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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-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_tffm0_1 = 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_tffm0_2 = 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_tffm0_3 = 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: 0x555560091f40></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-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> [====|====|====|====|====|====|====|====|====|====] 0:00:02 </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> [====|====|====|====|====|====|====|====|====|====] 0:00:04 </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> [====|====|====|====|====|====|====|====|====|====] 0:00:07 </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> Needed Covariates:</span>
<span class="r-out co"><span class="r-pr">#></span> [1] "CMT"</span>
<span class="r-msg co"><span class="r-pr">#></span> RxODE 1.1.4 using 8 threads (see ?getRxThreads)</span>
<span class="r-msg co"><span class="r-pr">#></span> no cache: create with `rxCreateCache()`</span>
<span class="r-out co"><span class="r-pr">#></span> <span style="font-weight: bold;">Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation</span>
<span class="r-out co"><span class="r-pr">#></span> F: Forward difference gradient approximation</span>
<span class="r-out co"><span class="r-pr">#></span> C: Central difference gradient approximation</span>
<span class="r-out co"><span class="r-pr">#></span> M: Mixed forward and central difference gradient approximation</span>
<span class="r-out co"><span class="r-pr">#></span> Unscaled parameters for Omegas=chol(solve(omega));</span>
<span class="r-out co"><span class="r-pr">#></span> Diagonals are transformed, as specified by foceiControl(diagXform=)</span>
<span class="r-out co"><span class="r-pr">#></span> |-----+---------------+-----------+-----------+-----------+-----------|</span>
<span class="r-out co"><span class="r-pr">#></span> | #| Objective Fun | DMTA_0 | log_k_M23 | log_k_M27 | log_k_M31 |</span>
<span class="r-out co"><span class="r-pr">#></span> |.....................| log_k1 | log_k2 | g_qlogis |f_DMTA_tffm0_1_qlogis |</span>
<span class="r-out co"><span class="r-pr">#></span> |.....................|f_DMTA_tffm0_2_qlogis |f_DMTA_tffm0_3_qlogis | sigma_low | rsd_high |</span>
<span class="r-out co"><span class="r-pr">#></span> |.....................| o1 | o2 | o3 | o4 |</span>
<span class="r-out co"><span class="r-pr">#></span> |.....................| o5 | o6 | o7 | o8 |</span>
<span class="r-out co"><span class="r-pr">#></span> <span style="text-decoration: underline;">|.....................| o9 | o10 |...........|...........|</span></span>
<span class="r-out co"><span class="r-pr">#></span> calculating covariance matrix</span>
<span class="r-out co"><span class="r-pr">#></span> done</span>
<span class="r-msg co"><span class="r-pr">#></span> Calculating residuals/tables</span>
<span class="r-msg co"><span class="r-pr">#></span> done</span>
<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span>
<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))</span>
<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>last objective function was not at minimum, possible problems in optimization</span>
<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>S matrix non-positive definite</span>
<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>using R matrix to calculate covariance</span>
<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>gradient problems with initial estimate and covariance; see $scaleInfo</span>
<span class="r-out co"><span class="r-pr">#></span> user system elapsed </span>
<span class="r-out co"><span class="r-pr">#></span> 553.721 10.570 564.258 </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-out co"><span class="r-pr">#></span> nlmixr version used for fitting: 2.0.6 </span>
<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.1.0 </span>
<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.1.2 </span>
<span class="r-out co"><span class="r-pr">#></span> Date of fit: Wed Mar 2 13:27:22 2022 </span>
<span class="r-out co"><span class="r-pr">#></span> Date of summary: Wed Mar 2 13:27:22 2022 </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Equations:</span>
<span class="r-out co"><span class="r-pr">#></span> d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span>
<span class="r-out co"><span class="r-pr">#></span> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span>
<span class="r-out co"><span class="r-pr">#></span> * DMTA</span>
<span class="r-out co"><span class="r-pr">#></span> d_M23/dt = + f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span>
<span class="r-out co"><span class="r-pr">#></span> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span>
