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authorJohannes Ranke <jranke@uni-bremen.de>2023-04-20 20:17:12 +0200
committerJohannes Ranke <jranke@uni-bremen.de>2023-04-20 20:17:12 +0200
commit7b7c4bf493ba15824ea43bed764661678b4aca03 (patch)
tree48de1e32327c36ee49fc476dc76ece8397ab55c6 /docs/articles/prebuilt/2022_dmta_parent.html
parent842998b688037c007d8876d7e1110c929fe2374c (diff)
parent9ae42bd20bc2543a94cf1581ba9820c2f9e3afbd (diff)
Merge branch 'v1.2.3_pkgdown'
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+ <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
+ </li>
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+ <div class="page-header toc-ignore">
+ <h1 data-toc-skip>Testing hierarchical parent degradation kinetics
+with residue data on dimethenamid and dimethenamid-P</h1>
+ <h4 data-toc-skip class="author">Johannes
+Ranke</h4>
+
+ <h4 data-toc-skip class="date">Last change on 5 January
+2023, last compiled on 20 April 2023</h4>
+
+ <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/prebuilt/2022_dmta_parent.rmd" class="external-link"><code>vignettes/prebuilt/2022_dmta_parent.rmd</code></a></small>
+ <div class="hidden name"><code>2022_dmta_parent.rmd</code></div>
+
+ </div>
+
+
+
+<div class="section level2">
+<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
+</h2>
+<p>The purpose of this document is to demonstrate how nonlinear
+hierarchical models (NLHM) based on the parent degradation models SFO,
+FOMC, DFOP and HS can be fitted with the mkin package.</p>
+<p>It was assembled in the course of work package 1.1 of Project Number
+173340 (Application of nonlinear hierarchical models to the kinetic
+evaluation of chemical degradation data) of the German Environment
+Agency carried out in 2022 and 2023.</p>
+<p>The mkin package is used in version 1.2.3. It contains the test data
+and the functions used in the evaluations. The <code>saemix</code>
+package is used as a backend for fitting the NLHM, but is also loaded to
+make the convergence plot function available.</p>
+<p>This document is processed with the <code>knitr</code> package, which
+also provides the <code>kable</code> function that is used to improve
+the display of tabular data in R markdown documents. For parallel
+processing, the <code>parallel</code> package is used.</p>
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span><span class="op">)</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://yihui.org/knitr/" class="external-link">knitr</a></span><span class="op">)</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">saemix</span><span class="op">)</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">parallel</span><span class="op">)</span></span>
+<span><span class="va">n_cores</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span></span>
+<span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.info.html" class="external-link">Sys.info</a></span><span class="op">(</span><span class="op">)</span><span class="op">[</span><span class="st">"sysname"</span><span class="op">]</span> <span class="op">==</span> <span class="st">"Windows"</span><span class="op">)</span> <span class="op">{</span></span>
+<span> <span class="va">cl</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makePSOCKcluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span>
+<span><span class="op">}</span> <span class="kw">else</span> <span class="op">{</span></span>
+<span> <span class="va">cl</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makeForkCluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span>
+<span><span class="op">}</span></span></code></pre></div>
+</div>
+<div class="section level2">
+<h2 id="data">Data<a class="anchor" aria-label="anchor" href="#data"></a>
+</h2>
+<p>The test data are available in the mkin package as an object of class
+<code>mkindsg</code> (mkin dataset group) under the identifier
+<code>dimethenamid_2018</code>. The following preprocessing steps are
+still necessary:</p>
+<ul>
+<li>The data available for the enantiomer dimethenamid-P (DMTAP) are
+renamed to have the same substance name as the data for the racemic
+mixture dimethenamid (DMTA). The reason for this is that no difference
+between their degradation behaviour was identified in the EU risk
+assessment.</li>
+<li>The data for transformation products and unnecessary columns are
+discarded</li>
+<li>The observation times of each dataset are multiplied with the
+corresponding normalisation factor also available in the dataset, in
+order to make it possible to describe all datasets with a single set of
+parameters that are independent of temperature</li>
+<li>Finally, datasets observed in the same soil (<code>Elliot 1</code>
+and <code>Elliot 2</code>) are combined, resulting in dimethenamid
+(DMTA) data from six soils.</li>
+</ul>
+<p>The following commented R code performs this preprocessing.</p>
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Apply a function to each of the seven datasets in the mkindsg object to create a list</span></span>
+<span><span class="va">dmta_ds</span> <span class="op">&lt;-</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> <span class="va">ds_i</span> <span class="op">&lt;-</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span><span class="op">[[</span><span class="va">i</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span> <span class="co"># Get a dataset</span></span>
+<span> <span class="va">ds_i</span><span class="op">[</span><span class="va">ds_i</span><span class="op">$</span><span class="va">name</span> <span class="op">==</span> <span class="st">"DMTAP"</span>, <span class="st">"name"</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"DMTA"</span> <span class="co"># Rename DMTAP to DMTA</span></span>
+<span> <span class="va">ds_i</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">ds_i</span>, <span class="va">name</span> <span class="op">==</span> <span class="st">"DMTA"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">)</span> <span class="co"># Select data</span></span>
+<span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">&lt;-</span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">*</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">f_time_norm</span><span class="op">[</span><span class="va">i</span><span class="op">]</span> <span class="co"># Normalise time</span></span>
+<span> <span class="va">ds_i</span> <span class="co"># Return the dataset</span></span>
+<span><span class="op">}</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co"># Use dataset titles as names for the list elements</span></span>
+<span><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">&lt;-</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></span>
+<span><span class="co"># Combine data for Elliot soil to obtain a named list with six elements</span></span>
+<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</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 class="co">#</span></span>
+<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="cn">NULL</span></span>
+<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">&lt;-</span> <span class="cn">NULL</span></span></code></pre></div>
+<p>The following tables show the 6 datasets.</p>
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="kw">for</span> <span class="op">(</span><span class="va">ds_name</span> <span class="kw">in</span> <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="op">{</span></span>
+<span> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mkin_long_to_wide.html">mkin_long_to_wide</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="va">ds_name</span><span class="op">]</span><span class="op">]</span><span class="op">)</span>,</span>
+<span> caption <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="va">ds_name</span><span class="op">)</span>,</span>
+<span> label <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="st">"tab:"</span>, <span class="va">ds_name</span><span class="op">)</span>, booktabs <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span>
+<span> <span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="st">"\n\\clearpage\n"</span><span class="op">)</span></span>
+<span><span class="op">}</span></span></code></pre></div>
+<table class="table">
+<caption>Dataset Calke</caption>
+<thead><tr class="header">
+<th align="right">time</th>
+<th align="right">DMTA</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="right">0</td>
+<td align="right">95.8</td>
+</tr>
+<tr class="even">
+<td align="right">0</td>
+<td align="right">98.7</td>
+</tr>
+<tr class="odd">
+<td align="right">14</td>
+<td align="right">60.5</td>
+</tr>
+<tr class="even">
+<td align="right">30</td>
+<td align="right">39.1</td>
+</tr>
+<tr class="odd">
+<td align="right">59</td>
+<td align="right">15.2</td>
+</tr>
+<tr class="even">
+<td align="right">120</td>
+<td align="right">4.8</td>
+</tr>
+<tr class="odd">
+<td align="right">120</td>
+<td align="right">4.6</td>
+</tr>
+</tbody>
+</table>
+<table class="table">
