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<!DOCTYPE html>
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published by EFSA. Kinetic evaluations shown for these datasets are intended
to illustrate and advance kinetic modelling. The fact that these data and
some results are shown here does not imply a license to use them in the
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<h1>Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018</h1>
<small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/dimethenamid_2018.R" class="external-link"><code>R/dimethenamid_2018.R</code></a></small>
<div class="hidden name"><code>dimethenamid_2018.Rd</code></div>
</div>
<div class="ref-description">
<p>The datasets were extracted from the active substance evaluation dossier
published by EFSA. Kinetic evaluations shown for these datasets are intended
to illustrate and advance kinetic modelling. The fact that these data and
some results are shown here does not imply a license to use them in the
context of pesticide registrations, as the use of the data may be
constrained by data protection regulations.</p>
</div>
<div id="ref-usage">
<div class="sourceCode"><pre class="sourceCode r"><code><span class="va">dimethenamid_2018</span></code></pre></div>
</div>
<div id="format">
<h2>Format</h2>
<p>An <a href="mkindsg.html">mkindsg</a> object grouping seven datasets with some meta information</p>
</div>
<div id="source">
<h2>Source</h2>
<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" class="external-link">https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716</a></p>
</div>
<div id="details">
<h2>Details</h2>
<p>The R code used to create this data object is installed with this package
in the 'dataset_generation' directory. In the code, page numbers are given for
specific pieces of information in the comments.</p>
</div>
<div id="ref-examples">
<h2>Examples</h2>
<div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">)</span></span>
<span class="r-out co"><span class="r-pr">#></span> <mkindsg> holding 7 mkinds objects</span>
<span class="r-out co"><span class="r-pr">#></span> Title $title: Aerobic soil degradation data on dimethenamid-P from the EU assessment in 2018 </span>
<span class="r-out co"><span class="r-pr">#></span> Occurrence of observed compounds $observed_n:</span>
<span class="r-out co"><span class="r-pr">#></span> DMTAP M23 M27 M31 DMTA </span>
<span class="r-out co"><span class="r-pr">#></span> 3 7 7 7 4 </span>
<span class="r-out co"><span class="r-pr">#></span> Time normalisation factors $f_time_norm:</span>
<span class="r-out co"><span class="r-pr">#></span> [1] 1.0000000 0.9706477 1.2284784 1.2284784 0.6233856 0.7678922 0.6733938</span>
<span class="r-out co"><span class="r-pr">#></span> Meta information $meta:</span>
<span class="r-out co"><span class="r-pr">#></span> study usda_soil_type study_moisture_ref_type rel_moisture</span>
<span class="r-out co"><span class="r-pr">#></span> Calke Unsworth 2014 Sandy loam pF2 1.00</span>
<span class="r-out co"><span class="r-pr">#></span> Borstel Staudenmaier 2009 Sand pF1 0.50</span>
<span class="r-out co"><span class="r-pr">#></span> Elliot 1 Wendt 1997 Clay loam pF2.5 0.75</span>
<span class="r-out co"><span class="r-pr">#></span> Elliot 2 Wendt 1997 Clay loam pF2.5 0.75</span>
<span class="r-out co"><span class="r-pr">#></span> Flaach König 1996 Sandy clay loam pF1 0.40</span>
<span class="r-out co"><span class="r-pr">#></span> BBA 2.2 König 1995 Loamy sand pF1 0.40</span>
<span class="r-out co"><span class="r-pr">#></span> BBA 2.3 König 1995 Sandy loam pF1 0.40</span>
<span class="r-out co"><span class="r-pr">#></span> study_ref_moisture temperature</span>
<span class="r-out co"><span class="r-pr">#></span> Calke NA 20</span>
<span class="r-out co"><span class="r-pr">#></span> Borstel 23.00 20</span>
<span class="r-out co"><span class="r-pr">#></span> Elliot 1 33.37 23</span>
<span class="r-out co"><span class="r-pr">#></span> Elliot 2 33.37 23</span>
<span class="r-out co"><span class="r-pr">#></span> Flaach NA 20</span>
<span class="r-out co"><span class="r-pr">#></span> BBA 2.2 NA 20</span>
<span class="r-out co"><span class="r-pr">#></span> BBA 2.3 NA 20</span>
<span class="r-in"><span class="va">dmta_ds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span>
<span class="r-in"> <span class="va">ds_i</span> <span class="op"><-</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span><span class="op">[[</span><span class="va">i</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span></span>
