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- <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/2022_wp_1.1_dmta_parent.rmd" class="external-link"><code>vignettes/2022_wp_1.1_dmta_parent.rmd</code></a></small> - <div class="hidden name"><code>2022_wp_1.1_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>The mkin package is used in version 1.2.2. 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"><-</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"><-</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"><-</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 class="section level3"> -<h3 id="preprocessing-of-test-data">Preprocessing of test data<a class="anchor" aria-label="anchor" href="#preprocessing-of-test-data"></a> -</h3> -<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"><-</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"><-</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">$</span><span class="va">name</span> <span class="op">==</span> <span class="st">"DMTAP"</span>, <span class="st">"name"</span><span class="op">]</span> <span class="op"><-</span> <span class="st">"DMTA"</span> <span class="co"># Rename DMTAP to DMTA</span></span> -<span> <span class="va">ds_i</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">ds_i</span>, <span class="va">name</span> <span class="op">==</span> <span class="st">"DMTA"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">)</span> <span class="co"># Select data</span></span> -<span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op"><-</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"><-</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"><-</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"><-</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"><-</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> -<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"><-</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"><-</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">|></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"><-</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">|></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"><-</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">|></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">|></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">|></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"><-</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">|></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(>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_wp_1.1_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_wp_1.1_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: 1.2.2 -R version used for fitting: 4.2.2 -Date of fit: Thu Jan 5 08:19:13 2023 -Date of summary: Thu Jan 5 08:19:13 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 4.075 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 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"><-</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_wp_1.1_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"><-</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_wp_1.1_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_wp_1.1_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="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: 1.2.2 -R version used for fitting: 4.2.2 -Date of fit: Thu Jan 5 08:19:06 2023 -Date of summary: Thu Jan 5 08:20:11 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 1.09 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: 1.2.2 -R version used for fitting: 4.2.2 -Date of fit: Thu Jan 5 08:19:07 2023 -Date of summary: Thu Jan 5 08:20:11 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 2.441 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: 1.2.2 -R version used for fitting: 4.2.2 -Date of fit: Thu Jan 5 08:19:06 2023 -Date of summary: Thu Jan 5 08:20:11 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 1.156 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: 1.2.2 -R version used for fitting: 4.2.2 -Date of fit: Thu Jan 5 08:19:07 2023 -Date of summary: Thu Jan 5 08:20:11 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 2.729 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: 1.2.2 -R version used for fitting: 4.2.2 -Date of fit: Thu Jan 5 08:19:07 2023 -Date of summary: Thu Jan 5 08:20:11 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 2.007 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: 1.2.2 -R version used for fitting: 4.2.2 -Date of fit: Thu Jan 5 08:19:08 2023 -Date of summary: Thu Jan 5 08:20:11 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 3.033 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: 1.2.2 -R version used for fitting: 4.2.2 -Date of fit: Thu Jan 5 08:19:07 2023 -Date of summary: Thu Jan 5 08:20:11 2023 - -Equations: -d_DMTA/dt = - ifelse(time <= tb, k1, k2) * DMTA - -Data: -155 observations of 1 variable(s) grouped in 6 datasets - -Model predictions using solution type analytical - -Fitted in 2.004 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: 1.2.2 -R version used for fitting: 4.2.2 -Date of fit: Thu Jan 5 08:19:08 2023 -Date of summary: Thu Jan 5 08:20:11 2023 - -Equations: -d_DMTA/dt = - ifelse(time <= tb, k1, k2) * DMTA - -Data: -155 observations of 1 variable(s) grouped in 6 datasets - -Model predictions using solution type analytical - -Fitted in 3.287 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_wp_1.1_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_wp_1.1_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_wp_1.1_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_wp_1.1_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_wp_1.1_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_wp_1.1_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_wp_1.1_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_wp_1.1_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 4.2.2 Patched (2022-11-10 r83330) -Platform: x86_64-pc-linux-gnu (64-bit) -Running under: Debian GNU/Linux bookworm/sid - -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_1.41 mkin_1.2.2 - -loaded via a namespace (and not attached): - [1] deSolve_1.34 zoo_1.8-11 tidyselect_1.2.0 xfun_0.35 - [5] bslib_0.4.2 purrr_1.0.0 lattice_0.20-45 colorspace_2.0-3 - [9] vctrs_0.5.1 generics_0.1.3 htmltools_0.5.4 yaml_2.3.6 -[13] utf8_1.2.2 rlang_1.0.6 pkgdown_2.0.7 jquerylib_0.1.4 -[17] pillar_1.8.1 glue_1.6.2 DBI_1.1.3 lifecycle_1.0.3 -[21] stringr_1.5.0 munsell_0.5.0 gtable_0.3.1 ragg_1.2.4 -[25] codetools_0.2-18 memoise_2.0.1 evaluate_0.19 fastmap_1.1.0 -[29] lmtest_0.9-40 fansi_1.0.3 highr_0.9 scales_1.2.1 -[33] cachem_1.0.6 desc_1.4.2 jsonlite_1.8.4 systemfonts_1.0.4 -[37] fs_1.5.2 textshaping_0.3.6 gridExtra_2.3 ggplot2_3.4.0 -[41] digest_0.6.31 stringi_1.7.8 dplyr_1.0.10 grid_4.2.2 -[45] rprojroot_2.0.3 cli_3.5.0 tools_4.2.2 magrittr_2.0.3 -[49] sass_0.4.4 tibble_3.1.8 pkgconfig_2.0.3 assertthat_0.2.1 -[53] rmarkdown_2.19 R6_2.5.1 mclust_6.0.0 nlme_3.1-161 -[57] compiler_4.2.2 </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> -</div> - -</div> - - - - <footer><div class="copyright"> - <p></p> -<p>Developed by Johannes Ranke.</p> -</div> - -<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> -</div> - - </footer> -</div> - - - - - - - </body> -</html> diff --git a/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-dfop-const-1.png b/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-dfop-const-1.png Binary files differdeleted file mode 100644 index 4f87b956..00000000 --- a/docs/dev/articles/2022_wp_1.1_dmta_parent_files/figure-html/convergence-saem-dfop-const-1.png +++ /dev/null diff --git 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aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li> </ul> </div> -<!--/.nav-collapse --> + + </div> -<!--/.container --> -</div> -<!--/.navbar --> +</nav><div class="container template-article"> + - - </header><div class="row"> - <div class="col-md-9 contents"> - <div class="page-header toc-ignore"> - <h1 data-toc-skip>Example evaluation of FOCUS Example Dataset -D</h1> + +<div class="row"> + <main id="main" class="col-md-9"><div class="page-header"> + + <h1>Example evaluation of FOCUS Example Dataset D</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> <h4 data-toc-skip class="date">Last change 31 January 2019 -(rebuilt 2023-05-19)</h4> +(rebuilt 2025-02-16)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/FOCUS_D.rmd" class="external-link"><code>vignettes/FOCUS_D.rmd</code></a></small> - <div class="hidden name"><code>FOCUS_D.rmd</code></div> - + <div class="d-none name"><code>FOCUS_D.rmd</code></div> </div> @@ -238,10 +186,10 @@ the <code>mkinparplot</code> function.</p> <code>summary</code> method for <code>mkinfit</code> objects.</p> <div class="sourceCode" id="cb11"><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">fit</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## mkin version used for fitting: 1.2.5 </span></span> -<span><span class="co">## R version used for fitting: 4.3.0 </span></span> -<span><span class="co">## Date of fit: Fri May 19 17:34:10 2023 </span></span> -<span><span class="co">## Date of summary: Fri May 19 17:34:10 2023 </span></span> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.10 </span></span> +<span><span class="co">## R version used for fitting: 4.4.2 </span></span> +<span><span class="co">## Date of fit: Sun Feb 16 17:04:33 2025 </span></span> +<span><span class="co">## Date of summary: Sun Feb 16 17:04:33 2025 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - k_parent * parent</span></span> @@ -249,7 +197,7 @@ the <code>mkinparplot</code> function.</p> <span><span class="co">## </span></span> <span><span class="co">## Model predictions using solution type analytical </span></span> <span><span class="co">## </span></span> -<span><span class="co">## Fitted using 401 model solutions performed in 0.05 s</span></span> +<span><span class="co">## Fitted using 401 model solutions performed in 0.051 s</span></span> <span><span class="co">## </span></span> <span><span class="co">## Error model: Constant variance </span></span> <span><span class="co">## </span></span> @@ -293,10 +241,10 @@ the <code>mkinparplot</code> function.</p> <span><span class="co">## Parameter correlation:</span></span> <span><span class="co">## parent_0 log_k_parent log_k_m1 f_parent_qlogis sigma</span></span> <span><span class="co">## parent_0 1.000e+00 5.174e-01 -1.688e-01 -5.471e-01 -1.172e-06</span></span> -<span><span class="co">## log_k_parent 5.174e-01 1.000e+00 -3.263e-01 -5.426e-01 -8.483e-07</span></span> -<span><span class="co">## log_k_m1 -1.688e-01 -3.263e-01 1.000e+00 7.478e-01 8.205e-07</span></span> +<span><span class="co">## log_k_parent 5.174e-01 1.000e+00 -3.263e-01 -5.426e-01 -8.479e-07</span></span> +<span><span class="co">## log_k_m1 -1.688e-01 -3.263e-01 1.000e+00 7.478e-01 8.211e-07</span></span> <span><span class="co">## f_parent_qlogis -5.471e-01 -5.426e-01 7.478e-01 1.000e+00 1.305e-06</span></span> -<span><span class="co">## sigma -1.172e-06 -8.483e-07 8.205e-07 1.305e-06 1.000e+00</span></span> +<span><span class="co">## sigma -1.172e-06 -8.479e-07 8.211e-07 1.305e-06 1.000e+00</span></span> <span><span class="co">## </span></span> <span><span class="co">## Backtransformed parameters:</span></span> <span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span> @@ -365,33 +313,26 @@ the <code>mkinparplot</code> function.</p> <span><span class="co">## 100 m1 33.13 31.98162 1.148e+00</span></span> <span><span class="co">## 120 m1 25.15 28.78984 -3.640e+00</span></span> <span><span class="co">## 120 m1 33.31 28.78984 4.520e+00</span></span></code></pre> - </div> - - <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - - </div> - + </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2> + </nav></aside> </div> - <footer><div class="copyright"> - <p></p> -<p>Developed by Johannes Ranke.</p> + <footer><div class="pkgdown-footer-left"> + <p>Developed by Johannes Ranke.</p> </div> -<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> +<div 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- </header><div class="row"> - <div class="col-md-9 contents"> - <div class="page-header toc-ignore"> - <h1 data-toc-skip>Example evaluation of FOCUS Laboratory Data L1 -to L3</h1> + + +<div class="row"> + <main id="main" class="col-md-9"><div class="page-header"> + + <h1>Example evaluation of FOCUS Laboratory Data L1 to L3</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> <h4 data-toc-skip class="date">Last change 18 May 2023 -(rebuilt 2023-05-19)</h4> +(rebuilt 2025-02-16)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/FOCUS_L.rmd" class="external-link"><code>vignettes/FOCUS_L.rmd</code></a></small> - <div class="hidden name"><code>FOCUS_L.rmd</code></div> - + <div class="d-none name"><code>FOCUS_L.rmd</code></div> </div> @@ -170,10 +118,10 @@ report.</p> <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">m.L1.SFO</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_L1_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> <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">m.L1.SFO</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## mkin version used for fitting: 1.2.5 </span></span> -<span><span class="co">## R version used for fitting: 4.3.0 </span></span> -<span><span class="co">## Date of fit: Fri May 19 17:34:11 2023 </span></span> -<span><span class="co">## Date of summary: Fri May 19 17:34:11 2023 </span></span> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.10 </span></span> +<span><span class="co">## R version used for fitting: 4.4.2 </span></span> +<span><span class="co">## Date of fit: Sun Feb 16 17:04:35 2025 </span></span> +<span><span class="co">## Date of summary: Sun Feb 16 17:04:35 2025 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - k_parent * parent</span></span> @@ -263,7 +211,9 @@ objects.</p> <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="../reference/mkinresplot.html">mkinresplot</a></span><span class="op">(</span><span class="va">m.L1.SFO</span>, ylab <span class="op">=</span> <span class="st">"Observed"</span>, xlab <span class="op">=</span> <span class="st">"Time"</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-5-1.png" width="576"></p> -<p>For comparison, the FOMC model is fitted as well, and the <span class="math inline">\(\chi^2\)</span> error level is checked.</p> +<p>For comparison, the FOMC model is fitted as well, and the +<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math> +error level is checked.</p> <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">m.L1.FOMC</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_L1_mkin</span>, quiet<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## Warning in mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet = TRUE): Optimisation did not converge:</span></span> @@ -274,20 +224,19 @@ objects.</p> <div class="sourceCode" id="cb9"><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">m.L1.FOMC</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre> -<pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre> -<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result</span></span> -<span><span class="co">## is doubtful</span></span></code></pre> -<pre><code><span><span class="co">## mkin version used for fitting: 1.2.5 </span></span> -<span><span class="co">## R version used for fitting: 4.3.0 </span></span> -<span><span class="co">## Date of fit: Fri May 19 17:34:12 2023 </span></span> -<span><span class="co">## Date of summary: Fri May 19 17:34:12 2023 </span></span> +<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(V) had non-positive or NA entries; the</span></span> +<span><span class="co">## non-finite result may be dubious</span></span></code></pre> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.10 </span></span> +<span><span class="co">## R version used for fitting: 4.4.2 </span></span> +<span><span class="co">## Date of fit: Sun Feb 16 17:04:35 2025 </span></span> +<span><span class="co">## Date of summary: Sun Feb 16 17:04:35 2025 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span></span> <span><span class="co">## </span></span> <span><span class="co">## Model predictions using solution type analytical </span></span> <span><span class="co">## </span></span> -<span><span class="co">## Fitted using 369 model solutions performed in 0.025 s</span></span> +<span><span class="co">## Fitted using 342 model solutions performed in 0.023 s</span></span> <span><span class="co">## </span></span> <span><span class="co">## Error model: Constant variance </span></span> <span><span class="co">## </span></span> @@ -315,22 +264,22 @@ objects.</p> <span><span class="co">## </span></span> <span><span class="co">## Results:</span></span> <span><span class="co">## </span></span> -<span><span class="co">## AIC BIC logLik</span></span> -<span><span class="co">## 95.88781 99.44929 -43.9439</span></span> +<span><span class="co">## AIC BIC logLik</span></span> +<span><span class="co">## 95.88782 99.44931 -43.94391</span></span> <span><span class="co">## </span></span> <span><span class="co">## Optimised, transformed parameters with symmetric confidence intervals:</span></span> <span><span class="co">## Estimate Std. Error Lower Upper</span></span> <span><span class="co">## parent_0 92.47 1.2820 89.720 95.220</span></span> -<span><span class="co">## log_alpha 13.78 NaN NaN NaN</span></span> -<span><span class="co">## log_beta 16.13 NaN NaN NaN</span></span> -<span><span class="co">## sigma 2.78 0.4598 1.794 3.766</span></span> +<span><span class="co">## log_alpha 13.20 NaN NaN NaN</span></span> +<span><span class="co">## log_beta 15.54 NaN NaN NaN</span></span> +<span><span class="co">## sigma 2.78 0.4607 1.792 3.768</span></span> <span><span class="co">## </span></span> <span><span class="co">## Parameter correlation:</span></span> -<span><span class="co">## parent_0 log_alpha log_beta sigma</span></span> -<span><span class="co">## parent_0 1.0000000 NaN NaN 0.0001671</span></span> -<span><span class="co">## log_alpha NaN 1 NaN NaN</span></span> -<span><span class="co">## log_beta NaN NaN 1 NaN</span></span> -<span><span class="co">## sigma 0.0001671 NaN NaN 1.0000000</span></span> +<span><span class="co">## parent_0 log_alpha log_beta sigma</span></span> +<span><span class="co">## parent_0 1.000000 NaN NaN 0.000603</span></span> +<span><span class="co">## log_alpha NaN 1 NaN NaN</span></span> +<span><span class="co">## log_beta NaN NaN 1 NaN</span></span> +<span><span class="co">## sigma 0.000603 NaN NaN 1.000000</span></span> <span><span class="co">## </span></span> <span><span class="co">## Backtransformed parameters:</span></span> <span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span> @@ -338,9 +287,9 @@ objects.</p> <span><span class="co">## for estimators of untransformed parameters.</span></span> <span><span class="co">## Estimate t value Pr(>t) Lower Upper</span></span> <span><span class="co">## parent_0 9.247e+01 NA NA 89.720 95.220</span></span> -<span><span class="co">## alpha 9.658e+05 NA NA NA NA</span></span> -<span><span class="co">## beta 1.010e+07 NA NA NA NA</span></span> -<span><span class="co">## sigma 2.780e+00 NA NA 1.794 3.766</span></span> +<span><span class="co">## alpha 5.386e+05 NA NA NA NA</span></span> +<span><span class="co">## beta 5.633e+06 NA NA NA NA</span></span> +<span><span class="co">## sigma 2.780e+00 NA NA 1.792 3.768</span></span> <span><span class="co">## </span></span> <span><span class="co">## FOCUS Chi2 error levels in percent:</span></span> <span><span class="co">## err.min n.optim df</span></span> @@ -348,43 +297,47 @@ objects.</p> <span><span class="co">## parent 3.619 3 6</span></span> <span><span class="co">## </span></span> <span><span class="co">## Estimated disappearance times:</span></span> -<span><span class="co">## DT50 DT90 DT50back</span></span> -<span><span class="co">## parent 7.25 24.08 7.25</span></span></code></pre> +<span><span class="co">## DT50 DT90 DT50back</span></span> +<span><span class="co">## parent 7.249 24.08 7.249</span></span></code></pre> <p>We get a warning that the default optimisation algorithm <code>Port</code> did not converge, which is an indication that the model is overparameterised, <em>i.e.</em> contains too many parameters that are ill-defined as a consequence.</p> <p>And in fact, due to the higher number of parameters, and the lower -number of degrees of freedom of the fit, the <span class="math inline">\(\chi^2\)</span> error level is actually higher for -the FOMC model (3.6%) than for the SFO model (3.4%). Additionally, the -parameters <code>log_alpha</code> and <code>log_beta</code> internally -fitted in the model have excessive confidence intervals, that span more -than 25 orders of magnitude (!) when backtransformed to the scale of -<code>alpha</code> and <code>beta</code>. Also, the t-test for -significant difference from zero does not indicate such a significant -difference, with p-values greater than 0.1, and finally, the parameter -correlation of <code>log_alpha</code> and <code>log_beta</code> is -1.000, clearly indicating that the model is overparameterised.</p> -<p>The <span class="math inline">\(\chi^2\)</span> error levels reported -in Appendix 3 and Appendix 7 to the FOCUS kinetics report are rounded to -integer percentages and partly deviate by one percentage point from the -results calculated by mkin. The reason for this is not known. However, -mkin gives the same <span class="math inline">\(\chi^2\)</span> error -levels as the kinfit package and the calculation routines of the kinfit -package have been extensively compared to the results obtained by the -KinGUI software, as documented in the kinfit package vignette. KinGUI -was the first widely used standard package in this field. Also, the -calculation of <span class="math inline">\(\chi^2\)</span> error levels -was compared with KinGUII, CAKE and DegKin manager in a project -sponsored by the German Umweltbundesamt <span class="citation">(Ranke -2014)</span>.</p> +number of degrees of freedom of the fit, the +<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math> +error level is actually higher for the FOMC model (3.6%) than for the +SFO model (3.4%). Additionally, the parameters <code>log_alpha</code> +and <code>log_beta</code> internally fitted in the model have excessive +confidence intervals, that span more than 25 orders of magnitude (!) +when backtransformed to the scale of <code>alpha</code> and +<code>beta</code>. Also, the t-test for significant difference from zero +does not indicate such a significant difference, with p-values greater +than 0.1, and finally, the parameter correlation of +<code>log_alpha</code> and <code>log_beta</code> is 1.000, clearly +indicating that the model is overparameterised.</p> +<p>The +<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math> +error levels reported in Appendix 3 and Appendix 7 to the FOCUS kinetics +report are rounded to integer percentages and partly deviate by one +percentage point from the results calculated by mkin. The reason for +this is not known. However, mkin gives the same +<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math> +error levels as the kinfit package and the calculation routines of the +kinfit package have been extensively compared to the results obtained by +the KinGUI software, as documented in the kinfit package vignette. +KinGUI was the first widely used standard package in this field. Also, +the calculation of +<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math> +error levels was compared with KinGUII, CAKE and DegKin manager in a +project sponsored by the German Umweltbundesamt <span class="citation">(Ranke 2014)</span>.</p> </div> <div class="section level2"> <h2 id="laboratory-data-l2">Laboratory Data L2<a class="anchor" aria-label="anchor" href="#laboratory-data-l2"></a> </h2> <p>The following code defines example dataset L2 from the FOCUS kinetics report, p. 287:</p> -<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">FOCUS_2006_L2</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span> <span> t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><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</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span><span class="op">)</span>, each <span class="op">=</span> <span class="fl">2</span><span class="op">)</span>,</span> <span> parent <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">96.1</span>, <span class="fl">91.8</span>, <span class="fl">41.4</span>, <span class="fl">38.7</span>,</span> @@ -397,15 +350,16 @@ report, p. 287:</p> <p>Again, the SFO model is fitted and the result is plotted. The residual plot can be obtained simply by adding the argument <code>show_residuals</code> to the plot command.</p> -<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">m.L2.SFO</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_L2_mkin</span>, quiet<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span> <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">m.L2.SFO</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>,</span> <span> main <span class="op">=</span> <span class="st">"FOCUS L2 - SFO"</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-8-1.png" width="672"></p> -<p>The <span class="math inline">\(\chi^2\)</span> error level of 14% -suggests that the model does not fit very well. This is also obvious -from the plots of the fit, in which we have included the residual -plot.</p> +<p>The +<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math> +error level of 14% suggests that the model does not fit very well. This +is also obvious from the plots of the fit, in which we have included the +residual plot.</p> <p>In the FOCUS kinetics report, it is stated that there is no apparent systematic error observed from the residual plot up to the measured DT90 (approximately at day 5), and there is an underestimation beyond that @@ -420,18 +374,20 @@ kinetics.</p> <div class="section level3"> <h3 id="fomc-fit-for-l2">FOMC fit for L2<a class="anchor" aria-label="anchor" href="#fomc-fit-for-l2"></a> </h3> -<p>For comparison, the FOMC model is fitted as well, and the <span class="math inline">\(\chi^2\)</span> error level is checked.</p> -<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r"> +<p>For comparison, the FOMC model is fitted as well, and the +<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math> +error level is checked.</p> +<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">m.L2.FOMC</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_L2_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> <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">m.L2.FOMC</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>,</span> <span> main <span class="op">=</span> <span class="st">"FOCUS L2 - FOMC"</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-9-1.png" width="672"></p> -<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb16"><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">m.L2.FOMC</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## mkin version used for fitting: 1.2.5 </span></span> -<span><span class="co">## R version used for fitting: 4.3.0 </span></span> -<span><span class="co">## Date of fit: Fri May 19 17:34:12 2023 </span></span> -<span><span class="co">## Date of summary: Fri May 19 17:34:12 2023 </span></span> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.10 </span></span> +<span><span class="co">## R version used for fitting: 4.4.2 </span></span> +<span><span class="co">## Date of fit: Sun Feb 16 17:04:35 2025 </span></span> +<span><span class="co">## Date of summary: Sun Feb 16 17:04:35 2025 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span></span> @@ -473,10 +429,10 @@ kinetics.