+<caption>Dataset Borstel</caption>
+<thead><tr class="header">
+<th align="right">time</th>
+<th align="right">DMTA</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="right">0.000000</td>
+<td align="right">100.5</td>
+</tr>
+<tr class="even">
+<td align="right">0.000000</td>
+<td align="right">99.6</td>
+</tr>
+<tr class="odd">
+<td align="right">1.941295</td>
+<td align="right">91.9</td>
+</tr>
+<tr class="even">
+<td align="right">1.941295</td>
+<td align="right">91.3</td>
+</tr>
+<tr class="odd">
+<td align="right">6.794534</td>
+<td align="right">81.8</td>
+</tr>
+<tr class="even">
+<td align="right">6.794534</td>
+<td align="right">82.1</td>
+</tr>
+<tr class="odd">
+<td align="right">13.589067</td>
+<td align="right">69.1</td>
+</tr>
+<tr class="even">
+<td align="right">13.589067</td>
+<td align="right">68.0</td>
+</tr>
+<tr class="odd">
+<td align="right">27.178135</td>
+<td align="right">51.4</td>
+</tr>
+<tr class="even">
+<td align="right">27.178135</td>
+<td align="right">51.4</td>
+</tr>
+<tr class="odd">
+<td align="right">56.297565</td>
+<td align="right">27.6</td>
+</tr>
+<tr class="even">
+<td align="right">56.297565</td>
+<td align="right">26.8</td>
+</tr>
+<tr class="odd">
+<td align="right">86.387643</td>
+<td align="right">15.7</td>
+</tr>
+<tr class="even">
+<td align="right">86.387643</td>
+<td align="right">15.3</td>
+</tr>
+<tr class="odd">
+<td align="right">115.507073</td>
+<td align="right">7.9</td>
+</tr>
+<tr class="even">
+<td align="right">115.507073</td>
+<td align="right">8.1</td>
+</tr>
+</tbody>
+</table>
+<table class="table">
+<caption>Dataset Flaach</caption>
+<thead><tr class="header">
+<th align="right">time</th>
+<th align="right">DMTA</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="right">0.0000000</td>
+<td align="right">96.5</td>
+</tr>
+<tr class="even">
+<td align="right">0.0000000</td>
+<td align="right">96.8</td>
+</tr>
+<tr class="odd">
+<td align="right">0.0000000</td>
+<td align="right">97.0</td>
+</tr>
+<tr class="even">
+<td align="right">0.6233856</td>
+<td align="right">82.9</td>
+</tr>
+<tr class="odd">
+<td align="right">0.6233856</td>
+<td align="right">86.7</td>
+</tr>
+<tr class="even">
+<td align="right">0.6233856</td>
+<td align="right">87.4</td>
+</tr>
+<tr class="odd">
+<td align="right">1.8701567</td>
+<td align="right">72.8</td>
+</tr>
+<tr class="even">
+<td align="right">1.8701567</td>
+<td align="right">69.9</td>
+</tr>
+<tr class="odd">
+<td align="right">1.8701567</td>
+<td align="right">71.9</td>
+</tr>
+<tr class="even">
+<td align="right">4.3636989</td>
+<td align="right">51.4</td>
+</tr>
+<tr class="odd">
+<td align="right">4.3636989</td>
+<td align="right">52.9</td>
+</tr>
+<tr class="even">
+<td align="right">4.3636989</td>
+<td align="right">48.6</td>
+</tr>
+<tr class="odd">
+<td align="right">8.7273979</td>
+<td align="right">28.5</td>
+</tr>
+<tr class="even">
+<td align="right">8.7273979</td>
+<td align="right">27.3</td>
+</tr>
+<tr class="odd">
+<td align="right">8.7273979</td>
+<td align="right">27.5</td>
+</tr>
+<tr class="even">
+<td align="right">13.0910968</td>
+<td align="right">14.8</td>
+</tr>
+<tr class="odd">
+<td align="right">13.0910968</td>
+<td align="right">13.4</td>
+</tr>
+<tr class="even">
+<td align="right">13.0910968</td>
+<td align="right">14.4</td>
+</tr>
+<tr class="odd">
+<td align="right">17.4547957</td>
+<td align="right">7.7</td>
+</tr>
+<tr class="even">
+<td align="right">17.4547957</td>
+<td align="right">7.3</td>
+</tr>
+<tr class="odd">
+<td align="right">17.4547957</td>
+<td align="right">8.1</td>
+</tr>
+<tr class="even">
+<td align="right">26.1821936</td>
+<td align="right">2.0</td>
+</tr>
+<tr class="odd">
+<td align="right">26.1821936</td>
+<td align="right">1.5</td>
+</tr>
+<tr class="even">
+<td align="right">26.1821936</td>
+<td align="right">1.9</td>
+</tr>
+<tr class="odd">
+<td align="right">34.9095915</td>
+<td align="right">1.3</td>
+</tr>
+<tr class="even">
+<td align="right">34.9095915</td>
+<td align="right">1.0</td>
+</tr>
+<tr class="odd">
+<td align="right">34.9095915</td>
+<td align="right">1.1</td>
+</tr>
+<tr class="even">
+<td align="right">43.6369893</td>
+<td align="right">0.9</td>
+</tr>
+<tr class="odd">
+<td align="right">43.6369893</td>
+<td align="right">0.7</td>
+</tr>
+<tr class="even">
+<td align="right">43.6369893</td>
+<td align="right">0.7</td>
+</tr>
+<tr class="odd">
+<td align="right">52.3643872</td>
+<td align="right">0.6</td>
+</tr>
+<tr class="even">
+<td align="right">52.3643872</td>
+<td align="right">0.4</td>
+</tr>
+<tr class="odd">
+<td align="right">52.3643872</td>
+<td align="right">0.5</td>
+</tr>
+<tr class="even">
+<td align="right">74.8062674</td>
+<td align="right">0.4</td>
+</tr>
+<tr class="odd">
+<td align="right">74.8062674</td>
+<td align="right">0.3</td>
+</tr>
+<tr class="even">
+<td align="right">74.8062674</td>
+<td align="right">0.3</td>
+</tr>
+</tbody>
+</table>
+<table class="table">
+<caption>Dataset BBA 2.2</caption>
+<thead><tr class="header">
+<th align="right">time</th>
+<th align="right">DMTA</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="right">0.0000000</td>
+<td align="right">98.09</td>
+</tr>
+<tr class="even">
+<td align="right">0.0000000</td>
+<td align="right">98.77</td>
+</tr>
+<tr class="odd">
+<td align="right">0.7678922</td>
+<td align="right">93.52</td>
+</tr>
+<tr class="even">
+<td align="right">0.7678922</td>
+<td align="right">92.03</td>
+</tr>
+<tr class="odd">
+<td align="right">2.3036765</td>
+<td align="right">88.39</td>
+</tr>
+<tr class="even">
+<td align="right">2.3036765</td>
+<td align="right">87.18</td>
+</tr>
+<tr class="odd">
+<td align="right">5.3752452</td>
+<td align="right">69.38</td>
+</tr>
+<tr class="even">
+<td align="right">5.3752452</td>
+<td align="right">71.06</td>
+</tr>
+<tr class="odd">
+<td align="right">10.7504904</td>
+<td align="right">45.21</td>
+</tr>
+<tr class="even">
+<td align="right">10.7504904</td>
+<td align="right">46.81</td>
+</tr>
+<tr class="odd">
+<td align="right">16.1257355</td>
+<td align="right">30.54</td>
+</tr>
+<tr class="even">
+<td align="right">16.1257355</td>
+<td align="right">30.07</td>
+</tr>
+<tr class="odd">
+<td align="right">21.5009807</td>
+<td align="right">21.60</td>
+</tr>
+<tr class="even">
+<td align="right">21.5009807</td>
+<td align="right">20.41</td>
+</tr>
+<tr class="odd">
+<td align="right">32.2514711</td>
+<td align="right">9.10</td>
+</tr>
+<tr class="even">
+<td align="right">32.2514711</td>
+<td align="right">9.70</td>
+</tr>
+<tr class="odd">
+<td align="right">43.0019614</td>
+<td align="right">6.58</td>
+</tr>
+<tr class="even">
+<td align="right">43.0019614</td>
+<td align="right">6.31</td>
+</tr>
+<tr class="odd">
+<td align="right">53.7524518</td>
+<td align="right">3.47</td>
+</tr>
+<tr class="even">
+<td align="right">53.7524518</td>
+<td align="right">3.52</td>
+</tr>
+<tr class="odd">
+<td align="right">64.5029421</td>
+<td align="right">3.40</td>
+</tr>
+<tr class="even">
+<td align="right">64.5029421</td>
+<td align="right">3.67</td>
+</tr>
+<tr class="odd">
+<td align="right">91.3791680</td>
+<td align="right">1.62</td>
+</tr>
+<tr class="even">
+<td align="right">91.3791680</td>
+<td align="right">1.62</td>
+</tr>
+</tbody>
+</table>
+<table class="table">
+<caption>Dataset BBA 2.3</caption>
+<thead><tr class="header">
+<th align="right">time</th>
+<th align="right">DMTA</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="right">0.0000000</td>
+<td align="right">99.33</td>
+</tr>
+<tr class="even">
+<td align="right">0.0000000</td>
+<td align="right">97.44</td>
+</tr>
+<tr class="odd">
+<td align="right">0.6733938</td>
+<td align="right">93.73</td>
+</tr>
+<tr class="even">
+<td align="right">0.6733938</td>
+<td align="right">93.77</td>
+</tr>
+<tr class="odd">
+<td align="right">2.0201814</td>
+<td align="right">87.84</td>
+</tr>
+<tr class="even">
+<td align="right">2.0201814</td>
+<td align="right">89.82</td>
+</tr>
+<tr class="odd">
+<td align="right">4.7137565</td>
+<td align="right">71.61</td>
+</tr>
+<tr class="even">
+<td align="right">4.7137565</td>
+<td align="right">71.42</td>
+</tr>
+<tr class="odd">
+<td align="right">9.4275131</td>
+<td align="right">45.60</td>
+</tr>
+<tr class="even">
+<td align="right">9.4275131</td>
+<td align="right">45.42</td>
+</tr>
+<tr class="odd">
+<td align="right">14.1412696</td>
+<td align="right">31.12</td>
+</tr>
+<tr class="even">
+<td align="right">14.1412696</td>
+<td align="right">31.68</td>
+</tr>
+<tr class="odd">
+<td align="right">18.8550262</td>
+<td align="right">23.20</td>
+</tr>
+<tr class="even">
+<td align="right">18.8550262</td>
+<td align="right">24.13</td>
+</tr>
+<tr class="odd">
+<td align="right">28.2825393</td>
+<td align="right">9.43</td>
+</tr>
+<tr class="even">
+<td align="right">28.2825393</td>
+<td align="right">9.82</td>
+</tr>
+<tr class="odd">
+<td align="right">37.7100523</td>
+<td align="right">7.08</td>
+</tr>
+<tr class="even">
+<td align="right">37.7100523</td>
+<td align="right">8.64</td>
+</tr>