<span class="r-in"> <span class="va">ds_i</span><span class="op">[</span><span class="va">ds_i</span><span class="op">$</span><span class="va">name</span> <span class="op">==</span> <span class="st">"DMTAP"</span>, <span class="st">"name"</span><span class="op">]</span> <span class="op"><-</span> <span class="st">"DMTA"</span></span>
<span class="r-in"> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op"><-</span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">*</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">f_time_norm</span><span class="op">[</span><span class="va">i</span><span class="op">]</span></span>
<span class="r-in"> <span class="va">ds_i</span></span>
<span class="r-in"><span class="op">}</span><span class="op">)</span></span>
<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="va">ds</span><span class="op">$</span><span class="va">title</span><span class="op">)</span></span>
<span class="r-in"><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span>, <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span>
<span class="r-in"><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="cn">NULL</span></span>
<span class="r-in"><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="cn">NULL</span></span>
<span class="r-in"><span class="co"># \dontrun{</span></span>
<span class="r-in"><span class="va">dfop_sfo3_plus</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span></span>
<span class="r-in"> DMTA <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M23"</span>, <span class="st">"M27"</span>, <span class="st">"M31"</span><span class="op">)</span><span class="op">)</span>,</span>
<span class="r-in"> M23 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
<span class="r-in"> M27 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span>
<span class="r-in"> M31 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M27"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span>
<span class="r-in"> quiet <span class="op">=</span> <span class="cn">TRUE</span></span>
<span class="r-in"><span class="op">)</span></span>
<span class="r-in"><span class="va">f_dmta_mkin_tc</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span></span>
<span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"DFOP-SFO3+"</span> <span class="op">=</span> <span class="va">dfop_sfo3_plus</span><span class="op">)</span>,</span>
<span class="r-in"> <span class="va">dmta_ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span>
<span class="r-in"><span class="fu"><a href="nlmixr.mmkin.html">nlmixr_model</a></span><span class="op">(</span><span class="va">f_dmta_mkin_tc</span><span class="op">)</span></span>
<span class="r-msg co"><span class="r-pr">#></span> 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>
<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>number of items to replace is not a multiple of replacement length</span>
<span class="r-out co"><span class="r-pr">#></span> function () </span>
<span class="r-out co"><span class="r-pr">#></span> {</span>
<span class="r-out co"><span class="r-pr">#></span> ini({</span>
<span class="r-out co"><span class="r-pr">#></span> DMTA_0 = 99</span>
<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 ~ 2.3</span>
<span class="r-out co"><span class="r-pr">#></span> log_k_M23 = -3.9</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M23 ~ 0.55</span>
<span class="r-out co"><span class="r-pr">#></span> log_k_M27 = -4.3</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M27 ~ 0.86</span>
<span class="r-out co"><span class="r-pr">#></span> log_k_M31 = -4.2</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M31 ~ 0.75</span>
<span class="r-out co"><span class="r-pr">#></span> log_k1 = -2.2</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 ~ 0.9</span>
<span class="r-out co"><span class="r-pr">#></span> log_k2 = -3.8</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k2 ~ 1.6</span>
<span class="r-out co"><span class="r-pr">#></span> g_qlogis = 0.44</span>
<span class="r-out co"><span class="r-pr">#></span> eta.g_qlogis ~ 3.1</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_1_qlogis = -2.1</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis ~ 0.3</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_2_qlogis = -2.2</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_2_qlogis ~ 0.3</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_3_qlogis = -2.1</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis ~ 0.3</span>
<span class="r-out co"><span class="r-pr">#></span> sigma_low_DMTA = 0.7</span>
<span class="r-out co"><span class="r-pr">#></span> rsd_high_DMTA = 0.026</span>
