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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
context of pesticide registrations, as the use of the data may be
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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="co"># We don't use DFOP for the parent compound, as this gives numerical</span></span>
<span class="r-in"><span class="co"># instabilities in the fits</span></span>
<span class="r-in"><span class="va">sfo_sfo3p</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">"SFO"</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">dmta_sfo_sfo3p_tc</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"SFO-SFO3+"</span> <span class="op">=</span> <span class="va">sfo_sfo3p</span><span class="op">)</span>,</span>
<span class="r-in"> <span class="va">dmta_ds</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
<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">dmta_sfo_sfo3p_tc</span><span class="op">)</span></span>
<span class="r-out co"><span class="r-pr">#></span> <mmkin> object</span>
<span class="r-out co"><span class="r-pr">#></span> Status of individual fits:</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> dataset</span>
<span class="r-out co"><span class="r-pr">#></span> model Calke Borstel Flaach BBA 2.2 BBA 2.3 Elliot</span>
<span class="r-out co"><span class="r-pr">#></span> SFO-SFO3+ OK OK OK OK OK OK </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> OK: No warnings</span>
<span class="r-in"><span class="co"># The default (test_log_parms = FALSE) gives an undue</span></span>
<span class="r-in"><span class="co"># influence of ill-defined rate constants that have</span></span>
<span class="r-in"><span class="co"># extremely small values:</span></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="fu"><a href="mixed.html">mixed</a></span><span class="op">(</span><span class="va">dmta_sfo_sfo3p_tc</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">FALSE</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"># If we disregards ill-defined rate constants, the results</span></span>
<span class="r-in"><span class="co"># look more plausible, but the truth is likely to be in</span></span>
<span class="r-in"><span class="co"># between these variants</span></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="fu"><a href="mixed.html">mixed</a></span><span class="op">(</span><span class="va">dmta_sfo_sfo3p_tc</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</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"># We can also specify a default value for the failing</span></span>
<span class="r-in"><span class="co"># log parameters, to mimic FOCUS guidance</span></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="fu"><a href="mixed.html">mixed</a></span><span class="op">(</span><span class="va">dmta_sfo_sfo3p_tc</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span>,</span>
<span class="r-in"> default_log_parms <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log</a></span><span class="op">(</span><span class="fl">2</span><span class="op">)</span><span class="op">/</span><span class="fl">1000</span><span class="op">)</span></span>
<span class="r-plt img"><img src="dimethenamid_2018-3.png" alt="" width="700" height="433"></span>
<span class="r-in"><span class="co"># As these attempts are not satisfying, we use nonlinear mixed-effects models</span></span>
<span class="r-in"><span class="co"># f_dmta_nlme_tc <- nlme(dmta_sfo_sfo3p_tc)</span></span>
<span class="r-in"><span class="co"># nlme reaches maxIter = 50 without convergence</span></span>
<span class="r-in"><span class="va">f_dmta_saem_tc</span> <span class="op"><-</span> <span class="fu"><a href="saem.html">saem</a></span><span class="op">(</span><span class="va">dmta_sfo_sfo3p_tc</span><span class="op">)</span></span>
<span class="r-in"><span class="co"># I am commenting out the convergence plot as rendering them</span></span>
<span class="r-in"><span class="co"># with pkgdown fails (at least without further tweaks to the</span></span>
<span class="r-in"><span class="co"># graphics device used)</span></span>
<span class="r-in"><span class="co">#saemix::plot(f_dmta_saem_tc$so, plot.type = "convergence")</span></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_saem_tc</span><span class="op">)</span></span>
<span class="r-out co"><span class="r-pr">#></span> saemix version used for fitting: 3.0 </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: 4.2.1 </span>
<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Jun 30 10:21:01 2022 </span>
<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Jun 30 10:21:01 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 = - k_DMTA * DMTA</span>
<span class="r-out co"><span class="r-pr">#></span> d_M23/dt = + f_DMTA_to_M23 * k_DMTA * DMTA - k_M23 * M23</span>
<span class="r-out co"><span class="r-pr">#></span> d_M27/dt = + f_DMTA_to_M27 * k_DMTA * 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 * k_DMTA * 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> Model predictions using solution type deSolve </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Fitted in 1845.619 s</span>