+<tr class="odd">
+<td align="right">47.1375654</td>
+<td align="right">4.41</td>
+</tr>
+<tr class="even">
+<td align="right">47.1375654</td>
+<td align="right">4.78</td>
+</tr>
+<tr class="odd">
+<td align="right">56.5650785</td>
+<td align="right">4.92</td>
+</tr>
+<tr class="even">
+<td align="right">56.5650785</td>
+<td align="right">5.08</td>
+</tr>
+<tr class="odd">
+<td align="right">80.1338612</td>
+<td align="right">2.13</td>
+</tr>
+<tr class="even">
+<td align="right">80.1338612</td>
+<td align="right">2.23</td>
+</tr>
+</tbody>
+</table>
+<table class="table">
+<caption>Dataset Elliot</caption>
+<thead><tr class="header">
+<th align="right">time</th>
+<th align="right">DMTA</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="right">0.000000</td>
+<td align="right">97.5</td>
+</tr>
+<tr class="even">
+<td align="right">0.000000</td>
+<td align="right">100.7</td>
+</tr>
+<tr class="odd">
+<td align="right">1.228478</td>
+<td align="right">86.4</td>
+</tr>
+<tr class="even">
+<td align="right">1.228478</td>
+<td align="right">88.5</td>
+</tr>
+<tr class="odd">
+<td align="right">3.685435</td>
+<td align="right">69.8</td>
+</tr>
+<tr class="even">
+<td align="right">3.685435</td>
+<td align="right">77.1</td>
+</tr>
+<tr class="odd">
+<td align="right">8.599349</td>
+<td align="right">59.0</td>
+</tr>
+<tr class="even">
+<td align="right">8.599349</td>
+<td align="right">54.2</td>
+</tr>
+<tr class="odd">
+<td align="right">17.198697</td>
+<td align="right">31.3</td>
+</tr>
+<tr class="even">
+<td align="right">17.198697</td>
+<td align="right">33.5</td>
+</tr>
+<tr class="odd">
+<td align="right">25.798046</td>
+<td align="right">19.6</td>
+</tr>
+<tr class="even">
+<td align="right">25.798046</td>
+<td align="right">20.9</td>
+</tr>
+<tr class="odd">
+<td align="right">34.397395</td>
+<td align="right">13.3</td>
+</tr>
+<tr class="even">
+<td align="right">34.397395</td>
+<td align="right">15.8</td>
+</tr>
+<tr class="odd">
+<td align="right">51.596092</td>
+<td align="right">6.7</td>
+</tr>
+<tr class="even">
+<td align="right">51.596092</td>
+<td align="right">8.7</td>
+</tr>
+<tr class="odd">
+<td align="right">68.794789</td>
+<td align="right">8.8</td>
+</tr>
+<tr class="even">
+<td align="right">68.794789</td>
+<td align="right">8.7</td>
+</tr>
+<tr class="odd">
+<td align="right">103.192184</td>
+<td align="right">6.0</td>
+</tr>
+<tr class="even">
+<td align="right">103.192184</td>
+<td align="right">4.4</td>
+</tr>
+<tr class="odd">
+<td align="right">146.188928</td>
+<td align="right">3.3</td>
+</tr>
+<tr class="even">
+<td align="right">146.188928</td>
+<td align="right">2.8</td>
+</tr>
+<tr class="odd">
+<td align="right">223.583066</td>
+<td align="right">1.4</td>
+</tr>
+<tr class="even">
+<td align="right">223.583066</td>
+<td align="right">1.8</td>
+</tr>
+<tr class="odd">
+<td align="right">0.000000</td>
+<td align="right">93.4</td>
+</tr>
+<tr class="even">
+<td align="right">0.000000</td>
+<td align="right">103.2</td>
+</tr>
+<tr class="odd">
+<td align="right">1.228478</td>
+<td align="right">89.2</td>
+</tr>
+<tr class="even">
+<td align="right">1.228478</td>
+<td align="right">86.6</td>
+</tr>
+<tr class="odd">
+<td align="right">3.685435</td>
+<td align="right">78.2</td>
+</tr>
+<tr class="even">
+<td align="right">3.685435</td>
+<td align="right">78.1</td>
+</tr>
+<tr class="odd">
+<td align="right">8.599349</td>
+<td align="right">55.6</td>
+</tr>
+<tr class="even">
+<td align="right">8.599349</td>
+<td align="right">53.0</td>
+</tr>
+<tr class="odd">
+<td align="right">17.198697</td>
+<td align="right">33.7</td>
+</tr>
+<tr class="even">
+<td align="right">17.198697</td>
+<td align="right">33.2</td>
+</tr>
+<tr class="odd">
+<td align="right">25.798046</td>
+<td align="right">20.9</td>
+</tr>
+<tr class="even">
+<td align="right">25.798046</td>
+<td align="right">19.9</td>
+</tr>
+<tr class="odd">
+<td align="right">34.397395</td>
+<td align="right">18.2</td>
+</tr>
+<tr class="even">
+<td align="right">34.397395</td>
+<td align="right">12.7</td>
+</tr>
+<tr class="odd">
+<td align="right">51.596092</td>
+<td align="right">7.8</td>
+</tr>
+<tr class="even">
+<td align="right">51.596092</td>
+<td align="right">9.0</td>
+</tr>
+<tr class="odd">
+<td align="right">68.794789</td>
+<td align="right">11.4</td>
+</tr>
+<tr class="even">
+<td align="right">68.794789</td>
+<td align="right">9.0</td>
+</tr>
+<tr class="odd">
+<td align="right">103.192184</td>
+<td align="right">3.9</td>
+</tr>
+<tr class="even">
+<td align="right">103.192184</td>
+<td align="right">4.4</td>
+</tr>
+<tr class="odd">
+<td align="right">146.188928</td>
+<td align="right">2.6</td>
+</tr>
+<tr class="even">
+<td align="right">146.188928</td>
+<td align="right">3.4</td>
+</tr>
+<tr class="odd">
+<td align="right">223.583066</td>
+<td align="right">2.0</td>
+</tr>
+<tr class="even">
+<td align="right">223.583066</td>
+<td align="right">1.7</td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="section level2">
+<h2 id="separate-evaluations">Separate evaluations<a class="anchor" aria-label="anchor" href="#separate-evaluations"></a>
+</h2>
+<p>In order to obtain suitable starting parameters for the NLHM fits,
+separate fits of the four models to the data for each soil are generated
+using the <code>mmkin</code> function from the <code>mkin</code>
+package. In a first step, constant variance is assumed. Convergence is
+checked with the <code>status</code> function.</p>
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">deg_mods</span> <span class="op">&lt;-</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">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span>, <span class="st">"HS"</span><span class="op">)</span></span>
+<span><span class="va">f_sep_const</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span></span>
+<span> <span class="va">deg_mods</span>,</span>
+<span> <span class="va">dmta_ds</span>,</span>
+<span> error_model <span class="op">=</span> <span class="st">"const"</span>,</span>
+<span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
+<span></span>
+<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_const</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+<table class="table">
+<thead><tr class="header">
+<th align="left"></th>
+<th align="left">Calke</th>
+<th align="left">Borstel</th>
+<th align="left">Flaach</th>
+<th align="left">BBA 2.2</th>
+<th align="left">BBA 2.3</th>
+<th align="left">Elliot</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left">SFO</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+</tr>
+<tr class="even">
+<td align="left">FOMC</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+</tr>
+<tr class="odd">
+<td align="left">DFOP</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+</tr>
+<tr class="even">
+<td align="left">HS</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+<td align="left">C</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+</tr>
+</tbody>
+</table>
+<p>In the table above, OK indicates convergence, and C indicates failure
+to converge. All separate fits with constant variance converged, with
+the sole exception of the HS fit to the BBA 2.2 data. To prepare for
+fitting NLHM using the two-component error model, the separate fits are
+updated assuming two-component error.</p>
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">f_sep_tc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_sep_const</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span>
+<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_tc</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+<table class="table">
+<thead><tr class="header">
+<th align="left"></th>
+<th align="left">Calke</th>
+<th align="left">Borstel</th>
+<th align="left">Flaach</th>
+<th align="left">BBA 2.2</th>
+<th align="left">BBA 2.3</th>
+<th align="left">Elliot</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left">SFO</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+</tr>
+<tr class="even">
+<td align="left">FOMC</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+<td align="left">C</td>
+<td align="left">OK</td>
+</tr>
+<tr class="odd">
+<td align="left">DFOP</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+<td align="left">C</td>
+<td align="left">OK</td>
+<td align="left">C</td>
+<td align="left">OK</td>
+</tr>
+<tr class="even">
+<td align="left">HS</td>
+<td align="left">OK</td>
+<td align="left">C</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+</tr>
+</tbody>
+</table>
+<p>Using the two-component error model, the one fit that did not
+converge with constant variance did converge, but other non-SFO fits
+failed to converge.</p>
+</div>
+<div class="section level2">
+<h2 id="hierarchichal-model-fits">Hierarchichal model fits<a class="anchor" aria-label="anchor" href="#hierarchichal-model-fits"></a>
+</h2>
+<p>The following code fits eight versions of hierarchical models to the
+data, using SFO, FOMC, DFOP and HS for the parent compound, and using
+either constant variance or two-component error for the error model. The