<span class="r-out co"><span class="r-pr">#></span> sigma_low_M23 = 0.7</span>
<span class="r-out co"><span class="r-pr">#></span> rsd_high_M23 = 0.026</span>
<span class="r-out co"><span class="r-pr">#></span> sigma_low_M27 = 0.7</span>
<span class="r-out co"><span class="r-pr">#></span> rsd_high_M27 = 0.026</span>
<span class="r-out co"><span class="r-pr">#></span> sigma_low_M31 = 0.7</span>
<span class="r-out co"><span class="r-pr">#></span> rsd_high_M31 = 0.026</span>
<span class="r-out co"><span class="r-pr">#></span> })</span>
<span class="r-out co"><span class="r-pr">#></span> model({</span>
<span class="r-out co"><span class="r-pr">#></span> DMTA_0_model = DMTA_0 + eta.DMTA_0</span>
<span class="r-out co"><span class="r-pr">#></span> DMTA(0) = DMTA_0_model</span>
<span class="r-out co"><span class="r-pr">#></span> k_M23 = exp(log_k_M23 + eta.log_k_M23)</span>
<span class="r-out co"><span class="r-pr">#></span> k_M27 = exp(log_k_M27 + eta.log_k_M27)</span>
<span class="r-out co"><span class="r-pr">#></span> k_M31 = exp(log_k_M31 + eta.log_k_M31)</span>
<span class="r-out co"><span class="r-pr">#></span> k1 = exp(log_k1 + eta.log_k1)</span>
<span class="r-out co"><span class="r-pr">#></span> k2 = exp(log_k2 + eta.log_k2)</span>
<span class="r-out co"><span class="r-pr">#></span> g = expit(g_qlogis + eta.g_qlogis)</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M23 = expit(f_DMTA_tffm0_1_qlogis + eta.f_DMTA_tffm0_1_qlogis)</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M23 = expit(f_DMTA_tffm0_2_qlogis + eta.f_DMTA_tffm0_2_qlogis)</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M23 = expit(f_DMTA_tffm0_3_qlogis + eta.f_DMTA_tffm0_3_qlogis)</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M23 = f_DMTA_tffm0_1</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M27 = f_DMTA_tffm0_2 * (1 - f_DMTA_tffm0_1)</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M31 = f_DMTA_tffm0_3 * (1 - f_DMTA_tffm0_2) * </span>
<span class="r-out co"><span class="r-pr">#></span> (1 - f_DMTA_tffm0_1)</span>
<span class="r-out co"><span class="r-pr">#></span> d/dt(DMTA) = -((k1 * g * exp(-k1 * time) + k2 * (1 - </span>
<span class="r-out co"><span class="r-pr">#></span> g) * exp(-k2 * time))/(g * exp(-k1 * time) + (1 - </span>
<span class="r-out co"><span class="r-pr">#></span> g) * exp(-k2 * time))) * DMTA</span>
<span class="r-out co"><span class="r-pr">#></span> d/dt(M23) = +f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + </span>
<span class="r-out co"><span class="r-pr">#></span> k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + </span>
<span class="r-out co"><span class="r-pr">#></span> (1 - g) * exp(-k2 * time))) * DMTA - k_M23 * M23</span>
<span class="r-out co"><span class="r-pr">#></span> d/dt(M27) = +f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + </span>
<span class="r-out co"><span class="r-pr">#></span> k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + </span>
<span class="r-out co"><span class="r-pr">#></span> (1 - g) * exp(-k2 * time))) * DMTA - k_M27 * M27 + </span>
<span class="r-out co"><span class="r-pr">#></span> k_M31 * M31</span>
<span class="r-out co"><span class="r-pr">#></span> d/dt(M31) = +f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + </span>
<span class="r-out co"><span class="r-pr">#></span> k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + </span>
<span class="r-out co"><span class="r-pr">#></span> (1 - g) * exp(-k2 * time))) * DMTA - k_M31 * M31</span>
<span class="r-out co"><span class="r-pr">#></span> DMTA ~ add(sigma_low_DMTA) + prop(rsd_high_DMTA)</span>
<span class="r-out co"><span class="r-pr">#></span> M23 ~ add(sigma_low_M23) + prop(rsd_high_M23)</span>
<span class="r-out co"><span class="r-pr">#></span> M27 ~ add(sigma_low_M27) + prop(rsd_high_M27)</span>
<span class="r-out co"><span class="r-pr">#></span> M31 ~ add(sigma_low_M31) + prop(rsd_high_M31)</span>
<span class="r-out co"><span class="r-pr">#></span> })</span>
<span class="r-out co"><span class="r-pr">#></span> }</span>
<span class="r-out co"><span class="r-pr">#></span> <environment: 0x55555fca3790></span>
<span class="r-in"><span class="co"># The focei fit takes about four minutes on my system</span></span>
<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span></span>
<span class="r-in"> <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" class="external-link">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>,</span>
<span class="r-in"> 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" class="external-link">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></span>
<span class="r-in"><span class="op">)</span></span>
<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>number of items to replace is not a multiple of replacement length</span>
<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
<span class="r-msg co"><span class="r-pr">#></span> → creating full model...</span>
<span class="r-msg co"><span class="r-pr">#></span> → pruning branches (`if`/`else`)...</span>
<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span>
<span class="r-msg co"><span class="r-pr">#></span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span>
<span class="r-msg co"><span class="r-pr">#></span> → creating full model...</span>
<span class="r-msg co"><span class="r-pr">#></span> → pruning branches (`if`/`else`)...</span>
<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span>
<span class="r-msg co"><span class="r-pr">#></span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span>
<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span>
<span class="r-msg co"><span class="r-pr">#></span> → calculate jacobian</span>
<span class="r-out co"><span class="r-pr">#></span> [====|====|====|====|====|====|====|====|====|====] 0:00:01 </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-msg co"><span class="r-pr">#></span> → calculate sensitivities</span>
<span class="r-out co"><span class="r-pr">#></span> [====|====|====|====|====|====|====|====|====|====] 0:00:03 </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-msg co"><span class="r-pr">#></span> → calculate ∂(f)/∂(η)</span>
<span class="r-out co"><span class="r-pr">#></span> [====|====|====|====|====|====|====|====|====|====] 0:00:01 </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-msg co"><span class="r-pr">#></span> → calculate ∂(R²)/∂(η)</span>
<span class="r-out co"><span class="r-pr">#></span> [====|====|====|====|====|====|====|====|====|====] 0:00:08 </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-msg co"><span class="r-pr">#></span> → finding duplicate expressions in inner model...</span>
<span class="r-out co"><span class="r-pr">#></span> [====|====|====|====|====|====|====|====|====|====] 0:00:07 </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-msg co"><span class="r-pr">#></span> → optimizing duplicate expressions in inner model...</span>
<span class="r-out co"><span class="r-pr">#></span> [====|====|====|====|====|====|====|====|====|====] 0:00:06 </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-msg co"><span class="r-pr">#></span> → finding duplicate expressions in EBE model...</span>
<span class="r-out co"><span class="r-pr">#></span> [====|====|====|====|====|====|====|====|====|====] 0:00:00 </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-msg co"><span class="r-pr">#></span> → optimizing duplicate expressions in EBE model...</span>
<span class="r-out co"><span class="r-pr">#></span> [====|====|====|====|====|====|====|====|====|====] 0:00:00 </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-msg co"><span class="r-pr">#></span> → compiling inner model...</span>
<span class="r-msg co"><span class="r-pr">#></span> </span>
<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span>
<span class="r-msg co"><span class="r-pr">#></span> → finding duplicate expressions in FD model...</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-msg co"><span class="r-pr">#></span> → optimizing duplicate expressions in FD model...</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-msg co"><span class="r-pr">#></span> → compiling EBE model...</span>
<span class="r-msg co"><span class="r-pr">#></span> </span>
<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span>
<span class="r-msg co"><span class="r-pr">#></span> → compiling events FD model...</span>
<span class="r-msg co"><span class="r-pr">#></span> </span>
<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span>
<span class="r-msg co"><span class="r-pr">#></span> Model:</span>
<span class="r-msg co"><span class="r-pr">#></span> cmt(DMTA);</span>
<span class="r-msg co"><span class="r-pr">#></span> cmt(M23);</span>
<span class="r-msg co"><span class="r-pr">#></span> cmt(M27);</span>
<span class="r-msg co"><span class="r-pr">#></span> cmt(M31);</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_14~ETA[1]+THETA[1];</span>