</p> <span><span class="co">## </span></span> <span><span class="co">## Parameter correlation:</span></span> <span><span class="co">## parent_0 log_alpha log_beta sigma</span></span> -<span><span class="co">## parent_0 1.000e+00 -1.151e-01 -2.085e-01 -7.828e-09</span></span> -<span><span class="co">## log_alpha -1.151e-01 1.000e+00 9.741e-01 -1.602e-07</span></span> -<span><span class="co">## log_beta -2.085e-01 9.741e-01 1.000e+00 -1.372e-07</span></span> -<span><span class="co">## sigma -7.828e-09 -1.602e-07 -1.372e-07 1.000e+00</span></span> +<span><span class="co">## parent_0 1.000e+00 -1.151e-01 -2.085e-01 -7.436e-09</span></span> +<span><span class="co">## log_alpha -1.151e-01 1.000e+00 9.741e-01 -1.617e-07</span></span> +<span><span class="co">## log_beta -2.085e-01 9.741e-01 1.000e+00 -1.386e-07</span></span> +<span><span class="co">## sigma -7.436e-09 -1.617e-07 -1.386e-07 1.000e+00</span></span> <span><span class="co">## </span></span> <span><span class="co">## Backtransformed parameters:</span></span> <span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span> @@ -496,26 +452,29 @@ kinetics.</p> <span><span class="co">## Estimated disappearance times:</span></span> <span><span class="co">## DT50 DT90 DT50back</span></span> <span><span class="co">## parent 0.8092 5.356 1.612</span></span></code></pre> -<p>The error level at which the <span class="math inline">\(\chi^2\)</span> test passes is much lower in this -case. Therefore, the FOMC model provides a better description of the -data, as less experimental error has to be assumed in order to explain -the data.</p> +<p>The error level at which the +<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math> +test passes is much lower in this case. Therefore, the FOMC model +provides a better description of the data, as less experimental error +has to be assumed in order to explain the data.</p> </div> <div class="section level3"> <h3 id="dfop-fit-for-l2">DFOP fit for L2<a class="anchor" aria-label="anchor" href="#dfop-fit-for-l2"></a> </h3> -<p>Fitting the four parameter DFOP model further reduces the <span class="math inline">\(\chi^2\)</span> error level.</p> -<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r"> +<p>Fitting the four parameter DFOP model further reduces the +<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math> +error level.</p> +<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">m.L2.DFOP</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">FOCUS_2006_L2_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> <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">m.L2.DFOP</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>,</span> <span> main <span class="op">=</span> <span class="st">"FOCUS L2 - DFOP"</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-10-1.png" width="672"></p> -<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb19"><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">m.L2.DFOP</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## mkin version used for fitting: 1.2.5 </span></span> -<span><span class="co">## R version used for fitting: 4.3.0 </span></span> -<span><span class="co">## Date of fit: Fri May 19 17:34:12 2023 </span></span> -<span><span class="co">## Date of summary: Fri May 19 17:34:12 2023 </span></span> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.10 </span></span> +<span><span class="co">## R version used for fitting: 4.4.2 </span></span> +<span><span class="co">## Date of fit: Sun Feb 16 17:04:36 2025 </span></span> +<span><span class="co">## Date of summary: Sun Feb 16 17:04:36 2025 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span></span> @@ -524,7 +483,7 @@ the data.</p> <span><span class="co">## </span></span> <span><span class="co">## Model predictions using solution type analytical </span></span> <span><span class="co">## </span></span> -<span><span class="co">## Fitted using 581 model solutions performed in 0.04 s</span></span> +<span><span class="co">## Fitted using 581 model solutions performed in 0.042 s</span></span> <span><span class="co">## </span></span> <span><span class="co">## Error model: Constant variance </span></span> <span><span class="co">## </span></span> @@ -555,18 +514,18 @@ the data.</p> <span><span class="co">## Optimised, transformed parameters with symmetric confidence intervals:</span></span> <span><span class="co">## Estimate Std. Error Lower Upper</span></span> <span><span class="co">## parent_0 93.950 9.998e-01 91.5900 96.3100</span></span> -<span><span class="co">## log_k1 3.112 1.842e+03 -4353.0000 4359.0000</span></span> +<span><span class="co">## log_k1 3.113 1.849e+03 -4369.0000 4375.0000</span></span> <span><span class="co">## log_k2 -1.088 6.285e-02 -1.2370 -0.9394</span></span> <span><span class="co">## g_qlogis -0.399 9.946e-02 -0.6342 -0.1638</span></span> <span><span class="co">## sigma 1.414 2.886e-01 0.7314 2.0960</span></span> <span><span class="co">## </span></span> <span><span class="co">## Parameter correlation:</span></span> <span><span class="co">## parent_0 log_k1 log_k2 g_qlogis sigma</span></span> -<span><span class="co">## parent_0 1.000e+00 6.783e-07 -3.390e-10 2.665e-01 -2.967e-10</span></span> -<span><span class="co">## log_k1 6.783e-07 1.000e+00 1.116e-04 -2.196e-04 -1.031e-05</span></span> -<span><span class="co">## log_k2 -3.390e-10 1.116e-04 1.000e+00 -7.903e-01 2.917e-09</span></span> -<span><span class="co">## g_qlogis 2.665e-01 -2.196e-04 -7.903e-01 1.000e+00 -4.408e-09</span></span> -<span><span class="co">## sigma -2.967e-10 -1.031e-05 2.917e-09 -4.408e-09 1.000e+00</span></span> +<span><span class="co">## parent_0 1.000e+00 6.763e-07 -8.944e-10 2.665e-01 -1.083e-09</span></span> +<span><span class="co">## log_k1 6.763e-07 1.000e+00 1.112e-04 -2.187e-04 -1.027e-05</span></span> +<span><span class="co">## log_k2 -8.944e-10 1.112e-04 1.000e+00 -7.903e-01 9.464e-09</span></span> +<span><span class="co">## g_qlogis 2.665e-01 -2.187e-04 -7.903e-01 1.000e+00 -1.532e-08</span></span> +<span><span class="co">## sigma -1.083e-09 -1.027e-05 9.464e-09 -1.532e-08 1.000e+00</span></span> <span><span class="co">## </span></span> <span><span class="co">## Backtransformed parameters:</span></span> <span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span> @@ -574,7 +533,7 @@ the data.</p> <span><span class="co">## for estimators of untransformed parameters.</span></span> <span><span class="co">## Estimate t value Pr(>t) Lower Upper</span></span> <span><span class="co">## parent_0 93.9500 9.397e+01 2.036e-12 91.5900 96.3100</span></span> -<span><span class="co">## k1 22.4800 5.553e-04 4.998e-01 0.0000 Inf</span></span> +<span><span class="co">## k1 22.4900 5.533e-04 4.998e-01 0.0000 Inf</span></span> <span><span class="co">## k2 0.3369 1.591e+01 4.697e-07 0.2904 0.3909</span></span> <span><span class="co">## g 0.4016 1.680e+01 3.238e-07 0.3466 0.4591</span></span> <span><span class="co">## sigma 1.4140 4.899e+00 8.776e-04 0.7314 2.0960</span></span> @@ -586,7 +545,7 @@ the data.</p> <span><span class="co">## </span></span> <span><span class="co">## Estimated disappearance times:</span></span> <span><span class="co">## DT50 DT90 DT50back DT50_k1 DT50_k2</span></span> -<span><span class="co">## parent 0.5335 5.311 1.599 0.03084 2.058</span></span></code></pre> +<span><span class="co">## parent 0.5335 5.311 1.599 0.03083 2.058</span></span></code></pre> <p>Here, the DFOP model is clearly the best-fit model for dataset L2 based on the chi^2 error level criterion.</p> </div> @@ -596,7 +555,7 @@ based on the chi^2 error level criterion.</p> </h2> <p>The following code defines example dataset L3 from the FOCUS kinetics report, p. 290.</p> -<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">FOCUS_2006_L3</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span> <span> t <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</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">30</span>, <span class="fl">60</span>, <span class="fl">91</span>, <span class="fl">120</span><span class="op">)</span>,</span> <span> parent <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">97.8</span>, <span class="fl">60</span>, <span class="fl">51</span>, <span class="fl">43</span>, <span class="fl">35</span>, <span class="fl">22</span>, <span class="fl">15</span>, <span class="fl">12</span><span class="op">)</span><span class="op">)</span></span> @@ -608,16 +567,22 @@ report, p. 290.</p> one or more datasets in one call to the function <code>mmkin</code>. The datasets have to be passed in a list, in this case a named list holding only the L3 dataset prepared above.</p> -<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="co"># Only use one core here, not to offend the CRAN checks</span></span> <span><span class="va">mm.L3</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mmkin.html">mmkin</a></span><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="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, cores <span class="op">=</span> <span class="fl">1</span>,</span> <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">"FOCUS L3"</span> <span class="op">=</span> <span class="va">FOCUS_2006_L3_mkin</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> <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">mm.L3</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-12-1.png" width="700"></p> -<p>The <span class="math inline">\(\chi^2\)</span> error level of 21% as -well as the plot suggest that the SFO model does not fit very well. The -FOMC model performs better, with an error level at which the <span class="math inline">\(\chi^2\)</span> test passes of 7%. Fitting the -four parameter DFOP model further reduces the <span class="math inline">\(\chi^2\)</span> error level considerably.</p> +<p>The +<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math> +error level of 21% as well as the plot suggest that the SFO model does +not fit very well. The FOMC model performs better, with an error level +at which the +<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math> +test passes of 7%. Fitting the four parameter DFOP model further reduces +the +<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math> +error level considerably.</p> </div> <div class="section level3"> <h3 id="accessing-mmkin-objects">Accessing mmkin objects<a class="anchor" aria-label="anchor" href="#accessing-mmkin-objects"></a> @@ -627,12 +592,12 @@ as a row index and datasets as a column index.</p> <p>We can extract the summary and plot for <em>e.g.</em> the DFOP fit, using square brackets for indexing which will result in the use of the summary and plot functions working on mkinfit objects.</p> -<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb23"><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">mm.L3</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## mkin version used for fitting: 1.2.5 </span></span> -<span><span class="co">## R version used for fitting: 4.3.0 </span></span> -<span><span class="co">## Date of fit: Fri May 19 17:34:13 2023 </span></span> -<span><span class="co">## Date of summary: Fri May 19 17:34:13 2023 </span></span> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.10 </span></span> +<span><span class="co">## R version used for fitting: 4.4.2 </span></span> +<span><span class="co">## Date of fit: Sun Feb 16 17:04:36 2025 </span></span> +<span><span class="co">## Date of summary: Sun Feb 16 17:04:36 2025 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span></span> @@ -679,11 +644,11 @@ summary and plot functions working on mkinfit objects.</p> <span><span class="co">## </span></span> <span><span class="co">## Parameter correlation:</span></span> <span><span class="co">## parent_0 log_k1 log_k2 g_qlogis sigma</span></span> -<span><span class="co">## parent_0 1.000e+00 1.732e-01 2.282e-02 4.009e-01 -9.664e-08</span></span> -<span><span class="co">## log_k1 1.732e-01 1.000e+00 4.945e-01 -5.809e-01 7.147e-07</span></span> +<span><span class="co">## parent_0 1.000e+00 1.732e-01 2.282e-02 4.009e-01 -9.696e-08</span></span> +<span><span class="co">## log_k1 1.732e-01 1.000e+00 4.945e-01 -5.809e-01 7.148e-07</span></span> <span><span class="co">## log_k2 2.282e-02 4.945e-01 1.000e+00 -6.812e-01 1.022e-06</span></span> -<span><span class="co">## g_qlogis 4.009e-01 -5.809e-01 -6.812e-01 1.000e+00 -7.926e-07</span></span> -<span><span class="co">## sigma -9.664e-08 7.147e-07 1.022e-06 -7.926e-07 1.000e+00</span></span> +<span><span class="co">## g_qlogis 4.009e-01 -5.809e-01 -6.812e-01 1.000e+00 -7.930e-07</span></span> +<span><span class="co">## sigma -9.696e-08 7.148e-07 1.022e-06 -7.930e-07 1.000e+00</span></span> <span><span class="co">## </span></span> <span><span class="co">## Backtransformed parameters:</span></span> <span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span> @@ -715,14 +680,15 @@ summary and plot functions working on mkinfit objects.</p> <span><span class="co">## 60 parent 22.0 23.26 -1.25919</span></span> <span><span class="co">## 91 parent 15.0 15.18 -0.18181</span></span> <span><span class="co">## 120 parent 12.0 10.19 1.81395</span></span></code></pre> -<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb25"><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">mm.L3</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-13-1.png" width="700"></p> <p>Here, a look to the model plot, the confidence intervals of the parameters and the correlation matrix suggest that the parameter estimates are reliable, and the DFOP model can be used as the best-fit -model based on the <span class="math inline">\(\chi^2\)</span> error -level criterion for laboratory data L3.</p> +model based on the +<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math> +error level criterion for laboratory data L3.</p> <p>This is also an example where the standard t-test for the parameter <code>g_ilr</code> is misleading, as it tests for a significant difference from zero. In this case, zero appears to be the correct value @@ -735,38 +701,40 @@ parameter <code>g</code> is quite narrow.</p> </h2> <p>The following code defines example dataset L4 from the FOCUS kinetics report, p. 293:</p> -<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">FOCUS_2006_L4</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span> <span> t <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</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">30</span>, <span class="fl">60</span>, <span class="fl">91</span>, <span class="fl">120</span><span class="op">)</span>,</span> <span> parent <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">96.6</span>, <span class="fl">96.3</span>, <span class="fl">94.3</span>, <span class="fl">88.8</span>, <span class="fl">74.9</span>, <span class="fl">59.9</span>, <span class="fl">53.5</span>, <span class="fl">49.0</span><span class="op">)</span><span class="op">)</span></span> <span><span class="va">FOCUS_2006_L4_mkin</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">FOCUS_2006_L4</span><span class="op">)</span></span></code></pre></div> <p>Fits of the SFO and FOMC models, plots and summaries are produced below:</p> -<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="co"># Only use one core here, not to offend the CRAN checks</span></span> <span><span class="va">mm.L4</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mmkin.html">mmkin</a></span><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="st">"SFO"</span>, <span class="st">"FOMC"</span><span class="op">)</span>, cores <span class="op">=</span> <span class="fl">1</span>,</span> <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">"FOCUS L4"</span> <span class="op">=</span> <span class="va">FOCUS_2006_L4_mkin</span><span class="op">)</span>,</span> <span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> <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">mm.L4</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-15-1.png" width="700"></p> -<p>The <span class="math inline">\(\chi^2\)</span> error level of 3.3% -as well as the plot suggest that the SFO model fits very well. The error -level at which the <span class="math inline">\(\chi^2\)</span> test -passes is slightly lower for the FOMC model. However, the difference -appears negligible.</p> -<div class="sourceCode" id="cb29"><pre class="downlit sourceCode r"> +<p>The +<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math> +error level of 3.3% as well as the plot suggest that the SFO model fits +very well. The error level at which the +<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math> +test passes is slightly lower for the FOMC model. However, the +difference appears negligible.</p> +<div class="sourceCode" id="cb28"><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">mm.L4</span><span class="op">[[</span><span class="st">"SFO"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## mkin version used for fitting: 1.2.5 </span></span> -<span><span class="co">## R version used for fitting: 4.3.0 </span></span> -<span><span class="co">## Date of fit: Fri May 19 17:34:13 2023 </span></span> -<span><span class="co">## Date of summary: Fri May 19 17:34:13 2023 </span></span> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.10 </span></span> +<span><span class="co">## R version used for fitting: 4.4.2 </span></span> +<span><span class="co">## Date of fit: Sun Feb 16 17:04:36 2025 </span></span> +<span><span class="co">## Date of summary: Sun Feb 16 17:04:37 2025 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - k_parent * parent</span></span> <span><span class="co">## </span></span> <span><span class="co">## Model predictions using solution type analytical </span></span> <span><span class="co">## </span></span> -<span><span class="co">## Fitted using 142 model solutions performed in 0.009 s</span></span> +<span><span class="co">## Fitted using 142 model solutions performed in 0.01 s</span></span> <span><span class="co">## </span></span> <span><span class="co">## Error model: Constant variance </span></span> <span><span class="co">## </span></span> @@ -798,9 +766,9 @@ appears negligible.</p> <span><span class="co">## </span></span> <span><span class="co">## Parameter correlation:</span></span> <span><span class="co">## parent_0 log_k_parent sigma</span></span> -<span><span class="co">## parent_0 1.000e+00 5.938e-01 3.387e-07</span></span> -<span><span class="co">## log_k_parent 5.938e-01 1.000e+00 5.830e-07</span></span> -<span><span class="co">## sigma 3.387e-07 5.830e-07 1.000e+00</span></span> +<span><span class="co">## parent_0 1.000e+00 5.938e-01 3.430e-07</span></span> +<span><span class="co">## log_k_parent 5.938e-01 1.000e+00 5.885e-07</span></span> +<span><span class="co">## sigma 3.430e-07 5.885e-07 1.000e+00</span></span> <span><span class="co">## </span></span> <span><span class="co">## Backtransformed parameters:</span></span> <span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span> @@ -819,12 +787,12 @@ appears negligible.</p> <span><span class="co">## Estimated disappearance times:</span></span> <span><span class="co">## DT50 DT90</span></span> <span><span class="co">## parent 106 352</span></span></code></pre> -<div class="sourceCode" id="cb31"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb30"><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">mm.L4</span><span class="op">[[</span><span class="st">"FOMC"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## mkin version used for fitting: 1.2.5 </span></span> -<span><span class="co">## R version used for fitting: 4.3.0 </span></span> -<span><span class="co">## Date of fit: Fri May 19 17:34:13 2023 </span></span> -<span><span class="co">## Date of summary: Fri May 19 17:34:13 2023 </span></span> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.10 </span></span> +<span><span class="co">## R version used for fitting: 4.4.2 </span></span> +<span><span class="co">## Date of fit: Sun Feb 16 17:04:36 2025 </span></span> +<span><span class="co">## Date of summary: Sun Feb 16 17:04:37 2025 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span></span> @@ -866,10 +834,10 @@ appears negligible.</p> <span><span class="co">## </span></span> <span><span class="co">## Parameter correlation:</span></span> <span><span class="co">## parent_0 log_alpha log_beta sigma</span></span> -<span><span class="co">## parent_0 1.000e+00 -4.696e-01 -5.543e-01 -2.468e-07</span></span> -<span><span class="co">## log_alpha -4.696e-01 1.000e+00 9.889e-01 2.478e-08</span></span> -<span><span class="co">## log_beta -5.543e-01 9.889e-01 1.000e+00 5.211e-08</span></span> -<span><span class="co">## sigma -2.468e-07 2.478e-08 5.211e-08 1.000e+00</span></span> +<span><span class="co">## parent_0 1.000e+00 -4.696e-01 -5.543e-01 -2.447e-07</span></span> +<span><span class="co">## log_alpha -4.696e-01 1.000e+00 9.889e-01 2.198e-08</span></span> +<span><span class="co">## log_beta -5.543e-01 9.889e-01 1.000e+00 4.923e-08</span></span> +<span><span class="co">## sigma -2.447e-07 2.198e-08 4.923e-08 1.000e+00</span></span> <span><span class="co">## </span></span> <span><span class="co">## Backtransformed parameters:</span></span> <span><span class="co">## Confidence intervals for internally transformed parameters are asymmetric.</span></span> @@ -901,35 +869,26 @@ Validierung von Modellierungssoftware als Alternative zu ModelMaker </div> </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> -</div> - + </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2> + </nav></aside> </div> - <footer><div class="copyright"> - <p></p> -<p>Developed by Johannes Ranke.</p> + <footer><div class="pkgdown-footer-left"> + <p>Developed by Johannes Ranke.</p> </div> -<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> +<div class="pkgdown-footer-right"> + <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p> </div> - </footer> + </footer> </div> - - + </body> </html> diff --git a/docs/dev/articles/FOCUS_L_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/dev/articles/FOCUS_L_files/accessible-code-block-0.0.1/empty-anchor.js deleted file mode 100644 index ca349fd6..00000000 --- a/docs/dev/articles/FOCUS_L_files/accessible-code-block-0.0.1/empty-anchor.js +++ /dev/null @@ -1,15 +0,0 @@ -// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> -// v0.0.1 -// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. - 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<p class="section-desc"></p> + <div class="section-desc"></div> - <dl><dt><a href="FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></dt> - <dd> - </dd><dt><a href="FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></dt> - <dd> - </dd><dt><a href="mkin.html">Short introduction to mkin</a></dt> - <dd> - </dd><dt><a href="prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></dt> + <dl><dt><a href="prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></dt> <dd> </dd><dt><a href="prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></dt> <dd> @@ -124,41 +70,45 @@ <dd> </dd><dt><a href="prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></dt> <dd> - </dd><dt><a href="twa.html">Calculation of time weighted average concentrations with mkin</a></dt> - <dd> - </dd><dt><a href="web_only/FOCUS_Z.html">Example evaluation of FOCUS dataset Z</a></dt> - <dd> - </dd><dt><a href="web_only/NAFTA_examples.html">Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance</a></dt> - <dd> </dd><dt><a href="web_only/benchmarks.html">Benchmark timings for mkin</a></dt> <dd> </dd><dt><a href="web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></dt> <dd> </dd><dt><a href="web_only/dimethenamid_2018.html">Example evaluations of the dimethenamid data from 2018</a></dt> <dd> + </dd><dt><a href="FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a></dt> + <dd> + </dd><dt><a href="FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a></dt> + <dd> + </dd><dt><a href="web_only/FOCUS_Z.html">Example evaluation of FOCUS dataset Z</a></dt> + <dd> + </dd><dt><a href="mkin.html">Short introduction to mkin</a></dt> + <dd> </dd><dt><a href="web_only/multistart.html">Short demo of the multistart method</a></dt> <dd> + </dd><dt><a href="web_only/NAFTA_examples.html">Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance</a></dt> + <dd> </dd><dt><a href="web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></dt> <dd> + </dd><dt><a href="twa.html">Calculation of time weighted average concentrations with mkin</a></dt> + <dd> </dd></dl></div> - 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- </header><div class="row"> - <div class="col-md-9 contents"> - <div class="page-header toc-ignore"> - <h1 data-toc-skip>Short introduction to mkin</h1> + +<div class="row"> + <main id="main" class="col-md-9"><div class="page-header"> + + <h1>Short introduction to mkin</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> <h4 data-toc-skip class="date">Last change 18 May 2023 -(rebuilt 2023-05-19)</h4> +(rebuilt 2025-02-16)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/mkin.rmd" class="external-link"><code>vignettes/mkin.rmd</code></a></small> - <div class="hidden name"><code>mkin.rmd</code></div> - + <div class="d-none name"><code>mkin.rmd</code></div> </div> @@ -222,8 +171,9 @@ was already available, and provided a good basis for developing a package specifically tailored to the task. The remaining challenge was to make it as easy as possible for the users (including the author of this vignette) to specify the system of differential equations and to -include the output requested by the FOCUS guidance, such as the <span class="math inline">\(\chi^2\)</span> error level as defined in this -guidance.</p> +include the output requested by the FOCUS guidance, such as the +<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math> +error level as defined in this guidance.</p> <p>Also, <code>mkin</code> introduced using analytical solutions for parent only kinetics for improved optimization speed. Later, Eigenvalue based solutions were introduced to <code>mkin</code> for the case of @@ -361,13 +311,14 @@ reliability of parameter estimates, based on the FOCUS guidance mentioned above. Therefore, the results of this one-sided t-test are included in the summary output from <code>mkin</code>.</p> <p>As it is not reasonable to test for significant difference of the -transformed parameters (<em>e.g.</em> <span class="math inline">\(log(k)\)</span>) from zero, the t-test is -calculated based on the model definition before parameter -transformation, <em>i.e.</em> in a similar way as in packages that do -not apply such an internal parameter transformation. A note is included -in the <code>mkin</code> output, pointing to the fact that the t-test is -based on the unjustified assumption of normal distribution of the -parameter estimators.</p> +transformed parameters (<em>e.g.</em> +<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>l</mi><mi>o</mi><mi>g</mi><mrow><mo stretchy="true" form="prefix">(</mo><mi>k</mi><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">log(k)</annotation></semantics></math>) +from zero, the t-test is calculated based on the model definition before +parameter transformation, <em>i.e.</em> in a similar way as in packages +that do not apply such an internal parameter transformation. A note is +included in the <code>mkin</code> output, pointing to the fact that the +t-test is based on the unjustified assumption of normal distribution of +the parameter estimators.</p> </div> </div> <div class="section level2"> @@ -438,35 +389,26 @@ Sensitivity and Monte Carlo Analysis in <span>R</span> Using Package </div> </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> -</div> - + </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2> + </nav></aside> </div> - <footer><div class="copyright"> - <p></p> -<p>Developed by Johannes Ranke.