,7 +76,7 @@ endpoints such as formation fractions and DT50 values. Optionally </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span> </span> </div> @@ -258,12 +258,12 @@ nlmixr authors for the parts inherited from nlmixr.</p> quiet <span class='op'>=</span> <span class='cn'>TRUE</span>, error_model <span class='op'>=</span> <span class='st'>"tc"</span>, cores <span class='op'>=</span> <span class='fl'>5</span><span class='op'>)</span> <span class='va'>f_saemix_dfop_sfo</span> <span class='op'><-</span> <span class='fu'>mkin</span><span class='fu'>::</span><span class='fu'><a href='saem.html'>saem</a></span><span class='op'>(</span><span class='va'>f_mmkin_dfop_sfo</span><span class='op'>)</span> </div><div class='output co'>#> Running main SAEM algorithm -#> [1] "Wed Aug 4 16:22:46 2021" +#> [1] "Thu Sep 16 14:35:21 2021" #> .... #> Minimisation finished -#> [1] "Wed Aug 4 16:22:59 2021"</div><div class='input'><span class='va'>f_nlme_dfop_sfo</span> <span class='op'><-</span> <span class='fu'>mkin</span><span class='fu'>::</span><span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span><span class='op'>(</span><span class='va'>f_mmkin_dfop_sfo</span><span class='op'>)</span> +#> [1] "Thu Sep 16 14:35:33 2021"</div><div class='input'><span class='va'>f_nlme_dfop_sfo</span> <span class='op'><-</span> <span class='fu'>mkin</span><span class='fu'>::</span><span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span><span class='op'>(</span><span class='va'>f_mmkin_dfop_sfo</span><span class='op'>)</span> </div><div class='output co'>#> <span class='warning'>Warning: Iteration 4, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#> <span class='warning'>Warning: Iteration 6, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='input'><span class='va'>f_nlmixr_dfop_sfo_saem</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_dfop_sfo</span>, est <span class='op'>=</span> <span class='st'>"saem"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'>→ generate SAEM model</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <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_m1</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 1.383 0.12 1.503</span></div><div class='input'><span class='co'># The following takes a very long time but gives</span> +</div><div class='output co'>#> <span class='message'>With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'>→ generate SAEM model</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <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_m1</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 1.412 0.101 1.525</span></div><div class='input'><span class='co'># The following takes a very long time but gives</span> <span class='va'>f_nlmixr_dfop_sfo_focei</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlmixr/man/nlmixr.html'>nlmixr</a></span><span class='op'>(</span><span class='va'>f_mmkin_dfop_sfo</span>, est <span class='op'>=</span> <span class='st'>"focei"</span><span class='op'>)</span> </div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span></div><div class='output co'>#> <span class='message'><span style='color: #00BBBB;'>ℹ</span> Need to run with the source intact to parse comments</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ creating full model...</span></div><div class='output co'>#> <span class='message'>→ pruning branches (<span style='color: #262626; background-color: #DADADA;'>`if`</span>/<span style='color: #262626; background-color: #DADADA;'>`else`</span>)...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ loading into <span style='color: #0000BB;'>symengine</span> environment...</span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ calculate jacobian</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate sensitivities</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(f)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ calculate ∂(R²)/∂(η)</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in inner model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in EBE model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling inner model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ finding duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ optimizing duplicate expressions in FD model...</span></div><div class='output co'>#> </div><div class='output co'>#> <span class='message'>→ compiling EBE model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>→ compiling events FD model...</span></div><div class='output co'>#> <span class='message'> </span></div><div class='output co'>#> <span class='message'><span style='color: #00BB00;'>✔</span> done</span></div><div class='output co'>#> <span class='message'>Model:</span></div><div class='output co'>#> <span class='message'>cmt(parent);</span> #> <span class='message'>cmt(m1);</span> @@ -323,7 +323,7 @@ nlmixr authors for the parts inherited from nlmixr.</p> #> <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_m1" "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$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 18.43 0.422 18.87</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span><span class='va'>f_nlmixr_dfop_sfo_saem</span><span class='op'>$</span><span class='va'>nm</span>, <span class='va'>f_nlmixr_dfop_sfo_focei</span><span class='op'>$</span><span class='va'>nm</span><span class='op'>)</span> +#> <span class='message'>dosenum=dosenum();</span></div><div class='output co'>#> <span class='message'>Needed Covariates:</span></div><div class='output co'>#> <span class='message'>[1] "f_parent_to_m1" "CMT" </span></div><div class='output co'>#> <span class='error'>Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$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$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 18.28 0.455 18.73</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span><span class='op'>(</span><span class='va'>f_nlmixr_dfop_sfo_saem</span><span class='op'>$</span><span class='va'>nm</span>, <span class='va'>f_nlmixr_dfop_sfo_focei</span><span class='op'>$</span><span class='va'>nm</span><span class='op'>)</span> </div><div class='output co'>#> <span class='error'>Error in AIC(f_nlmixr_dfop_sfo_saem$nm, f_nlmixr_dfop_sfo_focei$nm): object 'f_nlmixr_dfop_sfo_saem' not found</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/summary-methods.html'>summary</a></span><span class='op'>(</span><span class='va'>f_nlmixr_dfop_sfo_sfo</span>, data <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> </div><div class='output co'>#> <span class='error'>Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'f_nlmixr_dfop_sfo_sfo' not found</span></div><div class='input'><span class='co'># }</span> diff --git a/docs/dev/reference/tffm0.html b/docs/dev/reference/tffm0.html index d993e8ff..67f26b85 100644 --- a/docs/dev/reference/tffm0.html +++ b/docs/dev/reference/tffm0.html @@ -81,7 +81,7 @@ from RxODE." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.0.5</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">1.1.0</span> </span> </div> @@ -3,7 +3,7 @@ Loading required package: parallel ℹ Testing mkin ✔ | OK F W S | Context ✔ | 5 | AIC calculation -✔ | 5 | Analytical solutions for coupled models [3.3 s] +✔ | 5 | Analytical solutions for coupled models [3.4 s] ✔ | 5 | Calculation of Akaike weights ✔ | 2 | Export dataset for reading into CAKE ✔ | 12 | Confidence intervals and p-values [1.0 s] @@ -13,7 +13,7 @@ Loading required package: parallel ✔ | 14 | Results for FOCUS D established in expertise for UBA (Ranke 2014) [0.8 s] ✔ | 4 | Test fitting the decline of metabolites from their maximum [0.3 s] ✔ | 1 | Fitting the logistic model [0.2 s] -✔ | 35 1 | Nonlinear mixed-effects models [26.6 s] +✔ | 35 1 | Nonlinear mixed-effects models [26.3 s] ──────────────────────────────────────────────────────────────────────────────── Skip (test_mixed.R:161:3): saem results are reproducible for biphasic fits Reason: Fitting with saemix takes around 10 minutes when using deSolve @@ -36,7 +36,7 @@ Reason: Fitting with saemix takes around 10 minutes when using deSolve ✔ | 4 | Calculation of maximum time weighted average concentrations (TWAs) [2.3 s] ══ Results ═════════════════════════════════════════════════════════════════════ -Duration: 67.8 s +Duration: 67.6 s ── Skipped tests ────────────────────────────────────────────────────────────── • Fitting with saemix takes around 10 minutes when using deSolve (1) diff --git a/vignettes/FOCUS_D.html b/vignettes/FOCUS_D.html index 0e983b98..ba514c18 100644 --- a/vignettes/FOCUS_D.html +++ b/vignettes/FOCUS_D.html @@ -360,7 +360,7 @@ pre code { <h1 class="title toc-ignore">Example evaluation of FOCUS Example Dataset D</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">Last change 31 January 2019 (rebuilt 2021-09-15)</h4> +<h4 class="date">Last change 31 January 2019 (rebuilt 2021-09-16)</h4> </div> @@ -434,10 +434,10 @@ print(FOCUS_2006_D)</code></pre> <p><img src="data:image/png;base64,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width="768" /></p> <p>A comprehensive report of the results is obtained using the <code>summary</code> method for <code>mkinfit</code> objects.</p> <pre class="r"><code>summary(fit)</code></pre> -<pre><code>## mkin version used for fitting: 1.0.5 +<pre><code>## mkin version used for fitting: 1.1.0 ## R version used for fitting: 4.1.1 -## Date of fit: Wed Sep 15 17:39:28 2021 -## Date of summary: Wed Sep 15 17:39:28 2021 +## Date of fit: Thu Sep 16 13:57:32 2021 +## Date of summary: Thu Sep 16 13:57:33 2021 ## ## Equations: ## d_parent/dt = - k_parent * parent @@ -445,7 +445,7 @@ print(FOCUS_2006_D)</code></pre> ## ## Model predictions using solution type analytical ## -## Fitted using 401 model solutions performed in 0.143 s +## Fitted using 401 model solutions performed in 0.149 s ## ## Error model: Constant variance ## diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html index 717a01be..b6ebb606 100644 --- a/vignettes/FOCUS_L.html +++ b/vignettes/FOCUS_L.html @@ -27,11 +27,8 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) 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(max-width:991px){.hidden-sm{display:none!important}}@media (min-width:992px) and (max-width:1199px){.hidden-md{display:none!important}}@media (min-width:1200px){.hidden-lg{display:none!important}}.visible-print{display:none!important}@media print{.visible-print{display:block!important}table.visible-print{display:table!important}tr.visible-print{display:table-row!important}td.visible-print,th.visible-print{display:table-cell!important}}.visible-print-block{display:none!important}@media print{.visible-print-block{display:block!important}}.visible-print-inline{display:none!important}@media print{.visible-print-inline{display:inline!important}}.visible-print-inline-block{display:none!important}@media print{.visible-print-inline-block{display:inline-block!important}}@media print{.hidden-print{display:none!important}} @@ -1516,7 +1513,7 @@ div.tocify { <h1 class="title toc-ignore">Example evaluation of FOCUS Laboratory Data L1 to L3</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">Last change 17 November 2016 (rebuilt 2021-07-23)</h4> +<h4 class="date">Last change 17 November 2016 (rebuilt 2021-09-16)</h4> </div> @@ -1535,17 +1532,17 @@ FOCUS_2006_L1_mkin <- mkin_wide_to_long(FOCUS_2006_L1)</code></pre> <p>Since mkin version 0.9-32 (July 2014), we can use shorthand notation like <code>"SFO"</code> for parent only degradation models. The following two lines fit the model and produce the summary report of the model fit. This covers the numerical analysis given in the FOCUS report.</p> <pre class="r"><code>m.L1.SFO <- mkinfit("SFO", FOCUS_2006_L1_mkin, quiet = TRUE) summary(m.L1.SFO)</code></pre> -<pre><code>## mkin version used for fitting: 1.0.4 -## R version used for fitting: 4.1.0 -## Date of fit: Fri Jul 23 18:03:18 2021 -## Date of summary: Fri Jul 23 18:03:18 2021 +<pre><code>## mkin version used for fitting: 1.1.0 +## R version used for fitting: 4.1.1 +## Date of fit: Thu Sep 16 13:57:35 2021 +## Date of summary: Thu Sep 16 13:57:35 2021 ## ## Equations: ## d_parent/dt = - k_parent * parent ## ## Model predictions using solution type analytical ## -## Fitted using 133 model solutions performed in 0.032 s +## Fitted using 133 model solutions performed in 0.031 s ## ## Error model: Constant variance ## @@ -1636,10 +1633,10 @@ summary(m.L1.SFO)</code></pre> <pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> <pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is ## doubtful</code></pre> -<pre><code>## mkin version used for fitting: 1.0.4 -## R version used for fitting: 4.1.0 -## Date of fit: Fri Jul 23 18:03:19 2021 -## Date of summary: Fri Jul 23 18:03:19 2021 +<pre><code>## mkin version used for fitting: 1.1.0 +## R version used for fitting: 4.1.1 +## Date of fit: Thu Sep 16 13:57:35 2021 +## Date of summary: Thu Sep 16 13:57:35 2021 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent @@ -1741,17 +1738,17 @@ plot(m.L2.FOMC, show_residuals = TRUE, main = "FOCUS L2 - FOMC")</code></pre> <p><img 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" /><!-- --></p> <pre class="r"><code>summary(m.L2.FOMC, data = FALSE)</code></pre> -<pre><code>## mkin version used for fitting: 1.0.4 -## R version used for fitting: 4.1.0 -## Date of fit: Fri Jul 23 18:03:19 2021 -## Date of summary: Fri Jul 23 18:03:19 2021 +<pre><code>## mkin version used for fitting: 1.1.0 +## R version used for fitting: 4.1.1 +## Date of fit: Thu Sep 16 13:57:35 2021 +## Date of summary: Thu Sep 16 13:57:35 2021 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 239 model solutions performed in 0.049 s +## Fitted using 239 model solutions performed in 0.048 s ## ## Error model: Constant variance ## @@ -1819,10 +1816,10 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, main = "FOCUS L2 - DFOP")</code></pre> <p><img src="data:image/png;base64,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" /><!-- --></p> <pre class="r"><code>summary(m.L2.DFOP, data = FALSE)</code></pre> -<pre><code>## mkin version used for fitting: 1.0.4 -## R version used for fitting: 4.1.0 -## Date of fit: Fri Jul 23 18:03:19 2021 -## Date of summary: Fri Jul 23 18:03:19 2021 +<pre><code>## mkin version used for fitting: 1.1.0 +## R version used for fitting: 4.1.1 +## Date of fit: Thu Sep 16 13:57:36 2021 +## Date of summary: Thu Sep 16 13:57:36 2021 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -1919,10 +1916,10 @@ plot(mm.L3)</code></pre> <p>The objects returned by mmkin are arranged like a matrix, with models as a row index and datasets as a column index.</p> <p>We can extract the summary and plot for <em>e.g.</em> the DFOP fit, using square brackets for indexing which will result in the use of the summary and plot functions working on mkinfit objects.</p> <pre class="r"><code>summary(mm.L3[["DFOP", 1]])</code></pre> -<pre><code>## mkin version used for fitting: 1.0.4 -## R version used for fitting: 4.1.0 -## Date of fit: Fri Jul 23 18:03:19 2021 -## Date of summary: Fri Jul 23 18:03:20 2021 +<pre><code>## mkin version used for fitting: 1.1.0 +## R version used for fitting: 4.1.1 +## Date of fit: Thu Sep 16 13:57:36 2021 +## Date of summary: Thu Sep 16 13:57:36 2021 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -1931,7 +1928,7 @@ plot(mm.L3)</code></pre> ## ## Model predictions using solution type analytical ## -## Fitted using 376 model solutions performed in 0.081 s +## Fitted using 376 model solutions performed in 0.079 s ## ## Error model: Constant variance ## @@ -2027,10 +2024,10 @@ plot(mm.L4)</code></pre> <p><img 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" /><!-- --></p> <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 3.3% as well as the plot suggest that the SFO model fits very well. The error level at which the <span class="math inline"><em>χ</em><sup>2</sup></span> test passes is slightly lower for the FOMC model. However, the difference appears negligible.</p> <pre class="r"><code>summary(mm.L4[["SFO", 1]], data = FALSE)</code></pre> -<pre><code>## mkin version used for fitting: 1.0.4 -## R version used for fitting: 4.1.0 -## Date of fit: Fri Jul 23 18:03:20 2021 -## Date of summary: Fri Jul 23 18:03:20 2021 +<pre><code>## mkin version used for fitting: 1.1.0 +## R version used for fitting: 4.1.1 +## Date of fit: Thu Sep 16 13:57:36 2021 +## Date of summary: Thu Sep 16 13:57:36 2021 ## ## Equations: ## d_parent/dt = - k_parent * parent @@ -2091,10 +2088,10 @@ plot(mm.L4)</code></pre> ## DT50 DT90 ## parent 106 352</code></pre> <pre class="r"><code>summary(mm.L4[["FOMC", 1]], data = FALSE)</code></pre> -<pre><code>## mkin version used for fitting: 1.0.4 -## R version used for fitting: 4.1.0 -## Date of fit: Fri Jul 23 18:03:20 2021 -## Date of summary: Fri Jul 23 18:03:20 2021 +<pre><code>## mkin version used for fitting: 1.1.0 +## R version used for fitting: 4.1.1 +## Date of fit: Thu Sep 16 13:57:36 2021 +## Date of summary: Thu Sep 16 13:57:36 2021 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent diff --git a/vignettes/mkin.html b/vignettes/mkin.html index 80bc9a0d..58d49112 100644 --- a/vignettes/mkin.html +++ b/vignettes/mkin.html @@ -1591,7 +1591,7 @@ div.tocify { <h1 class="title toc-ignore">Introduction to mkin</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">Last change 15 February 2021 (rebuilt 2021-09-15)</h4> +<h4 class="date">Last change 15 February 2021 (rebuilt 2021-09-16)</h4> </div> diff --git a/vignettes/references.bib b/vignettes/references.bib index f7eb4692..55bd483c 100644 --- a/vignettes/references.bib +++ b/vignettes/references.bib @@ -133,6 +133,19 @@ } +@Article{ranke2021, + AUTHOR = {Ranke, Johannes and Wöltjen, Janina and Schmidt, Jana and Comets, Emmanuelle}, + TITLE = {Taking Kinetic Evaluations of Degradation Data to the Next Level with Nonlinear Mixed-Effects Models}, + JOURNAL = {Environments}, + VOLUME = {8}, + YEAR = {2021}, + NUMBER = {8}, + ARTICLE-NUMBER = {71}, + URL = {https://www.mdpi.com/2076-3298/8/8/71}, + ISSN = {2076-3298}, + ABSTRACT = {When data on the degradation of a chemical substance have been collected in a number of environmental media (e.g., in different soils), two strategies can be followed for data evaluation. Currently, each individual dataset is evaluated separately, and representative degradation parameters are obtained by calculating averages of the kinetic parameters. However, such averages often take on unrealistic values if certain degradation parameters are ill-defined in some of the datasets. Moreover, the most appropriate degradation model is selected for each individual dataset, which is time consuming and then requires workarounds for averaging parameters from different models. Therefore, a simultaneous evaluation of all available data is desirable. If the environmental media are viewed as random samples from a population, an advanced strategy based on assumptions about the statistical distribution of the kinetic parameters across the population can be used. Here, we show the advantages of such simultaneous evaluations based on nonlinear mixed-effects models that incorporate such assumptions in the evaluation process. The advantages of this approach are demonstrated using synthetically generated data with known statistical properties and using publicly available experimental degradation data on two pesticidal active substances.}, + DOI = {10.3390/environments8080071} +} @Article{gao11, Title = {Improving uncertainty analysis in kinetic evaluations using iteratively reweighted least squares}, diff --git a/vignettes/twa.html b/vignettes/twa.html index fe9586e0..25a4c396 100644 --- a/vignettes/twa.html +++ b/vignettes/twa.html @@ -253,7 +253,7 @@ code > span.er { color: #a61717; background-color: #e3d2d2; } <h1 class="title toc-ignore">Calculation of time weighted average concentrations with mkin</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">Last change 18 September 2019 (rebuilt 2021-09-15)</h4> +<h4 class="date">Last change 18 September 2019 (rebuilt 2021-09-16)</h4> diff --git a/vignettes/web_only/.build.timestamp b/vignettes/web_only/.build.timestamp deleted file mode 100644 index e69de29b..00000000 --- a/vignettes/web_only/.build.timestamp +++ /dev/null diff --git a/vignettes/web_only/dimethenamid_2018.R b/vignettes/web_only/dimethenamid_2018.R new file mode 100644 index 00000000..2c01bc14 --- /dev/null +++ b/vignettes/web_only/dimethenamid_2018.R @@ -0,0 +1,116 @@ +## ---- 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 df8200eb..aa1905da 100644 --- a/vignettes/web_only/dimethenamid_2018.html +++ b/vignettes/web_only/dimethenamid_2018.html @@ -27,11 +27,8 @@ document.addEventListener('DOMContentLoaded', function(e) { } }); </script> -<script>/*! jQuery v1.11.3 | (c) 2005, 2015 jQuery Foundation, Inc. | jquery.org/license */ -!function(a,b){"object"==typeof module&&"object"==typeof module.exports?module.exports=a.document?b(a,!0):function(a){if(!a.document)throw new Error("jQuery requires a window with a document");return b(a)}:b(a)}("undefined"!=typeof window?window:this,function(a,b){var c=[],d=c.slice,e=c.concat,f=c.push,g=c.indexOf,h={},i=h.toString,j=h.hasOwnProperty,k={},l="1.11.3",m=function(a,b){return new 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ke2C8vfjgV/Y9kkRQOJIn/xM9INZSpiBnqX0Q9GlQPpPKAyO5y+W5NMPSRdBCUlmuxl40ZfMCnf2Cp044uI9WLFtCi4YVxKjuRCOBWIb4XbIsGdbo4qtMQnNOQz4XDSui7W/N6l54qOynCqD3DpWQ+mpD7C40D8BZEWGJX3tlAaZBMj1yjvDYKwCJBa201u6nBKE5UE+7QSEhCwrXfbRZylAaAkplhBWX50dumrElePyNMRYUrC99UmcSSNgImhFhDI4BXjMtiqkgizUGCrZ8iwFxU6fQ8GEHCFdLewwxYWxgScAYMdMLmcZR6b7rZl95eQVDGVoUKcRMM1ixXQtXNkBETZkVVPg8LoSrdetHzkuM7DjZRHP02tCxA1fmkXKF3VzfN1pc1cv/8lbTIkkYpqKM9VOhp65ktYk+Q46myFWBapDfyWUCnsnI00QTBQmuFjMZTcd0V2NQ768Fhpby04k2IzNR1wKabuGJqYWwSly6ocMFGTeeI+ejsWDYgEvr66QgqdcIbFYDNgsm0x9UHY6SCd5+7tpsLpKdvhahIDyYmEJQCqMqtCF6UlrE5GXRmbu+vtm3BFSxI6ND6UxIE7GsGMgWqghXxSnaRJuGFveTcK5ZVSPJyjUxe1dKgI6kNF7EZhIZs8y8FVqwEfbM0Xk2ltORVDKZZM40SD3qQoQe0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY/iOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v+6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ/qDQK+bfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam+WpHG+0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3+J1eCBay8IYZ0wQRKGAqvCuZ/UgbQPyllosq+XtfKIZOzmeJqRazpmmoP/76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t/I4Jktu0XSgifO2ozFOiwd/0SssJDn0dn4xqk4GDTTKX73/wQyBLdqgJ+Wx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy/qqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh/wkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml/R4yrzow1Q2A5G+kzo/RhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj/UMMc34IBqTKLDTp76WzL/dMjCxK7MjhiGjeYAC/kj/jY/Rde7hpSM1xChrog6yZ7OWTuD56xBJnGFE+pT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G+9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36/dBySis4m9/DR8izaLJW6bWCkVgm5T+ius3ZXq4xI+GnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te/r4dPYMCl5qtiHNTn+TPbh1jCBHH+dMJNhwNgs3nT+OhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF+uRIWyXjQMAs2chdpaKPNaB+kSezYt0+CA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6/TzoA1/ZBG9bIUVHLAbi/kdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh+epgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF+zkJHIA7PwCDk1gGVmGUZSSoPhNf+Tklauz98QofOlCIQ/tCD4dosHYPqtPCXB3agggQQIqQJsSkB+qn0rkQ1toJjON/OtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU+TQ6NIw3ej+AtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb++W6Uk4q6F7/rg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK+EfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l+wM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l+DMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg+EWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb+Mw76Qy29iQ5up/X7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa/xnsMYcIO/vEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz/Chp/Vlpj2P7jJQmQRwGnltkTV5dbF9fE3/fxoSqTROgq9wFUlbuYzYcasE0ouzBo+dDCDzxKAfhbAZYxQiHrLzV2iVexnDX/QnT1fsT/xuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR+CtGdkPwYN2o7DOw/VGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO/f9Qua+pDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD/prQ84B1pVGkIpVUAHCG+iz3Bn3qm2AVrYcYWhock4jso5+J7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk+UILT7+VoX5mdxxA5fS42gISQVi/HTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8/6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk/wdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt+Su9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ/BfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk/gc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B//lHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO+CvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd/p1gi/07h8qfwHrByuSxglc9cI4QIg2oqvC/qm0i7tjPLTgDhoWTAKDO2ONW5oe+/eKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2/j4ODUwRkqrtBBCrDsDpt8jhZdXoy/1BCqw3sSGhgGGy0a5Jw6BP/TExoCmNFYjZl248A0osgPyGEmRA+fAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd/ocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE/EZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ/Ugd/JZQK8lvAm43uDRAbyW8gZ+ZGq0EVerVGUKUSm/Idn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ+QATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm+oOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724+zUQ7+vkCpZB+pGA562hYQiDxHVWOq0oDQl/QsoiY+cuI7iWq/ZIBtHcXJ7kks+h2fCNUPA82BzjnqktNts+RLdk1VSu+tqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy/44XYXdI5noQoRcvjZ1RMPACRqYg2V1+OwOepcOknRLLFdYgTkT5UApt/JhLM3jeFYprZV+Zow2g8fP+U68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr/A1SY9dXFz4RjzoU9ExfJCmx/I9FKEGT3n2cmzl2X42L3Jh+AbQq6sA+Ss1kitoa4TAYgKHaoybHUDJ51oETdeI/9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB+Un44zExFE4vLytcu5NwpWrUxO/0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL+BDqMyaN3RJPh/anbx+Iv+qgQdAa3M9Z5JmvYlh4qop+Ho1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs+gs37sFvi0PPVvA5dnCBgILTwoKd/+DoL9F6inlM7H4rOTzD79KJgKlZO/Zgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno+gBoKVXgIL/VI8dB1O5o/R3Suez/gD7M781ShjKpIIORM/nxG+jjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4/QftDbEn+AucIr1oxrLabRj9q4ae0+fXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd+eNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz/6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT+mAUmiYbV3YQVqFVp9dorv+TsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp+xJyYLv1OsxQCZwTB4a8BZ/5EdxTBJthApqyfd9u3ifr/WILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj/qn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9+W8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4/YtowhEmTs0vrvlD/CrisnoBNDAcUi/teY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O/4DcgV/dZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk+tgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb/n7qwhvGnrHuf5bX6Vh/n3xffU3PeHdR+FA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E+YbfL0adwNtHP7dT7t7b46DVZIkzaRJOM+S6KcrzYVg+T3wSRFRQashjfU18NutrKa/7PXbtuJvpIjbgPeqd+pjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir/8tNXJ/OsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG+FZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx/GdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD/Qi/EmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4/asthNMK5UQKCOhU97oaOYNGsTah+jfCKsZnTRn5TbhFX8ghg8CBYt/BjeYYYUrtUZ5jVij/op7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM+3SW6Opll/wgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy+QbSKVJcqkia+GvDefFwMOmgnD7h81TUtMn+mRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d/QK7Cr4uoCeOQ7/8JfKT77KiDzLImESHw/0wf73QeHu74hxv7uihi4fTX+XEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo/oVH5ww5OzLFsiTPDns7fS6EURSSWd/92BxMYQ8sBaH+j+wthQPdVgDGpTfi+JQIWMD8xKqULliRH01rTeyF8x8q/GBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE+/7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV/yTDRRP8Y2ww5RO6d2D94o+6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt+kqdae76ViWe3STan69yaF9+fESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw/A1zEdjWquIsQXXGIVEH0thC5M+W9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF+RVmI8L4HUYk4x+67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8/p5qIQrEo/H+1l/0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud+tJUfdaZ4CWNijzZtlRa8+CkmO/EwHYfPZFU/hzjFWH7vnzHRMo+aF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce/+/9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs/GWJS6SwEN/ULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e+G2zor8l+YaMxSEomDdLHGcD6YVQPegTaA74H8+V4WvJkFUrjMLGLlvSZQWvi8/QA7yzQ8GPno//5SJHRP/OqKObPCo81s/+6WgLqykYpGAgQZhVDEBPXWgU/WzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M+GWn6ASobIWC+LbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg/kpf3+CnAXKiMgIE8Jk/Mhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo+RJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B+SkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE+VMd3b1fhCynD0pQNhCG6/WCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp/YbHjo++7/Wj8S4YNa+ZdqAw1hDrKWFXv9+zaXpf8ZTDSbiqsxnwN/CzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m/NCW/HILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO+5EJ7Z6bCiRoPedRZ/P0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn/LvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl+11PoFYnNv2HwAODeNRkHj+8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij+bsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur/eWHjiB7SOVdqMAVmpBvfRiebsFjger7DC+8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l+kXRZ0KLZaGsFSIdQz/HXDxf3/TE30+DgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH+RptvRMVRaahu4cySjS3P5wxAUCPkmn+rhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik/zbrTQk5KmgxCg/f45L0jywebOWUYFJQAJia7XzCV0x89rpp/f3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl+5tfUWZNwBpEPXghzbBggYCw/dhy0ntds2yeHCDKkF/YxQjNIL/F/37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD+qNOhwMlfARQUdJ2tUX+MNJqOwIciWalZsmEjt07tfa8ma4cji9sqz+Q9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe+jGDiNJQLWnfx+drTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf/bkvo8PLVBsZl152y5S8+HRDfZIMCbYZ1WDp4yrdchOJw8k6R+/2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB+M4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5/iYp3ZdrCf7fL+en+sIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv+NRiJc8JAKqqgCA/PNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN/hCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj+bYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5/zBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8/i+jHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2+JrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk+Mz7wwstg6RFZb+TZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm/7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk+9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx+whVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC+XYuqMBMUun5YezKbRKmEPwuK+CLzijPEQgfhahQswBBLfg/GBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX+RCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk/4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug/AbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM+Zu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX/pcsfwXbLze2+D+u33OGBoJyAAL3jn3RuEcdp5If8O+a4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT++tm+haBCikRUUMrMhYKZJKYoVuv/bsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV/DMUxd9uFZmBfKXMCn/SqkWJyKPnT6lq+4zBZni6fYRByJn6OK+OgPBGRAJluwGSk4wxjOOzyce/PKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO/cEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm+bddRQu5F9s0XftGEJ9JSuSk+ZachCbdU45fEqbugzTIUokwoAKvpUQF/CvLbWW5BNQFqFkJg2f30E/48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J+1pT0tLkvFVZaNzfhs/Kd3+A9YsImlO4XK4vpCo/elHQi/9gkFg07xxnuXLt21unCIpDV+bbRxb7FC6nWYTsMFF8+1LUg4JFjVt3vqbuhHmDKbgQ4e+RGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB/KVijd1ARWkFEf3yiUw1v/WaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G+ZManTqDLPjyrOse7WiiwOJCG+J0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj/6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j/N46f+S2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb/0jQbaHJ2YRK8A+ls6WMhWmpCXYG5jqapGs5/eOJErxi2/2KWVHiPellTgh/fNl/2KYPKb7DUcAg+mCOPQFCiU9Mq/WLcU1xxC8aLePFZZlE+PCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh/nFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW+oKFLvpyvTBMM69tN1Ydwv1LIEhHsC+ueVG+w+kyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw+H/AuOx+aH+tBL88H57D0MsqyiZxhOEQkF/8DR1d2hSPMj/sNOa5rxcUnBgH8ictv2J+cb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd/PjMMtQfyDNZsOPd6XcAsnBE/mRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl/XPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG/VanIvcwycVA7+BE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP/MVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX/5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c/F1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J/5wkjpkre727p5PTRX5FGrSBIfJqhJE/IS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug+oRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U/5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7/BQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN+nA7pvF78/RII5ZHA09OAiE/66MF6HQ+qVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe+hXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz+JV/4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB+K3wBP/ineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q/9uocGsx41O4IZhViw/2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY/cQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE/om7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi/caHSM3FPRGRf7dB7YC+cD2ho6oL2zGDCkjlf/DFoQVl8GS/56wur3rdV6ggtzZW60MRB3g+U1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg/gBQIZMG/YcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK/G7F3mUc3GOAKqh60zM0v34v+ELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND+/GTk6M56Ig4yMsU6LUW1EWE+fIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP/IoRIZ/F6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg/RrPD/d3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl+Mu4xf0ezqeXD2PtPDnwMPo86sbwDV+9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD/OwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d/UfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH+14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC+OA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6+vAUUBoGhY3CT2tgwehdPqU/4Q7ZLYvhRl/ogOvR9O2+wkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn/dFSWBWzQ/VYk+Gezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n/yJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET/Hh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j+DXfpi12m1RbzYLg9J2wFergEwOxFyD0/JstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT/cUP6pE/mujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB+HEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu+vqQ02+KpJBjaLt9ye1Ab+BbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC+UwkXOoAjneU/xHplMQo2cXUlrVNqJYczgYlaOEczVCs/OCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38+xsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9+b8fH6+b8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj/0Q7PFUcC8hDrxESWdfgFRm+7vvWbkEppHB4T/1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y+g3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR/Rrs/JLKXgEx+qkmeDlFOD1/yTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW+xz+5FElFxWB28VjYIGZ0Yd+5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ+lT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ+2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1+JpI9psyNYIFuJogZa0/1AhOWdlHQxdAgbwacsHqPZo8u/ngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2+RdM+MAaYaZ0Y/ADkbNCZuAyAVQa2OcXOeICmDn9Q/eFkDeFQg5MgHEDXq/tVjj+jtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2/Bc0UxvseQCO2pQ2i+Krfhu/WeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt/U0Wf+phiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7+ExseJauyqo30hs+1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j/e1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la/QCTiVelFnU6O/GCvykqS/wZJDhKN9gBtSOp/1SP5VRgJcoVj+kmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn+8p6+vBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H+gDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D/GvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P+jAgN5TB3haSifDcApp6yymEi6Ij/GsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x/pChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11/yxyw0UnGig3MFdZklN5FI/qiT65T+jOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5+bqWiAYiAv6Jsf79/VUs4cIl+n6+WOjcgB/2l5TreoAV2717JzZbQIR0W1cl/dEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW/PhoawJDrGAP0JYWHgAVUByo/bGdiv2T2EMg8gsS14/rAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq+SCcTSx5NDtbFwNlh8VhjGGDu7JG5/TAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9+ogD8Kk/W7QoRN1UWPqM4+xdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c+4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS+o3F8YVVeikw13w+OEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX++7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9/O53DYi/5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD+P8sNh6e+XYHJXT/lkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp+pT0KYV5dGtknU64JhabdqcVQbG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) 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(max-width:991px){.hidden-sm{display:none!important}}@media (min-width:992px) and (max-width:1199px){.hidden-md{display:none!important}}@media (min-width:1200px){.hidden-lg{display:none!important}}.visible-print{display:none!important}@media print{.visible-print{display:block!important}table.visible-print{display:table!important}tr.visible-print{display:table-row!important}td.visible-print,th.visible-print{display:table-cell!important}}.visible-print-block{display:none!important}@media print{.visible-print-block{display:block!important}}.visible-print-inline{display:none!important}@media print{.visible-print-inline{display:inline!important}}.visible-print-inline-block{display:none!important}@media print{.visible-print-inline-block{display:inline-block!important}}@media print{.hidden-print{display:none!important}} @@ -1594,33 +1591,30 @@ 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 27 July 2021, built on 27 Jul 2021</h4> +<h4 class="date">Last change 16 September 2021, built on 16 Sep 2021</h4> </div> -<p><a href="http://www.jrwb.de">Wissenschaftlicher Berater, Kronacher Str. 12, 79639 Grenzach-Wyhlen, Germany</a><br /> <a href="http://chem.uft.uni-bremen.de/ranke">Privatdozent at the University of Bremen</a></p> +<p><a href="http://www.jrwb.de">Wissenschaftlicher Berater, Kronacher Str. 12, 79639 Grenzach-Wyhlen, Germany</a></p> <div id="introduction" class="section level1"> <h1>Introduction</h1> -<p>During the preparation of the journal article on nonlinear mixed-effects models in degradation kinetics (submitted) and the analysis of the dimethenamid degradation data analysed therein, a need for a more detailed analysis using not only nlme and saemix, but also nlmixr for fitting the mixed-effects models was identified.</p> +<p>During the preparation of the journal article on nonlinear mixed-effects models in degradation kinetics <span class="citation">(Ranke et al. 2021)</span> and the analysis of the dimethenamid degradation data analysed therein, a need for a more detailed analysis using not only nlme and saemix, but also nlmixr for fitting the mixed-effects models was identified.</p> <p>This vignette is an attempt to satisfy this need.</p> </div> <div id="data" class="section level1"> <h1>Data</h1> -<p>Residue data forming the basis for the endpoints derived in the conclusion on the peer review of the pesticide risk assessment of dimethenamid-P published by the European Food Safety Authority (EFSA) in 2018 <span class="citation">(EFSA 2018)</span> were transcribed from the risk assessment report <span class="citation">(Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria 2018)</span> which can be downloaded from the <a href="https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716">EFSA register of questions</a>.</p> +<p>Residue data forming the basis for the endpoints derived in the conclusion on the peer review of the pesticide risk assessment of dimethenamid-P published by the European Food Safety Authority (EFSA) in 2018 <span class="citation">(EFSA 2018)</span> were transcribed from the risk assessment report <span class="citation">(Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria 2018)</span> which can be downloaded from the Open EFSA repository <a href="https://open.efsa.europa.eu">https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716</a>.</p> <p>The data are <a href="https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html">available in the mkin package</a>. The following code (hidden by default, please use the button to the right to show it) treats the data available for the racemic mixture dimethenamid (DMTA) and its enantiomer dimethenamid-P (DMTAP) in the same way, as no difference between their degradation behaviour was identified in the EU risk assessment. The observation times of each dataset are multiplied with the corresponding normalisation factor also available in the dataset, in order to make it possible to describe all datasets with a single set of parameters.</p> <p>Also, datasets observed in the same soil are merged, resulting in dimethenamid (DMTA) data from six soils.</p> <pre class="r"><code>library(mkin) -dmta_ds <- lapply(1:8, function(i) { +dmta_ds <- lapply(1:7, function(i) { ds_i <- dimethenamid_2018$ds[[i]]$data ds_i[ds_i$name == "DMTAP", "name"] <- "DMTA" ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i] ds_i }) names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title) -dmta_ds[["Borstel"]] <- rbind(dmta_ds[["Borstel 1"]], dmta_ds[["Borstel 2"]]) -dmta_ds[["Borstel 1"]] <- NULL -dmta_ds[["Borstel 2"]] <- NULL dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]]) dmta_ds[["Elliot 1"]] <- NULL dmta_ds[["Elliot 2"]] <- NULL</code></pre> @@ -1637,159 +1631,168 @@ f_parent_mkin_tc <- mmkin(c("SFO", "DFOP"), dmta_ds, error_model = "tc", quiet = TRUE)</code></pre> <p>The plot of the individual SFO fits shown below suggests that at least in some datasets the degradation slows down towards later time points, and that the scatter of the residuals error is smaller for smaller values (panel to the right):</p> <pre class="r"><code>plot(mixed(f_parent_mkin_const["SFO", ]))</code></pre> -<p><img 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" 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" /><!-- --></p> <p>Using biexponential decline (DFOP) results in a slightly more random scatter of the residuals:</p> <pre class="r"><code>plot(mixed(f_parent_mkin_const["DFOP", ]))</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <p>The population curve (bold line) in the above plot results from taking the mean of the individual transformed parameters, i.e. of log k1 and log k2, as well as of the logit of the g parameter of the DFOP model). Here, this procedure does not result in parameters that represent the degradation well, because in some datasets the fitted value for k2 is extremely close to zero, leading to a log k2 value that dominates the average. This is alleviated if only rate constants that pass the t-test for significant difference from zero (on the untransformed scale) are considered in the averaging:</p> <pre class="r"><code>plot(mixed(f_parent_mkin_const["DFOP", ]), test_log_parms = TRUE)</code></pre> -<p><img src="data:image/png;base64,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" 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u1aWFiYvb29Wq0OCwu7cuUKTdNms3n48OEymczHx2fFihVz5sxRqVS3b99GCBEEcenSJT8/P+vqWVlZSqUyMjLy1q1bHTt2lEqlnp6eq1atYnSbaoVUKi39UgsEgtDQ0OvXr1c+a9y4cX369ClbCUVRHh4eCxcuLFv48kMKvXv3pmnaz89v9uzZlSwAAGAWDDYDAAAAsBBcMAMAAABYCBI8AAAAwEKQ4AEAAAAWggQPAAAAsBAkeGBL2aaMYrKCZ68BAADUMUjwwGYImlj9ZOHutF9sVeFrBzK3rdIR5WuvibLyj+c+7nbDkmWySW3Mjgd/7ty5li1bSiSSRo0a7d271yZbBACoIUjwwGb+yj3mxHdJKHkUX/zQVnVWPpD5mwoNDY2JibFVbDWhP5abMv2RqJksvleMrXI8U+PB63S6QYMGjRgxIj4+furUqSNGjHjw4IFNtggAUBOQ4IFtFBD6k7mHP3Ib96HLiD3pv1I0ZZNqKxnI/FVomjabzRXOys3Ntdiiz7gayj+lSZ3+yPdAswYrA9Sj3R6Hx1gybJDjmRoP/sqVK46OjjNmzHB1dZ08ebKnp+fVq1drvjkAgBqCBA9s44/MXR3swpwFrm2U7wtx0eW8c7atv9xA5jdu3OjQoYNMJgsKCrKO756dna1Sqc6cOePt7R0dHb18+XIPDw+JRNKpU6f4+HiEUOfOnVNTUyMiIqwDyDI1Qnz+KU3KpIe+vzcTt5IjhJymediPcXscYZscb1XH48F37Njx/Pnz1unMzEytVhsUFGSrbQEAVB/TXemBt8ZNffSGpz/EFz18efrHxG+n3PukhCi2ll/Snp1xf1S07nyFy1dd2T5WEUJBQUHZ2dk0TWu1WpVK9eOPP2ZnZx89elQqlV64cCErK4vP548ePTorK+vcuXMymezy5cupqakDBw7s16+ftUIvLy9rZ7dGo9Hb23vevHk5OTl///23i4vLiRMn0tLSEEIEQdT8syKLiaRRd5M+uZM+P6F0OumTO48jYmIUZx/3jilbnj4/IX1+wm3n80nDY62LWV/p8xNq/llVPouiKE9Pz507d9I0/eWXX4aGhlbeyrlz5+RyeVRU1Muz9Hp9SEiIVCpdtGhR1cMGANQeOIIHVeUp8g1RtHMWuJWb9hD56Mzafk5DRRyxtbyZvHWwrFVc8YMKl38j1ovHWVlZ8fHxzZs37969O0LoyJEjvr6+s2bNcnR07NOnz+jRo3fu3IkQMpvNc+bMcXJyMplMFEVpNBoHB4f9+/fv27evXLWnTp2yjhDv4OBQOkJ8zT+iUriIYzfYWTnQSRFuXzqtHOikGurEdeJzVdyy5bL3VYXndbIOSuWgZ4tZX4pw+zdqtMLPqvJZ5caDv3btWumAMeUYDIYvv/yyT58+27Ztq3C4d4lEsmzZsqlTp/70009Xrlx5o8gBALUBRpMDVaXmO6j5Di9PJ5bEFRB6OVd5XX/ZWvKw6I6HyHt/xvZeDv2lXFm55d9IhQOZp6Wl+fn5lS7j7+9/7Ngx67R1ANnw8PAVK1YsXrz4o48+CgkJ+fbbb8PCwspWW+sjxGNIEfE8PZedVvayj//glj4qV9JGoYiwJwuIhH63RE1lHmsbIaxGbTI1Hrxer0cIKZXKsLCwsLCwuLi4PXv2tG3btkYbAwCoMTiCBzWFIayxrNnN/Ogb+f+Wvh4XP2ihCDVQJTZsqHQgczc3t6SkpNLypKQkd3d367R1GNmkpKQuXbpcvXo1Jyenb9++I0aMKPfwm3WE+Mz/uXr16tq1a20YaiW4Dnz/qBYFp7Tp3ybYMLuX80bjwZee0Js5c+bL99Jbh3sPDw8/c+bMy9kdIbRu3brSEeURQm5ubkaj0ZYbAwCoHgYvDwBQOalUun//fo1Go9Fonj59OnLkSA8PD5PJlJubq1Ao1q5dq9PpTp48KZPJzp8/n5WVhf53BX3Lli3e3t537tzRaDRLly51dHSkKIqmaS8vr9OnT9M0nZeX5+jouHLlytzc3IsXLzo5Oe3bt8+G1+Bfy5xtut8q+o7/xZSZj2jKBhW+6rOqZNZff/3F5/Pz8vJKK4mOjkYIPX78uGzNhw4dsrOze/ToUcL/ZGRk0DR98ODBmzdv0jR969YtsVh88OBBnU535swZhUJx/PhxG2wSAKBmIMGD+quSMc6vXLny3nvvSaXSwMDA3bt30zRdNsFbLJZJkyY5ODiIxeL33nvv33//ta711VdfSaXSAwcO0DT98gjxdZngaZo2Z5tyfk21SXanGR0Pnqbpo0ePhoaGWju62bp1q202CQBQMzAePAAAAMBCcA0eAAAAYCFI8AAAAAALQYIHAAAAWAgSPAAAAMBCkOABAAAAFoIEDwAAALAQJHgAAACAhSDBAwAAACwECR4AAABgIUjwAAAAAAtBggcAAABYCBI8AAAAwEKQ4AEAAAAWggQPAAAAsBAkeAAAAICFIMEDAAAALAQJHgAAAGAhSPAAAAAAC0GCBwAAAFgIEjwAAADAQpDgAQAAABaCBA8AAACwECR4AAAAgIUgwQMAAAAsBAkeAAAAYCFI8AAAAAALQYIHAAAAWAgSPAAAAMBCkOABAAAAFoIEDwAAALAQJHgAAACAhSDBAwAAACwECR4AAABgIUjwAAAAAAtBggcAAABYCBI8AAAAwEKQ4AEAAAAWggQPAAAAsBAkeAAAAICFuEwHUFM0TcfGxpIkyXQgANSISCRq3Lgx01HUd/B9B+xQN993jKbp2m6jVsXFxTVv3jwoKIjpQACokdu3bxcXFwsEAqYDqdfg+w7YoW6+72/9ETxJkj4+Pjdu3GA6EABqRCwWUxTFdBT1HXzfATvUzfcdrsEDAAAALAQJHgAAAGAhSPAAAAAAC0GCBwAAAFgIEjwAAADAQpDgAQAAABZ66x+TK4cqvGZJW1rhLI5dX67T6DqOBwAAAGBEXSf4wsLCffv23bt3Lzs7myAIFxeXZs2aDR06VC6X26R+TOhJFd3geS7EBF7PS2nCnPAp1/kzmzQBAAAA1H91eor+8uXLbm5umzZtoigqMDCwadOmOI5v2bLF09Pz2rVrNmkC4zlxXWeS+rMcRefSF214jEuac1Q9bdIEAAAAUP/V6RH8tGnTFi1aNG3atHLlu3btmjJliq1yPM91siG2HZl/jqPoihCiiTxLxhpB46M2qRwAAAB4K9TpEXxiYmK/fv1eLu/fv39CQoLNmsH4fI/vLE+/RjSBELKkLuTYD8ZFjWxWPwAAAFDv1WmC79y58+zZs7Ozs8sWarXaWbNmdezY0YYNcez6YHy3u0mzdifMInXHeW6f27ByAAAAoP6r01P0mzdvHj16tJubm7e3t1qtxjBMp9MlJSWFhYXt3bvXNm1QRsrwCCFkUvd3efK5BBfp7D5wMSXTpmSM747x7G3TCgAAAFC/1WmCd3R0jIqKSk5Ovn//fmZmJk3TTk5OzZo18/DwsFUTZMEl06PBuCS4wKwRY1wHqiRHd8pUcp82xPOcx/M8vrNVQwAAAEB9xsBjcqdPny77mFxWVpYNH5PjKMNwWdt8Za+V+lsNnjpz00qM3WTtRF6tMpZxXSbapAkAAACg/mPbY3IIYTyvJXja8jbpzdp/1rHd930aHHeXZKygXCZiPEcbNQEAAADUd2/TY3KFhYUEQZQrLCgooGm6bMl1c6HA6NnmXEzxwA54Oqfx2RuuzagjlGB4zTcAAAAAeEvUaYKv5DG56dOnV75uUVFRQECA0WgsV04QRElJSelbM2U6fOla/7mzVOun37ndxZLg3mXOjrQ1Y+PfU6ZOftpA6FXjjQAAAADeAm/NY3JSqTQjI0P3kj179nC5z3+mPLwe23dKo11T5XsVve1DNxVPO5YoEM+eEt71Z+XDHTG1slUAAABA/VOnCX7z5s0FBQVubm7+/v5t27Z97733AgICnJ2dU1JStm3bZpMm3O65kSJuaqDqjv3XXpaS/qJD651+duSVpAVhPtG+NmkCAAAAqP/Y9phc7lDtgzsx08eH/DEb1+wfbXJJ9vOhnGNJWYn2/vxCP9TMVg0BAAAA9RkDw8V6enp6enrWUuUeRNbQ4TtQz99CTJyiWRxxITWFPo01oWk1QeMwViwAAIB3BdvGgxdLW2OUJXnT+LvOgXYJJocsMieAp9AVBy2cL1J2ZTo6AN4t9+/fv3btWlBQUGho6JUrV/bu3VtSUjJw4MCIiAimQwOA/eo0we/du/fixYuvmrtx48aaN1Fym5OzbYjl/87+Rb4f9N+8Hhef/PT5+2Oove63mhoee9qPq3kLAIAq2bNnz8iRIz09PbVa7RdffPHTTz+1b9/ezc3t448/Xr169ejRcEYNgNpVpwm+RYsWR48e3b9//yeffGJvXyvdwuvuFekvRSjH/u1ll5wZlKawu+hPNXqP/0fm1Z8FIr39OLfaaBQA8LIFCxasW7du0qRJmZmZ/v7+06dPX7x4MUKod+/eM2bMqDzB0zR9+fLllx+Lffr0aXFxcS0GDQCL1GmCDwwM3L179/nz52fPnh0UFFQbTWz0lhDvq/svHzF02pIfOV9gYkPXvC1P730Qm+Nw8lPHw7XRJACgIqmpqf3790cIubi4NGrUqGvXZ9fIQkJCUlNTK1+3uLh48eLFL3dspdFo0tLSaiNaANinrq/Bc7ncqVOnKhSKWqpfLcK3t7Ynz74//d7xzm6X8+0LQqmUY6e3bPjYvYmoTp8JBOAdFxAQcODAgRkzZiCEzp49K5FIrOXnzp177e97qVR68uTJl8uPHTs2ePBgm4cKACsxcJPd3Llza6/y6SGyEap87aPTOPZ1L/lHON/yYN+4LkNze7hj9j071F67AIByVq5cOWjQoI0bN54/f97FxQUhpNfrR4wYcfbs2cjISKajA4D92HZQy8WRRKe1c1K6T37/TkInhOi/pL5qDztxbq6QizEdHQDvkK5duyYmJq5evVomk5UWhoSEXL58uU+fPgwGBsA7gm0JHiHEc3MyP0mjCXKj/lNk4jf9S22Kf8Jr4Mx0XAC8c+zs7CIiIqRSqfWtUqn89ttvmzdvzmxUALwj2Jjg3Z05/l4Z83dNX2HBxEaXJEnur1J+u5b061cFAAAAWIJ1CZ4madPTf8Ka5v4pwHwyLDQ37rMYiiZPTzh901DIdHAAAABAHWFbT3ak/r+muI/Cs5zIpVi+PYdHWcK6/mLpI+5K5hi2DEBTNzEdIAAAAFAX2HYEz1F2Nyd75R0dJ+t1f5rsT32R2+5bKw/6bsb50uy1ndPnJTAdIAAAAFAX2JbgEcYpvDFD1XdL8c3cSaaiEkIqp/Lbpo/Pv97XnOuo7F0r3ecBAAAA9Q3bTtEjhBqsGJm2+yCxbeHBiBnTTZSvKMZXZ0xd1AM72FTSVsl0dAAAANiA0BywpC6tYAaG89y/4toPqfOIymNhgkcIiQeuxVy7TBnXBS2QtPA5pfl5xOp1qh/fh/voAQAA2AZH1sZCaASNfsf4zx/Dps05pkeDONIQBgMrxbpT9AghhI6SDomen4hm73ExGHAS+31kq8zQgo3Zp5mOCwAAAEtgAk+u83giZwcm8Cp9ETk7uU7jMKEP09EhxNYEP0omdXP+j6z5VUFgUjTW0TEk7T3z8bzCv+KLHzIdGgAAAJbguc4k8y9QhVesb6niWFL/F9ft/5iNqhQ7EzxCaGP2iVXyr+8bnETpXG1ku4FunyJE70nfTCM4UQ8AAMAWOBJeg2/NT+cgRCFEm5/O4Xl8g3HkTIf1DDsT/Cn9rT85PVI4eNqtUN+0/Nbbss8sMwi4qnRjypW8C0xHBwAAgCW4DkMRxiU0vxOag4gq5jp8xHREz7EzwT+llb7GU0OvqxrdcqXUJed/iAw5Xtx2ZzgH4ym4KqajAwAAwBoY32upJWWBJfV7vtcP9Sqr1qNQbKiFObrjlXueq0v+6iLKal1i9Ei9sHat479PWu/2SzU+ZTo6AAAA7IFLQzjy93FZG1zWhulYXsDCx+RoS3bTpKmBDirOTzEOFqMK18/NFQrs7dDy5Y3jPCwf2E8AACAASURBVC37xqNpTIcIAAD12ME4w6AAEdNRvE34vj8zHUIFWHgEj3HVlKWNOdn/HH/mbu3HdK5Sf7Izz+UrEhflPOpgyTAxHSAAANRfFoqeeyGf6SjeNhgXYfXugLneBVRzNJGHC2+KW9Ctb1qcPDgcfoHALUf3aJkIz1e0iHEZuJjpAAEAAIBax8YjeJ4Dz2UqLgkUucp44ybSJPdwUbjInEdbhE49lyIcYzpAAAAAoNax8AgeIcRzm2XJ3IdQZlJEakORobvistHgQtz3uaZz7DaY6eAAAKCesVD09UwzTSOEEEHTNI0upz27molhqI2LgMOug0HDg6InI+55rAmQdmDzc1XsTPDGOCp7wzDhmAOBo3+jzVz7ZlcsYs5a+cb+YxL1Qr6yjwPTAQIAQD2iNVBbYosJCiGEaESTNPrldrF1Fp+DAux4atFbn+ETLUQaQXQSCQ0PihL63lYPd3n8yT36t4BWXRyZDq22sDDB60pMxVf30zhfKOaLqRKcZ7aQokuCDhzVbZf+gryDdpDgAQCgLGcJZ1uEnXXaQtFNt2Tv7G3HbEg2x8XQN3n5X8cXu3340G2R39MBdmtaYtNHPyrcwZN1YudxPAsT/Pdnby1u9DnhK8SLMHuhEWG0WFTQ3vhP9+LTlnClsgN0Rw8AAO8cTy53q16UNuB29rdexAC7KRrd0h6uAXvtkobf9d7ehJU5/q0/6/KyMW1akBwnSuz+4OLP2tiWNI2dLvnw1q4vcESXePbHJRymAwQAAFDXyALC8EGsy7e+87vxP83VLbVTdhIJpe2U3tuCEofEmp4YmA7Q9liY4LfdMfysW8W3JP3dBv/z7mxTlovjdn//llElHGGyZB7T0QEAQL2GY1gjNQtP7nKkHFlHVcmhbNpI8zGUT9EIIUTRuv1ZkpZynrOA6QBtj4X/i2u7KRHqZbwfMor7Rbch58Kv+6jUiS5B19fYzSakdHumwwMAgPqMg6Ejg+yZjqIW4Jh+o//1cXfXzMiw29N0XH4eRtHBnz81PTH4HW6Ov/13Eb6MhZtkxfX/TUbmDdf+dllmUfU9Sgs8LBx9OyELf6MBAAB4rUcWy5S8vKa/NnFwEtL/uf+rWJkx6WF2YrHf4eZsvXTLwiN4Ky7fhe/wn4nk+lt5DkpBiajh9nniIA7Gzv9FAAAAlVPg+CYHdVM+j97U+OnY+0WtrrXzF/MPNmVrdkcsPoJHCPG9liCc27ZxWn6O0pznaPjnBtMRAQAAYIYLh9OUz0MIYVzMa2uQy9c+QYeaByiETMdVi9ic4BEuTJJ8R1jw/OzggvTM37N33y64xnRMAAAAGIZxMfuxbriYtcfuVqxO8AjNedB/w+HufK65WMAV55o15hymIwIAAADqAssTfL6J1mAeQl6xhiba/ivpKun2S8rKOwU3mY4LAAAAqF0sT/COIqy4sIAvLMwqKuY5O9xMP38979+tKWsJ2sJ0aAAAAEAtYuFd9BdTTYVm2jptIlGe0pcnPntBxyWHjjyYP89V7KAnNMezf+/v/DGzcQLAbjRN//bbb0eOHMFxfMKECb169bKWZ2Zmjh8//tixY8yGB6rOnDiRNmdVMAMXCBruRBi/etUWUtR/crQ/2ds14D67Fh5VYvjLYFytZmGvsYxgYYI/88SoN1HW6ewSqtC1DWW3+k6G9Em2BlPaD2849Ib+35O5R7qoeyl4sBsBUFsWL168du3azz77TKfTDRs2bNu2bYMGDUIIlZSUHD9+nOnowJvAeAjjcF2mlC0jNb/TprRqZ3eEkAzHP5FJRuZotjuqPbjckyWG5fqCLQ5sG+SGQXWd4AsLC/ft23fv3r3s7GyCIFxcXJo1azZ06FC5XG6rJhZ2VJROd9ydgxkNPGRuQd/Sex0LUbQLlrXyEze6rr+8M33TVK85tmoUAFDO5s2bf//9986dOyOEBg0a1Ldv3yZNmgQEBDAYEm1MoIrvVTgLl7fDeKwdNrSGeA2+Md5phwvcMaGftYQm9OaECYLAQzWs+UOJGCE0Kkc7Wib5tbB4i4NdQx6vpuGC/6nTBH/58uVevXr5+fm1a9cuMDAQIaTT6bZs2fLll1+ePn06NDTU5i3iGMYRiqhCCQe/jdEo4qqHpYFZzJEMdBn+R8bOJyXx3mJ/mzcKAEAIFRYW+vs/+3517dp14sSJkydPPnPmTFXWLS4unjBhgtlsLleekZFBEES1QyLyTlrSlnOU3V8opUlSd1zQ6ABH2a3aNbMbxnPgukw1J38jCNhnLbGkLePY9cHFQTWv/EOJ+J7ZskxfsMGm2d1EGQuJfHu+k60qfBvVaYKfNm3aokWLpk2bVq58165dU6ZMuXbN9g+pYxjN4QkthUKFIKWDbEDOjeitzS5/23BlmPqD0zl//pq6enHAegxhNm8XANC6deuFCxf+9NNPPB4PIbRgwYJWrVp98cUX48ePf+26IpHoww8/tFjK3wx78+bNmvyh4Dl9SmRt4TmNw+XPR6UgsrciQg/ZvXI8l88MObtI/VmOMowyPCa1h4TNom1S88kSwzmDcZJC9r0uf7sj14Nrm6y0P2P7vcKYxY1+5tXgIsLbDqNpus4aUyqVsbGxnp6e5coLCws9PT11Ol016jx27NjgwYONRmOFc4