+default parameter distribution model in mkin allows for variation of all
+degradation parameters across the assumed population of soils. In other
+words, each degradation parameter is associated with a random effect as
+a first step. The <code>mhmkin</code> function makes it possible to fit
+all eight versions in parallel (given a sufficient number of computing
+cores being available) to save execution time.</p>
+<p>Convergence plots and summaries for these fits are shown in the
+appendix.</p>
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">f_saem</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mhmkin.html">mhmkin</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="va">f_sep_const</span>, <span class="va">f_sep_tc</span><span class="op">)</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span></span></code></pre></div>
+<p>The output of the <code>status</code> function shows that all fits
+terminated successfully.</p>
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+<table class="table">
+<thead><tr class="header">
+<th align="left"></th>
+<th align="left">const</th>
+<th align="left">tc</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left">SFO</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+</tr>
+<tr class="even">
+<td align="left">FOMC</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+</tr>
+<tr class="odd">
+<td align="left">DFOP</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+</tr>
+<tr class="even">
+<td align="left">HS</td>
+<td align="left">OK</td>
+<td align="left">OK</td>
+</tr>
+</tbody>
+</table>
+<p>The AIC and BIC values show that the biphasic models DFOP and HS give
+the best fits.</p>
+<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
+<table class="table">
+<thead><tr class="header">
+<th align="left"></th>
+<th align="right">npar</th>
+<th align="right">AIC</th>
+<th align="right">BIC</th>
+<th align="right">Lik</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left">SFO const</td>
+<td align="right">5</td>
+<td align="right">796.3</td>
+<td align="right">795.3</td>
+<td align="right">-393.2</td>
+</tr>
+<tr class="even">
+<td align="left">SFO tc</td>
+<td align="right">6</td>
+<td align="right">798.3</td>
+<td align="right">797.1</td>
+<td align="right">-393.2</td>
+</tr>
+<tr class="odd">
+<td align="left">FOMC const</td>
+<td align="right">7</td>
+<td align="right">734.2</td>
+<td align="right">732.7</td>
+<td align="right">-360.1</td>
+</tr>
+<tr class="even">
+<td align="left">FOMC tc</td>
+<td align="right">8</td>
+<td align="right">720.4</td>
+<td align="right">718.8</td>
+<td align="right">-352.2</td>
+</tr>
+<tr class="odd">
+<td align="left">DFOP const</td>
+<td align="right">9</td>
+<td align="right">711.8</td>
+<td align="right">710.0</td>
+<td align="right">-346.9</td>
+</tr>
+<tr class="even">
+<td align="left">HS const</td>
+<td align="right">9</td>
+<td align="right">714.0</td>
+<td align="right">712.1</td>
+<td align="right">-348.0</td>
+</tr>
+<tr class="odd">
+<td align="left">DFOP tc</td>
+<td align="right">10</td>
+<td align="right">665.5</td>
+<td align="right">663.4</td>
+<td align="right">-322.8</td>
+</tr>
+<tr class="even">
+<td align="left">HS tc</td>
+<td align="right">10</td>
+<td align="right">667.1</td>
+<td align="right">665.0</td>
+<td align="right">-323.6</td>
+</tr>
+</tbody>
+</table>
+<p>The DFOP model is preferred here, as it has a better mechanistic
+basis for batch experiments with constant incubation conditions. Also,
+it shows the lowest AIC and BIC values in the first set of fits when
+combined with the two-component error model. Therefore, the DFOP model
+was selected for further refinements of the fits with the aim to make
+the model fully identifiable.</p>
+<div class="section level3">
+<h3 id="parameter-identifiability-based-on-the-fisher-information-matrix">Parameter identifiability based on the Fisher Information
+Matrix<a class="anchor" aria-label="anchor" href="#parameter-identifiability-based-on-the-fisher-information-matrix"></a>
+</h3>
+<p>Using the <code>illparms</code> function, ill-defined statistical
+model parameters such as standard deviations of the degradation
+parameters in the population and error model parameters can be
+found.</p>
+<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+<table class="table">
+<thead><tr class="header">
+<th align="left"></th>
+<th align="left">const</th>
+<th align="left">tc</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left">SFO</td>
+<td align="left"></td>
+<td align="left">b.1</td>
+</tr>
+<tr class="even">
+<td align="left">FOMC</td>
+<td align="left"></td>
+<td align="left">sd(DMTA_0)</td>
+</tr>
+<tr class="odd">
+<td align="left">DFOP</td>
+<td align="left">sd(k2)</td>
+<td align="left">sd(k2)</td>
+</tr>
+<tr class="even">
+<td align="left">HS</td>
+<td align="left"></td>
+<td align="left">sd(tb)</td>
+</tr>
+</tbody>
+</table>
+<p>According to the <code>illparms</code> function, the fitted standard
+deviation of the second kinetic rate constant <code>k2</code> is
+ill-defined in both DFOP fits. This suggests that different values would
+be obtained for this standard deviation when using different starting
+values.</p>
+<p>The thus identified overparameterisation is addressed by removing the
+random effect for <code>k2</code> from the parameter model.</p>
+<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">f_saem_dfop_tc_no_ranef_k2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>,</span>
+<span> no_random_effect <span class="op">=</span> <span class="st">"k2"</span><span class="op">)</span></span></code></pre></div>
+<p>For the resulting fit, it is checked whether there are still
+ill-defined parameters,</p>
+<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2</span><span class="op">)</span></span></code></pre></div>
+<p>which is not the case. Below, the refined model is compared with the
+previous best model. The model without random effect for <code>k2</code>
+is a reduced version of the previous model. Therefore, the models are
+nested and can be compared using the likelihood ratio test. This is
+achieved with the argument <code>test = TRUE</code> to the
+<code>anova</code> function.</p>
+<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>, <span class="va">f_saem_dfop_tc_no_ranef_k2</span>, test <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op">|&gt;</span></span>
+<span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>format.args <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>digits <span class="op">=</span> <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<table class="table">
+<colgroup>
+<col width="37%">
+<col width="6%">
+<col width="8%">
+<col width="8%">
+<col width="9%">
+<col width="9%">
+<col width="4%">
+<col width="15%">
+</colgroup>
+<thead><tr class="header">
+<th align="left"></th>
+<th align="right">npar</th>
+<th align="right">AIC</th>
+<th align="right">BIC</th>
+<th align="right">Lik</th>
+<th align="right">Chisq</th>
+<th align="right">Df</th>
+<th align="right">Pr(&gt;Chisq)</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left">f_saem_dfop_tc_no_ranef_k2</td>
+<td align="right">9</td>
+<td align="right">663.8</td>
+<td align="right">661.9</td>
+<td align="right">-322.9</td>
+<td align="right">NA</td>
+<td align="right">NA</td>
+<td align="right">NA</td>
+</tr>
+<tr class="even">
+<td align="left">f_saem[[“DFOP”, “tc”]]</td>
+<td align="right">10</td>
+<td align="right">665.5</td>
+<td align="right">663.4</td>
+<td align="right">-322.8</td>
+<td align="right">0.2809</td>
+<td align="right">1</td>
+<td align="right">0.5961</td>
+</tr>
+</tbody>
+</table>
+<p>The AIC and BIC criteria are lower after removal of the ill-defined
+random effect for <code>k2</code>. The p value of the likelihood ratio
+test is much greater than 0.05, indicating that the model with the
+higher likelihood (here the model with random effects for all
+degradation parameters <code>f_saem[["DFOP", "tc"]]</code>) does not fit
+significantly better than the model with the lower likelihood (the
+reduced model <code>f_saem_dfop_tc_no_ranef_k2</code>).</p>
+<p>Therefore, AIC, BIC and likelihood ratio test suggest the use of the
+reduced model.</p>
+<p>The convergence of the fit is checked visually.</p>
+<div class="figure" style="text-align: center">
+<img src="2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-no-ranef-k2-1.png" alt="Convergence plot for the NLHM DFOP fit with two-component error and without a random effect on 'k2'" width="864"><p class="caption">
+Convergence plot for the NLHM DFOP fit with two-component error and
+without a random effect on ‘k2’
+</p>
+</div>
+<p>All parameters appear to have converged to a satisfactory degree. The
+final fit is plotted using the plot method from the mkin package.</p>
+<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2</span><span class="op">)</span></span></code></pre></div>
+<div class="figure" style="text-align: center">