<span class="r-out co"><span class="r-pr">#></span> Using 300, 100 iterations and 9 chains</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_DMTA log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1 </span>
<span class="r-out co"><span class="r-pr">#></span> 95.5662 -2.9048 -3.8130 -4.1600 -4.1486 0.1341 </span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_2 f_DMTA_ilr_3 </span>
<span class="r-out co"><span class="r-pr">#></span> 0.1385 -1.6700 </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 computed by importance sampling</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> 2276 2272 -1120</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 88.5943 84.3961 92.7925</span>
<span class="r-out co"><span class="r-pr">#></span> log_k_DMTA -3.0466 -3.5609 -2.5322</span>
<span class="r-out co"><span class="r-pr">#></span> log_k_M23 -4.0684 -4.9340 -3.2029</span>
<span class="r-out co"><span class="r-pr">#></span> log_k_M27 -3.8628 -4.2627 -3.4628</span>
<span class="r-out co"><span class="r-pr">#></span> log_k_M31 -3.9803 -4.4804 -3.4801</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_1 0.1304 -0.2186 0.4795</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_2 0.1490 -0.2559 0.5540</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_3 -1.3970 -1.6976 -1.0964</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 l__DMTA lg__M23 lg__M27 lg__M31 f_DMTA__1 f_DMTA__2</span>
<span class="r-out co"><span class="r-pr">#></span> log_k_DMTA 0.0309 </span>
<span class="r-out co"><span class="r-pr">#></span> log_k_M23 -0.0231 -0.0031 </span>
<span class="r-out co"><span class="r-pr">#></span> log_k_M27 -0.0381 -0.0048 0.0039 </span>
<span class="r-out co"><span class="r-pr">#></span> log_k_M31 -0.0251 -0.0031 0.0021 0.0830 </span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_1 -0.0046 -0.0006 0.0417 -0.0437 0.0328 </span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_2 -0.0008 -0.0002 0.0214 -0.0270 -0.0909 -0.0361 </span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_3 -0.1832 -0.0135 0.0434 0.0804 0.0395 -0.0070 0.0059 </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Random effects:</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> SD.DMTA_0 3.3651 -0.9655 7.6956</span>
<span class="r-out co"><span class="r-pr">#></span> SD.log_k_DMTA 0.6415 0.2774 1.0055</span>
<span class="r-out co"><span class="r-pr">#></span> SD.log_k_M23 1.0176 0.3809 1.6543</span>
<span class="r-out co"><span class="r-pr">#></span> SD.log_k_M27 0.4538 0.1522 0.7554</span>
<span class="r-out co"><span class="r-pr">#></span> SD.log_k_M31 0.5684 0.1905 0.9464</span>
<span class="r-out co"><span class="r-pr">#></span> SD.f_DMTA_ilr_1 0.4111 0.1524 0.6699</span>
<span class="r-out co"><span class="r-pr">#></span> SD.f_DMTA_ilr_2 0.4788 0.1808 0.7768</span>
<span class="r-out co"><span class="r-pr">#></span> SD.f_DMTA_ilr_3 0.3501 0.1316 0.5685</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> est. lower upper</span>
<span class="r-out co"><span class="r-pr">#></span> a.1 0.9349 0.8395 1.0302</span>
<span class="r-out co"><span class="r-pr">#></span> b.1 0.1344 0.1176 0.1512</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 88.59431 84.396147 92.79246</span>
<span class="r-out co"><span class="r-pr">#></span> k_DMTA 0.04752 0.028413 0.07948</span>
<span class="r-out co"><span class="r-pr">#></span> k_M23 0.01710 0.007198 0.04064</span>
<span class="r-out co"><span class="r-pr">#></span> k_M27 0.02101 0.014084 0.03134</span>
<span class="r-out co"><span class="r-pr">#></span> k_M31 0.01868 0.011329 0.03080</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M23 0.14498 NA NA</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M27 0.12056 NA NA</span>
<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M31 0.11015 NA NA</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 0.1450</span>
<span class="r-out co"><span class="r-pr">#></span> DMTA_M27 0.1206</span>
<span class="r-out co"><span class="r-pr">#></span> DMTA_M31 0.1101</span>
<span class="r-out co"><span class="r-pr">#></span> DMTA_sink 0.6243</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</span>
<span class="r-out co"><span class="r-pr">#></span> DMTA 14.59 48.45</span>
<span class="r-out co"><span class="r-pr">#></span> M23 40.52 134.62</span>
<span class="r-out co"><span class="r-pr">#></span> M27 32.99 109.60</span>
<span class="r-out co"><span class="r-pr">#></span> M31 37.11 123.26</span>
<span class="r-in"><span class="co"># As the confidence interval for the random effects of DMTA_0</span></span>
<span class="r-in"><span class="co"># includes zero, we could try an alternative model without</span></span>
<span class="r-in"><span class="co"># such random effects</span></span>
<span class="r-in"><span class="co"># f_dmta_saem_tc_2 <- saem(dmta_sfo_sfo3p_tc,</span></span>
<span class="r-in"><span class="co"># covariance.model = diag(c(0, rep(1, 7))))</span></span>
<span class="r-in"><span class="co"># saemix::plot(f_dmta_saem_tc_2$so, plot.type = "convergence")</span></span>
<span class="r-in"><span class="co"># This does not perform better judged by AIC and BIC</span></span>
<span class="r-in"><span class="co"># saemix::compare.saemix(f_dmta_saem_tc$so, f_dmta_saem_tc_2$so)</span></span>
<span class="r-in"><span class="co"># }</span></span>
</code></pre></div>
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