<span class="r-out co"><span class="r-pr">#></span> exp(-k2 * time))) * DMTA - k_M23 * M23</span>
<span class="r-out co"><span class="r-pr">#></span> d_M27/dt = + f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span>
<span class="r-out co"><span class="r-pr">#></span> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span>
<span class="r-out co"><span class="r-pr">#></span> exp(-k2 * time))) * DMTA - k_M27 * M27 + k_M31 * M31</span>
<span class="r-out co"><span class="r-pr">#></span> d_M31/dt = + f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span>
<span class="r-out co"><span class="r-pr">#></span> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span>
<span class="r-out co"><span class="r-pr">#></span> exp(-k2 * time))) * DMTA - k_M31 * M31</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Data:</span>
<span class="r-out co"><span class="r-pr">#></span> 563 observations of 4 variable(s) grouped in 6 datasets</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Degradation model predictions using RxODE</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Fitted in 564.08 s</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Variance model: Two-component variance function </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Mean of starting values for individual parameters:</span>
<span class="r-out co"><span class="r-pr">#></span> DMTA_0 log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1 f_DMTA_ilr_2 </span>
<span class="r-out co"><span class="r-pr">#></span> 98.7132 -3.9216 -4.3306 -4.2442 0.1376 0.1388 </span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_3 log_k1 log_k2 g_qlogis </span>
<span class="r-out co"><span class="r-pr">#></span> -1.7554 -2.2352 -3.7758 0.4363 </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Mean of starting values for error model parameters:</span>
<span class="r-out co"><span class="r-pr">#></span> sigma_low rsd_high </span>
<span class="r-out co"><span class="r-pr">#></span> 0.70012 0.02577 </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Fixed degradation parameter values:</span>
<span class="r-out co"><span class="r-pr">#></span> None</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Results:</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Likelihood calculated by focei </span>
<span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span>
<span class="r-out co"><span class="r-pr">#></span> 1857 1952 -906.5</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Optimised parameters:</span>
<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span>
<span class="r-out co"><span class="r-pr">#></span> DMTA_0 98.0116 95.243 100.780</span>
<span class="r-out co"><span class="r-pr">#></span> log_k_M23 -4.0184 -5.213 -2.824</span>
<span class="r-out co"><span class="r-pr">#></span> log_k_M27 -4.2033 -5.013 -3.394</span>
<span class="r-out co"><span class="r-pr">#></span> log_k_M31 -4.1728 -4.999 -3.347</span>
<span class="r-out co"><span class="r-pr">#></span> log_k1 -2.4831 -3.398 -1.568</span>
<span class="r-out co"><span class="r-pr">#></span> log_k2 -3.8423 -5.450 -2.235</span>
<span class="r-out co"><span class="r-pr">#></span> g_qlogis 0.4682 -2.188 3.124</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_1_qlogis -2.0823 -2.591 -1.574</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_2_qlogis -2.1265 -2.686 -1.567</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_3_qlogis -2.0795 -2.735 -1.424</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Correlation: </span>
<span class="r-out co"><span class="r-pr">#></span> DMTA_0 lg__M23 lg__M27 lg__M31 log_k1 log_k2 g_qlogs</span>
<span class="r-out co"><span class="r-pr">#></span> log_k_M23 -0.0154 </span>
<span class="r-out co"><span class="r-pr">#></span> log_k_M27 -0.0164 0.0031 </span>
<span class="r-out co"><span class="r-pr">#></span> log_k_M31 -0.0131 0.0018 0.0541 </span>
<span class="r-out co"><span class="r-pr">#></span> log_k1 -0.0306 0.0045 0.0019 0.0011 </span>
<span class="r-out co"><span class="r-pr">#></span> log_k2 0.0527 -0.0043 -0.0037 -0.0003 0.0375 </span>
<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.1005 0.0076 0.0074 0.0013 0.0910 0.1151 </span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_1_qlogis -0.0308 0.0362 0.0024 0.0021 0.0058 -0.0070 0.0145</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_2_qlogis -0.0309 0.0062 0.0353 -0.0229 0.0047 -0.0082 0.0146</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_3_qlogis -0.0308 0.0061 0.0419 0.0547 0.0033 -0.0055 0.0104</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_0_1 f_DMTA_0_2</span>
<span class="r-out co"><span class="r-pr">#></span> log_k_M23 </span>
<span class="r-out co"><span class="r-pr">#></span> log_k_M27 </span>
<span class="r-out co"><span class="r-pr">#></span> log_k_M31 </span>