<span class="r-msg co"><span class="r-pr">#></span> DMTA(0)=rx_expr_14;</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_15~ETA[5]+THETA[5];</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_16~ETA[7]+THETA[7];</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_17~ETA[6]+THETA[6];</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_24~exp(rx_expr_15);</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_25~exp(rx_expr_17);</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_29~t*rx_expr_24;</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_30~t*rx_expr_25;</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_31~exp(-(rx_expr_16));</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_35~1+rx_expr_31;</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_40~1/(rx_expr_35);</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_42~(rx_expr_40);</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_43~1-rx_expr_42;</span>
<span class="r-msg co"><span class="r-pr">#></span> d/dt(DMTA)=-DMTA*(exp(rx_expr_15-rx_expr_29)/(rx_expr_35)+exp(rx_expr_17-rx_expr_30)*(rx_expr_43))/(exp(-t*rx_expr_24)/(rx_expr_35)+exp(-t*rx_expr_25)*(rx_expr_43));</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_18~ETA[2]+THETA[2];</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_26~exp(rx_expr_18);</span>
<span class="r-msg co"><span class="r-pr">#></span> d/dt(M23)=-rx_expr_26*M23+DMTA*(exp(rx_expr_15-rx_expr_29)/(rx_expr_35)+exp(rx_expr_17-rx_expr_30)*(rx_expr_43))*f_DMTA_tffm0_1/(exp(-t*rx_expr_24)/(rx_expr_35)+exp(-t*rx_expr_25)*(rx_expr_43));</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_19~ETA[3]+THETA[3];</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_20~ETA[4]+THETA[4];</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_21~1-f_DMTA_tffm0_1;</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_27~exp(rx_expr_19);</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_28~exp(rx_expr_20);</span>
<span class="r-msg co"><span class="r-pr">#></span> d/dt(M27)=-rx_expr_27*M27+rx_expr_28*M31+DMTA*(rx_expr_21)*(exp(rx_expr_15-rx_expr_29)/(rx_expr_35)+exp(rx_expr_17-rx_expr_30)*(rx_expr_43))*f_DMTA_tffm0_2/(exp(-t*rx_expr_24)/(rx_expr_35)+exp(-t*rx_expr_25)*(rx_expr_43));</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_22~1-f_DMTA_tffm0_2;</span>
<span class="r-msg co"><span class="r-pr">#></span> d/dt(M31)=-rx_expr_28*M31+DMTA*(rx_expr_22)*(rx_expr_21)*(exp(rx_expr_15-rx_expr_29)/(rx_expr_35)+exp(rx_expr_17-rx_expr_30)*(rx_expr_43))*f_DMTA_tffm0_3/(exp(-t*rx_expr_24)/(rx_expr_35)+exp(-t*rx_expr_25)*(rx_expr_43));</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_0~CMT==4;</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_1~CMT==2;</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_2~CMT==1;</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_3~CMT==3;</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_4~1-(rx_expr_0);</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_5~1-(rx_expr_1);</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_6~1-(rx_expr_3);</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_yj_~(rx_expr_4)*((2*(rx_expr_5)*(rx_expr_2)+2*(rx_expr_1))*(rx_expr_6)+2*(rx_expr_3))+2*(rx_expr_0);</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_7~(rx_expr_1);</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_8~(rx_expr_3);</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_9~(rx_expr_0);</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_13~(rx_expr_5);</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_32~rx_expr_13*(rx_expr_2);</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_lambda_~(rx_expr_4)*((rx_expr_32+rx_expr_7)*(rx_expr_6)+rx_expr_8)+rx_expr_9;</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_hi_~(rx_expr_4)*((rx_expr_32+rx_expr_7)*(rx_expr_6)+rx_expr_8)+rx_expr_9;</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_low_~0;</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_10~M31*(rx_expr_0);</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_11~M27*(rx_expr_3);</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_12~M23*(rx_expr_1);</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_23~DMTA*(rx_expr_5);</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_36~rx_expr_23*(rx_expr_2);</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_pred_=(rx_expr_4)*((rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_3)+((rx_expr_1)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))+(rx_expr_5)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_2))*(rx_expr_6))+(rx_expr_0)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)));</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_33~Rx_pow_di(THETA[12],2);</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_expr_34~Rx_pow_di(THETA[11],2);</span>