+<img src="2022_dmta_parent_files/figure-html/plot-saem-dfop-tc-no-ranef-k2-1.png" alt="Plot of the final NLHM DFOP fit" width="864"><p class="caption">
+Plot of the final NLHM DFOP fit
+</p>
+</div>
+<p>Finally, a summary report of the fit is produced.</p>
+<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2</span><span class="op">)</span></span></code></pre></div>
+<pre><code>saemix version used for fitting: 3.2
+mkin version used for pre-fitting: 1.2.3
+R version used for fitting: 4.2.3
+Date of fit: Thu Apr 20 14:07:09 2023
+Date of summary: Thu Apr 20 14:07:10 2023
+
+Equations:
+d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
+ time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
+ * DMTA
+
+Data:
+155 observations of 1 variable(s) grouped in 6 datasets
+
+Model predictions using solution type analytical
+
+Fitted in 4.175 s
+Using 300, 100 iterations and 9 chains
+
+Variance model: Two-component variance function
+
+Starting values for degradation parameters:
+ DMTA_0 k1 k2 g
+98.759266 0.087034 0.009933 0.930827
+
+Fixed degradation parameter values:
+None
+
+Starting values for random effects (square root of initial entries in omega):
+ DMTA_0 k1 k2 g
+DMTA_0 98.76 0 0 0
+k1 0.00 1 0 0
+k2 0.00 0 1 0
+g 0.00 0 0 1
+
+Starting values for error model parameters:
+a.1 b.1
+ 1 1
+
+Results:
+
+Likelihood computed by importance sampling
+ AIC BIC logLik
+ 663.8 661.9 -322.9
+
+Optimised parameters:
+ est. lower upper
+DMTA_0 98.228939 96.285869 100.17201
+k1 0.064063 0.033477 0.09465
+k2 0.008297 0.005824 0.01077
+g 0.953821 0.914328 0.99331
+a.1 1.068479 0.869538 1.26742
+b.1 0.029424 0.022406 0.03644
+SD.DMTA_0 2.030437 0.404824 3.65605
+SD.k1 0.594692 0.256660 0.93272
+SD.g 1.006754 0.361327 1.65218
+
+Correlation:
+ DMTA_0 k1 k2
+k1 0.0218
+k2 0.0556 0.0355
+g -0.0516 -0.0284 -0.2800
+
+Random effects:
+ est. lower upper
+SD.DMTA_0 2.0304 0.4048 3.6560
+SD.k1 0.5947 0.2567 0.9327
+SD.g 1.0068 0.3613 1.6522
+
+Variance model:
+ est. lower upper
+a.1 1.06848 0.86954 1.26742
+b.1 0.02942 0.02241 0.03644
+
+Estimated disappearance times:
+ DT50 DT90 DT50back DT50_k1 DT50_k2
+DMTA 11.45 41.4 12.46 10.82 83.54</code></pre>
+</div>
+<div class="section level3">
+<h3 id="alternative-check-of-parameter-identifiability">Alternative check of parameter identifiability<a class="anchor" aria-label="anchor" href="#alternative-check-of-parameter-identifiability"></a>
+</h3>
+<p>The parameter check used in the <code>illparms</code> function is
+based on a quadratic approximation of the likelihood surface near its
+optimum, which is calculated using the Fisher Information Matrix (FIM).
+An alternative way to check parameter identifiability <span class="citation">(Duchesne et al. 2021)</span> based on a multistart
+approach has recently been implemented in mkin.</p>
+<p>The graph below shows boxplots of the parameters obtained in 50 runs
+of the saem algorithm with different parameter combinations, sampled
+from the range of the parameters obtained for the individual datasets
+fitted separately using nonlinear regression.</p>
+<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">f_saem_dfop_tc_multi</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/multistart.html">multistart</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>, n <span class="op">=</span> <span class="fl">50</span>, cores <span class="op">=</span> <span class="fl">15</span><span class="op">)</span></span></code></pre></div>
+<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mar <span class="op">=</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="fl">6.1</span>, <span class="fl">4.1</span>, <span class="fl">2.1</span>, <span class="fl">2.1</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_multi</span>, lpos <span class="op">=</span> <span class="st">"bottomright"</span>, ylim <span class="op">=</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="fl">0.3</span>, <span class="fl">10</span><span class="op">)</span>, las <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></code></pre></div>
+<div class="figure" style="text-align: center">
+<img src="2022_dmta_parent_files/figure-html/multistart-full-par-1.png" alt="Scaled parameters from the multistart runs, full model" width="960"><p class="caption">
+Scaled parameters from the multistart runs, full model
+</p>
+</div>
+<p>The graph clearly confirms the lack of identifiability of the
+variance of <code>k2</code> in the full model. The overparameterisation
+of the model also indicates a lack of identifiability of the variance of
+parameter <code>g</code>.</p>
+<p>The parameter boxplots of the multistart runs with the reduced model
+shown below indicate that all runs give similar results, regardless of
+the starting parameters.</p>
+<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">f_saem_dfop_tc_no_ranef_k2_multi</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/multistart.html">multistart</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2</span>,</span>
+<span> n <span class="op">=</span> <span class="fl">50</span>, cores <span class="op">=</span> <span class="fl">15</span><span class="op">)</span></span></code></pre></div>
+<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mar <span class="op">=</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="fl">6.1</span>, <span class="fl">4.1</span>, <span class="fl">2.1</span>, <span class="fl">2.1</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2_multi</span>, ylim <span class="op">=</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="fl">0.5</span>, <span class="fl">2</span><span class="op">)</span>, las <span class="op">=</span> <span class="fl">2</span>,</span>
+<span> lpos <span class="op">=</span> <span class="st">"bottomright"</span><span class="op">)</span></span></code></pre></div>
+<div class="figure" style="text-align: center">
+<img src="2022_dmta_parent_files/figure-html/multistart-reduced-par-1.png" alt="Scaled parameters from the multistart runs, reduced model" width="960"><p class="caption">
+Scaled parameters from the multistart runs, reduced model
+</p>
+</div>
+<p>When only the parameters of the top 25% of the fits are shown (based
+on a feature introduced in mkin 1.2.2 currently under development), the
+scatter is even less as shown below.</p>
+<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mar <span class="op">=</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="fl">6.1</span>, <span class="fl">4.1</span>, <span class="fl">2.1</span>, <span class="fl">2.1</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2_multi</span>, ylim <span class="op">=</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="fl">0.5</span>, <span class="fl">2</span><span class="op">)</span>, las <span class="op">=</span> <span class="fl">2</span>, llquant <span class="op">=</span> <span class="fl">0.25</span>,</span>
+<span> lpos <span class="op">=</span> <span class="st">"bottomright"</span><span class="op">)</span></span></code></pre></div>
+<div class="figure" style="text-align: center">
+<img src="2022_dmta_parent_files/figure-html/multistart-reduced-par-llquant-1.png" alt="Scaled parameters from the multistart runs, reduced model, fits with the top 25\% likelihood values" width="960"><p class="caption">
+Scaled parameters from the multistart runs, reduced model, fits with the
+top 25% likelihood values
+</p>
+</div>
+</div>
+</div>
+<div class="section level2">
+<h2 id="conclusions">Conclusions<a class="anchor" aria-label="anchor" href="#conclusions"></a>
+</h2>
+<p>Fitting the four parent degradation models SFO, FOMC, DFOP and HS as
+part of hierarchical model fits with two different error models and
+normal distributions of the transformed degradation parameters works
+without technical problems. The biphasic models DFOP and HS gave the
+best fit to the data, but the default parameter distribution model was
+not fully identifiable. Removing the random effect for the second
+kinetic rate constant of the DFOP model resulted in a reduced model that
+was fully identifiable and showed the lowest values for the model
+selection criteria AIC and BIC. The reliability of the identification of
+all model parameters was confirmed using multiple starting values.</p>
+</div>
+<div class="section level2">
+<h2 id="acknowledgements">Acknowledgements<a class="anchor" aria-label="anchor" href="#acknowledgements"></a>
+</h2>
+<p>The helpful comments by Janina Wöltjen of the German Environment
+Agency are gratefully acknowledged.</p>
+</div>
+<div class="section level2">
+<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
+</h2>
+<div id="refs" class="references csl-bib-body hanging-indent">
+<div id="ref-duchesne_2021" class="csl-entry">
+Duchesne, Ronan, Anissa Guillemin, Olivier Gandrillon, and Fabien
+Crauste. 2021. <span>“Practical Identifiability in the Frame of
+Nonlinear Mixed Effects Models: The Example of the in Vitro
+Erythropoiesis.”</span> <em>BMC Bioinformatics</em> 22 (478). <a href="https://doi.org/10.1186/s12859-021-04373-4" class="external-link">https://doi.org/10.1186/s12859-021-04373-4</a>.