<span class="r-out co"><span class="r-pr">#></span> log_k1 </span>
<span class="r-out co"><span class="r-pr">#></span> log_k2 </span>
<span class="r-out co"><span class="r-pr">#></span> g_qlogis </span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_1_qlogis </span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_2_qlogis 0.0118 </span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_3_qlogis 0.0086 -0.0057 </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Random effects (omega):</span>
<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 eta.log_k_M23 eta.log_k_M27 eta.log_k_M31</span>
<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 4.224 0.000 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M23 0.000 1.041 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M27 0.000 0.000 0.4609 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M31 0.000 0.000 0.0000 0.4728</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 0.000 0.000 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k2 0.000 0.000 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.g_qlogis 0.000 0.000 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis 0.000 0.000 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_2_qlogis 0.000 0.000 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis 0.000 0.000 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 eta.log_k2 eta.g_qlogis</span>
<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 0.000 0.000 0.00</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M23 0.000 0.000 0.00</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M27 0.000 0.000 0.00</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M31 0.000 0.000 0.00</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 0.635 0.000 0.00</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k2 0.000 1.662 0.00</span>
<span class="r-out co"><span class="r-pr">#></span> eta.g_qlogis 0.000 0.000 4.36</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis 0.000 0.000 0.00</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_2_qlogis 0.000 0.000 0.00</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis 0.000 0.000 0.00</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis eta.f_DMTA_tffm0_2_qlogis</span>
<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M23 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M27 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M31 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k2 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.g_qlogis 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis 0.1909 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_2_qlogis 0.0000 0.2232</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis</span>
<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M23 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M27 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M31 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k2 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.g_qlogis 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_2_qlogis 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis 0.3149</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Variance model:</span>
<span class="r-out co"><span class="r-pr">#></span> sigma_low rsd_high </span>
<span class="r-out co"><span class="r-pr">#></span> 0.82408 0.03045 </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Backtransformed parameters:</span>
<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span>
<span class="r-out co"><span class="r-pr">#></span> DMTA_0 98.01163 95.243379 100.77988</span>
<span class="r-out co"><span class="r-pr">#></span> k_M23 0.01798 0.005443 0.05940</span>
<span class="r-out co"><span class="r-pr">#></span> k_M27 0.01495 0.006652 0.03358</span>
<span class="r-out co"><span class="r-pr">#></span> k_M31 0.01541 0.006746 0.03520</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M23 0.11083 NA NA</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M27 0.09474 NA NA</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M31 0.08827 NA NA</span>
<span class="r-out co"><span class="r-pr">#></span> k1 0.08348 0.033429 0.20848</span>
<span class="r-out co"><span class="r-pr">#></span> k2 0.02144 0.004296 0.10704</span>
<span class="r-out co"><span class="r-pr">#></span> g 0.61496 0.100857 0.95788</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Resulting formation fractions:</span>
<span class="r-out co"><span class="r-pr">#></span> ff</span>
<span class="r-out co"><span class="r-pr">#></span> DMTA_M23 0.11083</span>
<span class="r-out co"><span class="r-pr">#></span> DMTA_M27 0.09474</span>
<span class="r-out co"><span class="r-pr">#></span> DMTA_M31 0.08827</span>