</p> + <footer><div class="pkgdown-footer-left"> + <p>Developed by Johannes Ranke.</p> </div> -<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> +<div class="pkgdown-footer-right"> + <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p> </div> - </footer> + </footer> </div> - - + </body> </html> diff --git a/docs/dev/articles/mkin_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/dev/articles/mkin_files/accessible-code-block-0.0.1/empty-anchor.js deleted file mode 100644 index ca349fd6..00000000 --- a/docs/dev/articles/mkin_files/accessible-code-block-0.0.1/empty-anchor.js +++ /dev/null @@ -1,15 +0,0 @@ -// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> -// v0.0.1 -// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. - -document.addEventListener('DOMContentLoaded', function() { - const codeList = document.getElementsByClassName("sourceCode"); - for (var i = 0; i < codeList.length; i++) { - var linkList = codeList[i].getElementsByTagName('a'); - for (var j = 0; j < linkList.length; j++) { - if (linkList[j].innerHTML === "") { - linkList[j].setAttribute('aria-hidden', 'true'); - } - } - } -}); diff --git a/docs/dev/articles/mkin_files/anchor-sections-1.0/anchor-sections.css b/docs/dev/articles/mkin_files/anchor-sections-1.0/anchor-sections.css deleted file mode 100644 index 07aee5fc..00000000 --- a/docs/dev/articles/mkin_files/anchor-sections-1.0/anchor-sections.css +++ /dev/null @@ -1,4 +0,0 @@ -/* Styles for section anchors */ -a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;} -a.anchor-section::before {content: '#';} -.hasAnchor:hover a.anchor-section {visibility: visible;} diff --git a/docs/dev/articles/mkin_files/anchor-sections-1.0/anchor-sections.js b/docs/dev/articles/mkin_files/anchor-sections-1.0/anchor-sections.js deleted file mode 100644 index 570f99a0..00000000 --- a/docs/dev/articles/mkin_files/anchor-sections-1.0/anchor-sections.js +++ /dev/null @@ -1,33 +0,0 @@ -// Anchor sections v1.0 written by Atsushi Yasumoto on Oct 3rd, 2020. -document.addEventListener('DOMContentLoaded', function() { - // Do nothing if AnchorJS is used - if (typeof window.anchors === 'object' && anchors.hasOwnProperty('hasAnchorJSLink')) { - return; - } - - const h = document.querySelectorAll('h1, h2, h3, h4, h5, h6'); - - // Do nothing if sections are already anchored - if (Array.from(h).some(x => x.classList.contains('hasAnchor'))) { - return null; - } - - // Use section id when pandoc runs with --section-divs - const section_id = function(x) { - return ((x.classList.contains('section') || (x.tagName === 'SECTION')) - ? x.id : ''); - }; - - // Add anchors - h.forEach(function(x) { - const id = x.id || section_id(x.parentElement); - if (id === '') { - return null; - } - let anchor = document.createElement('a'); - anchor.href = '#' + id; - anchor.classList = ['anchor-section']; - x.classList.add('hasAnchor'); - x.appendChild(anchor); - }); -}); diff --git a/docs/dev/articles/mkin_files/figure-html/unnamed-chunk-2-1.png b/docs/dev/articles/mkin_files/figure-html/unnamed-chunk-2-1.png Binary files differindex 7ba861ea..65c1a613 100644 --- a/docs/dev/articles/mkin_files/figure-html/unnamed-chunk-2-1.png +++ b/docs/dev/articles/mkin_files/figure-html/unnamed-chunk-2-1.png diff --git a/docs/dev/articles/mkin_files/header-attrs-2.6/header-attrs.js b/docs/dev/articles/mkin_files/header-attrs-2.6/header-attrs.js deleted file mode 100644 index dd57d92e..00000000 --- a/docs/dev/articles/mkin_files/header-attrs-2.6/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. 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- </header><div class="row"> - <div class="col-md-9 contents"> - <div class="page-header toc-ignore"> - <h1 data-toc-skip>Testing hierarchical pathway kinetics with -residue data on cyantraniliprole</h1> + +<div class="row"> + <main id="main" class="col-md-9"><div class="page-header"> + + <h1>Testing hierarchical pathway kinetics with residue data on cyantraniliprole</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> - <h4 data-toc-skip class="date">Last change on 20 April 2023, -last compiled on 19 Mai 2023</h4> + <h4 data-toc-skip class="date">Last change on 13 February +2023, last compiled on 16 Februar 2025</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/prebuilt/2022_cyan_pathway.rmd" class="external-link"><code>vignettes/prebuilt/2022_cyan_pathway.rmd</code></a></small> - <div class="hidden name"><code>2022_cyan_pathway.rmd</code></div> - + <div class="d-none name"><code>2022_cyan_pathway.rmd</code></div> </div> @@ -156,7 +104,7 @@ be fitted with the mkin package.</p> 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 1.2.5 which is currently under +<p>The mkin package is used in version 1.2.10 which is currently under development. The newly introduced functionality that is used here is a simplification of excluding random effects for a set of fits based on a related set of fits with a reduced model, and the documentation of the @@ -1357,9 +1305,9 @@ when the two-component error model is used.</p> <tr class="odd"> <td align="left">SFORB tc</td> <td align="right">10</td> -<td align="right">700.1</td> -<td align="right">696.2</td> -<td align="right">-340.1</td> +<td align="right">700.0</td> +<td align="right">696.1</td> +<td align="right">-340.0</td> </tr> <tr class="even"> <td align="left">HS tc</td> @@ -1448,7 +1396,7 @@ pathway fits are performed for all datasets.</p> <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="even"> @@ -1512,7 +1460,7 @@ pathway fits are performed for all datasets.</p> <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_path_1</td> @@ -1525,7 +1473,7 @@ pathway fits are performed for all datasets.</p> <tr class="even"> <td align="left">sforb_path_1</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> @@ -1536,7 +1484,7 @@ pathway fits are performed for all datasets.</p> <td align="left">OK</td> <td align="left">C</td> <td align="left">OK</td> -<td align="left">OK</td> +<td align="left">C</td> </tr> </tbody> </table> @@ -1565,7 +1513,7 @@ which is currently under development.</p> <tbody> <tr class="odd"> <td align="left">sfo_path_1</td> -<td align="left">Fth, FO</td> +<td align="left">FO</td> <td align="left">Fth, FO</td> </tr> <tr class="even"> @@ -1585,8 +1533,8 @@ which is currently under development.</p> </tr> <tr class="odd"> <td align="left">hs_path_1</td> -<td align="left">Fth, FO</td> -<td align="left">Fth, FO</td> +<td align="left">FO</td> +<td align="left">E</td> </tr> </tbody> </table> @@ -1635,14 +1583,62 @@ sd(f_JCZ38_qlogis)</td> <tr class="odd"> <td align="left">hs_path_1</td> <td align="left">NA</td> -<td align="left">NA</td> +<td align="left">E</td> </tr> </tbody> </table> -<p>The model comparison below suggests that the pathway fits using DFOP +<p>The model comparisons below suggest that the pathway fits using DFOP or SFORB for the parent compound provide the best fit.</p> <div class="sourceCode" id="cb15"><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_1</span><span class="op">)</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>digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div> +<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_1</span><span class="op">[</span>, <span class="st">"const"</span><span class="op">]</span><span class="op">)</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>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_path_1 const</td> +<td align="right">16</td> +<td align="right">2693.0</td> +<td align="right">2686.8</td> +<td align="right">-1330.5</td> +</tr> +<tr class="even"> +<td align="left">fomc_path_1 const</td> +<td align="right">18</td> +<td align="right">2427.9</td> +<td align="right">2420.9</td> +<td align="right">-1196.0</td> +</tr> +<tr class="odd"> +<td align="left">dfop_path_1 const</td> +<td align="right">20</td> +<td align="right">2403.2</td> +<td align="right">2395.4</td> +<td align="right">-1181.6</td> +</tr> +<tr class="even"> +<td align="left">sforb_path_1 const</td> +<td align="right">20</td> +<td align="right">2401.4</td> +<td align="right">2393.6</td> +<td align="right">-1180.7</td> +</tr> +<tr class="odd"> +<td align="left">hs_path_1 const</td> +<td align="right">20</td> +<td align="right">2427.2</td> +<td align="right">2419.4</td> +<td align="right">-1193.6</td> +</tr> +</tbody> +</table> +<div class="sourceCode" id="cb16"><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_1</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">4</span>, <span class="op">]</span><span class="op">)</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>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> @@ -1655,30 +1651,30 @@ or SFORB for the parent compound provide the best fit.</p> <tr class="odd"> <td align="left">sfo_path_1 const</td> <td align="right">16</td> -<td align="right">2692.8</td> -<td align="right">2686.6</td> -<td align="right">-1330.4</td> +<td align="right">2693.0</td> +<td align="right">2686.8</td> +<td align="right">-1330.5</td> </tr> <tr class="even"> <td align="left">sfo_path_1 tc</td> <td align="right">17</td> -<td align="right">2657.7</td> -<td align="right">2651.1</td> -<td align="right">-1311.9</td> +<td align="right">2657.6</td> +<td align="right">2651.0</td> +<td align="right">-1311.8</td> </tr> <tr class="odd"> <td align="left">fomc_path_1 const</td> <td align="right">18</td> -<td align="right">2427.8</td> -<td align="right">2420.8</td> -<td align="right">-1195.9</td> +<td align="right">2427.9</td> +<td align="right">2420.9</td> +<td align="right">-1196.0</td> </tr> <tr class="even"> <td align="left">fomc_path_1 tc</td> <td align="right">19</td> -<td align="right">2423.4</td> -<td align="right">2416.0</td> -<td align="right">-1192.7</td> +<td align="right">2423.6</td> +<td align="right">2416.2</td> +<td align="right">-1192.8</td> </tr> <tr class="odd"> <td align="left">dfop_path_1 const</td> @@ -1695,45 +1691,31 @@ or SFORB for the parent compound provide the best fit.</p> <td align="right">-1180.7</td> </tr> <tr class="odd"> -<td align="left">hs_path_1 const</td> -<td align="right">20</td> -<td align="right">2427.3</td> -<td align="right">2419.5</td> -<td align="right">-1193.7</td> -</tr> -<tr class="even"> <td align="left">dfop_path_1 tc</td> <td align="right">20</td> <td align="right">2398.0</td> -<td align="right">2390.2</td> +<td align="right">2390.1</td> <td align="right">-1179.0</td> </tr> -<tr class="odd"> +<tr class="even"> <td align="left">sforb_path_1 tc</td> <td align="right">20</td> -<td align="right">2399.8</td> -<td align="right">2392.0</td> -<td align="right">-1179.9</td> -</tr> -<tr class="even"> -<td align="left">hs_path_1 tc</td> -<td align="right">21</td> -<td align="right">2422.3</td> -<td align="right">2414.1</td> -<td align="right">-1190.2</td> +<td align="right">2399.9</td> +<td align="right">2392.1</td> +<td align="right">-1180.0</td> </tr> </tbody> </table> <p>For these two parent model, successful fits are shown below. Plots of the fits with the other parent models are shown in the Appendix.</p> -<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb17"><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_1</span><span class="op">[[</span><span class="st">"dfop_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> <div class="figure" style="text-align: center"> <img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-7-1.png" alt="DFOP pathway fit with two-component error" width="700"><p class="caption"> DFOP pathway fit with two-component error </p> </div> -<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb18"><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_1</span><span class="op">[[</span><span class="st">"sforb_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> <div class="figure" style="text-align: center"> <img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-8-1.png" alt="SFORB pathway fit with two-component error" width="700"><p class="caption"> @@ -1743,7 +1725,7 @@ SFORB pathway fit with two-component error <p>A closer graphical analysis of these Figures shows that the residues of transformation product JCZ38 in the soils Tama and Nambsheim observed at later time points are strongly and systematically underestimated.</p> -<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">stopCluster</a></span><span class="op">(</span><span class="va">cl_path_1</span><span class="op">)</span></span></code></pre></div> </div> <div class="section level3"> @@ -1757,7 +1739,7 @@ corresponding amide (Addendum 2014, p. 109). As FOMC provided the best fit for the parent, and the biexponential models DFOP and SFORB provided the best initial pathway fits, these three parent models are used in the alternative pathway fits.</p> -<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">cyan_path_2</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> <span> fomc_path_2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> <span> cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"FOMC"</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">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span> @@ -1828,14 +1810,14 @@ alternative pathway fits.</p> <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>Using constant variance, separate fits converge with the exception of the fits to the Sassafras soil data.</p> -<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">f_sep_2_tc</span> <span class="op"><-</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_2_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_2_tc</span><span class="op">)</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="op">)</span></span></code></pre></div> <table class="table"> @@ -1851,7 +1833,7 @@ the fits to the Sassafras soil data.</p> <tr class="odd"> <td align="left">fomc_path_2</td> <td align="left">OK</td> -<td align="left">C</td> +<td align="left">OK</td> <td align="left">OK</td> <td align="left">C</td> <td align="left">OK</td> @@ -1859,7 +1841,7 @@ the fits to the Sassafras soil data.</p> <tr class="even"> <td align="left">dfop_path_2</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> @@ -1869,7 +1851,7 @@ the fits to the Sassafras soil data.</p> <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> </tbody> @@ -1877,11 +1859,11 @@ the fits to the Sassafras soil data.</p> <p>Using the two-component error model, all separate fits converge with the exception of the alternative pathway fit with DFOP used for the parent and the Sassafras dataset.</p> -<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">f_saem_2</span> <span class="op"><-</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_2_const</span>, <span class="va">f_sep_2_tc</span><span class="op">)</span>,</span> <span> no_random_effect <span class="op">=</span> <span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">cyan_saem_full</span><span class="op">[</span><span class="fl">2</span><span class="op">:</span><span class="fl">4</span>, <span class="op">]</span><span class="op">)</span>,</span> <span> cluster <span class="op">=</span> <span class="va">cl_path_2</span><span class="op">)</span></span></code></pre></div> -<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb23"><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_2</span><span class="op">)</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="op">)</span></span></code></pre></div> <table class="table"> <thead><tr class="header"> @@ -1892,8 +1874,8 @@ parent and the Sassafras dataset.</p> <tbody> <tr class="odd"> <td align="left">fomc_path_2</td> +<td align="left">E</td> <td align="left">OK</td> -<td align="left">FO</td> </tr> <tr class="even"> <td align="left">dfop_path_2</td> @@ -1908,8 +1890,9 @@ parent and the Sassafras dataset.</p> </tbody> </table> <p>The hierarchical fits for the alternative pathway completed -successfully.</p> -<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r"> +successfully, with the exception of the model using FOMC for the parent +compound and constant variance as the error model.</p> +<div class="sourceCode" id="cb24"><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_2</span><span class="op">)</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="op">)</span></span></code></pre></div> <table class="table"> <colgroup> @@ -1925,8 +1908,8 @@ successfully.</p> <tbody> <tr class="odd"> <td align="left">fomc_path_2</td> -<td align="left">sd(f_JCZ38_qlogis), sd(f_JSE76_qlogis)</td> -<td align="left">NA</td> +<td align="left">E</td> +<td align="left">sd(f_JSE76_qlogis)</td> </tr> <tr class="even"> <td align="left">dfop_path_2</td> @@ -1940,11 +1923,12 @@ successfully.</p> </tr> </tbody> </table> -<p>In both fits, the random effects for the formation fractions for the -pathways from JCZ38 to JSE76, and for the reverse pathway from JSE76 to -JCZ38 are ill-defined.</p> -<div class="sourceCode" id="cb24"><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_2</span><span class="op">)</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>digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div> +<p>In all biphasic fits (DFOP or SFORB for the parent compound), the +random effects for the formation fractions for the pathways from JCZ38 +to JSE76, and for the reverse pathway from JSE76 to JCZ38 are +ill-defined.</p> +<div class="sourceCode" id="cb25"><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_2</span><span class="op">[</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">)</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>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> @@ -1955,32 +1939,52 @@ JCZ38 are ill-defined.</p> </tr></thead> <tbody> <tr class="odd"> -<td align="left">fomc_path_2 const</td> -<td align="right">20</td> -<td align="right">2308.3</td> -<td align="right">2300.5</td> -<td align="right">-1134.2</td> -</tr> -<tr class="even"> <td align="left">fomc_path_2 tc</td> <td align="right">21</td> -<td align="right">2248.3</td> -<td align="right">2240.1</td> -<td align="right">-1103.2</td> +<td align="right">2249.0</td> +<td align="right">2240.8</td> +<td align="right">-1103.5</td> +</tr> +<tr class="even"> +<td align="left">dfop_path_2 tc</td> +<td align="right">22</td> +<td align="right">2234.4</td> +<td align="right">2225.8</td> +<td align="right">-1095.2</td> +</tr> +<tr class="odd"> +<td align="left">sforb_path_2 tc</td> +<td align="right">22</td> +<td align="right">2239.7</td> +<td align="right">2231.1</td> +<td align="right">-1097.9</td> </tr> +</tbody> +</table> +<div class="sourceCode" id="cb26"><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_2</span><span class="op">[</span><span class="fl">2</span><span class="op">:</span><span class="fl">3</span>,<span class="op">]</span><span class="op">)</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>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">dfop_path_2 const</td> <td align="right">22</td> -<td align="right">2289.6</td> -<td align="right">2281.0</td> -<td align="right">-1122.8</td> +<td align="right">2288.4</td> +<td align="right">2279.8</td> +<td align="right">-1122.2</td> </tr> <tr class="even"> <td align="left">sforb_path_2 const</td> <td align="right">22</td> -<td align="right">2284.1</td> -<td align="right">2275.5</td> -<td align="right">-1120.0</td> +<td align="right">2283.3</td> +<td align="right">2274.7</td> +<td align="right">-1119.7</td> </tr> <tr class="odd"> <td align="left">dfop_path_2 tc</td> @@ -1992,9 +1996,9 @@ JCZ38 are ill-defined.</p> <tr class="even"> <td align="left">sforb_path_2 tc</td> <td align="right">22</td> -<td align="right">2240.4</td> -<td align="right">2231.8</td> -<td align="right">-1098.2</td> +<td align="right">2239.7</td> +<td align="right">2231.1</td> +<td align="right">-1097.9</td> </tr> </tbody> </table> @@ -2004,21 +2008,21 @@ and BIC values and are plotted below. Compared with the original pathway, the AIC and BIC values indicate a large improvement. This is confirmed by the plots, which show that the metabolite JCZ38 is fitted much better with this model.</p> -<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb27"><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_2</span><span class="op">[[</span><span class="st">"fomc_path_2"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> <div class="figure" style="text-align: center"> <img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-13-1.png" alt="FOMC pathway fit with two-component error, alternative pathway" width="700"><p class="caption"> FOMC pathway fit with two-component error, alternative pathway </p> </div> -<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb28"><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_2</span><span class="op">[[</span><span class="st">"dfop_path_2"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> <div class="figure" style="text-align: center"> <img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-14-1.png" alt="DFOP pathway fit with two-component error, alternative pathway" width="700"><p class="caption"> DFOP pathway fit with two-component error, alternative pathway </p> </div> -<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb29"><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_2</span><span class="op">[[</span><span class="st">"sforb_path_2"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> <div class="figure" style="text-align: center"> <img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-15-1.png" alt="SFORB pathway fit with two-component error, alternative pathway" width="700"><p class="caption"> @@ -2036,7 +2040,7 @@ created below that can be indexed by row and column indices, and which contains the degradation parameter names for which random effects should be excluded for each of the hierarchical fits contained in <code>f_saem_2</code>.</p> -<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb30"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">no_ranef</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</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="op">)</span>, nrow <span class="op">=</span> <span class="fl">3</span>, ncol <span class="op">=</span> <span class="fl">2</span>, dimnames <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/dimnames.html" class="external-link">dimnames</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">)</span><span class="op">)</span></span> <span><span class="va">no_ranef</span><span class="op">[[</span><span class="st">"fomc_path_2"</span>, <span class="st">"const"</span><span class="op">]</span><span class="op">]</span> <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="st">"log_beta"</span>, <span class="st">"f_JCZ38_qlogis"</span>, <span class="st">"f_JSE76_qlogis"</span><span class="op">)</span></span> <span><span class="va">no_ranef</span><span class="op">[[</span><span class="st">"fomc_path_2"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span> <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="st">"cyan_0"</span>, <span class="st">"f_JCZ38_qlogis"</span>, <span class="st">"f_JSE76_qlogis"</span><span class="op">)</span></span> @@ -2051,7 +2055,7 @@ be excluded for each of the hierarchical fits contained in <span><span class="va">f_saem_3</span> <span class="op"><-</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_2</span>,</span> <span> no_random_effect <span class="op">=</span> <span class="va">no_ranef</span>,</span> <span> cluster <span class="op">=</span> <span class="va">cl_path_2</span><span class="op">)</span></span></code></pre></div> -<div class="sourceCode" id="cb29"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb31"><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_3</span><span class="op">)</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="op">)</span></span></code></pre></div> <table class="table"> <thead><tr class="header"> @@ -2081,7 +2085,7 @@ be excluded for each of the hierarchical fits contained in all updated fits completed successfully. However, the Fisher Information Matrix for the fixed effects (Fth) could not be inverted, so no confidence intervals for the optimised parameters are available.</p> -<div class="sourceCode" id="cb30"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb32"><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_3</span><span class="op">)</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="op">)</span></span></code></pre></div> <table class="table"> <thead><tr class="header"> @@ -2107,8 +2111,8 @@ confidence intervals for the optimised parameters are available.</p> </tr> </tbody> </table> -<div class="sourceCode" id="cb31"><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_3</span><span class="op">)</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>digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div> +<div class="sourceCode" id="cb33"><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_3</span><span class="op">[</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">)</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>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> @@ -2121,37 +2125,64 @@ confidence intervals for the optimised parameters are available.</p> <tr class="odd"> <td align="left">fomc_path_2 tc</td> <td align="right">19</td> -<td align="right">2250.9</td> -<td align="right">2243.5</td> -<td align="right">-1106.5</td> +<td align="right">2249.1</td> +<td align="right">2241.6</td> +<td align="right">-1105.5</td> </tr> <tr class="even"> -<td align="left">dfop_path_2 const</td> +<td align="left">dfop_path_2 tc</td> <td align="right">20</td> -<td align="right">2281.7</td> -<td align="right">2273.9</td> -<td align="right">-1120.8</td> +<td align="right">2237.3</td> +<td align="right">2229.5</td> +<td align="right">-1098.6</td> </tr> <tr class="odd"> -<td align="left">sforb_path_2 const</td> +<td align="left">sforb_path_2 tc</td> <td align="right">20</td> -<td align="right">2279.5</td> -<td align="right">2271.7</td> -<td align="right">-1119.7</td> +<td align="right">2241.3</td> +<td align="right">2233.5</td> +<td align="right">-1100.7</td> +</tr> +</tbody> +</table> +<div class="sourceCode" id="cb34"><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_3</span><span class="op">[</span><span class="fl">2</span><span class="op">:</span><span class="fl">3</span>,<span class="op">]</span><span class="op">)</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>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">dfop_path_2 const</td> +<td align="right">20</td> +<td align="right">2282.2</td> +<td align="right">2274.4</td> +<td align="right">-1121.1</td> </tr> <tr class="even"> -<td align="left">dfop_path_2 tc</td> +<td align="left">sforb_path_2 const</td> <td align="right">20</td> -<td align="right">2231.5</td> -<td align="right">2223.7</td> -<td align="right">-1095.8</td> +<td align="right">2279.7</td> +<td align="right">2271.9</td> +<td align="right">-1119.9</td> </tr> <tr class="odd"> +<td align="left">dfop_path_2 tc</td> +<td align="right">20</td> +<td align="right">2237.3</td> +<td align="right">2229.5</td> +<td align="right">-1098.6</td> +</tr> +<tr class="even"> <td align="left">sforb_path_2 tc</td> <td align="right">20</td> -<td align="right">2235.7</td> -<td align="right">2227.9</td> -<td align="right">-1097.9</td> +<td align="right">2241.3</td> +<td align="right">2233.5</td> +<td align="right">-1100.7</td> </tr> </tbody> </table> @@ -2159,7 +2190,7 @@ confidence intervals for the optimised parameters are available.</p> two-component error) are lower than in the previous fits with the alternative pathway, the practical value of these refined evaluations is limited as no confidence intervals are obtained.