+</div>
+</div>
+</div>
+<div class="section level2">
+<h2 id="appendix">Appendix<a class="anchor" aria-label="anchor" href="#appendix"></a>
+</h2>
+<div class="section level3">
+<h3 id="hierarchical-model-fit-listings">Hierarchical model fit listings<a class="anchor" aria-label="anchor" href="#hierarchical-model-fit-listings"></a>
+</h3>
+<caption>
+Hierarchical mkin fit of the SFO model with error model const
+</caption>
+<pre><code>
+saemix version used for fitting: 3.2
+mkin version used for pre-fitting: 1.2.3
+R version used for fitting: 4.2.3
+Date of fit: Thu Apr 20 14:07:02 2023
+Date of summary: Thu Apr 20 14:08:16 2023
+
+Equations:
+d_DMTA/dt = - k_DMTA * DMTA
+
+Data:
+155 observations of 1 variable(s) grouped in 6 datasets
+
+Model predictions using solution type analytical
+
+Fitted in 0.982 s
+Using 300, 100 iterations and 9 chains
+
+Variance model: Constant variance
+
+Starting values for degradation parameters:
+ DMTA_0 k_DMTA
+97.2953 0.0566
+
+Fixed degradation parameter values:
+None
+
+Starting values for random effects (square root of initial entries in omega):
+ DMTA_0 k_DMTA
+DMTA_0 97.3 0
+k_DMTA 0.0 1
+
+Starting values for error model parameters:
+a.1
+ 1
+
+Results:
+
+Likelihood computed by importance sampling
+ AIC BIC logLik
+ 796.3 795.3 -393.2
+
+Optimised parameters:
+ est. lower upper
+DMTA_0 97.28130 95.71113 98.8515
+k_DMTA 0.05665 0.02909 0.0842
+a.1 2.66442 2.35579 2.9731
+SD.DMTA_0 1.54776 0.15447 2.9411
+SD.k_DMTA 0.60690 0.26248 0.9513
+
+Correlation:
+ DMTA_0
+k_DMTA 0.0168
+
+Random effects:
+ est. lower upper
+SD.DMTA_0 1.5478 0.1545 2.9411
+SD.k_DMTA 0.6069 0.2625 0.9513
+
+Variance model:
+ est. lower upper
+a.1 2.664 2.356 2.973
+
+Estimated disappearance times:
+ DT50 DT90
+DMTA 12.24 40.65
+
+</code></pre>
+<p></p>
+<caption>
+Hierarchical mkin fit of the SFO model with error model tc
+</caption>
+<pre><code>
+saemix version used for fitting: 3.2
+mkin version used for pre-fitting: 1.2.3
+R version used for fitting: 4.2.3
+Date of fit: Thu Apr 20 14:07:03 2023
+Date of summary: Thu Apr 20 14:08:16 2023
+
+Equations:
+d_DMTA/dt = - k_DMTA * DMTA
+
+Data:
+155 observations of 1 variable(s) grouped in 6 datasets
+
+Model predictions using solution type analytical
+
+Fitted in 2.398 s
+Using 300, 100 iterations and 9 chains
+
+Variance model: Two-component variance function
+
+Starting values for degradation parameters:
+ DMTA_0 k_DMTA
+96.99175 0.05603
+
+Fixed degradation parameter values:
+None
+
+Starting values for random effects (square root of initial entries in omega):
+ DMTA_0 k_DMTA
+DMTA_0 96.99 0
+k_DMTA 0.00 1
+
+Starting values for error model parameters:
+a.1 b.1
+ 1 1
+
+Results:
+
+Likelihood computed by importance sampling
+ AIC BIC logLik
+ 798.3 797.1 -393.2
+
+Optimised parameters:
+ est. lower upper
+DMTA_0 97.271822 95.703157 98.84049
+k_DMTA 0.056638 0.029110 0.08417
+a.1 2.660081 2.230398 3.08976
+b.1 0.001665 -0.006911 0.01024
+SD.DMTA_0 1.545520 0.145035 2.94601
+SD.k_DMTA 0.606422 0.262274 0.95057
+
+Correlation:
+ DMTA_0
+k_DMTA 0.0169
+
+Random effects:
+ est. lower upper
+SD.DMTA_0 1.5455 0.1450 2.9460
+SD.k_DMTA 0.6064 0.2623 0.9506
+
+Variance model:
+ est. lower upper
+a.1 2.660081 2.230398 3.08976
+b.1 0.001665 -0.006911 0.01024
+
+Estimated disappearance times:
+ DT50 DT90
+DMTA 12.24 40.65
+
+</code></pre>
+<p></p>
+<caption>
+Hierarchical mkin fit of the FOMC model with error model const
+</caption>
+<pre><code>
+saemix version used for fitting: 3.2
+mkin version used for pre-fitting: 1.2.3
+R version used for fitting: 4.2.3
+Date of fit: Thu Apr 20 14:07:02 2023
+Date of summary: Thu Apr 20 14:08:16 2023
+
+Equations:
+d_DMTA/dt = - (alpha/beta) * 1/((time/beta) + 1) * DMTA
+
+Data:
+155 observations of 1 variable(s) grouped in 6 datasets
+
+Model predictions using solution type analytical
+
+Fitted in 1.398 s
+Using 300, 100 iterations and 9 chains
+
+Variance model: Constant variance
+
+Starting values for degradation parameters:
+ DMTA_0 alpha beta
+ 98.292 9.909 156.341
+
+Fixed degradation parameter values:
+None
+
+Starting values for random effects (square root of initial entries in omega):
+ DMTA_0 alpha beta
+DMTA_0 98.29 0 0
+alpha 0.00 1 0
+beta 0.00 0 1
+
+Starting values for error model parameters:
+a.1
+ 1
+
+Results:
+
+Likelihood computed by importance sampling
+ AIC BIC logLik
+ 734.2 732.7 -360.1
+
+Optimised parameters:
+ est. lower upper
+DMTA_0 98.3435 96.9033 99.784
+alpha 7.2007 2.5889 11.812
+beta 112.8746 34.8816 190.868
+a.1 2.0459 1.8054 2.286
+SD.DMTA_0 1.4795 0.2717 2.687
+SD.alpha 0.6396 0.1509 1.128
+SD.beta 0.6874 0.1587 1.216
+
+Correlation:
+ DMTA_0 alpha
+alpha -0.1125
+beta -0.1227 0.3632
+
+Random effects:
+ est. lower upper
+SD.DMTA_0 1.4795 0.2717 2.687
+SD.alpha 0.6396 0.1509 1.128
+SD.beta 0.6874 0.1587 1.216
+
+Variance model:
+ est. lower upper
+a.1 2.046 1.805 2.286
+
+Estimated disappearance times:
+ DT50 DT90 DT50back
+DMTA 11.41 42.53 12.8
+
+</code></pre>
+<p></p>
+<caption>
+Hierarchical mkin fit of the FOMC model with error model tc
+</caption>
+<pre><code>
+saemix version used for fitting: 3.2
+mkin version used for pre-fitting: 1.2.3
+R version used for fitting: 4.2.3
+Date of fit: Thu Apr 20 14:07:04 2023
+Date of summary: Thu Apr 20 14:08:16 2023
+
+Equations:
+d_DMTA/dt = - (alpha/beta) * 1/((time/beta) + 1) * DMTA
+
+Data:
+155 observations of 1 variable(s) grouped in 6 datasets
+
+Model predictions using solution type analytical
+
+Fitted in 3.044 s
+Using 300, 100 iterations and 9 chains
+
+Variance model: Two-component variance function
+
+Starting values for degradation parameters:
+DMTA_0 alpha beta
+98.772 4.663 92.597
+
+Fixed degradation parameter values:
+None
+
+Starting values for random effects (square root of initial entries in omega):
+ DMTA_0 alpha beta
+DMTA_0 98.77 0 0
+alpha 0.00 1 0
+beta 0.00 0 1
+
+Starting values for error model parameters:
+a.1 b.1
+ 1 1
+
+Results:
+
+Likelihood computed by importance sampling
+ AIC BIC logLik
+ 720.4 718.8 -352.2
+
+Optimised parameters:
+ est. lower upper
+DMTA_0 98.99136 97.26011 100.72261
+alpha 5.86312 2.57485 9.15138
+beta 88.55571 29.20889 147.90254
+a.1 1.51063 1.24384 1.77741
+b.1 0.02824 0.02040 0.03609
+SD.DMTA_0 1.57436 -0.04867 3.19739
+SD.alpha 0.59871 0.17132 1.02611
+SD.beta 0.72994 0.22849 1.23139
+
+Correlation:
+ DMTA_0 alpha
+alpha -0.1363
+beta -0.1414 0.2542
+
+Random effects:
+ est. lower upper
+SD.DMTA_0 1.5744 -0.04867 3.197
+SD.alpha 0.5987 0.17132 1.026