<span class="r-out co"><span class="r-pr">#></span> DMTA_sink 0.70616</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Estimated disappearance times:</span>
<span class="r-out co"><span class="r-pr">#></span> DT50 DT90 DT50back DT50_k1 DT50_k2</span>
<span class="r-out co"><span class="r-pr">#></span> DMTA 12.96 64.24 19.34 8.303 32.32</span>
<span class="r-out co"><span class="r-pr">#></span> M23 38.55 128.06 NA NA NA</span>
<span class="r-out co"><span class="r-pr">#></span> M27 46.38 154.06 NA NA NA</span>
<span class="r-out co"><span class="r-pr">#></span> M31 44.98 149.43 NA NA NA</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-plt img"><img src="dimethenamid_2018-1.png" alt="" width="700" height="433"></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: 12.58 0 12.58</span>
<span class="r-msg co"><span class="r-pr">#></span> Timing stopped at: 12.99 0.008 13</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-msg co"><span class="r-pr">#></span> </span>
<span class="r-msg co"><span class="r-pr">#></span> → generate SAEM model</span>
<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span>
<span class="r-out co"><span class="r-pr">#></span> 1: 98.7179 -3.4492 -3.2592 -3.6952 -2.1629 -2.7824 0.8990 -2.8080 -2.7380 -2.8041 2.7789 0.6848 0.8170 0.7125 0.8550 1.5200 2.9882 0.3073 0.2850 0.2877 4.0480 0.4153 4.5214 0.3775 4.4419 0.4181 3.7069 0.5935</span>
<span class="r-out co"><span class="r-pr">#></span> 500: 97.8519 -4.3891 -4.0888 -4.1247 -2.9246 -4.2755 2.6294 -2.1212 -2.1380 -2.0739 3.1293 1.2665 0.2763 0.3429 0.5743 1.5561 4.4991 0.1499 0.1551 0.3103 0.9514 0.0341 0.4846 0.1068 0.6597 0.0767 0.7836 0.0360</span>
<span class="r-msg co"><span class="r-pr">#></span> Calculating covariance matrix</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-msg co"><span class="r-pr">#></span> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span>
<span class="r-out co"><span class="r-pr">#></span> </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> → 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> Needed Covariates:</span>
<span class="r-out co"><span class="r-pr">#></span> [1] "CMT"</span>
<span class="r-msg co"><span class="r-pr">#></span> Calculating residuals/tables</span>
<span class="r-msg co"><span class="r-pr">#></span> done</span>
<span class="r-out co"><span class="r-pr">#></span> user system elapsed </span>
<span class="r-out co"><span class="r-pr">#></span> 785.825 3.841 153.598 </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-out co"><span class="r-pr">#></span> nlmixr version used for fitting: 2.0.6 </span>
<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.1.0 </span>
<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.1.2 </span>
<span class="r-out co"><span class="r-pr">#></span> Date of fit: Wed Mar 2 13:30:09 2022 </span>
<span class="r-out co"><span class="r-pr">#></span> Date of summary: Wed Mar 2 13:30:09 2022 </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Equations:</span>
<span class="r-out co"><span class="r-pr">#></span> d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span>
<span class="r-out co"><span class="r-pr">#></span> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span>
<span class="r-out co"><span class="r-pr">#></span> * DMTA</span>
<span class="r-out co"><span class="r-pr">#></span> d_M23/dt = + f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span>
<span class="r-out co"><span class="r-pr">#></span> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span>
<span class="r-out co"><span class="r-pr">#></span> exp(-k2 * time))) * DMTA - k_M23 * M23</span>
<span class="r-out co"><span class="r-pr">#></span> d_M27/dt = + f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span>
<span class="r-out co"><span class="r-pr">#></span> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span>
<span class="r-out co"><span class="r-pr">#></span> exp(-k2 * time))) * DMTA - k_M27 * M27 + k_M31 * M31</span>
<span class="r-out co"><span class="r-pr">#></span> d_M31/dt = + f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span>
<span class="r-out co"><span class="r-pr">#></span> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span>
<span class="r-out co"><span class="r-pr">#></span> exp(-k2 * time))) * DMTA - k_M31 * M31</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Data:</span>
<span class="r-out co"><span class="r-pr">#></span> 563 observations of 4 variable(s) grouped in 6 datasets</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Degradation model predictions using RxODE</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Fitted in 153.313 s</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Variance model: Two-component variance function </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Mean of starting values for individual parameters:</span>