</p> -<div class="sourceCode" id="cb32"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb35"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">stopCluster</a></span><span class="op">(</span><span class="va">cl_path_2</span><span class="op">)</span></span></code></pre></div> </div> </div> @@ -2187,21 +2218,21 @@ Agency are gratefully acknowledged.</p> <div class="section level3"> <h3 id="plots-of-fits-that-were-not-refined-further">Plots of fits that were not refined further<a class="anchor" aria-label="anchor" href="#plots-of-fits-that-were-not-refined-further"></a> </h3> -<div class="sourceCode" id="cb33"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb36"><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_1</span><span class="op">[[</span><span class="st">"sfo_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> <div class="figure" style="text-align: center"> <img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-20-1.png" alt="SFO pathway fit with two-component error" width="700"><p class="caption"> SFO pathway fit with two-component error </p> </div> -<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb37"><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_1</span><span class="op">[[</span><span class="st">"fomc_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> <div class="figure" style="text-align: center"> <img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-21-1.png" alt="FOMC pathway fit with two-component error" width="700"><p class="caption"> FOMC pathway fit with two-component error </p> </div> -<div class="sourceCode" id="cb35"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb38"><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_1</span><span class="op">[[</span><span class="st">"sforb_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> <div class="figure" style="text-align: center"> <img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-22-1.png" alt="HS pathway fit with two-component error" width="700"><p class="caption"> @@ -2219,11 +2250,11 @@ HS pathway fit with two-component error Hierarchical SFO path 1 fit with constant variance </caption> <pre><code> -saemix version used for fitting: 3.2 -mkin version used for pre-fitting: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 17:42:02 2023 -Date of summary: Fri May 19 18:14:01 2023 +saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.4.2 +Date of fit: Fri Feb 14 07:42:26 2025 +Date of summary: Sun Feb 16 17:03:40 2025 Equations: d_cyan/dt = - k_cyan * cyan @@ -2236,7 +2267,7 @@ Data: Model predictions using solution type deSolve -Fitted in 443.997 s +Fitted in 437.151 s Using 300, 100 iterations and 10 chains Variance model: Constant variance @@ -2245,7 +2276,7 @@ Starting values for degradation parameters: cyan_0 log_k_cyan log_k_JCZ38 log_k_J9Z38 log_k_JSE76 95.3304 -3.8459 -3.1305 -5.0678 -5.3196 f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis - 0.8158 22.5404 10.4289 + 0.8158 23.5335 11.8774 Fixed degradation parameter values: None @@ -2267,8 +2298,8 @@ log_k_JCZ38 0.0000 0.000 0.00 log_k_J9Z38 0.0000 0.000 0.00 log_k_JSE76 0.0000 0.000 0.00 f_cyan_ilr_1 0.7714 0.000 0.00 -f_cyan_ilr_2 0.0000 8.684 0.00 -f_JCZ38_qlogis 0.0000 0.000 13.48 +f_cyan_ilr_2 0.0000 9.247 0.00 +f_JCZ38_qlogis 0.0000 0.000 16.61 Starting values for error model parameters: a.1 @@ -2278,68 +2309,68 @@ Results: Likelihood computed by importance sampling AIC BIC logLik - 2693 2687 -1330 + 2693 2687 -1331 Optimised parameters: - est. lower upper -cyan_0 95.0946 NA NA -log_k_cyan -3.8544 NA NA -log_k_JCZ38 -3.0402 NA NA -log_k_J9Z38 -5.0109 NA NA -log_k_JSE76 -5.2857 NA NA -f_cyan_ilr_1 0.8069 NA NA -f_cyan_ilr_2 16.6623 NA NA -f_JCZ38_qlogis 1.3602 NA NA -a.1 4.8326 NA NA -SD.log_k_cyan 0.5842 NA NA -SD.log_k_JCZ38 1.2680 NA NA -SD.log_k_J9Z38 0.3626 NA NA -SD.log_k_JSE76 0.5244 NA NA -SD.f_cyan_ilr_1 0.2752 NA NA -SD.f_cyan_ilr_2 2.3556 NA NA -SD.f_JCZ38_qlogis 0.2400 NA NA + est. lower upper +cyan_0 95.1279 9.354e+01 9.671e+01 +log_k_cyan -3.8527 -4.367e+00 -3.338e+00 +log_k_JCZ38 -3.0381 -4.187e+00 -1.889e+00 +log_k_J9Z38 -5.0095 -5.623e+00 -4.396e+00 +log_k_JSE76 -5.3357 -6.025e+00 -4.646e+00 +f_cyan_ilr_1 0.8050 5.174e-01 1.093e+00 +f_cyan_ilr_2 12.4820 -1.050e+06 1.051e+06 +f_JCZ38_qlogis 1.2912 3.561e-01 2.226e+00 +a.1 4.8393 NA NA +SD.log_k_cyan 0.5840 NA NA +SD.log_k_JCZ38 1.2740 NA NA +SD.log_k_J9Z38 0.3172 NA NA +SD.log_k_JSE76 0.5677 NA NA +SD.f_cyan_ilr_1 0.2623 NA NA +SD.f_cyan_ilr_2 1.3724 NA NA +SD.f_JCZ38_qlogis 0.1464 NA NA Correlation is not available Random effects: est. lower upper -SD.log_k_cyan 0.5842 NA NA -SD.log_k_JCZ38 1.2680 NA NA -SD.log_k_J9Z38 0.3626 NA NA -SD.log_k_JSE76 0.5244 NA NA -SD.f_cyan_ilr_1 0.2752 NA NA -SD.f_cyan_ilr_2 2.3556 NA NA -SD.f_JCZ38_qlogis 0.2400 NA NA +SD.log_k_cyan 0.5840 NA NA +SD.log_k_JCZ38 1.2740 NA NA +SD.log_k_J9Z38 0.3172 NA NA +SD.log_k_JSE76 0.5677 NA NA +SD.f_cyan_ilr_1 0.2623 NA NA +SD.f_cyan_ilr_2 1.3724 NA NA +SD.f_JCZ38_qlogis 0.1464 NA NA Variance model: est. lower upper -a.1 4.833 NA NA +a.1 4.839 NA NA Backtransformed parameters: - est. lower upper -cyan_0 95.094581 NA NA -k_cyan 0.021186 NA NA -k_JCZ38 0.047825 NA NA -k_J9Z38 0.006665 NA NA -k_JSE76 0.005063 NA NA -f_cyan_to_JCZ38 0.757885 NA NA -f_cyan_to_J9Z38 0.242115 NA NA -f_JCZ38_to_JSE76 0.795792 NA NA + est. lower upper +cyan_0 95.127935 93.542456 96.713413 +k_cyan 0.021221 0.012687 0.035497 +k_JCZ38 0.047924 0.015189 0.151213 +k_J9Z38 0.006674 0.003612 0.012332 +k_JSE76 0.004817 0.002417 0.009601 +f_cyan_to_JCZ38 0.757402 NA NA +f_cyan_to_J9Z38 0.242597 NA NA +f_JCZ38_to_JSE76 0.784347 0.588098 0.902582 Resulting formation fractions: ff -cyan_JCZ38 7.579e-01 -cyan_J9Z38 2.421e-01 -cyan_sink 5.877e-10 -JCZ38_JSE76 7.958e-01 -JCZ38_sink 2.042e-01 +cyan_JCZ38 7.574e-01 +cyan_J9Z38 2.426e-01 +cyan_sink 9.839e-08 +JCZ38_JSE76 7.843e-01 +JCZ38_sink 2.157e-01 Estimated disappearance times: DT50 DT90 -cyan 32.72 108.68 -JCZ38 14.49 48.15 -J9Z38 103.99 345.46 -JSE76 136.90 454.76 +cyan 32.66 108.50 +JCZ38 14.46 48.05 +J9Z38 103.86 345.00 +JSE76 143.91 478.04 </code></pre> <p></p> @@ -2347,11 +2378,11 @@ JSE76 136.90 454.76 Hierarchical SFO path 1 fit with two-component error </caption> <pre><code> -saemix version used for fitting: 3.2 -mkin version used for pre-fitting: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 17:42:01 2023 -Date of summary: Fri May 19 18:14:01 2023 +saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.4.2 +Date of fit: Fri Feb 14 07:42:06 2025 +Date of summary: Sun Feb 16 17:03:40 2025 Equations: d_cyan/dt = - k_cyan * cyan @@ -2364,7 +2395,7 @@ Data: Model predictions using solution type deSolve -Fitted in 442.545 s +Fitted in 417.143 s Using 300, 100 iterations and 10 chains Variance model: Two-component variance function @@ -2373,7 +2404,7 @@ Starting values for degradation parameters: cyan_0 log_k_cyan log_k_JCZ38 log_k_J9Z38 log_k_JSE76 96.0039 -3.8907 -3.1276 -5.0069 -4.9367 f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis - 0.7937 20.0030 15.1336 + 0.7937 22.3422 17.8932 Fixed degradation parameter values: None @@ -2395,8 +2426,8 @@ log_k_JCZ38 0.0000 0.000 0.00 log_k_J9Z38 0.0000 0.000 0.00 log_k_JSE76 0.0000 0.000 0.00 f_cyan_ilr_1 0.7598 0.000 0.00 -f_cyan_ilr_2 0.0000 7.334 0.00 -f_JCZ38_qlogis 0.0000 0.000 11.78 +f_cyan_ilr_2 0.0000 8.939 0.00 +f_JCZ38_qlogis 0.0000 0.000 14.49 Starting values for error model parameters: a.1 b.1 @@ -2410,66 +2441,66 @@ Likelihood computed by importance sampling Optimised parameters: est. lower upper -cyan_0 94.72923 NA NA -log_k_cyan -3.91670 NA NA -log_k_JCZ38 -3.12917 NA NA -log_k_J9Z38 -5.06070 NA NA -log_k_JSE76 -5.09254 NA NA -f_cyan_ilr_1 0.81116 NA NA -f_cyan_ilr_2 39.97850 NA NA -f_JCZ38_qlogis 3.09728 NA NA -a.1 3.95044 NA NA -b.1 0.07998 NA NA -SD.log_k_cyan 0.58855 NA NA -SD.log_k_JCZ38 1.29753 NA NA -SD.log_k_J9Z38 0.62851 NA NA -SD.log_k_JSE76 0.37235 NA NA -SD.f_cyan_ilr_1 0.37346 NA NA -SD.f_cyan_ilr_2 1.41667 NA NA -SD.f_JCZ38_qlogis 1.81467 NA NA +cyan_0 94.81681 NA NA +log_k_cyan -3.91558 NA NA +log_k_JCZ38 -3.12715 NA NA +log_k_J9Z38 -5.04840 NA NA +log_k_JSE76 -5.10443 NA NA +f_cyan_ilr_1 0.80760 NA NA +f_cyan_ilr_2 48.66960 NA NA +f_JCZ38_qlogis 3.03397 NA NA +a.1 3.93879 NA NA +b.1 0.08057 NA NA +SD.log_k_cyan 0.58921 NA NA +SD.log_k_JCZ38 1.29813 NA NA +SD.log_k_J9Z38 0.68372 NA NA +SD.log_k_JSE76 0.35128 NA NA +SD.f_cyan_ilr_1 0.38352 NA NA +SD.f_cyan_ilr_2 4.98884 NA NA +SD.f_JCZ38_qlogis 1.75636 NA NA Correlation is not available Random effects: est. lower upper -SD.log_k_cyan 0.5886 NA NA -SD.log_k_JCZ38 1.2975 NA NA -SD.log_k_J9Z38 0.6285 NA NA -SD.log_k_JSE76 0.3724 NA NA -SD.f_cyan_ilr_1 0.3735 NA NA -SD.f_cyan_ilr_2 1.4167 NA NA -SD.f_JCZ38_qlogis 1.8147 NA NA +SD.log_k_cyan 0.5892 NA NA +SD.log_k_JCZ38 1.2981 NA NA +SD.log_k_J9Z38 0.6837 NA NA +SD.log_k_JSE76 0.3513 NA NA +SD.f_cyan_ilr_1 0.3835 NA NA +SD.f_cyan_ilr_2 4.9888 NA NA +SD.f_JCZ38_qlogis 1.7564 NA NA Variance model: est. lower upper -a.1 3.95044 NA NA -b.1 0.07998 NA NA +a.1 3.93879 NA NA +b.1 0.08057 NA NA Backtransformed parameters: - est. lower upper -cyan_0 94.729229 NA NA -k_cyan 0.019907 NA NA -k_JCZ38 0.043754 NA NA -k_J9Z38 0.006341 NA NA -k_JSE76 0.006142 NA NA -f_cyan_to_JCZ38 0.758991 NA NA -f_cyan_to_J9Z38 0.241009 NA NA -f_JCZ38_to_JSE76 0.956781 NA NA + est. lower upper +cyan_0 94.81681 NA NA +k_cyan 0.01993 NA NA +k_JCZ38 0.04384 NA NA +k_J9Z38 0.00642 NA NA +k_JSE76 0.00607 NA NA +f_cyan_to_JCZ38 0.75807 NA NA +f_cyan_to_J9Z38 0.24193 NA NA +f_JCZ38_to_JSE76 0.95409 NA NA Resulting formation fractions: ff -cyan_JCZ38 0.75899 -cyan_J9Z38 0.24101 +cyan_JCZ38 0.75807 +cyan_J9Z38 0.24193 cyan_sink 0.00000 -JCZ38_JSE76 0.95678 -JCZ38_sink 0.04322 +JCZ38_JSE76 0.95409 +JCZ38_sink 0.04591 Estimated disappearance times: DT50 DT90 -cyan 34.82 115.67 -JCZ38 15.84 52.63 -J9Z38 109.31 363.12 -JSE76 112.85 374.87 +cyan 34.78 115.54 +JCZ38 15.81 52.52 +J9Z38 107.97 358.68 +JSE76 114.20 379.35 </code></pre> <p></p> @@ -2477,11 +2508,11 @@ JSE76 112.85 374.87 Hierarchical FOMC path 1 fit with constant variance </caption> <pre><code> -saemix version used for fitting: 3.2 -mkin version used for pre-fitting: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 17:42:38 2023 -Date of summary: Fri May 19 18:14:01 2023 +saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.4.2 +Date of fit: Fri Feb 14 07:42:57 2025 +Date of summary: Sun Feb 16 17:03:40 2025 Equations: d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan @@ -2496,7 +2527,7 @@ Data: Model predictions using solution type deSolve -Fitted in 479.463 s +Fitted in 468.609 s Using 300, 100 iterations and 10 chains Variance model: Constant variance @@ -2505,7 +2536,7 @@ Starting values for degradation parameters: cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 101.2314 -3.3680 -5.1108 -5.9416 0.7144 f_cyan_ilr_2 f_JCZ38_qlogis log_alpha log_beta - 7.3870 15.7604 -0.1791 2.9811 + 7.0229 14.9234 -0.1791 2.9811 Fixed degradation parameter values: None @@ -2527,8 +2558,8 @@ log_k_JCZ38 0.00 0.00 0.0000 0.0000 log_k_J9Z38 0.00 0.00 0.0000 0.0000 log_k_JSE76 0.00 0.00 0.0000 0.0000 f_cyan_ilr_1 0.00 0.00 0.0000 0.0000 -f_cyan_ilr_2 12.33 0.00 0.0000 0.0000 -f_JCZ38_qlogis 0.00 20.42 0.0000 0.0000 +f_cyan_ilr_2 11.57 0.00 0.0000 0.0000 +f_JCZ38_qlogis 0.00 18.81 0.0000 0.0000 log_alpha 0.00 0.00 0.4144 0.0000 log_beta 0.00 0.00 0.0000 0.5077 @@ -2543,78 +2574,78 @@ Likelihood computed by importance sampling 2428 2421 -1196 Optimised parameters: - est. lower upper -cyan_0 101.0225 98.306270 103.7387 -log_k_JCZ38 -3.3786 -4.770657 -1.9866 -log_k_J9Z38 -5.2603 -5.902085 -4.6186 -log_k_JSE76 -6.1427 -7.318336 -4.9671 -f_cyan_ilr_1 0.7437 0.421215 1.0663 -f_cyan_ilr_2 0.9108 0.267977 1.5537 -f_JCZ38_qlogis 2.0487 0.524897 3.5724 -log_alpha -0.2268 -0.618049 0.1644 -log_beta 2.8986 2.700701 3.0964 -a.1 3.4058 3.169913 3.6416 -SD.cyan_0 2.5279 0.454190 4.6016 -SD.log_k_JCZ38 1.5636 0.572824 2.5543 -SD.log_k_J9Z38 0.5316 -0.004405 1.0677 -SD.log_k_JSE76 0.9903 0.106325 1.8742 -SD.f_cyan_ilr_1 0.3464 0.112066 0.5807 -SD.f_cyan_ilr_2 0.2804 -0.393900 0.9546 -SD.f_JCZ38_qlogis 0.9416 -0.152986 2.0362 -SD.log_alpha 0.4273 0.161044 0.6936 + est. lower upper +cyan_0 101.1664 98.51265 103.8202 +log_k_JCZ38 -3.3883 -4.78250 -1.9941 +log_k_J9Z38 -5.3087 -5.91564 -4.7017 +log_k_JSE76 -6.1313 -7.30061 -4.9619 +f_cyan_ilr_1 0.7456 0.43782 1.0534 +f_cyan_ilr_2 0.8181 0.24956 1.3866 +f_JCZ38_qlogis 2.0467 0.61165 3.4817 +log_alpha -0.2391 -0.62806 0.1499 +log_beta 2.8739 2.67664 3.0711 +a.1 3.4160 3.17960 3.6525 +SD.cyan_0 2.4355 0.40399 4.4671 +SD.log_k_JCZ38 1.5654 0.57311 2.5576 +SD.log_k_J9Z38 0.4645 -0.06533 0.9943 +SD.log_k_JSE76 0.9841 0.10738 1.8609 +SD.f_cyan_ilr_1 0.3285 0.10546 0.5515 +SD.f_cyan_ilr_2 0.2276 -0.38711 0.8424 +SD.f_JCZ38_qlogis 0.8340 -0.20970 1.8777 +SD.log_alpha 0.4250 0.16017 0.6898 Correlation: cyan_0 l__JCZ3 l__J9Z3 l__JSE7 f_cy__1 f_cy__2 f_JCZ38 log_lph -log_k_JCZ38 -0.0156 -log_k_J9Z38 -0.0493 0.0073 -log_k_JSE76 -0.0329 0.0018 0.0069 -f_cyan_ilr_1 -0.0086 0.0180 -0.1406 0.0012 -f_cyan_ilr_2 -0.2629 0.0779 0.2826 0.0274 0.0099 -f_JCZ38_qlogis 0.0713 -0.0747 -0.0505 0.1169 -0.1022 -0.4893 -log_alpha -0.0556 0.0120 0.0336 0.0193 0.0036 0.0840 -0.0489 -log_beta -0.2898 0.0460 0.1305 0.0768 0.0190 0.4071 -0.1981 0.2772 +log_k_JCZ38 -0.0159 +log_k_J9Z38 -0.0546 0.0080 +log_k_JSE76 -0.0337 0.0016 0.0074 +f_cyan_ilr_1 -0.0095 0.0194 -0.1573 0.0003 +f_cyan_ilr_2 -0.2733 0.0799 0.3059 0.0263 0.0125 +f_JCZ38_qlogis 0.0755 -0.0783 -0.0516 0.1222 -0.1155 -0.5231 +log_alpha -0.0567 0.0120 0.0351 0.0189 0.0040 0.0829 -0.0502 +log_beta -0.2980 0.0461 0.1382 0.0758 0.0209 0.4079 -0.2053 0.2759 Random effects: - est. lower upper -SD.cyan_0 2.5279 0.454190 4.6016 -SD.log_k_JCZ38 1.5636 0.572824 2.5543 -SD.log_k_J9Z38 0.5316 -0.004405 1.0677 -SD.log_k_JSE76 0.9903 0.106325 1.8742 -SD.f_cyan_ilr_1 0.3464 0.112066 0.5807 -SD.f_cyan_ilr_2 0.2804 -0.393900 0.9546 -SD.f_JCZ38_qlogis 0.9416 -0.152986 2.0362 -SD.log_alpha 0.4273 0.161044 0.6936 + est. lower upper +SD.cyan_0 2.4355 0.40399 4.4671 +SD.log_k_JCZ38 1.5654 0.57311 2.5576 +SD.log_k_J9Z38 0.4645 -0.06533 0.9943 +SD.log_k_JSE76 0.9841 0.10738 1.8609 +SD.f_cyan_ilr_1 0.3285 0.10546 0.5515 +SD.f_cyan_ilr_2 0.2276 -0.38711 0.8424 +SD.f_JCZ38_qlogis 0.8340 -0.20970 1.8777 +SD.log_alpha 0.4250 0.16017 0.6898 Variance model: est. lower upper -a.1 3.406 3.17 3.642 +a.1 3.416 3.18 3.652 Backtransformed parameters: est. lower upper -cyan_0 1.010e+02 9.831e+01 1.037e+02 -k_JCZ38 3.409e-02 8.475e-03 1.372e-01 -k_J9Z38 5.194e-03 2.734e-03 9.867e-03 -k_JSE76 2.149e-03 6.633e-04 6.963e-03 -f_cyan_to_JCZ38 6.481e-01 NA NA -f_cyan_to_J9Z38 2.264e-01 NA NA -f_JCZ38_to_JSE76 8.858e-01 6.283e-01 9.727e-01 -alpha 7.971e-01 5.390e-01 1.179e+00 -beta 1.815e+01 1.489e+01 2.212e+01 +cyan_0 1.012e+02 9.851e+01 103.82023 +k_JCZ38 3.377e-02 8.375e-03 0.13614 +k_J9Z38 4.948e-03 2.697e-03 0.00908 +k_JSE76 2.174e-03 6.751e-04 0.00700 +f_cyan_to_JCZ38 6.389e-01 NA NA +f_cyan_to_J9Z38 2.226e-01 NA NA +f_JCZ38_to_JSE76 8.856e-01 6.483e-01 0.97016 +alpha 7.873e-01 5.336e-01 1.16166 +beta 1.771e+01 1.454e+01 21.56509 Resulting formation fractions: ff -cyan_JCZ38 0.6481 -cyan_J9Z38 0.2264 -cyan_sink 0.1255 -JCZ38_JSE76 0.8858 -JCZ38_sink 0.1142 +cyan_JCZ38 0.6389 +cyan_J9Z38 0.2226 +cyan_sink 0.1385 +JCZ38_JSE76 0.8856 +JCZ38_sink 0.1144 Estimated disappearance times: DT50 DT90 DT50back -cyan 25.15 308.01 92.72 -JCZ38 20.33 67.54 NA -J9Z38 133.46 443.35 NA -JSE76 322.53 1071.42 NA +cyan 25.00 312.06 93.94 +JCZ38 20.53 68.19 NA +J9Z38 140.07 465.32 NA +JSE76 318.86 1059.22 NA </code></pre> <p></p> @@ -2622,11 +2653,11 @@ JSE76 322.53 1071.42 NA Hierarchical FOMC path 1 fit with two-component error </caption> <pre><code> -saemix version used for fitting: 3.2 -mkin version used for pre-fitting: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 17:42:53 2023 -Date of summary: Fri May 19 18:14:01 2023 +saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.4.2 +Date of fit: Fri Feb 14 07:42:50 2025 +Date of summary: Sun Feb 16 17:03:40 2025 Equations: d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan @@ -2641,41 +2672,41 @@ Data: Model predictions using solution type deSolve -Fitted in 494.121 s +Fitted in 460.309 s Using 300, 100 iterations and 10 chains Variance model: Two-component variance function Starting values for degradation parameters: cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 - 101.13827 -3.32493 -5.08921 -5.93478 0.71330 + 101.13294 -3.32499 -5.09097 -5.93566 0.71359 f_cyan_ilr_2 f_JCZ38_qlogis log_alpha log_beta - 10.05989 12.79248 -0.09621 3.10646 + 10.30315 14.62272 -0.09633 3.10634 Fixed degradation parameter values: None Starting values for random effects (square root of initial entries in omega): cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 -cyan_0 5.643 0.000 0.000 0.00 0.0000 -log_k_JCZ38 0.000 2.319 0.000 0.00 0.0000 -log_k_J9Z38 0.000 0.000 1.731 0.00 0.0000 -log_k_JSE76 0.000 0.000 0.000 1.86 0.0000 -f_cyan_ilr_1 0.000 0.000 0.000 0.00 0.7186 -f_cyan_ilr_2 0.000 0.000 0.000 0.00 0.0000 -f_JCZ38_qlogis 0.000 0.000 0.000 0.00 0.0000 -log_alpha 0.000 0.000 0.000 0.00 0.0000 -log_beta 0.000 0.000 0.000 0.00 0.0000 +cyan_0 5.649 0.000 0.00 0.00 0.0000 +log_k_JCZ38 0.000 2.319 0.00 0.00 0.0000 +log_k_J9Z38 0.000 0.000 1.73 0.00 0.0000 +log_k_JSE76 0.000 0.000 0.00 1.86 0.0000 +f_cyan_ilr_1 0.000 0.000 0.00 0.00 0.7183 +f_cyan_ilr_2 0.000 0.000 0.00 0.00 0.0000 +f_JCZ38_qlogis 0.000 0.000 0.00 0.00 0.0000 +log_alpha 0.000 0.000 0.00 0.00 0.0000 +log_beta 0.000 0.000 0.00 0.00 0.0000 f_cyan_ilr_2 f_JCZ38_qlogis log_alpha log_beta cyan_0 0.00 0.00 0.0000 0.0000 log_k_JCZ38 0.00 0.00 0.0000 0.0000 log_k_J9Z38 0.00 0.00 0.0000 0.0000 log_k_JSE76 0.00 0.00 0.0000 0.0000 f_cyan_ilr_1 0.00 0.00 0.0000 0.0000 -f_cyan_ilr_2 12.49 0.00 0.0000 0.0000 -f_JCZ38_qlogis 0.00 20.19 0.0000 0.0000 +f_cyan_ilr_2 12.85 0.00 0.0000 0.0000 +f_JCZ38_qlogis 0.00 18.54 0.0000 0.0000 log_alpha 0.00 0.00 0.3142 0.0000 -log_beta 0.00 0.00 0.0000 0.7331 +log_beta 0.00 0.00 0.0000 0.7333 Starting values for error model parameters: a.1 b.1 @@ -2685,74 +2716,74 @@ Results: Likelihood computed by importance sampling AIC BIC logLik - 2423 2416 -1193 + 2424 2416 -1193 Optimised parameters: est. lower upper -cyan_0 100.57649 NA NA -log_k_JCZ38 -3.46250 NA NA -log_k_J9Z38 -5.24442 NA NA -log_k_JSE76 -5.75229 NA NA -f_cyan_ilr_1 0.68480 NA NA -f_cyan_ilr_2 0.61670 NA NA -f_JCZ38_qlogis 87.97407 NA NA -log_alpha -0.15699 NA NA -log_beta 3.01540 NA NA -a.1 3.11518 NA NA -b.1 0.04445 NA NA -SD.log_k_JCZ38 1.40732 NA NA -SD.log_k_J9Z38 0.56510 NA NA -SD.log_k_JSE76 0.72067 NA NA -SD.f_cyan_ilr_1 0.31199 NA NA -SD.f_cyan_ilr_2 0.36894 NA NA -SD.f_JCZ38_qlogis 6.92892 NA NA -SD.log_alpha 0.25662 NA NA -SD.log_beta 0.35845 NA NA +cyan_0 100.65667 NA NA +log_k_JCZ38 -3.45782 NA NA +log_k_J9Z38 -5.23476 NA NA +log_k_JSE76 -5.71827 NA NA +f_cyan_ilr_1 0.68389 NA NA +f_cyan_ilr_2 0.61027 NA NA +f_JCZ38_qlogis 116.27482 NA NA +log_alpha -0.14484 NA NA +log_beta 3.03220 NA NA +a.1 3.11051 NA NA +b.1 0.04508 NA NA +SD.log_k_JCZ38 1.39961 NA NA +SD.log_k_J9Z38 0.57920 NA NA +SD.log_k_JSE76 0.68364 NA NA +SD.f_cyan_ilr_1 0.31477 NA NA +SD.f_cyan_ilr_2 0.37716 NA NA +SD.f_JCZ38_qlogis 5.52695 NA NA +SD.log_alpha 0.22823 NA NA +SD.log_beta 0.39161 NA NA Correlation is not available Random effects: est. lower upper -SD.log_k_JCZ38 1.4073 NA NA -SD.log_k_J9Z38 0.5651 NA NA -SD.log_k_JSE76 0.7207 NA NA -SD.f_cyan_ilr_1 0.3120 NA NA -SD.f_cyan_ilr_2 0.3689 NA NA -SD.f_JCZ38_qlogis 6.9289 NA NA -SD.log_alpha 0.2566 NA NA -SD.log_beta 0.3585 NA NA +SD.log_k_JCZ38 1.3996 NA NA +SD.log_k_J9Z38 0.5792 NA NA +SD.log_k_JSE76 0.6836 NA NA +SD.f_cyan_ilr_1 0.3148 NA NA +SD.f_cyan_ilr_2 0.3772 NA NA +SD.f_JCZ38_qlogis 5.5270 NA NA +SD.log_alpha 0.2282 NA NA +SD.log_beta 0.3916 NA NA Variance model: est. lower upper -a.1 3.11518 NA NA -b.1 0.04445 NA NA +a.1 3.11051 NA NA +b.1 0.04508 NA NA Backtransformed parameters: est. lower upper -cyan_0 1.006e+02 NA NA -k_JCZ38 3.135e-02 NA NA -k_J9Z38 5.277e-03 NA NA -k_JSE76 3.175e-03 NA NA -f_cyan_to_JCZ38 5.991e-01 NA NA -f_cyan_to_J9Z38 2.275e-01 NA NA +cyan_0 1.007e+02 NA NA +k_JCZ38 3.150e-02 NA NA +k_J9Z38 5.328e-03 NA NA +k_JSE76 3.285e-03 NA NA +f_cyan_to_JCZ38 5.980e-01 NA NA +f_cyan_to_J9Z38 2.273e-01 NA NA f_JCZ38_to_JSE76 1.000e+00 NA NA -alpha 8.547e-01 NA NA -beta 2.040e+01 NA NA +alpha 8.652e-01 NA NA +beta 2.074e+01 NA NA Resulting formation fractions: ff -cyan_JCZ38 0.5991 -cyan_J9Z38 0.2275 -cyan_sink 0.1734 +cyan_JCZ38 0.5980 +cyan_J9Z38 0.2273 +cyan_sink 0.1746 JCZ38_JSE76 1.0000 JCZ38_sink 0.0000 Estimated disappearance times: - DT50 DT90 DT50back -cyan 25.50 281.29 84.68 -JCZ38 22.11 73.44 NA -J9Z38 131.36 436.35 NA -JSE76 218.28 725.11 NA + DT50 DT90 DT50back +cyan 25.48 276.2 83.15 +JCZ38 22.01 73.1 NA +J9Z38 130.09 432.2 NA +JSE76 210.98 700.9 NA </code></pre> <p></p> @@ -2760,11 +2791,11 @@ JSE76 218.28 725.11 NA Hierarchical DFOP path 1 fit with constant variance </caption> <pre><code> -saemix version used for fitting: 3.2 -mkin version used for pre-fitting: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 17:43:22 2023 -Date of summary: Fri May 19 18:14:01 2023 +saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.4.2 +Date of fit: Fri Feb 14 07:43:33 2025 +Date of summary: Sun Feb 16 17:03:40 2025 Equations: d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -2783,16 +2814,16 @@ Data: Model predictions using solution type deSolve -Fitted in 523.691 s +Fitted in 504.014 s Using 300, 100 iterations and 10 chains Variance model: Constant variance Starting values for degradation parameters: cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 - 102.0644 -3.4008 -5.0024 -5.8613 0.6855 + 102.0643 -3.4008 -5.0024 -5.8612 0.6855 f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 g_qlogis - 1.2365 13.7245 -1.8641 -4.5063 -0.6468 + 1.2366 13.6901 -1.8641 -4.5063 -0.6468 Fixed degradation parameter values: None @@ -2816,7 +2847,7 @@ log_k_J9Z38 0.00 0.00 0.0000 0.0000 0.000 log_k_JSE76 0.00 0.00 0.0000 0.0000 0.000 f_cyan_ilr_1 0.00 0.00 0.0000 0.0000 0.000 f_cyan_ilr_2 1.28 0.00 0.0000 0.0000 0.000 -f_JCZ38_qlogis 0.00 16.11 0.0000 0.0000 0.000 +f_JCZ38_qlogis 0.00 16.08 0.0000 0.0000 0.000 log_k1 0.00 0.00 0.9866 0.0000 0.000 log_k2 0.00 0.00 0.0000 0.5953 0.000 g_qlogis 0.00 0.00 0.0000 0.0000 1.583 @@ -2833,72 +2864,72 @@ Likelihood computed by importance sampling Optimised parameters: est. lower upper -cyan_0 102.6079 NA NA -log_k_JCZ38 -3.4855 NA NA -log_k_J9Z38 -5.1686 NA NA -log_k_JSE76 -5.6697 NA NA -f_cyan_ilr_1 0.6714 NA NA -f_cyan_ilr_2 0.4986 NA NA -f_JCZ38_qlogis 55.4760 NA NA -log_k1 -1.8409 NA NA -log_k2 -4.4915 NA NA -g_qlogis -0.6403 NA NA -a.1 3.2387 NA NA -SD.log_k_JCZ38 1.4524 NA NA -SD.log_k_J9Z38 0.5151 NA NA -SD.log_k_JSE76 0.6514 NA NA -SD.f_cyan_ilr_1 0.3023 NA NA -SD.f_cyan_ilr_2 0.2959 NA NA -SD.f_JCZ38_qlogis 1.9984 NA NA -SD.log_k1 0.5188 NA NA -SD.log_k2 0.3894 NA NA -SD.g_qlogis 0.8579 NA NA +cyan_0 102.5565 NA NA +log_k_JCZ38 -3.4729 NA NA +log_k_J9Z38 -5.1533 NA NA +log_k_JSE76 -5.6669 NA NA +f_cyan_ilr_1 0.6665 NA NA +f_cyan_ilr_2 0.5191 NA NA +f_JCZ38_qlogis 37.0113 NA NA +log_k1 -1.8497 NA NA +log_k2 -4.4931 NA NA +g_qlogis -0.6383 NA NA +a.1 3.2397 NA NA +SD.log_k_JCZ38 1.4286 NA NA +SD.log_k_J9Z38 0.5312 NA NA +SD.log_k_JSE76 0.6627 NA NA +SD.f_cyan_ilr_1 0.3013 NA NA +SD.f_cyan_ilr_2 0.2980 NA NA +SD.f_JCZ38_qlogis 0.1637 NA NA +SD.log_k1 0.5069 NA NA +SD.log_k2 0.3828 NA NA +SD.g_qlogis 0.8641 NA NA Correlation is not available Random effects: est. lower upper -SD.log_k_JCZ38 1.4524 NA NA -SD.log_k_J9Z38 0.5151 NA NA -SD.log_k_JSE76 0.6514 NA NA -SD.f_cyan_ilr_1 0.3023 NA NA -SD.f_cyan_ilr_2 0.2959 NA NA -SD.f_JCZ38_qlogis 1.9984 NA NA -SD.log_k1 0.5188 NA NA -SD.log_k2 0.3894 NA NA -SD.g_qlogis 0.8579 NA NA +SD.log_k_JCZ38 1.4286 NA NA +SD.log_k_J9Z38 0.5312 NA NA +SD.log_k_JSE76 0.6627 NA NA +SD.f_cyan_ilr_1 0.3013 NA NA +SD.f_cyan_ilr_2 0.2980 NA NA +SD.f_JCZ38_qlogis 0.1637 NA NA +SD.log_k1 0.5069 NA NA +SD.log_k2 0.3828 NA NA +SD.g_qlogis 0.8641 NA NA Variance model: - est. lower upper -a.1 3.239 NA NA + est. lower upper +a.1 3.24 NA NA Backtransformed parameters: est. lower upper cyan_0 1.026e+02 NA NA -k_JCZ38 3.064e-02 NA NA -k_J9Z38 5.692e-03 NA NA -k_JSE76 3.449e-03 NA NA -f_cyan_to_JCZ38 5.798e-01 NA NA -f_cyan_to_J9Z38 2.243e-01 NA NA +k_JCZ38 3.103e-02 NA NA +k_J9Z38 5.780e-03 NA NA +k_JSE76 3.459e-03 NA NA +f_cyan_to_JCZ38 5.813e-01 NA NA +f_cyan_to_J9Z38 2.265e-01 NA NA f_JCZ38_to_JSE76 1.000e+00 NA NA -k1 1.587e-01 NA NA -k2 1.120e-02 NA NA -g 3.452e-01 NA NA +k1 1.573e-01 NA NA +k2 1.119e-02 NA NA +g 3.456e-01 NA NA Resulting formation fractions: ff -cyan_JCZ38 0.5798 -cyan_J9Z38 0.2243 -cyan_sink 0.1958 +cyan_JCZ38 0.5813 +cyan_J9Z38 0.2265 +cyan_sink 0.1922 JCZ38_JSE76 1.0000 JCZ38_sink 0.0000 Estimated disappearance times: DT50 DT90 DT50back DT50_k1 DT50_k2 -cyan 25.21 167.73 50.49 4.368 61.87 -JCZ38 22.62 75.15 NA NA NA -J9Z38 121.77 404.50 NA NA NA -JSE76 200.98 667.64 NA NA NA +cyan 25.23 167.94 50.55 4.407 61.97 +JCZ38 22.34 74.22 NA NA NA +J9Z38 119.92 398.36 NA NA NA +JSE76 200.41 665.76 NA NA NA </code></pre> <p></p> @@ -2906,11 +2937,11 @@ JSE76 200.98 667.64 NA NA NA Hierarchical DFOP path 1 fit with two-component error </caption> <pre><code> -saemix version used for fitting: 3.2 -mkin version used for pre-fitting: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 17:46:08 2023 -Date of summary: Fri May 19 18:14:01 2023 +saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.4.2 +Date of fit: Fri Feb 14 07:46:07 2025 +Date of summary: Sun Feb 16 17:03:40 2025 Equations: d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -2929,7 +2960,7 @@ Data: Model predictions using solution type deSolve -Fitted in 689.217 s +Fitted in 658.043 s Using 300, 100 iterations and 10 chains Variance model: Two-component variance function @@ -2938,7 +2969,7 @@ Starting values for degradation parameters: cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 101.3964 -3.3626 -4.9792 -5.8727 0.6814 f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 g_qlogis - 6.7799 13.7245 -1.9222 -4.5035 -0.7172 + 6.8713 13.6901 -1.9222 -4.5035 -0.7172 Fixed degradation parameter values: None @@ -2949,7 +2980,7 @@ cyan_0 5.317 0.000 0.000 0.000 0.0000 log_k_JCZ38 0.000 2.272 0.000 0.000 0.0000 log_k_J9Z38 0.000 0.000 1.633 0.000 0.0000 log_k_JSE76 0.000 0.000 0.000 1.271 0.0000 -f_cyan_ilr_1 0.000 0.000 0.000 0.000 0.6838 +f_cyan_ilr_1 0.000 0.000 0.000 0.000 0.6839 f_cyan_ilr_2 0.000 0.000 0.000 0.000 0.0000 f_JCZ38_qlogis 0.000 0.000 0.000 0.000 0.0000 log_k1 0.000 0.000 0.000 0.000 0.0000 @@ -2961,8 +2992,8 @@ log_k_JCZ38 0.00 0.00 0.0000 0.0000 0.000 log_k_J9Z38 0.00 0.00 0.0000 0.0000 0.000 log_k_JSE76 0.00 0.00 0.0000 0.0000 0.000 f_cyan_ilr_1 0.00 0.00 0.0000 0.0000 0.000 -f_cyan_ilr_2 11.77 0.00 0.0000 0.0000 0.000 -f_JCZ38_qlogis 0.00 16.11 0.0000 0.0000 0.000 +f_cyan_ilr_2 11.95 0.00 0.0000 0.0000 0.000 +f_JCZ38_qlogis 0.00 16.08 0.0000 0.0000 0.000 log_k1 0.00 0.00 0.9496 0.0000 0.000 log_k2 0.00 0.00 0.0000 0.5846 0.000 g_qlogis 0.00 0.00 0.0000 0.0000 1.719 @@ -2978,73 +3009,73 @@ Likelihood computed by importance sampling 2398 2390 -1179 Optimised parameters: - est. lower upper -cyan_0 100.8076 NA NA -log_k_JCZ38 -3.4684 NA NA -log_k_J9Z38 -5.0844 NA NA -log_k_JSE76 -5.5743 NA NA -f_cyan_ilr_1 0.6669 NA NA -f_cyan_ilr_2 0.7912 NA NA -f_JCZ38_qlogis 84.1825 NA NA -log_k1 -2.1671 NA NA -log_k2 -4.5447 NA NA -g_qlogis -0.5631 NA NA -a.1 2.9627 NA NA -b.1 0.0444 NA NA -SD.log_k_JCZ38 1.4044 NA NA -SD.log_k_J9Z38 0.6410 NA NA -SD.log_k_JSE76 0.5391 NA NA -SD.f_cyan_ilr_1 0.3203 NA NA -SD.f_cyan_ilr_2 0.5038 NA NA -SD.f_JCZ38_qlogis 3.5865 NA NA -SD.log_k2 0.3119 NA NA -SD.g_qlogis 0.8276 NA NA + est. lower upper +cyan_0 100.69709 NA NA +log_k_JCZ38 -3.46669 NA NA +log_k_J9Z38 -5.05076 NA NA +log_k_JSE76 -5.55558 NA NA +f_cyan_ilr_1 0.66045 NA NA +f_cyan_ilr_2 0.84275 NA NA +f_JCZ38_qlogis 64.22404 NA NA +log_k1 -2.17715 NA NA +log_k2 -4.55002 NA NA +g_qlogis -0.55920 NA NA +a.1 2.95785 NA NA +b.1 0.04456 NA NA +SD.log_k_JCZ38 1.39881 NA NA +SD.log_k_J9Z38 0.67788 NA NA +SD.log_k_JSE76 0.52603 NA NA +SD.f_cyan_ilr_1 0.32490 NA NA +SD.f_cyan_ilr_2 0.53923 NA NA +SD.f_JCZ38_qlogis 2.75576 NA NA +SD.log_k2 0.30694 NA NA +SD.g_qlogis 0.83619 NA NA Correlation is not available Random effects: est. lower upper -SD.log_k_JCZ38 1.4044 NA NA -SD.log_k_J9Z38 0.6410 NA NA -SD.log_k_JSE76 0.5391 NA NA -SD.f_cyan_ilr_1 0.3203 NA NA -SD.f_cyan_ilr_2 0.5038 NA NA -SD.f_JCZ38_qlogis 3.5865 NA NA -SD.log_k2 0.3119 NA NA -SD.g_qlogis 0.8276 NA NA +SD.log_k_JCZ38 1.3988 NA NA +SD.log_k_J9Z38 0.6779 NA NA +SD.log_k_JSE76 0.5260 NA NA +SD.f_cyan_ilr_1 0.3249 NA NA +SD.f_cyan_ilr_2 0.5392 NA NA +SD.f_JCZ38_qlogis 2.7558 NA NA +SD.log_k2 0.3069 NA NA +SD.g_qlogis 0.8362 NA NA Variance model: - est. lower upper -a.1 2.9627 NA NA -b.1 0.0444 NA NA + est. lower upper +a.1 2.95785 NA NA +b.1 0.04456 NA NA Backtransformed parameters: est. lower upper -cyan_0 1.008e+02 NA NA -k_JCZ38 3.117e-02 NA NA -k_J9Z38 6.193e-03 NA NA -k_JSE76 3.794e-03 NA NA -f_cyan_to_JCZ38 6.149e-01 NA NA -f_cyan_to_J9Z38 2.395e-01 NA NA +cyan_0 1.007e+02 NA NA +k_JCZ38 3.122e-02 NA NA +k_J9Z38 6.404e-03 NA NA +k_JSE76 3.866e-03 NA NA +f_cyan_to_JCZ38 6.187e-01 NA NA +f_cyan_to_J9Z38 2.431e-01 NA NA f_JCZ38_to_JSE76 1.000e+00 NA NA -k1 1.145e-01 NA NA -k2 1.062e-02 NA NA -g 3.628e-01 NA NA +k1 1.134e-01 NA NA +k2 1.057e-02 NA NA +g 3.637e-01 NA NA Resulting formation fractions: ff -cyan_JCZ38 0.6149 -cyan_J9Z38 0.2395 -cyan_sink 0.1456 +cyan_JCZ38 0.6187 +cyan_J9Z38 0.2431 +cyan_sink 0.1382 JCZ38_JSE76 1.0000 JCZ38_sink 0.0000 Estimated disappearance times: DT50 DT90 DT50back DT50_k1 DT50_k2 -cyan 26.26 174.32 52.47 6.053 65.25 -JCZ38 22.24 73.88 NA NA NA -J9Z38 111.93 371.82 NA NA NA -JSE76 182.69 606.88 NA NA NA +cyan 26.35 175.12 52.72 6.114 65.6 +JCZ38 22.20 73.75 NA NA NA +J9Z38 108.23 359.53 NA NA NA +JSE76 179.30 595.62 NA NA NA </code></pre> <p></p> @@ -3052,11 +3083,11 @@ JSE76 182.69 606.88 NA NA NA Hierarchical SFORB path 1 fit with constant variance </caption> <pre><code> -saemix version used for fitting: 3.2 -mkin version used for pre-fitting: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 17:43:37 2023 -Date of summary: Fri May 19 18:14:01 2023 +saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.4.2 +Date of fit: Fri Feb 14 07:43:36 2025 +Date of summary: Sun Feb 16 17:03:40 2025 Equations: d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound * @@ -3074,7 +3105,7 @@ Data: Model predictions using solution type deSolve -Fitted in 538.025 s +Fitted in 507.042 s Using 300, 100 iterations and 10 chains Variance model: Constant variance @@ -3087,7 +3118,7 @@ log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 f_cyan_ilr_2 -5.8613 0.6855 1.2366 f_JCZ38_qlogis - 13.7418 + 13.7395 Fixed degradation parameter values: None @@ -3125,7 +3156,7 @@ log_k_J9Z38 0.0000 0.00 0.00 log_k_JSE76 0.0000 0.00 0.00 f_cyan_ilr_1 0.6852 0.00 0.00 f_cyan_ilr_2 0.0000 1.28 0.00 -f_JCZ38_qlogis 0.0000 0.00 16.14 +f_JCZ38_qlogis 0.0000 0.00 16.13 Starting values for error model parameters: a.1 @@ -3139,77 +3170,77 @@ Likelihood computed by importance sampling Optimised parameters: est. lower upper -cyan_free_0 102.7803 NA NA -log_k_cyan_free -2.8068 NA NA -log_k_cyan_free_bound -2.5714 NA NA -log_k_cyan_bound_free -3.4426 NA NA -log_k_JCZ38 -3.4994 NA NA -log_k_J9Z38 -5.1148 NA NA -log_k_JSE76 -5.6335 NA NA -f_cyan_ilr_1 0.6597 NA NA -f_cyan_ilr_2 0.5132 NA NA -f_JCZ38_qlogis 37.2090 NA NA -a.1 3.2367 NA NA -SD.log_k_cyan_free 0.3161 NA NA -SD.log_k_cyan_free_bound 0.8103 NA NA -SD.log_k_cyan_bound_free 0.5554 NA NA -SD.log_k_JCZ38 1.4858 NA NA -SD.log_k_J9Z38 0.5859 NA NA -SD.log_k_JSE76 0.6195 NA NA -SD.f_cyan_ilr_1 0.3118 NA NA -SD.f_cyan_ilr_2 0.3344 NA NA -SD.f_JCZ38_qlogis 0.5518 NA NA +cyan_free_0 102.8136 NA NA +log_k_cyan_free -2.7935 NA NA +log_k_cyan_free_bound -2.5440 NA NA +log_k_cyan_bound_free -3.4303 NA NA +log_k_JCZ38 -3.5010 NA NA +log_k_J9Z38 -5.1226 NA NA +log_k_JSE76 -5.6314 NA NA +f_cyan_ilr_1 0.6609 NA NA +f_cyan_ilr_2 0.5085 NA NA +f_JCZ38_qlogis 44.0153 NA NA +a.1 3.2318 NA NA +SD.log_k_cyan_free 0.3211 NA NA +SD.log_k_cyan_free_bound 0.8408 NA NA +SD.log_k_cyan_bound_free 0.5724 NA NA +SD.log_k_JCZ38 1.4925 NA NA +SD.log_k_J9Z38 0.5816 NA NA +SD.log_k_JSE76 0.6037 NA NA +SD.f_cyan_ilr_1 0.3115 NA NA +SD.f_cyan_ilr_2 0.3436 NA NA +SD.f_JCZ38_qlogis 4.8937 NA NA Correlation is not available Random effects: est. lower upper -SD.log_k_cyan_free 0.3161 NA NA -SD.log_k_cyan_free_bound 0.8103 NA NA -SD.log_k_cyan_bound_free 0.5554 NA NA -SD.log_k_JCZ38 1.4858 NA NA -SD.log_k_J9Z38 0.5859 NA NA -SD.log_k_JSE76 0.6195 NA NA -SD.f_cyan_ilr_1 0.3118 NA NA -SD.f_cyan_ilr_2 0.3344 NA NA -SD.f_JCZ38_qlogis 0.5518 NA NA +SD.log_k_cyan_free 0.3211 NA NA +SD.log_k_cyan_free_bound 0.8408 NA NA +SD.log_k_cyan_bound_free 0.5724 NA NA +SD.log_k_JCZ38 1.4925 NA NA +SD.log_k_J9Z38 0.5816 NA NA +SD.log_k_JSE76 0.6037 NA NA +SD.f_cyan_ilr_1 0.3115 NA NA +SD.f_cyan_ilr_2 0.3436 NA NA +SD.f_JCZ38_qlogis 4.8937 NA NA Variance model: est. lower upper -a.1 3.237 NA NA +a.1 3.232 NA NA Backtransformed parameters: est. lower upper cyan_free_0 1.028e+02 NA NA -k_cyan_free 6.040e-02 NA NA -k_cyan_free_bound 7.643e-02 NA NA -k_cyan_bound_free 3.198e-02 NA NA -k_JCZ38 3.022e-02 NA NA -k_J9Z38 6.007e-03 NA NA -k_JSE76 3.576e-03 NA NA -f_cyan_free_to_JCZ38 5.787e-01 NA NA -f_cyan_free_to_J9Z38 2.277e-01 NA NA +k_cyan_free 6.120e-02 NA NA +k_cyan_free_bound 7.855e-02 NA NA +k_cyan_bound_free 3.238e-02 NA NA +k_JCZ38 3.017e-02 NA NA +k_J9Z38 5.961e-03 NA NA +k_JSE76 3.584e-03 NA NA +f_cyan_free_to_JCZ38 5.784e-01 NA NA +f_cyan_free_to_J9Z38 2.271e-01 NA NA f_JCZ38_to_JSE76 1.000e+00 NA NA Estimated Eigenvalues of SFORB model(s): cyan_b1 cyan_b2 cyan_g -0.15646 0.01235 0.33341 +0.15973 0.01241 0.33124 Resulting formation fractions: ff -cyan_free_JCZ38 0.5787 -cyan_free_J9Z38 0.2277 -cyan_free_sink 0.1936 +cyan_free_JCZ38 0.5784 +cyan_free_J9Z38 0.2271 +cyan_free_sink 0.1945 cyan_free 1.0000 JCZ38_JSE76 1.0000 JCZ38_sink 0.0000 Estimated disappearance times: - DT50 DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 -cyan 24.48 153.7 46.26 4.43 56.15 -JCZ38 22.94 76.2 NA NA NA -J9Z38 115.39 383.3 NA NA NA -JSE76 193.84 643.9 NA NA NA + DT50 DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 +cyan 24.51 153.18 46.11 4.34 55.87 +JCZ38 22.98 76.33 NA NA NA +J9Z38 116.28 386.29 NA NA NA +JSE76 193.42 642.53 NA NA NA </code></pre> <p></p> @@ -3217,11 +3248,11 @@ JSE76 193.84 643.9 NA NA NA Hierarchical SFORB path 1 fit with two-component error </caption> <pre><code> -saemix version used for fitting: 3.2 -mkin version used for pre-fitting: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 17:45:51 2023 -Date of summary: Fri May 19 18:14:01 2023 +saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.4.2 +Date of fit: Fri Feb 14 07:46:14 2025 +Date of summary: Sun Feb 16 17:03:40 2025 Equations: d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound * @@ -3239,7 +3270,7 @@ Data: Model predictions using solution type deSolve -Fitted in 672.309 s +Fitted in 664.11 s Using 300, 100 iterations and 10 chains Variance model: Two-component variance function @@ -3250,9 +3281,9 @@ Starting values for degradation parameters: log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 -3.4376 -3.3626 -4.9792 log_k_JSE76 f_cyan_ilr_1 f_cyan_ilr_2 - -5.8727 0.6814 6.8139 + -5.8727 0.6814 6.7399 f_JCZ38_qlogis - 13.7419 + 13.7395 Fixed degradation parameter values: None @@ -3289,8 +3320,8 @@ log_k_JCZ38 0.0000 0.00 0.00 log_k_J9Z38 0.0000 0.00 0.00 log_k_JSE76 0.0000 0.00 0.00 f_cyan_ilr_1 0.6838 0.00 0.00 -f_cyan_ilr_2 0.0000 11.84 0.00 -f_JCZ38_qlogis 0.0000 0.00 16.14 +f_cyan_ilr_2 0.0000 11.69 0.00 +f_JCZ38_qlogis 0.0000 0.00 16.13 Starting values for error model parameters: a.1 b.1 @@ -3304,77 +3335,77 @@ Likelihood computed by importance sampling Optimised parameters: est. lower upper -cyan_free_0 100.69983 NA NA -log_k_cyan_free -3.11584 NA NA -log_k_cyan_free_bound -3.15216 NA NA -log_k_cyan_bound_free -3.65986 NA NA -log_k_JCZ38 -3.47811 NA NA -log_k_J9Z38 -5.08835 NA NA -log_k_JSE76 -5.55514 NA NA -f_cyan_ilr_1 0.66764 NA NA -f_cyan_ilr_2 0.78329 NA NA -f_JCZ38_qlogis 25.35245 NA NA -a.1 2.99088 NA NA -b.1 0.04346 NA NA -SD.log_k_cyan_free 0.48797 NA NA -SD.log_k_cyan_bound_free 0.27243 NA NA -SD.log_k_JCZ38 1.42450 NA NA -SD.log_k_J9Z38 0.63496 NA NA -SD.log_k_JSE76 0.55951 NA NA -SD.f_cyan_ilr_1 0.32687 NA NA -SD.f_cyan_ilr_2 0.48056 NA NA -SD.f_JCZ38_qlogis 0.43818 NA NA +cyan_free_0 100.56004 NA NA +log_k_cyan_free -3.12657 NA NA +log_k_cyan_free_bound -3.16825 NA NA +log_k_cyan_bound_free -3.66003 NA NA +log_k_JCZ38 -3.47278 NA NA +log_k_J9Z38 -5.06823 NA NA +log_k_JSE76 -5.54327 NA NA +f_cyan_ilr_1 0.66631 NA NA +f_cyan_ilr_2 0.82898 NA NA +f_JCZ38_qlogis 38.31115 NA NA +a.1 2.98352 NA NA +b.1 0.04388 NA NA +SD.log_k_cyan_free 0.49145 NA NA +SD.log_k_cyan_bound_free 0.27347 NA NA +SD.log_k_JCZ38 1.41193 NA NA +SD.log_k_J9Z38 0.66073 NA NA +SD.log_k_JSE76 0.55885 NA NA +SD.f_cyan_ilr_1 0.33020 NA NA +SD.f_cyan_ilr_2 0.51367 NA NA +SD.f_JCZ38_qlogis 5.52122 NA NA Correlation is not available Random effects: est. lower upper -SD.log_k_cyan_free 0.4880 NA NA -SD.log_k_cyan_bound_free 0.2724 NA NA -SD.log_k_JCZ38 1.4245 NA NA -SD.log_k_J9Z38 0.6350 NA NA -SD.log_k_JSE76 0.5595 NA NA -SD.f_cyan_ilr_1 0.3269 NA NA -SD.f_cyan_ilr_2 0.4806 NA NA -SD.f_JCZ38_qlogis 0.4382 NA NA +SD.log_k_cyan_free 0.4914 NA NA +SD.log_k_cyan_bound_free 0.2735 NA NA +SD.log_k_JCZ38 1.4119 NA NA +SD.log_k_J9Z38 0.6607 NA NA +SD.log_k_JSE76 0.5589 NA NA +SD.f_cyan_ilr_1 0.3302 NA NA +SD.f_cyan_ilr_2 0.5137 NA NA +SD.f_JCZ38_qlogis 5.5212 NA NA Variance model: est. lower upper -a.1 2.99088 NA NA -b.1 0.04346 NA NA +a.1 2.98352 NA NA +b.1 0.04388 NA NA Backtransformed parameters: est. lower upper -cyan_free_0 1.007e+02 NA NA -k_cyan_free 4.434e-02 NA NA -k_cyan_free_bound 4.276e-02 NA NA -k_cyan_bound_free 2.574e-02 NA NA -k_JCZ38 3.087e-02 NA NA -k_J9Z38 6.168e-03 NA NA -k_JSE76 3.868e-03 NA NA -f_cyan_free_to_JCZ38 6.143e-01 NA NA -f_cyan_free_to_J9Z38 2.389e-01 NA NA +cyan_free_0 1.006e+02 NA NA +k_cyan_free 4.387e-02 NA NA +k_cyan_free_bound 4.208e-02 NA NA +k_cyan_bound_free 2.573e-02 NA NA +k_JCZ38 3.103e-02 NA NA +k_J9Z38 6.294e-03 NA NA +k_JSE76 3.914e-03 NA NA +f_cyan_free_to_JCZ38 6.188e-01 NA NA +f_cyan_free_to_J9Z38 2.412e-01 NA NA f_JCZ38_to_JSE76 1.000e+00 NA NA Estimated Eigenvalues of SFORB model(s): cyan_b1 cyan_b2 cyan_g -0.10161 0.01123 0.36636 +0.10044 0.01124 0.36580 Resulting formation fractions: - ff -cyan_free_JCZ38 6.143e-01 -cyan_free_J9Z38 2.389e-01 -cyan_free_sink 1.468e-01 -cyan_free 1.000e+00 -JCZ38_JSE76 1.000e+00 -JCZ38_sink 9.763e-12 + ff +cyan_free_JCZ38 0.6188 +cyan_free_J9Z38 0.2412 +cyan_free_sink 0.1400 +cyan_free 1.0000 +JCZ38_JSE76 1.0000 +JCZ38_sink 0.0000 Estimated disappearance times: DT50 DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 -cyan 25.91 164.4 49.49 6.822 61.72 -JCZ38 22.46 74.6 NA NA NA -J9Z38 112.37 373.3 NA NA NA -JSE76 179.22 595.4 NA NA NA +cyan 26.05 164.4 49.48 6.901 61.67 +JCZ38 22.34 74.2 NA NA NA +J9Z38 110.14 365.9 NA NA NA +JSE76 177.11 588.3 NA NA NA </code></pre> <p></p> @@ -3382,11 +3413,11 @@ JSE76 179.22 595.4 NA NA NA Hierarchical HS path 1 fit with constant variance </caption> <pre><code> -saemix version used for fitting: 3.2 -mkin version used for pre-fitting: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 17:43:26 2023 -Date of summary: Fri May 19 18:14:01 2023 +saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.4.2 +Date of fit: Fri Feb 14 07:43:42 2025 +Date of summary: Sun Feb 16 17:03:40 2025 Equations: d_cyan/dt = - ifelse(time <= tb, k1, k2) * cyan @@ -3401,27 +3432,27 @@ Data: Model predictions using solution type deSolve -Fitted in 526.974 s +Fitted in 512.818 s Using 300, 100 iterations and 10 chains Variance model: Constant variance Starting values for degradation parameters: cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 - 102.8738 -3.4490 -4.9348 -5.5989 0.6469 + 102.8845 -3.4495 -4.9355 -5.6040 0.6468 f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 log_tb - 1.2854 9.7193 -2.9084 -4.1810 1.7813 + 1.2396 9.7220 -2.9079 -4.1810 1.7813 Fixed degradation parameter values: None Starting values for random effects (square root of initial entries in omega): cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 -cyan_0 5.409 0.00 0.00 0.000 0.0000 +cyan_0 5.406 0.00 0.00 0.000 0.0000 log_k_JCZ38 0.000 2.33 0.00 0.000 0.0000 log_k_J9Z38 0.000 0.00 1.59 0.000 0.0000 -log_k_JSE76 0.000 0.00 0.00 1.006 0.0000 -f_cyan_ilr_1 0.000 0.00 0.00 0.000 0.6371 +log_k_JSE76 0.000 0.00 0.00 1.013 0.0000 +f_cyan_ilr_1 0.000 0.00 0.00 0.000 0.6367 f_cyan_ilr_2 0.000 0.00 0.00 0.000 0.0000 f_JCZ38_qlogis 0.000 0.00 0.00 0.000 0.0000 log_k1 0.000 0.00 0.00 0.000 0.0000 @@ -3433,11 +3464,11 @@ log_k_JCZ38 0.000 0.00 0.0000 0.0000 0.0000 log_k_J9Z38 0.000 0.00 0.0000 0.0000 0.0000 log_k_JSE76 0.000 0.00 0.0000 0.0000 0.0000 f_cyan_ilr_1 0.000 0.00 0.0000 0.0000 0.0000 -f_cyan_ilr_2 2.167 0.00 0.0000 0.0000 0.0000 -f_JCZ38_qlogis 0.000 10.22 0.0000 0.0000 0.0000 -log_k1 0.000 0.00 0.7003 0.0000 0.0000 +f_cyan_ilr_2 2.038 0.00 0.0000 0.0000 0.0000 +f_JCZ38_qlogis 0.000 10.33 0.0000 0.0000 0.0000 +log_k1 0.000 0.00 0.7006 0.0000 0.0000 log_k2 0.000 0.00 0.0000 0.8928 0.0000 -log_tb 0.000 0.00 0.0000 0.0000 0.6774 +log_tb 0.000 0.00 0.0000 0.0000 0.6773 Starting values for error model parameters: a.1 @@ -3447,220 +3478,76 @@ Results: Likelihood computed by importance sampling AIC BIC logLik - 2427 2420 -1194 + 2427 2419 -1194 Optimised parameters: - est. lower upper -cyan_0 101.84849 NA NA -log_k_JCZ38 -3.47365 NA NA -log_k_J9Z38 -5.10562 NA NA -log_k_JSE76 -5.60318 NA NA -f_cyan_ilr_1 0.66127 NA NA -f_cyan_ilr_2 0.60283 NA NA -f_JCZ38_qlogis 45.06408 NA NA -log_k1 -3.10124 NA NA -log_k2 -4.39028 NA NA -log_tb 2.32256 NA NA -a.1 3.32683 NA NA -SD.log_k_JCZ38 1.41427 NA NA -SD.log_k_J9Z38 0.54767 NA NA -SD.log_k_JSE76 0.62147 NA NA -SD.f_cyan_ilr_1 0.30189 NA NA -SD.f_cyan_ilr_2 0.34960 NA NA -SD.f_JCZ38_qlogis 0.04644 NA NA -SD.log_k1 0.39534 NA NA -SD.log_k2 0.43468 NA NA -SD.log_tb 0.60781 NA NA - -Correlation is not available - -Random effects: - est. lower upper -SD.log_k_JCZ38 1.41427 NA NA -SD.log_k_J9Z38 0.54767 NA NA -SD.log_k_JSE76 0.62147 NA NA -SD.f_cyan_ilr_1 0.30189 NA NA -SD.f_cyan_ilr_2 0.34960 NA NA -SD.f_JCZ38_qlogis 0.04644 NA NA -SD.log_k1 0.39534 NA NA -SD.log_k2 0.43468 NA NA -SD.log_tb 0.60781 NA NA - -Variance model: - est. lower upper -a.1 3.327 NA NA - -Backtransformed parameters: - est. lower upper -cyan_0 1.018e+02 NA NA -k_JCZ38 3.100e-02 NA NA -k_J9Z38 6.063e-03 NA NA -k_JSE76 3.686e-03 NA NA -f_cyan_to_JCZ38 5.910e-01 NA NA -f_cyan_to_J9Z38 2.320e-01 NA NA -f_JCZ38_to_JSE76 1.000e+00 NA NA -k1 4.499e-02 NA NA -k2 1.240e-02 NA NA -tb 1.020e+01 NA NA - -Resulting formation fractions: - ff -cyan_JCZ38 0.591 -cyan_J9Z38 0.232 -cyan_sink 0.177 -JCZ38_JSE76 1.000 -JCZ38_sink 0.000 - -Estimated disappearance times: - DT50 DT90 DT50back DT50_k1 DT50_k2 -cyan 29.09 158.91 47.84 15.41 55.91 -JCZ38 22.36 74.27 NA NA NA -J9Z38 114.33 379.80 NA NA NA -JSE76 188.04 624.66 NA NA NA - -</code></pre> -<p></p> -<caption> -Hierarchical HS path 1 fit with two-component error -</caption> -<pre><code> -saemix version used for fitting: 3.2 -mkin version used for pre-fitting: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 17:43:29 2023 -Date of summary: Fri May 19 18:14:01 2023 - -Equations: -d_cyan/dt = - ifelse(time <= tb, k1, k2) * cyan -d_JCZ38/dt = + f_cyan_to_JCZ38 * ifelse(time <= tb, k1, k2) * cyan - - k_JCZ38 * JCZ38 -d_J9Z38/dt = + f_cyan_to_J9Z38 * ifelse(time <= tb, k1, k2) * cyan - - k_J9Z38 * J9Z38 -d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 - -Data: -433 observations of 4 variable(s) grouped in 5 datasets - -Model predictions using solution type deSolve - -Fitted in 529.515 s -Using 300, 100 iterations and 10 chains - -Variance model: Two-component variance function - -Starting values for degradation parameters: - cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 - 101.168 -3.358 -4.941 -5.794 0.676 - f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 log_tb - 5.740 13.863 -3.147 -4.262 2.173 - -Fixed degradation parameter values: -None - -Starting values for random effects (square root of initial entries in omega): - cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 -cyan_0 5.79 0.000 0.000 0.000 0.0000 -log_k_JCZ38 0.00 2.271 0.000 0.000 0.0000 -log_k_J9Z38 0.00 0.000 1.614 0.000 0.0000 -log_k_JSE76 0.00 0.000 0.000 1.264 0.0000 -f_cyan_ilr_1 0.00 0.000 0.000 0.000 0.6761 -f_cyan_ilr_2 0.00 0.000 0.000 0.000 0.0000 -f_JCZ38_qlogis 0.00 0.000 0.000 0.000 0.0000 -log_k1 0.00 0.000 0.000 0.000 0.0000 -log_k2 0.00 0.000 0.000 0.000 0.0000 -log_tb 0.00 0.000 0.000 0.000 0.0000 - f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 log_tb -cyan_0 0.000 0.00 0.0000 0.0000 0.000 -log_k_JCZ38 0.000 0.00 0.0000 0.0000 0.000 -log_k_J9Z38 0.000 0.00 0.0000 0.0000 0.000 -log_k_JSE76 0.000 0.00 0.0000 0.0000 0.000 -f_cyan_ilr_1 0.000 0.00 0.0000 0.0000 0.000 -f_cyan_ilr_2 9.572 0.00 0.0000 0.0000 0.000 -f_JCZ38_qlogis 0.000 19.19 0.0000 0.0000 0.000 -log_k1 0.000 0.00 0.8705 0.0000 0.000 -log_k2 0.000 0.00 0.0000 0.9288 0.000 -log_tb 0.000 0.00 0.0000 0.0000 1.065 - -Starting values for error model parameters: -a.1 b.1 - 1 1 - -Results: - -Likelihood computed by importance sampling - AIC BIC logLik - 2422 2414 -1190 - -Optimised parameters: - est. lower upper -cyan_0 100.9521 NA NA -log_k_JCZ38 -3.4629 NA NA -log_k_J9Z38 -5.0346 NA NA -log_k_JSE76 -5.5722 NA NA -f_cyan_ilr_1 0.6560 NA NA -f_cyan_ilr_2 0.7983 NA NA -f_JCZ38_qlogis 42.7949 NA NA -log_k1 -3.1721 NA NA -log_k2 -4.4039 NA NA -log_tb 2.3994 NA NA -a.1 3.0586 NA NA -b.1 0.0380 NA NA -SD.log_k_JCZ38 1.3754 NA NA -SD.log_k_J9Z38 0.6703 NA NA -SD.log_k_JSE76 0.5876 NA NA -SD.f_cyan_ilr_1 0.3272 NA NA -SD.f_cyan_ilr_2 0.5300 NA NA -SD.f_JCZ38_qlogis 6.4465 NA NA -SD.log_k1 0.4135 NA NA -SD.log_k2 0.4182 NA NA -SD.log_tb 0.6035 NA NA + est. lower upper +cyan_0 101.9660 1.005e+02 1.035e+02 +log_k_JCZ38 -3.4698 -4.716e+00 -2.224e+00 +log_k_J9Z38 -5.0947 -5.740e+00 -4.450e+00 +log_k_JSE76 -5.5977 -6.321e+00 -4.875e+00 +f_cyan_ilr_1 0.6595 3.734e-01 9.456e-01 +f_cyan_ilr_2 0.5905 1.664e-01 1.015e+00 +f_JCZ38_qlogis 25.8627 -4.224e+05 4.225e+05 +log_k1 -3.0884 -3.453e+00 -2.723e+00 +log_k2 -4.3877 -4.778e+00 -3.998e+00 +log_tb 2.3057 1.715e+00 2.896e+00 +a.1 3.3228 NA NA +SD.log_k_JCZ38 1.4071 NA NA +SD.log_k_J9Z38 0.5774 NA NA +SD.log_k_JSE76 0.6214 NA NA +SD.f_cyan_ilr_1 0.3058 NA NA +SD.f_cyan_ilr_2 0.3470 NA NA +SD.f_JCZ38_qlogis 0.0644 NA NA +SD.log_k1 0.3994 NA NA +SD.log_k2 0.4373 NA NA +SD.log_tb 0.6419 NA NA Correlation is not available Random effects: est. lower upper -SD.log_k_JCZ38 1.3754 NA NA -SD.log_k_J9Z38 0.6703 NA NA -SD.log_k_JSE76 0.5876 NA NA -SD.f_cyan_ilr_1 0.3272 NA NA -SD.f_cyan_ilr_2 0.5300 NA NA -SD.f_JCZ38_qlogis 6.4465 NA NA -SD.log_k1 0.4135 NA NA -SD.log_k2 0.4182 NA NA -SD.log_tb 0.6035 NA NA +SD.log_k_JCZ38 1.4071 NA NA +SD.log_k_J9Z38 0.5774 NA NA +SD.log_k_JSE76 0.6214 NA NA +SD.f_cyan_ilr_1 0.3058 NA NA +SD.f_cyan_ilr_2 0.3470 NA NA +SD.f_JCZ38_qlogis 0.0644 NA NA +SD.log_k1 0.3994 NA NA +SD.log_k2 0.4373 NA NA +SD.log_tb 0.6419 NA NA Variance model: est. lower upper -a.1 3.059 NA NA -b.1 0.038 NA NA +a.1 3.323 NA NA Backtransformed parameters: - est. lower upper -cyan_0 1.010e+02 NA NA -k_JCZ38 3.134e-02 NA NA -k_J9Z38 6.509e-03 NA NA -k_JSE76 3.802e-03 NA NA -f_cyan_to_JCZ38 6.127e-01 NA NA -f_cyan_to_J9Z38 2.423e-01 NA NA -f_JCZ38_to_JSE76 1.000e+00 NA NA -k1 4.191e-02 NA NA -k2 1.223e-02 NA NA -tb 1.102e+01 NA NA + est. lower upper +cyan_0 1.020e+02 1.005e+02 1.035e+02 +k_JCZ38 3.112e-02 8.951e-03 1.082e-01 +k_J9Z38 6.129e-03 3.216e-03 1.168e-02 +k_JSE76 3.706e-03 1.798e-03 7.639e-03 +f_cyan_to_JCZ38 5.890e-01 NA NA +f_cyan_to_J9Z38 2.318e-01 NA NA +f_JCZ38_to_JSE76 1.000e+00 0.000e+00 1.000e+00 +k1 4.558e-02 3.164e-02 6.565e-02 +k2 1.243e-02 8.417e-03 1.835e-02 +tb 1.003e+01 5.557e+00 1.811e+01 Resulting formation fractions: - ff -cyan_JCZ38 0.6127 -cyan_J9Z38 0.2423 -cyan_sink 0.1449 -JCZ38_JSE76 1.0000 -JCZ38_sink 0.0000 + ff +cyan_JCZ38 5.890e-01 +cyan_J9Z38 2.318e-01 +cyan_sink 1.793e-01 +JCZ38_JSE76 1.000e+00 +JCZ38_sink 5.861e-12 Estimated disappearance times: DT50 DT90 DT50back DT50_k1 DT50_k2 -cyan 29.94 161.54 48.63 16.54 56.68 -JCZ38 22.12 73.47 NA NA NA -J9Z38 106.50 353.77 NA NA NA -JSE76 182.30 605.60 NA NA NA +cyan 29.02 158.51 47.72 15.21 55.77 +JCZ38 22.27 73.98 NA NA NA +J9Z38 113.09 375.69 NA NA NA +JSE76 187.01 621.23 NA NA NA </code></pre> <p></p> @@ -3669,14 +3556,14 @@ JSE76 182.30 605.60 NA NA NA <h4 id="pathway-2">Pathway 2<a class="anchor" aria-label="anchor" href="#pathway-2"></a> </h4> <caption> -Hierarchical FOMC path 2 fit with constant variance +Hierarchical FOMC path 2 fit with two-component error </caption> <pre><code> -saemix version used for fitting: 3.2 -mkin version used for pre-fitting: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 17:54:58 2023 -Date of summary: Fri May 19 18:14:01 2023 +saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.4.2 +Date of fit: Fri Feb 14 07:54:36 2025 +Date of summary: Sun Feb 16 17:03:40 2025 Equations: d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan @@ -3691,88 +3578,89 @@ Data: Model predictions using solution type deSolve -Fitted in 519.639 s +Fitted in 491.928 s Using 300, 100 iterations and 10 chains -Variance model: Constant variance +Variance model: Two-component variance function Starting values for degradation parameters: cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 - 101.8173 -1.8998 -5.1449 -2.5415 0.6705 + 102.4477 -1.8631 -5.1087 -2.5114 0.6826 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_alpha log_beta - 4.4669 16.1281 13.3327 -0.2314 2.8738 + 4.7944 15.9616 13.1566 -0.1564 2.9781 Fixed degradation parameter values: None Starting values for random effects (square root of initial entries in omega): cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 -cyan_0 5.742 0.000 0.000 0.00 0.0000 -log_k_JCZ38 0.000 1.402 0.000 0.00 0.0000 -log_k_J9Z38 0.000 0.000 1.718 0.00 0.0000 -log_k_JSE76 0.000 0.000 0.000 3.57 0.0000 -f_cyan_ilr_1 0.000 0.000 0.000 0.00 0.5926 -f_cyan_ilr_2 0.000 0.000 0.000 0.00 0.0000 -f_JCZ38_qlogis 0.000 0.000 0.000 0.00 0.0000 -f_JSE76_qlogis 0.000 0.000 0.000 0.00 0.0000 -log_alpha 0.000 0.000 0.000 0.00 0.0000 -log_beta 0.000 0.000 0.000 0.00 0.0000 +cyan_0 7.701 0.000 0.000 0.000 0.0000 +log_k_JCZ38 0.000 1.448 0.000 0.000 0.0000 +log_k_J9Z38 0.000 0.000 1.724 0.000 0.0000 +log_k_JSE76 0.000 0.000 0.000 3.659 0.0000 +f_cyan_ilr_1 0.000 0.000 0.000 0.000 0.6356 +f_cyan_ilr_2 0.000 0.000 0.000 0.000 0.0000 +f_JCZ38_qlogis 0.000 0.000 0.000 0.000 0.0000 +f_JSE76_qlogis 0.000 0.000 0.000 0.000 0.0000 +log_alpha 0.000 0.000 0.000 0.000 0.0000 +log_beta 0.000 0.000 0.000 0.000 0.0000 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_alpha log_beta cyan_0 0.00 0.00 0.00 0.0000 0.0000 log_k_JCZ38 0.00 0.00 0.00 0.0000 0.0000 log_k_J9Z38 0.00 0.00 0.00 0.0000 0.0000 log_k_JSE76 0.00 0.00 0.00 0.0000 0.0000 f_cyan_ilr_1 0.00 0.00 0.00 0.0000 0.0000 -f_cyan_ilr_2 10.56 0.00 0.00 0.0000 0.0000 -f_JCZ38_qlogis 0.00 12.04 0.00 0.0000 0.0000 -f_JSE76_qlogis 0.00 0.00 15.26 0.0000 0.0000 -log_alpha 0.00 0.00 0.00 0.4708 0.0000 -log_beta 0.00 0.00 0.00 0.0000 0.4432 +f_cyan_ilr_2 10.32 0.00 0.00 0.0000 0.0000 +f_JCZ38_qlogis 0.00 12.23 0.00 0.0000 0.0000 +f_JSE76_qlogis 0.00 0.00 14.99 0.0000 0.0000 +log_alpha 0.00 0.00 0.00 0.3924 0.0000 +log_beta 0.00 0.00 0.00 0.0000 0.5639 Starting values for error model parameters: -a.1 - 1 +a.1 b.1 + 1 1 Results: Likelihood computed by importance sampling AIC BIC logLik - 2308 2301 -1134 + 2249 2241 -1104 Optimised parameters: - est. lower upper -cyan_0 101.9586 99.22024 104.69700 -log_k_JCZ38 -2.4861 -3.17661 -1.79560 -log_k_J9Z38 -5.3926 -6.08842 -4.69684 -log_k_JSE76 -3.1193 -4.12904 -2.10962 -f_cyan_ilr_1 0.7368 0.42085 1.05276 -f_cyan_ilr_2 0.6196 0.06052 1.17861 -f_JCZ38_qlogis 4.8970 -4.68003 14.47398 -f_JSE76_qlogis 4.4066 -1.02087 9.83398 -log_alpha -0.3021 -0.68264 0.07838 -log_beta 2.7438 2.57970 2.90786 -a.1 2.9008 2.69920 3.10245 -SD.cyan_0 2.7081 0.64216 4.77401 -SD.log_k_JCZ38 0.7043 0.19951 1.20907 -SD.log_k_J9Z38 0.6248 0.05790 1.19180 -SD.log_k_JSE76 1.0750 0.33157 1.81839 -SD.f_cyan_ilr_1 0.3429 0.11688 0.56892 -SD.f_cyan_ilr_2 0.4774 0.09381 0.86097 -SD.f_JCZ38_qlogis 1.5565 -7.83970 10.95279 -SD.f_JSE76_qlogis 1.6871 -1.25577 4.63000 -SD.log_alpha 0.4216 0.15913 0.68405 + est. lower upper +cyan_0 101.55265 9.920e+01 103.9059 +log_k_JCZ38 -2.32302 -2.832e+00 -1.8142 +log_k_J9Z38 -5.13082 -5.942e+00 -4.3199 +log_k_JSE76 -3.01756 -4.262e+00 -1.7736 +f_cyan_ilr_1 0.70850 3.657e-01 1.0513 +f_cyan_ilr_2 0.95775 2.612e-01 1.6543 +f_JCZ38_qlogis 3.86105 9.248e-01 6.7973 +f_JSE76_qlogis 7.51583 -1.120e+02 127.0392 +log_alpha -0.15308 -4.508e-01 0.1446 +log_beta 2.99165 2.711e+00 3.2720 +a.1 2.04034 1.843e+00 2.2382 +b.1 0.06924 5.749e-02 0.0810 +SD.log_k_JCZ38 0.50818 1.390e-01 0.8774 +SD.log_k_J9Z38 0.86597 2.652e-01 1.4667 +SD.log_k_JSE76 1.38092 4.864e-01 2.2754 +SD.f_cyan_ilr_1 0.38204 1.354e-01 0.6286 +SD.f_cyan_ilr_2 0.55129 7.198e-02 1.0306 +SD.f_JCZ38_qlogis 1.88457 1.711e-02 3.7520 +SD.f_JSE76_qlogis 2.64018 -2.450e+03 2454.9447 +SD.log_alpha 0.31860 1.047e-01 0.5325 +SD.log_beta 0.24195 1.273e-02 0.4712 Correlation: cyan_0 l__JCZ3 l__J9Z3 l__JSE7 f_cy__1 f_cy__2 f_JCZ38 f_JSE76 -log_k_JCZ38 -0.0167 -log_k_J9Z38 -0.0307 0.0057 -log_k_JSE76 -0.0032 0.1358 0.0009 -f_cyan_ilr_1 -0.0087 0.0206 -0.1158 -0.0009 -f_cyan_ilr_2 -0.1598 0.0690 0.1770 0.0002 -0.0007 -f_JCZ38_qlogis 0.0966 -0.1132 -0.0440 0.0182 -0.1385 -0.4583 -f_JSE76_qlogis -0.0647 0.1157 0.0333 -0.0026 0.1110 0.3620 -0.8586 -log_alpha -0.0389 0.0113 0.0209 0.0021 0.0041 0.0451 -0.0605 0.0412 -log_beta -0.2508 0.0533 0.0977 0.0098 0.0220 0.2741 -0.2934 0.1999 +log_k_JCZ38 -0.0235 +log_k_J9Z38 -0.0442 0.0047 +log_k_JSE76 -0.0023 0.0966 0.0006 +f_cyan_ilr_1 -0.0032 0.0070 -0.0536 -0.0001 +f_cyan_ilr_2 -0.5189 0.0452 0.1152 0.0013 -0.0304 +f_JCZ38_qlogis 0.1088 -0.0848 -0.0240 0.0040 -0.0384 -0.2303 +f_JSE76_qlogis -0.0545 0.1315 0.0195 0.0020 0.0252 0.1737 -0.5939 +log_alpha -0.0445 0.0056 0.0261 0.0019 -0.0055 0.0586 -0.0239 -0.0284 +log_beta -0.2388 0.0163 0.0566 0.0040 -0.0078 0.2183 -0.0714 -0.0332 log_lph log_k_JCZ38 log_k_J9Z38 @@ -3782,199 +3670,54 @@ f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_alpha -log_beta 0.2281 - -Random effects: - est. lower upper -SD.cyan_0 2.7081 0.64216 4.7740 -SD.log_k_JCZ38 0.7043 0.19951 1.2091 -SD.log_k_J9Z38 0.6248 0.05790 1.1918 -SD.log_k_JSE76 1.0750 0.33157 1.8184 -SD.f_cyan_ilr_1 0.3429 0.11688 0.5689 -SD.f_cyan_ilr_2 0.4774 0.09381 0.8610 -SD.f_JCZ38_qlogis 1.5565 -7.83970 10.9528 -SD.f_JSE76_qlogis 1.6871 -1.25577 4.6300 -SD.log_alpha 0.4216 0.15913 0.6840 - -Variance model: - est. lower upper -a.1 2.901 2.699 3.102 - -Backtransformed parameters: - est. lower upper -cyan_0 101.95862 99.220240 1.047e+02 -k_JCZ38 0.08323 0.041727 1.660e-01 -k_J9Z38 0.00455 0.002269 9.124e-03 -k_JSE76 0.04419 0.016098 1.213e-01 -f_cyan_to_JCZ38 0.61318 NA NA -f_cyan_to_J9Z38 0.21630 NA NA -f_JCZ38_to_JSE76 0.99259 0.009193 1.000e+00 -f_JSE76_to_JCZ38 0.98795 0.264857 9.999e-01 -alpha 0.73924 0.505281 1.082e+00 -beta 15.54568 13.193194 1.832e+01 - -Resulting formation