<span class="r-msg co"><span class="r-pr">#></span> rx_r_=(rx_expr_4)*((rx_expr_33*Rx_pow_di(((rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_3)+((rx_expr_1)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))+(rx_expr_5)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_2))*(rx_expr_6)),2)+rx_expr_34)*(rx_expr_3)+((rx_expr_1)*(rx_expr_33*Rx_pow_di(((rx_expr_1)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))+(rx_expr_5)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_2)),2)+rx_expr_34)+(rx_expr_33*Rx_pow_di(((rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_2)),2)+rx_expr_34)*(rx_expr_5)*(rx_expr_2))*(rx_expr_6))+(rx_expr_0)*(rx_expr_33*Rx_pow_di(((rx_expr_4)*((rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_3)+((rx_expr_1)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))+(rx_expr_5)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_2))*(rx_expr_6))+(rx_expr_0)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))),2)+rx_expr_34);</span>
<span class="r-msg co"><span class="r-pr">#></span> DMTA_0=THETA[1];</span>
<span class="r-msg co"><span class="r-pr">#></span> log_k_M23=THETA[2];</span>
<span class="r-msg co"><span class="r-pr">#></span> log_k_M27=THETA[3];</span>
<span class="r-msg co"><span class="r-pr">#></span> log_k_M31=THETA[4];</span>
<span class="r-msg co"><span class="r-pr">#></span> log_k1=THETA[5];</span>
<span class="r-msg co"><span class="r-pr">#></span> log_k2=THETA[6];</span>
<span class="r-msg co"><span class="r-pr">#></span> g_qlogis=THETA[7];</span>
<span class="r-msg co"><span class="r-pr">#></span> f_DMTA_tffm0_1_qlogis=THETA[8];</span>
<span class="r-msg co"><span class="r-pr">#></span> f_DMTA_tffm0_2_qlogis=THETA[9];</span>
<span class="r-msg co"><span class="r-pr">#></span> f_DMTA_tffm0_3_qlogis=THETA[10];</span>
<span class="r-msg co"><span class="r-pr">#></span> sigma_low=THETA[11];</span>
<span class="r-msg co"><span class="r-pr">#></span> rsd_high=THETA[12];</span>
<span class="r-msg co"><span class="r-pr">#></span> eta.DMTA_0=ETA[1];</span>
<span class="r-msg co"><span class="r-pr">#></span> eta.log_k_M23=ETA[2];</span>
<span class="r-msg co"><span class="r-pr">#></span> eta.log_k_M27=ETA[3];</span>
<span class="r-msg co"><span class="r-pr">#></span> eta.log_k_M31=ETA[4];</span>
<span class="r-msg co"><span class="r-pr">#></span> eta.log_k1=ETA[5];</span>
<span class="r-msg co"><span class="r-pr">#></span> eta.log_k2=ETA[6];</span>
<span class="r-msg co"><span class="r-pr">#></span> eta.g_qlogis=ETA[7];</span>
<span class="r-msg co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis=ETA[8];</span>
<span class="r-msg co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_2_qlogis=ETA[9];</span>
<span class="r-msg co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis=ETA[10];</span>
<span class="r-msg co"><span class="r-pr">#></span> DMTA_0_model=rx_expr_14;</span>
<span class="r-msg co"><span class="r-pr">#></span> k_M23=rx_expr_26;</span>
<span class="r-msg co"><span class="r-pr">#></span> k_M27=rx_expr_27;</span>
<span class="r-msg co"><span class="r-pr">#></span> k_M31=rx_expr_28;</span>
<span class="r-msg co"><span class="r-pr">#></span> k1=rx_expr_24;</span>
<span class="r-msg co"><span class="r-pr">#></span> k2=rx_expr_25;</span>
<span class="r-msg co"><span class="r-pr">#></span> g=1/(rx_expr_35);</span>
<span class="r-msg co"><span class="r-pr">#></span> f_DMTA_to_M23=1/(1+exp(-(ETA[8]+THETA[8])));</span>
<span class="r-msg co"><span class="r-pr">#></span> f_DMTA_to_M23=1/(1+exp(-(ETA[9]+THETA[9])));</span>
<span class="r-msg co"><span class="r-pr">#></span> f_DMTA_to_M23=1/(1+exp(-(ETA[10]+THETA[10])));</span>
<span class="r-msg co"><span class="r-pr">#></span> f_DMTA_to_M23=f_DMTA_tffm0_1;</span>
<span class="r-msg co"><span class="r-pr">#></span> f_DMTA_to_M27=(rx_expr_21)*f_DMTA_tffm0_2;</span>
<span class="r-msg co"><span class="r-pr">#></span> f_DMTA_to_M31=(rx_expr_22)*(rx_expr_21)*f_DMTA_tffm0_3;</span>
<span class="r-msg co"><span class="r-pr">#></span> tad=tad();</span>
<span class="r-msg co"><span class="r-pr">#></span> dosenum=dosenum();</span>
<span class="r-msg co"><span class="r-pr">#></span> Needed Covariates:</span>
<span class="r-msg co"><span class="r-pr">#></span> [1] "f_DMTA_tffm0_1" "f_DMTA_tffm0_2" "f_DMTA_tffm0_3" "CMT" </span>
<span class="r-err co"><span class="r-pr">#></span> <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):</span> Not all the covariates are in the dataset.</span>
<span class="r-msg co"><span class="r-pr">#></span> Timing stopped at: 119.8 9.331 129.2</span>
<span class="r-msg co"><span class="r-pr">#></span> Timing stopped at: 120 9.331 129.3</span>