<span class="r-out co"><span class="r-pr">#></span> DMTA_0 log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1 f_DMTA_ilr_2 </span>
<span class="r-out co"><span class="r-pr">#></span> 98.7132 -3.9216 -4.3306 -4.2442 0.1376 0.1388 </span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_3 log_k1 log_k2 g_qlogis </span>
<span class="r-out co"><span class="r-pr">#></span> -1.7554 -2.2352 -3.7758 0.4363 </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Mean of starting values for error model parameters:</span>
<span class="r-out co"><span class="r-pr">#></span> sigma_low_DMTA rsd_high_DMTA sigma_low_M23 rsd_high_M23 sigma_low_M27 </span>
<span class="r-out co"><span class="r-pr">#></span> 0.70012 0.02577 0.70012 0.02577 0.70012 </span>
<span class="r-out co"><span class="r-pr">#></span> rsd_high_M27 sigma_low_M31 rsd_high_M31 </span>
<span class="r-out co"><span class="r-pr">#></span> 0.02577 0.70012 0.02577 </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Fixed degradation parameter values:</span>
<span class="r-out co"><span class="r-pr">#></span> None</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Results:</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Likelihood calculated by focei </span>
<span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span>
<span class="r-out co"><span class="r-pr">#></span> 1966 2088 -955.2</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Optimised parameters:</span>
<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span>
<span class="r-out co"><span class="r-pr">#></span> DMTA_0 97.852 95.86386 99.840</span>
<span class="r-out co"><span class="r-pr">#></span> log_k_M23 -4.389 -5.35084 -3.427</span>
<span class="r-out co"><span class="r-pr">#></span> log_k_M27 -4.089 -4.54432 -3.633</span>
<span class="r-out co"><span class="r-pr">#></span> log_k_M31 -4.125 -4.63280 -3.617</span>
<span class="r-out co"><span class="r-pr">#></span> log_k1 -2.925 -3.54158 -2.308</span>
<span class="r-out co"><span class="r-pr">#></span> log_k2 -4.275 -5.81760 -2.733</span>
<span class="r-out co"><span class="r-pr">#></span> g_qlogis 2.629 -0.01785 5.277</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_1_qlogis -2.121 -2.44462 -1.798</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_2_qlogis -2.138 -2.47804 -1.798</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_3_qlogis -2.074 -2.53581 -1.612</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Correlation: </span>
<span class="r-out co"><span class="r-pr">#></span> DMTA_0 lg__M23 lg__M27 lg__M31 log_k1 log_k2 g_qlogs</span>
<span class="r-out co"><span class="r-pr">#></span> log_k_M23 -0.0164 </span>
<span class="r-out co"><span class="r-pr">#></span> log_k_M27 -0.0267 0.0028 </span>
<span class="r-out co"><span class="r-pr">#></span> log_k_M31 -0.0179 0.0023 0.0755 </span>
<span class="r-out co"><span class="r-pr">#></span> log_k1 0.0385 -0.0034 -0.0054 -0.0029 </span>
<span class="r-out co"><span class="r-pr">#></span> log_k2 0.0381 0.0115 0.0087 0.0093 0.0786 </span>
<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.0656 0.0021 0.0051 0.0001 -0.1177 -0.4389 </span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_1_qlogis -0.0604 0.0554 0.0054 0.0039 -0.0082 -0.0022 0.0119</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_2_qlogis -0.0601 0.0091 0.0577 -0.0350 -0.0081 -0.0057 0.0137</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_3_qlogis -0.0515 0.0083 0.0569 0.0729 -0.0059 0.0005 0.0073</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_0_1 f_DMTA_0_2</span>
<span class="r-out co"><span class="r-pr">#></span> log_k_M23 </span>
<span class="r-out co"><span class="r-pr">#></span> log_k_M27 </span>
<span class="r-out co"><span class="r-pr">#></span> log_k_M31 </span>
<span class="r-out co"><span class="r-pr">#></span> log_k1 </span>
<span class="r-out co"><span class="r-pr">#></span> log_k2 </span>
<span class="r-out co"><span class="r-pr">#></span> g_qlogis </span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_1_qlogis </span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_2_qlogis 0.0167 </span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_3_qlogis 0.0145 -0.0060 </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Random effects (omega):</span>
<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 eta.log_k_M23 eta.log_k_M27 eta.log_k_M31</span>
<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 3.129 0.000 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M23 0.000 1.266 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M27 0.000 0.000 0.2763 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M31 0.000 0.000 0.0000 0.3429</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 0.000 0.000 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k2 0.000 0.000 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.g_qlogis 0.000 0.000 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis 0.000 0.000 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_2_qlogis 0.000 0.000 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis 0.000 0.000 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 eta.log_k2 eta.g_qlogis</span>