fractions: - ff -cyan_JCZ38 0.613182 -cyan_J9Z38 0.216298 -cyan_sink 0.170519 -JCZ38_JSE76 0.992586 -JCZ38_sink 0.007414 -JSE76_JCZ38 0.987950 -JSE76_sink 0.012050 - -Estimated disappearance times: - DT50 DT90 DT50back -cyan 24.157 334.68 100.7 -JCZ38 8.328 27.66 NA -J9Z38 152.341 506.06 NA -JSE76 15.687 52.11 NA - -</code></pre> -<p></p> -<caption> -Hierarchical FOMC path 2 fit with two-component error -</caption> -<pre><code> -saemix version used for fitting: 3.2 -mkin version used for pre-fitting: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 17:54:41 2023 -Date of summary: Fri May 19 18:14:01 2023 - -Equations: -d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan -d_JCZ38/dt = + f_cyan_to_JCZ38 * (alpha/beta) * 1/((time/beta) + 1) * - cyan - k_JCZ38 * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 -d_J9Z38/dt = + f_cyan_to_J9Z38 * (alpha/beta) * 1/((time/beta) + 1) * - cyan - k_J9Z38 * J9Z38 -d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 - -Data: -433 observations of 4 variable(s) grouped in 5 datasets - -Model predictions using solution type deSolve - -Fitted in 503.222 s -Using 300, 100 iterations and 10 chains - -Variance model: Two-component variance function - -Starting values for degradation parameters: - cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 - 101.9028 -1.9055 -5.0249 -2.5646 0.6807 - f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_alpha log_beta - 4.8883 16.0676 9.3923 -0.1346 3.0364 - -Fixed degradation parameter values: -None - -Starting values for random effects (square root of initial entries in omega): - cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 -cyan_0 6.321 0.000 0.000 0.000 0.0000 -log_k_JCZ38 0.000 1.392 0.000 0.000 0.0000 -log_k_J9Z38 0.000 0.000 1.561 0.000 0.0000 -log_k_JSE76 0.000 0.000 0.000 3.614 0.0000 -f_cyan_ilr_1 0.000 0.000 0.000 0.000 0.6339 -f_cyan_ilr_2 0.000 0.000 0.000 0.000 0.0000 -f_JCZ38_qlogis 0.000 0.000 0.000 0.000 0.0000 -f_JSE76_qlogis 0.000 0.000 0.000 0.000 0.0000 -log_alpha 0.000 0.000 0.000 0.000 0.0000 -log_beta 0.000 0.000 0.000 0.000 0.0000 - f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_alpha log_beta -cyan_0 0.00 0.00 0.00 0.0000 0.0000 -log_k_JCZ38 0.00 0.00 0.00 0.0000 0.0000 -log_k_J9Z38 0.00 0.00 0.00 0.0000 0.0000 -log_k_JSE76 0.00 0.00 0.00 0.0000 0.0000 -f_cyan_ilr_1 0.00 0.00 0.00 0.0000 0.0000 -f_cyan_ilr_2 10.41 0.00 0.00 0.0000 0.0000 -f_JCZ38_qlogis 0.00 12.24 0.00 0.0000 0.0000 -f_JSE76_qlogis 0.00 0.00 15.13 0.0000 0.0000 -log_alpha 0.00 0.00 0.00 0.3701 0.0000 -log_beta 0.00 0.00 0.00 0.0000 0.5662 - -Starting values for error model parameters: -a.1 b.1 - 1 1 - -Results: - -Likelihood computed by importance sampling - AIC BIC logLik - 2248 2240 -1103 - -Optimised parameters: - est. lower upper -cyan_0 101.55545 9.920e+01 1.039e+02 -log_k_JCZ38 -2.37354 -2.928e+00 -1.819e+00 -log_k_J9Z38 -5.14736 -5.960e+00 -4.335e+00 -log_k_JSE76 -3.07802 -4.243e+00 -1.913e+00 -f_cyan_ilr_1 0.71263 3.655e-01 1.060e+00 -f_cyan_ilr_2 0.95202 2.701e-01 1.634e+00 -f_JCZ38_qlogis 3.58473 1.251e+00 5.919e+00 -f_JSE76_qlogis 19.03623 -1.037e+07 1.037e+07 -log_alpha -0.15297 -4.490e-01 1.431e-01 -log_beta 2.99230 2.706e+00 3.278e+00 -a.1 2.04816 NA NA -b.1 0.06886 NA NA -SD.log_k_JCZ38 0.56174 NA NA -SD.log_k_J9Z38 0.86509 NA NA -SD.log_k_JSE76 1.28450 NA NA -SD.f_cyan_ilr_1 0.38705 NA NA -SD.f_cyan_ilr_2 0.54153 NA NA -SD.f_JCZ38_qlogis 1.65311 NA NA -SD.f_JSE76_qlogis 7.51468 NA NA -SD.log_alpha 0.31586 NA NA -SD.log_beta 0.24696 NA NA - -Correlation is not available +log_beta 0.2135 Random effects: - est. lower upper -SD.log_k_JCZ38 0.5617 NA NA -SD.log_k_J9Z38 0.8651 NA NA -SD.log_k_JSE76 1.2845 NA NA -SD.f_cyan_ilr_1 0.3870 NA NA -SD.f_cyan_ilr_2 0.5415 NA NA -SD.f_JCZ38_qlogis 1.6531 NA NA -SD.f_JSE76_qlogis 7.5147 NA NA -SD.log_alpha 0.3159 NA NA -SD.log_beta 0.2470 NA NA + est. lower upper +SD.log_k_JCZ38 0.5082 1.390e-01 0.8774 +SD.log_k_J9Z38 0.8660 2.652e-01 1.4667 +SD.log_k_JSE76 1.3809 4.864e-01 2.2754 +SD.f_cyan_ilr_1 0.3820 1.354e-01 0.6286 +SD.f_cyan_ilr_2 0.5513 7.198e-02 1.0306 +SD.f_JCZ38_qlogis 1.8846 1.711e-02 3.7520 +SD.f_JSE76_qlogis 2.6402 -2.450e+03 2454.9447 +SD.log_alpha 0.3186 1.047e-01 0.5325 +SD.log_beta 0.2420 1.273e-02 0.4712 Variance model: - est. lower upper -a.1 2.04816 NA NA -b.1 0.06886 NA NA + est. lower upper +a.1 2.04034 1.84252 2.238 +b.1 0.06924 0.05749 0.081 Backtransformed parameters: - est. lower upper -cyan_0 1.016e+02 99.20301 103.9079 -k_JCZ38 9.315e-02 0.05349 0.1622 -k_J9Z38 5.815e-03 0.00258 0.0131 -k_JSE76 4.605e-02 0.01436 0.1477 -f_cyan_to_JCZ38 6.438e-01 NA NA -f_cyan_to_J9Z38 2.350e-01 NA NA -f_JCZ38_to_JSE76 9.730e-01 0.77745 0.9973 -f_JSE76_to_JCZ38 1.000e+00 0.00000 1.0000 -alpha 8.582e-01 0.63824 1.1538 -beta 1.993e+01 14.97621 26.5262 + est. lower upper +cyan_0 1.016e+02 9.920e+01 103.9059 +k_JCZ38 9.798e-02 5.890e-02 0.1630 +k_J9Z38 5.912e-03 2.627e-03 0.0133 +k_JSE76 4.892e-02 1.410e-02 0.1697 +f_cyan_to_JCZ38 6.432e-01 NA NA +f_cyan_to_J9Z38 2.362e-01 NA NA +f_JCZ38_to_JSE76 9.794e-01 7.160e-01 0.9989 +f_JSE76_to_JCZ38 9.995e-01 2.268e-49 1.0000 +alpha 8.581e-01 6.371e-01 1.1556 +beta 1.992e+01 1.505e+01 26.3646 Resulting formation fractions: ff -cyan_JCZ38 6.438e-01 -cyan_J9Z38 2.350e-01 -cyan_sink 1.212e-01 -JCZ38_JSE76 9.730e-01 -JCZ38_sink 2.700e-02 -JSE76_JCZ38 1.000e+00 -JSE76_sink 5.403e-09 +cyan_JCZ38 0.6432301 +cyan_J9Z38 0.2361657 +cyan_sink 0.1206042 +JCZ38_JSE76 0.9793879 +JCZ38_sink 0.0206121 +JSE76_JCZ38 0.9994559 +JSE76_sink 0.0005441 Estimated disappearance times: DT50 DT90 DT50back -cyan 24.771 271.70 81.79 -JCZ38 7.441 24.72 NA -J9Z38 119.205 395.99 NA -JSE76 15.052 50.00 NA +cyan 24.759 271.61 81.76 +JCZ38 7.075 23.50 NA +J9Z38 117.249 389.49 NA +JSE76 14.169 47.07 NA </code></pre> <p></p> @@ -3982,11 +3725,11 @@ JSE76 15.052 50.00 NA Hierarchical DFOP path 2 fit with constant variance </caption> <pre><code> -saemix version used for fitting: 3.2 -mkin version used for pre-fitting: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 17:55:53 2023 -Date of summary: Fri May 19 18:14:01 2023 +saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.4.2 +Date of fit: Fri Feb 14 07:55:32 2025 +Date of summary: Sun Feb 16 17:03:40 2025 Equations: d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -4006,25 +3749,25 @@ Data: Model predictions using solution type deSolve -Fitted in 574.8 s +Fitted in 548.554 s Using 300, 100 iterations and 10 chains Variance model: Constant variance Starting values for degradation parameters: cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 - 102.4358 -2.3107 -5.3123 -3.7120 0.6753 + 102.4380 -2.3107 -5.3123 -3.7120 0.6757 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 - 1.1462 12.4095 12.3630 -1.9317 -4.4557 + 1.1439 13.1194 12.3492 -1.9317 -4.4557 g_qlogis - -0.5648 + -0.5644 Fixed degradation parameter values: None Starting values for random effects (square root of initial entries in omega): cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 -cyan_0 4.594 0.0000 0.000 0.0 0.0000 +cyan_0 4.591 0.0000 0.000 0.0 0.0000 log_k_JCZ38 0.000 0.7966 0.000 0.0 0.0000 log_k_J9Z38 0.000 0.0000 1.561 0.0 0.0000 log_k_JSE76 0.000 0.0000 0.000 0.8 0.0000 @@ -4036,17 +3779,17 @@ log_k1 0.000 0.0000 0.000 0.0 0.0000 log_k2 0.000 0.0000 0.000 0.0 0.0000 g_qlogis 0.000 0.0000 0.000 0.0 0.0000 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 -cyan_0 0.000 0.00 0.0 0.000 0.0000 -log_k_JCZ38 0.000 0.00 0.0 0.000 0.0000 -log_k_J9Z38 0.000 0.00 0.0 0.000 0.0000 -log_k_JSE76 0.000 0.00 0.0 0.000 0.0000 -f_cyan_ilr_1 0.000 0.00 0.0 0.000 0.0000 -f_cyan_ilr_2 1.797 0.00 0.0 0.000 0.0000 -f_JCZ38_qlogis 0.000 13.85 0.0 0.000 0.0000 -f_JSE76_qlogis 0.000 0.00 14.1 0.000 0.0000 -log_k1 0.000 0.00 0.0 1.106 0.0000 -log_k2 0.000 0.00 0.0 0.000 0.6141 -g_qlogis 0.000 0.00 0.0 0.000 0.0000 +cyan_0 0.000 0.00 0.00 0.000 0.0000 +log_k_JCZ38 0.000 0.00 0.00 0.000 0.0000 +log_k_J9Z38 0.000 0.00 0.00 0.000 0.0000 +log_k_JSE76 0.000 0.00 0.00 0.000 0.0000 +f_cyan_ilr_1 0.000 0.00 0.00 0.000 0.0000 +f_cyan_ilr_2 1.797 0.00 0.00 0.000 0.0000 +f_JCZ38_qlogis 0.000 13.86 0.00 0.000 0.0000 +f_JSE76_qlogis 0.000 0.00 13.91 0.000 0.0000 +log_k1 0.000 0.00 0.00 1.106 0.0000 +log_k2 0.000 0.00 0.00 0.000 0.6141 +g_qlogis 0.000 0.00 0.00 0.000 0.0000 g_qlogis cyan_0 0.000 log_k_JCZ38 0.000 @@ -4068,45 +3811,45 @@ Results: Likelihood computed by importance sampling AIC BIC logLik - 2290 2281 -1123 + 2288 2280 -1122 Optimised parameters: - est. lower upper -cyan_0 102.6903 101.44420 103.9365 -log_k_JCZ38 -2.4018 -2.98058 -1.8230 -log_k_J9Z38 -5.1865 -5.92931 -4.4437 -log_k_JSE76 -3.0784 -4.25226 -1.9045 -f_cyan_ilr_1 0.7157 0.37625 1.0551 -f_cyan_ilr_2 0.7073 0.20136 1.2132 -f_JCZ38_qlogis 4.6797 0.43240 8.9269 -f_JSE76_qlogis 5.0080 -1.01380 11.0299 -log_k1 -1.9620 -2.62909 -1.2949 -log_k2 -4.4894 -4.94958 -4.0292 -g_qlogis -0.4658 -1.34443 0.4129 -a.1 2.7158 2.52576 2.9059 -SD.log_k_JCZ38 0.5818 0.15679 1.0067 -SD.log_k_J9Z38 0.7421 0.16751 1.3167 -SD.log_k_JSE76 1.2841 0.43247 2.1356 -SD.f_cyan_ilr_1 0.3748 0.13040 0.6192 -SD.f_cyan_ilr_2 0.4550 0.08396 0.8261 -SD.f_JCZ38_qlogis 2.0862 -0.73390 4.9062 -SD.f_JSE76_qlogis 1.9585 -3.14773 7.0647 -SD.log_k1 0.7389 0.25761 1.2201 -SD.log_k2 0.5132 0.18143 0.8450 -SD.g_qlogis 0.9870 0.35773 1.6164 + est. lower upper +cyan_0 102.7204 1.014e+02 1.040e+02 +log_k_JCZ38 -2.8925 -4.044e+00 -1.741e+00 +log_k_J9Z38 -5.1430 -5.828e+00 -4.457e+00 +log_k_JSE76 -3.5577 -4.174e+00 -2.941e+00 +f_cyan_ilr_1 0.6929 3.788e-01 1.007e+00 +f_cyan_ilr_2 0.6066 5.342e-02 1.160e+00 +f_JCZ38_qlogis 9.8071 -2.819e+03 2.838e+03 +f_JSE76_qlogis 2.2229 5.684e-01 3.877e+00 +log_k1 -1.9339 -2.609e+00 -1.258e+00 +log_k2 -4.4709 -4.935e+00 -4.007e+00 +g_qlogis -0.4987 -1.373e+00 3.757e-01 +a.1 2.7368 2.545e+00 2.928e+00 +SD.log_k_JCZ38 1.2747 4.577e-01 2.092e+00 +SD.log_k_J9Z38 0.6758 1.418e-01 1.210e+00 +SD.log_k_JSE76 0.5869 1.169e-01 1.057e+00 +SD.f_cyan_ilr_1 0.3392 1.161e-01 5.622e-01 +SD.f_cyan_ilr_2 0.4200 8.501e-02 7.550e-01 +SD.f_JCZ38_qlogis 0.8511 -1.137e+06 1.137e+06 +SD.f_JSE76_qlogis 0.3767 -5.238e-01 1.277e+00 +SD.log_k1 0.7475 2.601e-01 1.235e+00 +SD.log_k2 0.5179 1.837e-01 8.521e-01 +SD.g_qlogis 0.9817 3.553e-01 1.608e+00 Correlation: cyan_0 l__JCZ3 l__J9Z3 l__JSE7 f_cy__1 f_cy__2 f_JCZ38 f_JSE76 -log_k_JCZ38 -0.0170 -log_k_J9Z38 -0.0457 0.0016 -log_k_JSE76 -0.0046 0.1183 0.0005 -f_cyan_ilr_1 0.0079 0.0072 -0.0909 0.0003 -f_cyan_ilr_2 -0.3114 0.0343 0.1542 0.0023 -0.0519 -f_JCZ38_qlogis 0.0777 -0.0601 -0.0152 0.0080 -0.0520 -0.2524 -f_JSE76_qlogis -0.0356 0.0817 0.0073 0.0051 0.0388 0.1959 -0.6236 -log_k1 0.0848 -0.0028 0.0010 -0.0010 -0.0014 -0.0245 0.0121 -0.0177 -log_k2 0.0274 -0.0001 0.0075 0.0000 -0.0023 -0.0060 0.0000 -0.0130 -g_qlogis 0.0159 0.0002 -0.0095 0.0002 0.0029 -0.0140 -0.0001 0.0149 +log_k_JCZ38 -0.0351 +log_k_J9Z38 -0.0541 0.0043 +log_k_JSE76 -0.0078 0.0900 -0.0014 +f_cyan_ilr_1 -0.0249 0.0268 -0.0962 0.0000 +f_cyan_ilr_2 -0.3560 0.0848 0.1545 -0.0022 0.0463 +f_JCZ38_qlogis 0.2005 -0.1226 -0.0347 0.0514 -0.1840 -0.5906 +f_JSE76_qlogis -0.1638 0.1307 0.0266 0.0001 0.1645 0.5181 -0.9297 +log_k1 0.0881 -0.0071 0.0005 -0.0070 -0.0064 -0.0346 0.0316 -0.0341 +log_k2 0.0238 -0.0003 0.0082 -0.0022 -0.0017 -0.0017 -0.0002 -0.0076 +g_qlogis 0.0198 -0.0002 -0.0109 0.0034 0.0017 -0.0176 0.0044 0.0051 log_k1 log_k2 log_k_JCZ38 log_k_J9Z38 @@ -4116,56 +3859,56 @@ f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 -log_k2 0.0280 -g_qlogis -0.0278 -0.0310 +log_k2 0.0276 +g_qlogis -0.0283 -0.0309 Random effects: - est. lower upper -SD.log_k_JCZ38 0.5818 0.15679 1.0067 -SD.log_k_J9Z38 0.7421 0.16751 1.3167 -SD.log_k_JSE76 1.2841 0.43247 2.1356 -SD.f_cyan_ilr_1 0.3748 0.13040 0.6192 -SD.f_cyan_ilr_2 0.4550 0.08396 0.8261 -SD.f_JCZ38_qlogis 2.0862 -0.73390 4.9062 -SD.f_JSE76_qlogis 1.9585 -3.14773 7.0647 -SD.log_k1 0.7389 0.25761 1.2201 -SD.log_k2 0.5132 0.18143 0.8450 -SD.g_qlogis 0.9870 0.35773 1.6164 + est. lower upper +SD.log_k_JCZ38 1.2747 4.577e-01 2.092e+00 +SD.log_k_J9Z38 0.6758 1.418e-01 1.210e+00 +SD.log_k_JSE76 0.5869 1.169e-01 1.057e+00 +SD.f_cyan_ilr_1 0.3392 1.161e-01 5.622e-01 +SD.f_cyan_ilr_2 0.4200 8.501e-02 7.550e-01 +SD.f_JCZ38_qlogis 0.8511 -1.137e+06 1.137e+06 +SD.f_JSE76_qlogis 0.3767 -5.238e-01 1.277e+00 +SD.log_k1 0.7475 2.601e-01 1.235e+00 +SD.log_k2 0.5179 1.837e-01 8.521e-01 +SD.g_qlogis 0.9817 3.553e-01 1.608e+00 Variance model: est. lower upper -a.1 2.716 2.526 2.906 +a.1 2.737 2.545 2.928 Backtransformed parameters: est. lower upper -cyan_0 1.027e+02 1.014e+02 103.93649 -k_JCZ38 9.056e-02 5.076e-02 0.16154 -k_J9Z38 5.591e-03 2.660e-03 0.01175 -k_JSE76 4.603e-02 1.423e-02 0.14890 -f_cyan_to_JCZ38 6.184e-01 NA NA -f_cyan_to_J9Z38 2.248e-01 NA NA -f_JCZ38_to_JSE76 9.908e-01 6.064e-01 0.99987 -f_JSE76_to_JCZ38 9.934e-01 2.662e-01 0.99998 -k1 1.406e-01 7.214e-02 0.27393 -k2 1.123e-02 7.086e-03 0.01779 -g 3.856e-01 2.068e-01 0.60177 +cyan_0 102.72037 1.014e+02 104.00464 +k_JCZ38 0.05544 1.752e-02 0.17539 +k_J9Z38 0.00584 2.942e-03 0.01159 +k_JSE76 0.02850 1.539e-02 0.05279 +f_cyan_to_JCZ38 0.59995 NA NA +f_cyan_to_J9Z38 0.22519 NA NA +f_JCZ38_to_JSE76 0.99994 0.000e+00 1.00000 +f_JSE76_to_JCZ38 0.90229 6.384e-01 0.97971 +k1 0.14459 7.357e-02 0.28414 +k2 0.01144 7.192e-03 0.01819 +g 0.37784 2.021e-01 0.59284 Resulting formation fractions: - ff -cyan_JCZ38 0.618443 -cyan_J9Z38 0.224770 -cyan_sink 0.156787 -JCZ38_JSE76 0.990803 -JCZ38_sink 0.009197 -JSE76_JCZ38 0.993360 -JSE76_sink 0.006640 + ff +cyan_JCZ38 5.999e-01 +cyan_J9Z38 2.252e-01 +cyan_sink 1.749e-01 +JCZ38_JSE76 9.999e-01 +JCZ38_sink 5.506e-05 +JSE76_JCZ38 9.023e-01 +JSE76_sink 9.771e-02 Estimated disappearance times: - DT50 DT90 DT50back DT50_k1 DT50_k2 -cyan 21.674 161.70 48.68 4.931 61.74 -JCZ38 7.654 25.43 NA NA NA -J9Z38 123.966 411.81 NA NA NA -JSE76 15.057 50.02 NA NA NA + DT50 DT90 DT50back DT50_k1 DT50_k2 +cyan 21.93 159.83 48.11 4.794 60.6 +JCZ38 12.50 41.53 NA NA NA +J9Z38 118.69 394.27 NA NA NA +JSE76 24.32 80.78 NA NA NA </code></pre> <p></p> @@ -4173,11 +3916,11 @@ JSE76 15.057 50.02 NA NA NA Hierarchical DFOP path 2 fit with two-component error </caption> <pre><code> -saemix version used for fitting: 3.2 -mkin version used for pre-fitting: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 17:58:32 2023 -Date of summary: Fri May 19 18:14:01 2023 +saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.4.2 +Date of fit: Fri Feb 14 07:57:56 2025 +Date of summary: Sun Feb 16 17:03:40 2025 Equations: d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -4197,59 +3940,59 @@ Data: Model predictions using solution type deSolve -Fitted in 733.398 s +Fitted in 691.67 s Using 300, 100 iterations and 10 chains Variance model: Two-component variance function Starting values for degradation parameters: cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 - 101.7523 -1.5948 -5.0119 -2.2723 0.6719 + 101.7393 -1.4493 -5.0118 -2.1269 0.6720 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 - 5.1681 12.8238 12.4130 -2.0057 -4.5526 + 7.3362 13.4423 13.2659 -2.0061 -4.5527 g_qlogis - -0.5805 + -0.5806 Fixed degradation parameter values: None Starting values for random effects (square root of initial entries in omega): cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 -cyan_0 5.627 0.000 0.000 0.000 0.0000 -log_k_JCZ38 0.000 2.327 0.000 0.000 0.0000 -log_k_J9Z38 0.000 0.000 1.664 0.000 0.0000 -log_k_JSE76 0.000 0.000 0.000 4.566 0.0000 -f_cyan_ilr_1 0.000 0.000 0.000 0.000 0.6519 -f_cyan_ilr_2 0.000 0.000 0.000 0.000 0.0000 -f_JCZ38_qlogis 0.000 0.000 0.000 0.000 0.0000 -f_JSE76_qlogis 0.000 0.000 0.000 0.000 0.0000 -log_k1 0.000 0.000 0.000 0.000 0.0000 -log_k2 0.000 0.000 0.000 0.000 0.0000 -g_qlogis 0.000 0.000 0.000 0.000 0.0000 +cyan_0 5.604 0.00 0.000 0.000 0.0000 +log_k_JCZ38 0.000 2.77 0.000 0.000 0.0000 +log_k_J9Z38 0.000 0.00 1.662 0.000 0.0000 +log_k_JSE76 0.000 0.00 0.000 5.021 0.0000 +f_cyan_ilr_1 0.000 0.00 0.000 0.000 0.6519 +f_cyan_ilr_2 0.000 0.00 0.000 0.000 0.0000 +f_JCZ38_qlogis 0.000 0.00 0.000 0.000 0.0000 +f_JSE76_qlogis 0.000 0.00 0.000 0.000 0.0000 +log_k1 0.000 0.00 0.000 0.000 0.0000 +log_k2 0.000 0.00 0.000 0.000 0.0000 +g_qlogis 0.000 0.00 0.000 0.000 0.0000 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 -cyan_0 0.0 0.00 0.00 0.0000 0.0000 -log_k_JCZ38 0.0 0.00 0.00 0.0000 0.0000 -log_k_J9Z38 0.0 0.00 0.00 0.0000 0.0000 -log_k_JSE76 0.0 0.00 0.00 0.0000 0.0000 -f_cyan_ilr_1 0.0 0.00 0.00 0.0000 0.0000 -f_cyan_ilr_2 10.1 0.00 0.00 0.0000 0.0000 -f_JCZ38_qlogis 0.0 13.99 0.00 0.0000 0.0000 -f_JSE76_qlogis 0.0 0.00 14.15 0.0000 0.0000 -log_k1 0.0 0.00 0.00 0.8452 0.0000 -log_k2 0.0 0.00 0.00 0.0000 0.5968 -g_qlogis 0.0 0.00 0.00 0.0000 0.0000 +cyan_0 0.00 0.00 0.00 0.0000 0.0000 +log_k_JCZ38 0.00 0.00 0.00 0.0000 0.0000 +log_k_J9Z38 0.00 0.00 0.00 0.0000 0.0000 +log_k_JSE76 0.00 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_1 0.00 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_2 13.37 0.00 0.00 0.0000 0.0000 +f_JCZ38_qlogis 0.00 14.21 0.00 0.0000 0.0000 +f_JSE76_qlogis 0.00 0.00 14.58 0.0000 0.0000 +log_k1 0.00 0.00 0.00 0.8453 0.0000 +log_k2 0.00 0.00 0.00 0.0000 0.5969 +g_qlogis 0.00 0.00 0.00 0.0000 0.0000 g_qlogis -cyan_0 0.000 -log_k_JCZ38 0.000 -log_k_J9Z38 0.000 -log_k_JSE76 0.000 -f_cyan_ilr_1 0.000 -f_cyan_ilr_2 0.000 -f_JCZ38_qlogis 0.000 -f_JSE76_qlogis 0.000 -log_k1 0.000 -log_k2 0.000 -g_qlogis 1.691 +cyan_0 0.00 +log_k_JCZ38 0.00 +log_k_J9Z38 0.00 +log_k_JSE76 0.00 +f_cyan_ilr_1 0.00 +f_cyan_ilr_2 0.00 +f_JCZ38_qlogis 0.00 +f_JSE76_qlogis 0.00 +log_k1 0.00 +log_k2 0.00 +g_qlogis 1.69 Starting values for error model parameters: a.1 b.1 @@ -4262,42 +4005,42 @@ Likelihood computed by importance sampling 2234 2226 -1095 Optimised parameters: - est. lower upper -cyan_0 101.10667 9.903e+01 103.18265 -log_k_JCZ38 -2.49437 -3.297e+00 -1.69221 -log_k_J9Z38 -5.08171 -5.875e+00 -4.28846 -log_k_JSE76 -3.20072 -4.180e+00 -2.22163 -f_cyan_ilr_1 0.71059 3.639e-01 1.05727 -f_cyan_ilr_2 1.15398 2.981e-01 2.00984 -f_JCZ38_qlogis 3.18027 1.056e+00 5.30452 -f_JSE76_qlogis 5.61578 -2.505e+01 36.28077 -log_k1 -2.38875 -2.517e+00 -2.26045 -log_k2 -4.67246 -4.928e+00 -4.41715 -g_qlogis -0.28231 -1.135e+00 0.57058 -a.1 2.08190 1.856e+00 2.30785 -b.1 0.06114 5.015e-02 0.07214 -SD.log_k_JCZ38 0.84622 2.637e-01 1.42873 -SD.log_k_J9Z38 0.84564 2.566e-01 1.43464 -SD.log_k_JSE76 1.04385 3.242e-01 1.76351 -SD.f_cyan_ilr_1 0.38568 1.362e-01 0.63514 -SD.f_cyan_ilr_2 0.68046 7.166e-02 1.28925 -SD.f_JCZ38_qlogis 1.25244 -4.213e-02 2.54700 -SD.f_JSE76_qlogis 0.28202 -1.515e+03 1515.87968 -SD.log_k2 0.25749 7.655e-02 0.43843 -SD.g_qlogis 0.94535 3.490e-01 1.54174 + est. lower upper +cyan_0 101.25496 99.14662 103.36331 +log_k_JCZ38 -2.55593 -3.32972 -1.78215 +log_k_J9Z38 -5.07103 -5.85423 -4.28783 +log_k_JSE76 -3.25468 -4.17577 -2.33360 +f_cyan_ilr_1 0.70139 0.35924 1.04355 +f_cyan_ilr_2 1.07712 0.17789 1.97636 +f_JCZ38_qlogis 3.57483 0.05990 7.08976 +f_JSE76_qlogis 4.54884 -7.25628 16.35395 +log_k1 -2.38201 -2.51639 -2.24763 +log_k2 -4.66741 -4.91865 -4.41617 +g_qlogis -0.28446 -1.14192 0.57300 +a.1 2.05925 1.86481 2.25369 +b.1 0.06172 0.05062 0.07282 +SD.log_k_JCZ38 0.81137 0.25296 1.36977 +SD.log_k_J9Z38 0.83542 0.25395 1.41689 +SD.log_k_JSE76 0.97903 0.30100 1.65707 +SD.f_cyan_ilr_1 0.37878 0.13374 0.62382 +SD.f_cyan_ilr_2 0.67274 0.10102 1.24446 +SD.f_JCZ38_qlogis 1.35327 -0.42359 3.13012 +SD.f_JSE76_qlogis 1.43956 -19.14972 22.02884 +SD.log_k2 0.25329 0.07521 0.43138 +SD.g_qlogis 0.95167 0.35149 1.55184 Correlation: cyan_0 l__JCZ3 l__J9Z3 l__JSE7 f_cy__1 f_cy__2 f_JCZ38 f_JSE76 -log_k_JCZ38 -0.0086 -log_k_J9Z38 -0.0363 -0.0007 -log_k_JSE76 0.0015 0.1210 -0.0017 -f_cyan_ilr_1 -0.0048 0.0095 -0.0572 0.0030 -f_cyan_ilr_2 -0.4788 0.0328 0.1143 0.0027 -0.0316 -f_JCZ38_qlogis 0.0736 -0.0664 -0.0137 0.0145 -0.0444 -0.2175 -f_JSE76_qlogis -0.0137 0.0971 0.0035 0.0009 0.0293 0.1333 -0.6767 -log_k1 0.2345 -0.0350 -0.0099 -0.0113 -0.0126 -0.1652 0.1756 -0.2161 -log_k2 0.0440 -0.0133 0.0199 -0.0040 -0.0097 -0.0119 0.0604 -0.1306 -g_qlogis 0.0438 0.0078 -0.0123 0.0029 0.0046 -0.0363 -0.0318 0.0736 +log_k_JCZ38 -0.0265 +log_k_J9Z38 -0.0392 0.0024 +log_k_JSE76 0.0011 0.1220 -0.0016 +f_cyan_ilr_1 -0.0161 0.0217 -0.0552 0.0034 +f_cyan_ilr_2 -0.4718 0.0829 0.1102 0.0042 0.0095 +f_JCZ38_qlogis 0.1609 -0.1318 -0.0277 0.0081 -0.1040 -0.4559 +f_JSE76_qlogis -0.1289 0.1494 0.0219 0.0012 0.1004 0.4309 -0.8543 +log_k1 0.2618 -0.0739 -0.0167 -0.0148 -0.0444 -0.2768 0.3518 -0.3818 +log_k2 0.0603 -0.0217 0.0174 -0.0058 -0.0197 -0.0533 0.0923 -0.1281 +g_qlogis 0.0362 0.0115 -0.0111 0.0040 0.0095 -0.0116 -0.0439 0.0651 log_k1 log_k2 log_k_JCZ38 log_k_J9Z38 @@ -4307,56 +4050,56 @@ f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 -log_k2 0.3198 -g_qlogis -0.1666 -0.0954 +log_k2 0.3269 +g_qlogis -0.1656 -0.0928 Random effects: - est. lower upper -SD.log_k_JCZ38 0.8462 2.637e-01 1.4287 -SD.log_k_J9Z38 0.8456 2.566e-01 1.4346 -SD.log_k_JSE76 1.0439 3.242e-01 1.7635 -SD.f_cyan_ilr_1 0.3857 1.362e-01 0.6351 -SD.f_cyan_ilr_2 0.6805 7.166e-02 1.2893 -SD.f_JCZ38_qlogis 1.2524 -4.213e-02 2.5470 -SD.f_JSE76_qlogis 0.2820 -1.515e+03 1515.8797 -SD.log_k2 0.2575 7.655e-02 0.4384 -SD.g_qlogis 0.9453 3.490e-01 1.5417 + est. lower upper +SD.log_k_JCZ38 0.8114 0.25296 1.3698 +SD.log_k_J9Z38 0.8354 0.25395 1.4169 +SD.log_k_JSE76 0.9790 0.30100 1.6571 +SD.f_cyan_ilr_1 0.3788 0.13374 0.6238 +SD.f_cyan_ilr_2 0.6727 0.10102 1.2445 +SD.f_JCZ38_qlogis 1.3533 -0.42359 3.1301 +SD.f_JSE76_qlogis 1.4396 -19.14972 22.0288 +SD.log_k2 0.2533 0.07521 0.4314 +SD.g_qlogis 0.9517 0.35149 1.5518 Variance model: est. lower upper -a.1 2.08190 1.85595 2.30785 -b.1 0.06114 0.05015 0.07214 +a.1 2.05925 1.86481 2.25369 +b.1 0.06172 0.05062 0.07282 Backtransformed parameters: est. lower upper -cyan_0 1.011e+02 9.903e+01 103.18265 -k_JCZ38 8.255e-02 3.701e-02 0.18411 -k_J9Z38 6.209e-03 2.809e-03 0.01373 -k_JSE76 4.073e-02 1.530e-02 0.10843 -f_cyan_to_JCZ38 6.608e-01 NA NA -f_cyan_to_J9Z38 2.419e-01 NA NA -f_JCZ38_to_JSE76 9.601e-01 7.419e-01 0.99506 -f_JSE76_to_JCZ38 9.964e-01 1.322e-11 1.00000 -k1 9.174e-02 8.070e-02 0.10430 -k2 9.349e-03 7.243e-03 0.01207 -g 4.299e-01 2.432e-01 0.63890 +cyan_0 1.013e+02 9.915e+01 103.36331 +k_JCZ38 7.762e-02 3.580e-02 0.16828 +k_J9Z38 6.276e-03 2.868e-03 0.01373 +k_JSE76 3.859e-02 1.536e-02 0.09695 +f_cyan_to_JCZ38 6.520e-01 NA NA +f_cyan_to_J9Z38 2.418e-01 NA NA +f_JCZ38_to_JSE76 9.727e-01 5.150e-01 0.99917 +f_JSE76_to_JCZ38 9.895e-01 7.052e-04 1.00000 +k1 9.236e-02 8.075e-02 0.10565 +k2 9.397e-03 7.309e-03 0.01208 +g 4.294e-01 2.420e-01 0.63945 Resulting formation fractions: - ff -cyan_JCZ38 0.660808 -cyan_J9Z38 0.241904 -cyan_sink 0.097288 -JCZ38_JSE76 0.960085 -JCZ38_sink 0.039915 -JSE76_JCZ38 0.996373 -JSE76_sink 0.003627 + ff +cyan_JCZ38 0.65203 +cyan_J9Z38 0.24181 +cyan_sink 0.10616 +JCZ38_JSE76 0.97274 +JCZ38_sink 0.02726 +JSE76_JCZ38 0.98953 +JSE76_sink 0.01047 Estimated disappearance times: - DT50 DT90 DT50back DT50_k1 DT50_k2 -cyan 24.359 186.18 56.05 7.555 74.14 -JCZ38 8.397 27.89 NA NA NA -J9Z38 111.631 370.83 NA NA NA -JSE76 17.017 56.53 NA NA NA + DT50 DT90 DT50back DT50_k1 DT50_k2 +cyan 24.26 185.34 55.79 7.504 73.77 +JCZ38 8.93 29.66 NA NA NA +J9Z38 110.45 366.89 NA NA NA +JSE76 17.96 59.66 NA NA NA </code></pre> <p></p> @@ -4364,11 +4107,11 @@ JSE76 17.017 56.53 NA NA NA Hierarchical SFORB path 2 fit with constant variance </caption> <pre><code> -saemix version used for fitting: 3.2 -mkin version used for pre-fitting: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 17:55:51 2023 -Date of summary: Fri May 19 18:14:01 2023 +saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.4.2 +Date of fit: Fri Feb 14 07:55:26 2025 +Date of summary: Sun Feb 16 17:03:40 2025 Equations: d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound * @@ -4386,20 +4129,20 @@ Data: Model predictions using solution type deSolve -Fitted in 572.747 s +Fitted in 542.162 s Using 300, 100 iterations and 10 chains Variance model: Constant variance Starting values for degradation parameters: cyan_free_0 log_k_cyan_free log_k_cyan_free_bound - 102.4394 -2.7673 -2.8942 + 102.4395 -2.7673 -2.8942 log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 -3.6201 -2.3107 -5.3123 log_k_JSE76 f_cyan_ilr_1 f_cyan_ilr_2 -3.7120 0.6754 1.1448 f_JCZ38_qlogis f_JSE76_qlogis - 13.2672 13.3538 + 14.8408 15.4734 Fixed degradation parameter values: None @@ -4430,17 +4173,17 @@ f_cyan_ilr_2 0.000 0.0000 0.000 0.0 f_JCZ38_qlogis 0.000 0.0000 0.000 0.0 f_JSE76_qlogis 0.000 0.0000 0.000 0.0 f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis -cyan_free_0 0.0000 0.000 0.00 0.00 -log_k_cyan_free 0.0000 0.000 0.00 0.00 -log_k_cyan_free_bound 0.0000 0.000 0.00 0.00 -log_k_cyan_bound_free 0.0000 0.000 0.00 0.00 -log_k_JCZ38 0.0000 0.000 0.00 0.00 -log_k_J9Z38 0.0000 0.000 0.00 0.00 -log_k_JSE76 0.0000 0.000 0.00 0.00 -f_cyan_ilr_1 0.6349 0.000 0.00 0.00 -f_cyan_ilr_2 0.0000 1.797 0.00 0.00 -f_JCZ38_qlogis 0.0000 0.000 13.84 0.00 -f_JSE76_qlogis 0.0000 0.000 0.00 14.66 +cyan_free_0 0.0000 0.000 0.0 0.00 +log_k_cyan_free 0.0000 0.000 0.0 0.00 +log_k_cyan_free_bound 0.0000 0.000 0.0 0.00 +log_k_cyan_bound_free 0.0000 0.000 0.0 0.00 +log_k_JCZ38 0.0000 0.000 0.0 0.00 +log_k_J9Z38 0.0000 0.000 0.0 0.00 +log_k_JSE76 0.0000 0.000 0.0 0.00 +f_cyan_ilr_1 0.6349 0.000 0.0 0.00 +f_cyan_ilr_2 0.0000 1.797 0.0 0.00 +f_JCZ38_qlogis 0.0000 0.000 15.6 0.00 +f_JSE76_qlogis 0.0000 0.000 0.0 17.52 Starting values for error model parameters: a.1 @@ -4450,45 +4193,45 @@ Results: Likelihood computed by importance sampling AIC BIC logLik - 2284 2275 -1120 + 2283 2275 -1120 Optimised parameters: - est. lower upper -cyan_free_0 102.7730 1.015e+02 1.041e+02 -log_k_cyan_free -2.8530 -3.167e+00 -2.539e+00 -log_k_cyan_free_bound -2.7326 -3.543e+00 -1.922e+00 -log_k_cyan_bound_free -3.5582 -4.126e+00 -2.990e+00 -log_k_JCZ38 -2.3810 -2.921e+00 -1.841e+00 -log_k_J9Z38 -5.2301 -5.963e+00 -4.497e+00 -log_k_JSE76 -3.0286 -4.286e+00 -1.771e+00 -f_cyan_ilr_1 0.7081 3.733e-01 1.043e+00 -f_cyan_ilr_2 0.5847 7.846e-03 1.162e+00 -f_JCZ38_qlogis 9.5676 -1.323e+03 1.342e+03 -f_JSE76_qlogis 3.7042 7.254e-02 7.336e+00 -a.1 2.7222 2.532e+00 2.913e+00 -SD.log_k_cyan_free 0.3338 1.086e-01 5.589e-01 -SD.log_k_cyan_free_bound 0.8888 3.023e-01 1.475e+00 -SD.log_k_cyan_bound_free 0.6220 2.063e-01 1.038e+00 -SD.log_k_JCZ38 0.5221 1.334e-01 9.108e-01 -SD.log_k_J9Z38 0.7104 1.371e-01 1.284e+00 -SD.log_k_JSE76 1.3837 4.753e-01 2.292e+00 -SD.f_cyan_ilr_1 0.3620 1.248e-01 5.992e-01 -SD.f_cyan_ilr_2 0.4259 8.145e-02 7.704e-01 -SD.f_JCZ38_qlogis 3.5332 -1.037e+05 1.037e+05 -SD.f_JSE76_qlogis 1.6990 -2.771e-01 3.675e+00 + est. lower upper +cyan_free_0 102.6517 101.40815 103.8952 +log_k_cyan_free -2.8729 -3.18649 -2.5593 +log_k_cyan_free_bound -2.7803 -3.60525 -1.9552 +log_k_cyan_bound_free -3.5845 -4.16644 -3.0026 +log_k_JCZ38 -2.3411 -2.89698 -1.7852 +log_k_J9Z38 -5.2487 -6.01271 -4.4847 +log_k_JSE76 -3.0259 -4.28274 -1.7690 +f_cyan_ilr_1 0.7289 0.38214 1.0756 +f_cyan_ilr_2 0.6891 0.18277 1.1954 +f_JCZ38_qlogis 4.2162 0.47015 7.9622 +f_JSE76_qlogis 5.8911 -20.19088 31.9730 +a.1 2.7159 2.52587 2.9060 +SD.log_k_cyan_free 0.3354 0.10979 0.5610 +SD.log_k_cyan_free_bound 0.9061 0.30969 1.5025 +SD.log_k_cyan_bound_free 0.6376 0.21229 1.0628 +SD.log_k_JCZ38 0.5499 0.14533 0.9545 +SD.log_k_J9Z38 0.7457 0.15106 1.3404 +SD.log_k_JSE76 1.3822 0.47329 2.2912 +SD.f_cyan_ilr_1 0.3820 0.13280 0.6313 +SD.f_cyan_ilr_2 0.4317 0.06803 0.7953 +SD.f_JCZ38_qlogis 1.8258 -0.25423 3.9059 +SD.f_JSE76_qlogis 2.2348 -83.33679 87.8065 Correlation: cyn_f_0 lg_k_c_ lg_k_cyn_f_ lg_k_cyn_b_ l__JCZ3 l__J9Z3 -log_k_cyan_free 0.2126 -log_k_cyan_free_bound 0.0894 0.0871 -log_k_cyan_bound_free 0.0033 0.0410 0.0583 -log_k_JCZ38 -0.0708 -0.0280 -0.0147 0.0019 -log_k_J9Z38 -0.0535 -0.0138 0.0012 0.0148 0.0085 -log_k_JSE76 -0.0066 -0.0030 -0.0021 -0.0005 0.1090 0.0010 -f_cyan_ilr_1 -0.0364 -0.0157 -0.0095 -0.0015 0.0458 -0.0960 -f_cyan_ilr_2 -0.3814 -0.1104 -0.0423 0.0146 0.1540 0.1526 -f_JCZ38_qlogis 0.2507 0.0969 0.0482 -0.0097 -0.2282 -0.0363 -f_JSE76_qlogis -0.1648 -0.0710 -0.0443 -0.0087 0.2002 0.0226 +log_k_cyan_free 0.1944 +log_k_cyan_free_bound 0.0815 0.0814 +log_k_cyan_bound_free 0.0106 0.0426 0.0585 +log_k_JCZ38 -0.0231 -0.0106 -0.0089 -0.0051 +log_k_J9Z38 -0.0457 -0.0108 0.0019 0.0129 0.0032 +log_k_JSE76 -0.0054 -0.0024 -0.0017 -0.0005 0.1108 0.0009 +f_cyan_ilr_1 0.0051 -0.0005 -0.0035 -0.0056 0.0131 -0.0967 +f_cyan_ilr_2 -0.3182 -0.0771 -0.0309 -0.0038 0.0680 0.1643 +f_JCZ38_qlogis 0.0834 0.0369 0.0302 0.0172 -0.1145 -0.0204 +f_JSE76_qlogis -0.0553 -0.0365 -0.0441 -0.0414 0.1579 0.0175 l__JSE7 f_cy__1 f_cy__2 f_JCZ38 log_k_cyan_free log_k_cyan_free_bound @@ -4496,63 +4239,63 @@ log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76 -f_cyan_ilr_1 0.0001 -f_cyan_ilr_2 0.0031 0.0586 -f_JCZ38_qlogis 0.0023 -0.1867 -0.6255 -f_JSE76_qlogis 0.0082 0.1356 0.4519 -0.7951 +f_cyan_ilr_1 -0.0002 +f_cyan_ilr_2 0.0020 -0.0415 +f_JCZ38_qlogis 0.0052 -0.0665 -0.3437 +f_JSE76_qlogis 0.0066 0.0635 0.3491 -0.7487 Random effects: - est. lower upper -SD.log_k_cyan_free 0.3338 1.086e-01 5.589e-01 -SD.log_k_cyan_free_bound 0.8888 3.023e-01 1.475e+00 -SD.log_k_cyan_bound_free 0.6220 2.063e-01 1.038e+00 -SD.log_k_JCZ38 0.5221 1.334e-01 9.108e-01 -SD.log_k_J9Z38 0.7104 1.371e-01 1.284e+00 -SD.log_k_JSE76 1.3837 4.753e-01 2.292e+00 -SD.f_cyan_ilr_1 0.3620 1.248e-01 5.992e-01 -SD.f_cyan_ilr_2 0.4259 8.145e-02 7.704e-01 -SD.f_JCZ38_qlogis 3.5332 -1.037e+05 1.037e+05 -SD.f_JSE76_qlogis 1.6990 -2.771e-01 3.675e+00 + est. lower upper +SD.log_k_cyan_free 0.3354 0.10979 0.5610 +SD.log_k_cyan_free_bound 0.9061 0.30969 1.5025 +SD.log_k_cyan_bound_free 0.6376 0.21229 1.0628 +SD.log_k_JCZ38 0.5499 0.14533 0.9545 +SD.log_k_J9Z38 0.7457 0.15106 1.3404 +SD.log_k_JSE76 1.3822 0.47329 2.2912 +SD.f_cyan_ilr_1 0.3820 0.13280 0.6313 +SD.f_cyan_ilr_2 0.4317 0.06803 0.7953 +SD.f_JCZ38_qlogis 1.8258 -0.25423 3.9059 +SD.f_JSE76_qlogis 2.2348 -83.33679 87.8065 Variance model: est. lower upper -a.1 2.722 2.532 2.913 +a.1 2.716 2.526 2.906 Backtransformed parameters: est. lower upper -cyan_free_0 1.028e+02 1.015e+02 104.06475 -k_cyan_free 5.767e-02 4.213e-02 0.07894 -k_cyan_free_bound 6.505e-02 2.892e-02 0.14633 -k_cyan_bound_free 2.849e-02 1.614e-02 0.05028 -k_JCZ38 9.246e-02 5.390e-02 0.15859 -k_J9Z38 5.353e-03 2.572e-03 0.01114 -k_JSE76 4.838e-02 1.376e-02 0.17009 -f_cyan_free_to_JCZ38 6.011e-01 5.028e-01 0.83792 -f_cyan_free_to_J9Z38 2.208e-01 5.028e-01 0.83792 -f_JCZ38_to_JSE76 9.999e-01 0.000e+00 1.00000 -f_JSE76_to_JCZ38 9.760e-01 5.181e-01 0.99935 +cyan_free_0 1.027e+02 1.014e+02 103.89517 +k_cyan_free 5.654e-02 4.132e-02 0.07736 +k_cyan_free_bound 6.202e-02 2.718e-02 0.14153 +k_cyan_bound_free 2.775e-02 1.551e-02 0.04966 +k_JCZ38 9.622e-02 5.519e-02 0.16777 +k_J9Z38 5.254e-03 2.447e-03 0.01128 +k_JSE76 4.852e-02 1.380e-02 0.17051 +f_cyan_free_to_JCZ38 6.197e-01 5.643e-01 0.84429 +f_cyan_free_to_J9Z38 2.211e-01 5.643e-01 0.84429 +f_JCZ38_to_JSE76 9.855e-01 6.154e-01 0.99965 +f_JSE76_to_JCZ38 9.972e-01 1.703e-09 1.00000 Estimated Eigenvalues of SFORB model(s): cyan_b1 cyan_b2 cyan_g -0.13942 0.01178 0.35948 +0.13466 0.01165 0.36490 Resulting formation fractions: - ff -cyan_free_JCZ38 6.011e-01 -cyan_free_J9Z38 2.208e-01 -cyan_free_sink 1.780e-01 -cyan_free 1.000e+00 -JCZ38_JSE76 9.999e-01 -JCZ38_sink 6.996e-05 -JSE76_JCZ38 9.760e-01 -JSE76_sink 2.403e-02 + ff +cyan_free_JCZ38 0.619745 +cyan_free_J9Z38 0.221083 +cyan_free_sink 0.159172 +cyan_free 1.000000 +JCZ38_JSE76 0.985460 +JCZ38_sink 0.014540 +JSE76_JCZ38 0.997244 +JSE76_sink 0.002756 Estimated disappearance times: DT50 DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 -cyan 23.390 157.60 47.44 4.971 58.82 -JCZ38 7.497 24.90 NA NA NA -J9Z38 129.482 430.13 NA NA NA -JSE76 14.326 47.59 NA NA NA +cyan 23.293 158.67 47.77 5.147 59.5 +JCZ38 7.203 23.93 NA NA NA +J9Z38 131.918 438.22 NA NA NA +JSE76 14.287 47.46 NA NA NA </code></pre> <p></p> @@ -4560,11 +4303,11 @@ JSE76 14.326 47.59 NA NA NA Hierarchical SFORB path 2 fit with two-component error </caption> <pre><code> -saemix version used for fitting: 3.2 -mkin version used for pre-fitting: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 17:58:21 2023 -Date of summary: Fri May 19 18:14:01 2023 +saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.4.2 +Date of fit: Fri Feb 14 07:58:06 2025 +Date of summary: Sun Feb 16 17:03:40 2025 Equations: d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound * @@ -4582,20 +4325,20 @@ Data: Model predictions using solution type deSolve -Fitted in 722.847 s +Fitted in 701.582 s Using 300, 100 iterations and 10 chains Variance model: Two-component variance function Starting values for degradation parameters: cyan_free_0 log_k_cyan_free log_k_cyan_free_bound - 101.751 -2.837 -3.016 + 101.7511 -2.8370 -3.0162 log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 - -3.660 -2.299 -5.313 + -3.6600 -2.2988 -5.3129 log_k_JSE76 f_cyan_ilr_1 f_cyan_ilr_2 - -3.699 0.672 5.873 + -3.6991 0.6722 4.8596 f_JCZ38_qlogis f_JSE76_qlogis - 13.216 13.338 + 13.4678 14.2149 Fixed degradation parameter values: None @@ -4626,17 +4369,17 @@ f_cyan_ilr_2 0.000 0.0000 0.000 0.0000 f_JCZ38_qlogis 0.000 0.0000 0.000 0.0000 f_JSE76_qlogis 0.000 0.0000 0.000 0.0000 f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis -cyan_free_0 0.0000 0.00 0.00 0.00 -log_k_cyan_free 0.0000 0.00 0.00 0.00 -log_k_cyan_free_bound 0.0000 0.00 0.00 0.00 -log_k_cyan_bound_free 0.0000 0.00 0.00 0.00 -log_k_JCZ38 0.0000 0.00 0.00 0.00 -log_k_J9Z38 0.0000 0.00 0.00 0.00 -log_k_JSE76 0.0000 0.00 0.00 0.00 -f_cyan_ilr_1 0.6519 0.00 0.00 0.00 -f_cyan_ilr_2 0.0000 10.78 0.00 0.00 -f_JCZ38_qlogis 0.0000 0.00 13.96 0.00 -f_JSE76_qlogis 0.0000 0.00 0.00 14.69 +cyan_free_0 0.0000 0.000 0.00 0.00 +log_k_cyan_free 0.0000 0.000 0.00 0.00 +log_k_cyan_free_bound 0.0000 0.000 0.00 0.00 +log_k_cyan_bound_free 0.0000 0.000 0.00 0.00 +log_k_JCZ38 0.0000 0.000 0.00 0.00 +log_k_J9Z38 0.0000 0.000 0.00 0.00 +log_k_JSE76 0.0000 0.000 0.00 0.00 +f_cyan_ilr_1 0.6518 0.000 0.00 0.00 +f_cyan_ilr_2 0.0000 9.981 0.00 0.00 +f_JCZ38_qlogis 0.0000 0.000 14.26 0.00 +f_JSE76_qlogis 0.0000 0.000 0.00 16.17 Starting values for error model parameters: a.1 b.1 @@ -4646,45 +4389,45 @@ Results: Likelihood computed by importance sampling AIC BIC logLik - 2240 2232 -1098 + 2240 2231 -1098 Optimised parameters: - est. lower upper -cyan_free_0 101.10205 98.99221 103.2119 -log_k_cyan_free -3.16929 -3.61395 -2.7246 -log_k_cyan_free_bound -3.38259 -3.63022 -3.1350 -log_k_cyan_bound_free -3.81075 -4.13888 -3.4826 -log_k_JCZ38 -2.42057 -3.00756 -1.8336 -log_k_J9Z38 -5.07501 -5.85138 -4.2986 -log_k_JSE76 -3.12442 -4.21277 -2.0361 -f_cyan_ilr_1 0.70577 0.35788 1.0537 -f_cyan_ilr_2 1.14824 0.15810 2.1384 -f_JCZ38_qlogis 3.52245 0.43257 6.6123 -f_JSE76_qlogis 5.65140 -21.22295 32.5257 -a.1 2.07062 1.84329 2.2980 -b.1 0.06227 0.05124 0.0733 -SD.log_k_cyan_free 0.49468 0.18566 0.8037 -SD.log_k_cyan_bound_free 0.28972 0.07188 0.5076 -SD.log_k_JCZ38 0.58852 0.16800 1.0090 -SD.log_k_J9Z38 0.82500 0.24730 1.4027 -SD.log_k_JSE76 1.19201 0.40313 1.9809 -SD.f_cyan_ilr_1 0.38534 0.13640 0.6343 -SD.f_cyan_ilr_2 0.72463 0.10076 1.3485 -SD.f_JCZ38_qlogis 1.38223 -0.20997 2.9744 -SD.f_JSE76_qlogis 2.07989 -72.53027 76.6901 + est. lower upper +cyan_free_0 100.73014 9.873e+01 1.027e+02 +log_k_cyan_free -3.19634 -3.641e+00 -2.752e+00 +log_k_cyan_free_bound -3.43533 -3.674e+00 -3.197e+00 +log_k_cyan_bound_free -3.83282 -4.163e+00 -3.503e+00 +log_k_JCZ38 -2.51065 -3.225e+00 -1.796e+00 +log_k_J9Z38 -5.02539 -5.825e+00 -4.226e+00 +log_k_JSE76 -3.24777 -4.163e+00 -2.333e+00 +f_cyan_ilr_1 0.70640 3.562e-01 1.057e+00 +f_cyan_ilr_2 1.42704 3.170e-01 2.537e+00 +f_JCZ38_qlogis 2.84779 1.042e+00 4.654e+00 +f_JSE76_qlogis 8.63674 -6.407e+02 6.580e+02 +a.1 2.07082 1.877e+00 2.265e+00 +b.1 0.06227 5.098e-02 7.355e-02 +SD.log_k_cyan_free 0.49674 1.865e-01 8.069e-01 +SD.log_k_cyan_bound_free 0.28537 6.809e-02 5.027e-01 +SD.log_k_JCZ38 0.74846 2.305e-01 1.266e+00 +SD.log_k_J9Z38 0.86077 2.713e-01 1.450e+00 +SD.log_k_JSE76 0.97613 3.030e-01 1.649e+00 +SD.f_cyan_ilr_1 0.38994 1.382e-01 6.417e-01 +SD.f_cyan_ilr_2 0.82869 3.917e-02 1.618e+00 +SD.f_JCZ38_qlogis 1.05000 -2.808e-02 2.128e+00 +SD.f_JSE76_qlogis 0.44681 -3.985e+05 3.985e+05 Correlation: cyn_f_0 lg_k_c_ lg_k_cyn_f_ lg_k_cyn_b_ l__JCZ3 l__J9Z3 -log_k_cyan_free 0.1117 -log_k_cyan_free_bound 0.1763 0.1828 -log_k_cyan_bound_free 0.0120 0.0593 0.5030 -log_k_JCZ38 -0.0459 -0.0230 -0.0931 -0.0337 -log_k_J9Z38 -0.0381 -0.0123 -0.0139 0.0237 0.0063 -log_k_JSE76 -0.0044 -0.0038 -0.0175 -0.0072 0.1120 0.0003 -f_cyan_ilr_1 -0.0199 -0.0087 -0.0407 -0.0233 0.0268 -0.0552 -f_cyan_ilr_2 -0.4806 -0.1015 -0.2291 -0.0269 0.1156 0.1113 -f_JCZ38_qlogis 0.1805 0.0825 0.3085 0.0963 -0.1674 -0.0314 -f_JSE76_qlogis -0.1586 -0.0810 -0.3560 -0.1563 0.2025 0.0278 +log_k_cyan_free 0.0936 +log_k_cyan_free_bound 0.1302 0.1627 +log_k_cyan_bound_free 0.0029 0.0525 0.5181 +log_k_JCZ38 -0.0116 -0.0077 -0.0430 -0.0236 +log_k_J9Z38 -0.0192 -0.0077 -0.0048 0.0229 -0.0005 +log_k_JSE76 0.0007 -0.0020 -0.0134 -0.0072 0.1225 -0.0016 +f_cyan_ilr_1 -0.0118 -0.0027 -0.0132 -0.0118 0.0127 -0.0505 +f_cyan_ilr_2 -0.4643 -0.0762 -0.1245 0.0137 0.0497 0.1003 +f_JCZ38_qlogis 0.0710 0.0371 0.1826 0.0925 -0.0869 -0.0130 +f_JSE76_qlogis -0.0367 -0.0270 -0.2274 -0.1865 0.1244 0.0098 l__JSE7 f_cy__1 f_cy__2 f_JCZ38 log_k_cyan_free log_k_cyan_free_bound @@ -4692,63 +4435,63 @@ log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76 -f_cyan_ilr_1 0.0024 -f_cyan_ilr_2 0.0087 0.0172 -f_JCZ38_qlogis -0.0016 -0.1047 -0.4656 -f_JSE76_qlogis 0.0119 0.1034 0.4584 -0.8137 +f_cyan_ilr_1 0.0036 +f_cyan_ilr_2 0.0050 -0.0201 +f_JCZ38_qlogis 0.0142 -0.0529 -0.2698 +f_JSE76_qlogis 0.0064 0.0345 0.2015 -0.7058 Random effects: - est. lower upper -SD.log_k_cyan_free 0.4947 0.18566 0.8037 -SD.log_k_cyan_bound_free 0.2897 0.07188 0.5076 -SD.log_k_JCZ38 0.5885 0.16800 1.0090 -SD.log_k_J9Z38 0.8250 0.24730 1.4027 -SD.log_k_JSE76 1.1920 0.40313 1.9809 -SD.f_cyan_ilr_1 0.3853 0.13640 0.6343 -SD.f_cyan_ilr_2 0.7246 0.10076 1.3485 -SD.f_JCZ38_qlogis 1.3822 -0.20997 2.9744 -SD.f_JSE76_qlogis 2.0799 -72.53027 76.6901 + est. lower upper +SD.log_k_cyan_free 0.4967 1.865e-01 8.069e-01 +SD.log_k_cyan_bound_free 0.2854 6.809e-02 5.027e-01 +SD.log_k_JCZ38 0.7485 2.305e-01 1.266e+00 +SD.log_k_J9Z38 0.8608 2.713e-01 1.450e+00 +SD.log_k_JSE76 0.9761 3.030e-01 1.649e+00 +SD.f_cyan_ilr_1 0.3899 1.382e-01 6.417e-01 +SD.f_cyan_ilr_2 0.8287 3.917e-02 1.618e+00 +SD.f_JCZ38_qlogis 1.0500 -2.808e-02 2.128e+00 +SD.f_JSE76_qlogis 0.4468 -3.985e+05 3.985e+05 Variance model: - est. lower upper -a.1 2.07062 1.84329 2.2980 -b.1 0.06227 0.05124 0.0733 + est. lower upper +a.1 2.07082 1.87680 2.26483 +b.1 0.06227 0.05098 0.07355 Backtransformed parameters: - est. lower upper -cyan_free_0 1.011e+02 9.899e+01 103.21190 -k_cyan_free 4.203e-02 2.695e-02 0.06557 -k_cyan_free_bound 3.396e-02 2.651e-02 0.04350 -k_cyan_bound_free 2.213e-02 1.594e-02 0.03073 -k_JCZ38 8.887e-02 4.941e-02 0.15984 -k_J9Z38 6.251e-03 2.876e-03 0.01359 -k_JSE76 4.396e-02 1.481e-02 0.13054 -f_cyan_free_to_JCZ38 6.590e-01 5.557e-01 0.95365 -f_cyan_free_to_J9Z38 2.429e-01 5.557e-01 0.95365 -f_JCZ38_to_JSE76 9.713e-01 6.065e-01 0.99866 -f_JSE76_to_JCZ38 9.965e-01 6.067e-10 1.00000 + est. lower upper +cyan_free_0 1.007e+02 9.873e+01 102.72898 +k_cyan_free 4.091e-02 2.623e-02 0.06382 +k_cyan_free_bound 3.221e-02 2.537e-02 0.04090 +k_cyan_bound_free 2.165e-02 1.557e-02 0.03011 +k_JCZ38 8.122e-02 3.975e-02 0.16594 +k_J9Z38 6.569e-03 2.954e-03 0.01461 +k_JSE76 3.886e-02 1.556e-02 0.09703 +f_cyan_free_to_JCZ38 6.785e-01 6.102e-01 0.97309 +f_cyan_free_to_J9Z38 2.498e-01 6.102e-01 0.97309 +f_JCZ38_to_JSE76 9.452e-01 7.392e-01 0.99056 +f_JSE76_to_JCZ38 9.998e-01 5.580e-279 1.00000 Estimated Eigenvalues of SFORB model(s): cyan_b1 cyan_b2 cyan_g -0.08749 0.01063 0.40855 +0.08426 0.01051 0.41220 Resulting formation fractions: - ff -cyan_free_JCZ38 0.65905 -cyan_free_J9Z38 0.24291 -cyan_free_sink 0.09805 -cyan_free 1.00000 -JCZ38_JSE76 0.97132 -JCZ38_sink 0.02868 -JSE76_JCZ38 0.99650 -JSE76_sink 0.00350 + ff +cyan_free_JCZ38 0.6784541 +cyan_free_J9Z38 0.2498405 +cyan_free_sink 0.0717054 +cyan_free 1.0000000 +JCZ38_JSE76 0.9452043 +JCZ38_sink 0.0547957 +JSE76_JCZ38 0.9998226 +JSE76_sink 0.0001774 Estimated disappearance times: - DT50 DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 -cyan 24.91 167.16 50.32 7.922 65.19 -JCZ38 7.80 25.91 NA NA NA -J9Z38 110.89 368.36 NA NA NA -JSE76 15.77 52.38 NA NA NA + DT50 DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 +cyan 25.237 168.51 50.73 8.226 65.95 +JCZ38 8.535 28.35 NA NA NA +J9Z38 105.517 350.52 NA NA NA +JSE76 17.837 59.25 NA NA NA </code></pre> <p></p> @@ -4761,11 +4504,11 @@ Hierarchical FOMC path 2 fit with reduced random effects, two-component error </caption> <pre><code> -saemix version used for fitting: 3.2 -mkin version used for pre-fitting: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 18:12:10 2023 -Date of summary: Fri May 19 18:14:01 2023 +saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.4.2 +Date of fit: Fri Feb 14 08:11:01 2025 +Date of summary: Sun Feb 16 17:03:41 2025 Equations: d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan @@ -4780,27 +4523,27 @@ Data: Model predictions using solution type deSolve -Fitted in 816.454 s +Fitted in 773.693 s Using 300, 100 iterations and 10 chains Variance model: Two-component variance function Starting values for degradation parameters: cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 - 101.9028 -1.9055 -5.0249 -2.5646 0.6807 + 102.4477 -1.8631 -5.1087 -2.5114 0.6826 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_alpha log_beta - 4.8883 16.0676 9.3923 -0.1346 3.0364 + 4.7944 15.9616 13.1566 -0.1564 2.9781 Fixed degradation parameter values: None Starting values for random effects (square root of initial entries in omega): cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 -cyan_0 6.321 0.000 0.000 0.000 0.0000 -log_k_JCZ38 0.000 1.392 0.000 0.000 0.0000 -log_k_J9Z38 0.000 0.000 1.561 0.000 0.0000 -log_k_JSE76 0.000 0.000 0.000 3.614 0.0000 -f_cyan_ilr_1 0.000 0.000 0.000 0.000 0.6339 +cyan_0 7.701 0.000 0.000 0.000 0.0000 +log_k_JCZ38 0.000 1.448 0.000 0.000 0.0000 +log_k_J9Z38 0.000 0.000 1.724 0.000 0.0000 +log_k_JSE76 0.000 0.000 0.000 3.659 0.0000 +f_cyan_ilr_1 0.000 0.000 0.000 0.000 0.6356 f_cyan_ilr_2 0.000 0.000 0.000 0.000 0.0000 f_JCZ38_qlogis 0.000 0.000 0.000 0.000 0.0000 f_JSE76_qlogis 0.000 0.000 0.000 0.000 0.0000 @@ -4812,11 +4555,11 @@ log_k_JCZ38 0.00 0.00 0.00 0.0000 0.0000 log_k_J9Z38 0.00 0.00 0.00 0.0000 0.0000 log_k_JSE76 0.00 0.00 0.00 0.0000 0.0000 f_cyan_ilr_1 0.00 0.00 0.00 0.0000 0.0000 -f_cyan_ilr_2 10.41 0.00 0.00 0.0000 0.0000 -f_JCZ38_qlogis 0.00 12.24 0.00 0.0000 0.0000 -f_JSE76_qlogis 0.00 0.00 15.13 0.0000 0.0000 -log_alpha 0.00 0.00 0.00 0.3701 0.0000 -log_beta 0.00 0.00 0.00 0.0000 0.5662 +f_cyan_ilr_2 10.32 0.00 0.00 0.0000 0.0000 +f_JCZ38_qlogis 0.00 12.23 0.00 0.0000 0.0000 +f_JSE76_qlogis 0.00 0.00 14.99 0.0000 0.0000 +log_alpha 0.00 0.00 0.00 0.3924 0.0000 +log_beta 0.00 0.00 0.00 0.0000 0.5639 Starting values for error model parameters: a.1 b.1 @@ -4826,76 +4569,76 @@ Results: Likelihood computed by importance sampling AIC BIC logLik - 2251 2244 -1106 + 2249 2242 -1106 Optimised parameters: - est. lower upper -cyan_0 101.05768 NA NA -log_k_JCZ38 -2.73252 NA NA -log_k_J9Z38 -5.07399 NA NA -log_k_JSE76 -3.52863 NA NA -f_cyan_ilr_1 0.72176 NA NA -f_cyan_ilr_2 1.34610 NA NA -f_JCZ38_qlogis 2.08337 NA NA -f_JSE76_qlogis 1590.31880 NA NA -log_alpha -0.09336 NA NA -log_beta 3.10191 NA NA -a.1 2.08557 1.85439 2.31675 -b.1 0.06998 0.05800 0.08197 -SD.log_k_JCZ38 1.20053 0.43329 1.96777 -SD.log_k_J9Z38 0.85854 0.26708 1.45000 -SD.log_k_JSE76 0.62528 0.16061 1.08995 -SD.f_cyan_ilr_1 0.35190 0.12340 0.58039 -SD.f_cyan_ilr_2 0.85385 0.15391 1.55378 -SD.log_alpha 0.28971 0.08718 0.49225 -SD.log_beta 0.31614 0.05938 0.57290 + est. lower upper +cyan_0 101.24524 NA NA +log_k_JCZ38 -2.85375 NA NA +log_k_J9Z38 -5.07729 NA NA +log_k_JSE76 -3.53511 NA NA +f_cyan_ilr_1 0.67478 NA NA +f_cyan_ilr_2 0.97152 NA NA +f_JCZ38_qlogis 213.48001 NA NA +f_JSE76_qlogis 2.02040 NA NA +log_alpha -0.11041 NA NA +log_beta 3.06575 NA NA +a.1 2.05279 1.85495 2.2506 +b.1 0.07116 0.05912 0.0832 +SD.log_k_JCZ38 1.21713 0.44160 1.9927 +SD.log_k_J9Z38 0.88268 0.27541 1.4900 +SD.log_k_JSE76 0.59452 0.15005 1.0390 +SD.f_cyan_ilr_1 0.35370 0.12409 0.5833 +SD.f_cyan_ilr_2 0.78186 0.18547 1.3782 +SD.log_alpha 0.27781 0.08168 0.4739 +SD.log_beta 0.32608 0.06490 0.5873 Correlation is not available Random effects: est. lower upper -SD.log_k_JCZ38 1.2005 0.43329 1.9678 -SD.log_k_J9Z38 0.8585 0.26708 1.4500 -SD.log_k_JSE76 0.6253 0.16061 1.0900 -SD.f_cyan_ilr_1 0.3519 0.12340 0.5804 -SD.f_cyan_ilr_2 0.8538 0.15391 1.5538 -SD.log_alpha 0.2897 0.08718 0.4923 -SD.log_beta 0.3161 0.05938 0.5729 +SD.log_k_JCZ38 1.2171 0.44160 1.9927 +SD.log_k_J9Z38 0.8827 0.27541 1.4900 +SD.log_k_JSE76 0.5945 0.15005 1.0390 +SD.f_cyan_ilr_1 0.3537 0.12409 0.5833 +SD.f_cyan_ilr_2 0.7819 0.18547 1.3782 +SD.log_alpha 0.2778 0.08168 0.4739 +SD.log_beta 0.3261 0.06490 0.5873 Variance model: - est. lower upper -a.1 2.08557 1.854 2.31675 -b.1 0.06998 0.058 0.08197 + est. lower upper +a.1 2.05279 1.85495 2.2506 +b.1 0.07116 0.05912 0.0832 Backtransformed parameters: est. lower upper -cyan_0 1.011e+02 NA NA -k_JCZ38 6.506e-02 NA NA -k_J9Z38 6.257e-03 NA NA -k_JSE76 2.935e-02 NA NA -f_cyan_to_JCZ38 6.776e-01 NA NA -f_cyan_to_J9Z38 2.442e-01 NA NA -f_JCZ38_to_JSE76 8.893e-01 NA NA -f_JSE76_to_JCZ38 1.000e+00 NA NA -alpha 9.109e-01 NA NA -beta 2.224e+01 NA NA +cyan_0 1.012e+02 NA NA +k_JCZ38 5.763e-02 NA NA +k_J9Z38 6.237e-03 NA NA +k_JSE76 2.916e-02 NA NA +f_cyan_to_JCZ38 6.354e-01 NA NA +f_cyan_to_J9Z38 2.447e-01 NA NA +f_JCZ38_to_JSE76 1.000e+00 NA NA +f_JSE76_to_JCZ38 8.829e-01 NA NA +alpha 8.955e-01 NA NA +beta 2.145e+01 NA NA Resulting formation fractions: - ff -cyan_JCZ38 0.67761 -cyan_J9Z38 0.24417 -cyan_sink 0.07822 -JCZ38_JSE76 0.88928 -JCZ38_sink 0.11072 -JSE76_JCZ38 1.00000 -JSE76_sink 0.00000 + ff +cyan_JCZ38 0.6354 +cyan_J9Z38 0.2447 +cyan_sink 0.1200 +JCZ38_JSE76 1.0000 +JCZ38_sink 0.0000 +JSE76_JCZ38 0.8829 +JSE76_sink 0.1171 Estimated disappearance times: DT50 DT90 DT50back -cyan 25.36 256.37 77.18 -JCZ38 10.65 35.39 NA -J9Z38 110.77 367.98 NA -JSE76 23.62 78.47 NA +cyan 25.07 259.21 78.03 +JCZ38 12.03 39.96 NA +J9Z38 111.14 369.19 NA +JSE76 23.77 78.98 NA </code></pre> <p></p> @@ -4904,11 +4647,11 @@ Hierarchical DFOP path 2 fit with reduced random effects, constant variance </caption> <pre><code> -saemix version used for fitting: 3.2 -mkin version used for pre-fitting: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 18:13:38 2023 -Date of summary: Fri May 19 18:14:01 2023 +saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.4.2 +Date of fit: Fri Feb 14 08:12:27 2025 +Date of summary: Sun Feb 16 17:03:41 2025 Equations: d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -4928,25 +4671,25 @@ Data: Model predictions using solution type deSolve -Fitted in 904.25 s +Fitted in 859.906 s Using 300, 100 iterations and 10 chains Variance model: Constant variance Starting values for degradation parameters: cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 - 102.4358 -2.3107 -5.3123 -3.7120 0.6753 + 102.4380 -2.3107 -5.3123 -3.7120 0.6757 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 - 1.1462 12.4095 12.3630 -1.9317 -4.4557 + 1.1439 13.1194 12.3492 -1.9317 -4.4557 g_qlogis - -0.5648 + -0.5644 Fixed degradation parameter values: None Starting values for random effects (square root of initial entries in omega): cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 -cyan_0 4.594 0.0000 0.000 0.0 0.0000 +cyan_0 4.591 0.0000 0.000 0.0 0.0000 log_k_JCZ38 0.000 0.7966 0.000 0.0 0.0000 log_k_J9Z38 0.000 0.0000 1.561 0.0 0.0000 log_k_JSE76 0.000 0.0000 0.000 0.8 0.0000 @@ -4958,17 +4701,17 @@ log_k1 0.000 0.0000 0.000 0.0 0.0000 log_k2 0.000 0.0000 0.000 0.0 0.0000 g_qlogis 0.000 0.0000 0.000 0.0 0.0000 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 -cyan_0 0.000 0.00 0.0 0.000 0.0000 -log_k_JCZ38 0.000 0.00 0.0 0.000 0.0000 -log_k_J9Z38 0.000 0.00 0.0 0.000 0.0000 -log_k_JSE76 0.000 0.00 0.0 0.000 0.0000 -f_cyan_ilr_1 0.000 0.00 0.0 0.000 0.0000 -f_cyan_ilr_2 1.797 0.00 0.0 0.000 0.0000 -f_JCZ38_qlogis 0.000 13.85 0.0 0.000 0.0000 -f_JSE76_qlogis 0.000 0.00 14.1 0.000 0.0000 -log_k1 0.000 0.00 0.0 1.106 0.0000 -log_k2 0.000 0.00 0.0 0.000 0.6141 -g_qlogis 0.000 0.00 0.0 0.000 0.0000 +cyan_0 0.000 0.00 0.00 0.000 0.0000 +log_k_JCZ38 0.000 0.00 0.00 0.000 0.0000 +log_k_J9Z38 0.000 0.00 0.00 0.000 0.0000 +log_k_JSE76 0.000 0.00 0.00 0.000 0.0000 +f_cyan_ilr_1 0.000 0.00 0.00 0.000 0.0000 +f_cyan_ilr_2 1.797 0.00 0.00 0.000 0.0000 +f_JCZ38_qlogis 0.000 13.86 0.00 0.000 0.0000 +f_JSE76_qlogis 0.000 0.00 13.91 0.000 0.0000 +log_k1 0.000 0.00 0.00 1.106 0.0000 +log_k2 0.000 0.00 0.00 0.000 0.6141 +g_qlogis 0.000 0.00 0.00 0.000 0.0000 g_qlogis cyan_0 0.000 log_k_JCZ38 0.000 @@ -4994,74 +4737,74 @@ Likelihood computed by importance sampling Optimised parameters: est. lower upper -cyan_0 102.5254 NA NA -log_k_JCZ38 -2.9358 NA NA -log_k_J9Z38 -5.1424 NA NA -log_k_JSE76 -3.6458 NA NA -f_cyan_ilr_1 0.6957 NA NA -f_cyan_ilr_2 0.6635 NA NA -f_JCZ38_qlogis 4984.8163 NA NA -f_JSE76_qlogis 1.9415 NA NA -log_k1 -1.9456 NA NA -log_k2 -4.4705 NA NA -g_qlogis -0.5117 NA NA -a.1 2.7455 2.55392 2.9370 -SD.log_k_JCZ38 1.3163 0.47635 2.1563 -SD.log_k_J9Z38 0.7162 0.16133 1.2711 -SD.log_k_JSE76 0.6457 0.15249 1.1390 -SD.f_cyan_ilr_1 0.3424 0.11714 0.5677 -SD.f_cyan_ilr_2 0.4524 0.09709 0.8077 -SD.log_k1 0.7353 0.25445 1.2161 -SD.log_k2 0.5137 0.18206 0.8453 -SD.g_qlogis 0.9857 0.35651 1.6148 +cyan_0 102.6036 NA NA +log_k_JCZ38 -2.9348 NA NA +log_k_J9Z38 -5.1617 NA NA +log_k_JSE76 -3.6396 NA NA +f_cyan_ilr_1 0.6991 NA NA +f_cyan_ilr_2 0.6341 NA NA +f_JCZ38_qlogis 4232.3011 NA NA +f_JSE76_qlogis 1.9658 NA NA +log_k1 -1.9503 NA NA +log_k2 -4.4745 NA NA +g_qlogis -0.4967 NA NA +a.1 2.7461 2.59274 2.8994 +SD.log_k_JCZ38 1.3178 0.47602 2.1596 +SD.log_k_J9Z38 0.7022 0.15061 1.2538 +SD.log_k_JSE76 0.6566 0.15613 1.1570 +SD.f_cyan_ilr_1 0.3409 0.11666 0.5652 +SD.f_cyan_ilr_2 0.4385 0.09482 0.7821 +SD.log_k1 0.7381 0.25599 1.2202 +SD.log_k2 0.5133 0.18152 0.8450 +SD.g_qlogis 0.9866 0.35681 1.6164 Correlation is not available Random effects: est. lower upper -SD.log_k_JCZ38 1.3163 0.47635 2.1563 -SD.log_k_J9Z38 0.7162 0.16133 1.2711 -SD.log_k_JSE76 0.6457 0.15249 1.1390 -SD.f_cyan_ilr_1 0.3424 0.11714 0.5677 -SD.f_cyan_ilr_2 0.4524 0.09709 0.8077 -SD.log_k1 0.7353 0.25445 1.2161 -SD.log_k2 0.5137 0.18206 0.8453 -SD.g_qlogis 0.9857 0.35651 1.6148 +SD.log_k_JCZ38 1.3178 0.47602 2.1596 +SD.log_k_J9Z38 0.7022 0.15061 1.2538 +SD.log_k_JSE76 0.6566 0.15613 1.1570 +SD.f_cyan_ilr_1 0.3409 0.11666 0.5652 +SD.f_cyan_ilr_2 0.4385 0.09482 0.7821 +SD.log_k1 0.7381 0.25599 1.2202 +SD.log_k2 0.5133 0.18152 0.8450 +SD.g_qlogis 0.9866 0.35681 1.6164 Variance model: est. lower upper -a.1 2.745 2.554 2.937 +a.1 2.746 2.593 2.899 Backtransformed parameters: est. lower upper -cyan_0 1.025e+02 NA NA -k_JCZ38 5.309e-02 NA NA -k_J9Z38 5.844e-03 NA NA -k_JSE76 2.610e-02 NA NA -f_cyan_to_JCZ38 6.079e-01 NA NA -f_cyan_to_J9Z38 2.272e-01 NA NA +cyan_0 1.026e+02 NA NA +k_JCZ38 5.314e-02 NA NA +k_J9Z38 5.732e-03 NA NA +k_JSE76 2.626e-02 NA NA +f_cyan_to_JCZ38 6.051e-01 NA NA +f_cyan_to_J9Z38 2.251e-01 NA NA f_JCZ38_to_JSE76 1.000e+00 NA NA -f_JSE76_to_JCZ38 8.745e-01 NA NA -k1 1.429e-01 NA NA -k2 1.144e-02 NA NA -g 3.748e-01 NA NA +f_JSE76_to_JCZ38 8.772e-01 NA NA +k1 1.422e-01 NA NA +k2 1.140e-02 NA NA +g 3.783e-01 NA NA Resulting formation fractions: ff -cyan_JCZ38 0.6079 -cyan_J9Z38 0.2272 -cyan_sink 0.1649 +cyan_JCZ38 0.6051 +cyan_J9Z38 0.2251 +cyan_sink 0.1698 JCZ38_JSE76 1.0000 JCZ38_sink 0.0000 -JSE76_JCZ38 0.8745 -JSE76_sink 0.1255 +JSE76_JCZ38 0.8772 +JSE76_sink 0.1228 Estimated disappearance times: DT50 DT90 DT50back DT50_k1 DT50_k2 -cyan 22.29 160.20 48.22 4.85 60.58 -JCZ38 13.06 43.37 NA NA NA -J9Z38 118.61 394.02 NA NA NA -JSE76 26.56 88.22 NA NA NA +cyan 22.05 160.35 48.27 4.873 60.83 +JCZ38 13.04 43.33 NA NA NA +J9Z38 120.93 401.73 NA NA NA +JSE76 26.39 87.68 NA NA NA </code></pre> <p></p> @@ -5070,11 +4813,11 @@ Hierarchical DFOP path 2 fit with reduced random effects, two-component error </caption> <pre><code> -saemix version used for fitting: 3.2 -mkin version used for pre-fitting: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 18:14:00 2023 -Date of summary: Fri May 19 18:14:01 2023 +saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.4.2 +Date of fit: Fri Feb 14 08:12:54 2025 +Date of summary: Sun Feb 16 17:03:41 2025 Equations: d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -5094,59 +4837,59 @@ Data: Model predictions using solution type deSolve -Fitted in 926.094 s +Fitted in 886.298 s Using 300, 100 iterations and 10 chains Variance model: Two-component variance function Starting values for degradation parameters: cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 - 101.7523 -1.5948 -5.0119 -2.2723 0.6719 + 101.7393 -1.4493 -5.0118 -2.1269 0.6720 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 - 5.1681 12.8238 12.4130 -2.0057 -4.5526 + 7.3362 13.4423 13.2659 -2.0061 -4.5527 g_qlogis - -0.5805 + -0.5806 Fixed degradation parameter values: None Starting values for random effects (square root of initial entries in omega): cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 -cyan_0 5.627 0.000 0.000 0.000 0.0000 -log_k_JCZ38 0.000 2.327 0.000 0.000 0.0000 -log_k_J9Z38 0.000 0.000 1.664 0.000 0.0000 -log_k_JSE76 0.000 0.000 0.000 4.566 0.0000 -f_cyan_ilr_1 0.000 0.000 0.000 0.000 0.6519 -f_cyan_ilr_2 0.000 0.000 0.000 0.000 0.0000 -f_JCZ38_qlogis 0.000 0.000 0.000 0.000 0.0000 -f_JSE76_qlogis 0.000 0.000 0.000 0.000 0.0000 -log_k1 0.000 0.000 0.000 0.000 0.0000 -log_k2 0.000 0.000 0.000 0.000 0.0000 -g_qlogis 0.000 0.000 0.000 0.000 0.0000 +cyan_0 5.604 0.00 0.000 0.000 0.0000 +log_k_JCZ38 0.000 2.77 0.000 0.000 0.0000 +log_k_J9Z38 0.000 0.00 1.662 0.000 0.0000 +log_k_JSE76 0.000 0.00 0.000 5.021 0.0000 +f_cyan_ilr_1 0.000 0.00 0.000 0.000 0.6519 +f_cyan_ilr_2 0.000 0.00 0.000 0.000 0.0000 +f_JCZ38_qlogis 0.000 0.00 0.000 0.000 0.0000 +f_JSE76_qlogis 0.000 0.00 0.000 0.000 0.0000 +log_k1 0.000 0.00 0.000 0.000 0.0000 +log_k2 0.000 0.00 0.000 0.000 0.0000 +g_qlogis 0.000 0.00 0.000 0.000 0.0000 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 -cyan_0 0.0 0.00 0.00 0.0000 0.0000 -log_k_JCZ38 0.0 0.00 0.00 0.0000 0.0000 -log_k_J9Z38 0.0 0.00 0.00 0.0000 0.0000 -log_k_JSE76 0.0 0.00 0.00 0.0000 0.0000 -f_cyan_ilr_1 0.0 0.00 0.00 0.0000 0.0000 -f_cyan_ilr_2 10.1 0.00 0.00 0.0000 0.0000 -f_JCZ38_qlogis 0.0 13.99 0.00 0.0000 0.0000 -f_JSE76_qlogis 0.0 0.00 14.15 0.0000 0.0000 -log_k1 0.0 0.00 0.00 0.8452 0.0000 -log_k2 0.0 0.00 0.00 0.0000 0.5968 -g_qlogis 0.0 0.00 0.00 0.0000 0.0000 +cyan_0 0.00 0.00 0.00 0.0000 0.0000 +log_k_JCZ38 0.00 0.00 0.00 0.0000 0.0000 +log_k_J9Z38 0.00 0.00 0.00 0.0000 0.0000 +log_k_JSE76 0.00 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_1 0.00 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_2 13.37 0.00 0.00 0.0000 0.0000 +f_JCZ38_qlogis 0.00 14.21 0.00 0.0000 0.0000 +f_JSE76_qlogis 0.00 0.00 14.58 0.0000 0.0000 +log_k1 0.00 0.00 0.00 0.8453 0.0000 +log_k2 0.00 0.00 0.00 0.0000 0.5969 +g_qlogis 0.00 0.00 0.00 0.0000 0.0000 g_qlogis -cyan_0 0.000 -log_k_JCZ38 0.000 -log_k_J9Z38 0.000 -log_k_JSE76 0.000 -f_cyan_ilr_1 0.000 -f_cyan_ilr_2 0.000 -f_JCZ38_qlogis 0.000 -f_JSE76_qlogis 0.000 -log_k1 0.000 -log_k2 0.000 -g_qlogis 