<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_dmta_nlmixr_focei</span><span class="op">)</span></span>
<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in summary(f_dmta_nlmixr_focei):</span> object 'f_dmta_nlmixr_focei' not found</span>
<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_dmta_nlmixr_focei</span><span class="op">)</span></span>
<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in plot(f_dmta_nlmixr_focei):</span> object 'f_dmta_nlmixr_focei' not found</span>
<span class="r-in"><span class="co"># Using saemix takes about 18 minutes</span></span>
<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span></span>
<span class="r-in"> <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>
<span class="r-in"><span class="op">)</span></span>
<span class="r-out co"><span class="r-pr">#></span> DINTDY- T (=R1) illegal </span>
<span class="r-out co"><span class="r-pr">#></span> In above message, R1 = 115.507</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> T not in interval TCUR - HU (= R1) to TCUR (=R2) </span>
<span class="r-out co"><span class="r-pr">#></span> In above message, R1 = 112.133, R2 = 113.577</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> DLSODA- At T (=R1), too much accuracy requested </span>
<span class="r-out co"><span class="r-pr">#></span> for precision of machine.. See TOLSF (=R2) </span>
<span class="r-out co"><span class="r-pr">#></span> In above message, R1 = 55.3899, R2 = nan</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in out[available, var]:</span> (subscript) logical subscript too long</span>
<span class="r-msg co"><span class="r-pr">#></span> Timing stopped at: 11.84 0.008 11.85</span>
<span class="r-msg co"><span class="r-pr">#></span> Timing stopped at: 12.16 0.008 12.17</span>
<span class="r-in"></span>
<span class="r-in"><span class="co"># nlmixr with est = "saem" is pretty fast with default iteration numbers, most</span></span>
<span class="r-in"><span class="co"># of the time (about 2.5 minutes) is spent for calculating the log likelihood at the end</span></span>
<span class="r-in"><span class="co"># The likelihood calculated for the nlmixr fit is much lower than that found by saemix</span></span>
<span class="r-in"><span class="co"># Also, the trace plot and the plot of the individual predictions is not</span></span>
<span class="r-in"><span class="co"># convincing for the parent. It seems we are fitting an overparameterised</span></span>
<span class="r-in"><span class="co"># model, so the result we get strongly depends on starting parameters and control settings.</span></span>
<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span></span>
<span class="r-in"> <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" class="external-link">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>,</span>
<span class="r-in"> 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" class="external-link">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>
<span class="r-in"><span class="op">)</span></span>
<span class="r-msg co"><span class="r-pr">#></span> 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>
<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span>
<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span>
<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in eval(substitute(expr), data, enclos = parent.frame()):</span> Cannot run SAEM since some of the parameters are not mu-referenced (eta.f_DMTA_tffm0_1_qlogis, eta.f_DMTA_tffm0_2_qlogis, eta.f_DMTA_tffm0_3_qlogis)</span>
<span class="r-msg co"><span class="r-pr">#></span> Timing stopped at: 0.892 0.004 0.896</span>
<span class="r-msg co"><span class="r-pr">#></span> Timing stopped at: 1.096 0.005 1.1</span>
<span class="r-in"><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></span>
<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in traceplot(f_dmta_nlmixr_saem$nm):</span> could not find function "traceplot"</span>
<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_dmta_nlmixr_saem</span><span class="op">)</span></span>
<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in summary(f_dmta_nlmixr_saem):</span> object 'f_dmta_nlmixr_saem' not found</span>
<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_dmta_nlmixr_saem</span><span class="op">)</span></span>
<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in plot(f_dmta_nlmixr_saem):</span> object 'f_dmta_nlmixr_saem' not found</span>
<span class="r-in"><span class="co"># }</span></span>
</code></pre></div>
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