f9qS3KTlnm9eFBdXAOX0QbDRY+LuAIEEIETZop01e+iwKkTarRLgC2JRaLtVqtSCRiOhCbSUxM7Ny5c35+/po1a8aMGYMQSkhIiIiI0Gq1Op2uen95Kv++VwWpPWzJWC1s8jfCOAghmtAbY9sIAg/Z5GCU3ci8U5aU74TBl0xxH3GU3bnOE2pe58kSw1J9gfXM/B/FJRvyC63X42tYbarh6cqk+Z4iH39JYG+nwTWP0+bq5vtep4/Jde7cefbs2dnZ2WULtVrtrFmzOnbsWBstru+hCldmmwsceXmNxFyJwMxT4HIRRyLiSGRcuZrvIOSw5+8pAPWKr69vYmJiVFTU+++/by3x8/O7d+/eli1b5s+fz1RUHPUAxJETubutb214qpn1OKpwjO9uejyKNqVyncbUvMJCilqhL9jqoLaemf9QIh4vly7RF9S85v2Zv/V3/mhkg0lnco9qzbk1r/AtVaen6Ddv3jx69Gg3Nzdvb2+1Wo1hmE6nS0pKCgsL27t3b220qBbhje35+C2JU6rl626/5vy4RdC5hTAoSICzuf9hAOoJPp9fmt1LSwYMGDBgwACmQkII8T2Xmh59yLHrT1uybXiq+V3A81pivNNBEBCJMBtcLJfh+DnXF66RD5NKhkklNaz2Rv6/+Za8jnbdcIzT2T78cNaecR4zXl6MRshC0nwOmy/R1mmCd3R0jIqKSk5Ovn//fmZmJk3TTk5OzZo18/DweO26RUVFgwcPfvn+Go1GU8lNN5lFZCzhGlLMlSkNCCH1uKGXLJdTM3d84maDk0sAgLcRLmnKUfUi0n+kDA95brMwrprpiN4auChA1DwGEzRgOpBXstDmAxk7RrpPxDEOQqi344dz46bEFz/0lwSWW/JcsvF4gnF1mJKJMOtIXT8mFxMTc/Xq1fbt20dERERFRW3fvt1isQwdOvSjjz6qfEWJRDJ37tyXr71FR0c/ePDgVWs9yScu62UNi3CpgxEhxFHKGhqb6PTVudgPAGANXoO5xtg2GM/BJqea3yn1ObsjhE7lHPES+QbJmlvf8nHBAOfh+9K3fNPwx3L3U5tJZCHr7hY0RtRpgt+9e/eoUaMaN248a9asefPmbdy4ccKECTRNz5gxQ6/XT5w4sZJ1MQzr0KHDy+UGgwHDKj7HQuafa07HLG9syNOmKVw1RMZKhJADQr0xAaJKEC62yUYBAN46GM+B77MG47nY5FQzqCf0Ft2JnEN9nYZe118uLeRhPK0lNzrvfDtVFwZjY0SdJviFCxdu27ZtxIgRUVFRvXv3vnz5crt27RBC7du3nzRpUuUJvhrMSTMwU5oLLlUHlXC5yJy2FiFEE0YMt5DCAKGq+2trAACwFceuL9MhABsrJAuC5a2eGhKeGhLKljeSNiVpkqmoGFSnCT49Pb19+/YIIeu/zZo1s5YHBwenp6fbvDm+1w+muOGn+H8+WPjF4I+Rc8RBhUKR/2dDsyt5l7R0tXl7AAAAmNNA6DXR84tKFoh8ULLpdpF1uoSgDWa6895nY4g3VPE292Jb5+V1muBbtmy5atWq2bNnr1+/HsOwyMjIsWPHIoQiIyODg4Nt3x5N0lyHtsWDC1sb+Q64KW2VWavnOWmKeT3VOAs74QcAAFCJQY1E7d0F1ukLqaZzT43fv/+sa3O5gIW309dpnlu3bt0HH3ywYcMGX1/fq1ev9u/ff/v27RRF3blz5+TJkzZvjiy8TGO4Atf5e4iltNFs0JAlf9IWmQJdcJGttXlzAAAA6jMejjWQPxu5Ti3Cxbznb1mpThN88+bNk5OTU1NTPT09cRy/efPm8ePHzWbz3r17X+7eruZ4bl8SuQevFLdv5nmFYxHqjBwVz0STwbSh4VNLvidXbX2OAgAAAGCfuj5TzeVyvb29rdPOzs7jxo2rvbYwrpLj9jlP/weXR3OFxa5YJNeuD1l4U9j4yIb0ZX2dhgZKa+G6AAAAgHoPQ+gVD2CxR512VVv3BM5jglRFt5JcMYwmaC5leMz3WoQJZd3se8u5bO7fAAAAQFmFFJVFPr+X/v0GgrGtJDkkm++uZ3mCRxhH4b/Uz1VPkty4kmYY145j1wch1Erxnpvw9d3nAQAAqBv6Y7lpXz2mbdT5DEFbCgh92ZI4CzEkW5NgedbzqRGn55n1F40mmzRXP7E9wSO0LamVpsSJLpD6Ce/yvJYghHJXbiV1+bcLbD86LQCAZUwP+5dcUVX0UtPmTKajYw/90ZzU6Y9KYgqTJzywSY7/PXPnsoSvCfp5R+YhAv4cpXxMrvaxxaIlqdG5ml5i0SAJm3s8Y//TYrvuF5uKBjUMXvGgIKituAlCiCouMSen7xVucRG4OwlcmQ4QAFB/cR1H0oTurvuiw9n7i8nCRQE/y7hyS8YaqvA6xndhOjqW0B/NSZkW53eomTBQmjgk9uno+17bgjBu9a+QZ5rSruRdcBN6ntVE9XToV1reSyxCCI3J1Ukw1FciniyX1SRs2pRGFd2ocBYubVUf+vRl/xH86jClROia98j/dnYLawnX1cmSkTPYZaSUK2c2NgAYRFaK6ejqC456AM2RJaV8P6rBpPdUnQ9l7aYtOUTmer7n90yHxhKl2V3cUo6LcN8DzYg8y9Mx92mi+sfxkenbejsOHuU+KSrnYD6RV3ZWqEDAQ7SWoruLajqmKFn4rylhPKE9Quj+LPsyJUwgCy7WsHKbYH+Cb2zPkyuUuhQn3v/O1fBcnSwZ2a2V7SUcKbOxAcAgbqWYjq4eiZZ26WaK8xe493MaFltwI+/JF1yHTzChH9NxsUHBWV3qjDi/P5uLW8rzfs9OnvwQ42LWHJ8641H16owtuKEx53S1j3AUuLRXdTmSta90lvXM/IdSyUKVYlyu7rHFUpPgufYf4uImXHU/gf9vpS+uegAu9OHaD6lJzbbC/q/xlDN5StLHRYcp5anWEr63u/F+PI3oC9rTndXhzIYHAFPS0tJeNUuj0dRlJPVZnkV7pPBOiF1vS/oKkeeiYco2ZOoCTsgrR7CsOxRV9M9108NETMAXt20ubPxW/uDAJThNIaqI1O3PSp+XIGoseTLynsdPjahCgqOoTnoiaXJ/xm8fuY3lYByEUD/nYV8/mvy0JMFL7KclqZG5ml5ikfXMPInQuFzdbw5qX1618yDO91pqih8rUoUjXIQQQpTJnPI93/tHhNWL3FovgqhVj/OIJhKxXkNK7DKsJcJAX2GgL0WTBzJ2tFF2FHHYfJMFAK/i5uZWOn379m2tVmudLi4uHjVqlE4HoyojhNCBjO1h6giZOsx4px3lODI4788T4pYNCu+1VrZnMiyazl66iSZIaYcQymDQrN8t6/m+om8YkyFVi7St0mdv08QPYzEe1vBUK4GvKGn43fvNo1X9Hd0W+Vejwr80xx0FLk1lLa1vhbion9OwfRlbZ/stSSeJIRLJCJnEOqu3WMRDKIkgapDgES5rg/AmhX9NJ3V9hU0DOMqjuLgJR1Ffhq1jf4I3kbSGEB7TX+/uqihbjmOciV5fCHABU4EBUE989913y5YtE4lEXC5XpVIlJibOmTOH6aDqhfjih4+K7g1xHVWCCzDnicZHAxBH7eW5LDLjt2by1nycz1RgxdG3aJPZ+fvp1r5aJB1CMmYukXYM5ShrdNcYIyyZJoyHIRpZcs08NwGRY+IqOJZME22mMMGbXUQuIPKPZR8Y6Dz8QWFsaaGa75Btyrih/7e1sn0w/4X/sp5iUQ2DLzh+ruhvF+XAg1RxR92Orcq+e0QhF2pYpw2xP8GTJDJQXEeySXuCTPsizn1FQOms0l95ALzLfv3116ioKEdHxwULFvzxxx/r1q0zm81MB1Uv3Cq4RtCWb+OmI4Q4iPqMKDmOeyanrkcIPTXEN5QEMRWYKSFZ3Dq4tCc2jlLO9/UwP0kVtWjMVEjVk39Sk/ZVfMMzrSxZ5icj7vGc+dIOKvfFfkmf3E2e/NBry5t9wjqLxkvsF1NwtVy5u8gr/8Vn4m2CyNXlH/nL9cfvqUIHrtM/oiCs6FJjXE0LA23eVDWxM8HPOZ9/+onROl1gokTZloF5Q7jSzSmb02OyLH13NdHtPCzw9+K0afS35lQvxwHMRgsAs/R6fXBwsL29fXx8PEJo3LhxwcHBn3/+OdNxMW+Iy8ghLiPLljRiKpQX4WIRVVxStoQqLsElNT0erXs8Jz5N0OYUo7y72ntnk+JovfOX3oTOYk41Kls4vGltXiLfz32q9HQDobWkffXYdZ4P36v6H5op/qkwyI+jlHPkMwy3QxGicNEC08NEYaBvteu0LXbeRT//ffnfHztYX60o+oc1SWYdn3Aq4s+0bxCVkzzt0T1ne3NiCo7wo9n7KRqeCALvtMDAwMjISBzHLRZLSkpKTk5Obm4u00GByohDmhSei7ZkPvtvKv43hiwo4nu7MxtVNYhbyn33Bz8ddz//hEbWUeX8lTehNcd/ECPvpnb52qeWGiW0lvjeMYTO8jgixvTEUO16MD6PNlsQQggX831/4vv8RJlwjM+zWaA1xs4j+GPxhls5FoSQOMc8df5jsxBbNMNpl7zgp1by/0yXkGuSM/SyRkFZfFwwx28pjCkH3nFLliwZMGBAz549x4wZExISwuFw+vXr9/rVQI0VmWkpvzrdufC93FXD+2bNW8VzdaQMJtpscZw1DuPVo9RSdZI2Ct8DzRKHxHpubCwJlcf3viXvpnZbVFsPBZB6IqH/LXmY2m2Rn2Z7RvwHMf5RLQXe1TmOFwR4azftMyWmCHw9OIouRI62+N9Vzt9MsXnM1cbOBO8u4xIUQgj5Do8twdHib/2M7loeRdrxdTvHebbimNv+kJGfgTl+hTxE3kwHCwDDevbsqdFoeDzezJkzGzdunJeXN2RIvXiKl/Wm/Ddveoi0hVN1btaTdmojDm1mTk7H+XyepxvGeYtPxz7L8UPvcJRc1QBH1+9q6xQ3qSfi+8TIOtu5LfRDCNmPckUIVTvHc2RS+4kf5yzdJPD1QFyu6WGCcnhfXoN61L8hOxN8Wzd+Wzc+QuiBHY9INfJ1pPefv5FTpI+9zY581CsD6XGMI6cRQnkW7VnNiQ9d/sN0yAAwZuvWreVKdu7cOXbsWEaCeaeYSdpcgyuEuEgobFRfLvfWkKSNwvdAcPHNAseJtdXDK1VCPg6/qYiwd/32+YdmP8oVkXT8BzEB51vzHN/4l5aoVRO3tfOMj5IQQag/HcJR1q/eUdmZ4K3Igot+x+LjFiauuXPmXounGIl1Mx/usFtG64vdFo90nNwVIcTHBRd0Zx4X35/uPQ86tgPvpj/++MM6QdN0VlZWbGxs3759IcGDOiYJVUhCFZUsQJNFpPYgQhV0YYsJPF//9DmOYQKcKiIRjVCZCyNkMYlxMYxTza7vcYlY3KpJ9datbaxO8JqDZP7ZtMHtA2LyWxt5GE33vnVViASSqbcXeg3/ASGEkIQjDZQGxxc9+DNr38dunzIcMQBMOHnyZNm3v/766+bNm5kKBoBXokrMT+dwFB0x3gunwcn8sxxFp9cmeFyI+x9rkdDvVsqMRx5rGllzfM7PKbm/pDY80YqrfivvYKgcOxP8vVxLSgHJp2a1thw/VDzuYVfX7/4T31S4whzdmhiTcEgxUipsgGiaJshkIjm++MFc/+WL4r/sqO7hLvRkOnYAGPbJJ59MnDiR6ShYq/9BTUrBs/PyRRZq/Mk869VzDKGFHRURvjUdAYXVcFwUSJU85ijKJHiyiDbnYsIqdXvHkXP9/nye43PWp+RsTG14ohXfk50fOzsT/PVMc0y2GSFpPn/sEPnymYZ1q+arf70rT+2T1tDlanPPf+MojuHu44LT/+wbkNRG+f43j6f2chgYmb71c98FTMcOAJNomt63b59arWY6ENba21dtoZ6dZP70pO6zFtJWzs8u/Ur5b/GNcnUB49GmJzRNYFwpJnx2HZ3I/g3jyJClqqMncORcvyMt4vveetzzpiXLFHAmhOfG2v5Mq5Tg4+PjDxw4MHfu3NqOxlZGB0tGIwlCCNGzkqPbRmiiQ7hkvlnqF/RPttvcdhL7dghZ7FXX+fcsFDfDmEpSZKYpPZ/Q3y643lzemunwAahTMtnz/k1JkjQajatWrWIwHnYT87DSK8BcHJPwcMUbdsj6zsK4Cp7710T2NjL/H2GD+QjjkAUXaXMuwhDXbVbV6+EouP5HWzyY/Tj+a98g9mZ3VHlHN0+ePFm+fHnLli0bNmz4tl6Tw/inTV+NUS7JbuCW1LeIhxk+x3rEWSwIIdpRdrZVelv5+zmmzBWNf00ofthR3SMyfStB12gAQQDeOrfLuHv3rkajmTFjBtNBAVABrtNohOGIMhG5exBNWp7OQTwFr8FcjFvZ3Xkv4yi4aV/4nDNXf8j5t0IFR/ApKSm///77gQMHrl27hhCaOXPmhg0b2rRpU+ex2UYcHd4I22VWnGuTcf6JxXuOUvlpru5XB7v72kM+uYp/FKeHeY5VcFUDnYefyT3mKmzwV+7xcOi8FrwD/u///q+SuatXr66zSN5ZGFbanTyoGozD81piTphoSV1EEzpEkxjO4zp8wnRY9dQLCX7dunX79++Pjo728fEZNGjQihUrOnXqtHLlSqaCs4kmjpwNwi+2aj9Mi3cXemE9xSIKocV5+TJNlNnbiCi0NWUtRVMW2kwhKt2Y8tSQCAkevAuEwmc3FuXl5W3evLlz585NmzZNTk4+ffr0vHnzmI2tHlr/dHlzeev2dl1tWOe8dnJfJTtvhLIVjYGiaeQgfn6ymSPviEtb0qYMS+oijOcg8N6CoDfSV3hh35o+fbq3t/d///vfsLAwhJDJZGIoKlsa3UQ6GnUyiFed//KvHt/cRgj1Eot6iUU61U/fP5451XuOi8C9hCzelLxisOvITckrv/X/kemQAagLS5cutU4MHDjwp59+mjx5svXt9u3bjx49ylxcdYEqJmkLzalyco0tuPHUkJBQ/KiFoo2YI7FVGIFsfDTLtiIflFA0mhbyQiclfM+FxrthuLwDxlXh8vZVr+2BxnIx9Vlei9MRSXril1tF1rcN5Fz2PcLwwjX47777js/nh4eHR0RE7Nq1Kz8/n6mwbMhC0RtiikSu/9mcfF4gKywsLraWR2X/3lze2kXgjhAScyQzfeY3EHo3FDf+SxPFaLwA1LVz584NHDiw9G1ERMS5c+cYjKe2kXricfjNRx2vWdKrdAxD0MT+jG0j3Cc2V7Q+ln2gtsMDZVE0ol/q2QYTeHGdRlKFV/keVRo7rlSxhc43P3uVELSFQqVvNQYWjjr2wg/Y+fPnz58//9atW5GRkd98882nn36KENq+fXv//v2VSiVDEdaUkUC/xhZPail1Ifzl1O0UTXZjiQ9FU7EFN0yUMUYfXXoRjEY0hrB0Uwr0XAveKQ0aNDh+/Lj1+44QOn78uIeHR82r3bt378WLF181d+PGjZWvXlBQQJLl/+YWFRXVMCoyn4jvd0vaXsV3F8T1uFGVZ6D/yj3mLHBrKmvpJfKbGzelg12Ym9AGnw8opKiRudqf1Xau3Gfn2E+VGM4YjKvUqteuy3P7gqMIwwRv9h/R2oXf2uXZQ4knk4xRCYYv28gqX+WtVsEZqhYtWrRo0WLZsmXR0dGRkZGzZ8+eMGFCz54939KzdjI+tjlchRDyEfiaiuIM2ifI0wfH8B8bb0EIpX4613XlHI5cmlgcdzBr95e+C5mOF4C6tnTp0kGDBp04cSIgICAuLu7EiRMHDx6sebUtWrQ4evTo/v37P/nkE3t7+zdat6ioqFGjRkajsVy5xWKxWKr/nAuZT8T3vSV9T+m+7Fm/KI8jblae4wuI/JO5h+f4LUMIybjyDxwHRWZsm+Uzv9ox1H80QjvuFo9qarMrEa8iw/GPpJL/5Gq2O9g34HJOlxiW6Au2ONiZSdpAPDtqNxI0wuh8E2V9K+JifGufsrjojU7Ov5teSPD+/v7R0dHWryKGYe3atWvXrt3q1avPnz+/b98+hiK0AetPNlzJNeQKTZwnZWfxXBwtyemcpgHuIk8Zt36NEwBA3ejdu3dsbOyePXvS09ODg4OXL1/esGHDmlcbGBi4e/fu8+fPz549Oygo6I3WlUqlGRkZL5cfO3Zs8ODB1Yvn5ezuOMUDYVjlOf5g5q72qq7OAlfr2272vS9oz9wtjGkqa1m9MOo/gqKXRRfWQYJHCA2WiBFCo3I1Y6TSXwqLtjjYNeTxFlwuOPL42TDtJoKmMRT54NnbgQGiee3gD3VVvZDgExISXj4nxuFwwsLCrLfdvaU23ioaGyzhqflEoUqneKiRzAAAIABJREFUyCw7i+/lak5OFzYNEODCiZ5fMBUhAMxq1KjRwoW2P33F5XKnTp2qULzZM8q2pduXJW2n5HsKM5c+wXBUmt2tHCc3KLqcl/5NgvfOCsYLSTEk3Sm4uaTR+tISDsYZ5jp6T/qviwJ+4mJwA7wNDJaI75stS/LzN9qrG/J4CKFv28u/bf8si6+7UUQjenqI7U+ke8o5LZ2rM1BvVRC0pYQslnMZvrT9Tuyge+6X9PcXCR1FZJF9Jt9QdhbPw9UU9+yYnqCJPIvWge/ERIwA1DUej7d+/frx48fzeBXcy12TM+FlMdsDZtaPT3M3pWF8rOGJli6zveP7xKTPS3Bb5Fe6gGZbesntQv+oig/H/9H9lU/kTb738cuz4osfBEqDayvud8npEsM5g3GiXPZ9nt56rr5u2m1sz2tsX1tPMRzM3B1bcH1BwDpmfwWWb3vUqFGlT8eWc/jw4dqPp1aIuVgJQUtdZLRGyUF5ZWeJmgTQRrN1Os2YvCN1w3cN3+7n/gGoooSEBGuf8wkJCUzHUiuy16Zod2c2utg6/5TmcUSMf1RL3z1P808t1h/mi5pIEUKmJwaqpNh3rwoRHKpwAi5rW66GT9zGf+I2nonY65qJpM8lm2gaIYRImqZodCLx2Q0QOIbCvAQ8vFZ65DldYlisL7CemXficEb973p8bbRVZ7JM6f/m/e0m9Dirierp0I/BSMoneG9vb4mkLi691CURDzNYaEUDJbqv5GMvPPvHdbaX9342yGADoVeA9M2uFALw9vL09Cw3gRAiSRLHceyt7V+NRuhYiaG3WJS7NkXzW3rDky1NiL4+SNUaofgPYvwOScXNb+bumELkqDhKbv4JjcscH45Sb075ht/gne7bR2+kTiYarQ+kUYimaXQy6dnJTgEHa+PKVwltv0sUUtTy/2V3hNBgiZig6cX6/E32dqXL8DiIot+yvTEyY1uE46AW8tBFCV+1VXVUcF//UEAtKZ/gv/vuOycntp2jlvCwEoJWu9hvyYxr8+pOKjgYZ5jrmLoMDID64PHjx19++eW+ffsuX748cuRIkiSPHDnStm35w9n6L6OIdJZy/igqzl+V3OpgXsOTLYvTjQ/73iLel6v3tKAJOmFgsu+eti7/J078pDlZQDQ82YrvKTQnTeU5f1Y6Otm7yUnCWdf92QVjC0U3fZL9U/daT0syHD/n+kK6+Ugq+Uj6wp/oMcFv2QHn/cLbWaaMKV6zuRivvarLkax9I90nMRXMOzGK0dx28qYOPAcHB78EbztUUG4uka0h9c8KzZTpcfGDOg8QACaNGzeOy+XyeLwFCxbMmjVrxowZn3/+OdNBVUfv3zVGgt7soFae0z9oIynOMt0ffPvqMs9AxHky8p6ssx1VQlmy55D6df4nGzS6GMr3FFLFd8i8/3LdZjIdO6iYgIMJOG/NETxFk5EZ2z52HcfFeAihfs7DYgtuPC1h7BLYCwm+W7dufH5t3VXIoCB7noiLOTg4uKZ62qPCcnOLLlwrPPWsOw49kbc5GQbKBO+WGzdurF27liTJmzdvTpgwYdy4cbdv32Y6qOogaZqikBDDuh5upbhW+KjHjatLPKaO8Pbd1ZQsJB51uOax+jRX2Y0mtOa4IHO8Y8kVlfFuJ9qSbbjuRereyn4+QL3ylyZKwVMFy1tZ3wpxUT+nYXsztrzcGV/deCHB796922w2Z78CI/HZxJknxgcai4ODQ46OVHDKH8HzPVzNyWnWaUe+c7hj/zoPEAAmqdVq6xgzTZs2lUgkGRkZb/uNOESSQawj8p14bleKMIQsWWZTokHgLSo8F4pxlYKgoxjPQRCwh+e5BBc3EgTswbhqXP4+01HXFxwMK+3uDVRdEVFwLPtAc3nog8LY0pc93zHHlHldf5mRkF64Bu/s7FzJojT9tg6d+2+6yU/F+6iRen7mpgEcC0kSHM7zDed7u5u3P++3q5t9byZiBIAxkydPDg8Ppyjqxx9/fPToUf/+/avdmQzjxp3U/dxTeWfU3ZT/OAya6X854vrhcbEBl4ucZ3nZfeScMX8yTRSaHg7HcL7p8RiESIznYHo8FtEmKv9vjnrg6xt4Q8VX8wsv5jl/7mXzmmsPjqFdfexev1ydo42JxnvdUUVHw7iii8B/W92HVJbGkusp9r1dcO02ula23F3klU/kvWqtWvVCgg8NDY2NjQ0LCxs0aFCPHj1EIpHN2yssLNy3b9+9e/eys7MJgnBxcWnWrNnQoUPl8lrsnGhMsFTExTgcvDW3jdlwSZf71MH5+YOwXEc1VWKkCotxmQQhZKZM1/WXbTsoJAD12ezZs9u0aWMymcLDw5OSkubOnTtq1Cimg6oSvZFqtyvHRD7/i38909z+QXbDac4z56VlhDq8f6L1f4feujHJaeRo16fj7tPEEIz7F8IwQUCkOWkGJmrIUYVb0pYhRHNUH9g8vKIr+qRhd7kOPCLX7L7cBp0DvuMwoQ8mCuDa9eY4DC9TTJru9+KqmT/z6iXy/dznzQa/qW0vnKK/evVqfHx8z549d+zY0aRJk8mTJ587d04oFKrVauvzsjV0+fJlNze3TZs2URQVGBjYtGlTHMe3bNni6el57dq1169fXR5yjnU44YZiP0OxIjs3/oXZGKYa0R/xnv3WIWlyb8YWgiZqLx4A6huDwbBp06Y+ffoghCQSCYfzdjyIrBTiDz79f/bOO7yp6n3g586splldabonLWVvRPYWpYACIktAAdnigC9DpowfKkMQCiJDQRyArMoQFNmyymhL915pRpNm3fn7I7W0JV00bSjm8/R5zDn3jDeSe997znmHV+oMufXPBYfcBUgHb/zAu4Gdj7Ul5iUy9/VRp9vfHC3JmPaYLqECdvdGvWdDuJzI/B+nxY+4/+dk9ioI0Lj/OgBz7Cub4UZJ2pgH/jsjwv/oaLhZkvNpkoPOYV8mIDxgA5m3FUAwhIqtf7TqNwjzQqRvOFq2FxK2GgoLC3ft2jVw4EBXV9fo6OiDBw9W17LutG/ffsuWLc/WHzhwoFOnTs835okTJzgcTs1t7hUSf2SYWZZdHPFh8s9dT1/fXX6JIYiS3y4Urt9V9OVew/V71sq4ktsMyzyfPE6cPB88Hs9oNDpk6gMHDsjl8lWrVnl5eWVmZnp6em7evNkhktRKDfd7lo5qEZPfdX/h8iLtqIKiEpouva69F3j5gx+TL42/lzziHmOmWZZlGYvxXgfjvQ5U8VEia635QW9zfLTd5Sy9ro3z+0sbq7QWqRIysdet7E+eOJ8rDceSOpfIWGb9zJAa4+0w2vDQsSI9B01zv1frJufh4fH++++fPXs2NjY2OTl5wgQ7ZFBNTU0dPtxGWJ/o6OhGjaUVX0xeyjQDABApalG7Gs1l4ehZmi5Ysc2SkunStyu/Y5T219813/8GAGjt2gECzcYxw4mTBrJjx47Dhw8vW7YMAODn5/fzzz9v3rzZ0ULVFRPFvh+rmXhKPeeclqBZrYV5co1S5zB9UoqmGMmtmxXvzs32tUDBP7aGODAAAEA47rcCYgkicwlV+C1jycb8P7evSMY7utSxDwL2RokGl+XQQ1zRkN/alV4vyV3+cgYNbEow36VU8WHGlAwAIHM2INJhMN9GHgEnoAY/+EePHq1YsSIqKmrUqFG9evX6448/Gj5Z7969Fy1aVMUgX6VSLVy4sGfPng0fvzp4KGRNPshx51JKEUMrrfWGq3dhHsf9wyn8jq0AAF4r5pX+eYMq1rCAPZz3bePJ48TJC0VCQkJkZGR5MSIiQqVSOVCeesFFoaltBNPbCl4P4QIAuAg0viV/tae4Cx9/HEp07OXe+k63oO9bfW0ypJJl526IdBjLDS2A3VluIOL2FsyPsK9IlJaCYIB5VDJEh3kw5oGTeRb7zvUfBMLcUe+5ZNYyxpREq37BfP/naIleXKoq+Pv37y9dujQ8PHzw4MEajeabb77Jzc3dvn173752MDqLiYnR6XQKhSI0NLRr167dunULDw/38vLKysrau7cRDSD5GGQkWQAAz0vAFIkRVm2tJ9KzeW0jAACAZYt3HgYwxAkNIDJyIADdLblRTBQ1nkhOnLw4dOnSZevWreXZZQ4cONChQwfHilR3IAC6eOOv+HCi3DEAAAeBOsnxIDmagpBduZzzJrPRG4dwOABDpyhVVh1vYtmVwoWuZDZkycZ8PrW7SK79pL5fhSe/fs94t8wplyWYtAkPIRz23xlZc18ndQHzms6YUixPxmHeH0KoHezDXlYqWdEHBwfn5OT0799/0aJFXbt2hWEYAJCcXGaSFh4e3sDJPDw8Tp8+nZmZ+fjx4/z8fJZlPT0927Rp4+fnV2tfkiS//vprgiCq1CckJDAMU3NfPgqZKJa1ZPlH6jgptAeWwxjuAwBQNyVjzGJMvjAvFPf3JtJzaI0OEbsCAD4KWinF3J73izpx0pzYvn37gAED9u7dq9FoevTokZycfO7cOUcLVScSVOTJlLKcKGlaCgBgoNhND0v/8jJ2JbhbwyRflejeLVJ95yF7nc9DAZiiVG13k27S6nz4kUKvd2FuCNQ4CT3Fwz0AACkj40KOtuFFuaRNeAghcOD+KAirw9kfbSCylgPWxmMN4igwRbMMMmhnIBwPWEdmrUK9pjlalBeaSgo+LS0NAHDmzJkzZ84825S1hx/8lStX8vLyRo8ezTDMjh07tm/fzufzx48fP3JkLR6oDMOo1epnU1iWlpbWKlhHOS7jwZbEwX3aKYuCyAicIdIWAAAwOUmpcswPNvA7ZeBBfvozfzJmCx7gAwDw5Hg34Fs6cdJssFgsubm5iYmJsbGx6enpcrl8yJAhjs3gXncwGBLhZSrT+p5P0MyfXsbOJk53lgMAWCByJVjdvGLNfg/ZED6PZNl3iop7cbmrpGJYukxDqvSmND9eUGPIJh7uAViQ+lYcN8IFcUXrqt0BADCH0V2BBa1h11cqVlNFB2FAN4aozRFEPAARD3C0FC86lRQ8RTWub9jWrVs/+uijVatWAQCWLFmyb9++t99+WywWz5gxo7i4+P33a0rLyOFwVq9e/Wz9yZMnT506VfO8PBSKdMMo8DGTtP7PDf17fR7r1uqS9ZLx5ijTXYPm0EamRA8gyGvth9C/mQp3ZGyc4jeHC9s/GIATJy8OOI5PmjTp1q1b0dGO9ySuLxgMvvqnlAEs+Df6CcWAkqvgvMFyAVg8BEhff86nYtc0kgIAmFn2qMHUDsfiCCKVpEIx9Lvsr/PM2Z+32I7b20fOijjaA0BAd17l+1WLump3AACEYgEbiPQP8aCvy533WHM6mbUGCz/UGHI6eVmppOAb2/l1/fr1R44cGTFiBABgz549f/zxR+vWrQEAPXv2nD59es0KviEYKfbgQ8P0dm/Cadu6++rc6DIrP0Z/C8KeSKfdoJQGlqIZowmrkNrIwpgzjWnOBLJOXm4gCIqJiVm8ePHixYv9/PzKHwLNwhXeX4QeHi4laQAA2H7HcCXXLMaRz7u6SngwgEFHL5wx3LMkjJQDYATAyLJfAsCDIJJlzTlssmtvFSQMFoTHFh0b7jW2uilo3WXWnGbjAsRBZSNr9Z4XD/ewbtfXC0TUG+aFkYUxmHyOtYbI/B/qPRfCHJPqkyo6yFI27C4hiIN6vQ+gZvBT+W9SNV0sRVGHDh06e/Zseno6ACAwMHDgwIHjx4+3y92OIEhERAQAgGVZkiQDAgKs9S1atCgqakSLNr2FOfDIOL2dCytf6TtuEgSRgDEDGCcyFuH+qyDMBfN2ebbXnMDFCFT1/48TJy8fY8aMMRqNVQxd7XIk19ikaqlbeWV2OUVGGgBAMsyjYsIFhyU8uLMXDvitIVyBes34H/uKKwwvlohgALishXrQez06eKZni2Cu32dJC3pI+8lwd5tTUAV7WFMiLKy0W86S+XTJ34h4ANQIS3+WKjHHdQGMidacp3K/AABiWQowRkZ3nSn5kxNxtCGDp5GUgWVb4Vh5TQJBohAUitX0rKOLf2YZA+JaydeJMcQxlgzU672GyOOkUan0j6rT6Xr06FFYWDh69OgOHTpAEJSenv7pp59++eWXf//9d8OjyY4ZM2bq1KkxMTEtW7YcN27c//3f/61evZqm6XXr1jWqm5yHAPl6oBgAIFX0+mGPcUBfxGzMRo3XAYwhshHV9bLm+3Pi5KUnMTHR0SI8J2aKVVvYG7kWmgUFRhoAYKLB2QwCRwAHAfHF5NQ2Lt4B64jUOSWKvn0EEgTlAQDI3C+Kcb/HeLsIl3ABBPWTDf0l/8B0/4U2p8D9VpgfDcR8F0PY04W4JfldTDEfahw7XAgVwS7tYH4UoEpYuhTzX2WJH4YqPqaVB2FxnwYOXsoyHyjVX7lJO3FwAEAcQcxSqje71RJ5HvNfa0l8E/WeD6H/GmewtPlhT9x/DXCugl5gKv3bLFiwQCQS3bx5s2IU+o0bNw4aNGjhwoW7d+9u4GQbNmxYsGBB165dRSKRm5tbXFzc7t27SZIMDw8/fvx4AwevAQiAdp44AACG4QeHBkQPPp+X+7vcsIMTfghUCGhjfpwMWJYb9TRk9Ob01SO8xvnzghtPNidOHI6np2M2fhtOjp7O1VMwBCAASNp6Es9CgEEh2I2HtJBiLhiEcF+FBa1XmQ+NoSbQALyG6S15OxZKd051FQogCAAwxGPk0iezkwyPwwQ2zuMgbhDiPpbMXocHfWWtYfQ3mdLbnOAdjfe9cP/PzY8GcKJ+tzweBrJxCPeGMBljyeJ4NvQcszWOb3GTzitWb5JJuDA0S6leKxV35NSSOw4WtEIkQ6jcTZh/mSEUVbgXQt0QydAGyuOkUamk4C9cuLB///4qOWa4XO6aNWsmTpzY8MkQBNm6dev69euvX79eWFio1WqlUmlYWFj79u0bPnjNbLmtn91BiEDA3dJCqY33gNYgHmNgQbuKbajCYvPj5IoKPkwQmW/OdSp4J05eTMIk6NCgsufVqis6E00DFhoZxvd1RT0FcAevMr2F+692fdhvX+TYyVpdcMmSv/nREVzoPbGi7CqMj5JPOJT77fLQTTBkI/YX5vOpOa4zY5gIC9oBwBAZi3C/1aAxzW8hbiDq8Q6VtxXzXUykf8Rt9SeR8p69AuZ34OBb3KSzlCoGgI0ySW8ety69MN8l5gfdEY+JMC+UpbRk7iZOxLGGC+OkUamk4LOyssLCbKQ8Cg8Pz8jIsNeUfD6/X79+9hqtjux7aJzcSiDiwKwY5KaHYmLYx3dplTZ4iH/Jbxcq1gz1GNWEMjpx4qR+oAiks5T5i1v/AwFgIBidhYEqGK1DHH/Uc5J3/tovJRMFOX8dk654Dy44U1TxMJtVEgVXNRdflfZ/dhYIEWI+nxIZi7ktY6miAwDmIDIbIbftC6r42BzXCfWYyG0Zy5RchjBPRDLIXoMjEGAABADLhepq2w9h7qj3PDJzKafFETLnc0Q2HOY7g/aUUWigPQUvoqVh1eMTm8Z0zcKetmb4KETo79FmfUDXPC5lySXl3sZ4AOIBABDMgYXdAAC4jxetLmEMJljw9N28lNJxET7qPGdy4uTFo9hIxynJ+GKSZkEpwQIAGIg9k27hogQXgRJU5LhIvpcAAQBg3h+WxnVy1VyP81igRrrfY1Pa0PkVh+ojG1xDbCvUYyJVuJ8q+oHM2VDlaK8RYGh1LGBJRDyESJmBes8nc9Zjik9o1XEI84Bduzdw9PsEMUup3igTC2HYulfflVunjQHM631T4X4y/2tadZzb5kYDxXhp0JiZkUdVVyfU21eiCaiqt/bv3/9sjIvS0tKmkqex4GMQJ3suAYwdBsAC2AxQI5W3BQDAkoUskc9rnwhgDoBhPEBBZOdxWzzdkz9d9KsYkw5yb/QXdidOmpgFCxbUcPWrr75qMkmemys5xO0CosTCsCyg/jX715gZGALufFiAQvC/K9RMlrNP+OE000+vB8xsSdJTlPzOoh6v8+u+zQ7j/mvM8W+gHhOqHO01hIuZlvZemJhT6VyApY1E6kyIHwHhcoZSEVlLIcydNtxmivZBuCc3spawHzWTQJCzleoNMkkPLgcA8KWb5MNizXZ3aRu8lmN4AKzx49ZaEsfgARsgtBa7vP8OFAPoF9XlpJKCj4qKOnjwoM12UVHNO10PH4NUsiVyzYprd6f6PCzgfXxeHnEMAGBJfBP1mFR+suU2ewIirfR+00n8SoYx1QESO3HSyHC5ZYevGo0mJiamd+/erVq1yszMPHv27NKlVQ+wXkyCxUhrtzJvlys5hNpMc2CoqxznolCgGJ3e7qn76xpNyVDvcQrBNABAEIbGuEs/KFZ3wswsbZBzfeoyF+z6Cu6/BnF7y47y74krnYcJu3hXUq4Q4oIqPmL01zmh+xj9DUvSZG7UBQjCTHGdcb8VDZwRhaAtbtIO/1rVdeZwdrhL8Tpv1CPigXjITlTmPLtsHlRS8A8fPnSUHI3N2p4iH9kQxvhdq073tedbihAtAIDW/M5aslHPKeXNUM+qe3RB/LAgvg27BCdOmjvr1q2zfhg5cuS2bdtmzZplLe7bt+/EiROOk6seDA3mDQ0uW4W/d0ZzNY925cDLe7g+eyC6271SSpIwDLsg99ySvibXnL22xTYMqsP6FQBUPrO+EtKlNJlv4Yby69ULk88wFR2ktX8g4n689g8BhJHZqxFRT9ilY30FqMKz/u6t67J2rwDqNqaBMjhpMv4rR8tR7hiZvRbAwjDvoyfH/Noepom0uZTqBCLsTGQuwf3XgOrv8BJSY2JMXs7o9E5eUi5evLh9+/by4tChQ+fPn+9AeZ4PLgpwxPZKlFaXFO+PY9QWyRh/q5tM8d7c3LzMgjfzbjyYESs5+YZXoyxJaT2VMvy+6XFp8E9thL0k9egJ4bj/ajLzf4joCoAw1pJJFe7ntr7SGEI6eQ4eKckRR4uZChvzwTvLTDpccOjaBE9B3SMTNyb/FQV/PMnU3fLEFVEReJsOrrmAzWNJNczxATCP0d8EFWLaqHYdFo8ZhoiF5TWpxqS/VGcXBC13hOBOnDQ6vr6+p06deu+9spBkp06dqkuCxxcNVw4s4cJDgniulY+0Tffi85ac0d9tAyGQ8f6frv2uAV6/wm3ZGrRwjHHc8UCvs0VbXpH2rC6S3XNj1e68li6KNSFp7zwM/K6lsLcUAPCgiNQTZZb/JRbmkZKk/lUULd0wMbdMeEQymCrcQxV+i3rNIDKXofIPINzLvhI6eW6i3LHk6XLrZ6WRef0X5Y2JL2Iwif+Kgr+aY4nJ+/DXFm/iAUfcC0fqzDxcf40bedL8ZBwn+JuKNrG0VmdJTOV3bVteEyVsW0rrHCG1EydNwbp160aNGnXmzJnw8PAnT56cOXPm119/dbRQ9eZtt/2z8W/FOAweAlOFerJAx6JzQk90RiRY8jAuVZRPFCcrfzY+hB8PmjPwnYeFYa8NOlbwwzQ/e25alGt3v60tAASCDrVKG1em4/c9NBSbyhR8Xil9MsX8V3bZ82daG0FP36cG7Zj/55b41yDMizE84ITE2FE8J/8R/isKfnyU4FjJNUgQieR/mBuvULySwCD+lsylABEyhjhY2KU8XwInNMCSlF5RweMwp6fUmZfQyUvLsGHD4uLifvjhh9zc3NatW2/YsMFmPIwXnEi/bpYnmznhpyDk6YF3SeyfqMtS+ZI3BJ1FAAC3KYr8zy0ur6lPcE5+HLza66R3RLeb7be13THxy2RDQqggoobxjRT7e6p5ZHjthvdVtDsAwKWbuFzHf9nvqf35uBOqeR2rGtmVA/PCENkoS8o0TuheANcpHM3Lx4UMMwCgf8B/9Os3EBthm6pA03SzSDtRMwohMsD1JKD1rDkjAcrWA2EpLWD1N2FERBbE/JtqEgAAOKGBlqSMKt2NtOGq+mKTSuzESROSlpb28OFDpVI5adKku3fvOlqc5wEWdkLEA2jVMYgTYP0rve5K5R803X1V0CUIAKD6IV91IE/8P27+ZYng+5nr/xbO/0e/9j3/1BMany+mzzhbOPe8psBQbcL1PD29636dHIbVPxYQOWafDaEVveVduollb3vlLq+fSw7mswhTfIJI36hXr5eJ+GLqcTHpaClqwkAyGvMLqiJtK/ikpKTo6GiTyXThwgU/Pz+5XH7jRvMOa/CgiPiq4FPWkof6rojy5lzJ6E1RalQ+m6VLcP/VFfMl4CF+ZFFxle4sYA/nfftd9rYH+ttNK7gTJ43OwYMHp02b1qFDh9u3b6MoOn/+/C1btjhaqPrDGFGvqVTBHkZ/lbVkrCmMy8j9DvfNI/MUprhHWRS1kSqlLYwm93qJl759scnfPcnfPYlDMlzK7Ck3i6W3wuSpUm7ta55acZuiEPaSpIy4z1R4XVAdyFP/XBiwu1L0NwPJWuiadAOEijGfTxoukpPGQ4DBrpwXwqTuWWxv0U+bNs3DwwPDsFWrVi1cuJAgiI8++ujKlWZmwxlfTH5wTmP9XEKwOnP4GVUfc/HDLph7FJohYPMhVAyhMkQ6rGIvmMf12b6yylACxCXa8+1Ded/eKbn+ddShJvoCTpw0CTt27Dh8+HCvXr127Njh5+f3888/T5w4cd68eY6Wq36QuZvI/B0A5psTxkCY+1SGdgkpoM0i8Zi92ee00zmL3NtRSIvHzIG2rn0MWcsS3GGAFWDLvrFoxyst01VvAJc2rmx1dvj1AkIg/52RmTPiU0beDznaFhYgqgN5eavTQk+144YLKrYsKKULq98zcNJcsMevplGwreBv376dnJxM0/SdO3diY2NNJtOaNWuaWLKGE+GG/fC6zGqgeijesPu+oUvHlZK0HhfPDez35uFc2o+Tt8VmcmXIVmrkU0W/thK2Syp9/GPu3rGKKc82cOKkmZKQkBAZ+XRlGRERoVKp7DKyXq8/fPjwo0ePCgsLKYqSy+Vt2rQZM2ZMw3NPPwsiiyaKNLY6AAAgAElEQVQLvsX9PiOyP0e9pppKn+SVPrzl2nNMwU9pp0ZKupE+GiWcGyjoJkYfcboa+iOuaNKUOwe7CFeufJVTzRNaZ2Gu5JTlmy8w0KUEeybVbC0KcehV32ojvJbp+Pfj095+IBrmXvhlZtjv7TnBVb3hgyWon+t/xRCq7pxPN98rKtuWv5NPsBCw0Hprsb0n9uKcx9OqY2TeVh4DdvqR5oeV0otj8tmIm+PDAdn+bclksszMzDt37rRq1UogEKSmpgoEApstX2QgABTCMtM5uQsCAPCRelKW+RGdDl4++Hbb8ScR6RiYbyNB5LPEKo8bKJ0QdR3pPeGnvO+Geb7pgtr/CeXEiUPo0qXL1q1bly8vcwQ9cOBAhw4dGj7s1atXhwwZEhIS0r1794iICACAWq3es2fPJ598cvbs2c6dOzd8iorQ6liaMRmy1nBwTyprhRfLeEFwhOUB4LHd1kx7xYJAfAA2QbpzE2TvzEwefg/mwm5TfU7w8ar7dRXI1tOxaWUm+aUEW0oy5UUOAnVXcJDqd/QhBPKPicx8P75gU0ZYrA3t7qQ6OCgkwsteuTgIBCC2vIjBL8RiOb6YnHVOo8C8tyoS1xRsSzOI3VRlPwUPTLvUazYkaOVYCa3YVvCzZs0aPHgwwzCbNm1KTEyMjo5+6y17BmhsegQYbD3pQr3eE7lvk/G0QrgU811kszFLkHkffq7YthxAEACAYqjj+T8M9RyZYUyVcxQiTPJN5qaPg1c1ofhOnDQi27dvHzBgwN69ezUaTY8ePZKTk8+dO9fwYefOnbtmzZq5c+dWqT948ODs2bNv3bpVQ1+CILZt20ZRVJX6hIQEiqI2bNhgLQYGBo4ePRoAYDKZYn6m3+oisCDo0fT+k2RKAQ/E8qPDDH8QEH71+P86n9N5DojlRGVi8jBMnOSxKvTisT+yzH93ZMK/2vQkqMI427dvp2m6fPxt/9av+eb7h9xXfe7+VmXeKu2r1EOhAAlAEu7kjQ4uq/+/HXsJpkxL5Un6FRkF2TrabDYfPHiQS+kRwNRr/Je+vmPQUBawE1sg1vobANx4AeT051Ov5h9lGCYO6hiNfjvbtGvbcAUAwGw2K6+PvJ8d9felYzWPA5oE2wp+0aJFXbp0sVgsgwcPTktLW7JkyeTJk5tGIDvyuJiM/tVGsKER7NJVb82hGBiqJqMzhGMsw5D5RZi3JwBgV/aXOMwZ4fWOmTHhEP5BwCdrkz9NMSSGCFo0yfdw4qRxCQkJSUxMjI2NTU9Pl8vlQ4YMscuwqampw4fbyNIUHR1d6wE/y7KlpaUmk6lKvTXxlUZTZlsjl8vL26u1lisPWrWPSh6kOKUlhDwybZBuN2ABRXBDBs60jGAFrBEwbP5xqZDNFk7vmpiQbtFYvMENLQDmCuPo9XqLxfLs+AaDgcZo69TyZ9pzUSJUnuGB51LFAADAEhY/139IkgQAFOtkZrNHefvfsW6lcNlq3kIJPrtSKMQ5gIVKOL1bGf5WGFNtjv+sPM56B9ZDgIV0+ZTFcqkk7IP+RyL1j3xd/QAARjpZInm0/cJbJsL276TKOI0NVJ0L3JkzZ2JiYmia3rJly61bt8aOHds0AtWXkydPvvXWW2azueZmShMz6Efl3Xc9AQAPrz8gN+zjv3swbOA1mBdqu/2W/byoMJd+3ZRE4aKEmR8EfNxB1K386vqU/2nI4g0RztATTuwGn89XqVQ8Xt3zm9mNwMDA2bNnL1y4sLyGx+M9q1zrS3R0NI/H27x5s6fn0yBfKpVq8eLFRUVFx48ff44xa7vfGfPD/irK9BfaaqDpDM6SuRl9yY/Ham/2LVJ9GEIkhQhaZk8aBeNU2MWBEFq/zd4UDTXrnObsGNsB76jCfUT6Agjm0gCuuG0PMUYTtwW/zTWbmddfP5rO8dg1MjBknGJavYT577D1dikL2HkdhbU3dRCanH0pKQc69f4DAMiSMBKRvo56vltrr6a5322fIL0kbjMVoBiW/NcdRRHu0+vk7mzgxRLZ1bXntggyJ6YCAL7J2Agg9nDetx8lTPsoYdoHj96e+3hiviVXSRTd0v7dRNI7cdKYZGVlHThwYOLEibW+KNeLmJgYnU6nUChCQ0O7du3arVu38PBwLy+vrKysvXv32nGiCsB4wDqUKu5rOo8zFoShJckit7cy2tz6YYDpoheVZeHOcGl5mWWFuUuS6zu0EIdCJNUaxKGekyFMhoj67QtPnOh/n+qYze+cy215ggXwcvFnZlvrKIqllETBMI83b2mv5Jqz6ivPf4S2nlgbj/qlw3lOaANLaW380bUEP6Bl41FA0qpjtPo3lshHPSY0hbR1w/bv9eVwm6mIzsIaqbJ7TCKRrOB+rJGn60uSxaK+NttzW7ewpGQCAIZ4jkotTSyvL7Lka2lNGD8SABDMd27RO3kZgGH42rVr48eP79Wr17Fjx7y97ZNXycPD4/Tp05mZmY8fP87Pz2dZ1tPTs02bNo0U6J4q/oXWnL5rIbwA6oYAGvAgxijs8yvLdGdNyWwRbCluT+z+0aXncO2fhMsr4vqO7ylAtg+sKWEMFrSTSBrzgW8+AbwmK1V73WV04qRkTqdPfPqLYRtLqd+VxzlwcIRLSwU+6nDuno+cZj226Fm9q4J9MT14hSWLILjSdCxLApbhd8qsmK+kCjIeWuS/hsiaAwCCB2+tGFXF4dgWpfHcZhwFCj8NKgVBkD/uU1KoVHGeiKt51GByd7dZ4wEAnUTdO4m6N5GUTpw4CIFAcPTo0WXLlnXu3PnoURu+o8+Nv7+/v7+/HQesDgjhGzUXfhWtWCDpieauRBHXHG5rjj5Nhl6GhQoI53m33kS2kaSOjFOsDxW/4WF3AVBJf5IfRaRM/LD1DQB0u1O+nkEW+LS95IPaeMzqKO055Qkf/gYuCnWSDb2sOn9f909b1052l8pJHcG859Cq3ziRlRIlE6kzIdy7Bu0OAIAAiAzsZUnuCFgKcX21kcWsH7YVfCO5zTgQEQcWV4hRZXAxqQt4Zvf6hY20QrGUiTYKnW5yTl4uIAhas2ZNVFTU4MGDy62BmhGIZGipoONCF0qumE5hXIgbGMJvZbgTARg+paRhyTKglaWOvKdYHyodXZaWjcxew5TesTUYhAdvg3BFfWXghH5vvt+WKv7Jnze4j2b9Wdd3+uMymy1/ytvfWzrw9Qgvq9/X24ppB7J3RAnbojXqEieNB+oxmSr8jlafRKSvW2sYw31ae4nb9p+6dOcE72hM6Z4T2wq+kdxmHIuFYo0ky8cgAMDFHjf1iMdrdH4N7VmaYXR6RCKqUp9kiD9R8OOikM8bUVYnTpqQirnjxo4dGxIS8u233zpQnudGEbTeEv866/km6jEJAKD6/hzXD4EEfNQtKOUdbwi65/NFuGREhbU7KlKV5Hq02FjRAI7RXaOKf4JQ24q5ZmCuLyobZUhbyOWf5sBIlmLlFKVqr7usyhZ9hin1cen9z8O3l3t1R7q0lnN9LhSfHuwe/RzzNjZU8RGWsPG0hGAX1GtKXXKa1BEzYzqQs3OSz0xO0yfXgRAsYB2ROocnHgBgLgAskbEI81sBIbXY9xlIdt4FzZ4h0pqbOQTbCv5ZtxmRqKqee/HJLKG+uKW3HrxbaGCm2VnnNC44BADwRCZJ/7olDFfW0J3IyFHvPiJf/3GV+jBBpDe3+WXLduKkOt54o1Iuk44dO3bs2NFRwjQEmBeOSIeTORvwgI3KnVmIYGXJH7P4rS4KB24MOqSgdbRoUCW1jXlNL0naK2UorrR/WRVLk5lLcf/Vz5297ZTXxr7qE31KT+AhMR+KxASrm6ZUHfRw41V4ifgp7zsuzNuXs71iRwNderLwp17SgTzkhQuJQxXuA4BGhK9UrGSMDxnTE9RzEoDspuBPFf58r+SmG+4x0usde41ZdxDXV2FBKzL/G0yxgFIeAYwFdR9day8jyT5S2kiHQ7M08m+SUkdhW8GXv7+7uroaDIZffvkFADB16tSmk8seyHjI4KAyJwQ9wf6Zae4o5wSKEABAJmwufihyRUoAS1VnE4H7K8jcQsZgggWVPBlQCJ3oM6OxhXfipLHBMGz79u3vv/8+htnYFrb6cDc7MN8l5rgu6itDS/+5InqbZRNHqX4epj7GBO4TQdgzjmoQviX/k6/ES4Ckl/WclSraB6HS8k3a54CDcCi/dVztcdTtLQDAIrHrj6UGkmUrKvhor7dLSO2zfREIdcDKtQ7gAessiWPRFr9WWM4y5of9cb9VNZ9P1wslUXhZfeHT4DVfpq/qKe3vhnvW3sfe4P6rzQ/7o7LXyexVnNC9z705cV558k7JjU9D1kDAkaH3bOs2q0YHALAsW1BQEBcX98YbbzQ7Be+CQ0ODy+6WYhOz4grUyh3t6cv5SKUJiXIV/YVqaaErUZCPyGcVq/d6yCSVt9EgFMFD/C2JqbwOUc8Ofl93q62rnWNtOnHSlKSkpMhkMusHR8tiNyBUTGRNw9ynekzVEfleLh3Gu/YxABqUXgKoGw5gDJXPhBAXAADMj4T5UZf1fQD+K1W4F/WazlJaMmejzfwUAACGBWlaqgZPOSuv8XmA/y6QP/WEHutSNc53mKBOEbJfHGBBW0Tcl8r7CvMtM8yiig4AGEdk9jxQ+DFv72D36AB+yAC3YT/l7f8gwAFp9CBOAOoxwfx4CCLqCwu7WitZwKYantQ9spmOKjlV9LML4npLe6WL2JFmd7Z/rLGxsRWLu3fvjolp9kFdIMAaSRYAMFskXBIpf43Jzc2jeaa0d034eBeBxJYfC7dliDk+xaaC/ylvvwiVBPJtx8lx4uTFZ/PmzTVc/eqrr5pMEjuiPlKQv6pLwK5vGAN/v/GLD169TxWsAwzGMhBrpgHCkpnLaIZFILMB8r8sOC3Hc/8mpnbOnHWjuHWAZZ+vsCeECFhLJsTxA5XXXhkl1Ozzmt9H2w5089KD+a0wx3VH3d+BuMEsXUrmbOCEHwL2W54mlD7IMWXM9P8IADDYI3pJ4pzE0kctXGw8exsbVLGQLr2F+a0or7mh+WtP1paloRvLH/gZJdQXt8ry31gooCOYOefLoitiMNQi+Mgrkr4dRN2+yfy/tq6dHLgrU6f9h/Hjx9+7d6+xRWlsBBhszf4UgKIf3bGEa92Vpd77lY/GuwgmCG2n0hF072CNVvssk31nibCanGKdOHnB4f6LyWTasmVLXFwcwzDp6ek7d+50c3NztHTPCSeIT6nZrIXrKBeBxYvBAz6BUHdTcru0dw4ytDs38jTVJns/dYJloZPEypCSuUdCR7Q0LmIYqpt+lC/5K1t60/z4NdO9dqw5rcrIDAuqCfv5nwDCPFD5B0TWSgAAlbMBEQ+CBe3sNTjDModzvx3jPcXqRIBB+FvyiYfz9jAsY68p6g6EuHAjT0N4WTRZC2P+Nf/73rJBh/L2sKDsF+DOR4YE8ax/ff25HAQqL3b0KYkvvfm65+hgQXioICK26FjTf4VyanfJZ1n28OHD1q285ouYA8/tKBRgEABAuTsHbC6CWND6dlvX4NQ+odUmysO8PTBv2/6yYYJIm/VOnDQX1q1bZ/0wcuTIbdu2zZo1y1rct2/fiRMnqu/34lJKsOnJRgaBLYXSxMwZwxUr4gvacaAN8rCp7nN3UUWREL+lKwJPE86EkPZTot5gSr1z7o3z6PI3B2bM9ztgimWofCaZs54xJUPcYEd/mxcOzHu2Ka47VbiXUh7mtrlmx5EvqWKFqKi9qEt5TSfxK5dUsZfV53vLBtlxoufgTNHRcJeW432mr0n+5Ibmr26S3gAAAfb0/FdpZDbfflpcnxIz0v1tq6XkaO/JnyUt6CHt54bbP+5CXbCt4IXCp44BNE2bzeYvv/yyqURqFFAYBEuQPzLNbf/SZH6STL7pVvJnsYVStLh5/QRTcK01d5HY1Wa0qRq4pb0iwWShgohGktmJk6bh4sWL27c/tegeOnTo/PnzHSjPc/NXtvl8qnkUw3h9vTpElAhQhkprh5gQlg+7vnIR0DzT7UAsYBNjSuO2fwAAgF063irtNqxgK+S3lNv6MoR5sUQeVbCH2+qSdUAWAJ2lbBGpJxiGBSX/FjEY4j9rtfdycV196UHpvel+H5aVIRz3+8ySNBHzXwVhdtNYLGBPFB7BYc6qpIUV6/W07reCHx2r4NVk8SXV75+FfgEBaJxi2o6Mje1FXWvYcr+p/dvMmHpI+lmLEkzWz23oL/kHZvh/1FQiV8K2gr9//37FokQikUpfRCe/epGkpuCfC6XbMs1vus1Y7uba6a6nBz+GzPccmcAeDhINsh26ktEbLGlZvDY2tDjDMueUJ5wK3klzx9fX99SpU++99561eOrUqUaKJtvYvBbMe21ZgK6bUPXdYP0kInfle+HL1hriT6CSj/kRNxFZLwgVkdmfoW5j4X+D2Gwp/HioezTrMQ7iBgEAiMxlqNc0iONrvXojl5h1ruxslWaBiWT6HCrzrXXBoUtve9SQD765wwBmf+5OgiFekfSNEra1ViLS1/HATfYNtw4BaFHI5xbGRmwlDtxEcWqr48fcvQPchslwdwBAMD883CXqTNHREV7jbDYmGOKX/APjvKeZGGN5ZS/pwJXJC5MMjx1iWVlJwS9YsKCGps3U6KacwUXmvK8yZBPle1d6jkEQ7HbE3aBkqHeh0R9/c2yqJlch5dt43aE0JerdPym+/uzZS10kr4a7NDNrWCdOnmXdunWjRo06c+ZMeHj4kydPzpw5UzH0TbPDtb8MgOmW7DNJI4rDmD64bD7ul4tKhlKaM4jbWMCQeMjT1FltFQrEayaRtYoTto/R/8Pob3KCt5Vf7abArSkoQW3Z5F4+vs3aCkNwG1GHPVlfbW65v7we9bS/O5Wc42P3MesNY2QZomJFqjEp3/h4quLp931LPvGzpAWvSvtX3HKXcOE5HYQAgAxTspk27c3eBp7hvu624xU8l1u286DRaGJiYnr37t2qVavMzMyzZ88uXbq06YWzL64yPB8BlgzTVrEEQqGD2fn5ga0ZAHuyWkrgIqnmVRz3lTNmC1WkQj2qWiFAAJJgzds0wYkTi8UiFArj4uJ++OGH3Nzc1q1bb9iwISwszNFyPQ8Wmi0yMAAA0Fl8PfWz11q+n7Nlk//SeYaALaz+OAKhdPFhPHBTxefetgESwMw2PehOl1wks9fifisB/GLFmaF1FOLa1PlLtJT6pvbyVJ857cTd5j4af7Lop9c9ag/50qwxxXWvkmxGShs/Aix59/ebwd+x/4ZL8eb4/Jy/f6b/0wBoKAzeackHAIQJWm6L+r6Jxa6ZSr+bl8/opiIlIfwnX7YI/+hJ8mv3fE61TW/vLUgugvw9IXFui+MTIE41e20QxG0VZnr4RNjPRsqZUkq3JX3t/0LXOzaagRMnzw2O45MmTbp169bq1asdLUtDOZNq3nK7zHlJx2ntqu3Fi764NO+svChrsdddr5D1dNFhGwtQmIP7LSeS3oX4EYjbyKYWukZKYovTJzz02RjmNqXegfEbwta0tZ4c727SPgCAoR6jThb8PMgtGoebJG2rg8DkH9Dq05zI36xFiiWP5u1rpzpiQVzSzRnlzeRcH1e03qkIHQZrC5FIlJeXV14sLCwUiUQ2WzqcEydOcDicurSMKySG/6LU/am6K7l4os+NWZcenBMfz/m/19Zf33nTbK6ho/7SjaKvvqvu6saUZSpCWV+xnTipAo/HMxqNDpn67NmzU6ZMSU5Otlgs1L84RJJaqfv9/keGeX5skvF2CG18YorrQamO19zekjKTLr1fQ4MMLTn0pya907VnlHEBl9W/FjyMuKL8NqfJ5o3XxU2Ji84ypZfXzH04YVvGuiYTwDEwlCmuO6U+U15Bl9433g5nqJLGmK1p7nfbOz8vjdFNRYQ4pCdYYS/phT3BfaamThleStLc2DY+b3qpJhZrTsk9bMa6AQAIurXD5NUevH3szOLspJkzZswYo9G4d+/eipVs83f6LmVliNtb5rjuAMaIzBUgc0XFq4h0KO6/tryI15YNzF+EHnqjWltjxpRM5dseAXbtirqNqYfcAAAAdOdUWbMSgn9qI+jkKugoShp6l6VZ9/ea4qx6Z9aXbYSdfLkB5TVTfeduyVhb6JnryW3SjYQmBUIw/8+J9AU8UV8AcwBgiYzFmN8yCGnGiUNtK/iXzOjGipAD6wkGADBpuIIjdMlekJibnn79p9/f+XjgUV/36rQ7AADi4JzwoBpG1pJqISpyeF4BJ06ej8TExCo1xcXFDpHE7uC+/6MK92HyD1CP8eWVLENaEoYjol71HU1U3UEeABDiQhX/hMlnlFvgWyGz18H8eltX6c6pMmfEBx1pI+jkCgDA/bhhZ9onDb0LAGhsHX9J9bue0j4pfTzncSVTeRawMVlfLQvb1KizOxZE1AvmtSALdmLe86jiXwFjQN3fdrRQDcK2gh82bNjLYXRTEREHKiVYAIAHgoABUuHNLnp5SdLtXLPuiSfSIN18svAnd47Xi5nn0YmTWvH09Lx//75KpbIWDQbD5MmT1Wq1Y6VqIBwEcBAIwC6c0O/IrGWYz6flaVHo/B0wLxwRD7TjdBAuxxTzGWM8x3dZeSWtPg2hEtRzcr2GorVU6tgHQQdbWbW7FdyPG3yoVeKr/whflXBbVBubq1bSKaqUYVvhTzPEJJIkAqBQrEwXtBF2MnuZbPYNcAl57nmbC7j/WvOjAahsOJm9ihOyq4GZcIu/yxX2lnICebU3bRyqNc5s0aLFS2B0UxEMho6PehqAExYgLMQSPaey5Nla+9Jave70Jck7b9i82t9tWJIh3m6COnHStHz22Wfr16/n8XgoikokktTU1MWLFztaqIbyig+nkxwHACCSQVThHqpgDyqfCQBgKQ2Zt5kTWWY1fDnb0tOXAwC4YrZ04eBYhZxvf5rMvXjculvPYvI5priujO4q7PoKAACwBJH1GR6wobp8ldUBuxjCTo+FIMJ4o+qloB+iuGGX6zVaFXQM84FSvcVN2pGDAwAeEMQHSvVXbk+PHqS4bIjniIZM0ayBuIGoxzvmR4Ng0auwsFtDhspblao+VFCwMSP0THtH6fhKrycYhlmTymC2sMt8er0+JiZm7ty5Y8aMGTVq1OzZs3fv3q3T6ewyeK2ESSvdaSahBRV1gIGJpUtr7ghxMP3vl1kLYfOqnOvTS2bP1YATJ03J7t27T58+ffny5d69eycnJ2/evFkieRnyLOBImXbG/D8n875kKRUAgMxejbi9BfPKMoPN+F1joVkAwDGDcYFKQ/5rebBBq/tap7cZCZ3WlFBKtY3A9DAX91tBZC4GLA0AIPN3wPwWiLhfvWQuYZj7NA91G0bkzkifcQbzLzB2UqWF5+es+IPM7yzoOBnADXLYaYPjW92k84vVNy2WxwT5QbF6tVTcifMym8fXF1TxEcQNwP1WNmSQ/DVp2t+U4X918vo0MHnoXUu67U2RxqaSgk9JSRk3bpz1w7M0fLKrV68qFIqdO3cyDBMREdGqVSsYhvfs2ePv73/r1q2Gj18r2++WFhmf3rO0GytJ0hWRUtaSWXNHmMfFg/3Mj5Kqa2BhzFfVF+0mqBMnTYhWq23dunVUVFRycjIAYNq0aTt37nS0UPYE5oUisjfJ7M8ZUyKtPoUpbMQN3SAVAwBmF2sIlv2yRHfTYtnjLqtydEdk5eV9sjHvk40Fyzfnzl1ljq/0VGRpHSzqDWA+WbiLMSVT+V9jioUspbXq+zpSwjBzVOqbnks5/j97TIcThtyd/09uyZiHor43OEE61HPSc3z9KnTg4FvcpPOUminFxasl4j68FzEDvQOBECG35VkIf35zwvw1aZpjRaFn2mOeuNtkbwfq+EorWn9//yofAAA0TcMwDEF28POeO3fumjVr5s6dW6X+4MGDs2fPbgIdfzXH0lmOe/DLXlexUA5WqM2kPILNaaA2Qxhe20jjvXibqWOtHMn7LkrYzplizkmzIyIi4scff5w7dy5JkllZWQzDKJVKRwtlZzCfT81xXRj9DcxnMYTauElRCNosk8xXaYYVKAUw9J27rEpyCtZCFG2MEY8a7NKnKwDAFJeg/Oo77w0fI1IxAIDRXTXHD4NQMWBpUn+LhFdCALYkjGRpI+o+Dg+qaxhQPxTd4y57T6na4zZZ3m/X7/kLPxyU7D7V3XXgt1jApvru9lcHF4IABFgG8Bu2H/BfY5NWJ0HgqUIXAAClJvVGai5uWCoRhVfY4c5fk6Y9qQw72x51K1M0bpO9AcUmD70b9nsH3L9JX6dsWxAkJSVFR0ebTKYLFy74+fnJ5fIbN545Dqo/qampw4cPf7Y+OjraLjsEtfJ5L1Fbz6f/EqLF8vPgajrrzZjTa+3L7xhFJGdUd5UDcxcELeejz2/84sSJo/j8888XLVr05MmTKVOmdOzYsVu3bjbv02YNhIoxn0UAQlCPiYkq8pGy7I9lwePiss+pGjoARTQMLYFg/jNLGktSOuoutWp3AACvTQS/Q5TxzmNrEXbtBgva4oFf8TploR6TIETM7ZDEbfcAwqT1XXa3wLDd7rKZ2AS17mbYzILwsx28PjwP81sioj4N//8AAHhMkNOLVeuk4m/cZQuLNTctNoLAO7HJu0KXYwbjNzo9WUgk9Prn8Su3OmXTYZXPr/WXNS7dxKis0qmHS08xY6TNqUbQtEA2vV179uzp4eHx448/9u3bNzo6miCIU6dOXblypYGTRUdH83i8zZs3e3o+TbKuUqkWL15cVFR0/Pjx5xjz5MmTb731ltlsfo6+Baezd0/ebzhuWSXX4UFbam3PkhSENXXMSCf/Efh8vkql4vEcY4xjNBoxDEMQ5Pfff9doNKNHj7aX2Y19acj9DgAALKE0oe//rmZYAACI4lyjzelyl7Kz+lIpwxdDk10FJ43mf1yGb3L3xiGISHmf1hpANAUAACAASURBVP8DAGDMFtZCIKKyTJsQzLGkzGVpsXj0UGsNo79pSZnGa3OLZUnWkgPzI4nMpYA21H35Xk4xzYxTKtvrTi007XOL+Nn8oAc36qxdMtgmkuS0ItXnMnFPLhcAcMNsWajSfOMubY07j+HrhIpmZiUWzBmThhSRCARxOHDYuQ4V/RpoHZUy/J6gs8hnfZg1wKk52Zg87K7is2DpOHl5s6a5322rq9u3bycnJ9M0fefOndjYWJPJtGbNmoZPFhMT8+677yoUisDAQJlMBkGQWq1OS0vr16/foUOHGj5+rZxNN/NQyGo0CwAQuruGEB6npFKzbmddft21avdzyhMSTNZJ/EqDJXXipNGpIbnU7du3m3tyKQAAQbPldnZlQLg7HxwbWeZNQ6Sezcr9S6EYgEDgAUEU0kwvhIOVFI0quZTA7/uhSrPNTQq7dGRMKZywvWReoWrnYc9lsyEMpYp/oTVnTfcLXN9oVT42LOwCu3Qk87/GFB9D/EjWnEYrf3yOvOnFNDNZWTyKL+glm5Dx6Afk5kAXnwn2yk+PAGiLm7TDv1Z1Xbmc7e5SrLZI2yylArTexgWYb8e8sc0CkZpaNDbdVEieW6dY0NIjdWRc0sA7FXU84oqG/NYuZfi9rPmJfptbmFNsaPcmw7bGkslkmZmZd+7cadWqlUAgSE1NFQjssPns4eFx+vTpzMzMx48f5+fnsyzr6enZpk2bOobJy8rKoiiqSmVhYWHdQ26lqCkLzZYreE4IH8GRlFIpoOt6QEDmFepOXSJzChCZxPW13pwQ/4pX/XnBvyuPORW8k2bBy51c6mqO5XC88euBNdnEYL5L+fmxsHw+zvdPLjW8xue5wDCR8h7mPXelZ/gPpQYGANRzClW0nzEm4IFDMP+Uoo2nXId1A+wu072xAMX4HSsZ5eD+a8wPeqFub0McHyJjEaZYUF/9p6KZ8UXFb7nwpwpdAMMm/jIbtP3swZb+7b9nYK4dctOGVl6lKGNyeEcKgn9uA2rMB25+NBBQWlAlphtjZGkdv2MGcHRS1yaDUhIJfW9bColHG/xvDXX5no9MONompbKO/0yjHc7ntzneLmX4vcwZ8dpL6gsfe33wtpdjJLYZwHbdunVCoVAgEHzzzTcJCQmBgYGzZs1qjEi5BoPBx8enLi11Ol3Lli2DnsHT0xNBkDpOd+ChYfllbcWaMWPGYN7e2qsKhiyutTuRW5D+1mz1oROmx8m6c1ey3vuf8c6jOk7txEnNODAW/YgRI77++uvy4nfffTdixAiHSFIr9YpFP+2MqtZmX/y23PhkcnmR1t0y3mnJ0oaKbSjtn8Z77VjazDKM/sI17U8jtEf7lpz4gyHIZwckstZYkt+jtBeN99qzdE1JLmySS1K/lRpZlmUZNnNewpP+t+OLjTcmxyW/cZc20fUdrWaKdmU/jLySNS8xoftNUkXU0JIs3Gd6NJBlmYqVlpQZRNYq+4r0gpM09dEt8R9796cxLFtM0a/lF+4o0en/Vt+VXHwy6La1zVWTuVtO/k2zmSoh41+/s2TXk01aG9HsHRmLftGiRV26dLFYLIMHD05LS1uyZMnkyZMb/jKRnp5+4MCBijUEQeTk5KxcuRIA8NlnNnKulyMUCh89evRsvfVMro4CiDiQ1lJpuT/1zuirBX8r9a5CUzIkrCX3q/bIGdxPgck9uJEh3MgQTOGp2n1E0b6q+b2GVDnTyDppRly8eHH79u3lxaFDh86fP9+B8jQlJ3XTZhiG/RudhiEyF2N+K6pkjK0YwVTQQ25++IDb+gqE216T/SZ+f1BSH1T3Nx60BcAcGoAYnX6Mi0BafTDsinijyBsoD7Aga0Gi+bEh5HhbWICwe1plTo9PG/sg6MfWdlnHAwCK9+YWbs4MPd2eE8jLW52W/Nrd0NPtUaltwwvUYwJVuJ9WHUdkZTFwGMN9WnuJ2/YfuwjTXHjgBvnicN9rJmg8K0Pgve6yd5WqHjdKIRTCPMpOPbpzOZtkkvnFmtVS0dYY3548zkKRw6LZV/tbMZlMO3fufP311wEAAoEAaVgwVysIgnz//ffr1q27f//+o0ePHj16FB8fDwCwfm74+LUi5sIGslLsCswVC+8y8onFizFVDcf9LERmruCVDsZbcdYiNyKYKlKxJFml2drkTwssefaS2YmTxsaaXKq8+HIkl6ojl8b7cv6NTkMpDwMIRt1GPdsM919L5W1jyUIicxkqn1WddgcAuKLCDaKPDYLOiHggDcD/1Jp/LIRLvdyMWZA5K8GcaLRqdwAAhED+OyMREZo27gFrsRl9p34U780t2JQRero97sclCyzey4JEQ9ySh92j1FWfZv8C4wHricxlgLE6c1sTsayAEGHDhWlGjFwb4T7UTXtCmflBAmBYDwT59pCR/iKb11Lg/01kebPuXM5nUvG8Yk0ojjpQu4PqzuAPHjz46aefzpw5c8eOHSiKzp8/v7CwcN68eQ2czM/P7/79+/PmzUtISDhw4EBwcLDBYHBxcfn5558bOHId6a7gBIgqfWVOAN873/MRZBxgrF3BIxIR5u1ZcuwcayEgDk6XlMJcDvSMsfFI+XiDTYMUJ05eSF6a5FLHkkzl+eBNFDAQdO9DRdZioAj97jXb58yIbARZ+C1VsJPM384JOwhsWZxZI5hanoxnyWJOyO4aZBjI4wLFmGj1oG8I8odSQx5Fx7jL8PooeFpPlZxU+mwKs2r3MgFQyH26T0r0fSLHzAnm19C9VkpOF+etTG1xuTOm4GRMfaw7rwo92c57eTCtpdLGPAg738FmL1jYGRZ2JvO2YT6fUMU/AcaMuo9uiBjNEQiBAva0zJj2WPNbEUAh3IdbtDWL14Ifeqp9xX8sNcPsKNEP5POumSy3LJbOHMfZKNjcuO/ateuff/7JsqyXlxfLspcvXw4ICLDjwcAvv/zi5+cXExOj1+urk6GO1P1Mzibxi+9+GLhiQuw68+PXa22sv3Qjd+G64pjDlKaENhgL1+9S7z/63FM7cVIRB57BsyybkJCwdOnSd999d/ny5U+ePHGUGLVS8/1uppisEsr6dyTe+M6J4vJisbGmA2y6NM5wQ2ZJmVlDG4bSGW+HUaqTdZEz1mhqk5U3ukBpYpjaWz+DMU73IOiy5rei8prSG9o4v7+0sXZISG/JNj2KulqwLTN96qPk4fc0vxU9CLysjVU+an2tcFtmDR0ZS47xn2DamGS8G0XrrjdckmYKQzFpEx/e9/7zvvzPJwNu05V/WiqafiO/yHruXn4e/+wgjjyDT0hIiIx8uuEQERFRnmnKLowaNapr166TJk366aef7DhsrVAM+OxKydqeovIa99aeXrs4V6UdaUNMrd1denehtfqSY2dNcYlMSamgV2fx26/bbFlEFOSZs9u6drKb6E6cNBpms/nmzZtisVgsFgMATp48CQBYuHCho+WqNxwE8nUtW0gla2Ae+rRYM7CgNR60FRH3r6ENhAh57eIAXHskMhqAP03mABzNoahUkmqJ1yOiQCZF/WWyTGwtDDnWNmXEfQBA6iCR5obWY2Ki/65I0WC3WkeoFdyHG3KqXUKXm7gXp8WNLjAXZsxM2tgHsncVHrNrOpqBcAXqOcXyeDAi7gcLuzZckmYKhEABe1tmTHtMFxFBv7SFeZVOuj9WafrzuHNEQgBAdy7n/2SSD4s15709efaIBltfbCv4Ll26bN26dfny5dbigQMHOnSwvW/z3CgUivPnz2/bts3Lq0n9B44nmSoqeF6AwB+4ayVBtBqwZCGEedbQFwAgiu7v+npfuliNSEXPbs6Xw7DMvuztX0R+60wS7+TFZ+LEiRcuXBgwYABu12gnhw4d+vvvv6u7+s0339hxroaDuo+rvVHdtPsStVZJ0z96uF02W6YrVbvcZXXX8VIYPm40FtH0R61dg39tmzji3plxkqE/aAK+bena3z6muyzN5q9K47d1JXLMyh3Z0nfkBRvSpWO8Sn4rMrwjF3Ss6cwYU8xn9DcwvxV2kaT5AiFQ4He2w5ZvlkmEFQwqX+FyYuUeDtHuoDoFv3379gEDBuzdu1ej0fTo0SM5OfncuXN2nxuCoGfj0jcqKAx+H+NesYYTymdcWTInuwj15hsfI6JaFDwAAEJgxkIArR51r9Z11IvjPSdwsVO7O2kWnDlz5tq1a61bt7bvsO3atTtx4sSRI0fGjx/v5maHpWe9gACAaovfUpErZktXDo5WeBD/ZTL3rE+6WCv/p9UpaXq7m5QLQQN5XFYimlmsPubpLkPqZP0uhOH97rKpStUGra5/BPf/dvl9/GFu6B67aXcAgPpwge6CKir+FUpNJg+9W/R1lsc8f895fvmfp2W+Hx95t8alOcwvz7TrxCbCZ9wlnq1pMmwr+JCQkMTExNjY2PT0dLlcPmTIEJFIZLNls8NXWEnpojLs4ZtpRDqaJHf3M8Yjor51GcT8MInIynWb+U4NbYL54Q0S1ImTpkIgECgUz587qzoiIiK+//77P//8c9GiRS1b1pLMye508cblLvV4sB41GH8sNWyWSVAIYkn27Dv3TGqSOtERqP+PMT2x1QPGAzZAWNUXl7cEfB8U4fz7ojCIzwvCMFF9croIYfhbd9nYouIfSw3f9JR3eBRQ9751QTraU3uyKOO9+MD9UaGx7Q3/6CQjPEqvapW7cwMPVJtMy0lzpHK6JJY9dOjQrFmz1q5dm56eHh0d3aVLF71ev2vXrrZt2zpKRPvy5S19oaFS9sahp16V3sl5qJOwxvg6DsLv2tZ46wFL1ZIF8mj+9/9orz6noE6cNBUrV6587733UlJSzGaz5V/sMjKKonPmzHmO5YFer/fx8ZE+w7hx48hnHFNtwsegFrJ6HH5vlIoBALOKNRaCOT/unoago4JcMkbFsQTBmpJR6fCKfxDqxhjiINTGbnYwhnIq78eGYihazx3aJJLS0owcQf4y2z8TDITDQQdbsTSTPukR5smRjPAovaZNG/8w8ECU8FVnMkzbqIhq8yvSam3pXzf1f1wj81+8HIwVLe6s8SmjoqIGDBjg7e1tjTwTGRnZp0+fmTNrsi91IPW1on/7t+LrOZaKNf+8eqkb3uWdN1ubHvSq+zh5S74w3n1cc5uk0viYzC/rPqaT/zIOtKIXi8XwM7uIDpGkIiUlJepnOHToUEO8ZmqGZJjZBcV7R/9zZOhNtZFkGTZzbkLy0L+NtyMqGY0zpCmuG6WOtcuktzRXCKbSE+m22fJKTv51k1n3/+zdd3QUVRcA8Pumbm/Z9J6QQIAk9CZNUJEiUgQROyICKmLFLiKI5VMBC4qABSmKCkgRUIogGHoNLYX0nt1sL1Pe98eGACFACiRA5nf2eGZnZ967G13vlDf3CcKoopIPzDXUQWs40SOkjz6c8cBR63bTkagd1n9M16OXW8MRy/7xR0YUuHMv/ci+c1/OuFdL5/1Y9tXSnCdeq1j9Vy3bbJzf+0U/49DQ0CVLlviWV6xYAQCrVq263hE0UF0T/ORNpg0ZrgvXZLyd2oVsH2BUO/YEYbGG2pM1sqzbVjZ/WR0ClUiuqAkTfNklTp06dT066tevXwNbaOBjsVcm8uKGBw7+MDBlfG6px/d4m4izp5zMfWm263AfjCufhuIK57tP3HtNejxjPzHu8LC1Rb9UrUn3cj3yClPOPVhVIQjDi0oWWW3XpLtqRLeQPvLwoYBttl3m69H+rYETuddOTp539v1PM96t9hFvtuQ88ao3v6jyrcWWO/EtT2YNxwGXapzf+0WH7QUFBX379vUt9+7dGwAGDx58/S8iNCqDjCh3XVQKKuadhLJgu+zrH3jkX5uJ4X3Ud/RQ3927NltmOs9gqO10OBJJ4/Pz88vNzT18zq5du7p37349Otq9u85TqzWmP944KWS5B/7WgZURz5aZvRgDgojPWrozBnhyeL5sJQBg3szlf0ZHzm54dxjwioLFo4If2Vy2tuoKcDBF/hBg7HquNIqWIH7w9+sru/ro/XpALBGzLLH1/m6qHrrr0T4AiDYHX1QG4jWovtdUtpStD2CDn458xcSVHbXuv/Ajz+mzbMtYOqRyaDapUSm7t3cdq3HERtOofg++ahJo3wMzN+ac0A1hVBCWi2s9WjaVv0U+x7aIK3UHYWdqLdtBLMNE1Wpc0srCH/ZX3ND/X5M0c++8807Xrl1Hjhx5//33T5w4cdiwYZMmTWrqoBrbJxbrn0M0ESUCubb8Mz89heCFcjMGsG41OQ/ZqYAPuJzpWLBxubNIv+GEIqHhPf5r2kIickDAsL5+A1YVLfWtVCDU4uIJ39QEEXO1iarrDTEEE35djh4EU0Xxe1/kTZlRNPOL3IlvOf47dD16ud6svGVDyW9jQh4nEPlAyBPLChbx+IIhIBhXf1CDIEC8gU7nmmz4flMZl6R6qM1FhR5p4+ejV7x52NUrmN7nSXvcmaKvenHZ12DSzAdCxmtpaeiK5Mb17bffrl+/fseOHX379k1LS5szZ45ef13+i73Rnn2/UAhJftQjJH5D+/y30m0riuf46TuyjGWLKXvCidifk5Rde5KaPt7MKYJpLR02reHduUXXqqJlD4Q8gQANCbjvtCM1zXGy4c3eKDAu+XQx2zImYtHssC+mB742yfTdr97M3KYOq85WFS3tob89iA0FgNbq5CA2ZEvZhqpP2fgoz6kMvqSyCpzocDn+OyRrG9c0sdak+oHh559/rlarAcDhcADAhx9+WPXRtGnX4D/rJqdmULVa06SeEMpQiVfJItCxfOX8zZgXvYUYvFdurWT218YpjxJK+RW2iZBHNzhqieQ6qqioSEpKMhqNaWlpADB+/PikpKSXXnrpmnd0TSalvE4eUCkBAOKVcWvbp91zKBRgZACT/WRqzIokZRctANAR77iOdGbCpyPqGhz9/FH8S6K6Q7QiDgAYgh0eNHZ5/sK34v9Xp2f3b1h8qUkwWXSjBgJCAMBEh2kG9XXsPsDEhDd1aHWQ4zp72LJvVqsvqtaMDRk/M31aN31vLaUHANKg048dWvj6/xRd2yOacqYcUvXrzraIbLqQq7voDL5nz57//PPPunXr1q1bt23btp49e667QFOFeG3l24RP9140EwwdPAJjTHDCx3mDATF0zFw6Zi4ACQCU31UmokUM7Ui5+qUnHvMLc+aI+CqP1UkkTSIhIWHFihUEQXAcl5OTU1JSUlp64z3w01hk8cq4Ve1zpp7KuP9I9NJEX3YHAMQEy9puowIfb3gXJd6if01bRgSdL6TRXd+XJpj/zNsb3viNQDBbSL0WLng4kDToBJOlCUOqh+UFC5M0HbOc6SdsR3yvMm9JpDxmddHyqm1U/boHzXyBCQ+ijLqAVyfqRg+abq447D1/ZmgSxafLTC7cNNftLzqDv0JdyVuGi8dbsj0vdDk/yyGhTHQc7OnXYRevlAEhI+RxguOIyOWRuv6E6irF5JW9u1jXblH373HlzShEWXnLcduhJE2na/AdJJJr6v333x8+fPiAAQPGjRvXqVMnkiTvvffepg6q6WAo/TZP1kKBFGTp13nKzlpEVSYqQn5trr7+UvC9jjL8Vbb2wpUKUvlr4ZJO2tsY4loWDG4SdEQIl1coVFhJXWWpAPfhEzfX6TuHvSwhL+dKN5T+Xu0jHvMXvqWD/Om7zxdIvUMuf6bU9IW/oR3DmETx8ZLy/nJZU5WqRbiJjiyulbVr144aNcrtdtdyewxQZBeCVefr2Zl/K8575b+YHyZ4gMSyJF3MM96zUzDvlLc/coVZnytbE4S8J98I/uBlKuAqhSQFLBCIuDWuv0muB4VCUV5eLpdf6XbP9eN0OmmaJkly48aNZrN59OjRN+YA27r+3q/Mk/aEaNlabaXoEjAPsqQtpDo286FjiCGiv29bleOvid3m7RWc6dL1JCL7GwdR6Eb8y9eV5ffN9p37dCMGEGqlc89h98mMkA9fQexNf+xSG7vcnpfLzTMNurkWWy8Z+5KuhoJIjfN7v16DM29YCOCi7L6qJO/lNET556+4J3TsKntxKS+fjzkrFTL1qtkdABBJqgf14fKLr5rgSURiwOXeUj/G/8pbSiSN7Lfffhs5cqRvedCgQQCwcuXKUaOucn/qFkAZhnDO42ybDb5bcoAh/610gl6lH7WX8msBgGJ+Ssx8+NjZx45f2xzfQ9/3WjV1w9KOuIsOD3bs3Cc6XWx8dPDsl5pJdgeA22Ts2wbdlDLT3Qp5jdm90TS7BA8Ab+6wvNZdo6SReVVJ3otn1H0OGEZssFRUACbUhizBdhYQhe37PCeHU4GPkYarXKvU3Tewlv3aeMuMtBffb/WVklQ1+EtIJNfAwYMHOY6bMGFCWFhY1UqLxTJu3LjmkOBJv+Fc8SLBtJYKeAwAij7KsqfYIz9ZxcYs8Q3FRQwR82Ni+rDD+a+laWfFLTnumNxB+vHWlqJzoqJzYlNH0QRMojjfYrtLId/t9uzzeDs33ZFNc0zw+wq9+TYh4F9T3ktnKH+Ki21BBhxxLpu0X71pwGOnAEFFxTg6pofy7EQ64u1r2K+G0o0IesgjuqUEL7lBTJw4sbS01GKxjBkz5sL1Tz/9dFOF1MiYyNmeU/eRhuGI0qp668SKpaL7NkJ1fnZsV6rddcoRMCWi2CGsSXNJCf5W5U2fiLniGj4g1WzcIqj1fRPffffecvZFrWa32/NcmWmu0dBUOb45Jng/OWFyieqTTt7KaQf7pb/Y7mD6iNui0ybntN6R4wkJzf0jpffg+P+0xlGEsn1tGsSC6E3LYlvFXHXLPn53NTh8ieSa2bt3LwD07dt3+/btTR1L0yCUiaR+EJf/MRM5U9m+goCtWU9/Fv4/k/p2AwA4j9gyRh2J/LyVdqCx1MxftTXJTQwRgGgqePKF64SyX7Anv/bZHQBeKTffIZc9q1UDQA8Z+7Gf/vky018hgU0yzq7ZFboBAD85Ue4WuEI3E8DY/7VYizw/G6bK7/qvSwA+Nu9189GOrTU7tWXL6fA3atkg5rji2V8LNnttNi7xFv1+rmqVRHIjqMruWVlZmZmZTRpLE6DD3xDKfhZdad7sN+mIZyO/7JP1RKptm8l5xJYx4nDE3FbaIdK4mVsfHTFdtB8k2HBS29f3IpTthIotdNT7dWrnUz+9L7v73CZjNwQHNNUo+uaY4I1y0urBQS9FAYHoECZ+5Mk7nH6rZE9+kZxhU4ZYT7VJ6LFOHvocogNr2SAhYxWdEx3b99ZmYx2l/8+8vcxb0oBvIJFcG3PmzKmaqf29996Ljo6OjY3t06dPWVlZ0wbWmBBtpEOmes88JDpS6aBJym7a6J8Sz45LTR92WPNRy1/CFN8csn9zyP7LSafZLfqWvzlkX5rqvLkfQJJcDNEBVPDT3px3q9ZwebNJw1BC0aZO7WgumZjx0jWNpjkm+Oc7q9r602SILO7PDt5sNxXM3P5QepuHu8qUFW38du0dSCmhlA56qk5tqu/qad20A2rxzCFDsDPi5xqZgPqGL5FcG1999dW8efNef/11AEhPT585c+aiRYuKiorUavX06dObOrpGRQU9CUAwUe8DwQKAqocu9uek6MVt+P4Gs0e0eLHFi20cFjH4li1eXOoUbqSi45KGch3uwOXOEExrq0qV80UL+OKFzn1hcGH9+ZtKc7wHr2GJN3eYZvfRtg6Txf3ZIW3gQW8kWwKMesEj2nd/6EyRGCOMvQjY2rfJxkcTcpn7ZLqs9dVLYchJBY85k7csgA1uwPeQSBpkxYoVCxYsuOOOOwBg+fLl7dq1GzduHABMnz591KhRX3zxxdUauIUgRpb834UrfAXs1ACvdK283Jpu5g8WeaveSm4xVMAjon0/Zbyfy50lS/rXc3oMqbtLdKX57s03dXT11BzP4E+UccUOwXfwzYTJxD+SSjOcmjLelNMxTfAvQf5H6PYF5l11bTbg5SeZqLCrbwcAAGXektkZr7sEJwZ8xlHbKewkkmvo8OHDSUlJvuW///57xIgRvuWgoKDi4pqGE0skty46eLLoPAWEAtGBnrRx2JNHaHoKpjXXZG6hptIcE7yTxxVuXDVpbHkQ/eXKmOx1bTXl4vPLoqeWTz4h74Ktda7aSwX4EYraliUKYkPvC3qYx/xu07YP099Md5yqa3cSSQPpdLojR44AgMVi+e+///r37+9bf/bs2djY2CYNTSJpdIhhIt7lst+gI98TzBvoqPe5nLfp0JcRZWjqyOqvGSV4J4ctHtHiEVvoKV7E+wo9vrflXuHeCO1jCX50qCwmAxduS3F7W8ts/17veG4z9KMJ+veipd31fZcVLMQg3dCTNKqhQ4d+8MEHu3fvnjJlSosWLTp06AAADodj+vTp/fr1a+robjgGGdE1pLnUYmueSMNgxIaJtj3ydgdAcGFPHhX4WFMH1SDNJcFjgKG/lt2+rPT2ZaX9lpVigAWHXb6376+yxVbQACBPUPYhIko//EDclq/0ZmG+vO7d4LxnposOZy03/7XwRwHzKRX/YIx3marXxJZIrquZM2eqVKo+ffqkpKR89dVXBEFs3LgxLCzMarXOnj27qaO74RjkxIxe2qaOQnJ90ZGzuPyPAbHenHfoyFkNvPvuzXVjrinP3JrLIDsE8PcD5x9mjf26cHIH5dMXF6Xyeygkcm80td9/994DTw3oIlp3k4Z76tgNkiW0sG3+Vzv86gVtSr3Feyv+9QjuNqp2NsHyW9FPHbXd5aSibj1KJPWl0+nWrFnj9XoZpvLENCEh4ffff+/duzdJklfeVyK5HkQsbilff4dxSFPNy0XIW5KGe92pAwhFW1LXvyFN2XaaM+47oulniP4xEdHSc/DX2Y/HHVXPsALArjxP1dscqwAA6v6GNgltA956+4BcQWh6CXW/DQ8Amnv6Wf/cgfmrT/3+c8HiKHlskqbT1Ji3AEOwLGx9ya/16FEiaYiq7A4AkZGRt99+iO9DiwAAIABJREFUu5TdJU1lh2nzivzFO01/N2EMdPhriJAxke9d+tGvDud+z/m53jHAV1ZbDl9DiUP7fxVnHzke81MiAGSOOYLPDflqZM0lwYsAc/fb5x6ofAHAgWKu6u3OXA8AcHlu8n0X5OZYAgItQlvRUp8Ez0SEMGGBjt0Hr7zZSfvRbFdGlitzVPAjAJCs7VzsKdhp+qvYU1CPTiUSieRm5xDsa4p+fiJiyqrCZS6htjc6rzlEGWTJe5CshtLjkRT1XJlpr8cDABjgPbNlp8tjvOSA2P5fRebYY9Hft9Xc6Rf9YyJiiMyxR5skxzeXBE8A7Hwo4L+HK18AMChG5lve+0jgg20UAMBEy7lib4ILZElJ/x6owFwp5upTb0734L2EXHaFDUQsLstfiICIUcSdtB/7p2zzP+WbKKBlhGJl4Q/1+4ISiURyU/uj6OcO2q499LcnazqtLf6lqcOpQWeWmWc0vFBm/s/tmWm2nPRy3/r7KS4uQ1uV3dV99ACAaNSEOb65JHgAUFBIyxK+F4lAxVS+VTGV/3oQicLej+vib5AnJe/anUJoutfvJJ6NjbjyJIkiiCGycCtvUVPaLFd6tjujpaptmCyygjf7SRXuJBJJ81Pozkup+Gd40FgAGBH80C7z1iJPflMHVYOOLDPHaJhcZtrj9nzr76ciqt9cz55wImBSuC+7+yAaRS5o7dhrNa9q7ArlzSjBX4giUcegGp54UffR9w/vIFgqtqelkZregnXH9eidRGS5t7SDtluSumNrVXJrVXIHTbdu+t7R8jgHb7sePUokEsmNbEXB4iGBo1SUBgA0lHZgwIifC75r6qBqgAH+dLrCSMosise93ks3iP4xsfTrXMuG87M5YI+YNS5V3Vevv6+285tcK800wWsYwiiv4btbt5sidwS9+2SZ3hprRx2Eir+hvo+nW9dv96Rn1/gRj7kANljA/H7L7gtfhZ5cm5TgJRJJM3PEur+MK+nnN6hqzR3GIcWewmO2qwxmamQYYKbZcsLLLQ80zjMaXio3p7g91bZRtFfH/tYu55mTvhyPPWLmQ8cIGRH1XVtENfZY+ubymFw1s/pot+V4eoZVrzbPhMhsa8tLWle8duzOfQuLew6Wi45jhDKpHl0QKrn5pzVB06dc+hGNmAkRz1+6PteVleGUStpJJJJmBAP+ueA7Ban8Kf+bC9fLScXPBd8ltuzQVIFd6mur7YSX812Z78gynxr1z5eZlwYao6iLMqmivTpmeVLGmKMR81qVLcondVTUojaIbIIn5Zppgg9VEUdLqk8QxBV7895IBwxMpPs92V+zPuvP9+xNVWyqX4JX9uxsWbnRfSKtNtPP+ITLo8LlUfXoSyKRSG5SCNDQwNEesfqpcJS8hYysbfHvxjFYIX9Mraqa3L0Lyy4LNAbW9Fipsqs2dkVS+rDDmgF+TZXdodkm+FZ+9OJBF1UY5oq9aYMO+j0QVPpNbveNUXKH/fOI7979sE/IG7/QoS/XowtEEtrRg8zL1gbPfKFOO/5TvpnD3B3GwfXoVCKRSG463fR9mjqEWomgqmfMyEvWVFF21bY+2I0KYJoqu0OzvQcPAGrm/B8d8/jMgAP6UYGiR8QcDnwtKttgHVDUT/HUANGR5Tpypn5dqHp1ovx0ostdp73aqtvvNm+rX48SiUQiuUHQwWwTZndozgn++S0VRY7KenOIQqru2vIfCyxrS4JejuIzvBunGbITZPnPnnUe70Go/qxnHwj5Pz/uys/EX8qP8X877n/17FEikUgkEgBotpfoAaDMKaab+SBl5e0TOpAV7AIdyOpGBChzPR1PKh0xRvGM4Ertp+2/FOC5Rg7Pylv2mHfc6V/HYvgSiUQiaRSi/YBgWlfjR6RhMKHq1MjxXKr5nsGHqskCe+UZPOZx+dLCkNei5cmqrMdS7bvND+2O6XVU87Hxm4p1saKrWHTV8yq9j/mnNdhTwxOTV8AS7PbyTafsxxrSr0TShDDGixcvHjp06LBhw/788/xlsMLCwnvukY5cb1wihqc3m5s6ikpnTPxpUw3F3m8EmK/gCr8EUgmU9vyLVHGFX2He1NTRATTnBB+sIgps5y/Rx//ZoXhujrKTho9hs2dlKnaS8yeJT5Y8pJkectww+K+8ZQ3piy8pt6yt22ywLCF7M+6jOGXrhvQrkTShWbNmTZs2LTk5OTQ0dMyYMb/99ptvvdPpXLeu5vMeyY1AwHhLVvUx7U1lY6Z7Y6arqaOoGanrT2r7IkJOh0yteiFSSWq6k7qrTyjaCBr7Er3NZlu+fPnx48eLi4t5ng8ODk5OTr7//vs1Gk0jRxKlpQ4Xn39Sjm2hiFvXPm3IoeA3o8sLPYd18MKHzgUdT6naBKrZu2eapwFMr3df+ofuLZz2ser2bpSfrvZ7+aaOPWo9EKtsqSRVV91eIrmhLFiwYOXKlX379gWAkSNHDh06tG3bti1btmzquCSSa4aJ+sB9/C7SOArRAQCAeTOX/xmbsLqp46rUqGfwu3btCg0N/frrr0VRTEhISExMJAhi4cKFkZGRe/fubcxIAGBonPz1HuoL17AtFC3Wti94LzO2g7b9Tufvrxgnv7dpIjttpu1DwlvgPnqb5/hdvpf71H116osK8FMP7G3+/rd6xJnhPP1D7lf12FEiaVo2my0urrIIRL9+/SZNmvT000+LYtPMmymRXA9IFkP6j+FyZ/vecrkzSb8RhCKhaaOq0qhn8FOmTJk5c+aUKdWLuy1ZsuSZZ55p5ByPAOhL5gmQxSlUa5JPDzsUnKx23OmXZElz2LgSRU8ZGagX7MAEA4Bo349wnZ980A6/s+yrZSCKQNTtoOrewDH5npy6dieRNLnOnTu/9957n3/+OU3TADBjxoyOHTu+/PLLEyZMuOq+oiiuXbvWe0mt7wMHDkiHCNdDuUucu98mYAAAEWMBwxs7LL6PWBI910mlZRvvbDDXKuRYK++7+xZ25VXeMojWUSGqGgrLNCE6bJr7SBfR8SgQcsG0Vpac0tQRnYcwrmet9XrQ6XRHjhyJjIystt5ms0VGRppM9RmVsHbt2lGjRrnddXvW3Gfor2WrRxovyfIwz2ILGpa68+kAA5r9UtiqUcatiwJpw5n75O2Pip4C9+F2TIv5lHF0PXqsN6/oKfDkRcljG7NTSWNSKBTl5eVy+Y1VuqshMjIy+vbta7FY5syZM27cOABIT08fNGhQeXm5yWS68v95HA7HhAkTOK56ucmCgoKUlBSev0FHXd28nBz+I93lm3lDwPjdf20zelXeNkUI7o2TyxqxjvqCw/Z/8yqP7XKtAgYcoak8F70tlHmq/Q13v5Iv+Z4v+xUhijTcQwU+UZtdGuf33qgJftiwYXK5fM6cOYGB5yfVKS8vf+2110pKSlavrs99i4Yk+L7LStbd569iqv+HO89is80+qxLxffcO05EWEREEoUSiAxEsFj0AmJDFypIb9XqDiSubcebFF2KmR8ijG7NfSaO59RI8AHi93j179gQFBVVdq/d6vevXrz969Og777xTjwYb8nuX1BIn4sSFxacmBDV1IAAA8/bbMeDnOqmvvmkTwoL7WF8AQZa4A1Ctros3zu+9Ue/BL1iwwGq1hoaGxsXFdevWrXv37i1btgwKCsrJyVm8eHFjRuLz95iAS7N7Ds/vdnsevzus5V6HmnRwIkliwYFlQGqxyAMWAJHYW1TvTssXrOAK6jwrsIE2Ph/ztpaqwxg9iaTJMQzTq1evquzev39/hmGGDx9ev+wukdygEMnELWbivqtldm80jRpNQEDA+vXrs7OzU1NTCwsLMcaBgYHJyckRERFX3ddut3fu3PnSe3IOh+PS63i1RNV0eBNBUSsCjb+S9tATnn3qJzvZFx5Kg4QYrOiW6twXSqo6Co6jVPhr9esRAOjw4LIvlgTPfL6uN+Mj5bEAUOTJ11A6BamsdwASSVPZvXt3U4cgkVwXhLy2k4o1pkZN8Bjj7777bvXq1QRBPPXUUwMHDvStLywsnDBhwtq1a6+wr0ql2rx586W5fOvWrU8//XT94vn9tKvEKUy85I7OSofza84xJ1HVtuxZu2yZLDaCgQzPqZEIkODOQLSRDnqyfj0CgObu3s7dB61/7tAM7luP3Q9b9x227Hu1xax6ByCRSCRXQBFoUocb5RTCT06IjXcb+VbTqAl+1qxZc+fOnThxoslkGjNmzOLFi0eOHAm1LnwRHh5+6crU1FSE6jn6Q06jw5dMGpvD8wustu/9jVR/h+uYN+WO1/pVvPlvRVxvtJvU9RcqttLx80T7QcFW87kIZbgHya44FA4hv6cfKnr9E3mHNnSwf11jHuB/bwATXNe9JJIbwfz585s6BMnVIYAb5573g20UTR3CTaxRE/yNVvgiRkedrag+HDeCov4KDgQAPC0aMHSEcZkHF7UST521D4ym/kO0P3ZniHw5n/8JFTj+wh2x6OSLviXVt10lwQPQQf7GqY8jsj4DIBCgDtquAJDmONlC2QpBU05VJJHUyWOPPdbUIUgkzUijDrK70QpfRGnJXKsgXKZ/RKIzd+4PseGPFNN1GkLJ70CCm4n9ii/8ijQMR2wkoepAR7xT9UKUnjSOINSda9O1PKklFeDXkOBXFS1dV7yyIS1IJBKJ5BbWqAneV/ii6j76jBkzCgoKXn755aZ6qpUlUWsjbeMue4RBBTC2v00J+uRNRS38tS5B/wCp609oevIlP9BRs73Zb4FY+bgO9hbwRQuZ8LqNDcaCIJgq6hf8s1GvJ2uafrYiiUQikdyYGjXBz58/f/369X5+fr6H4uRy+R9//LF27doePXo0ZhgX+nW4n+7yFZr0IwM92a4R/v7zyWc27ebWpLQGADrsFb7wS1LZgVAmcoWV9xS92W9RQU8itoZRAlfAF5UVvPIRX1afiZvkpCJCHuMVPfsqdtW4gY23OgR7PVqWSCQSyS2gUe/Bx8bGZmRk+Apf+Na0aNHi+PHjvsIXjRlJLRlGBwlWPoihHjELI19y9Or104OPTCDkrUhtX65gLhP5nvvYHZT/A9iTI9r2sLGf17V9OjRQM+T20k8XB82Yiqj61F/EgP8o/pnDXA9932offZn1AUMyL0RPr0ezEolEIrnZNfZ0sdUKX/jWNGHhi7Xprhm7rFfY4OxDx2z/mO8bPVoXErpz587jx48DABXyHF/0jeg8QxoGeDOneDOfpQPHia4zouMwiHWb2VB77x2kXmNa9Ev94mcJ2estPminqX7jf79ld447M91+6qj1QP1alkgkEslNrfnOB+8ToaGK7MIZ02UHAWgGGE0rCtNppvUvvwLAV199BQCCaS0Wnd6MiYL9uGDZJnrzedNab+ZU97F+QsWWukWAkPHZR7jCUtfhU57TmaK7ztMwy0mFglQWefLXFK/AgAGAw96f8hYoSY2MkH+f/xWP61kISCKRSCQ3r+ae4JMDaIOcWHnKebkN9CMDKtaWJoikJzyc9g9YsmSJ2Wymgp5CpB4QxUZ/KGu9Tp64XZa4jQ6ZQigTScOgusbAZeXzJeUVy/8w/bg6/5l3Hf8dqscX0dN+p+3Hj1kPAsCGkt+9oufRsEkPhU10C46/S69eY0AikUgkt5jmnuAB4Ew5f/ryZ/B0EKtIUnv+Mt2hUnZ66WW73b5gwQJE6eiwVxAV4Ml4llAmI1kLEN3enHeZyNl1/ZOKTlfpZ4v9nrw/+MNXgme9EPDGZNOilVxhaV2/BUvIXomdmaTpaObKN5asjlHEt1W376DtGi6LXlP8s4Wvzzg+iUQikdy8pAQPZ8yc3XulZ/H1owLNK4uGKeSq4cMBYO7cuW63mwp8HBAgxujNnQkAXMEcQtWJ0NT5cQDPmSw6PFjevjUACBW2ktlfy5MTXIdS6/tt4NucOV7sGRb8gO/to2GTMeBfC5bUu0GJRCKR3IyaaYLPsvC78jy+FydArk2oeptrFaptrBsWYN1i6iRQnFLVbtjwwsLChQsXAiLpqPexJ18oWymY1vNFC5mI6fWIRHQ4CWVlLUZSp9Y/dK9z3xG+uKx+3yvDeTrNcSJMFpXnzDphO3LCdqSCM8WrWqeYt+e4ztavTYlEIpHcjG6sue0azcpTzv/yK4eecSK2uPEneysfGe8TyT7X8aLpZyg9HfFFKwLBMKXi2BtvHl696sMPP3zyySdZTW9CmYRItSd9AhU0vq4PwfuwsZGmH36vSvPK2zpULF3j3H1IO/wuUqepa2sZjlMixhjEf0ybBRA4kZMRMgEEESDTeUaaS14ikUiaj2aa4GkCCbhyiiIEIAJUvZXVdFFDc4efeWXxyAcDfwkOSerQId5wLGPbnbGxsSB6hYrfAGOhdLnHnQ0I0UGTa1mt1ocKMqr7dS98/RP13b0RRdm372FbxTDREa4jp1R9utT1e+lpY4I60bfsxd5id3qwPExD6jRqnY7W17U1iUQikdy8mmmCn9pZPbVz5XRJ3X8scXJ4zUjjFbZHJMp7I6313cbZfvrSN9/8ad79gfJjPDOSlSkQKUeUTrDsAr5EsB9gwuo8VbxuzBA2oYVr/zHM8ZpBfZU92kN958frrLuts+62qrd23kYRlIyQ1681iUQikdy8mmmCX3nKWTVRrMMrIgK9scPie9spiBkeXz0jEkpSPyLQtKKo93MRMHy4OTWKZUrc2R8S6sBzmwiidQ+AKDpPkPL4usYjT24lT2516XquoBixLOWnq2uDPipKDQB23jrn7MwnIqYEs2H1a0cikUgkN51mmuCjdVTVJHKbM90IQUsDxRAIAKK0Nf9NQme2EJ0CAMyz2MIGzbeWj8TY5Yr8ISwyCQBEx1H3ySEgOLF42Ufq68F7Nt+8ZFXA65OYiJB6N6KiNHcYB+e4zkoJXiKRSJqPZprgOwUxnYIY3/L6DHeRQ7B68DMdlVfYhdRQJV/mKtqpA3rJt0a0t2+N6xB9Jmvjw2FPHQEAb/arCCmpqBlc7ixAFGUcfU3iVN7WAQAXz/jc/6XxslZXmWb+Crrp+/gW/inf3Fl3m4K80jeVSCQSyS2gmSb4C7UPpM0eSnv5OeWqKNqpC2dlDtvZ+QuL7YGRy8jDXTrEZG/fOL9X50BwZxOKlnTgY6Smu+fkMAQkaRzZwMC4vI9E10kqAPwm2lyH7seuWELle6AOMVEfIDqgHm0WefIX5Hw2NfrNBsYmkUgkkhuclOABAIKUxMNtFVfdTHu3sWB6hneL6ZEuqtUc29M2KpH+GYrfdPsHEtjFRM4GAELekk1Y5Tk5guZKqOBJDYsLY+dJOuxVygCUwUbIWMQygi1FMG9CpLZ+Ld4f8rhvoYIzqSg1heiGRSiRSCSSG1QzLXRzoR6hbLcQJtcm2Lz4Kpsi0A0LyH8r/WGVMsXjaXn/5wUmdXigUG6ykv4PEIoE31aEvJWszSa+9Cfv2RcAe+sdGB3yHBZdiA4g/YYxMQ9TIaNJwz1ixU4q4A0g2Ho367O9fNPbp6e66zj3nUQikUhuFlKCh26hTMcg5sdjjl8uP+WMj2VDWckXOe6TDvs3eY+pVQvszsD2X8oZBLz976OJF26J2HC2zUbMlbhTB2F3fUvIETIm4h1v9muAK4vr8cXfYa+8aPoh15FT9WzznGFBDzwV+QJLyADANwedRCKR3Ngw9mRjT1YNL97U1LHdiKQEX6ldILOv8Epn29bN5dkTUgEDzxI5r6aNzMb7PV6x1aAMR69PvA8/NnNOVlbWhdsjUs3GL6GMo9ypd/Gly+oXFek3AkgVX7oUADBfweV/zLad6//8Y+Xzl5q+/x1zDZoHNlIeiwBlOE+/cnJCpvNMQ5qSSCSS6w27M12H2rtTB7tPDL/odbSXN2NyU0d3I5ISfKUuwcy+Aq94mVNZ6+byrHHHAaEWq9uJ81pwLDoz5NBnxQwN6Msei/8j7y4/ceKJEY+b91SrIY+ooKfYhNV80QLPqfuwO70egTGRs7ncWViwcnmzScNQQtFG1jou5JPXBLOlbN6P9WiwmlhFywkRlafyEolEcsNCsljSbzgV8Ii8/aHzr+TdQOnokKl1bc3F41xb9ZlHbjHSILtK/gpCJyMyzHycofrfxPJnWdb4VESg2N+SlV20x1qR6R+GjnwhT7j36KfLYzXxcrpdh/Zte00+MurUgH0JKzrr7r6oKB6haCNr+7c3+zX3sduRqhOpH4Auzqak4R5E+V0aklCxBXtzAQDJoz0n7sHuTCr8Vb7kewBEGUf5P/84Fq40CV7txSkrRw98mPFGnLL1iKAHr7Bxqu1wa3UygnrW2pNIJJJ6YyLfcx/tTfmPrZr7g8v/hFTfRqi71bWpHbme9emueXfeyjW8pQR/3otd1Ca3+Oxf5s8v/ldeMi9HdIvx69sru2gBYLxGNWdEUOYvFS122eJ+NX37gv+3SCVzTFtELtlq27nw4c/afN9FO/jiwreIwu6zGHPYfkC07gRSg0gNAALgsKeQZcJJXf9L4/GmjceCBYAAwAAiAMFlvQMgAmDERpLavoisvABj/nEVHRmi6t2l3jVufSZFvnLMegDO3ZW/NIufsh3/NHP6yOBHBgWMaEhHEolEUg+ICaGCxntz32VbLAQA7Mnji7+XJW6vR1Mihstdsr1lSJfozxsUKyt2Cin51e/EkwaaVFMl8/OwUPmfwxOEgraLIoJiBUqygufeY0FjI4b+NjqDyhpvev7E4/ss66vP90po+gLmmNDn6YgZhDwO+ApC2Q7R/kCQhDKpxnh86+mId5iYeUz420z0HCZmLqINABSpvmgeGmXPjrY/dxS+/onnVGZD/gIaSnuboR8AHLcdevnEk+mOi4byYcDf5n5GEcya4hUu4VoW7JNIJJJaokOmira9onU3AHiz36CCJyGpRudlSAn+IhvSXZc+LBf1bWtZS4V9d0XWuFQsYJuN33bPgYAC7+4ROm8k8+jYjB13qVOfC+7fvd9v81amEZlPlE89OW5/tRxP6e9CpI4rXkwoEuiw16jIWZgrFe2HEROKnTUPiWdb/gQI8WUrqIAHqdDnqcCHgTFizsSEvgjERU/tMzERwbNf0gzqUzrnO8uqzQ3/OySqO0yJfl1P+wFAmbdExAIAbC79w8JXvBP/CQnkD3lfNbwXiUQiqTNCxoS/481+TbTuFh0H6eA6DK8TMFg8ou/l5DAn4qq31+iG541FukR/keNl3KUXbQgF2WJVu/Thh+27KrKfOpF5xmbI9x7tpbp9fpviB1O1ed7dj/ltzzYFTsgLT9eueOOn0e+NXWvZ5Jzm7Td4aFUjXNECwBz2FHBZbyAmEACw8xggCruzvJnPAMGSxtGUcRRiI893TKrp4Ge5gnlixVZC1w+wwKWNR6SKCp9WQ+gIKXt1VnRr38Ch9VUi5DG+hVVFS7OcGW/Gf7SqaFkvff8QNnxMyLgf8+cXuHNDZOHXpC+JRCKpPdI4gite6Dn9ABM7D+oyW+byE85P99p8y5wInCDevqzU97aNkV5yj+Hax9qkEMY3912ItWvXjho1yu1217uFXXmebw47fMtpJq7UKfYIqywjo2NR1RAM0SGkDT3kzXV7RZzSS9lrfpskGQsC/q/Vv15RhGA2s7Xs3tHheU+k5seVunfbn3K/+PjLT3zwwQcIIQDAXIn7SA+CDRfcmYqOp4SKv73p4wn9EABg4xaJ9v182S9C+RrEBJL6QaR+0Lnr9qJrbyQQpLxTJpf7Ppf/KdvqZ1J3Z22+V+ncH0SHUzOorzy5VQPvzVdwpl8Lf9xv+W9U8CM9Df1ZQjbt1FMsks1oObchzUoupFAoysvL5fJbam7f1NTUvXv3tmnTpkuXLikpKcuWLXM6nSNGjBg0aFD9Gmz4711yaxAdx/ii+Uxs/S8l/pnpXp/u+uKuphlk1zi/d+kSPXgEXOEWfS8RAwaoeuvkzh/9EEoy7o/2bIzCdZe+z/y2STIWAIBEHTZ1VJXy2gzP0dfD5nYkVff4B+zVC5PVFZT1o48+GjNmzEGL9aMKK3afJTTdsOgG0ek5/YAnYzKQBtG8iVC2wbyJUHVioj6SdzzJRH0MosuTNs51KMl79kXBtJ6O/gzzFm/Ou1zBXKRoWcvsDgDGyQ8qu7Uz/7Qm/7mZ9n/2NORP5MXelIqdw4MeOOM4sal0DQBMjHi50JO737K7Ic1Kbm1Lly5NTk6eOXPmXXfdNWvWrGHDhuXn5ysUirFjx3733XdNHZ3k5kYoExuS3ZsJ6RI9IARKpvIEl3YDAsAAKgYBgIy66ACIUJLxGztcONm7YOWznkg13BdoWVc2fmT6iUFa527Lgv+FPvwe8cdHG+5/Z+QfqSeOFpdEyWTPw5+ibS8WPYgOEiw7ABFA6RAdzuW+T+qHIMp3aYgg1F0JdVc64l3RdVqs+JsvWSLaUoBg+YJ5AIiN/boO34umVP26q/p1d5/M8GblNeRPND/rY5ZgWELeWpUMAP+Ubz5pP0oh5vu8LztouhFIOkyU1GDGjBnz5s2bPHlyYWFhXFzcc889N2vWLAAYMmTI1KlTH3/88aYOUCK5xUkJHtoYmYfbVC6vTnMWOTwyEh5uo4Rzaf4Kyn8q5Iq98Rs7uqc6T9+xv/VaHL+xw3CN+N9BR9zvnu8OHHhDwILLlTLs3l9emzKiNcGEvcgXLwVKTai6ic5UQt2TUPdARItLWybkLQl5Syr4acCcYFrvSRuHmADPyWFAGUhVZ0LViVC1IxRtoRYFamQJsbKEyqlm3cfPlC/4Wdmzo7J3ZzrIHwBz2e9gwXbpXog20OFv+ZZNXKlH9CzL//bCDUQseDFyi86zzvQQWbhvRJ5EUiU3N3fYsGEAEBwc3KpVq379+vnWd+rUKTc3t0lDk0iAJoAkbvF6HlKChwAFMSi2Mk0eLfVuz/ZmVAhdQxg/+dVPTP2fCnPut2aMORqzPCk5r7cny2X+reQg7+qzzTH9+4gCGa1FKGTe3NUnT8547GNldImxAAAgAElEQVTTW4ZHhq6WyUNJv6FCxRYq6Cm+cB6m/j7RZYfxiZCwD+Jr7gPRpN8wGRtGKJMBUaLrjGjbK9oP8qU/ia7ThCyGUCYTirZI0YZQJp67EnBZsrbx/lMftf+zt+jNz0it2vj0g6JrD6IMpH7AhZsJpg0X1t37KGEBj/lLWyOAkJOKM44TS/O/ndXqC6n6jeRCLVu2/OWXX6ZOnQoAW7ZsUSqVvvVbt25t06bNFXeVSK672yNlXUMaOmvXDU4aZHeR7446/rfHPipBpqSJl7uqa7MLFnD2kyd4MxezPAl7xFO99nkLPe+vij4VxfCAw0jy1wD/vUPX6v9Rfsp/OeXb7W7ZgC6R2wllMiLVgqPXmQFJlJHmirz+T4WFfXiZHH/Zvr2i84ToOCo6j4vOVOw8AYglFAlI3tJ3AQDJ4xFtvMy+2JOWRQcHAJHuOXU/Fr8HpJS1jSdkLOYt7iNd2FY/E8p2V+mf4wAA0ZVzzrpF16snJ/X1GzAs6IG6fRHJrTjIbuvWrSNHjgwICNi+fXtwcDAAVFRUPPLII1u2bFmxYsU999xzhX3tdvvo0aO5Sx4JKSsrO3bsGM/XcLgpkdxEGuf3Lp3BXyRGT4aoiQntVK9ut9RyF0SiyAWtzz52/OzDxzT9DdgrkI8GlWsIlYmLC1Yc93BLnzl623G9SPBPy8f/Odc29q0/v/rVMHBI22jZjrShT7FxipbbOpuWFea+cBrzOPyTlnUIFzGEst2FaRh7C0TXKew8JTqO8GUrsCsNACNZC0Ieh2RxhDwGsTGELAZIJSDExkcDAEAyqevPl/xg+6t12dwfmMhQZd/9bMygK2d3Lq+odO73XE4hIKAjQ/2nPEqHBsoI+YyWc01cGQDkurO2lf15u3FguCyqDt9Icgvp169fRkZGSkqKWn3+WLlTp04zZsxo1+4qx45KpfLVV1/1eqtXncrKynrvvfeufawSya1IOoO/iNkt7sj13BtX56MqzOOzjx137reGbWh/J2vVWYWPB2ceGWlwV3CtNlU4Itj+PySnDz/sKnby055ffyp97B1K/PUrzrJOHfb2JWQEAJQtzs994bTxidC65firBsaVYXe66ErD7gzRnYHdmaI7E5EaJIshZFGIjRJsKQh40baP0PUDUAiWYhBSSMNAggmhoz6w/L6JNOiYmHA6NKiqMq7ocudNehsIQnN3b8DYunEnwmLo/BmE/PyAAI/o3lH+lz8b1E7TOduVUeQpaK/pyhDMNfxqt5hb7wz+Uv3799+yZUtDWjhx4sSoUaNSU1OvVUgSSZOQzuCbgF5GVGX3Y6VcnJ6SUbW6r4woFP1DW9EpeFRkeLFjXpx/4H/+9MADnnSXKZj+bXHMoFhF/F+dzty5Hy1965W5zzj3t/03xTjWdXe3Ad1ffvnlwYMHG8eFik4x7/UzZDtVyMOhFzZuE0U1Uc+R6og2ItpYbSYG7C3A7rOiJwu7z4rWfwAQYCyYNwLQgAVAtGDeJIBI+t1LahyuI3mW3zfz5WZFp0T/5x8HAPuW3djrDV84m1DIAUAzpF/uk6/bt6VoBvWt6oIlZHf6V16DFbCwt+JfDaVNUCXlurIwiFVVdCTNyu7d0nOVEknjkRL8ZS1LdUZoyUntVbXcHpGIVFMKgN+D/AHAVezyprlkLeSBkbIpg9OKJ7k1U8Lj/+p0oKdQ9MEUwdY6QqaKl8Xt2LEj8+FHlFOfnxAeMXBrL0SgaX6eEQ7ncGVlMdqfHc6vrbZtwYHX8KshJgQxIQTcBoD5wi+x6EaUBgs23yA5hACLXoQob84M0i9P2a1E1cuAqBCAQm92GsGE8yWpVEBZ0VuvUvpoMjBY3i6BDvL3nMqECxL8hWIU8c9GveZbPutK+7t0/eSoV4LYkLPONA2l82P8r+FXk0gkEomPlOAv67nOqpNlDRjLQwAgEBx8y7kJgo03rSgam1vy8OsFkRa+4t8eW/YkDh2TvqTgy9mr1UcT/bI/+pjJfdxGmn7rtKksesJ7ZgsGGKFUrHQ4PzBb2rPX78o2okJf5PL/R2puJ42jPGceYlv9whUvwpZtVNgbdMgUAAAsYK4Ee3KxNx97C0VvLh1xhjTkMGEk5ktBVAJnUHTlSX2050yWdd1RRAUgeSipDGfbdJYnJ/q6wYLou8Lf23Bnb0NlrZ7D1n2x+bPkwCNAPOYIIAhEnvvrkWz88stNwyO5Sc2fP7+pQ5BImhHpHvx1VPZdfv7bGYSCaLm5ExMpqzhiTRtwENyiOV42bWnE86ucUQuL6QpevaWddeJ++ih+3v3WLn4vksmiV/4qb922k9N+RGdIZOhv/f3Yq9WaLZiRoequ09xZ94fRRbfzUDLwZbLkFOzOQUyo+9htQPkr2h8BouZnSPiS8vwp78mSW+ruuxvEcuumP/jCE37j+yHWJtrzREcO9pZgsRSRVkRrEWUEbHAdLhI5JRb0iPBj45IVXfogOgBzSvfRQYDOILZfHhQ6wJGgSsYAdts+ubdA0fEookNrDOCW1BzuwTecdA9ecmuQ7sHfED7aY5ORaEon1ahVZffEyR9pq6z9vsbHQwGg4O30MwMOhH/SMmfSCYZEQUsTU3vL/YttBZkOvQKFForeHgdlBOE/MXz5iJ9/TVm1bNmyA6Pui/l7y57wKP5shmXe3B/uvHPgwIHh4Zed2SXryRPmFYVFJGrxc5JmwGWei7scQsZGf+RJf8pz+lF58m73kc4IMUzMnMtldwCgAvz8Jo41Lfy5+NR8AABBMDz5ChPb5dItMVeGuVLMFVNhJdheIDryRHchIrd4M3/DXBn2lgNwAFh0/xWCEUKkwOUDIIXoBgDTomkeZ9y61kXxus59u40DAKHCav9nL6GQI4ZGDM1Gh1NBRgAAUeRLTQCAFDJEEIRcBvUdsiCRSCS3DOkM/irMbvG+VeVPJCv/t8fWN4L5tH8NMxOUOMUAxWUzSunCvMJ3MwSbQCjJ6O/aau7yA4AJpeW7PR4RwxAHNW58lquzGnE4OFmj7qI93W+/y5/49UH9gduY21fZC/ZsLjy03YwtujB9m65tE+9q1/vO3hEREVXt5zx9svynwqgf21rWlZp/KY5emqgbUue72u7jd4r2g2TAw0LJj4S6i6zNxqvuItjsnlNnAYBtFU2qaztSoRpPxlSh7EcAkg58EosOLNhF217w5mFEIjIWixZRNAECkjaKpCbba2I82gBLMPYyFiC0xtaqtl0QpecKXaaFG0U3K9i8IGLN4L660YMAwJOWVTzzK0RTiGUAQH1XT+29dwCA92yeeckqXwCIYRQ9Oqh6dwYALr/Yum5rVWzyjm0VnRIBgC812TbtBAAgCEIhY1vG+CoDCmaL/Z99AJhQKgCAjY1gYsJ9fxlnypGqdth4fzqIAQDR6facyZS1jUcU5esbMSFVm0ln8LUhncFLbg2N83uXEvzV5ViFMWvKTS7xtnB60cDq18CLnUK/ZaWp44Mu3dEjYABgSVS6MC/v1bTYZUlV2T3F4/05wFgmipPKyu9TKhIZZonNUSAIbRhaxokZZ51hOuZ//vrvvkzzlnLGw+nCsRy5IFOBci2/6ah44lvZpxzF5/YtjbCG0HuBjZZTfjShplwnHUKxJ+DZCMODwUUfnCXU1OpeMlesfOxPFQBAKElMozUOZ2uG7nx3EBspK12cDwDau/zokEyhaBAQXiyyzqM/ic44AFD11NEhrPmX4mrLF1L10DHhMtPPRQCg6q1nQljTiqLq2/TUMWEy3/oLlwEAIa+ySz9EuDEaykR8Zf4tVdXpLkBA6WeZ199duQ3hQXQFUBZItpJ6Cxwwc0TJWeMBg5YMKlWIpIknS1naQ9B2AECEDtE63qTAnJryMyKZxn2SEL1K4FSA9BhrQVDLE4Owx23ZCFhg5G1JRXKAdRsPGLCX8+YUAAYAkLclFB2C7SkUYFC0o7nsE7ZdACLGHE8F+dPB/r5tvGmH7Xso7OHYWK+qZ6zrtAEwCDaH50RlHUA21qsdPAMR+SDSgACwCBQJAIAwiB621UpSd4dvSynB14aU4CW3BukS/Y0iQkPOu1M3Zk15jZ/mWgQ3X/NB0uSNFRQJ39yt9x8fZrg/iFRTAPCGqWKPx/M20iUwNADM9fObUl4eT9Org/wrRPGYl3uh3ERFsWkIDeMtf7zTZlKpKZsPmaZR2TIyjqaklP/Fnd2a26niThVSvrzxmSgq8GPumzKLuVVAq1bG+KiHo0K3+RV/mavsqtWNCBRtfI9W8imkTRdP3KdQ8g7hO5PNpSKGaOSIRYgmKD0NGIBAiG7jzR9Ih23w5g7xFkQBcAAgOgQQQLDwGGPBLtA89i1f+B0FhyB6RL6CAwDBwot+dA3bWHnRJVRuc8EyAAAgZ+rzysRZCFaLjldkLZ7HgAAHYOZhvqLg3DYEgAHAoI7Vs/7yslMFANC69yusQV62toDDXJ4rS0mqA9ggi1i4OXZp5zZJLVIDOKLMTGQHB2sRZ0G0mVDkAGUnKDuibYCdhMKuG2oFQAipMadVtGYxpxYFpSxOKXJKEORMuD+p9iPkHBbkgAIUPfy9JQ7Mq7DAYl5dGT+hV9/d31NcAACq3no2Wm5L8cXMMC1b+0JX9dYT9JtYFEVBiWjSV/MAAICrwAAA5wYVSiQSybUmncFfyX2ry3Otgm+5zCkyFFbTJCBAAKNayl/qqgaA/YXe+9eUZ0wMvnT3Xj+VUATaNvaiC+YnvJyKQP0Wle180D9YRQLAEa83gCSDycr/1y+zO+5TKhiE3BjLEHJj/Gp5BYmgWBCKBaFEEJUIKXmuoAx9c/dRt+B6dMwODwmizSra7aLLFV0iW3hq4j9+h7e12RYfHx8eH48T2nwXG9uPZTNFsVgQ/gwONNZ0ixpzZZ4zD7EtlyOqsSdIdh/ujD05iA4QvQWAkCxhHaHpdvXdauIVvQDAEEyJt+jv0nWtVIkdtF2PWvd/nf3J/SGP9/G7K9eVddCa0knbI1QWwQt2p7dIjQALNsxbQLBhwQaCHQt2EGxYsJxbdmDBigUbiA4QnFiwAqlEhBIIBaK0QCgQqQBSjQglEHJEqoBUIUIGpBIRaiBYvvQnwbyJYKNIv6GYoBGhwoKDz/8EEJJ3OF1VS1g6g68N6QxecmuQzuCbXqSWJM4dAJmcgoJEDIEtHhynp0JU9RzG1ZqhAQBjbD93EpvMXPQU3FhV5Tg+GUK+f84xXpRxTaJYLogDd5fiP3v633Po939Hr1sRlGU3F9usypOuV99i02PFL6ZFZI/dsGHDhqj1f9JGf+Ty/AUIAJAo9knLlLucY/7eHBQUtKJrdzvDUBRFEASDkMy4BMp5HVG+0N8PACaWmcoEAQBIQMpz0y5pCeIzPz0AvFRuNolitW+nJ4hP/PQA8Eq5ueySTw0E8T8/PQBMM5lLhfOfhvp/Ni1vGOHNQ4ghNN3fFVpZy83V9lUiNNOgA4B3zBVWsfpRqQKhWQYdAMyyOK0iBnAAsCAbuY+HJeVmBWrxSetFJKLeMVeU8mwZn7DWgtUOs5krz3ZlxMoiFga3P2rdP90u6tlEDa11ky4Hb9codDSi5Qje0cllhOzCfhnRwWAXg5160TpZRWHB+Yu1SOAdtOhmBIdMNFHYzQguBbYl0RgEJwYkerLMJSsxIqhWqzVZzwAAFfriZWcKkEgkkgZr7ARvs9mWL19+/Pjx4uJinueDg4OTk5Pvv/9+jUbTyJHURucgJlBReWJ9pITXyIiBMfICmyCC+PZO61s7rVVbxn5d6FtgSNj9cJBedh3nVTMQhIEgsAVwAJNwoNvJjiljxpYkpHT1pDtOjd6vbK8euSGx3dnMU7//np6enrl2zdns7OzX3xL9/QmWsf75Z9Fr0zDPH3A4AIA0GAiVGgBYljUEBhpDQ/V6PalUvCCKBoMhKiIiweiv0WgUGjWt1qh8zhWsHadWWS5J4fJzxwGPqFW2y3/6kEplv+jTzjZLX411GwBmWiwYLMpNQvV9deeuOtyjUPgOOy6kPffpIIXcfMm+WoKQkywADFJgs8CCUncuoFDQhPr2baFMuE/IYCiFipQVespPuI62ZNuGySLzHEefS/3k8fBnBym67LOeSLUf6qTtEaqMLPYU5rnLY9UJhCrYwpsZVMJSwRSiMWDAWECEC4AhCFbGAkBOwbehOdMYQn4oYVcv/rhg24tIFRPy4uX/JUskEklDNeol+l27dg0cOLBFixY9evQwGo0AYDKZ9uzZc+bMmU2bNnXpUsNzVlfVaM/Bt/m2uFsY5Rtktyvf88IWi8UjCgKmSOQVsJ+c9D2pbvUKIFTOm8qJGADR50712wdRH/atPBfvu6zk+8F+UVrfeCsIU5N1PSKI/brwu0H63hEyb477ROc9dCDN5XsU7TXxmzvABZMciwD3FpUWCfyGQP/DRSXPY9y6vLzH9q05OTnFxcV5eXklJSV5eXl2u72W/apUKo1Go1ar1Wq1TqfTaDQKhUKhUOj1eplMJpfLtVoty7IqlUoul8tkMrVaTVGURqMhSdK3rFAoWLb6M3iYL3cfbEMGPsFEzqrjX6LxuARntiszTBapotQ5rrP7K3Z11vUMl0cdtKRsLv1jSOCotur228s3/pS/4OnIae21XXea/t5StmF0yGOtVW2t+1tRfKk16iNt3sfAlzHxS0jD4Asbly7R14Z0iV5ya7gFR9F37Njx0UcfnTJlSrX1S5Ys+fzzz/fu3VuPNhs3wdOLBl404XqxQ1h0xLHoqOPpjqoXOqtzrML4DSYHj2UUwgC5Vp4AFKYhAcAr4kKr8P/27j2oibPfA/iTzYVbgAQIkaAgYsBTiugpWpW+VQd7RMXq1Et1dPSgU9RxWuX0JsqccSz2Mh0ZHBXLtKXUKj1t1SMVPS++VDuK1da3tlgZqAoCFhIgIMg9m2TPH1tjhCBIZZ+8y/fzV3bz5MmXZy+/7Cbs2m+YznHE8dI178/0XTrB89EB2s3cqm+bzPePTm80s6O9pZ5yhhCiMpnf2HXTO1oZUfhQdSeEbDQ1/9JtLtQF8kfAP/WY1zWatqt87V8E8Lq6uhobGw0Gg8lkanJgMplaWlru3r179+5d/sGTWmFkMhl/kzGlUimXyxmGeVrvYWiSW22MSvXnETb/gcCxMU+tfvCdBf8xwrFnX19f5uEfGfR6uSPHrpzq29uAVCqVRCIhhLAc28TV+0k0ColbT8/FkNYtEglDOBvjoXeP+bHXq1DgBwMFHsRBhN/BV1RULFq0qO/8xYsXb9my5dGv7e7uTk9P73sf6Fu3bln7nLAdDqN9mIma3peM1XpJ/yPM/dNrHf81xZsQonZnVj/tOU4le26027UGdtn/mqwcV9Nq8VYw62O83KTkTqv1bE3P0xr5mdvdL473KG9iCSESCfmuusdTzswPd69oseRc65gd4jZnrPvtFssn1zrs7zU1yM3QYTVbbT1WImMIIaSpy9rQYSUSQhTM/o+eWj3JO4KRVLVaPi3pmBXqFh/qXtVq8b4hnWVx+7CyzU0qeX6M26wQt8+9AgpLu74P6pkV4lbdavmqvOv+O6inBmkXPPvnzDlBij4NyNQgxb+resoM945XWMIVbeNIfWVzz8V2f5Zlu7u7LRaLur3aw/BrC/G64zdZbiyV3/nnXc6zK+IF/hNYT08PW3ml5er/cb467+dWdd+83Hn9u3aZr3fsSkLIeUJIMOm+ebmzqEiuGWtvwD8m9z/COc58qAGxN/jHQA0G7GFYGpw+oJsxppbjJIqIQ4+/AgIAPB5BC/ysWbO2bduWmZmp1T64dUpTU1Nqaurzzz//6NfKZDKtVtvZ2dlrfnBw8IAHYU/EqijP2FFOrgnveCDurZDYL3U3MVAe6CWVMZKilZp2s81DJlFIJRYbWdzI6pTMmdvd/zlRWddmsf9cLMJPRgjx92AmauQ6pZQQonJnojVycr9BuEr601ptU5ftH7e7ozTyxcdM789StZsfHE8HekkJIV5yJipAHugpJYR4K5hnVHL+2R4rp3JnCCEhbjKNm4x/7Cln1G6M7f5BuePM/hr4+PiMkynHd3VN0QVPCoxu7LRJbnTZGzwbPGdSoLyx03biRtcU3cv2xw4N3PiZx8rao1SWp/0k1ab2wjvEarPxHwImKJcFWhpbLbIf21Q6bmawZEObTfHPTv+e+7cGHz13omfLC50Sj1uK8FFx47zbnmthpdVeE+wD4TnOw3tGeI9MafSLUI11850R3inxqFGOsTfw9jd7/Zsfq/BpHjXGJje5j5JY3VXtQQH2Bu7y8faZFnMQ4/aMzUPNqrzsDWRjtLL7M6UOj/kG3d3dnMqTjBtHvPyJQkLuP/7vwri/Jx2qujdposeTvCMwAIBTgp6ib2hoSEpKKiwsDAsL8/f3l0gkzc3NlZWV8fHxeXl5fn5+A3fRB/VTdo2dtjn/01CyzsmFbpz+mxwv/CPD318O1KuH/m/Q9u/gh9wDCM9276LEa6JE6uT7ApyiHwzq2zvAEyHCU/SBgYGnTp2qrq4uLS01GAwcx2m12piYGMcLr/7L0XgyTqs7IWRasJuivx/PSYgcl0sfeRifONoRAGCkoPB/8KGhoaGhocK/r/A+nO3b31MrJniM8flLVzHzkjNhvvK/0gMAAIgYLnRDx+6ZqoEbPdK19dqBGwEAwEiF08QAAAAihAIPAAAgQijwAAAAIoQCDwAAIEIo8AAAACL0L/8reqlUWllZGRsb2/cpk8lkMBj4i5m7CI7jLBaLXO5C/97GcRzLsgqFk4v0UWQ2m10tEsuyUVFRUulf+ufGRzCbzY970fsRyMW3d5ZlpVIp3eVIfdux2Ww2mw0LwmKxREdH9/esMNu7oFeyGw4cx5WUlDi9HP3x48eLi4tfeeUV4VP1p7S09OTJk9u2baMd5AGj0bhnz54PP/yQdpAHWJbdsGFDTk4O7SAP2bx587Fjx4bvusgeHh5PPfXUMHUuGi6+ve/Zsyc+Pn7SpEm0ApjN5k2bNn366ae0AhBCLly4UFZWlpycTDFDRkbG7NmzJ0+eTCsAy7IbN2784Ycf+msg0PbOiVdOTk5SUhLtFA8pKiqKj4+nneIhv//+e0REBO0UD+nq6nJ3d6edojeNRtPQ0EA7BfTLFbb3BQsWFBQUUAzQ0dHh6elJMQDHcbm5uWvXrqWbITEx8eTJkxQDdHZ2enh4UAzAwylBAAAAEUKBBwAAECEUeAAAABFCgQcAABAhFHgAAAAREnOBl8lkw/dfy0PjmpFc6lIBhBCGYVzqUgE8FxwocOQKG5dUKqW7krjCtoMFQVxjQRAR/B/8I/T09LS3t/v7+9MO8oDNZmtoaBg1ahTtIA+pq6vT6XS0UzwEkeBxucL2Xl9fHxAQQLe8UV9RzWbzvXv3AgICKGbAguCJucADAACMWGI+RQ8AADBiocADAACIEAo8AACACKHAAwAAiBAKPAAAgAihwAMAAIgQCjwAAIAIocADAACIkGgLfENDw0svvaRWq5955pmLFy/SirFu3TqJg/z8fLrZysrK8vLy7JNOkwgcr1ckuiNWWVmZkJCgVqtDQkLS09P5ma4wSvBotBbH4FeY4VZTU3P79m0qAaxW65tvvhkSEhIcHLxv3z4qGX799deZM2cqlcoJEyYcOXJE4AwuuF/libbAJyUlKRSKX375Zc2aNfPmzWtra6MSo6KiYu/eveX3xcfHU8xmtVp37dp14cIF+xynSYSM1zcSxREzm80zZ84MDQ397bffPv/888zMzJycnP7e3UVWMOBRWRyPtcIMq46Ojvj4+EOHDvGTAgdISUm5ePFiYWHhvn37Xn/99eLiYoEzWCyWxMTEqKiokpKS7du3r1279urVq4JlcMH96gOcGFVVVUmlUqPRyE9Onjw5OzubShKdTnft2jXHObSy7d69m78G/saNGx+RRMh4fSNxVEfs/Pnz3t7eZrOZn0xLS1u4cCH1UYIB0Vocg19hhjvJ+vXr3dzcdu7cyQk+Gh0dHUql8vr16/zke++999VXXwmcobKykhBSU1PDT0ZHR2dlZQmTwQX3q47EeQRfWloaFham1Wr5yRkzZly/fl34GJ2dnXV1dampqT4+Pnq9Pjs7m2K2NWvWFBUVrV692j7HaRIh4/WNRHfERo0alZmZab8HVHNzM8Mw1EcJBkRrcQx+hRnWGMePHy8rK5s3bx4/KXCAS5cu+fr6RkVFcRxHCNm2bdvy5csFzhAaGhoeHp6VldXS0nL69Olbt27FxcUJk8EF96uOxFngDQaD402lAgIC6uvrhY9RUVHBMMzcuXPLy8t3796dkpJy+vRpWtlGjx4dFRXleIsnp0mEjNc3Et0R0+v169at4x+fO3fu8OHDycnJ1EcJBkRrcQx+hRm+DLW1tSkpKYcOHbLfHVXgAPx92zZv3uzn56fRaFJTU202m8AZGIb58ssvP/jgA7VavWDBgrS0tIkTJwqTwQX3q47EWeA5jpNIJI5zLBaL8DGio6PNZvOrr76q0+mWL1+elJT0xRdfuEg20s8o0Y3nCiPW1dX11ltvLVy4MCcnZ/78+S44StAL3cUxmBVmmN6a47i1a9empaWFh4c7zhRyNFpaWkpKStRqdVVVVVFRUW5ublZWlsAZjEbj4sWLP/7447a2tuLi4oMHD546dYrWWuFSewxxFnitVtvc3GyfbG5uDgoKopLE8YbEEyZMMBgMrpPNaRLq8eiOWEVFRWxs7NWrV3/++eclS5YQVx0lcERxcQxyhRmmd9+3b59EIlm5cmVHR4fFYjGbzZ2dnQKPhp+fn1arTU9P9/X1jYmJWblyZUFBgcAZCgoKxo0bt379eqVSGRcXt2HDhtzcXFprhUvtMcRZ4GNiYiorK5uamvjJH3/8MSYmRvgYeXl5q1atsk9WVFSEh4e7SDbSzyjRjUd3xFiWnTdvXkJCwpkzZyIjI/mZLjhK0AutxTH4FWaYAoTLCZQAAAZRSURBVFy5cqWoqMjb21upVJ44ceLdd9+NjIwUeDQiIiJYlrVarfykSqXy9PQUOIPZbLbZbPZJjuNYlqW1VrjWHkOAH/JRMXfu3OTk5La2tsOHD6tUqpaWFuEzlJaWSqXS9957r7a2Nj8/38fH58qVK3Szbd261fEn606TCBzPMRLdETt+/Lifn195efmt++rq6vp7d1dYwcCOyuJ4rBVmuC1dupT/Fb3wAaZPn/7aa68Zjcbvv/9eo9EcPXpU4AxVVVVKpXL//v0mk+ns2bOBgYF5eXlCZnDB/SpPtAW+qanpxRdfVKlUsbGxly9fphXj7Nmzzz77rFKpjImJ+fbbb6ln67UiOk0icLxekSiO2M6dO3t9/E1MTOzv3V1kBQMelcXxWCvMcHMs8AIHMBqNiYmJvr6+er3+o48+opLh/Pnz06ZN8/Ly0uv1Bw4cEDiDC+5XeRKO44Q4UQAAAAACEud38AAAACMcCjwAAIAIocADAACIEAo8AACACKHAAwAAiBAKPAAAgAihwAMAAIgQCjwAAIAIocADAACIEAo8AACACKHAAwAAiBAKPAAAgAihwAMAAIgQCjwAAIAIocADAACIEAo8AACACKHAAwAAiBAKPAAAgAihwAMAAIgQCjwAAIAIocADAACIEAr8yPXCCy9I+tDr9dXV1XK5nHY6ABisKVOm2Ddhb2/vOXPmlJWVDaGf1NTUlJSUJ7sH4Pt8Ur3BY0GBH7m+/vpro9FoNBo3bdqUkJDAP7506ZK/v/+BAwdopwOAx7Br1y5+Ey4uLu7u7l62bNmQuxpwDzB16tSrV68OuX8QjIx2AKBGrVbzD7y8vNzd3bVarf2p5ORkSqEAYCh8fHz4TVir1e7YsWP+/PkGgyEoKIh/luM4lmUVCsVgulIqlY/eAzQ2NrIs+9czw3DDETz0Zj9B19TUpFKptm/f7uvrq9PpDh48mJ2dPXbsWLVa/f777/ONKyoqEhISVCpVXFzc4cOHqQYHgAdYlq2vr1er1WfOnAkLC7t06ZLTrbWgoCA6OlqtVi9duvTu3bvEYQ9ACLl8+fK0adN8fHymT59+5coVQsisWbPu3Lkzf/78Y8eOkX72AH37BDo4GPHeeOONxYsX2yerqqpkMhnHcSaTiRCyevXq2trajIwMQsiKFStaW1uzsrIYhmlvb+/u7g4LC0tLS2toaDh37lxQUNDp06fp/R0AI1RsbGx6errJZDKZTKWlpX/729/0er3NZjMajQqFIikpyWg0Ot1ay8vLFQpFdnZ2bW3t/v37JRLJ1q1b7XuA+vp6Hx+fTz75pK6u7p133tFoNCzLchw3duzYy5cvcxw3+D4pD9BIhQIPAxT4O3fucBzHfwwvKSnhOI4/O/fHH3+cOHFCp9OZzWb+hbt27Vq0aBGNvwBgRIuNjbUfs8nl8unTp1+7do3jOKPRSAi5ceMGx3FOt9a0tDTHbX/27NmOBT4zMzM+Pp5/ymq1Hjx40GQycQ4FfvB9CjAI0Be+g4cBaDQaQgh/yi44OJgQIpP9udpUVlY2NTWFhITYGzvuaABAMJmZmVu2bHH6VGhoKOlna62trY2MjLTPiY6OdnxhdXW1Xq/nHzMMs3Hjxl49D6FPEBIKPAxdUFBQbGxscXExP1lTU2OxWOhGAoBepFIp6Wdrzc3NvX79ur1ldXV1WFiYfTI4OPi7777jH3Mct3PnzjVr1oSHh9sbDKFPEBJ+ZAdDl5CQcPPmzYyMDJPJVFxcPHXq1J9++ol2KABwwunWumrVqlOnTn322WeNjY25ubmFhYWOL1m2bNn58+ePHDliMpn27Nmzd+9ef39//qnW1tah9QlCQoGHoVOpVIWFhfn5+WFhYatXr3777bdXrFhBOxQAOOF0a42MjDx69GhGRsb48eNPnDixY8cOx5eEhIScPHkyIyMjNDQ0Ly8vPz9fpVIRQl5++eUlS5Z88803Q+gThCThOI52BgAAAHjCcAQPAAAgQijwAAAAIoQCDwAAIEIo8AAAACKEAg8AACBCKPAAAAAihAIPAAAgQijwAAAAIoQCDwAAIEIo8AAAACKEAg8AACBCKPAAAAAihAIPAAAgQijwAAAAIoQCDwAAIEIo8AAAACKEAg8AACBCKPAAAAAihAIPAAAgQijwAAAAIvT/a4o/Z+xlbDIAAAAASUVORK5CYII=" /><!-- --></p> <p>While this is visually much more satisfactory, such an average procedure could introduce a bias, as not all results from the individual fits enter the population curve with the same weight. This is where nonlinear mixed-effects models can help out by treating all datasets with equally by fitting a parameter distribution model together with the degradation model and the error model (see below).</p> <p>The remaining trend of the residuals to be higher for higher predicted residues is reduced by using the two-component error model:</p> <pre class="r"><code>plot(mixed(f_parent_mkin_tc["DFOP", ]), test_log_parms = TRUE)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> </div> <div id="nonlinear-mixed-effects-models" class="section level2"> <h2>Nonlinear mixed-effects models</h2> <p>Instead of taking a model selection decision for each of the individual fits, we fit nonlinear mixed-effects models (using different fitting algorithms as implemented in different packages) and do model selection using all available data at the same time. In order to make sure that these decisions are not unduly influenced by the type of algorithm used, by implementation details or by the use of wrong control parameters, we compare the model selection results obtained with different R packages, with different algorithms and checking control parameters.</p> <div id="nlme" class="section level3"> <h3>nlme</h3> -<p>The nlme package was the first R extension providing facilities to fit nonlinear mixed-effects models. We use would like to do model selection from all four combinations of degradation models and error models based on the AIC. However, fitting the DFOP model with constant variance and using default control parameters results in an error, signalling that the maximum number of 50 iterations was reached, potentially indicating overparameterisation. However, the algorithm converges when the two-component error model is used in combination with the DFOP model. This can be explained by the fact that the smaller residues observed at later sampling times get more weight when using the two-component error model which will counteract the tendency of the algorithm to try parameter combinations unsuitable for fitting these data.</p> +<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> <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", ]) -# maxIter = 50 reached +# 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 overparameterisation is also indicated by warnings obtained when fitting SFO or DFOP with the two-component error model (‘false convergence’ in the ‘LME step’ in some iterations). In addition to these fits, attempts were also made to include correlations between random effects by using the log Cholesky parameterisation of the matrix specifying them. The code used for these attempts can be made visible below.</p> -<pre class="r"><code>f_parent_nlme_sfo_const_logchol <- nlme(f_parent_mkin_const["SFO", ], - random = pdLogChol(list(DMTA_0 ~ 1, log_k_DMTA ~ 1))) -anova(f_parent_nlme_sfo_const, f_parent_nlme_sfo_const_logchol) # not better -#f_parent_nlme_dfop_tc_logchol <- update(f_parent_nlme_dfop_tc, -# random = pdLogChol(list(DMTA_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1))) -# using log Cholesky parameterisation for random effects (nlme default) does -# not converge here and gives lots of warnings about the LME step not converging</code></pre> -<p>The model comparison function of the nlme package can directly be applied to these fits showing a similar goodness-of-fit of the SFO model, but a much lower AIC for the DFOP model fitted with the two-component error model. Also, the likelihood ratio test indicates that this difference is significant. as the p-value is below 0.0001.</p> +<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> <pre class="r"><code>anova( f_parent_nlme_sfo_const, f_parent_nlme_sfo_tc, f_parent_nlme_dfop_tc )</code></pre> <pre><code> Model df AIC BIC logLik Test L.Ratio p-value -f_parent_nlme_sfo_const 1 5 818.63 834.00 -404.31 -f_parent_nlme_sfo_tc 2 6 820.61 839.06 -404.31 1 vs 2 0.014 0.9049 -f_parent_nlme_dfop_tc 3 10 687.84 718.59 -333.92 2 vs 3 140.771 <.0001</code></pre> +f_parent_nlme_sfo_const 1 5 796.60 811.82 -393.30 +f_parent_nlme_sfo_tc 2 6 798.60 816.86 -393.30 1 vs 2 0.00 0.998 +f_parent_nlme_dfop_tc 3 10 671.91 702.34 -325.96 2 vs 3 134.69 <.0001</code></pre> +<p>In addition to these fits, attempts were also made to include correlations between random effects by using the log Cholesky parameterisation of the matrix specifying them. The code used for these attempts can be made visible below.</p> +<pre class="r"><code>f_parent_nlme_sfo_const_logchol <- nlme(f_parent_mkin_const["SFO", ], + random = nlme::pdLogChol(list(DMTA_0 ~ 1, log_k_DMTA ~ 1))) +anova(f_parent_nlme_sfo_const, f_parent_nlme_sfo_const_logchol) +f_parent_nlme_sfo_tc_logchol <- nlme(f_parent_mkin_tc["SFO", ], + random = nlme::pdLogChol(list(DMTA_0 ~ 1, log_k_DMTA ~ 1))) +anova(f_parent_nlme_sfo_tc, f_parent_nlme_sfo_tc_logchol) +f_parent_nlme_dfop_tc_logchol <- nlme(f_parent_mkin_const["DFOP", ], + random = nlme::pdLogChol(list(DMTA_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1))) +anova(f_parent_nlme_dfop_tc, f_parent_nlme_dfop_tc_logchol)</code></pre> +<p>While the SFO variants converge fast, the additional parameters introduced by this lead to convergence warnings for the DFOP model. The model comparison clearly show that adding correlations between random effects does not improve the fits.</p> <p>The selected model (DFOP with two-component error) fitted to the data assuming no correlations between random effects is shown below.</p> <pre class="r"><code>plot(f_parent_nlme_dfop_tc)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> </div> <div id="saemix" class="section level3"> <h3>saemix</h3> -<p>The saemix package provided the first Open Source implementation of the Stochastic Approximation to the Expectation Maximisation (SAEM) algorithm. SAEM fits of degradation models can be performed using an interface to the saemix package available in current development versions of the mkin package.</p> -<p>The corresponding SAEM fits of the four combinations of degradation and error models are fitted below. As there is no convergence criterion implemented in the saemix package, the convergence plots need to be manually checked for every fit.</p> -<p>The convergence plot for the SFO model using constant variance is shown below.</p> +<p>The saemix package provided the first Open Source implementation of the Stochastic Approximation to the Expectation Maximisation (SAEM) algorithm. SAEM fits of degradation models can be conveniently performed using an interface to the saemix package available in current development versions of the mkin package.</p> +<p>The corresponding SAEM fits of the four combinations of degradation and error models are fitted below. As there is no convergence criterion implemented in the saemix package, the convergence plots need to be manually checked for every fit. 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) -f_parent_saemix_sfo_const <- mkin::saem(f_parent_mkin_const["SFO", ], quiet = TRUE, - transformations = "saemix") +saemix_control <- saemixControl(nbiter.saemix = c(800, 300), nb.chains = 15, + print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE)</code></pre> +<p>The convergence plot for the SFO model using constant variance is shown below.</p> +<pre class="r"><code>f_parent_saemix_sfo_const <- mkin::saem(f_parent_mkin_const["SFO", ], quiet = TRUE, + control = saemix_control, transformations = "saemix") plot(f_parent_saemix_sfo_const$so, plot.type = "convergence")</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <p>Obviously the default number of iterations is sufficient to reach convergence. This can also be said for the SFO fit using the two-component error model.</p> <pre class="r"><code>f_parent_saemix_sfo_tc <- mkin::saem(f_parent_mkin_tc["SFO", ], quiet = TRUE, - transformations = "saemix") + control = saemix_control, transformations = "saemix") plot(f_parent_saemix_sfo_tc$so, plot.type = "convergence")</code></pre> -<p><img src="data:image/png;base64,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" 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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> <pre class="r"><code>f_parent_saemix_dfop_const <- mkin::saem(f_parent_mkin_const["DFOP", ], quiet = TRUE, - control = saemixControl(nbiter.saemix = c(800, 200), print = FALSE, - save = FALSE, save.graphs = FALSE, displayProgress = FALSE), - transformations = "saemix") + control = saemix_control, transformations = "saemix") plot(f_parent_saemix_dfop_const$so, plot.type = "convergence")</code></pre> -<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAIAAAD17khjAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOzdeTzV2f848HMXu2wlZEnF2FpVRLTMVEwp0mZGiwltU01TaZkU6tOiTftMpdRoVDKVJKSitIhskSVNRPb1uriWu/z+ON95/+7ci8i9Ltfr+cc87j3e9/0+7zun+3qfncThcBAAAAAAxAtZ1BkAAAAAgOBBgAcAAADEEAR4AAAAQAxBgAcAAADEEAR4AAAAQAxBgAcAAADEEAR4AAAAQAxBgAcAAADEEAR4AAAAQAxBgAcAAADEEAR4AAAAQAxBgAcAAADEEAR4AAAAQAxBgAcAAADEEAR4AAAAQAxBgAcAAADEEAR4AAAAQAxBgAcAAADEEAR4AAAAQAxBgAcAAADEEAR4AAAAQAxBgP8aaWlppH8NGTLEzc2NTqcjhEJDQ3FiTk4O99u0tDR9fX3Sf4WGhiKEMjIySCSShoYGm83u4Ip79+5VV1c3MjK6d+9ez9wj6D8uXbpEIpHKysp40svLyw8cOCCSLAFA4C+fTCbTzc1NWVlZS0vr+PHjIsxbLwcB/uutWbMmODh42bJlAQEBXl5eRDqJRIqPj0cIJSYmkkgknHj69OmQkBAHBweE0NWrV0NCQiZOnIgQunnzJolEKi0tjYuLa+9CUVFRXl5e+/btmz59+qJFi6qrq4V7Y6Dfo9PpoaGhCxcu5C7YAPQSgYGBly5d2rt3r4ODw5YtW16/fi3qHPVSEOC/3oQJExYtWuTr67to0aLz588TVXBDQ8NXr14hhBISEoyMjHCira3tggULDA0NEUL29vYLFiwYMmQIQujmzZsLFy5UVFS8detWexcKDw/X1dV1d3f38PBoaWmJiYkR+r2BfunUqVNSUlJxcXFJSUk7duzIysoSdY4A+P+I8llYWGhhYbFhwwYPDw+EUGZmpqiz1ktBgBeA8ePHNzY2FhUV4bcWFhbx8fEcDicxMXHSpEkdfDA5OTk3N9fJyWnu3LkhISHttdKXlZUNHDgQIYT/W1FRIeg7AADFxsZu3br10qVL1tbW06ZNy8rKWr9+vagzBcD/4S6fe/bsefnyJUIoIiICIdTxz2x/BgFeAIh2eMzCwiIjIyM5ObmxsdHU1LSDD964cUNGRsbW1tbR0bGsrKy9Vnoi8JPJZIQQh8MRUMYB+P/c3Nw0NTV//PFHUWcEgDbwl8/Lly//8ssvPj4+REMp4AEBXgCSk5NlZGQ0NTXxW11dXVVV1bNnz44bN05aWrq9T3E4nODgYAaDIScn5+joiBAKDg5u80hVVdXy8nKEUGVlJUJIXV1d8PcA+j0rK6v8/PyAgABRZwSANvCUz82bN69bt+7ixYt79uwRbcZ6MwjwXy8lJeXu3bu7d+8ODg5evXo1rl4jhEgkkoWFRVBQkIWFRQcfj4+P//Tp044dO0JCQkJCQr799tu///67zVZ6GxubwsJCPz8/Hx8faWnpqVOnCuV+QP8WEBAwa9as3bt3NzQ0iDovAPDiLp/379/38/NbvHixoqJiaGhofn6+qHPXS0GA/3pnz56dP3/+pUuXXFxc9u7dy/0nS0vL5ubmjnuGbt68KSkpuXPnzgULFixYsGDlypVlZWXPnj3jP9Le3n7Pnj2+vr5Pnz4NDAxUUVER8J0AgBCJRNq3b19JScmRI0dEnRcAeHGXz+joaIRQYGCgg4ODg4NDVFSUqHPXS5GgQxcAAAAQP1RRZwD8x7t377Zu3cqTGBQUpKysLJL8AAAA6KOgBg8AAACIIeiDBwAAAMQQBHgAAABADEGABwAAAMQQBHgAAABADEGABwAAAMQQBHgAAABADEGABwAAAMQQBHgAAABADEGABwAAAMQQLFUL+ik2m52WllZaWspkMjU1NceOHUvsBwiAyEH5BN0HAR70RzExMWvXrtXQ0NDU1EQIFRcXFxYWXrp0acqUKaLOGgBQPoFgwFr0oD8aNWrU3bt3R4wYQaQUFhYuXLjw9evXIswVABiUTyAQ0OYD+iMWi6WhocGdoqamBg+7oJeA8gkEQohN9DQaTUFBgUQiJSUlZWVljR8/3sjISHiXA6Dz1q9fP378eHt7ey0tLRKJVFxcfO/evfXr14s6XwAgBOUTCIiwmugvXLhw/Pjx9PT0I0eO+Pv7W1tbx8bGenp6uru7C+NyAHRVYWFhREREcXExQkhDQ2P27Nna2tqizhQA/wfKJ+g+YQX4oUOHpqenKygoaGtrp6WlqaioVFRUWFpa5ubmCuNyAHRVc3NzQkJCWVmZjIyMtrb26NGjRZ0jANp24cKFVatWiToXoO8RVhO9qqpqS0sLQkhJSQnP7qBQKEK6FgBdFR0d7erqamBgEB8fjycgVVdXBwcHQy8S6A0ePHhQUVFBvN27d6+UlBRCaMWKFaLLFOh7hBXg9+/fb2lpOW/evG+++WbatGmzZs0KDw/fsGGDkC4HQJds2LDh8ePH+vr6BQUFTk5OcXFxb9++XbVqVVxcnKizBgB69+6dp6enk5OTiooKQqipqSk1NRVBgAddJMRpcrW1taGhoYWFhQwGQ1VV1dbW1tDQsPMff/HiRVhYmJDy1g+REEJNTSQSiS0lJbyr6OjorFu3TnjnF5Rhw4a9e/dOVla2paXFwMAgLy+PxWIZGBh8+PChk2eA8ilYUD55vHjxYufOnV5eXt99992oUaPS09O7+nEonwLUR8tnj86DZ7FYnW+o37Zt27t372BhB4FhMCg+PkhGhuXlJaQr0Gi0wMDAwsJCIZ1fgLZs2ZKWlmZvb//w4cOBAwcGBATMnDnT0NDwzJkznTwDlE8Bg/LJp66ubsOGDSoqKuHh4e/fv+/SZ6F8ClgfLZ+cnlJaWtqly3l4eBw+fJh4W1VVpaamdvr0aSFkrV9oqqk5gtApJSXhXaKwsFBLS0t45xcgNpt97dq1X3755ffff29paWGz2a9fv+7SGdosn2fOnBF0TvsLKJ/tuX79uq2tbVc/xVM+ORxOdXX15MmTBZev/qWPls+eW6pWVVUVx/ivo6Kism7duqKiIgFmCfRbJBLphx9+MDY2Li0tjYyM1NTUnDBhQgfHNzc3NzY28qRwv1VRUVmzZg2e1ARA9xFr0cvJye3fv5/NZnewFj2DwWhqauJJYbPZ3Cnv37/HHfmg/xBigG9oaEAIycnJFRcXx8XFGRoajhkzpjsnlJGRqa+vF1Du2lVUVFRdXT1q1ChhXwiIUFfX+ra3t09ISOBOodPp48eP9/DwIFIkJSVbW1uFmm3QT3S1fDo6OvKsYkun001NTbdv306kVFdXMxiMuro6BQUFoWYe9B7CCvCBgYEbNmygUqne3t5+fn4WFhbbtm3z8PBobzGm8+fP5+XlcaeEh4ePHz/+P3mlUplMppAyTAgNDU1MTAwICBD2hYAIbdy4MTw8vPNrfUdGRvKkmJubDxo0iDuFRIKdHYBgdLV8RkRE8KSYm5urqqpyp9BoNDab/fjx4/nz5ws8w6B3ElaA9/b2zs7OlpaW1tbWjoyMnDx5cmVlpZmZWXsBXkFBQVlZmTulrq6uoKDgP3mlUvHceqFiMpnl5eXCvgoQLWGs9U0mk3kaRQH4OsIon7h2dOfOHQjw/YewAjybzVZRUaFSqXJycriiIy8vTyKR2jv+hx9+4Em5ffu2vLw8dwqFQklKShJGbrmx2ew2OwLCw8MtLCzwtFTQ1wljrW+owQNBEUb5xAGee/0cIPaEtZvc4sWLzc3NLSwsZs6c6eLicubMmQULFtja2nbnnFQqlWcgSV1d3fHjx7uXU14sFguPHuCxY8eOxMREwV4LiMq6desePnw4fPjwysrKiooKHR2dBw8erF69ujvnhAAPBEUY5ZPFYsnIyNDpdEFlEvR+wqrB+/r6zpkzh0KhTJ48+fHjxxEREQ4ODitXruzOOSkUCs8gpqKiovPnz2/evLl7mf0PFovFM14a+/z5M4PB6M6ZfX19XVxc1NTUunMSjMPhVFdXDxgwQFJSsvtn65+0tbV51vfu0joN/CDAAwESePlksVgjR44kai++vr6bNm2SEuayLUDkhLgfvLm5OZvNDgkJaWpqWr58ubu7ezeXo6dSqTwBnsViMZlMNpvd1WWeEEJZWVk//vgjf/rTp0+52wkePnyIEGKz2TQarc3A33l//fUXz0DC9jQ2Nnp6enZwwJ07d6ytrTU1NaHTV1DKysqo1G4970KAB8LTcflcsGDBiP/CU+y4j2GxWMrKyrW1tRs2bMjMzPT29o6JiRF+xoEoCasGL4zNPPhr8Gw2m8Vi5efnOzg4JCYm8nSQh4aG1tbWtrd6c3l5OXe4zc/P19XVRQhlZWVxD+WbO3cujUYjk8kcDqekpITJZHY1DFRXVysrK5NIpIaGBhaL1ZmPVFRU+Pv7/+9//3N2dv7pp59mzJiBEEpNTaVSqSNHjkQInTp1KisrCyHEZDKhEi8QHa/T8Pr1a54hn9XV1YqKitwpEOCB8HRcPv39/WtqarhTHB0deRoLWSyWnp7e8+fPz507h1ds9Pf372a3KejlhBXghbGZx+jRo/lr8LgS//Hjx1mzZr1584b7r1lZWZWVldwply9fzs/Pt7W1tbS0bGlpSU9P//z5s5aWVkNDw5gxY2g0GkIIh3PiI62trSwWC1eUt27dqqWltWTJkjazFxAQ4OLiwj+QcMGCBfv27bOysqqvr+9gmt/Tp09PnTr1999/I4SYTGZTUxODwfj06RPOFUIoODhYWloaB3iiLeGLTwxsNpvNZnezbiqWiIVEmEympqbm2LFjO+g9uXv37j///MOdUl1dzTNNDkbRAwHqUvlUVlbmmYUkJSXF81vEYrEkJCSMjY3x76SEhMSnT5+ElHnQSwjrd7+5uRkv0aCurl5SUoIQMjExwS++mpqaGnfd+vbt215eXriJHiFUV1fHczybzeYOqE1NTSEhIS9evKivr7e0tGQwGA0NDeXl5VpaWjig4sPq6+ulpaXxaxaLxeFw2Gw2UTPjX6osMzPzm2++ef/+vaur64IFC/gXkaivr8ed901NTSwWKz09/eDBg0FBQTyHVVRUEOv04XEArq6uL168ILbge/Pmjbm5eU5ODkKIGA3wxYUBzpw5U1tbu2fPno4P62+6upDIwYMHeVLMzc0HDhzInQI1eCAoXS2fnYG78Ldv375s2bLjx4+/f/8+OjpacFkGvZGwAryjo+O8efPwZh5Tp07lcDg2NjYdNAeFh4fzLENbXl4uJyfHnaKsrEyj0TgcDolEqqysPHLkSHZ2trKyMq7FNjQ0vH37dvTo0cTxLBbr/v37hw4dwgH77du3ERERUlJSaWlpaWlpr169Qv8GSDabjdsGmExmS0sL8eSL/8pisYi88U+RX758+cqVKy9fvszhcBobG/kDfFNTEz55S0sLk8nMzMzMz88n/spgMM6cOePh4dHY2Igr61lZWRwOp7W19fr164irjl5dXV1cXDx37txFixYRAT45OXnq1KnE2S5fvjxp0iRjY2MihU6n8z/6gK4uJNIZEOCBoAijfOIAP3PmzIMHD65du/aff/6B7ebEnrAC/NGjR4OCghITE+fMmePq6ooQOnDggJmZWXvHZ2Zm8uzU2djYyNMgLykpKSsrW1NTExER8eHDh/j4eIRQdXX1jh07EELFxcV2dnbcHaVsNvuff/6h0Wg4wONo3dLS8uTJExsbGxyqiQDP4XAWLVqEG8FaWlpycnLKy8tNTU3xX8+cOSMjI8Nisaqrq/HJV61a1dzcrKqqymAwPnz4gBd5bnMUHoPBSE5OfvfuXUtLC4vFotPpb968aW5uxuNXy8vLd+3apaSk5O/vj1sjRo8ezT0fj+h4YzKZeXl5ubm5DAaDaLdvaGgoLCyk0+k4qIeHh8vJyRkbG7979666utra2prJZHay479fEcZCIhDggaAIo3ziAK+oqLhp0yaEkIyMTDenBYHeT1gBnkQiOTs7Ozs7EykdRHeEEPea3lhqairPICaEkJKSUm1t7du3b4mnAVxNx6+JBvyrV6+uWLECBzYmk1lVVbVmzZqhQ4cihPA/Ejqdjl8QAR4h9Pr1axw4ORzOrVu33r17h/8BhIeHX79+XUlJ6cCBA9u3b1dVVXV3d8/KyiotLW1oaJCVlW1sbCRaEYisHjlyRF9f38HBobm5ec+ePRoaGnhIIK7QEwGexWK1trZmZGSkpqZqamomJCQwmUzulXZyc3OJO8VzWOl0OvGcwWKxQkJCPn78ePr0aYRQa2trcnLytGnTIiMjP336hAN8D6zv2+fAQjegN+tq+dy4cSMedUvIzs7m7oOn0Wg3b960s7MjUgYNGlRVVYUbRIVxC6A36GNjrxQUFOh0Om5I5/8rrvE3Nze7urquWLECh+2qqqrq6uqQkBDuI7l73J8+fWpoaIgQqqysJNLPnDlTXV2NT3jp0iUajaagoEClUsvLy/fv36+jo4Nr89XV1RQKhai4cwf49+/f4xDOZDI5HA6OzURnf3Nzc0NDQ3h4+NixY9G/wwVYLBbe9Zn7PEQzBhH4i4qKiMFcbDYbbyT1888/b9y4kclkHj58ODc3d/z48UT/ApPJ3Lx584Hdu7vxxYubdevWzZ07NyIiAg+qwAuJaGtrd+ecMMgOCEpXy6erqyvPEnXr16/n7i6Ul5f38fGZOHEikSIpKamoqFhWVqauri6EOwC9Qh8L8NLS0jdv3mQymW1u20Wj0UxNTUNDQ1ks1rRp02praxFCjo6OuHbLjfghvnv37p9//onbrJqamnANTEJCoqysjDi4qqoKIUSlUomx6FVVVcR6ONwL4xw6dOju3btxcXFjxoxpbm7G1Xr8XxybW1pasrOzEUJHjx5NS0vLzs7Gu0TgLclZLBY+jLsGzx3gcYvChw8f5OXl8bNCRUXFoUOHHBwcnj179uTJE7yHaXZ29siRI3GAZzKZOTk5MTExe3fu/OqvXSzxLyTSTVCDBwLUpfLJv1GnoqIi9wRaCoUyb948nmMMDAwSEhJmzpxJJpNhxRux1McCvKSk5OvXrw0NDV++fMn/VxaLlZKSEhoaihAqKSnBY9kaGxs76Gp6+fJlc3Ozr68v+rf1fuTIkRISEikpKcQxeH7UgAEDiAD/7t274uJiohWBCPD3799ns9m7du3y9PQkAjwOtPjkKSkply5dQgjV1NRERUWRyeSjR4+ifzcXx7Vt1E4NnsVi4cf5uro6eXl5SUnJgoKCn3/+uaWlBe9Wzmaz8TNNfn7+7du3zc3N4+Pjy8rK8KwY6InvjnPnzvHMgy8sLORZgUCENXiixweAzpsxY0ZiYuLz588HDx68detWUWcHCJ4QV7ITBklJSTwWvYMVlR89eoQQamxsxAGYTqffunWrvYPfvXvHkyIlJSUrK8udwmAwZGVlg4ODJSQkcMpff/31+fNn4tccD/dDCLFYrCtXrsTFxVVVVcXFxeHBAdyRlWgYwM8EbDYbP4XgAE+j0c6fP4/4mvrxYFfc1I/vSEpK6rvvvkP/Djv48OEDfo7B7f8MBuPdu3dMJvPQoUOPHz/G39Xvv//e3pcAvmjw4MHK/0WlUonygImqBh8fHz958uSevy7o60aNGpWamlpQUIAbKSMjI3H1A4iNvleDp9PpmZmZHRyDwyFRq66vr79x40bnL6Gvr8//9CAtLW1gYMAzzp/AvYbU2bNnEULHjx8vKSnBS09wj3ELDw/HL54+fYpf4OFyycnJCCEGg4Fv7c6dO8RHkpKSLl68OGfOHKLdvrGxUVJSkju6pKamSkpKEksCYLgjgxhVcPz48W3/Pm2wWCwajSYlJYUnIsJAmy9auHAhT8rt27d5HgRFFeCfPn2K+30EpaSkhE6nf/PNNwI8JzdcCLu5cDXovuHDhz969EhSUnLYsGFqamr19fVPnjzBk564ff78OS8vz9raWiSZBN3RxwK8lJRUS0sLdwDGdXruY3CQ++p14+fPn19cXExEYkxPTw8hxL8eHPdvOh5Oj0M1d6s4/i8+kphG//nzZ/wC/zTjsXUEngUoqqurf//9d+7HCAkJCe4Az2azefbZQwilpqbiQfv4LX7R2tp68eJFT0/P8vLyESNGVFZWWlhYvHz50s7OTldXNz093djYWFpaWkpKKjExkclk6urqjh8/Xk5OTkNDg0ajlZaWstlsEomUkZFRX18/aNCgJUuW4HGCQFQB/ujRow0NDfX19TzbK3+169evv379+ubNmwI5G79Dhw49fPiQeMYF3deZLiR+xsbGKioqxcXFaWlpBw4cWLJkSZubekRHR1+/fh3vygH6lt4S4FNTU3mWlaXRaPzT5OTk5Gg0Gnej97Bhw/Dibjxwo/dXkJaWXrZs2a+//sqdOGzYMIQQfzfngAEDiGVkrK2to6KiuP/K3QdvaWn54sUL/ssRTycSEhL8Iwdxt25eXh5uuifwBPg24cYA4qEEZ6O1tXXVqlWBgYFOTk5nzpzR1dVtbW319PR88+bNp0+fnJyccnNzm5ubm5qaHB0dZWRkPnz4EBkZ2dLSUlhYOHDgQC0tLbxwr56enp6eXn19Pfeyvv2cSAJ8VVUVk8nU0tKqqKgQVICn0+lC3TX8/fv3qampxcXFQ4YMEd5V+pXBgwfztDvydyHxk5CQ2L59e0VFxa1bt0pKSvAC9WVlZTxr4jIYDGJeLuhbekuAv3r1akZGBndKaWkpz+rKCCEFBYW8vDwlJSUiBb82MzOrrKz8+PEjkc7zU0uhUDo5yoxKpfI0vQ4ePHjBggUIISsrq9WrV1+5coV4elBSUiICvJ6eHk+Az8/Pf/bsGb6uvr5+mwGeaFTftWuXt7c3z1///vtv3KJQXFxMpVKZTCaZTHZ0dMzPz//iv16MZxI8i8Xat2/f0qVLEUJ47gAGnbjdJ5IAHxoaamRkpKysnJiYiB9DsZycnOrqagsLC4RQVVVVc3Nz56Ppx48fuUeBCFZTU9OjR4/q6upcXV3xFBLQfZ3pQmrTxo0bEUL79u2bMmVKUlKSpKRkdXU1d4B/+PAhjUaDJXH6qN5S/fLz84v+LwMDA561vhFCU6ZMIZPJeKw4NmDAAISQgoIC/xqx6N+aN0JIS0urkzmhUqnEPg34X4ixsfGiRYvwn7S0tLj3rONuY+Cv3zOZTLw2xciRI318fBYvXsz9V5xzhBCuePGsy4szoKOjQ/SOm5iY7Nu3j0KhLFiwQEJCwsHBwdzcvJM3xQ360oSkh0fRv337Njw83Nvb28bGZuzYsTzzSqysrCwtLQMDA9ls9unTp/HDXCd3VS4rK+NeTVmAMjMz582bV1xc7Ovr++TJkzdv3uCJo3hfJWFcEXTSunXrRo8ebWBgQKySiRBis9n29va+vr4FBQU83YigT+gtAb6TnJycuB9LSSSShoYGmUzW0NAYN24csUkMQohMJru5uc2cORO/xZ9qs9ZLoVC4Z5FSqVQymYy7r9atWzdu3DjurncKhcIdyBUUFIgAzBPgcWbws4iurq6Ojo6lpSX3AcRjMn5ikJeXl5GRwTlHCK1evVpCQoJ7MJ2srKyWlpampiZOtLS05Fm2grhx/nvkTtTX1+c/AHRfD9fgT506ZWdnV1hYuGnTpgkTJly8eJEYd9Lc3Iw7vJYvXx4dHf3gwYOoqKiPHz+OGzfui+1YLS0tFRUV5eXl3Wmlr6io2LJlC54z8unTJ2KrHnt7++jo6EOHDnl4eCxdutTMzMzIyMjc3NzFxQVvqQCTOUVl/vz5ly9f1tXVvXbtGk45ffq0kZFRU1MTjUarr68n0rGkpCT+cT+gt+ljAR5xBelRo0bt3bt38eLFXl5epqamly9fVldXJ3bwnD59+sWLF4mRujh2btmyhf+EcnJyy5Yt09TUxHVoHM5xe8CmTZsmTpzYQYBXUVEh3hIv5OTk5OXlcUUZ/87iPEtKSnI3JBBVdhybZWRkDAwMlixZMnv2bJwNHMiJ+5WTk3NxccnLyyMSJ0yYgLM0cODAU6dO4SYBCQkJ7i4MjOjsIJFI0PH5FVasWDHhv969e8e9GhLq8QCP+1wHDhyoqKg4depUBQUFYgsD3KA6fvz44cOH29raJiUl0en0n376icVi4QlRbQoPD585c+b8+fPxQNFff/0Vdyo1NTXhnZk60NjYaGVlRWwGHRMTc/z4cbzA1NSpU3/77beGhgYWi5WXlycrK2tlZUUikdauXcvhcD5+/Ein058/f47/qdLp9KtXr969e5cYm8LhcLKzszMyMvLy8mpqajrZRpKXlxcVFXXr1i1oGOgkKSkpIyOjFStWvH//ns1m0+n06OhootY+YsQIPPJ32bJleADyTz/99MVSAUSut/TBdx4R8HR1dT09PRFCxALLOLbhmIobz4nYjAP8pEmT8FsqlaqtrZ2Xl4cQWrBgAZVKVVFRaWlp2bp1K95gJiIiYsKECVQqdcCAAdz1fhkZmQEDBigqKjY2Ng4aNGjp0qU5OTn4nwHRfqCioiIhITF16tTo6Gj8U4tj/4gRI5ydnY8ePXr37t25c+fKycnhgXU4ME+dOjUsLOy7777T09N78OCBBBd5eXkymUz0QWhra48aNYo4rZSUlLKy8urVqy9cuJCcnCwpKTlq1CjipxYhJC8v/8MPP1w6cwb92/IPumrfvn08NVoXFxdVVVXulB4O8NnZ2WPGjMGBUEVFxdHRMT09HdeDm5qaVFRUHjx44OPjc+7cOWlpaUVFxaSkJIRQXl7e4MGD2zzhkydP8BoSJBJp+/bthw4dUldXnzx58rFjx/bs2RMfH8/dYlRfX19aWtrU1EQmkxMSEgYPHvzixYuHDx9aW1s3NTU9ffpUXV394MGDw4YNKywsRAg9ePAgNjZWUVExIyMDb6MyatSoIUOG1NTUMBiMkpKSezdvZi9eLCEhcerUKWlp6R9++GHIkCFNTU10Ol1VVVVGRqahoYFGozU3N8vLy+NerQEDBmhpaRFNepWVlXV1dXiMeNIAACAASURBVHV1dZWVlbKyssbGxkpKSqqqqngTij6Hfz/4Dga0PnjwgJiYg/HvxtkZ2traGRkZOjo6FhYWlZWVeNYSh8OZPXs2nrubl5d34cKF77//Pjc3l7urFPROfTLADx069Pvvvye2WSPIysrKy8szGIyioiIcNXlq8ESoHjt2LJ5WbmBg4OXldf/+fTKZTKFQvvvuO/zbgVsCcCM592yT1atXjxgxYv/+/cbGxk+ePFm0aNG3336Lf+iJGjzuxSdWeNbV1cXbsc+aNcvCwuLEiRO4rV5OTm7w4MFFRUV2dnYhISG6urpr164dOnSonp7evHnz8CBYTE1NbefOnTY2NkTm8cy0ESNGTJkyJSMjAz/H4P8qKysT/7BJJBKFQjl69CgxwhYC/NfR0dHR0dHhTpGVleX5we3JAB8YGJiVlXXkyJHg4GCcwj3es7m5ecCAAYMHD9bU1FRRUdmzZ8/58+dLSkq0tbXT0tLy8vKcnJx4Ttja2nr79u1vv/02Pz/fzc3N3d392LFjx44d+/bbb//55x82mz1jxozS0lL87wgh9Ouvv/r7++PX+F+Zjo7OmzdvysrKNm7cGBwcfO7cuRMnTqxdu3bChAlKSko3btxITU3V0tIiNkmTkpIqKiratm1bSkpKcnKypqZmNkJSUlL4QaS1tRVP9JKXl+dukWpsbKTT6XgMIJ1OLygoIBqK8QJECgoKysrKREteH9XV/eAzMjLwgpsEYqWvLhk2bBidTsc7dKiqqi5cuDAzMzMlJcXa2trf359Opzc2NpLJ5LS0tKamptu3b1tYWHRyKfsHDx4EBQXxtPMDYeuTAV5RUdHX15e/8U1RUVFWVnbatGl//fUXDvBEDR6/xdPZEUIrV648d+6crKzsjRs3hg4diqMpjvH4AFwdx+ncAV5SUlJXV9fQ0HDkyJHPnj0jkUhENMUB3sLCwtzcPDY2lviJUVFRMTIyIk5rZmYmJyeH55o7ODikpKRQqdThw4cjhPDidPgeqVTqDz/8MHDgQElJye3bt9va2vLMXUEITZw48eHDh2/fvn379i3Orb6+fkJCwvv37yMjI9G/6+erq6vb2tqGh4Sg168hwGN460z8OiUlJTMzc8KECQYGBt05Z48F+MrKyuXLly9fvtzFxWXGjBk4UVlZuaSkBL8mtkjeuHHjunXrlJSU/vrrr23bttXU1GzdupVOp1tbW2tqara0tOCynZeX980337DZ7A8fPsTFxenq6g4aNGjWrFnx8fG//PILmUweMWLEP//88/LlS2tra0lJSQ6H8/TpU2tr64KCAjqd7uTkdO7cuc2bN2/atGncuHElJSWmpqZLliwxMTGZOnXqsmXL1NTU8AhTngUeEEKHDx/OzMwMDg7GxZv4nyIhIYH/UfCQlZXlHoXDvwa7eOjqfvDbtm3jSUlNTeXvqvsiOTm5UaNG1dbWZmVltbS0JCYmZmdnHzx4cOrUqZKSkl5eXv/884+2tjZupb927dq8efPwAOQvKiwsfPLkSVfzA7qp7/XBHz582MvLS0FBATdTcxs5ciSFQpGUlBw4cCCOr/j3QkJCAldZiEqYnJwcrprjqjAO5BQKhT/AS0pK8oyeMzY2vnTpEpVKxQcTH8HPEO7u7n5+fq6urqNHj8Yf5x76JyEh8ezZMykpqffv31OpVA0NjSVLlujq6vLciKysLJVKPXnypKys7KxZsxwdHfmjOyYlJTVx4kS8+BQeHqikpGRmZoYH7hH3NXToUOEtTNYXEdUOPz8/Ozu7yMhIGxubP//8szvnpFKpbe6BJHB4UrKGhoaioqKJiQlOHD58eHBw8MqVK/39/RctWoT/dxPV30OHDu3evXvbtm34xrOysg4ePKioqHj//v2cnBxfX18mk6mnp0ehUKZNm4YL5JYtW/z8/D58+FBQUBATE7Nt27a5c+fi54mJEyeWlZX9/fff+fn5eP50YmLi+vXrZ82aVVJS4ujo+ObNGxUVFUtLy6FDh+JeJyUlJRcXF+IRlpuxsbG3tzeeMgMPoJgw9oPvpJcvX2ZmZqqpqbm7u8vKypqamt66dWvw4MGWlpb+/v51dXWZmZkXL16cPXs2hULx9PTkX0WxqKgId56mpKQQVfaamprS0tKe+QcCCEKsweNmNDk5ueLi4ri4OENDww4etwsKCnj6OPHuKfxHdvDAOGPGjICAACkpKQ8PDzyeDneH29nZaWtrc8dve3t7HR0dPLsd/RvIuQ/ATZH8NXgChULhbhg3NzfH/yDxg8X69es5HM6VK1fWrVvHHeAJ2traKioqJiYmDg4O/H8dNGgQ0Ztw+fLl9u6Xh6KiIrFYUHFxsYqKCs4kvil8F/yL8fVzx44de/PmjYaGRkVFxeTJk5cvX/7Vp5KTk2tsbPzw4cObN2/428C/yNvbe/369XhxoQ8fPhCFkweDwdi3bx/6d/IFYfz48bKysgEBAaWlpR8/fuR5WPn2228RQnp6ekFBQbt27XJzcystLaVSqbGxsbt27crNzf3uu+/8/Py4PzJ9+nSE0IcPH2bMmKGtrb18+fLDhw+/ePEiMjIyNTV12bJluGcKlys83nPt2rUUCsXLywvHaSqVSky3416HEXxRV/eDFzgjIyOen2tpaWnc0zdhwoTExMTjx4+rqKhcu3YtMjISb7dN8PHxiY2N/d///vfkyZOoqCi86kZpaSmHwxk3blxYWBj3gg1AqIT1cx8YGLhhwwYqlert7e3n52dhYbFt2zYPD4/2yuju3bt59n3Jy8trMzR2YNSoUQsXLkxPT8c1YITQtm3bMjIyTExMJk2a9Ntvv+FQh0fJGRgYEH2o/E30kpKSlpaWEhISUlJSbe7TRTwNkMlkEon09OnToqKiESNGECvGkEgkZ2fnDRs2tNcd2MG+DqtWrfqKlbo3bdr0yy+/4NdSUlIzZsx4/vw5rsGjf9skuvqVir1BgwYRT2bdXB1dTk6uoaHhf//7X2JiYlcDPIPB2L9/P950QF1d/cqVKyUlJTo6OrNnz+Z5Jps/f35UVJSLiwteooQwdOjQ+Pj4uXPnPnv2TElJqb01EiZMmLBjx46lS5fOnj3b1dXV2dm5vr5+6tSpeHgdPx8fH/wC17Nv3bo1e/ZsQ0PDgIAA/oMdHBzafGAFXdXV/eAFLiYmhidFWloa/0jOmTPnw4cP33//Pd5yk5i7gTU0NOCHy9OnT3NX2/Cw33fv3qWnp/fdAN/nZgYKK8B7e3tnZ2dLS0tra2tHRkZOnjy5srLSzMysvQB/9epVnhRzc3OeUcpfhNdPbWho4P5NxFVYEom0d+9ehBCFQsGby5HJZO4Az1ODRwjhOUJOTk5trupF1OARQkFBQVJSUsOHD+ffjYZMJi9ZsqRLd4EQ+rrmdCKWY1evXjU3Nye+jS8uTN2vSEhImJiY6Ojo1NXVXb16dfny5W5ublZWVu0d39jYyLP4MbG5H2HIkCGJiYnv37/vYCpam6qqqk6cOMFkMouLix89emRkZIRnt7NYrOjoaKKXHSFUWlr66tWr7du379q1i/9ZTVlZ+fnz505OTnieW3umT5/u5+enp6dnamo6c+bMT58+7d69+4uZJJFIXl5eixcvHjlyZJu940CwurQf/D///MMzpr2hoYF/JdDukJeXHzlyZGFhoYGBgaampqys7Pr16wcPHhwWFtbU1GRra3vixImxY8fm5uZqamq2trZu3LhRR0eHTCanpKSsWbMmNzdXR0enoKAgIyNj3rx5f/31l66urqAW0Pztt9/Gjx/fXqOXADU1NX369KkPzcsQVoBns9kqKipUKlVOTg5XYeXl5Xumg41ncgjumSbe7tq1Cw9iV1VVJWoh/DV4Qnud3xQKhWhF76C6xv0cIGw8AV5JSSknJ8fExAT/HNvZ2b34bxtsf1ZcXIxXT8vPz1dXV+dwOMOHD9+8eXN7xy9cuJDYFBij0+k85dnAwGDChAmxsbEIIWL8Go+amhpnZ+dTp04R4z0RQnv37j116tTGjRsXLFhgb2//4MEDhBCLxZKSkpo3bx6dTqdQKDU1NVVVVUFBQYaGhocOHerg1vDYt45vn1hU8eTJk9LS0jyt/R0wMjLKz89vb1tFICoHDx5MTU3lTsnPz/+KaXIdOHToUG1t7fz5883MzHA/qbq6uoWFxZ9//pmRkfH06dPp06cnJyfX1tZqa2sPGzbsypUrBQUFsrKyZ86cSUhI+O6776SlpUtKSgICAvT09A4dOmRiYjJ58mQ2m52QkEBMYO5Adnb2vXv3+IcTIoT++OOPmTNnVldXz549G887+KLCwsKTJ08+f/58woQJmzZt4v732AESieTh4XH+/HnBPjwJj7Biz+LFi83NzSUlJWfOnOni4uLs7BwREWFrayuky3WA6ITGiCZHMpmMl5RBCE2ZMkVDQyM3N7fzbUcSEhI8o2DaxP0cIGyampr8ddDk5GTcxWBqatrGUvj9WEtLi4GBgaGhYVJSUlBQEN5cp72DcdDl1mYL07hx4zIzMxsaGuh0Ov9Cy3fv3t23b19ycrKhoWFiYuK9e/cqKiqmTJny+vVrSUnJgwcPysrKRkRE+Pj47Ny5MzY2dtq0aQsXLiwrK2tqahozZsw333yTnJzMsw0Sv1WrVhELOH7RV6x6pK2t3ZNtxf3TH3/80eZue9evX2/zeGLKIuErWkA7pqKioqKikpaWhhDC834RQqqqqhUVFUlJSQMHDqyqqtq2bZu8vLy6urq7u3twcLCNjU1iYmJUVJS7u/upU6d8fX2rq6tfvXrl7OxMpVILCwvpdHpubu6UKVN+/PHHnTt34mksra2tVCq1paWlqqqKu3zu2bPn+fPnePtsYlGQ+vr6Z8+eMRiMe/fuRURE0Gi0rVu3duZ2du3aFRgYqKure/bs2fPnz3/+/DklJeXEiRNWVlZ4hCBCqKGh4c2bN0pKSidPnjzk5YUQkpWVHTx4sJ6e3vTp0/FMVLyQSXt7m1EoFIV/4Sd+Go3GZrNZLBaLxWpoaJg3bx4xx0oYhBXgfX1958yZQ6FQJk+e/Pjx44iICAcHh5UrVwrpch0YNWoUzwxmfqqqqqqqqjxLyXbMzs6uM0+dPRngtbW1T548yZPY5gACcOHChePHj6enpx85csTf39/a2nrXrl2enp7u7u7dOe3AgQPxInf19fU8Ab68vHzFihV1dXVqamplZWXR0dF4b6GzZ8/q6uoWFhbi2V+TJk3CW7DgGc8qKio7duyYOXNmfX09bnj/4qwkCQkJmDHR1y1btuz169d1dXXr1q0TdV46oqWl1dLScuDAgSNHjly6dOnOnTssFsvX19fCwuLVq1ejR492cnK6efMmHmu8atUqOzs7MzMzJpNpY2NTWFhoZmb2/ffft7a23rlzZ+DAgceOHfvw4cP8+fM3b95cUVFx/Phxd3f3uXPnamtrl5eXR0ZG0un04OBgGRmZAwcOtLS0PH/+fPny5RwO5+DBgzQa7cSJE7t37zY2NsY1t8bGRgsLiwsXLpibm3M4nLCwsP379zOZzHnz5tnb24eFhc2dO/fEiRNnz569fv366dOnjx492tzcTCaTN27cePPmTV9fX19f35ycnN27d1+9erX806dpCJHJ5DNnzuzcuTM+Pr62trayspJKpTY2NsrKyvJsByohIYF38a6pqcnPz6+rq2ttbZWQkJCRkcFLlJJIJGLcohBxehDutuwkMzOzOXPmCC8zPUNHRyc2NlbUueBwOJymmpojCJ1SUhLeJQoLC7W0tIR3fgHS0dGh0WgcDkdLS6uqqorD4ZSXl+vp6XX+DG2Wz9DQ0AMHDpiYmMyfP3/u3LlNTU3En8LDwxUVFV++fImHm+BKNvecizaNGzcOIbRkyZIRI0aoqKh4eno2NjZ27Vb7CCifPNLS0nbu3PnVH++x309PT085ObmKigoOhzNx4kQ5ObnU1FTir0eOHEEIFRYWEimamppUKtXNze1///sfQohYV5FMJt++fRsPB1m6dOmkSZPwKJPhw4ebmJjgEcTa2tp4vsaIESMmTpxoZ2enrKxsaGhYW1vL4XAaGhrc3d1HjBjxxx9//Pzzz/gRWVpaetGiRXhOtba2NolEIpFIxsbGu3btIrK0YcMGaWnpcePG8ey/h9cv19LSkpeXl0GoL5bPngvweOG5zh8vHgF+2LBhL168EHUuOBz4Af2v8ePH45+kkSNH1tTUcDicqqqq7gd47IcffsA/EMbGxkwms7m5mcPhbN682dvbGx9w/vx5/Mv18uVLDw+P+vr69q6C+1ykpKTc3d3xecQVlE/B6rHfzw8fPhw6dAi/xv+UuN29e1dOTo7FYhEpmzZtOnbsWEJCQkNDg4+Pj4KCwuLFi8lksrq6uqmpqZGREV6GgUKhBAcH29rajhw5Uk5OTkZGJiYmpqqq6uTJkyQSCQ+rolAonz594r4ck8nkHnkgISExZ84chBCJRFJUVAwODs7Ozj59+vQ333xTVFREfIrFYuHzFBcXm5mZSUhIjB071sbG5tGjR7NmzaJSqc7OzpPGjOmL5bPnAjyLxcJTITtJPAK8np5eQkKCqHPB4cAP6H9FRkbq6+tv2bLF0dFxzJgxHh4exsbGJ0+e7PwZOiifycnJAwYMWL9+vYKCgpOTk5aWlpubm5qamr+/P3GMubl5enr6F69y/PhxeXl5vKpr5/PWF0H55MFisZKTkx88eHDv3r2kpCTuGMmvsbGx+r9MTU1nz57dY7ltT21t7cWLFzs+hk6nnzhx4syZM6qqqiQS6dOnT0OGDNm+fTtxwPbt21VVVfGq+EwmMy0t7fnz59HR0fr6+vxni4yMtLe3f/r06cGDBysqKphM5rNnz548eZKcnNzJR+TS0lKinSwmJubw4cMtLS11ZWV9sXwKqw/+0KFDK1as4B6GRiaT2xuRjhBiMBg8Uwz5pyH1RWQyucf64EHn2djYJCQkhIaGKikpGRoaqqqq/v333zzrdXw1ExOTEydOrFy5sq6u7s8//8RVAYQQ9yAPnjH57fn111/Xrl2bnp4OewD2K11di97R0ZFnFVs6nd7BoNEeo6io6Obm1vEx8vLyeAGP4ODg1NRUHR2d9PR07pkdeK96/ENKoVBGjx6N09PT0/nPZmNjg7ftIL4rvLFn53HHqWnTpk2bNg0hxO6b04yFFeD37t374MEDJycnd3f3zkS4XltAu4lnrxrQeygoKIwePXrw4MF4t66OB6bFxcXxbG5UXV2NFyfmJykpiceTbtq06caNG6dOnUpKSrp169bXDZeVlpbm3sYN9AddXYseD8zktm3bNsGOou8BTk5ONBoN8a3SqKOj8+OPP/IfDyOIv0hYAV5OTu7JkyenT5+eNGmSs7OznZ1dxz+gbRZQCoXy8eNH/BbvhCHAkM9ms5ubmwV4QiaTiecucydu27atrKyMZxXeTsKbQQnq+YBVX48z2eb0m6/DYDCGDRtGPMDx7+/Xa3W1hvTw4cOcnBzulObmZiqV+sXyefHixZKSEryzcJe++Z4pn90B5VN4BLIWfXV1dd/6/dTV1fXw8BBUAYDyiRAS1v5XeH4kQohOp1+4cCEkJKSystLW1vb06dOdPMPvv/9+9OhR4i2dTsdzEgSVQzabzeFwurk6qVBPyGKxENdmNt1EQWgck8kikVIEmkNFRUXuNR8MDQ3xTlO93KhRo+7evdv5GhI/KJ9QPoXn3Llzp0+f5l+LfvXq1Z08A5RPKJ8ICW2a3KBBg3hSSkpKLly48NUnvH//vmDH3AUGBi5dulSAJzx58uTGjRsFeEJvb28vLy8BnnDTpk1+fn4CPOHy5cuvXr0qwBP2GCMjo4aGBu6U5ubmiRMnfvUJoXx2H5RPbgUFBefPn/fy8vLy8vrjjz8KCgq6czYon93XF8unsJro+Vf9xMsbCelyAHSJyHfrAqBj/GvRs1gsAVZwQX8grAC/c+fO5ubmhISEsrIyGRkZbW1tYugjACIn8t26AOiSsrIyvGmCqDMC+hJhBfjo6GhXV1cDA4P4+PixY8eSyeTq6urg4GChrrsLQOdBDQn0Iaqqqn1okCDoJYQV4Dds2PD48WN9ff2CggInJ6e4uLi3b9+uWrUK7woMQG8DNSTQq7DZ7LS0tNLSUjyNc+zYsR2sIwJAm4QV4Jubm/EEJHV19ZKSEoSQiYkJfvF1eDaF674+cUI2my3AEwp879qe3AxX2LpZQ+oTxQnKZ1/R1WmcX9QnihOUT4ET1jS5LVu2pKWl2dvbP3z4cODAgQEBATNnzjQ0NDxz5szXnZDJZFZWVqqrqwsqh83NzXV1dQJcC6KxsbG5uVmA+wTX1dUhhIiNEbuvpqZGUlJSgLtEV1RUKCgo9NHlJvhrSHiB668D5bP7oHwSuj+NkweUz+7ri+VTWAGew+EEBQUlJiYaGhq6urpSqdTExEQzMzNhXAuArhJ4DQkAATI2Nn7z5g335mYtLS1WVlYJCQkizBXoc4QV4AHozQReQwJAgLq/0A0ASHh98AD0ZgJZChQAIYFpnEAgIMCD/ggWugG9HP80TgC6CproQT9VWFhI1JA0NDRmz54NNSQAgDiBAA8AAACIoa+fFwQAAACAXqtvBPhz584ZGxuPHj06JibmKz6+b98+Y2NjbW3tgwcPtnfCbl6im6KioiZOnDh06NALFy70thw2NDT88ccfxNvOZEy0X2bPg/IpwhxC+fwiKJ8izKGIy6dQ96oTiIKCAkNDw4aGhtzcXAMDAxaL1aWPP3r0yNTUlMFgVFRU6OjoJCUl8Z+wm5foppqaGn19/YqKChqNZmBgQKPRek8O3759u3LlSicnJ/y2MxkT7ZfZ86B8QvnszaB89ufy2Qdq8BEREfb29rKysnp6eurq6mlpaV36eFVV1erVq6WlpQcNGmRlZYWHVvGcsJuX6KbQ0NB58+YNGjRIQUEhPT19wIABvSeH169fr62tJd52JmOi/TJ7HpRPKJ+9GZTP/lw++0CALykp0dLSwq+1tLS6umD44sWL8WyT9PT0ly9fTps2jf+E3bxENxUWFubn548aNUpHR+fo0aMkEqn35PDAgQPr1q0j3nYmY6L9MnselE8on70ZlM/+XD77wDx4NptNIpGIt0wms6tn4HA4J06c+P3332/fvq2oqMh/wu5fojsYDEZubu6zZ884HM6ECRNmzZrV23JI6EzGeklWewyUT5HnkADlkx+UT5HnkNDz5bMPBPghQ4YUFBTg18XFxUOGDOnSx1ks1qJFi5SUlN68eYM3HuA/YTcv0U2qqqo2NjZ4lwVra+vs7OzelkNCZzLWS7LaY6B8ijyHBCif/KB8ijyHBBGUz+6NIegJnz59GjlyZHNzc2FhoZ6eXlcHHQQFBS1ZsqTjE3bzEt2UlZVlYmJSVVVVXl6uo6OTmZnZq3L46NEjYpBIZzIm2i+z50H5hPLZm0H57M/lsw/U4HV0dH7++WcrKyuEkL+/f1f39IyLi4uIiCAWHr948aKdnR3PCbt5iW4yNDRctWqVubk5h8P57bffjIyMEEK9KocE/mx0JkUkWe0xUD5FnkMClE9+UD5FnkNCz5dPWMkOAAAAEENi/vQKAAAA9E8Q4AEAAAAxBAEeAAAAEEMQ4AEAAAAxBAEeAAAAEEMQ4AEAAAAxBAEeAAAAEEMQ4AEAAAAxBAEeAAAAEEMQ4AEAAAAxBAEeAAAAEEMQ4AEAAAAxBAEeAAAAEEMQ4AEAAAAxBAEeAAAAEEMQ4AEAAAAxBAEeAAAAEEMQ4LtrwIABDQ0NdXV1Fy9e7OpniU+FhoZu2LBBCLkD/R2UT9CbQfkUKgjwgkGn0//888+Oj2ltbW3vUzY2Nvv27RNW5kC/B+UT9GZQPoUEArxgbNu2LSMjY//+/Qghb29vPT29sWPHXrlyBSEUFRW1Zs2amTNnhoWFubm56evrDx069MiRI9yfevz48e7duzkcztatW42NjU1MTIKCghBCjx8/dnR0nDx5srGx8W+//SbSWwR9GJRP0JtB+RQWDugeeXn5+vr6z58/W1lZcTic8PDw7777rqGhobq6+ptvvklJSYmMjBwyZEh5eXl2draLiwubza6trdXQ0OBwOMSn7t+/v379+tu3b0+bNo3JZJaXl2toaJSUlDx69EheXr66urqlpWXo0KHl5eUivlvQ10D5BL0ZlE+hoor6AUPcxMXFlZaWLlmyBCHEYDBSUlKGDBlibW2tqqqqqqr622+/Xb58OSUlpaamps3POjo6UigUVVVVc3PzxMREWVnZ6dOnKysrI4T09fXr6upUVVV7+paAGIHyCXozKJ+CBU30AiYjI7Nly5awsLCwsLDMzMylS5cihAYMGIAQevLkycKFC0kk0po1a9TU1Pg/y+FwSCQSfk2hUNhsNkJo4MCBPZh9IOagfILeDMqnYEGAFxgWi4UQmj59ekBAQEtLC41GGzlyZGlpKXHAmzdvlixZsnLlSjqdXl5ejssf/hRmZWV1584dNptdVVX14sWLiRMn9vxdAHEF5RP0ZlA+hQECvGAMGjSovr5+z5491tbWM2fOHD16tKmp6a5du7S1tYljnJ2dY2JizM3NL1++PH36dE9PT+JT+ABHR8cxY8aMHj162rRpx44dGzJkiIjuBogbKJ+gN4PyKSQkDocj6jwAAAAAQMCgBg8AAACIIQjwAAAAgBiCAA8AAACIIQjwAAAAgBiCAA8AAACIIQjwAAAAgBiCAA8AAACIIQjwAAAAgBiCAA8AAACIIQjwAAAAgBiCAA8AAACIIQjwAAAAgBiCAA8AAACIIQjwAAAAgBiCAA8AAACIIQjwAAAAgBiCAA8AAACIIQjwAAAAgBiCAA8AAACIIQjwAAAAgBiCAA8AAACIIQjwAAAAgBiCAA8AAACIIQjwAAAAgBiCAA8AAACIIQjwAAAAgBiCAA8AAACIIQjwAAAAgBiCAA8A/RScZgAAIABJREFUAACIIQjwAAAAgBiCAA8AAACIIQjwAAAAgBiCAA8AAACIIQjwAAAAgBiCAA8AAACIIQjwAAAAgBiCAA8AAACIIQjwAPRHTCbTzc1NWVlZS0vr+PHj3H9KS0sj/WvIkCFubm50Oh0hFBoaihNzcnK436alpenr65P+KzQ0FCGUkZFBIpE0NDTYbHYHmdm7d6+6urqRkdG9e/eEedNAbHVQni9dukQikcrKyng+Ul5efuDAgR7MowhAgAegPwoMDLx06dLevXsdHBy2bNny+vVrngPWrFkTHBy8bNmygIAALy8vIp1EIsXHxyOEEhMTSSQSTjx9+nRISIiDgwNC6OrVqyEhIRMnTkQI3bx5k0QilZaWxsXFtZeTqKgoLy+vffv2TZ8+fdGiRdXV1QK/WSD2vlieudHp9NDQ0IULF3IXbLEEAb6n+fn5DRo0aPbs2SNHjvzpp59EnR3QSx0/flxXV1dZWXnJkiWVlZVXr14lkUgODg5ycnKzZs3y8/NTU1OztLTMzMxsampavXr1wIEDx4wZc/nyZfxxnmJWV1c3d+5ceXn5QYMGbd++HSFUWFhoYWGxYcMGDw8PhFBmZiZPBiZMmLBo0SJfX99FixadP3+eqIIbGhq+evUKIZSQkGBkZIQTbW1tFyxYYGhoiBCyt7dfsGDBkCFDEEI3b95cuHChoqLirVu32rvT8PBwXV1dd3d3Dw+PlpaWmJgYwX6ToDcQeXlGCJ06dUpKSiouLi4pKWnHjh1ZWVk9+Q2IBgf0oISEBBKJ5O7uvm/fPoSQi4uLqHMEeqOIiAiE0Jo1ay5duqSkpOTs7HzlyhWE0IoVK3x8fBBCY8eO/fPPPwcMGLBhw4a9e/cqKyvHxsaePHmSTCY/f/6cv5gdO3ZMQUHh3r17W7ZsQQh9/PiRuNbvv/+OEMrMzCRSUlNTEUL+/v747eHDhxFCBQUFd+/eRQitXLlyzJgxbDZbSUlp5cqVCKHU1FR85I4dOxBCtbW1+G1SUhJC6O+//166dKmamhqLxWrzZhcvXjx+/HgOh0Oj0RBCv//+uzC+UiBCoi3P/v7+CKEbN25ISEgEBgYS6d7e3lQqtUe/iB5H7cFnCYCioqLIZPLx48fl5OROnz4t6uyAXioyMlJWVvbEiRNSUlJPnz6NiIiYOXMmQmjPnj0KCgpeXl4uLi7Lli07e/ZsYWFhZmYmjUaztbXlcDhsNjs2NpbD4fAUs82bN5uamt69exf/1NbV1eELXb58+ZdffvHx8SHq4vyIdnjMwsLi6tWrycnJjY2NpqamRB2L340bN2RkZGxtbUkk0rVr1+Li4qZOncp/GNE2QCaTEUIcDqerXxfo5XpDeXZzc9PU1Pzxxx979tZFDJroexSDwSCTyRQKhUQiSUpKijo7oJficDh4qBpCiEKhECFQSkoKJ0pLS6N/Q6+CgsLUqVMZDEZTUxOTyfztt9/4i9np06ft7OwMDQ1XrVpFXGXz5s3r1q27ePHinj17OshMcnKyjIyMpqYmfqurq6uqqnr27Nlx48bhbLR3C8HBwQwGQ05OztHRESEUHBzc5pGqqqrl5eUIocrKSoSQurp6p78n0Df0hvJsZWWVn58fEBAg/NvtRSDA96gpU6a0trbu2LHD09Pz8+fPos4O6KVmzZrV0NCwZcuWwMDAO3fuzJo1q4ODZ8yY8fr164cPH546dUpWVjY7O5u/mKWlpcnLyw8fPjwqKgohxOFw7t+/7+fnt3jxYkVFxdDQ0Pz8/IyMjJ07d+bl5eHTpqSk3L17d/fu3cHBwatXr8bVa4QQiUSysLAICgqysLDoIFfx8fGfPn3asWNHSEhISEjIt99++/fff7c5lt7GxqawsNDPz8/Hx0daWrrNWj7o03pDeQ4ICJg1a9bu3bsbGhp64JZ7C1H1DfRb27dvV1ZWnjZtmoGBAfTBg/b4+vrq6OgoKiouXLiwvLwc91l+/vwZV3P/+OMPDoczadIkBweH5ubmNWvWKCsra2pqXr58GX+cp5glJSWNHTtWU1Nz48aNCKFjx47hF4Q//vgjJCQEIRQbG4v74DENDQ1XV9e6ujoOh4P74B89enTkyBGE0I0bN3DvZpt98L/88oukpCSNRsN/unbtGkIoJiamzZvds2ePmprasGHDbt26JeTvFYiGCMszLqWlpaV4aL2Xlxc+Z3/ogydxoMerB7169SooKGjt2rWysrKjRo3y8PDouHUUgK8AxQyIEyjPXw0G2fUoExOTysrKyZMnk8lkOzu7zZs3izpHQAz12mL27t27rVu38iQGBQUpKyuLJD+gT+i15bn3gxo8AAAAIIZgkB0AAAAghiDAAwAAAGIIAjwAAAAghiDAAwAAAGIIAjwAAAAghiDAAwAAAGIIAjwAAAAghiDAAwAAAGIIAjwAAAAghiDAAwAAAGIIAjwAAAAghoQV4FksFvE6JSXlr7/+ysnJEdK1AAAAAMBDWAFeXV0dv/Dz87Ozs4uMjLSxsfnzzz+FdDkAAAAAcBPWbnKqqqoVFRUIIS0trcTERA0NjYqKismTJ79//14YlwMAAAAAN6H3wQ8aNEhDQwO/oFAowr4cAAAAAJDwAryEhISJicn3339fV1d39epVDofj5uZmZWUlpMsBAAAAgBtVSOctLi5mMBifPn3Kz89XV1fncDjDhw/fvHmzkC4HAAAAAG7C6oNHCDU3NyckJJSVlcnIyGhra48ePVpIFwIAAAAAD2HV4KOjo11dXQ0MDOLj48eOHUsmk6urq4ODg42MjDp5hhcvXoSFhQkpe/0QCSHU1EQikdhSUsK7io6Ozrp164R3/t4DyqdgQfkULCifgtVHy6ewavCGhoZhYWH6+voFBQVOTk4vX758+/btzz//HBcX18kzbNu27d27d1OmTBFG9vojBoPi44NkZFheXkK6Ao1GCwwMLCwsFNL5RWj69OmxsbE8if0nWvQEKJ8CBb+fAtZHyydHOHR1dRsaGjgcTnNzs66uLofDYTKZI0aM6PwZPDw8Dh8+LKTsCcm1a9eMjY1v3bol6oy0oamm5ghCp5SUhHeJwsJCLS0t4Z2/VzEzM5szZw7xtqqqSk1N7cSJEyLMUp8G5bM7bG1tlf+LSqVOnjyZOACXz5MnT4owk31aHy2fwmqid3R0nDdvnr29/cOHD6dOncrhcGxsbGxtbds7fuPGjVlZWdwpaWlpEydO9PDwEFIOheHp06eZmZnJyckLFy4UdV5Aj1JRUfH29n79+rWoMwL6o9u3bzc1NXGnzJgxQ1FRkXiroqLi6emZnp7e41kDoiSsAH/06NGgoKDExMQ5c+a4uroihA4cOGBmZtbe8W5ubuXl5dwp69evb21tFVL2hKS8vHzChAlVVVXdPM+KFSv27NkzYsQIgeQK9IwRI0aEhISIOhegP5KRkZGRkeFOoVKpJBKJO0VHRyc6Orpn8wVETFgBnkQiOTs7Ozs7EylmZmYsFqu9tW74x9grKipKSkoKKXvCUF5e/v79exsbm/z8fEdHx9LS0pcvX7Z38JMnT3JyctauXcuTXldXt2TJksjIyKVLl+IA39TUJC0tHRcXN3r0aO5Hch6lpaVMJlNLSwu//fXXX2NjY318fObNmyeImwNfpqOj00/6d0EPaGhoQAjJyckVFxfHxcUZGhqOGTOmOyfU0NAoKSkRUO5A39Bzu8mVlZVRqcJ6nugZERERxsbGTCazzb/6+/tnZWWZmZndvXv3zp07mZmZOD0qKorFYn3+/Jn74IsXLx4+fPjVq1fciT/99NNff/0VGRmJEKLRaAghBoOhpqb26dOnWbNmPX78uL2MFRUVaWtru7i4cDicuLg4Z2fnEydOpKambt26NSMjo5t3La7YbHZKSkpERERYWFhycjKbze7mCXGA5wht3inoPwIDAzU1NYcOHXrmzBlra+uwsLB58+adOXOmO+eEAN8P9VzEVVVVLS0t7bHLdUlRUZGamhqVSo2IiLC0tGyvohwWFpaVlRUUFLR8+XL+v+bk5JBIpBkzZpDJZBMTk6ysrJaWlrq6uu+//97Ozi4mJiYkJKSoqOj777+/ePFidHQ0jUaztLTMy8vT1dUlzp+fn29oaDh06NDc3NympqatW7fW1dV5eno2NTXl5+e3l/+MjAwZGZnHjx9nZ2fPnj27sbFRT09v/Pjx0dHRtra2J06cgDEBPGJiYtauXauhoaGpqYkQKi4uLiwsvHTpUndGHcvIyCgoKJSWluK1mQH4at7e3tnZ2dLS0tra2pGRkZMnT66srDQzM1u/fv1Xn1NdXb28vLyDZlQgfoRYg+epISGE1NTUhHe5r8NkMk+dOrVw4cJHjx4hhDw8PG7cuLF79+42Dy4oKFBQUNi+fTt/be/GjRs5OTm7d+9WVVU1MzP76aefDA0NIyMjt2zZwuFwwsLC6uvrN23a5Orq6uPj4+XlJSkpiXvIQkJCNmzYgBBqbW2trq6OjY2dP3/+8uXL79y589NPP507dw4hdO3aNRUVlefPnxcUFPBc94cffrh//76/v7+bm5u+vv6yZcvq6+vZbPaVK1du3Lhx5MiRoqKi69evDx48OCEhQeDfXt+1cePG8PDwmJiYa9f+H3v3GddU0j4Of5JAQgkQpLegdAFRROkWxLIrVkSxdxQRELtiV+wiYlcEF7HuKlJELCCWVdcugiiCtNA7oQZS/i/O88uTOyCiJBDC9X3hhxzmzJnE4VyZOVMuX758+dGjR48ePer8iM6+fftmZ2cLpISgN2Oz2X369JGXl5eVlVVWVkYIkclkvmfqv0pCQqJPnz7YHmCglxBWC14YLSRhSEtLW716tby8fGVlZUZGRlpa2smTJ2tqavbs2dM6cVFR0dSpU8PDw0+dOoVFZYQQi8V6+/btokWLWlpabt++jRBycnLS0dGxsbHx9vam0WiqqqpEIjE/P//r169kMjkiIgIhZGxsLCMjU15e7u/vz2Qyt23bJikpifXu2traqqqqvnnz5s2bN1QqVV9fPykpycPDIzg4+OnTpx8+fNDS0nJ1dd2wYYOmpmZ0dLSamtqDBw8+ffqUnp5+//597C5gYGCAEOrXrx9CKDIyEiG0c+fOPxFisVgIoSNHjsTExFCp1NGjR1taWkpKSubm5mITafT19fv161dbW0un0zMzM5lMZlNTU01NTV5eHh6PJxAIVCqVSqVqaGjIy8tTKJRO3nS6C4vF4mtnq6mptdO7vnLlSr6NEL9+/do6mZ6eXnZ2tr29vaDKCXqnGTNm2NjYEInEMWPGLFy4cM6cOfHx8e3MQuogTU3NgoIC7l7eQOwJK8BjLSTeceA0Gs3Nza0b5xHR6XQpKSm+gXvh4eFsNru6urqqqioxMdHAwCA1NVVRUbH16Ww2u6CgICoqavjw4WvXrkUIycjIuLm5PX/+3MXFBXuJxYx9+/YhhIhEYmho6OjRo/fu3auurm5sbNzU1GRpaYkt9aOkpBQeHv7w4cOlS5cihNLS0hobG0kk0vTp07Fhca6uriUlJZGRkTU1NTt27BgzZgyDwQgODh41apSbm1t0dLSFhcW+fftYLFZYWJiNjY2urq6Njc3du3f37du3dOlSFRUVhBD2L0Jo4MCBL1++xAL8oUOHwsPDg4ODc3NzY2JigoODGxoatLW11dXVi4uLMzMz8/LyyGSyvLy8oaGhrKyshISErKystra2jIxMbW3tq1evaDRaYWFhXV1dbW1taGhomw8sRJy3t7eVldXkyZO1tbVxOFxhYWFMTEw7/Z8rV64sLCzky6F1PdHT0/v+/btQSgx6k4MHD7q4uBAIBAcHh8TExPj4+ClTpixevLiT2Wpra+fn51tZWQmkkED0CSvA/2oLqQusXr2aTCYHBwe7urqqqKg4OztramoGBQWRSCQGg/H9+/eMjIzVq1fv37+/qKgIi6O865Slp6cTCARtbe3hw4dXV1cnJCSkp6dTKJRHjx5JSEjY29vzBQAzMzMcDhcZGSknJ4cQqqqqunfvHplMHjNmjKurq6amJpVKnT9/vqenp4KCQkZGxsqVK0eOHIm17xFCDg4OGhoaqqqqqqqqV69eRQg5Ozuz2ezjx48HBgZSKJTnz5+zWKz+/fvX1dVNnDgRIWRtbS0vL79u3TpJSUksEyzAh4aGjhkzxkRXF3E4DAbjxpUrsbGxenp6CCFsBmMv5OXlNXHixPj4eOx/jUql3r17V0dH50fpTU1NTU1NeY8oKCi0HjRqYGDw6NEjYRQY9Dbczk5nZ2dnZ2f0f91vbUpLS+O7/9TU1FAoFL5kWIAXdEmB6BJWgP/VFpKwsVise/fuycvLR0RE3L59W01N7f79+x4eHhwO5+HDh+fPnz958mRLS8uiRYs8PDzCwsKSkpIYDAZvgH/8+LGTkxMOh9PV1ZWXl6+pqamtrZ0zZw6Dwbh586a+vv7Bgwd5r9ivX7/jx49j0R0hJCUlNWXKlKqqqoMHD/r5+WFT/CUlJcPCwgoLC/39/VtaWni3021z572jR48mJCQoKiquW7du+vTpsrKyBgYGhw8fxiKTra3tyZMnudEdIaSkpGRsbDxjxgwymbxlyxYUEMBisQ4cOIBF915OS0tr6NCh2PRCLS0t7FlSJxkYGJw/f77z+QDAp6SkBNuWs83fnj59Oj09nfdIUVERBHggrAD/qy2kuLi4goIC3iOlpaWysrKCKk9paWlRUVFJSYmfnx9CqKSkBCF06tQpWVlZBweHwsLCy5cvz5s3b+TIka6urq6urubm5tiUkoKCghMnThw4cCAmJgZbqEdSUvL69etYtzwej0cIWVpa6unpXbt2jfeKeDy+9RcaRUXFDRs2IIS4TwrmzZuXk5OzadMmWVnZnz5jIxAI27dvf/jwoYaGBpPJXLt2ra+vr6amJvZbCoUyb9483vQSEhLcR8Vr1649ERBAIBCcnJx+/fMTN0IaI2JkZMT3qB4AgWh/FlLrGXQ2NjbY6Dxe2H1Y8IUDokqI0+R0dHSWLVvWwcRpaWmZmZm8RxoaGjqzkl1qaqqOjg424S02NnbdunXYaLI3b97IyspqaGjIyMh8+vTp7t27eDx+8uTJ06dPP336NJlMRgiZmpqGhISsWrUqLy9vxowZ3759c3FxuXfv3ogRI7DMLSwscDichYXF8ePH2Wx2JxvEysrKUlJS27Zta2elPy53d3d3d/fc3FyEkI6ODje6d5CcnJyUlNRvFlSMCGmMiKqqKofDKS8vb31vBeCXsNns5ORkbg/ToEGDOj8LiUqlYrcO0EuIysozrWcoffz4sZ2F235qwYIFS5Ys8fLyunDhQnJy8rdv31auXFlaWtrU1OTv73/8+HEPD4/NmzePGzcOISQlJfX3339zz8XhcEuWLNm9e7eFhQW24AzWsOMuBqmlpaWkpGRkZDRs2LDfLiEXmUz++vWrrq5ux0/R0dGxsrJSUlLq/NV7J+GNETExMfn69Svv0xYAfpWQepj69u3bznIaQPyISoAXuOLi4vDw8IkTJ3p4eGBd/SNGjLh//76+vv6UKVMsLCzKysr69euH9bG3hsPhtm7dunz58vHjxxcWFn78+BGHw/GOmo6JicEmoQnEL0V3hBAej4+NjW1ztD/oCOGNEenfv//nz58hwIPOEFIPk6amZnl5OYPBIAlzU3MgOsQ2wFdUVNTU1Dx9+hQhxGKxpkyZMnbs2JcvX2poaBCJRBMTExMTk7i4uHZymD17dmJiYkBAQGJi4ooVK/bu3Tt37lzub21tbYX+HtoFy6V1xq+OEQkNDc3IyOA9QqPR2nzYYW5u/vnzZ4EXGPQqQuphIhAIOjo6ubm5RkZGncwK9AhiGODZbPbWrVuJRKKNjc2uXbucnJwcHBykpKQUFBSWL1/O+9W19ShTXmQy+caNGwihkpKS7du3r1279kfNfdAT/dIoeikpKb7+EgKB0OYiPwMGDMDWOwLgtwmvh6lfv35ZWVkQ4HsJMQzw9+7d279/v7a29s6dOx0dHadMmbJjxw7sV8bGxr+RoaOjI/S4iplffcbJuy8iJjIyEhuSyWfgwIGfPn3icDg9dI0/IAp+tYep4/T19WEtpt5DDAM8tq+8nJycg4PD8uXLR48e3dN3sQMCJ7yVFpWVleXl5bOysngzB+BX/dIspI4zNDTke9gExJgYRr7q6mocDqeqqooQOnv2bHcXB4gioa60OGTIkLdv30KAB13m7NmzfMPjaTRamyPpDA0N29l4GogZcQjwTCaTwWBwV8V5+fLlmjVr9u/f372lAqJMqCstWltbv3r1yt3dXSC5AfBTFAql9RiRNscMGRsb8615B8SYqAT4hoYGBoPBe4TJZHakRdXU1BQREZGQkLB//35swZlHjx5t2bKFd8VWAPgI7xknQsjW1nbTpk0CyQqAjpg5cybfkR+NEdHT0yssLGxqaoIFr3oDIQb41isxtTMKfcaMGS9evOA9Ultb25FhSubm5nJych8/fnzy5AmVStXS0mpoaBDGsysgZuTl5T08PHA43Lt37758+VJXVyeonK2trVNTUxsaGmRkZASVJwACISEhoa+vn56ePnDgwO4uCxA6UdkP/s6dO3xHbGxsuLud/khwcDCNRmtubkYIlZSUlJSUvHnzxtzcHG6soH3nz58/evRoSkrK4cOHL1y4MGzYsC1btmzdutXDw6PN9KmpqXwrgdPp9B9Ns5SWlh40aNCLFy9Gjx4t+KID0DlmZmapqakQ4HuDnr0f/KFDh5qbm4lEopqaGo1GQwgNHTq09b4LAPDZu3dvSkqKpKTkmTNnkpOT+/TpU1ZWZm9v/6MAf/bsWb4nl4WFhe0spezs7JyQkAABHoigAQMGpKSkdHcpQFfo2fvBl5WV4fH4qVOnWltb37x5s6qqauHChR3ZsgX0cioqKljHD4VCwZ4cEQiEdtJ3cLcurnHjxq1YseLAgQOCKCwAgmRhYXH69OnuLgXoCj14P3hsFF55eTk2fLSgoACHw/Hu4A7Aj+zdu9fe3n7SpElGRkYjR44cO3ZsXFycj4+PoPK3trYuKirKzc391V0GABA2S0vL9+/fd3cpQFcQ1tqrXl5eDx480NPTKy8vLysrw0YpL1++XICXaGhokJaW5k4OmTdv3sKFCwWYPxBj48aNe/369YABAywtLV1cXDQ1NW/duuXr6yuo/AkEwqRJkyIjIwWVIQCCgjW6jh8/zm3Hp6amBgcHV1ZWYpt0BwQENDQ0dG8hgUAIcRR9nz595syZIysrW1hY+OzZs8rKSkFNQ8LQaDTewXSDBg0SYOZA7FEolAULFggv/xkzZvj7+69evVp4lwDg91hbW69atQqHw02bNi0xMTEiIuLevXuBgYFTp06dN2/etm3b8Hi8v7//T/OJi4szNjY2MDDogjKD3yCsAB8REeHj4yMhIbFz586goCA7O7sNGzasX79egL30T58+/b215QHoAqNGjSopKUlJSRkwYEB3lwWIuRkzZrx79473SGFhobm5+Y/Sb968uV+/ft++fbtw4cKpU6eKiopMTU2/fv36zz//NDQ0DB069NatWz8N8O7u7lFRUbq6ut++fWs/JZvNzs7Ofvr0KYvFotPpa9as6fhbA50hrAC/c+fOr1+/SklJ6ejo3Lt3z8HBoby83NraWiABfvny5X5+fqWlpT+adAdAtyMQCEuXLj116hSslwyE7dSpU7W1tbxHpk6d2s40Yzs7Ozs7u7CwsGXLlrm7u8+YMcPa2jo+Pv7+/fsXLlz4+++/PTw8ysvLfzSMdMWKFVOnTo2OjnZ1dU1MTLSzs4uNjeVNXFtbKykpia2lU19fb2ZmlpubixDC4/Ha2toKCgpLliwRzDsH