+SD.beta 0.7299 0.22849 1.231
+
+Variance model:
+ est. lower upper
+a.1 1.51063 1.2438 1.77741
+b.1 0.02824 0.0204 0.03609
+
+Estimated disappearance times:
+ DT50 DT90 DT50back
+DMTA 11.11 42.6 12.82
+
+</code></pre>
+<p></p>
+<caption>
+Hierarchical mkin fit of the DFOP model with error model const
+</caption>
+<pre><code>
+saemix version used for fitting: 3.2
+mkin version used for pre-fitting: 1.2.3
+R version used for fitting: 4.2.3
+Date of fit: Thu Apr 20 14:07:02 2023
+Date of summary: Thu Apr 20 14:08:16 2023
+
+Equations:
+d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
+ time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
+ * DMTA
+
+Data:
+155 observations of 1 variable(s) grouped in 6 datasets
+
+Model predictions using solution type analytical
+
+Fitted in 1.838 s
+Using 300, 100 iterations and 9 chains
+
+Variance model: Constant variance
+
+Starting values for degradation parameters:
+ DMTA_0 k1 k2 g
+98.64383 0.09211 0.02999 0.76814
+
+Fixed degradation parameter values:
+None
+
+Starting values for random effects (square root of initial entries in omega):
+ DMTA_0 k1 k2 g
+DMTA_0 98.64 0 0 0
+k1 0.00 1 0 0
+k2 0.00 0 1 0
+g 0.00 0 0 1
+
+Starting values for error model parameters:
+a.1
+ 1
+
+Results:
+
+Likelihood computed by importance sampling
+ AIC BIC logLik
+ 711.8 710 -346.9
+
+Optimised parameters:
+ est. lower upper
+DMTA_0 98.092481 96.573898 99.61106
+k1 0.062499 0.030336 0.09466
+k2 0.009065 -0.005133 0.02326
+g 0.948967 0.862079 1.03586
+a.1 1.821671 1.604774 2.03857
+SD.DMTA_0 1.677785 0.472066 2.88350
+SD.k1 0.634962 0.270788 0.99914
+SD.k2 1.033498 -0.205994 2.27299
+SD.g 1.710046 0.428642 2.99145
+
+Correlation:
+ DMTA_0 k1 k2
+k1 0.0246
+k2 0.0491 0.0953
+g -0.0552 -0.0889 -0.4795
+
+Random effects:
+ est. lower upper
+SD.DMTA_0 1.678 0.4721 2.8835
+SD.k1 0.635 0.2708 0.9991
+SD.k2 1.033 -0.2060 2.2730
+SD.g 1.710 0.4286 2.9914
+
+Variance model:
+ est. lower upper
+a.1 1.822 1.605 2.039
+
+Estimated disappearance times:
+ DT50 DT90 DT50back DT50_k1 DT50_k2
+DMTA 11.79 42.8 12.88 11.09 76.46
+
+</code></pre>
+<p></p>
+<caption>
+Hierarchical mkin fit of the DFOP model with error model tc
+</caption>
+<pre><code>
+saemix version used for fitting: 3.2
+mkin version used for pre-fitting: 1.2.3
+R version used for fitting: 4.2.3
+Date of fit: Thu Apr 20 14:07:04 2023
+Date of summary: Thu Apr 20 14:08:16 2023
+
+Equations:
+d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
+ time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
+ * DMTA
+
+Data:
+155 observations of 1 variable(s) grouped in 6 datasets
+
+Model predictions using solution type analytical
+
+Fitted in 3.297 s
+Using 300, 100 iterations and 9 chains
+
+Variance model: Two-component variance function
+
+Starting values for degradation parameters:
+ DMTA_0 k1 k2 g
+98.759266 0.087034 0.009933 0.930827
+
+Fixed degradation parameter values:
+None
+
+Starting values for random effects (square root of initial entries in omega):
+ DMTA_0 k1 k2 g
+DMTA_0 98.76 0 0 0
+k1 0.00 1 0 0
+k2 0.00 0 1 0
+g 0.00 0 0 1
+
+Starting values for error model parameters:
+a.1 b.1
+ 1 1
+
+Results:
+
+Likelihood computed by importance sampling
+ AIC BIC logLik
+ 665.5 663.4 -322.8
+
+Optimised parameters:
+ est. lower upper
+DMTA_0 98.377019 96.447952 100.30609
+k1 0.064843 0.034607 0.09508
+k2 0.008895 0.006368 0.01142
+g 0.949696 0.903815 0.99558
+a.1 1.065241 0.865754 1.26473
+b.1 0.029340 0.022336 0.03634
+SD.DMTA_0 2.007754 0.387982 3.62753
+SD.k1 0.580473 0.250286 0.91066
+SD.k2 0.006105 -4.920337 4.93255
+SD.g 1.097149 0.412779 1.78152
+
+Correlation:
+ DMTA_0 k1 k2
+k1 0.0235
+k2 0.0595 0.0424
+g -0.0470 -0.0278 -0.2731
+
+Random effects:
+ est. lower upper
+SD.DMTA_0 2.007754 0.3880 3.6275
+SD.k1 0.580473 0.2503 0.9107
+SD.k2 0.006105 -4.9203 4.9325
+SD.g 1.097149 0.4128 1.7815
+
+Variance model:
+ est. lower upper
+a.1 1.06524 0.86575 1.26473
+b.1 0.02934 0.02234 0.03634
+
+Estimated disappearance times:
+ DT50 DT90 DT50back DT50_k1 DT50_k2
+DMTA 11.36 41.32 12.44 10.69 77.92
+
+</code></pre>
+<p></p>
+<caption>
+Hierarchical mkin fit of the HS model with error model const
+</caption>
+<pre><code>
+saemix version used for fitting: 3.2
+mkin version used for pre-fitting: 1.2.3
+R version used for fitting: 4.2.3
+Date of fit: Thu Apr 20 14:07:03 2023
+Date of summary: Thu Apr 20 14:08:16 2023
+
+Equations:
+d_DMTA/dt = - ifelse(time &lt;= tb, k1, k2) * DMTA
+
+Data:
+155 observations of 1 variable(s) grouped in 6 datasets
+
+Model predictions using solution type analytical
+
+Fitted in 1.972 s
+Using 300, 100 iterations and 9 chains
+
+Variance model: Constant variance
+
+Starting values for degradation parameters:
+ DMTA_0 k1 k2 tb
+97.82176 0.06931 0.02997 11.13945
+
+Fixed degradation parameter values:
+None
+
+Starting values for random effects (square root of initial entries in omega):
+ DMTA_0 k1 k2 tb
+DMTA_0 97.82 0 0 0
+k1 0.00 1 0 0
+k2 0.00 0 1 0
+tb 0.00 0 0 1
+
+Starting values for error model parameters:
+a.1
+ 1
+
+Results:
+
+Likelihood computed by importance sampling
+ AIC BIC logLik
+ 714 712.1 -348
+
+Optimised parameters:
+ est. lower upper
+DMTA_0 98.16102 96.47747 99.84456
+k1 0.07876 0.05261 0.10491
+k2 0.02227 0.01706 0.02747
+tb 13.99089 -7.40049 35.38228
+a.1 1.82305 1.60700 2.03910
+SD.DMTA_0 1.88413 0.56204 3.20622
+SD.k1 0.34292 0.10482 0.58102
+SD.k2 0.19851 0.01718 0.37985
+SD.tb 1.68168 0.58064 2.78272
+
+Correlation:
+ DMTA_0 k1 k2
+k1 0.0142
+k2 0.0001 -0.0025
+tb 0.0165 -0.1256 -0.0301
+
+Random effects:
+ est. lower upper
+SD.DMTA_0 1.8841 0.56204 3.2062
+SD.k1 0.3429 0.10482 0.5810
+SD.k2 0.1985 0.01718 0.3798
+SD.tb 1.6817 0.58064 2.7827
+
+Variance model:
+ est. lower upper
+a.1 1.823 1.607 2.039
+
+Estimated disappearance times:
+ DT50 DT90 DT50back DT50_k1 DT50_k2
+DMTA 8.801 67.91 20.44 8.801 31.13
+
+</code></pre>
+<p></p>
+<caption>
+Hierarchical mkin fit of the HS model with error model tc
+</caption>
+<pre><code>
+saemix version used for fitting: 3.2
+mkin version used for pre-fitting: 1.2.3
+R version used for fitting: 4.2.3
+Date of fit: Thu Apr 20 14:07:04 2023
+Date of summary: Thu Apr 20 14:08:16 2023
+
+Equations:
+d_DMTA/dt = - ifelse(time &lt;= tb, k1, k2) * DMTA
+
+Data:
+155 observations of 1 variable(s) grouped in 6 datasets
+
+Model predictions using solution type analytical
+
+Fitted in 3.378 s
+Using 300, 100 iterations and 9 chains
+
+Variance model: Two-component variance function
+