<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 0.0000 0.000 0.000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M23 0.0000 0.000 0.000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M27 0.0000 0.000 0.000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M31 0.0000 0.000 0.000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 0.5743 0.000 0.000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k2 0.0000 1.556 0.000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.g_qlogis 0.0000 0.000 4.499</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis 0.0000 0.000 0.000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_2_qlogis 0.0000 0.000 0.000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis 0.0000 0.000 0.000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis eta.f_DMTA_tffm0_2_qlogis</span>
<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M23 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M27 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M31 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k2 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.g_qlogis 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis 0.1499 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_2_qlogis 0.0000 0.1551</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis 0.0000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis</span>
<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M23 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M27 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M31 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.log_k2 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.g_qlogis 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_2_qlogis 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis 0.3103</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Variance model:</span>
<span class="r-out co"><span class="r-pr">#></span> sigma_low_DMTA rsd_high_DMTA sigma_low_M23 rsd_high_M23 sigma_low_M27 </span>
<span class="r-out co"><span class="r-pr">#></span> 0.95135 0.03412 0.48455 0.10682 0.65969 </span>
<span class="r-out co"><span class="r-pr">#></span> rsd_high_M27 sigma_low_M31 rsd_high_M31 </span>
<span class="r-out co"><span class="r-pr">#></span> 0.07670 0.78365 0.03598 </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Backtransformed parameters:</span>
<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span>
<span class="r-out co"><span class="r-pr">#></span> DMTA_0 97.85189 95.863863 99.83992</span>
<span class="r-out co"><span class="r-pr">#></span> k_M23 0.01241 0.004744 0.03247</span>
<span class="r-out co"><span class="r-pr">#></span> k_M27 0.01676 0.010627 0.02643</span>
<span class="r-out co"><span class="r-pr">#></span> k_M31 0.01617 0.009727 0.02687</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M23 0.10705 NA NA</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M27 0.09417 NA NA</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M31 0.08919 NA NA</span>
<span class="r-out co"><span class="r-pr">#></span> k1 0.05369 0.028968 0.09950</span>
<span class="r-out co"><span class="r-pr">#></span> k2 0.01391 0.002975 0.06500</span>
<span class="r-out co"><span class="r-pr">#></span> g 0.93273 0.495538 0.99492</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Resulting formation fractions:</span>
<span class="r-out co"><span class="r-pr">#></span> ff</span>
<span class="r-out co"><span class="r-pr">#></span> DMTA_M23 0.10705</span>
<span class="r-out co"><span class="r-pr">#></span> DMTA_M27 0.09417</span>
<span class="r-out co"><span class="r-pr">#></span> DMTA_M31 0.08919</span>
<span class="r-out co"><span class="r-pr">#></span> DMTA_sink 0.70959</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Estimated disappearance times:</span>
<span class="r-out co"><span class="r-pr">#></span> DT50 DT90 DT50back DT50_k1 DT50_k2</span>
<span class="r-out co"><span class="r-pr">#></span> DMTA 13.81 49.3 14.84 12.91 49.85</span>
<span class="r-out co"><span class="r-pr">#></span> M23 55.85 185.5 NA NA NA</span>
<span class="r-out co"><span class="r-pr">#></span> M27 41.36 137.4 NA NA NA</span>
<span class="r-out co"><span class="r-pr">#></span> M31 42.87 142.4 NA NA NA</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-plt img"><img src="dimethenamid_2018-2.png" alt="" width="700" height="433"></span>
<span class="r-in"><span class="co"># }</span></span>
</code></pre></div>
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