7RLWM3g2m92nTx95eXlZWVnsP55MJgtqf61r165du3bt27dv0IIHoszT0/Off/7hm0APgMCpqKjo/S8pKamfbm89bty4vn37rlmzZvLkyRoaGosXLz58+LCPj8+0adOcnZ1v376dnp5eXFy8ceNG3pXyPnz4cO3atYCAABMTk2vXrm3evDkrK2vUqFHPnj1DCNXU1Jw+fVpbW3vq1KmhoaGOjo7u7u59+vS5d+9eSEiImZkZnU4PCAgQ7scBuDjCsWHDhkGDBllbW8+dO9fW1vbEiRPjx4/38vL6UfpFixZZ/S9ZWVkbG5s2E0tISNjY2AwcODApKUlI5Rc/TVVVhxE6TqEI7xI0Gk1bW1t4+XejESNGtP7bGTx48E9PXL16tbe3dxeUsKeD+snn7du3UVFR1dXV3COxsbEdP93a2trFxeW3r37r1i0pKSkymezs7IzV9oSEBA6Hk5KSoqSkhO2evGPHDixxeXm5hITE+PHjGxoazMzMEEKjRo3CzsKGRp07d46bc0xMjKqq6vfv33+7bN2ih9ZPYQV4Dofz5MmTf//9l8PhJCQkrF279vz589jEtjZ9//797f9yd3ffvn1765TcnihpaenGxkbhlV/M9NAKKrI6eAMtLy9XVVX9+PFjFxSpR4P6yWvfvn1UKnX27Nl9+/Z9//49dlBdXb3jOXQywLPZ7KFDhw4dOhQhtGrVKmxAlb+/v4yMzMSJEzkcTmFhIYvF4qaPiYnB4/FmZmYyMjK+vr40Gm337t1BQUF1dXUvX77ky3zBggWHDh1q87rNzc0NDQ3cl/X19bm5uf/9999vvxFB6aH1U4ij6LkPyJ2dnbGvgSwW60eJsX1ieFGp1NabJZSWlgYHB2M/m5iYwH4JQMQpKSkdPHhwwYIFL1++lJaW7u7igJ7h1KlTycnJSkpKKSkp06ZNe/funZycXFcWAIfD/fvvv2w2u7i4uG/fvggheXn5ffv2rVy5Eht8x7eO2cSJE318fF6/fv3s2bPBgwcjhLZt24b9ytbWli9zT0/PyZMnr1mzpvXqUgsWLHj+/HlOTg72PNfHxyc8PFxTU3PcuHHHjx+Hv6ATRR5bAAAgAElEQVRfJaxn8K2VlJRISHT2+8SVK1f27ds3depUbW1t7v6wAIiyhQsXmpubL168mM1md3dZQM8gJyeH3d8GDBiwYsUKPz+/ri8DkUiUkpLCojtCKCAgoK6u7sSJE3yhnevYsWMvXrzAonv7bG1ttbS07t+/z3c8JCTk6dOnUlJS9vb2mZmZbm5uV65cmTJlCp1Ov3DhwsWLFxFCLS0tnLZWRG1sbLx27dr48eMjIiJ+6W2Kt64L8CoqKp0fbYRt7mlvb9+/f38I8KCnuHDhQmlp6bx58/j2RAagTbNmzXJwcDh//jxCyM/Pr7KycubMmd2++IwAN/GaPn36hAkTbty4gb309vZ2cnLasGHD4cOHDx48SCQShw4dGhsbu3Hjxps3bz569Oj69et79+4NDAx0cHD4448/Kisrs7KyEhMT6+rqxo8fP3v2bEtLy9mzZ+fl5fn6+h4+fFhQ5ezpunS7WDU1tc5kyGQyP336ZGdnZ2lpaWho+PHjR0EVFQChkpKSunPnzuLFi+3s7C5cuNCRVg7ozbZv3+7g4FBRUYEQwuFwN2/e/Pvvv7mrdoqBxYsXl5eXr1+/3tLSMjY29syZMwoKCvPmzZs1axZCaMyYMdu2bfP19cX6DwYPHjx48ODHjx+vW7dOVVWVw+FYWVnp6Og8e/bM0tIyLy+voqLCyMjo5s2bEydOzMrKsra2vnz58suXL2VkZDgcTlRUlJWVlYaGxsOHD/fu3Xv9+nXBLrkmykRlu9ifqqur8/DwePDgwZ07d7An+pMnTxZkiUEv0/oL6E+nFXWGtLT0tWvXwsPDJ0yYYGdnt2zZMmdn584/tALiijt8HSFEIBBmzZo1Y8aMHyUuKyvjmwff1NQkyo+E1NTUAgMDX79+PXr0aBqNNnPmzIMHD6qrq2O/lZWVPXr0KN8pp06dmjhx4ujRoysrKz09PVNSUm7fvr1jx47nz58/f/58wYIF2BN9ExOTY8eOHT9+3MnJafTo0U1NTceOHWOz2QoKClJSUkwmU09P79y5c3Pnzs3Nzb169aqvr6+ioiKDwSCRSLt373706JGUlNTRo0dNTU0RQlu3btXT06PRaHOnTcOK0dzcfPv27aioqKqqKlNTU21tbTKZ3NTURCQS8Xg8kUiUlZXF9jjlLTwej1dQUKBQKJKSkvLy8hQKpWu+rvWY7WKJRCI25q71yDsAfpXAv4B20IIFC6ZPn37p0qWdO3fOnDnTwcHBysqqf//+urq62traSkpKAuwFBeKkpKQEG0Xf5m+9vLz49o8pKSlRUlLqkqL9vqtXrx4+fHjDhg3a2to/TYzH48ePH48QUldXj4qKwg5OmTIFIcS3IMrixYtHjhw5a9as4ODgfv36HTt2bNiwYYmJiS0tLStXrjx9+rSXl5efn19TU5OKisqbN2+GDRsWEBAgJydHp9MZDIapqam5ubmlpWVLS0tGRgaLxSISieFnz3ohVF9fr6ura2JismjRIhUVlZSUlPz8/Pr6ehKJxGazmUxmU1NTS0sLHo/nexiHLeFXVVXV0tJCp9Orq6urq6sRQmFhYYsWLRLU59laj9kulkgkHj16NCkpSV5evtOlA73dr34B9fX1/fLlC++R9PT032vxy8jIeHp6enp6lpaWPn/+/MOHD7dv387NzS0oKCgvL2ez2bKyshQKBYfDycnJcZv4MjIyJBLpNy7XU0gymaMQqqurGzNmjACz9fX1nThxogAz7C7tj2H6559/+I5s2LChnZXsRISOjs7x48eFkbOent6rV6/q6+slJCSwPxzuZiUbN250cXFJTU1lMplubm4uLi7nzp1bs2ZNcXHxnDlzlJSU+vfvHxMT8+DBA0lJyX379llaWmZkZAQfOICKiyUlJRMSErC5/gihP//8UxiFFyCR3i4WG0mB/cxmszMzM3fu3FldXf3kyZPOl5DNZjMYDAHOu2AymSwWS4B3YWzDcr6unt/GqqtDCDGZTIF8epjGxsZ+/fpJSkpiL3vQkm2/+gV0yZIlZWVlvEdOnDihqanJVz9/tTr16dOHO4kU09zczGAwamtrORxOdXU1N8AzGAysPvw2NpvNYrG4/1mdh816bT3T6Tc1NnIeP5aUlPzjjz8EkyFCDAZDWVmZ+3/Ug+onEsQYptb3TwHe7nr6/RP783/16tX27du5BzkcTnl5+bNnzxQVFd3d3bGDmZmZOBzOw8Mj7f59PB5fXl4uqFtoF9w/cZ1pVbePRqPFx8dj4941NDTGjx//S0Mbzpw5c+TIEe7L2tpabL0kQRWPzWZzOByB3Z6EkKFgb6AEhCyZTBYO90GgJVRQUOB9mGRiYhIXFyeo/IXn9OnTJ06caP0FtOM7GkP9hPopPJ1/hAT1E+onQkJbqlbg7ty505mFmVqLiIiYO3euADMMDg729fUVYIY7d+7kLgYpEH5+fkFBQQLMcP78+eHh4QLMsCvl5eWdO3dux44dO3bsOHv2bF5eXmdyg/rZeVA/uczNzTMzM3mP5OXlWVtb/3aGUD87ryfWTxjEC3opHR2dZcuWdXcpAGiDwMcwgd4JAjwAAIgWgYxhAgACPAAAiBYvL6+JEydyxzBRqdS7d+/2nuVZgKBAgAcAAJEDj5BA5/WYAC8hISHAAZY9JUPBrkVFIBAEu3SawDPsuXpEdYL62Wv1iOoE9VPghDhNTrCYTGZ5eTl3LcPOYzAYdDpdgGtBNDQ0MBgMAS5ASKfTEUICXNinqqoKW0lRUBmWlZXJy8uL9wIsHQT1s/OgfgoP1M/O64n1s8cEeAAAAAB0XNdtFwsAAACALgMBHgAAABBDEOABAAAAMQQBHgAAABBDEOABAAAAMQQBHgAAABBDPSPAnz592tTU1MLCIikp6TdO37Nnj6mpqY6Ozv79+3+UYScv0Un3798fOnSorq7u+fPnRa2E9fX1Z8+e5b7sSMG698PselA/u7GEUD9/CupnN5awm+unUPeqE4i8vDwTE5P6+vqMjAxjY2MWi/VLpyckJAwePLixsbGsrIxKpb579651hp28RCdVVVUZGhqWlZXV1NQYGxvX1NSITgk/ffq0ePHimTNnYi87UrDu/TC7HtRPqJ+iDOpnb66fPaAFHx8fP3nyZBkZGQMDA3V19eTk5F86vaKiYvny5VJSUsrKyo6OjjQarXWGnbxEJ0VHR0+aNElZWVleXj4lJUVOTk50Snjt2rXq6mruy44UrHs/zK4H9RPqpyiD+tmb62cPCPBFRUXa2trYz9ra2sXFxb90+owZM7A9G1JSUl68eDFy5MjWGXbyEp1Eo9FycnIGDBhApVKPHDmCw+FEp4T79u3z8vLivuxIwbr3w+x6UD+hfooyqJ+9uX72gJ0Y2Gw2DofjvmQymb+aA4fDOXbs2JkzZyIjIxUUFFpn2PlLdEZjY2NGRsbTp085HM6QIUPGjh0raiXk6kjBRKSoXQbqZ7eXkAvqZ2tQP7u9hFxdXz97QIDX1NTMy8vDfi4sLNTU1Pyl01ks1vTp0ykUytu3b7GNB1pn2MlLdJKKisq4ceOwXRaGDRv29etXUSshV0cKJiJF7TJQP7u9hFxQP1uD+tntJeTqhvrZuTEEXSE3N9fc3JzBYNBoNAMDg18ddHD16lV3d/f2M+zkJTrpy5cvZmZmFRUVpaWlVCo1LS1NpEqYkJDAHSTSkYJ174fZ9aB+Qv0UZVA/e3P97AEteCqVunLlSkdHR4TQhQsX8PhfGzfw7Nmz+Ph4DQ0N7GVISMiECRP4MuzkJTrJxMRk2bJlNjY2HA7H39+/f//+CCGRKiFX62J05Ei3FLXLQP3s9hJyQf1sDepnt5eQq+vrJ2wXCwAAAIghMf/2CgAAAPROEOABAAAAMQQBHgAAABBDEOABAAAAMQQBHgAAABBDEOABAAAAMQQBHgAAABBDEOABAAAAMQQBHgAAABBDEOABAAAAMQQBHgAAABBDEOABAAAAMQQBHgAAABBDEOABAAAAMQQBHgAAABBDEOABAAAAMQQBHgAAABBDEOA7S05Orr6+nk6nh4SE/Oq53LOio6N9fHyEUDrQ20H9BKIM6qdQQYAXjNra2kuXLrWfpqWl5UdnjRs3bs+ePcIqHOj1oH4CUQb1U0ggwAvGhg0bUlNT9+7dixDauXOngYHBoEGD/vrrL4TQ/fv3PT09x4wZExsbu3TpUkNDQ11d3cOHD/OelZiYuG3bNg6Hs27dOlNTUzMzs6tXryKEEhMTXV1dHRwcTE1N/f39u/Utgh4M6icQZVA/hYUDOodMJtfV1eXn5zs6OnI4nLi4OGdn5/r6+srKSiMjow8fPty7d09TU7O0tPTr168LFy5ks9nV1dUaGhocDod71p07d7y9vSMjI0eOHMlkMktLSzU0NIqKihISEshkcmVlZXNzs66ubmlpaTe/W9DTQP0Eogzqp1BJdPcXDHHz7Nmz4uJid3d3hFBjY+OHDx80NTWHDRumoqKioqLi7+8fFhb24cOHqqqqNs91dXUlEAgqKio2NjZv3ryRkZFxcnJSVFRECBkaGtLpdBUVla5+S0CMQP0Eogzqp2BBF72ASUtLr127NjY2NjY2Ni0tbe7cuQghOTk5hNCjR4/c3NxwOJynp6eamlrrczkcDg6Hw34mEAhsNhshpKSk1IXFB2IO6icQZVA/BQsCvMCwWCyEkJOT08WLF5ubm2tqaszNzYuLi7kJ3r596+7uvnjx4tra2tLSUqz+YWdhHB0db9++zWazKyoqnj9/PnTo0K5/F0BcQf0EogzqpzBAgBcMZWXlurq67du3Dxs2bMyYMRYWFoMHD96yZYuOjg43zZw5c5KSkmxsbMLCwpycnLZu3co9C0vg6uo6cOBACwuLkSNHBgYGampqdtO7AeIG6icQZVA/hQTH4XC6uwwAAAAAEDBowQMAAABiCAI8AAAAIIYgwAMAAABiCAI8AAAAIIYgwAMAAABiCAI8AAAAIIYgwAMAAABiCAI8AAAAIIYgwAMAAABiCAI8AAAAIIYgwAMAAABiCAI8AAAAIIYgwAMAAABiCAI8AAAAIIYgwAMAAABiCAI8AAAAIIYgwAMAAABiCAI8AAAAIIYgwAMAAABiCAI8AAAAIIYgwAMAAABiCAI8AAAAIIYgwAMAAABiCAI8AAAAIIYgwAMAAABiCAI8AAAAIIYgwAMAAABiCAI8AAAAIIYgwAMAAABiCAI8AAAAIIYgwAMAAABiCAI8AAAAIIYgwAMAAABiCAI8AAAAIIYgwAMAAABiCAI8AAAAIIYgwAMAAABiCAK8qGAymUuXLlVUVNTW1j569Cjvr0JDQ3E4XElJSUcSAyBUUPeA6CgtLd23b193l0J0QYAXFREREaGhobt3754yZcratWtfvXolqMQACBDUPSAKamtro6Oj3dzcduzY0d1lEV0Q4Dvq6NGjffv2VVRUdHd3Ly8vDw8Px+FwU6ZMkZWVHTt2bFBQkJqamr29fVpaWlNT0/Lly5WUlAYOHBgWFoadHhQUpKysPH78eHNz80WLFtHp9IkTJ5LJZGVl5Y0bNyKEaDSanZ2dj4/P+vXrEUJpaWmty3D8+HESifTs2bOOJAa9U/dWVL7Tu/7tA3HSuvpxvXv3btOmTV++fOmusvUMHNAB8fHxCCFPT8/Q0FAKhTJnzpy//voLIbRgwYJdu3YhhAYNGnTp0iU5OTkfH5/du3crKio+fvw4ODgYj8f/+++/r1+/xuFwHh4ee/bsQQgtXLgwMDBQXl4+JiZm7dq1CKGsrCzutc6cOYMQSktL4x65cOECQuj69euSkpIRERG8BWudGPRm3VtRW5/eHZ8BEB/tVD/Mzp07JSQkuqVsPQIE+A5ZtWqVjIxMU1MTh8OZP3++iooKdt/8/v17WVkZQujYsWMcDsfGxmbKlCnOzs54PF5KSopEIiGEAgIC9uzZQyAQamtr2Wy2qqoqduNLSkpatWqVkZERQujjx4/YhUJDQ4lE4q5du3ivjgV4Mpnct29fFovFPd5mYtCbdW9FbfN0ADqjzerHBQG+fdBF3yEcDgeHw+FwOIQQgUBgs9nYcRKJhB2UkpJCCGE/y8vLjxgxorGxsampiclk+vv7NzY24vF4AoGAw+GIRCJC6MSJExMmTDAxMVm2bBn3KmvWrPHy8goJCdm+fXvrMjg6Oubk5Fy8eLEjiUHv1L0VtfXpAHRGm9UPdBwE+A4ZO3ZsfX392rVrIyIibt++PXbs2HYSjx49+tWrVw8ePDh+/LiMjMzXr1+HDx/e0tKyadOmrVu35ufnI4SSk5PJZLKent79+/cRQhwO586dO0FBQTNmzFBQUIiOjs7JyUlNTd28eXN2djaW7cWLF8eOHbtt27b6+vrWiYX/GYAeoHsrauvTAeiM1tWP764IfqJb+w96koMHD1KpVAUFBTc3t9LSUqznMz8/v7y8HCF09uxZDodja2s7ZcoUBoPh6empqKiopaUVFhaGnb5x40ZFRcWRI0caGxsvXLjw3bt3gwYN0tLS8vX1RQgFBgZiP3CdPXv25s2bCKHHjx9jXfTFxcXYiOUdO3a0Ttytnw0QId1YUVuf3p0fBOj5Wlc/3srGgS76n8FxOBzhfHMA/7+XL19evXp1xYoVMjIyAwYMWL9+PfSrAxHUyYoK9RwAkSLR3QXoFczMzMrLyx0cHPB4/IQJE9asWdPdJQKgDZ2sqFDPARAp0IIHAAAAxBAMsgMAAADEEAR4AAAAQAxBgAcAAADEEAR4AAAAQAxBgAcAAADEEAR4AAAAQAxBgAcAAADEEAR4AAAAQAxBgAcAAADEEAR4AAAAQAxBgAcAAADEEAR4AAAAQAxBgAcAAADEEAR4AAAAQAxBgAcAAADEEAR4AAAAQAxBgAcAAADEEAR4AAAAQAxJdHcBfuj58+exsbHdXQrxgUMINTXhcDg2iSS8q1CpVC8vL+Hl313i4+NpNBrvkTdv3jAYDE1Nze4qkpiB+ilYcP8UrB5aP3EcDkeA2QnQhg0bPn/+PHz48O4uiLhobCTs2oWkpVk7dgjpCjU1NREREXyBUDwcPHgwKyuL90hUVJSMjIynp2d3FUncQP0UKLh/CljPrJ8iHeBVVFTWr18vkNwOHDigqak5f/58geTWEzGqq08oKpIoFJ+qKiFdIj8/387OrpfcQG1sbFRUVO7cudPdBempRo4ceeXKFS0tLewl1E/Ban3/TEhIiI+PDwwM7MZS9Vw9tH72lmfwV65cSUlJ6e5SANCd8vLy/P39f5qMwWB8//5d2IVJTk5OTk4W9lUAV0lJyZcvX7q7FKBLiX+AZ7PZ//77b2pqan5+fneXBYiKAwcOFBUVdXcpulp6evrff//902QJCQlz586l0+nCK8nNmzfr6up64X+BkEybNk3/f508ebJ191JlZWW3FA90F9EdZCco8fHxbm5uCKFe0jUHOmL37t13796dOXOmh4eHpKRkdxenixQWFnYkptbW1v7333/79u07cOCAkEqyZs0aJpNZUFAgpPx7mwsXLlT9b9exq6srmUzmPcJmsyHA9zbiH+Dz8vKampqIRCIEeMAlKyv76NGjEydO2NrazpkzZ8KECUZGRu2kv3nzJl+vdVFRkaysrJCLKWA5OTkNDQ1VVVWKiortJKuvr0cICa/H6+PHj1jmhYWFQrpEb6OoqMj3f0oikXA4HF8yCPC9jTh30X/69AkhVFZWhhDS0dEpKSlhMpndXSggKiQkJFavXv348WMOh7NgwQJDQ0MfH58fJS4tLa36X0wms6WlpSsL3BFFRUUPHz780W+bm5tRByI3FuCF17wODQ3FxvZCC74rcTicqqoqNpvd3QUBXUdsW/D//PPPzJkz6XQ61nMlLS2tpqYWGRl5/fr1yMjI7i4dEBVycnJr165du3ZtcXFxO/OGW09OTUpKUlBQEHLpftmzZ8/OnTs3ZsyYNn+LBfjMzMwBAwa0k4mwW/D5+fk4HI5MJsOwmK7E4XDYbHZ1dXWfPn26uyygi4htCz4gIIDNZufk5DQ1NSGEJCUl+/bt+99//2HNetDLrVmzhu+Iurq6h4dHtxSmTXQ6va6u7ke/TUhIuH79OvZzSEjIq1evsJ/r6+vbiZrNzc0SEhJ5eXntX7qurs7Dw6OgoEBIc2gbGhqGDx++a9eu3NzchoYGYVwCtIb9b1ZUVHR3QUDXEZUW/J07d/geyL148aL9dkb7sO7T7Ozs5uZmZWVlSUnJfv36paWl5eXlsVgsAoHQ2RKDnmzz5s3dXYSfWLVqVXR0dElJSZtjAKOjowsKCmbOnIkQSkpKqqqqsrGxQQg9fvyYRqNxOJzWz18RQs3Nzbq6unyDUQ4ePOjr6ystLc09Ultba25uTiaTy8rKVFVVBfzGEKqvr9+6deu4ceO2bt0aGRk5d+5cgV9CDLDZ7OTk5OLiYiaTqaWlNWjQIDxeAO2x8vJyQ0PDzucDegRRCfBfv37NyMjgPZKRkdGZCo0F+KysrJaWFhMTExKJZGRkdO/evZaWFhqN1rdv304WGAChqqurq6qqysvLU1FRkZeX5/ttZWUl1hB//Phxamoqt9M1KyursbGxvLxcRUWldZ5NTU2Ghoa8LfiWlpatW7eOHTvW0tKSe5BOp8vLy+vo6OTl5QkjwDMYDGVlZRwOR6VSs7OzBZ6/GEhKSlqxYoWGhga2EFBhYSGNRgsNDe3MynRYC768vFxgpQQiT1QC/Lp16/iOfPz4sfV9reNaWlqMjIy+ffvW3Ny8cOHC0aNH//fffyUlJQihmJgYX1/fThUX9DJXrlzh6/ouLCzkbfUKVmJiYnR0NA6Hi4qKun79+ps3b/gSVFZW5uTkZGVleXh4ZGZmYl9Ys7KyUlJSiERibm5umwG+rKxsyJAhDx484B6pqKhgMpl5eXmtA7yurm5ubu6QIUME+L7YbPaxY8cYDAaRSEQILV++PDU1VYD5iw1fX9+4uDh9fX3uERqN5ubmxn0W8xsgwPdCYvsMHmuaxMTENDc3Kyoq6urqGhgYIISIROK7d+86k7OPjw88OOxtGhsb+UbRs9lswT6i3rt3b1xcHPZzcnJyS0uLlpbW27dv09PT+Zq5lZWVlZWVFRUVS5YsyczMRAjl5OQghD5//tzY2GhpaYm95BUXF5eUlFRSUjJ06FDe31ZXV3NP52IwGFJSUliAF+AbRAhlZ2evXbs2Pz+fRCIhhMzMzFoXFSCEWCyWhoYG7xE1NbVO1jcI8L2QqLTgBa6lpWX48OG1tbUFBQVYcwEL8EZGRpcuXdqzZw+VSv2NbFks1tmzZxcuXGhlZSXgEgMRtnTpUr4jSUlJcnJyArzEx48fm5ubXVxcEEJ1dXU6OjojR45MTEysra0NCQnZt28fluz79+9jxowhkUjS0tKPHz9GCAUGBu7YsQMhRKfTJ02aRKVS37x5o6io6OzszM08PDxcUVGxsrLS1NS0rq6uvr4em8TPYDAQQnxfIJqbm4lEYr9+/fiemv2258+fL1my5MGDB9iwwaqqKuxPsm/fvhDg2+Tt7W1lZTV58mRtbW0cDldYWBgTE+Pt7d2ZPLEAj00bBr2E2LbgGQwGhULp379/Wloa1lxQUFBQV1c3NzdHCKWnp/9etliX5rdv3wRZVgAQqqqq4gbauro6b2/vP//8s7CwkEAgPH78mLtOWW1tbV5eXkVFxf79+/F4/MiRI9esWUMikcrKyqqrq1VUVPT09E6dOrV9+3bezOvq6s6fP5+Tk4P1vXMv1GaAx7rQ+/Xr15EH5FgfwPHjx9tZvt7Hxyc9Pf3Tp0/ceQFYgNfV1S0oKGAymXQ6XWR3veoWXl5eDx480NPTKy8vLysro1Kpd+/eXb58eWfy5HA4JBIJe0wJegnxbMEzmczGxkY5OTkTE5OXL1+S/m8HXxMTE2Nj4zFjxqSmpv5ornD7sPvs169fBVlc0OtlZGR8+fLl8+fPRUVFCxcuVFRU1NfXxwbG7927d9OmTQ8ePHB3d0cINTU1sVissrKyxYsXZ2dnDx48GCGkp6f3/fv3b9++KSkp6enp1dfX821uW1tbixBis9nS0tL6+vqpqakGBgZSUlIMBkNBQQGLzZmZmZcuXdq9e3dzczOJRNLT02vzi2xQUNDq1au5xf7jjz++f/9++fJleXl53mfGvLBx+/fu3eM+6cdWUSUSiaqqqjQazcvLy2vRIsF8lOJCR0dn2bJlvEfamf6Tl5fH1zRvaGhovaaNtrb2TydJAnEini34iooKRUVFPB7fv39/hBA3wPfv319aWnrp0qVPnz7FjjQ3N/v5+WHtmI74+PEjQgg2ZQICxOFwLCwsioqKiouLnz59+uDBg6dPn6qrq+vq6i5atGjx4sUIIexZO0IIW9dBQkKCTCYfO3YM2wHZwMAgMjIyOzvbzMwMi7IlJSV1dXVfv359//49Qqi+vh77RosFeA8Pj4CAAIQQg8EwNTXNzs7mcDjfv3+/evVqY2Pj58+fiUSiiYlJY2NjWloat5xnzpypqKhYs2ZNbGwsNkvl8+fPubm5TU1NmZmZP2rBt7S0VFRU6Onpffny5d9//122bJmKigp3jSBra+vTp0/TaDTuGwRtKikpkZD4YXts27Zty/9XdnY2X8jncDiDBg36+PFjO+srADEjni34iooKJSUlhJCpqSniCfDbtm2TkJCoqKjYunUrdqSqqio4ONjW1habUtyOnJwcJSWlzMxMMzOzz58/C7P4QOScPXuW72kxjUbj1qtOqqmpaWpq0tDQkJSUjIiIQAgVFRWRyWQCgRAWFoYQGjVqFBanq6qqsGXmFBUVeWe6GxgY7NmzR11dfdWqVf369SORSEwmMyMj4/bt269evbp3715ZWdmpU6dSU1MlJCQMDQ3r6upCQkICAgIaGxuVlJQoFMrDhw8bGxtzc3Pz8/OZTKaqqiqBQLC0tExPT8f+iBBCu3btwvakUFEAACAASURBVGauTpo0KSEhwdnZOTExkcViPX36tKqqqs0H9nfu3CkuLlZSUkpMTBwyZEhZWdn+/fuPHz/OTeDj4+Pl5ZWVlfXo0aMR/3fw/fv3WM8E4FJRUSkuLv7Rb8PDw/mO2NjY8M2k4HA4ysrKlpaWM2bMuHXrlvDmgADR0YNb8N+/f+ftErxx48aVK1ewn3Nzc7W1tRFC2BN37qYgGhoaKioqBgYGJSUl2LNDbPHOjuxLPWrUqEWLFpWUlEybNi07O7vjjX4gBigUiuL/IhAIAll4BP3fIHYFBYUNGzZwB9Lz3n937doVHR3NYDAmT558+fJlhBDfaqOurq7q6urFxcUyMjKSkpLv37+fNGnSunXrwsLC0tLSysrK8vPzqVQqtpYUtq0OnU5//PjxwoUL5eTkjIyMzp8/39DQwGQy79y5o6enh02/NjY2fvv2Lfcq9fX1N2/exH7GRrFg00nu3r0rLS3dZn/+pUuXPDw8FBQUqFQqHo9PSUlRUlLi/WJkbGyclpbW2Nj46NEj7MjOnTutrKw2bNgggkv9d6V3795FR0fX1NRgL/F4fOvZkr8EW/4oJiampKRkyZIlN27cwO5+QIz14BZ8cnJyVlZWYWGhpqZmS0vL5s2b8Xj8nDlzEEK5ubl6enoIISqVunjx4n79+vGeKCEhYWVl9fr16+Li4gsXLqD/25amfZWVlbdu3dLX11+5cqW+vv6tW7dmz54tnHcGRE7rDp7IyEi+7Th/29GjR2VlZbkjQCUkJJhMJm+At7OzGzx48IULF0pKSp49e/bHH3+MGDGCN4dBgwY5OTk9evTIwsICIWRqampqarp37148Hi8tLX3y5Ek9PT1NTU1ubn5+fm/evHn48GFlZaW8vLyTk9OmTZuwlvrz589lZGSwlKNHj/b09Pz48aOqqmpYWFhDQ8PLly9dXV3Nzc2/ffuWl5f3/v17LS2tyMjISZMmxcXFtV5Br7GxESGkpKSEx+OvX78+fvz4oUOH8iZQU1MbPnx4WlpaXVkZQqilpSUsLOzZs2d79+61tbW1tLS0srL69u1bU1OTnp6ei4uLkZFROz3VXEwms7KyEofDKSoqdiS9qNm/f//Zs2cdHR39/PwiIyOxsQseHh4d2e33R7D/HTKZfPXqVV9f30WLFgUGBs6aNWvcuHEUCoVbPYA4EZWq/+rVK77RH5WVlbybebDZ7Li4uKFDh6qrq2NHsK2oPn78qKmpef78+ezsbBwOh3XOl5aWYitw4XC40NDQ1pfDnkUxmcxnz57Jy8t/+PChtrZ27dq158+f50uZmppqbm7OZrOxYUrfv3+XkZGZNWtWZGTk7NmzW1pavL29AwIC2lxXBICOeP36dXx8vJyc3IABA8aPH+/p6Tl//nzeGkUgEFauXLly5Uqsf37w4MGbNm3iy8TCwuLLly/cziosJCgpKWlra+/Zs2f+/Pnc0Esmk4OCgjw9PW/fvo0Q0tTUnDdvnq+vb3x8vI6Ozp07d7jLpVlZWeXk5OTk5BgaGjIYDDabXV9fTyaTbW1tjx07hsV+V1fXyMhIXV1dCoVCo9H4Zp+WlpYqKipi37aHDRu2devW1sPEFixYcPHixa+NjaiurqGhYcWWLY6OjnFxcXFxcZmZma9fv9bX1yeTyZmZmRMmTCgpKenfvz+VSmWz2QUFBerq6vLy8kQiMSsri0ajVVdXy8jItLS0lJeXY50cwcHBPfGL+KlTp5KTk5WUlFJSUqZNm/bu3bvOz8nkfv0yNja+f/9+UVHRxo0b/f39N2zYICMjg62GW1lZuXDhwqioKEVFxXnz5t2/fz83N3fgwIHa2tqxsbHOzs54PB4bTsFgMLS1tRUVFdPS0r5//z5p0qRhw4YhhOh0elFRkYqKCofDqa2tZbFYWD8rNvcSIVRVVXXixAl/f/9r165RqVQbG5sXL15cu3bN398fG6vx6NEj7AlUZmZmVFSUjo6Ovr6+k5NTfn5+RUUFd8JqRUVFXl6enp5eRUXF169f7e3tk5KSrKyssK4srB5mZ2fX1NRISUkVFhaam5tjg6OHDRtWXl6+fv36VatW1dbW6unpnT17dsCAAe/fv9fV1X316tW5c+eqq6srKyvr6+vxeLypqSmBQMDGXfW8uR4c0bBp06bp/6tPnz62trbcBNXV1ba2tl5eXtwj/v7+mpqacnJyI0aMUFZW7tOnj4WFRXx8PIfD8fPzO3r0aDuXi4iI0NTUxJ7TW1paKisrP336FIfDVVdX//PPP7W1tdyURCJx/fr1dXV10tLS06ZNQwj99ddfOTk5GhoaGRkZGzZswI4I4SMRsKaqqsMIHadQhHcJGo2mra0tvPyF59y5c796irW1tYuLS+cvHRQURCKRmpqa2k+GdTKZm5sPGjTo4MGDrRM0Nja+fPmS98jBgwcHDhyI9WkFBATwpcfWs3NycsrNzeVwOAEBASQSadasWQghHx8fbjKsr55AIOjo6MjJyf3xxx+rVq3KycnR1tbGljR/+PChhITErl27xowZc/fuXd5LYN0D//33X/tvra6u7u3bt2+fPTuM0G6EEhMT20/8/PnzW7duRUVF/ffff1FRUZcvXw4LC0tKSkpPT6+pqcnLyyssLGzz3B5UP7ERjtjPR48eXbx4MYfDUVdX73gOrevnyZMnV65cyZeMTqf//fff+/bt8/HxWbp0qY2NDYFA0NDQkJKSQgjhcDhZWVns+xm2riiRSMS+JfTp04f7qAWHw+Hx+AEDBpDJZAMDA+wlHo+XkpJSUFBQUVExMTFRVVVVU1PT19fHvnhxl0Du27evnp4eDocjkUjYuBNpaWkZGRkikUgikTQ1NRUVFVVUVKSkpCgUColEkpGRMTIy6tOnj6SkJA6Hw77eIYQkJCRIJJKSkhKWv6amppqaGoVCweFwWAJlZWXsiqampthbwP7F+ngIBAK2HCS2wgQ3PmLdvRoaGtIIHUZoDw5naWmpp6enp6dHpVIVOy08PJz7fyGM+ikqLfj9+/fzHbGxscECMEZBQSEwMNDPz497pLKy0t/ff+vWrU+ePEEIxcfHP3nyJDo6evfu3cbGxgMHDmzncsOGDautra2trbWwsFBXV1dTU9u0aROHw3F3d79//35sbOyECRMQQs3Nzc3NzdeuXfP19ZWTkwsMDLx16xa2yBeLxXrw4MHRo0cRQm/evFmwYMGmTZvWrFkjjLW7gcDdvXuXd4zx7t27sbvVggULuqwMmZmZFArl9evXgwYN+ul4PRMTE01NzW3btjk6OraZWEpKytbWlvfIrFmzVFRUtLS0MjIyuHc3Lqwz/88//8TaOgMHDmQwGNj9F7uhY4YPH06j0b59+0aj0ZSVlZctW0Ymk6lUKp1OLysrO3v2rIODg4GBAYVCMTc3T01N/fPPP7nnurm50en09v8SEUKysrJWVlaM6uqk/3un7Se2t7dvJ0Fn1rcWHbNmzXJwcFi+fPmyZcv8/PxcXV1nzpwpjAU05eTkpk+fznsE6wRtbm4uLy+Xl5evqKiQkZEpKipSU1O7fv36/PnzJSUls7Oz9fT0UlNTWSxWc3Ozqanp9evXS0tL+/bte/DgwfPnz1dWViYnJ+vr66uqqr59+zY3N1dfX3/cuHHV1dVSUlLDhg3Lzs7mrsjEYDCw9tLTp08HDx48atQoIpH4/v17T0/P5uZmAoFQUVERHx/fv3//rKys+vr6169fv3nzZvTo0VZWVtra2h8/fnRxcVm1atWWLVv+/fff7OzsUaNGJScnDxw4UFJSUlZW1sjIKDMz88WLFyNGjMjKynr8+PHx48elpaVJJFJRUdHbt2/9/PxIJBKRSIyIiLC1tb18+fLAgQNVVFTS0tIGDhx4+/Ztc3Nz1NhY7uUlJycXGhqqoKCAdUTJy8tjXyCwxR8ZDAaHp4kvISHB4XCwkTptDijB4/FC33JasN8XBKj1N9CGhgZZWdmIiIiCgoLi4mJNTc1Hjx59/vx5woQJGhoaLBbr9u3b2Jeyfv363bp1q/38sR2+w8LCMjMzsYXAEELYzl3Lly/ncDipqalxcXEkEklRUXHx4sV6enocDkddXT06OprD4UyYMOGPP/5ACElLSxsbG3M4HG1t7Rs3bgjr4+g0aMHzOnToEJFInD9/vp+fn5+fn5KSEvZDO+mX/S9VVVVHR8eOXIvNZnt7e3t6ekZHR2PDxTkcTmNjo52dnaenJ0Jo+/btAntjbamvr29paWl9PCAgID8/H/u5urra0NAwKChoxIgRISEh3DRYzzzWDaClpcU9bm9vj8fjmUwmh8Px8vJKTEy8ePHi3LlzuQlKS0sRQgQCoYOFxOrnHhwOWwNYGHpQ/eRwOAkJCdz7CZPJvHr1qqenZ8dPb33/PHHihLe3tyCL2BYsyImfHnr/FJUWfEdIS0ubmZnNmzdv06ZNo0ePNjQ0dHJyQghFRkZiPUJ2dnYIITKZnJ2dzdv6b9PGjRvd3NxsbGxkZGSmTJkSGBg4ZMiQx48fW1paYrPkIyMjg4ODlZWV7e3tb9y4gW2Hs2LFCqxn0s7ObsuWLZqamvb29o8fP05JSSkvL3/x4sWMGTOE/kGATlu/fr29vf3mzZvnz5/v7OyckJAQFBTUTnpTU1O+79oPHjxocyPX1r58+XLy5EllZWV5efn8/Pxbt245OTllZWW9fPkyLy/PwMBA2FsfcQfN8dmyZQv3ZwUFBWwYvIGBATbMHoN1b06fPp1Op/NuDDNo0KC0tDSsHXPq1CmE0IcPH3bt2lVRUUEkEsPCwtTU1CwtLel0+i8VVUFBoc2Nbnsh3pWGCQTCrFmzsAcov43zg02EBQurMEBECCvA8y669OHDh7S0tCFDhhgbG3cy22HDhqWlpQUHB1+5cmXkyJHYQe59Vk1NzdjYeNq0aefOnXNwcGg/KyqVyh0QNGjQoEmTJo0cOfLJkydXr161t7cvLS2tqqqqqKhwcXEZNmzYP//8g8V17gqg2ECk48ePT5s2bc2aNdeuXWtqauKunwNEn4ODw507d3x8fO7cufPTSY/YEvG8QkNDfxQ4+URFRSGEysvL//vvP4TQhw8fXr9+HRISQiKRCgoKtm/f/tMvo10JezjFZ/LkyYMHD+YuYIcQsrS0vHfvHm8aMzOzoqKikJAQCoWybt06GxubUaNGHTlyROglBh3TNQEeiBRhzYPnjnUPCgqaMGHCvXv3xo0bd+nSpU5mu3z5cux2SaPRsIjLJyYmZufOnfn5+b86N+bKlSseHh6bN2/W1dV1dHS0tbW9evWqioqKnJzc5MmTZ86cybe7jK2trZycHPYVwdnZ+e+//9bU1MzKyoLNmnoQeXn58PBwGxubH62xKhCnT5+eNm2anJzchw8fwsLCqqqqXFxcLl265OPjQ6FQOv+tt2vo6OhwJ8EjhNzc3A4dOsSbgEgkuru7v379+vXr15qams+fPxf680XwKyDA90JC76IPDAx8+/athoZGWVmZg4MDtrLmbzM0NDQ0NPzy5cu8efMcHR3bTIAQ+u2Zr3v37kUIjRkzJjY2FiF07NixPn36UKnUa9eu8aWUkJC4ePEiNnXY2dm5pKTEzMxMS0srICDg2LFjv3d10C1mzpz503UMf8+FCxdSU1NLSkoiIiIYDMacOXOePHkyZcqUqKgoEol0+PDh9evXKyoqCuPSwkahULBJJbx27dplb2/f0tISERHh4uLSQ9+auIIA3wsJPcArKytjGxsrKyv/aKeEX6WrqyvUznBsJLCBgcG8efP4lgzjxb3BSUlJjRo1Co/Hb9u2zc3NDQI8wPj4+DQ1NVEoFGlpaWlpaTs7u9ra2sDAwLq6Omzam5jNucAmGmGz6RwdHVsP3QdCcv369dzcXN4jhYWFfIvRQoDvhYQV4CUlJc3MzLDpNOHh4fPnz1+6dGmbbW4RZGBgEBUVNWnSpI7/PWzevLmysnLgwIEsFuvz589mZmZCLSHoYjt37uTddgUhlJmZ2X5HUVNTE7YxDHda19SpU7FJaFOmTBHXRzmOjo4VFRUIoUuXLqmpqXV3cXqL6upq7obCGBaLxbebHAT4XkhYAb6wsBDbuyInJwdbn0FPT2/NmjVCupzATZ48+ZfSc6cgu7q6jhkzJj09vfMrTwHRMWrUKL4vbR8+fGh/uw5s9vmWLVsGDRqEHTEzM8MyWbRo0ahRo4RX2m7k4uLy/PlzhJCurm53l6UXweZb8kpKSuJbEgACfC8kxC56aWlpExMT7rIVW7ZsYbFYP0qcm5vL16Zpcz9j0TdnzpygoKAbN25wl1QEYoC7eivXkSNHsNW+fuThw4dWVla8SzNxycjIYBsZi58FCxZ05WJBAIB2dN08+JKSEqwp3+Zvt2/fzrcHK41G64ldfIMHD/77779nz56dkZGxb98+QQ07AD1OYGAgtpIxAKIAWvC9UNcF+F/dz3jDhg09dAeXqVOnTp069dChQ9bW1q1HGgPxVl1dXVBQEBwcXFpaumjRou4uDgD/HwjwvZAQAzybzU5OTi4uLmYymdhWRT2xRf4bCATCmTNnqFSqv79/c3NzJ9efAj3LoUOHbt26VVJSIiMjIx7rooNu0fr+ia1q/tsgwPdCwgrwSUlJK1as0NDQwHajKiwspNFooaGhrZ9liqU+ffocOnToyZMny5cv79+/P4PBsLGx4Uvz4sULOzs7Yf/JvXjxwsLCQlA7l/da/v7+mZmZvEcyMzNb33AzMzP3798vJSXF4XA6ueQD6M2Ecf+EAN8LCSvA+/r6xsXF8S4QRqPR3NzcXr16JaQrihocDnflypUDBw5gU+SjoqKwWYJ0Oj00NHTw4MHjxo0jk8lPnjwR3py6v/76y8PDY9GiRSdPnsSmbIHfM3HixPz8fN4jHz58aD1RIiwsbMaMGQsXLjx16tT58+e7sIBArAjj/gkBvhcS4lr02Po2XGpqaj8aYSeuDA0NT5069fr165aWlmnTpj1//jwjI2Pjxo3p6enNzc0UCkVbW3vEiBExMTH29vbYLo3cczMyMrS1tXknYtXU1HRw7c/ExMQhQ4bcunXLw8PDycnp2bNnRkZGehoa4xH66aLroE3YPka8jhw5wrevRn19/cOHD9esWfPnn3/y7poKwK+C+ycQCGEFeG9vbysrq8mTJ2tra+NwuMLCwpiYGG9vbyFdTmRJSUn9+++/kpKSly9fdnFxMTAwUFJSevr0KZVKZbFY2traISEhw4cPnz17dkJCwrhx40JCQkpLS5OSklatWjV06NDp06c/fvzY0dHx4sWLWVlZixYtOnDgQDuXa2hoOHHixLZt2xQUFKqqqubMmRMaGlpUVLRx48aCzEyEEIPBaGpqan9+F/g9MTExZWVl3D2QAPhtwrh/Qgu+FxJWgPfy8po4cWJ8fHxhYSFCiEql3r17V0dHR0iXE2VYs3vZsmWVlZX+/v63bt3ifR7v4eHh6urq7e09atSoioqKCRMmWFhYHD582M7OTkNDY8mSJVpaWg8fPiSTyUOHDn3w4EFlZWVQUFBMTMyYMWN4lwJ99erVzp0737x5Q6fTDQ0NnZ2di4uL9+3bJykpia2lX1NcHKKhgcfj+/btu3jxYgsLCyMjIwKBQCKRyGSysrIyRP1Oqq2tHTduHF/DC4DfIIz7JwT4XkiIo+h1dHSWLVsmvPx7nE2bNg0ZMmTIkCF8x5WUlLDNbJhM5pYtW0JCQq5duzZw4EATE5N58+Y5OTnV1taSyWQcDldbW+vt7a2lpcVgMPr06TNv3jwPDw9VVdV//vnHy8vL1dX1yJEjs2bNqqura70DKRa/yWTyw4cPr169Gh0d/fXrV4RQc3NzTU1NeXk5m80mkUgcDgfreaZQKHg8nkKhtP+mNm/eDFMBMQ0NDR3cQBaAn2p9/+Tdg/s3QIDvhbpuHjxACI0ePbqd30pISBw8ePDgwYPcI05OTggh7mAuOTm58PDwN2/e9O3bNzs7G9vqlMViMZnMixcvuru7Y+O6SSRSO1cZMGDA/v37Wx9vaWlhs9lsNrulpYXFYlVVVbHZbDqdTiQS2Ww2i8WSlpaur6/nO6vNTXtF39mzZ588edL6eOttAzG+vr5fvnzhPZKens47ir66uvr8+fOurq6CLScAmPYXCutI/YyIiHB3dxduKYGIEekAX1lZmZWVhf3MZrMzMzPbX/37l7DZbAaDIcAMmUwmi8VqP7j+kubmZoQQ30guDLbxyYwZM6ZNm/b9+3cDAwM8Hv/s2bN2cmPV1WGFbDOwdQSdTuc70tjYSCaTuWsMt7OQkaiZN2/eq1ev6HS6l5dXR9IvXbq0tLSU98iJEyc0NTW59ZPFYi1cuNDY2Pi3P14+Pbp+/obO18/WGhsb+/XrJykpib3sQfWztfYXCutI/Vy8eDHUz9/WQ+snTmRHZp45c+bIkSPcl7W1teXl5b+90XtrbDabw+EIcClZgWeILd0vqAwJCFkymSwc7oNAS6igoMC77beJiUlcXJyg8heqT58+Xb9+fd++fb93OtRPqJ+iDOon1E+EEOL0EHfu3HFxcRFghhEREXPnzhVghsHBwb6+vgLMcOfOnTt27BBghn5+fkFBQQLMcP78+eHh4QLMsOeC+tl5UD+FB+pn5/XE+inSXfQAANAL/eoYEQDaBAEeAABEy6+OEQGgTRDgAQBAtMjKyq5evfr69evOzs7dXRbQg0GABwAAkWNhYWFhYdHdpQA9W48J8BISEgIcYNlTMmSz2QLMkEAgCHAYrTAy7Ll6RHWC+tlr9YjqBPVT4ER3mhwfJpNZXl6urq4uqAwZDAadTldRURFUhg0NDQwGg3fOQydh884FuKd4VVUVkUiUlZUVVIZlZWXy8v+vvfsLaer/4zj+Wf4uSlx/aKMcOm9c2rQVkuyiCZMwb6JgVAvqIiRMqK6KLtYfAqkuKuiqIsOiC7szxMKEmZDUTcYowwRvykWOiX+mjpHpzvdicPjy3ffP/J1t5+zj83G1I+dzzvscXvBycuRszOK/rhYu8qkd+cwd8qldIeazYAoeAABkbt1/7wIAAAoNBQ8AgIQoeAAAJETBAwAgIQoeAAAJUfAAAEioMAr+/v37TqfT5XINDg7+H8vb29udTmd5efmtW7f+6YAaT6FRf39/fX19RUXFo0ePjDZhPB5/+PChupnJYPrezPwjnzpOSD7/E/nUcUKd85nTd9VlxcTERHV1dTweHx8fr6qqWllZWdXyYDBYV1eXSCSmpqbsdvvHjx/TD6jxFBrNzs46HI6pqalYLFZVVRWLxYwz4efPn1taWo4fP57azGQwfW9m/pFP8mlk5HMt57MAvsH39fUdPny4uLi4srJy+/btnz59WtXy6enpM2fOrF+/3mKxeDyecDicfkCNp9Cop6fn0KFDFotl48aNIyMjZrPZOBM+f/58bm5O3cxkMH1vZv6RT/JpZORzLeezAAp+cnKyrKws9bmsrCwSiaxq+bFjx1pbW4UQIyMj79+/93q96QfUeAqNwuHwt2/fdu3aZbfb79y5YzKZjDPhzZs3//zCykwG0/dm5h/5JJ9GRj7Xcj4L4E0MyWTSZDKpm8vLy6s9gqIo9+7de/DgQXd396ZNm9IPqP0UWiQSifHx8bdv3yqKsnfv3gMHDhhtQlUmgxlk1Lwhn7pPqCKf6cin7hOq8p/PAih4m802MTGR+vzz50+bzbaq5SsrK0ePHt28efPw8HDqxQPpB9R4Co2sVmtzc3PqLQsNDQ1jY2NGm1CVyWAGGTVvyKfuE6rIZzryqfuEKh3yqe0Zgnz4/v17bW3tr1+/wuFwZWXlah866Orq8vv9/35AjafQ6OvXrzU1NdPT09Fo1G63j46OGmrCYDCoPiSSyWD63sz8I5/k08jI51rOZwF8g7fb7WfPnvV4PEKIx48fr1u3uucGhoaG+vr6SktLU5sdHR0HDx78ywE1nkKj6urq1tZWt9utKEogENi5c6cQwlATqtLHyOQnuoyaN+RT9wlV5DMd+dR9QlX+88nrYgEAkJDkv70CALA2UfAAAEiIggcAQEIUPAAAEqLgAQCQEAUPAICEKHgAACREwQMAICEKHgAACVHwAABIiIIHAEBCFDwAABKi4AEAkBAFDwCAhCh4AAAkRMEDACAhCh4AAAlR8FqZzeZ4PD4/P9/R0bHateqqnp6e8+fP52A6rHXkE0ZGPnOKgs+OhYWFZ8+e/fs+v3///qdVzc3N7e3tuRoOax75hJGRzxyh4LPj0qVLX758uXHjhhDi+vXrlZWVe/bsefr0qRCiv7+/ra2tqampt7f39OnTDoejoqLi9u3bf141MDBw9epVRVEuXrzodDpramq6urqEEAMDAz6fb9++fU6nMxAI6HqJKGDkE0ZGPnNFgTYlJSWLi4s/fvzweDyKorx69Wr//v3xeHxmZmbHjh2hUOj169c2my0ajY6NjZ06dSqZTM7NzZWWliqKoq56+fLluXPnuru7vV7v8vJyNBotLS2dnJwMBoMlJSUzMzNLS0sVFRXRaFTnq0WhIZ8wMvKZU//T+xcM2QwNDUUiEb/fL4RIJBKhUMhmszU0NFitVqvVGggEOjs7Q6HQ7Ozs3671+XxFRUVWq9Xtdn/48KG4uLixsXHLli1CCIfDMT8/b7Va831JkAj5hJGRz+ziT/RZtmHDhgsXLvT29vb29o6Ojp48eVIIYTabhRBv3rw5cuSIyWRqa2vbtm1b+lpFUUwmU+pzUVFRMpkUQmzdujWP40Ny5BNGRj6zi4LPmpWVFSFEY2PjkydPlpaWYrFYbW1tJBJRdxgeHvb7/S0tLQsLC9FoNJW/1KoUj8fz4sWLZDI5PT397t27+vr6/F8FZEU+YWTkMxco+OywWCyLi4vXrl1raGhoampyuVx1dXWXL18uLy9X9zlx4sTg4KDb7e7s7GxsbLxy5Yq6KrWDz+fbvXu3y+Xyer137961dsPvhAAAAG5JREFU2Ww6XQ1kQz5hZOQzR0yKoug9AwAAyDK+wQMAICEKHgAACVHwAABIiIIHAEBCFDwAABKi4AEAkBAFDwCAhCh4AAAkRMEDACAhCh4AAAlR8AAASIiCBwBAQhQ8AAASouABAJAQBQ8AgIT+AKkNq/4/kCw3AAAAAElFTkSuQmCC" /><!-- --></p> -<p>The same applies to the case where the DFOP model is fitted with the two-component error model. Convergence of the variance of k2 is enhanced by using the two-component error, it remains more or less stable already after 200 iterations of the first phase.</p> -<pre class="r"><code>f_parent_saemix_dfop_tc_moreiter <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE, - control = saemixControl(nbiter.saemix = c(800, 200), print = FALSE, - save = FALSE, save.graphs = FALSE, displayProgress = FALSE), - transformations = "saemix") -plot(f_parent_saemix_dfop_tc_moreiter$so, plot.type = "convergence")</code></pre> -<p><img 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" /><!-- --></p> -<p>The four combinations can be compared using the model comparison function from the saemix package:</p> -<pre class="r"><code>compare.saemix(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so, - f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc_moreiter$so)</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> +<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$so, plot.type = "convergence")</code></pre> +<p><img 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" /><!-- --> The four combinations and including the variations of the DFOP/tc combination can be compared using the model comparison function from the saemix package:</p> +<pre class="r"><code>compare.saemix( + f_parent_saemix_sfo_const$so, + f_parent_saemix_sfo_tc$so, + f_parent_saemix_dfop_const$so, + f_parent_saemix_dfop_tc$so)</code></pre> <pre><code>Likelihoods calculated by importance sampling</code></pre> <pre><code> AIC BIC -1 818.37 817.33 -2 820.38 819.14 -3 725.91 724.04 -4 683.64 681.55</code></pre> -<p>As in the case of nlme fits, the DFOP model fitted with two-component error (number 4) gives the lowest AIC. The numeric values are reasonably close to the ones obtained using nlme, considering that the algorithms for fitting the model and for the likelihood calculation are quite different.</p> +1 796.37 795.33 +2 798.37 797.13 +3 713.16 711.28 +4 666.10 664.01</code></pre> +<p>As in the case of nlme fits, the DFOP model fitted with two-component error (number 4) gives the lowest AIC. Using more iterations and/or more chains does not have a large influence on the final AIC (not shown).</p> <p>In order to check the influence of the likelihood calculation algorithms implemented in saemix, the likelihood from Gaussian quadrature is added to the best fit, and the AIC values obtained from the three methods are compared.</p> -<pre class="r"><code>f_parent_saemix_dfop_tc_moreiter$so <- - llgq.saemix(f_parent_saemix_dfop_tc_moreiter$so) -AIC(f_parent_saemix_dfop_tc_moreiter$so)</code></pre> -<pre><code>[1] 683.64</code></pre> -<pre class="r"><code>AIC(f_parent_saemix_dfop_tc_moreiter$so, method = "gq")</code></pre> -<pre><code>[1] 683.7</code></pre> -<pre class="r"><code>AIC(f_parent_saemix_dfop_tc_moreiter$so, method = "lin")</code></pre> -<pre><code>[1] 683.17</code></pre> -<p>The AIC values based on importance sampling and Gaussian quadrature are quite similar. Using linearisation is less accurate, but still gives a similar value.