+Starting values for degradation parameters:
+ DMTA_0 k1 k2 tb
+98.45190 0.07525 0.02576 19.19375
+
+Fixed degradation parameter values:
+None
+
+Starting values for random effects (square root of initial entries in omega):
+ DMTA_0 k1 k2 tb
+DMTA_0 98.45 0 0 0
+k1 0.00 1 0 0
+k2 0.00 0 1 0
+tb 0.00 0 0 1
+
+Starting values for error model parameters:
+a.1 b.1
+ 1 1
+
+Results:
+
+Likelihood computed by importance sampling
+ AIC BIC logLik
+ 667.1 665 -323.6
+
+Optimised parameters:
+ est. lower upper
+DMTA_0 97.76570 95.81350 99.71791
+k1 0.05855 0.03080 0.08630
+k2 0.02337 0.01664 0.03010
+tb 31.09638 29.38289 32.80987
+a.1 1.08835 0.88590 1.29080
+b.1 0.02964 0.02257 0.03671
+SD.DMTA_0 2.04877 0.42607 3.67147
+SD.k1 0.59166 0.25621 0.92711
+SD.k2 0.30698 0.09561 0.51835
+SD.tb 0.01274 -0.10914 0.13462
+
+Correlation:
+ DMTA_0 k1 k2
+k1 0.0160
+k2 -0.0070 -0.0024
+tb -0.0668 -0.0103 -0.2013
+
+Random effects:
+ est. lower upper
+SD.DMTA_0 2.04877 0.42607 3.6715
+SD.k1 0.59166 0.25621 0.9271
+SD.k2 0.30698 0.09561 0.5183
+SD.tb 0.01274 -0.10914 0.1346
+
+Variance model:
+ est. lower upper
+a.1 1.08835 0.88590 1.29080
+b.1 0.02964 0.02257 0.03671
+
+Estimated disappearance times:
+ DT50 DT90 DT50back DT50_k1 DT50_k2
+DMTA 11.84 51.71 15.57 11.84 29.66
+
+</code></pre>
+<p></p>
+</div>
+<div class="section level3">
+<h3 id="hierarchical-model-convergence-plots">Hierarchical model convergence plots<a class="anchor" aria-label="anchor" href="#hierarchical-model-convergence-plots"></a>
+</h3>
+<div class="figure" style="text-align: center">
+<img src="2022_dmta_parent_files/figure-html/convergence-saem-sfo-const-1.png" alt="Convergence plot for the NLHM SFO fit with constant variance" width="864"><p class="caption">
+Convergence plot for the NLHM SFO fit with constant variance
+</p>
+</div>
+<div class="figure" style="text-align: center">
+<img src="2022_dmta_parent_files/figure-html/convergence-saem-sfo-tc-1.png" alt="Convergence plot for the NLHM SFO fit with two-component error" width="864"><p class="caption">
+Convergence plot for the NLHM SFO fit with two-component error
+</p>
+</div>
+<div class="figure" style="text-align: center">
+<img src="2022_dmta_parent_files/figure-html/convergence-saem-fomc-const-1.png" alt="Convergence plot for the NLHM FOMC fit with constant variance" width="864"><p class="caption">
+Convergence plot for the NLHM FOMC fit with constant variance
+</p>
+</div>
+<div class="figure" style="text-align: center">
+<img src="2022_dmta_parent_files/figure-html/convergence-saem-fomc-tc-1.png" alt="Convergence plot for the NLHM FOMC fit with two-component error" width="864"><p class="caption">
+Convergence plot for the NLHM FOMC fit with two-component error
+</p>
+</div>
+<div class="figure" style="text-align: center">
+<img src="2022_dmta_parent_files/figure-html/convergence-saem-dfop-const-1.png" alt="Convergence plot for the NLHM DFOP fit with constant variance" width="864"><p class="caption">
+Convergence plot for the NLHM DFOP fit with constant variance
+</p>
+</div>
+<div class="figure" style="text-align: center">
+<img src="2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-1.png" alt="Convergence plot for the NLHM DFOP fit with two-component error" width="864"><p class="caption">
+Convergence plot for the NLHM DFOP fit with two-component error
+</p>
+</div>
+<div class="figure" style="text-align: center">
+<img src="2022_dmta_parent_files/figure-html/convergence-saem-hs-const-1.png" alt="Convergence plot for the NLHM HS fit with constant variance" width="864"><p class="caption">
+Convergence plot for the NLHM HS fit with constant variance
+</p>
+</div>
+<div class="figure" style="text-align: center">
+<img src="2022_dmta_parent_files/figure-html/convergence-saem-hs-tc-1.png" alt="Convergence plot for the NLHM HS fit with two-component error" width="864"><p class="caption">
+Convergence plot for the NLHM HS fit with two-component error
+</p>
+</div>
+</div>
+<div class="section level3">
+<h3 id="session-info">Session info<a class="anchor" aria-label="anchor" href="#session-info"></a>
+</h3>
+<pre><code>R version 4.2.3 (2023-03-15)
+Platform: x86_64-pc-linux-gnu (64-bit)
+Running under: Debian GNU/Linux 12 (bookworm)
+
+Matrix products: default
+BLAS: /usr/lib/x86_64-linux-gnu/openblas-serial/libblas.so.3
+LAPACK: /usr/lib/x86_64-linux-gnu/openblas-serial/libopenblas-r0.3.21.so
+
+locale:
+ [1] LC_CTYPE=de_DE.UTF-8 LC_NUMERIC=C
+ [3] LC_TIME=de_DE.UTF-8 LC_COLLATE=de_DE.UTF-8
+ [5] LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=de_DE.UTF-8
+ [7] LC_PAPER=de_DE.UTF-8 LC_NAME=C
+ [9] LC_ADDRESS=C LC_TELEPHONE=C
+[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C
+
+attached base packages:
+[1] parallel stats graphics grDevices utils datasets methods
+[8] base
+
+other attached packages:
+[1] saemix_3.2 npde_3.3 knitr_1.42 mkin_1.2.3
+
+loaded via a namespace (and not attached):
+ [1] highr_0.10 pillar_1.9.0 bslib_0.4.2 compiler_4.2.3
+ [5] jquerylib_0.1.4 tools_4.2.3 mclust_6.0.0 digest_0.6.31
+ [9] tibble_3.2.1 jsonlite_1.8.4 evaluate_0.20 memoise_2.0.1
+[13] lifecycle_1.0.3 nlme_3.1-162 gtable_0.3.3 lattice_0.21-8
+[17] pkgconfig_2.0.3 rlang_1.1.0 DBI_1.1.3 cli_3.6.1
+[21] yaml_2.3.7 pkgdown_2.0.7 xfun_0.38 fastmap_1.1.1
+[25] gridExtra_2.3 dplyr_1.1.1 stringr_1.5.0 generics_0.1.3
+[29] desc_1.4.2 fs_1.6.1 vctrs_0.6.1 sass_0.4.5
+[33] systemfonts_1.0.4 tidyselect_1.2.0 rprojroot_2.0.3 lmtest_0.9-40
+[37] grid_4.2.3 glue_1.6.2 R6_2.5.1 textshaping_0.3.6
+[41] fansi_1.0.4 rmarkdown_2.21 purrr_1.0.1 ggplot2_3.4.2
+[45] magrittr_2.0.3 codetools_0.2-19 scales_1.2.1 htmltools_0.5.5
+[49] colorspace_2.1-0 ragg_1.2.5 utf8_1.2.3 stringi_1.7.12
+[53] munsell_0.5.0 cachem_1.0.7 zoo_1.8-12 </code></pre>
+</div>
+<div class="section level3">
+<h3 id="hardware-info">Hardware info<a class="anchor" aria-label="anchor" href="#hardware-info"></a>
+</h3>
+<pre><code>CPU model: AMD Ryzen 9 7950X 16-Core Processor</code></pre>
+<pre><code>MemTotal: 64936316 kB</code></pre>
+</div>
+</div>
+ </div>
+
+ <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+
+ <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
+ </nav>
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+
+</div>
+
+
+
+ <footer><div class="copyright">
+ <p></p>
+<p>Developed by Johannes Ranke.</p>
+</div>
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+<div class="pkgdown">
+ <p></p>
+<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
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