</p> +<pre class="r"><code>f_parent_saemix_dfop_tc$so <- + llgq.saemix(f_parent_saemix_dfop_tc$so) +AIC(f_parent_saemix_dfop_tc$so)</code></pre> +<pre><code>[1] 666.1</code></pre> +<pre class="r"><code>AIC(f_parent_saemix_dfop_tc$so, method = "gq")</code></pre> +<pre><code>[1] 666.03</code></pre> +<pre class="r"><code>AIC(f_parent_saemix_dfop_tc$so, method = "lin")</code></pre> +<pre><code>[1] 665.48</code></pre> +<p>The AIC values based on importance sampling and Gaussian quadrature are very similar. Using linearisation is known to be less accurate, but still gives a similar value.</p> </div> <div id="nlmixr" class="section level3"> <h3>nlmixr</h3> -<p>In the last years, a lot of effort has been put into the nlmixr package which is designed for pharmacokinetics, where nonlinear mixed-effects models are routinely used, but which can also be used for related data like chemical degradation data. A current development branch of the mkin package provides an interface between mkin and nlmixr. Here, we check if we get equivalent results when using a refined version of the First Order Conditional Estimation (FOCE) algorithm used in nlme, namely First Order Conditional Estimation with Interaction (FOCEI), and the SAEM algorithm as implemented in nlmixr.</p> -<p>First, the focei algorithm is used for the four model combinations and the goodness of fit of the results is compared.</p> +<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> <pre class="r"><code>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")</code></pre> -<pre class="r"><code>AIC(f_parent_nlmixr_focei_sfo_const$nm, f_parent_nlmixr_focei_sfo_tc$nm, - f_parent_nlmixr_focei_dfop_const$nm, f_parent_nlmixr_focei_dfop_tc$nm)</code></pre> -<pre><code> df AIC -f_parent_nlmixr_focei_sfo_const$nm 5 818.63 -f_parent_nlmixr_focei_sfo_tc$nm 6 820.61 -f_parent_nlmixr_focei_dfop_const$nm 9 728.11 -f_parent_nlmixr_focei_dfop_tc$nm 10 687.82</code></pre> +<pre class="r"><code>aic_nlmixr_focei <- sapply( + list(f_parent_nlmixr_focei_sfo_const$nm, f_parent_nlmixr_focei_sfo_tc$nm, + f_parent_nlmixr_focei_dfop_const$nm, f_parent_nlmixr_focei_dfop_tc$nm), + AIC)</code></pre> <p>The AIC values are very close to the ones obtained with nlme which are repeated below for convenience.</p> -<pre class="r"><code>AIC( - f_parent_nlme_sfo_const, f_parent_nlme_sfo_tc, f_parent_nlme_dfop_tc +<pre class="r"><code>aic_nlme <- sapply( + list(f_parent_nlme_sfo_const, NA, f_parent_nlme_sfo_tc, f_parent_nlme_dfop_tc), + function(x) if (is.na(x[1])) NA else AIC(x)) +aic_nlme_nlmixr_focei <- data.frame( + "Degradation model" = c("SFO", "SFO", "DFOP", "DFOP"), + "Error model" = rep(c("constant variance", "two-component"), 2), + "AIC (nlme)" = aic_nlme, + "AIC (nlmixr with FOCEI)" = aic_nlmixr_focei, + check.names = FALSE )</code></pre> -<pre><code> df AIC -f_parent_nlme_sfo_const 5 818.63 -f_parent_nlme_sfo_tc 6 820.61 -f_parent_nlme_dfop_tc 10 687.84</code></pre> -<p>Secondly, we use the SAEM estimation routine and check the convergence plots for SFO with constant variance</p> +<p>Secondly, we use the SAEM estimation routine and check the convergence plots. The control parameters also used for the saemix fits are defined beforehand.</p> +<pre class="r"><code>nlmixr_saem_control <- saemControl(logLik = TRUE, + nBurn = 1000, nEm = 300, nmc = 15)</code></pre> +<p>The we fit SFO with constant variance</p> <pre class="r"><code>f_parent_nlmixr_saem_sfo_const <- nlmixr(f_parent_mkin_const["SFO", ], est = "saem", - control = nlmixr::saemControl(logLik = TRUE)) + control = nlmixr_saem_control) traceplot(f_parent_nlmixr_saem_sfo_const$nm)</code></pre> -<p><img 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" /><!-- --></p> -<p>for SFO with two-component error</p> +<p><img src="data:image/png;base64,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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::saemControl(logLik = TRUE)) + control = nlmixr_saem_control) traceplot(f_parent_nlmixr_saem_sfo_tc$nm)</code></pre> -<p><img src="data:image/png;base64,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" /><!-- --></p> -<p>For DFOP with constant variance, the convergence plots show considerable instability of the fit, which can be alleviated by increasing the number of iterations and the number of parallel chains for the first phase of algorithm.</p> +<p><img src="data:image/png;base64,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" /><!-- --></p> +<p>For DFOP with constant variance, the convergence plots show considerable instability of the fit, which indicates overparameterisation which was already observed earlier for this model combination.</p> <pre class="r"><code>f_parent_nlmixr_saem_dfop_const <- nlmixr(f_parent_mkin_const["DFOP", ], est = "saem", - control = nlmixr::saemControl(logLik = TRUE, nBurn = 1000), nmc = 15) + control = nlmixr_saem_control) traceplot(f_parent_nlmixr_saem_dfop_const$nm)</code></pre> -<p><img src="data:image/png;base64,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" /><!-- --></p> -<p>For DFOP with two-component error, the same increase in iterations and parallel chains was used, but using the two-component error appears to lead to a less erratic convergence, so this may not be necessary to this degree.</p> +<p><img src="data:image/png;base64,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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::saemControl(logLik = TRUE, nBurn = 1000, nmc = 15)) + control = nlmixr_saem_control) traceplot(f_parent_nlmixr_saem_dfop_tc$nm)</code></pre> -<p><img src="data:image/png;base64,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" /><!-- --></p> -<p>The AIC values are internally calculated using Gaussian quadrature. For an unknown reason, the AIC value obtained for the DFOP fit using the two-component error model is given as Infinity.</p> +<p><img src="data:image/png;base64,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" /><!-- --></p> +<p>The AIC values are internally calculated using Gaussian quadrature. For an unknown reason, the AIC value obtained for the DFOP fit using constant error is given as Infinity.</p> <pre class="r"><code>AIC(f_parent_nlmixr_saem_sfo_const$nm, f_parent_nlmixr_saem_sfo_tc$nm, f_parent_nlmixr_saem_dfop_const$nm, f_parent_nlmixr_saem_dfop_tc$nm)</code></pre> <pre><code> df AIC -f_parent_nlmixr_saem_sfo_const$nm 5 820.54 -f_parent_nlmixr_saem_sfo_tc$nm 6 835.26 -f_parent_nlmixr_saem_dfop_const$nm 9 842.84 -f_parent_nlmixr_saem_dfop_tc$nm 10 684.51</code></pre> +f_parent_nlmixr_saem_sfo_const$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$nm 10 669.57</code></pre> <p>The following table gives the AIC values obtained with the three packages.</p> <pre class="r"><code>AIC_all <- data.frame( + check.names = FALSE, + "Degradation model" = c("SFO", "SFO", "DFOP", "DFOP"), + "Error model" = c("const", "tc", "const", "tc"), nlme = c(AIC(f_parent_nlme_sfo_const), AIC(f_parent_nlme_sfo_tc), NA, AIC(f_parent_nlme_dfop_tc)), nlmixr_focei = sapply(list(f_parent_nlmixr_focei_sfo_const$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_moreiter$so), AIC), + f_parent_saemix_dfop_const$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) ) @@ -1797,6 +1800,8 @@ kable(AIC_all)</code></pre> <table> <thead> <tr class="header"> +<th align="left">Degradation model</th> +<th align="left">Error model</th> <th align="right">nlme</th> <th align="right">nlmixr_focei</th> <th align="right">saemix</th> @@ -1805,28 +1810,36 @@ kable(AIC_all)</code></pre> </thead> <tbody> <tr class="odd"> -<td align="right">818.63</td> -<td align="right">818.63</td> -<td align="right">818.37</td> -<td align="right">820.54</td> +<td align="left">SFO</td> +<td align="left">const</td> +<td align="right">796.60</td> +<td align="right">796.62</td> +<td align="right">796.37</td> +<td align="right">798.68</td> </tr> <tr class="even"> -<td align="right">820.61</td> -<td align="right">820.61</td> -<td align="right">820.38</td> -<td align="right">835.26</td> +<td align="left">SFO</td> +<td align="left">tc</td> +<td align="right">798.60</td> +<td align="right">798.61</td> +<td align="right">798.37</td> +<td align="right">808.88</td> </tr> <tr class="odd"> +<td align="left">DFOP</td> +<td align="left">const</td> <td align="right">NA</td> -<td align="right">728.11</td> -<td align="right">725.91</td> -<td align="right">842.84</td> +<td align="right">750.91</td> +<td align="right">713.16</td> +<td align="right">815.95</td> </tr> <tr class="even"> -<td align="right">687.84</td> -<td align="right">687.82</td> -<td align="right">683.64</td> -<td align="right">684.51</td> +<td align="left">DFOP</td> +<td align="left">tc</td> +<td align="right">671.91</td> +<td align="right">666.60</td> +<td align="right">666.10</td> +<td align="right">669.57</td> </tr> </tbody> </table> @@ -1840,6 +1853,9 @@ kable(AIC_all)</code></pre> <div id="ref-efsa_2018_dimethenamid"> <p>EFSA. 2018. “Peer Review of the Pesticide Risk Assessment of the Active Substance Dimethenamid-P.” <em>EFSA Journal</em> 16 (4): 5211.</p> </div> +<div id="ref-ranke2021"> +<p>Ranke, Johannes, Janina Wöltjen, Jana Schmidt, and Emmanuelle Comets. 2021. “Taking Kinetic Evaluations of Degradation Data to the Next Level with Nonlinear Mixed-Effects Models.” <em>Environments</em> 8 (8). <a href="https://doi.org/10.3390/environments8080071">https://doi.org/10.3390/environments8080071</a>.</p> +</div> <div id="ref-dimethenamid_rar_2018_b8"> <p>Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria. 2018. “Renewal Assessment Report Dimethenamid-P Volume 3 - B.8 Environmental fate and behaviour, Rev. 2 - November 2017.” <a href="https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716">https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716</a>.</p> </div> diff --git a/vignettes/web_only/dimethenamid_2018.rmd b/vignettes/web_only/dimethenamid_2018.rmd index c152e578..e5c8764d 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 4 August 2021, built on `r format(Sys.Date(), format = "%d %b %Y")` +date: Last change 16 September 2021, built on `r format(Sys.Date(), format = "%d %b %Y")` output: html_document: toc: true @@ -14,8 +14,7 @@ vignette: > %\VignetteEncoding{UTF-8} --- -[Wissenschaftlicher Berater, Kronacher Str. 12, 79639 Grenzach-Wyhlen, Germany](http://www.jrwb.de)<br /> -[Privatdozent at the University of Bremen](http://chem.uft.uni-bremen.de/ranke) +[Wissenschaftlicher Berater, Kronacher Str. 12, 79639 Grenzach-Wyhlen, Germany](http://www.jrwb.de) ```{r, include = FALSE} require(knitr) @@ -29,10 +28,11 @@ opts_chunk$set( # Introduction -During the preparation of the journal article on nonlinear mixed-effects models in -degradation kinetics (submitted) and the analysis of the dimethenamid degradation -data analysed therein, a need for a more detailed analysis using not only nlme and saemix, -but also nlmixr for fitting the mixed-effects models was identified. +During the preparation of the journal article on nonlinear mixed-effects models +in degradation kinetics [@ranke2021] and the analysis of the dimethenamid +degradation data analysed therein, a need for a more detailed analysis using +not only nlme and saemix, but also nlmixr for fitting the mixed-effects models +was identified. This vignette is an attempt to satisfy this need. @@ -42,8 +42,8 @@ Residue data forming the basis for the endpoints derived in the conclusion on the peer review of the pesticide risk assessment of dimethenamid-P published by the European Food Safety Authority (EFSA) in 2018 [@efsa_2018_dimethenamid] were transcribed from the risk assessment report [@dimethenamid_rar_2018_b8] -which can be downloaded from the -[EFSA register of questions](https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716). +which can be downloaded from the Open EFSA repository +[https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716](https://open.efsa.europa.eu). The data are [available in the mkin package](https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html). The @@ -60,16 +60,13 @@ Also, datasets observed in the same soil are merged, resulting in dimethenamid ```{r dimethenamid_data} library(mkin) -dmta_ds <- lapply(1:8, function(i) { +dmta_ds <- lapply(1:7, function(i) { ds_i <- dimethenamid_2018$ds[[i]]$data ds_i[ds_i$name == "DMTAP", "name"] <- "DMTA" ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i] ds_i }) names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title) -dmta_ds[["Borstel"]] <- rbind(dmta_ds[["Borstel 1"]], dmta_ds[["Borstel 2"]]) -dmta_ds[["Borstel 1"]] <- NULL -dmta_ds[["Borstel 2"]] <- NULL dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]]) dmta_ds[["Elliot 1"]] <- NULL dmta_ds[["Elliot 2"]] <- NULL @@ -150,7 +147,7 @@ packages, with different algorithms and checking control parameters. ### nlme The nlme package was the first R extension providing facilities to fit nonlinear -mixed-effects models. We use would like to do model selection from all four +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 @@ -165,34 +162,20 @@ fitting these data. ```{r f_parent_nlme, warning = FALSE} library(nlme) f_parent_nlme_sfo_const <- nlme(f_parent_mkin_const["SFO", ]) -#f_parent_nlme_dfop_const <- nlme(f_parent_mkin_const["DFOP", ]) -# maxIter = 50 reached +# 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", ]) ``` - -Note that overparameterisation is also indicated by warnings obtained when -fitting SFO or DFOP with the two-component error model ('false convergence' in -the 'LME step' in some iterations). In addition to these fits, attempts -were also made to include correlations between random effects by using the -log Cholesky parameterisation of the matrix specifying them. The code -used for these attempts can be made visible below. - -```{r f_parent_nlme_logchol, warning = FALSE, eval = FALSE} -f_parent_nlme_sfo_const_logchol <- nlme(f_parent_mkin_const["SFO", ], - random = pdLogChol(list(DMTA_0 ~ 1, log_k_DMTA ~ 1))) -anova(f_parent_nlme_sfo_const, f_parent_nlme_sfo_const_logchol) # not better -#f_parent_nlme_dfop_tc_logchol <- update(f_parent_nlme_dfop_tc, -# random = pdLogChol(list(DMTA_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1))) -# using log Cholesky parameterisation for random effects (nlme default) does -# not converge here and gives lots of warnings about the LME step not converging -``` +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 +`r f_parent_nlme_dfop_tc$numIter` iterations, we can ignore this warning. The model comparison function of the nlme package can directly be applied -to these fits showing a similar goodness-of-fit of the SFO model, but a much -lower AIC for the DFOP model fitted with the two-component error model. -Also, the likelihood ratio test indicates that this difference is significant. -as the p-value is below 0.0001. +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. ```{r AIC_parent_nlme} anova( @@ -200,6 +183,27 @@ anova( ) ``` +In addition to these fits, attempts were also made to include correlations +between random effects by using the log Cholesky parameterisation of the matrix +specifying them. The code used for these attempts can be made visible below. + +```{r f_parent_nlme_logchol, warning = FALSE, eval = FALSE} +f_parent_nlme_sfo_const_logchol <- nlme(f_parent_mkin_const["SFO", ], + random = nlme::pdLogChol(list(DMTA_0 ~ 1, log_k_DMTA ~ 1))) +anova(f_parent_nlme_sfo_const, f_parent_nlme_sfo_const_logchol) +f_parent_nlme_sfo_tc_logchol <- nlme(f_parent_mkin_tc["SFO", ], + random = nlme::pdLogChol(list(DMTA_0 ~ 1, log_k_DMTA ~ 1))) +anova(f_parent_nlme_sfo_tc, f_parent_nlme_sfo_tc_logchol) +f_parent_nlme_dfop_tc_logchol <- nlme(f_parent_mkin_const["DFOP", ], + random = nlme::pdLogChol(list(DMTA_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1))) +anova(f_parent_nlme_dfop_tc, f_parent_nlme_dfop_tc_logchol) +``` + +While the SFO variants converge fast, the additional parameters introduced +by this lead to convergence warnings for the DFOP model. The model comparison +clearly show that adding correlations between random effects does not improve +the fits. + The selected model (DFOP with two-component error) fitted to the data assuming no correlations between random effects is shown below. @@ -211,29 +215,37 @@ plot(f_parent_nlme_dfop_tc) The saemix package provided the first Open Source implementation of the Stochastic Approximation to the Expectation Maximisation (SAEM) algorithm. -SAEM fits of degradation models can be performed using an interface to the -saemix package available in current development versions of the mkin package. +SAEM fits of degradation models can be conveniently performed using an +interface to the saemix package available in current development versions of +the mkin package. 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. +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. + +```{r saemix_control} +library(saemix) +saemix_control <- saemixControl(nbiter.saemix = c(800, 300), 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. -```{r f_parent_saemix_sfo_const, results = 'hide'} -library(saemix) +```{r f_parent_saemix_sfo_const, results = 'hide', dependson = "saemix_control"} f_parent_saemix_sfo_const <- mkin::saem(f_parent_mkin_const["SFO", ], quiet = TRUE, - transformations = "saemix") + control = saemix_control, transformations = "saemix") plot(f_parent_saemix_sfo_const$so, plot.type = "convergence") ``` Obviously the default number of iterations is sufficient to reach convergence. This can also be said for the SFO fit using the two-component error model. -```{r f_parent_saemix_sfo_tc, results = 'hide'} +```{r f_parent_saemix_sfo_tc, results = 'hide', dependson = "saemix_control"} f_parent_saemix_sfo_tc <- mkin::saem(f_parent_mkin_tc["SFO", ], quiet = TRUE, - transformations = "saemix") + control = saemix_control, transformations = "saemix") plot(f_parent_saemix_sfo_tc$so, plot.type = "convergence") ``` @@ -243,56 +255,52 @@ iterations above). Therefore, the number of iterations in the first phase of the algorithm was increased, leading to visually satisfying convergence. -```{r f_parent_saemix_dfop_const, results = 'hide'} +```{r f_parent_saemix_dfop_const, results = 'hide', dependson = "saemix_control"} f_parent_saemix_dfop_const <- mkin::saem(f_parent_mkin_const["DFOP", ], quiet = TRUE, - control = saemixControl(nbiter.saemix = c(800, 200), print = FALSE, - save = FALSE, save.graphs = FALSE, displayProgress = FALSE), - transformations = "saemix") + control = saemix_control, transformations = "saemix") plot(f_parent_saemix_dfop_const$so, plot.type = "convergence") ``` -The same applies to the case where the DFOP model is fitted with the -two-component error model. Convergence of the variance of k2 is enhanced -by using the two-component error, it remains more or less stable already after 200 -iterations of the first phase. - -```{r f_parent_saemix_dfop_tc_moreiter, results = 'hide'} -f_parent_saemix_dfop_tc_moreiter <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE, - control = saemixControl(nbiter.saemix = c(800, 200), print = FALSE, - save = FALSE, save.graphs = FALSE, displayProgress = FALSE), - transformations = "saemix") -plot(f_parent_saemix_dfop_tc_moreiter$so, plot.type = "convergence") -``` - -The four combinations can be compared using the model comparison function from the -saemix package: +The same applies in the case where the DFOP model is fitted with the +two-component error model. Convergence of the variance of k2 is enhanced by +using the two-component error, it remains more or less stable already after +200 iterations of the first phase. -```{r AIC_parent_saemix} -compare.saemix(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so, - f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc_moreiter$so) +```{r f_parent_saemix_dfop_tc, results = 'hide', dependson = "saemix_control"} +f_parent_saemix_dfop_tc <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE, + control = saemix_control, transformations = "saemix") +plot(f_parent_saemix_dfop_tc$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: + +```{r AIC_parent_saemix, cache = FALSE} +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) ``` As in the case of nlme fits, the DFOP model fitted with two-component error -(number 4) gives the lowest AIC. The numeric values are reasonably close to -the ones obtained using nlme, considering that the algorithms for fitting the -model and for the likelihood calculation are quite different. +(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). In order to check the influence of the likelihood calculation algorithms implemented in saemix, the likelihood from Gaussian quadrature is added to the best fit, and the AIC values obtained from the three methods are compared. -```{r AIC_parent_saemix_methods} -f_parent_saemix_dfop_tc_moreiter$so <- - llgq.saemix(f_parent_saemix_dfop_tc_moreiter$so) -AIC(f_parent_saemix_dfop_tc_moreiter$so) -AIC(f_parent_saemix_dfop_tc_moreiter$so, method = "gq") -AIC(f_parent_saemix_dfop_tc_moreiter$so, method = "lin") +```{r AIC_parent_saemix_methods, cache = FALSE} +f_parent_saemix_dfop_tc$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") ``` - -The AIC values based on importance sampling and Gaussian quadrature are quite -similar. Using linearisation is less accurate, but still gives a similar value. - +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. ### nlmixr @@ -302,11 +310,11 @@ routinely used, but which can also be used for related data like chemical degradation data. A current development branch of the mkin package provides an interface between mkin and nlmixr. Here, we check if we get equivalent results when using a refined version of the First Order Conditional Estimation -(FOCE) algorithm used in nlme, namely First Order Conditional Estimation with -Interaction (FOCEI), and the SAEM algorithm as implemented in nlmixr. +(FOCE) algorithm used in nlme, namely the First Order Conditional Estimation +with Interaction (FOCEI), and the SAEM algorithm as implemented in nlmixr. -First, the focei algorithm is used for the four model combinations and the -goodness of fit of the results is compared. +First, the focei algorithm is used for the four model combinations. A number of +warnings are produced with unclear significance. ```{r f_parent_nlmixr_focei, results = "hide", message = FALSE, warning = FALSE} library(nlmixr) @@ -316,78 +324,92 @@ f_parent_nlmixr_focei_dfop_const <- nlmixr(f_parent_mkin_const["DFOP", ], est = f_parent_nlmixr_focei_dfop_tc<- nlmixr(f_parent_mkin_tc["DFOP", ], est = "focei") ``` -```{r AIC_parent_nlmixr_focei} -AIC(f_parent_nlmixr_focei_sfo_const$nm, f_parent_nlmixr_focei_sfo_tc$nm, - f_parent_nlmixr_focei_dfop_const$nm, f_parent_nlmixr_focei_dfop_tc$nm) +```{r AIC_parent_nlmixr_focei, cache = FALSE} +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) ``` The AIC values are very close to the ones obtained with nlme which are repeated below for convenience. -```{r AIC_parent_nlme_rep} -AIC( - f_parent_nlme_sfo_const, f_parent_nlme_sfo_tc, f_parent_nlme_dfop_tc +```{r AIC_parent_nlme_rep, cache = FALSE} +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 ) ``` -Secondly, we use the SAEM estimation routine and check the convergence plots for -SFO with constant variance +Secondly, we use the SAEM estimation routine and check the convergence plots. The +control parameters also used for the saemix fits are defined beforehand. + +```{r nlmixr_saem_control} +nlmixr_saem_control <- saemControl(logLik = TRUE, + nBurn = 1000, nEm = 300, nmc = 15) +``` + +The we fit SFO with constant variance -```{r f_parent_nlmixr_saem_sfo_const, results = "hide", warning = FALSE, message = FALSE} +```{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::saemControl(logLik = TRUE)) + control = nlmixr_saem_control) traceplot(f_parent_nlmixr_saem_sfo_const$nm) ``` -for SFO with two-component error +and SFO with two-component error. -```{r f_parent_nlmixr_saem_sfo_tc, results = "hide", warning = FALSE, message = FALSE} +```{r f_parent_nlmixr_saem_sfo_tc, results = "hide", warning = FALSE, message = FALSE, dependson = "nlmixr_saem_control"} f_parent_nlmixr_saem_sfo_tc <- nlmixr(f_parent_mkin_tc["SFO", ], est = "saem", - control = nlmixr::saemControl(logLik = TRUE)) + control = nlmixr_saem_control) traceplot(f_parent_nlmixr_saem_sfo_tc$nm) ``` -For DFOP with constant variance, the convergence plots show considerable instability -of the fit, which can be alleviated by increasing the number of iterations and -the number of parallel chains for the first phase of algorithm. +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. -```{r f_parent_nlmixr_saem_dfop_const, results = "hide", warning = FALSE, message = FALSE} +```{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::saemControl(logLik = TRUE, nBurn = 1000), nmc = 15) + control = nlmixr_saem_control) traceplot(f_parent_nlmixr_saem_dfop_const$nm) ``` -For DFOP with two-component error, the same increase in iterations and parallel -chains was used, but using the two-component error appears to lead to a less -erratic convergence, so this may not be necessary to this degree. - +For DFOP with two-component error, a less erratic convergence is seen. -```{r f_parent_nlmixr_saem_dfop_tc, results = "hide", warning = FALSE, message = FALSE} +```{r f_parent_nlmixr_saem_dfop_tc, results = "hide", warning = FALSE, message = FALSE, dependson = "nlmixr_saem_control"} f_parent_nlmixr_saem_dfop_tc <- nlmixr(f_parent_mkin_tc["DFOP", ], est = "saem", - control = nlmixr::saemControl(logLik = TRUE, nBurn = 1000, nmc = 15)) + control = nlmixr_saem_control) traceplot(f_parent_nlmixr_saem_dfop_tc$nm) ``` The AIC values are internally calculated using Gaussian quadrature. For an -unknown reason, the AIC value obtained for the DFOP fit using the two-component -error model is given as Infinity. +unknown reason, the AIC value obtained for the DFOP fit using constant error +is given as Infinity. -```{r AIC_parent_nlmixr_saem} +```{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) ``` The following table gives the AIC values obtained with the three packages. -```{r AIC_all} +```{r AIC_all, cache = FALSE} AIC_all <- data.frame( + check.names = FALSE, "Degradation model" = c("SFO", "SFO", "DFOP", "DFOP"), "Error model" = c("const", "tc", "const", "tc"), nlme = c(AIC(f_parent_nlme_sfo_const), AIC(f_parent_nlme_sfo_tc), NA, AIC(f_parent_nlme_dfop_tc)), 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 = 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