1.691 +cyan_0 0.00 +log_k_JCZ38 0.00 +log_k_J9Z38 0.00 +log_k_JSE76 0.00 +f_cyan_ilr_1 0.00 +f_cyan_ilr_2 0.00 +f_JCZ38_qlogis 0.00 +f_JSE76_qlogis 0.00 +log_k1 0.00 +log_k2 0.00 +g_qlogis 1.69 Starting values for error model parameters: a.1 b.1 @@ -5156,78 +4899,78 @@ Results: Likelihood computed by importance sampling AIC BIC logLik - 2232 2224 -1096 + 2237 2229 -1099 Optimised parameters: est. lower upper -cyan_0 101.20051 NA NA -log_k_JCZ38 -2.93542 NA NA -log_k_J9Z38 -5.03151 NA NA -log_k_JSE76 -3.67679 NA NA -f_cyan_ilr_1 0.67290 NA NA -f_cyan_ilr_2 0.99787 NA NA -f_JCZ38_qlogis 348.32484 NA NA -f_JSE76_qlogis 1.87846 NA NA -log_k1 -2.32738 NA NA -log_k2 -4.61295 NA NA -g_qlogis -0.38342 NA NA -a.1 2.06184 1.83746 2.28622 -b.1 0.06329 0.05211 0.07447 -SD.log_k_JCZ38 1.29042 0.47468 2.10617 -SD.log_k_J9Z38 0.84235 0.25903 1.42566 -SD.log_k_JSE76 0.56930 0.13934 0.99926 -SD.f_cyan_ilr_1 0.35183 0.12298 0.58068 -SD.f_cyan_ilr_2 0.77269 0.17908 1.36631 -SD.log_k2 0.28549 0.09210 0.47888 -SD.g_qlogis 0.93830 0.34568 1.53093 +cyan_0 101.00243 NA NA +log_k_JCZ38 -2.80828 NA NA +log_k_J9Z38 -5.04449 NA NA +log_k_JSE76 -3.66981 NA NA +f_cyan_ilr_1 0.72564 NA NA +f_cyan_ilr_2 1.37978 NA NA +f_JCZ38_qlogis 1.98726 NA NA +f_JSE76_qlogis 414.80884 NA NA +log_k1 -2.38601 NA NA +log_k2 -4.63632 NA NA +g_qlogis -0.33920 NA NA +a.1 2.10837 1.91261 2.30413 +b.1 0.06223 0.05085 0.07361 +SD.log_k_JCZ38 1.30902 0.48128 2.13675 +SD.log_k_J9Z38 0.83882 0.25790 1.41974 +SD.log_k_JSE76 0.58104 0.14201 1.02008 +SD.f_cyan_ilr_1 0.35421 0.12398 0.58443 +SD.f_cyan_ilr_2 0.79373 0.12007 1.46739 +SD.log_k2 0.27476 0.08557 0.46394 +SD.g_qlogis 0.96170 0.35463 1.56878 Correlation is not available Random effects: - est. lower upper -SD.log_k_JCZ38 1.2904 0.4747 2.1062 -SD.log_k_J9Z38 0.8423 0.2590 1.4257 -SD.log_k_JSE76 0.5693 0.1393 0.9993 -SD.f_cyan_ilr_1 0.3518 0.1230 0.5807 -SD.f_cyan_ilr_2 0.7727 0.1791 1.3663 -SD.log_k2 0.2855 0.0921 0.4789 -SD.g_qlogis 0.9383 0.3457 1.5309 + est. lower upper +SD.log_k_JCZ38 1.3090 0.48128 2.1367 +SD.log_k_J9Z38 0.8388 0.25790 1.4197 +SD.log_k_JSE76 0.5810 0.14201 1.0201 +SD.f_cyan_ilr_1 0.3542 0.12398 0.5844 +SD.f_cyan_ilr_2 0.7937 0.12007 1.4674 +SD.log_k2 0.2748 0.08557 0.4639 +SD.g_qlogis 0.9617 0.35463 1.5688 Variance model: est. lower upper -a.1 2.06184 1.83746 2.28622 -b.1 0.06329 0.05211 0.07447 +a.1 2.10837 1.91261 2.30413 +b.1 0.06223 0.05085 0.07361 Backtransformed parameters: est. lower upper -cyan_0 1.012e+02 NA NA -k_JCZ38 5.311e-02 NA NA -k_J9Z38 6.529e-03 NA NA -k_JSE76 2.530e-02 NA NA -f_cyan_to_JCZ38 6.373e-01 NA NA -f_cyan_to_J9Z38 2.461e-01 NA NA -f_JCZ38_to_JSE76 1.000e+00 NA NA -f_JSE76_to_JCZ38 8.674e-01 NA NA -k1 9.755e-02 NA NA -k2 9.922e-03 NA NA -g 4.053e-01 NA NA +cyan_0 1.010e+02 NA NA +k_JCZ38 6.031e-02 NA NA +k_J9Z38 6.445e-03 NA NA +k_JSE76 2.548e-02 NA NA +f_cyan_to_JCZ38 6.808e-01 NA NA +f_cyan_to_J9Z38 2.440e-01 NA NA +f_JCZ38_to_JSE76 8.795e-01 NA NA +f_JSE76_to_JCZ38 1.000e+00 NA NA +k1 9.200e-02 NA NA +k2 9.693e-03 NA NA +g 4.160e-01 NA NA Resulting formation fractions: - ff -cyan_JCZ38 0.6373 -cyan_J9Z38 0.2461 -cyan_sink 0.1167 -JCZ38_JSE76 1.0000 -JCZ38_sink 0.0000 -JSE76_JCZ38 0.8674 -JSE76_sink 0.1326 + ff +cyan_JCZ38 0.68081 +cyan_J9Z38 0.24398 +cyan_sink 0.07521 +JCZ38_JSE76 0.87945 +JCZ38_sink 0.12055 +JSE76_JCZ38 1.00000 +JSE76_sink 0.00000 Estimated disappearance times: DT50 DT90 DT50back DT50_k1 DT50_k2 -cyan 24.93 179.68 54.09 7.105 69.86 -JCZ38 13.05 43.36 NA NA NA -J9Z38 106.16 352.67 NA NA NA -JSE76 27.39 91.00 NA NA NA +cyan 25.00 182.05 54.8 7.535 71.51 +JCZ38 11.49 38.18 NA NA NA +J9Z38 107.55 357.28 NA NA NA +JSE76 27.20 90.36 NA NA NA </code></pre> <p></p> @@ -5236,11 +4979,11 @@ Hierarchical SFORB path 2 fit with reduced random effects, constant variance </caption> <pre><code> -saemix version used for fitting: 3.2 -mkin version used for pre-fitting: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 18:13:34 2023 -Date of summary: Fri May 19 18:14:01 2023 +saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.4.2 +Date of fit: Fri Feb 14 08:12:32 2025 +Date of summary: Sun Feb 16 17:03:41 2025 Equations: d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound * @@ -5258,20 +5001,20 @@ Data: Model predictions using solution type deSolve -Fitted in 900.859 s +Fitted in 865.121 s Using 300, 100 iterations and 10 chains Variance model: Constant variance Starting values for degradation parameters: cyan_free_0 log_k_cyan_free log_k_cyan_free_bound - 102.4394 -2.7673 -2.8942 + 102.4395 -2.7673 -2.8942 log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 -3.6201 -2.3107 -5.3123 log_k_JSE76 f_cyan_ilr_1 f_cyan_ilr_2 -3.7120 0.6754 1.1448 f_JCZ38_qlogis f_JSE76_qlogis - 13.2672 13.3538 + 14.8408 15.4734 Fixed degradation parameter values: None @@ -5302,17 +5045,17 @@ f_cyan_ilr_2 0.000 0.0000 0.000 0.0 f_JCZ38_qlogis 0.000 0.0000 0.000 0.0 f_JSE76_qlogis 0.000 0.0000 0.000 0.0 f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis -cyan_free_0 0.0000 0.000 0.00 0.00 -log_k_cyan_free 0.0000 0.000 0.00 0.00 -log_k_cyan_free_bound 0.0000 0.000 0.00 0.00 -log_k_cyan_bound_free 0.0000 0.000 0.00 0.00 -log_k_JCZ38 0.0000 0.000 0.00 0.00 -log_k_J9Z38 0.0000 0.000 0.00 0.00 -log_k_JSE76 0.0000 0.000 0.00 0.00 -f_cyan_ilr_1 0.6349 0.000 0.00 0.00 -f_cyan_ilr_2 0.0000 1.797 0.00 0.00 -f_JCZ38_qlogis 0.0000 0.000 13.84 0.00 -f_JSE76_qlogis 0.0000 0.000 0.00 14.66 +cyan_free_0 0.0000 0.000 0.0 0.00 +log_k_cyan_free 0.0000 0.000 0.0 0.00 +log_k_cyan_free_bound 0.0000 0.000 0.0 0.00 +log_k_cyan_bound_free 0.0000 0.000 0.0 0.00 +log_k_JCZ38 0.0000 0.000 0.0 0.00 +log_k_J9Z38 0.0000 0.000 0.0 0.00 +log_k_JSE76 0.0000 0.000 0.0 0.00 +f_cyan_ilr_1 0.6349 0.000 0.0 0.00 +f_cyan_ilr_2 0.0000 1.797 0.0 0.00 +f_JCZ38_qlogis 0.0000 0.000 15.6 0.00 +f_JSE76_qlogis 0.0000 0.000 0.0 17.52 Starting values for error model parameters: a.1 @@ -5322,83 +5065,83 @@ Results: Likelihood computed by importance sampling AIC BIC logLik - 2279 2272 -1120 + 2280 2272 -1120 Optimised parameters: est. lower upper -cyan_free_0 102.5621 NA NA -log_k_cyan_free -2.8531 NA NA -log_k_cyan_free_bound -2.6916 NA NA -log_k_cyan_bound_free -3.5032 NA NA -log_k_JCZ38 -2.9436 NA NA -log_k_J9Z38 -5.1140 NA NA -log_k_JSE76 -3.6472 NA NA -f_cyan_ilr_1 0.6887 NA NA -f_cyan_ilr_2 0.6874 NA NA -f_JCZ38_qlogis 4063.6389 NA NA -f_JSE76_qlogis 1.9556 NA NA -a.1 2.7460 2.55451 2.9376 -SD.log_k_cyan_free 0.3131 0.09841 0.5277 -SD.log_k_cyan_free_bound 0.8850 0.29909 1.4710 -SD.log_k_cyan_bound_free 0.6167 0.20391 1.0295 -SD.log_k_JCZ38 1.3555 0.49101 2.2200 -SD.log_k_J9Z38 0.7200 0.16166 1.2783 -SD.log_k_JSE76 0.6252 0.14619 1.1042 -SD.f_cyan_ilr_1 0.3386 0.11447 0.5627 -SD.f_cyan_ilr_2 0.4699 0.09810 0.8417 +cyan_free_0 102.6532 NA NA +log_k_cyan_free -2.8547 NA NA +log_k_cyan_free_bound -2.7004 NA NA +log_k_cyan_bound_free -3.5078 NA NA +log_k_JCZ38 -2.9255 NA NA +log_k_J9Z38 -5.1089 NA NA +log_k_JSE76 -3.6263 NA NA +f_cyan_ilr_1 0.6873 NA NA +f_cyan_ilr_2 0.6498 NA NA +f_JCZ38_qlogis 3624.2149 NA NA +f_JSE76_qlogis 1.9991 NA NA +a.1 2.7472 2.55559 2.9388 +SD.log_k_cyan_free 0.3227 0.10296 0.5423 +SD.log_k_cyan_free_bound 0.8757 0.29525 1.4562 +SD.log_k_cyan_bound_free 0.6128 0.20220 1.0233 +SD.log_k_JCZ38 1.3431 0.48474 2.2014 +SD.log_k_J9Z38 0.6881 0.14714 1.2291 +SD.log_k_JSE76 0.6461 0.15321 1.1390 +SD.f_cyan_ilr_1 0.3361 0.11376 0.5585 +SD.f_cyan_ilr_2 0.4286 0.08419 0.7730 Correlation is not available Random effects: est. lower upper -SD.log_k_cyan_free 0.3131 0.09841 0.5277 -SD.log_k_cyan_free_bound 0.8850 0.29909 1.4710 -SD.log_k_cyan_bound_free 0.6167 0.20391 1.0295 -SD.log_k_JCZ38 1.3555 0.49101 2.2200 -SD.log_k_J9Z38 0.7200 0.16166 1.2783 -SD.log_k_JSE76 0.6252 0.14619 1.1042 -SD.f_cyan_ilr_1 0.3386 0.11447 0.5627 -SD.f_cyan_ilr_2 0.4699 0.09810 0.8417 +SD.log_k_cyan_free 0.3227 0.10296 0.5423 +SD.log_k_cyan_free_bound 0.8757 0.29525 1.4562 +SD.log_k_cyan_bound_free 0.6128 0.20220 1.0233 +SD.log_k_JCZ38 1.3431 0.48474 2.2014 +SD.log_k_J9Z38 0.6881 0.14714 1.2291 +SD.log_k_JSE76 0.6461 0.15321 1.1390 +SD.f_cyan_ilr_1 0.3361 0.11376 0.5585 +SD.f_cyan_ilr_2 0.4286 0.08419 0.7730 Variance model: est. lower upper -a.1 2.746 2.555 2.938 +a.1 2.747 2.556 2.939 Backtransformed parameters: est. lower upper -cyan_free_0 1.026e+02 NA NA -k_cyan_free 5.767e-02 NA NA -k_cyan_free_bound 6.777e-02 NA NA -k_cyan_bound_free 3.010e-02 NA NA -k_JCZ38 5.267e-02 NA NA -k_J9Z38 6.012e-03 NA NA -k_JSE76 2.606e-02 NA NA -f_cyan_free_to_JCZ38 6.089e-01 NA NA -f_cyan_free_to_J9Z38 2.299e-01 NA NA +cyan_free_0 1.027e+02 NA NA +k_cyan_free 5.758e-02 NA NA +k_cyan_free_bound 6.718e-02 NA NA +k_cyan_bound_free 2.996e-02 NA NA +k_JCZ38 5.364e-02 NA NA +k_J9Z38 6.042e-03 NA NA +k_JSE76 2.662e-02 NA NA +f_cyan_free_to_JCZ38 6.039e-01 NA NA +f_cyan_free_to_J9Z38 2.285e-01 NA NA f_JCZ38_to_JSE76 1.000e+00 NA NA -f_JSE76_to_JCZ38 8.761e-01 NA NA +f_JSE76_to_JCZ38 8.807e-01 NA NA Estimated Eigenvalues of SFORB model(s): cyan_b1 cyan_b2 cyan_g - 0.1434 0.0121 0.3469 + 0.1426 0.0121 0.3484 Resulting formation fractions: ff -cyan_free_JCZ38 0.6089 -cyan_free_J9Z38 0.2299 -cyan_free_sink 0.1612 +cyan_free_JCZ38 0.6039 +cyan_free_J9Z38 0.2285 +cyan_free_sink 0.1676 cyan_free 1.0000 JCZ38_JSE76 1.0000 JCZ38_sink 0.0000 -JSE76_JCZ38 0.8761 -JSE76_sink 0.1239 +JSE76_JCZ38 0.8807 +JSE76_sink 0.1193 Estimated disappearance times: DT50 DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 -cyan 23.94 155.06 46.68 4.832 57.28 -JCZ38 13.16 43.71 NA NA NA -J9Z38 115.30 383.02 NA NA NA -JSE76 26.59 88.35 NA NA NA +cyan 23.84 154.95 46.65 4.86 57.31 +JCZ38 12.92 42.93 NA NA NA +J9Z38 114.71 381.07 NA NA NA +JSE76 26.04 86.51 NA NA NA </code></pre> <p></p> @@ -5407,11 +5150,11 @@ Hierarchical SFORB path 2 fit with reduced random effects, two-component error </caption> <pre><code> -saemix version used for fitting: 3.2 -mkin version used for pre-fitting: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 18:13:37 2023 -Date of summary: Fri May 19 18:14:01 2023 +saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.4.2 +Date of fit: Fri Feb 14 08:12:36 2025 +Date of summary: Sun Feb 16 17:03:41 2025 Equations: d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound * @@ -5429,20 +5172,20 @@ Data: Model predictions using solution type deSolve -Fitted in 903.326 s +Fitted in 868.952 s Using 300, 100 iterations and 10 chains Variance model: Two-component variance function Starting values for degradation parameters: cyan_free_0 log_k_cyan_free log_k_cyan_free_bound - 101.751 -2.837 -3.016 + 101.7511 -2.8370 -3.0162 log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 - -3.660 -2.299 -5.313 + -3.6600 -2.2988 -5.3129 log_k_JSE76 f_cyan_ilr_1 f_cyan_ilr_2 - -3.699 0.672 5.873 + -3.6991 0.6722 4.8596 f_JCZ38_qlogis f_JSE76_qlogis - 13.216 13.338 + 13.4678 14.2149 Fixed degradation parameter values: None @@ -5473,17 +5216,17 @@ f_cyan_ilr_2 0.000 0.0000 0.000 0.0000 f_JCZ38_qlogis 0.000 0.0000 0.000 0.0000 f_JSE76_qlogis 0.000 0.0000 0.000 0.0000 f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis -cyan_free_0 0.0000 0.00 0.00 0.00 -log_k_cyan_free 0.0000 0.00 0.00 0.00 -log_k_cyan_free_bound 0.0000 0.00 0.00 0.00 -log_k_cyan_bound_free 0.0000 0.00 0.00 0.00 -log_k_JCZ38 0.0000 0.00 0.00 0.00 -log_k_J9Z38 0.0000 0.00 0.00 0.00 -log_k_JSE76 0.0000 0.00 0.00 0.00 -f_cyan_ilr_1 0.6519 0.00 0.00 0.00 -f_cyan_ilr_2 0.0000 10.78 0.00 0.00 -f_JCZ38_qlogis 0.0000 0.00 13.96 0.00 -f_JSE76_qlogis 0.0000 0.00 0.00 14.69 +cyan_free_0 0.0000 0.000 0.00 0.00 +log_k_cyan_free 0.0000 0.000 0.00 0.00 +log_k_cyan_free_bound 0.0000 0.000 0.00 0.00 +log_k_cyan_bound_free 0.0000 0.000 0.00 0.00 +log_k_JCZ38 0.0000 0.000 0.00 0.00 +log_k_J9Z38 0.0000 0.000 0.00 0.00 +log_k_JSE76 0.0000 0.000 0.00 0.00 +f_cyan_ilr_1 0.6518 0.000 0.00 0.00 +f_cyan_ilr_2 0.0000 9.981 0.00 0.00 +f_JCZ38_qlogis 0.0000 0.000 14.26 0.00 +f_JSE76_qlogis 0.0000 0.000 0.00 16.17 Starting values for error model parameters: a.1 b.1 @@ -5493,83 +5236,83 @@ Results: Likelihood computed by importance sampling AIC BIC logLik - 2236 2228 -1098 + 2241 2233 -1101 Optimised parameters: est. lower upper -cyan_free_0 100.72760 NA NA -log_k_cyan_free -3.18281 NA NA -log_k_cyan_free_bound -3.37924 NA NA -log_k_cyan_bound_free -3.77107 NA NA -log_k_JCZ38 -2.92811 NA NA -log_k_J9Z38 -5.02759 NA NA -log_k_JSE76 -3.65835 NA NA -f_cyan_ilr_1 0.67390 NA NA -f_cyan_ilr_2 1.15106 NA NA -f_JCZ38_qlogis 827.82299 NA NA -f_JSE76_qlogis 1.83064 NA NA -a.1 2.06921 1.84443 2.29399 -b.1 0.06391 0.05267 0.07515 -SD.log_k_cyan_free 0.50518 0.18962 0.82075 -SD.log_k_cyan_bound_free 0.30991 0.08170 0.53813 -SD.log_k_JCZ38 1.26661 0.46578 2.06744 -SD.log_k_J9Z38 0.88272 0.27813 1.48730 -SD.log_k_JSE76 0.53050 0.12561 0.93538 -SD.f_cyan_ilr_1 0.35547 0.12461 0.58633 -SD.f_cyan_ilr_2 0.91446 0.20131 1.62761 +cyan_free_0 100.95469 NA NA +log_k_cyan_free -3.18706 NA NA +log_k_cyan_free_bound -3.38455 NA NA +log_k_cyan_bound_free -3.75788 NA NA +log_k_JCZ38 -2.77024 NA NA +log_k_J9Z38 -5.03665 NA NA +log_k_JSE76 -3.60289 NA NA +f_cyan_ilr_1 0.72263 NA NA +f_cyan_ilr_2 1.45352 NA NA +f_JCZ38_qlogis 2.00778 NA NA +f_JSE76_qlogis 941.58570 NA NA +a.1 2.11130 1.91479 2.30780 +b.1 0.06299 0.05152 0.07445 +SD.log_k_cyan_free 0.50098 0.18805 0.81390 +SD.log_k_cyan_bound_free 0.31671 0.08467 0.54875 +SD.log_k_JCZ38 1.25865 0.45932 2.05798 +SD.log_k_J9Z38 0.86833 0.27222 1.46444 +SD.log_k_JSE76 0.59325 0.14711 1.03940 +SD.f_cyan_ilr_1 0.35705 0.12521 0.58890 +SD.f_cyan_ilr_2 0.88541 0.13797 1.63286 Correlation is not available Random effects: - est. lower upper -SD.log_k_cyan_free 0.5052 0.1896 0.8207 -SD.log_k_cyan_bound_free 0.3099 0.0817 0.5381 -SD.log_k_JCZ38 1.2666 0.4658 2.0674 -SD.log_k_J9Z38 0.8827 0.2781 1.4873 -SD.log_k_JSE76 0.5305 0.1256 0.9354 -SD.f_cyan_ilr_1 0.3555 0.1246 0.5863 -SD.f_cyan_ilr_2 0.9145 0.2013 1.6276 + est. lower upper +SD.log_k_cyan_free 0.5010 0.18805 0.8139 +SD.log_k_cyan_bound_free 0.3167 0.08467 0.5487 +SD.log_k_JCZ38 1.2587 0.45932 2.0580 +SD.log_k_J9Z38 0.8683 0.27222 1.4644 +SD.log_k_JSE76 0.5933 0.14711 1.0394 +SD.f_cyan_ilr_1 0.3571 0.12521 0.5889 +SD.f_cyan_ilr_2 0.8854 0.13797 1.6329 Variance model: est. lower upper -a.1 2.06921 1.84443 2.29399 -b.1 0.06391 0.05267 0.07515 +a.1 2.11130 1.91479 2.30780 +b.1 0.06299 0.05152 0.07445 Backtransformed parameters: est. lower upper -cyan_free_0 1.007e+02 NA NA -k_cyan_free 4.147e-02 NA NA -k_cyan_free_bound 3.407e-02 NA NA -k_cyan_bound_free 2.303e-02 NA NA -k_JCZ38 5.350e-02 NA NA -k_J9Z38 6.555e-03 NA NA -k_JSE76 2.578e-02 NA NA -f_cyan_free_to_JCZ38 6.505e-01 NA NA -f_cyan_free_to_J9Z38 2.508e-01 NA NA -f_JCZ38_to_JSE76 1.000e+00 NA NA -f_JSE76_to_JCZ38 8.618e-01 NA NA +cyan_free_0 1.010e+02 NA NA +k_cyan_free 4.129e-02 NA NA +k_cyan_free_bound 3.389e-02 NA NA +k_cyan_bound_free 2.333e-02 NA NA +k_JCZ38 6.265e-02 NA NA +k_J9Z38 6.495e-03 NA NA +k_JSE76 2.724e-02 NA NA +f_cyan_free_to_JCZ38 6.844e-01 NA NA +f_cyan_free_to_J9Z38 2.463e-01 NA NA +f_JCZ38_to_JSE76 8.816e-01 NA NA +f_JSE76_to_JCZ38 1.000e+00 NA NA Estimated Eigenvalues of SFORB model(s): cyan_b1 cyan_b2 cyan_g -0.08768 0.01089 0.39821 +0.08751 0.01101 0.39586 Resulting formation fractions: ff -cyan_free_JCZ38 0.65053 -cyan_free_J9Z38 0.25082 -cyan_free_sink 0.09864 +cyan_free_JCZ38 0.68444 +cyan_free_J9Z38 0.24633 +cyan_free_sink 0.06923 cyan_free 1.00000 -JCZ38_JSE76 1.00000 -JCZ38_sink 0.00000 -JSE76_JCZ38 0.86184 -JSE76_sink 0.13816 +JCZ38_JSE76 0.88161 +JCZ38_sink 0.11839 +JSE76_JCZ38 1.00000 +JSE76_sink 0.00000 Estimated disappearance times: DT50 DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 -cyan 25.32 164.79 49.61 7.906 63.64 -JCZ38 12.96 43.04 NA NA NA -J9Z38 105.75 351.29 NA NA NA -JSE76 26.89 89.33 NA NA NA +cyan 25.36 163.36 49.18 7.921 62.95 +JCZ38 11.06 36.75 NA NA NA +J9Z38 106.71 354.49 NA NA NA +JSE76 25.44 84.51 NA NA NA </code></pre> <p></p> @@ -5578,13 +5321,13 @@ JSE76 26.89 89.33 NA NA NA <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 4.3.0 (2023-04-21) -Platform: x86_64-pc-linux-gnu (64-bit) +<pre><code>R version 4.4.2 (2024-10-31) +Platform: x86_64-pc-linux-gnu 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; LAPACK version 3.11.0 +BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0 +LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0 locale: [1] LC_CTYPE=de_DE.UTF-8 LC_NUMERIC=C @@ -5602,63 +5345,50 @@ attached base packages: [8] base other attached packages: -[1] saemix_3.2 npde_3.3 knitr_1.42 mkin_1.2.5 +[1] saemix_3.3 npde_3.5 knitr_1.49 mkin_1.2.10 loaded via a namespace (and not attached): - [1] sass_0.4.6 utf8_1.2.3 generics_0.1.3 stringi_1.7.12 - [5] lattice_0.21-8 digest_0.6.31 magrittr_2.0.3 evaluate_0.21 - [9] grid_4.3.0 fastmap_1.1.1 cellranger_1.1.0 rprojroot_2.0.3 -[13] jsonlite_1.8.4 processx_3.8.1 pkgbuild_1.4.0 deSolve_1.35 -[17] DBI_1.1.3 mclust_6.0.0 ps_1.7.5 gridExtra_2.3 -[21] purrr_1.0.1 fansi_1.0.4 scales_1.2.1 codetools_0.2-19 -[25] textshaping_0.3.6 jquerylib_0.1.4 cli_3.6.1 crayon_1.5.2 -[29] rlang_1.1.1 munsell_0.5.0 cachem_1.0.8 yaml_2.3.7 -[33] inline_0.3.19 tools_4.3.0 memoise_2.0.1 dplyr_1.1.2 -[37] colorspace_2.1-0 ggplot2_3.4.2 vctrs_0.6.2 R6_2.5.1 -[41] zoo_1.8-12 lifecycle_1.0.3 stringr_1.5.0 fs_1.6.2 -[45] ragg_1.2.5 callr_3.7.3 pkgconfig_2.0.3 desc_1.4.2 -[49] pkgdown_2.0.7 bslib_0.4.2 pillar_1.9.0 gtable_0.3.3 -[53] glue_1.6.2 systemfonts_1.0.4 highr_0.10 xfun_0.39 -[57] tibble_3.2.1 lmtest_0.9-40 tidyselect_1.2.0 htmltools_0.5.5 -[61] nlme_3.1-162 rmarkdown_2.21 compiler_4.3.0 prettyunits_1.1.1 -[65] readxl_1.4.2 </code></pre> + [1] gtable_0.3.6 jsonlite_1.8.9 dplyr_1.1.4 compiler_4.4.2 + [5] tidyselect_1.2.1 gridExtra_2.3 jquerylib_0.1.4 systemfonts_1.1.0 + [9] scales_1.3.0 textshaping_0.4.1 yaml_2.3.10 fastmap_1.2.0 +[13] lattice_0.22-6 ggplot2_3.5.1 R6_2.5.1 generics_0.1.3 +[17] lmtest_0.9-40 MASS_7.3-61 htmlwidgets_1.6.4 tibble_3.2.1 +[21] desc_1.4.3 munsell_0.5.1 bslib_0.8.0 pillar_1.9.0 +[25] rlang_1.1.4 utf8_1.2.4 inline_0.3.20 cachem_1.1.0 +[29] xfun_0.49 fs_1.6.5 sass_0.4.9 cli_3.6.3 +[33] pkgdown_2.1.1 magrittr_2.0.3 digest_0.6.37 grid_4.4.2 +[37] mclust_6.1.1 lifecycle_1.0.4 nlme_3.1-166 vctrs_0.6.5 +[41] evaluate_1.0.1 glue_1.8.0 ragg_1.3.3 zoo_1.8-12 +[45] fansi_1.0.6 colorspace_2.1-1 rmarkdown_2.29 pkgconfig_2.0.3 +[49] tools_4.4.2 htmltools_0.5.8.1</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: 64925476 kB</code></pre> +<pre><code>MemTotal: 64927788 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> -</div> - + </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2> + </nav></aside> </div> - <footer><div class="copyright"> - <p></p> -<p>Developed by Johannes Ranke.</p> + <footer><div class="pkgdown-footer-left"> + <p>Developed by Johannes Ranke.</p> </div> -<div class="pkgdown"> - <p></p> -<p>Site built with <a 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- <div class="col-md-9 contents"> - <div class="page-header toc-ignore"> - <h1 data-toc-skip>Testing hierarchical parent degradation kinetics -with residue data on dimethenamid and dimethenamid-P</h1> +<div class="row"> + <main id="main" class="col-md-9"><div class="page-header"> + + <h1>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 19 Mai 2023</h4> +2023, last compiled on 16 Februar 2025</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 class="d-none name"><code>2022_dmta_parent.rmd</code></div> </div> @@ -155,7 +103,7 @@ FOMC, DFOP and HS can be fitted with the mkin package.</p> 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 1.2.5. It contains the test data +<p>The mkin package is used in version 1.2.10. 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> @@ -1006,7 +954,7 @@ updated assuming two-component error.</p> <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">OK</td> <td align="left">C</td> <td align="left">OK</td> @@ -1014,7 +962,7 @@ updated assuming two-component error.</p> <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> @@ -1112,9 +1060,9 @@ the best fits.</p> <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> +<td align="right">720.7</td> +<td align="right">719.1</td> +<td align="right">-352.4</td> </tr> <tr class="odd"> <td align="left">DFOP const</td> @@ -1133,9 +1081,9 @@ the best fits.</p> <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> +<td align="right">665.7</td> +<td align="right">663.6</td> +<td align="right">-322.9</td> </tr> <tr class="even"> <td align="left">HS tc</td> @@ -1216,12 +1164,12 @@ achieved with the argument <code>test = TRUE</code> to the <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="38%"> +<col width="7%"> <col width="8%"> <col width="8%"> <col width="9%"> -<col width="9%"> +<col width="8%"> <col width="4%"> <col width="15%"> </colgroup> @@ -1239,8 +1187,8 @@ achieved with the argument <code>test = TRUE</code> to the <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">663.7</td> +<td align="right">661.8</td> <td align="right">-322.9</td> <td align="right">NA</td> <td align="right">NA</td> @@ -1249,12 +1197,12 @@ achieved with the argument <code>test = TRUE</code> to the <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">665.7</td> +<td align="right">663.6</td> +<td align="right">-322.9</td> +<td align="right">0</td> +<td align="right">1</td> <td align="right">1</td> -<td align="right">0.5961</td> </tr> </tbody> </table> @@ -1286,11 +1234,11 @@ Plot of the final NLHM DFOP fit <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: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 18:14:27 2023 -Date of summary: Fri May 19 18:14:28 2023 +<pre><code>saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.4.2 +Date of fit: Fri Feb 14 08:13:19 2025 +Date of summary: Fri Feb 14 08:13:19 2025 Equations: d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -1302,21 +1250,21 @@ Data: Model predictions using solution type analytical -Fitted in 4.477 s +Fitted in 4.154 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 + DMTA_0 k1 k2 g +98.71186 0.08675 0.01374 0.93491 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 +DMTA_0 98.71 0 0 0 k1 0.00 1 0 0 k2 0.00 0 1 0 g 0.00 0 0 1 @@ -1329,40 +1277,40 @@ Results: Likelihood computed by importance sampling AIC BIC logLik - 663.8 661.9 -322.9 + 663.7 661.8 -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 +DMTA_0 98.256267 96.286112 100.22642 +k1 0.064037 0.033281 0.09479 +k2 0.008469 0.006002 0.01094 +g 0.954167 0.914460 0.99387 +a.1 1.061795 0.878608 1.24498 +b.1 0.029550 0.022593 0.03651 +SD.DMTA_0 2.068581 0.427178 3.70998 +SD.k1 0.598285 0.258235 0.93833 +SD.g 1.016689 0.360061 1.67332 Correlation: DMTA_0 k1 k2 -k1 0.0218 -k2 0.0556 0.0355 -g -0.0516 -0.0284 -0.2800 +k1 0.0213 +k2 0.0541 0.0344 +g -0.0521 -0.0286 -0.2744 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 +SD.DMTA_0 2.0686 0.4272 3.7100 +SD.k1 0.5983 0.2582 0.9383 +SD.g 1.0167 0.3601 1.6733 Variance model: est. lower upper -a.1 1.06848 0.86954 1.26742 -b.1 0.02942 0.02241 0.03644 +a.1 1.06180 0.87861 1.24498 +b.1 0.02955 0.02259 0.03651 Estimated disappearance times: - DT50 DT90 DT50back DT50_k1 DT50_k2 -DMTA 11.45 41.4 12.46 10.82 83.54</code></pre> + DT50 DT90 DT50back DT50_k1 DT50_k2 +DMTA 11.45 41.32 12.44 10.82 81.85</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> @@ -1462,11 +1410,11 @@ Erythropoiesis.”</span> <em>BMC Bioinformatics</em> 22 (478). <a href="https:/ 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: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 18:14:19 2023 -Date of summary: Fri May 19 18:15:34 2023 +saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.4.2 +Date of fit: Fri Feb 14 08:13:11 2025 +Date of summary: Fri Feb 14 08:14:22 2025 Equations: d_DMTA/dt = - k_DMTA * DMTA @@ -1476,7 +1424,7 @@ Data: Model predictions using solution type analytical -Fitted in 1.091 s +Fitted in 0.869 s Using 300, 100 iterations and 9 chains Variance model: Constant variance @@ -1534,11 +1482,11 @@ DMTA 12.24 40.65 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: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 18:14:21 2023 -Date of summary: Fri May 19 18:15:34 2023 +saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.4.2 +Date of fit: Fri Feb 14 08:13:13 2025 +Date of summary: Fri Feb 14 08:14:22 2025 Equations: d_DMTA/dt = - k_DMTA * DMTA @@ -1548,7 +1496,7 @@ Data: Model predictions using solution type analytical -Fitted in 2.517 s +Fitted in 2.423 s Using 300, 100 iterations and 9 chains Variance model: Two-component variance function @@ -1576,13 +1524,13 @@ Likelihood computed by importance sampling 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 + est. lower upper +DMTA_0 97.271822 95.70316 98.84049 +k_DMTA 0.056638 0.02911 0.08417 +a.1 2.660081 2.27492 3.04525 +b.1 0.001665 -0.14451 0.14784 +SD.DMTA_0 1.545520 0.14301 2.94803 +SD.k_DMTA 0.606422 0.26227 0.95057 Correlation: DMTA_0 @@ -1590,13 +1538,13 @@ k_DMTA 0.0169 Random effects: est. lower upper -SD.DMTA_0 1.5455 0.1450 2.9460 +SD.DMTA_0 1.5455 0.1430 2.9480 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 + est. lower upper +a.1 2.660081 2.2749 3.0452 +b.1 0.001665 -0.1445 0.1478 Estimated disappearance times: DT50 DT90 @@ -1608,11 +1556,11 @@ DMTA 12.24 40.65 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: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 18:14:20 2023 -Date of summary: Fri May 19 18:15:34 2023 +saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.4.2 +Date of fit: Fri Feb 14 08:13:11 2025 +Date of summary: Fri Feb 14 08:14:22 2025 Equations: d_DMTA/dt = - (alpha/beta) * 1/((time/beta) + 1) * DMTA @@ -1622,7 +1570,7 @@ Data: Model predictions using solution type analytical -Fitted in 1.25 s +Fitted in 1.228 s Using 300, 100 iterations and 9 chains Variance model: Constant variance @@ -1654,7 +1602,7 @@ 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 +beta 112.8745 34.8816 190.867 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 @@ -1685,11 +1633,11 @@ DMTA 11.41 42.53 12.8 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: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 18:14:21 2023 -Date of summary: Fri May 19 18:15:34 2023 +saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.4.2 +Date of fit: Fri Feb 14 08:13:13 2025 +Date of summary: Fri Feb 14 08:14:22 2025 Equations: d_DMTA/dt = - (alpha/beta) * 1/((time/beta) + 1) * DMTA @@ -1699,7 +1647,7 @@ Data: Model predictions using solution type analytical -Fitted in 2.666 s +Fitted in 2.87 s Using 300, 100 iterations and 9 chains Variance model: Two-component variance function @@ -1725,38 +1673,38 @@ Results: Likelihood computed by importance sampling AIC BIC logLik - 720.4 718.8 -352.2 + 720.7 719.1 -352.4 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 +DMTA_0 99.10577 97.33296 100.87859 +alpha 5.46260 2.52199 8.40321 +beta 81.66080 30.46664 132.85497 +a.1 1.50219 1.25801 1.74636 +b.1 0.02893 0.02048 0.03739 +SD.DMTA_0 1.61887 -0.03843 3.27618 +SD.alpha 0.58145 0.17364 0.98925 +SD.beta 0.68205 0.21108 1.15302 Correlation: DMTA_0 alpha -alpha -0.1363 -beta -0.1414 0.2542 +alpha -0.1321 +beta -0.1430 0.2467 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 + est. lower upper +SD.DMTA_0 1.6189 -0.03843 3.2762 +SD.alpha 0.5814 0.17364 0.9892 +SD.beta 0.6821 0.21108 1.1530 Variance model: - est. lower upper -a.1 1.51063 1.2438 1.77741 -b.1 0.02824 0.0204 0.03609 + est. lower upper +a.1 1.50219 1.25801 1.74636 +b.1 0.02893 0.02048 0.03739 Estimated disappearance times: - DT50 DT90 DT50back -DMTA 11.11 42.6 12.82 + DT50 DT90 DT50back +DMTA 11.05 42.81 12.89 </code></pre> <p></p> @@ -1764,11 +1712,11 @@ DMTA 11.11 42.6 12.82 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: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 18:14:20 2023 -Date of summary: Fri May 19 18:15:34 2023 +saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.4.2 +Date of fit: Fri Feb 14 08:13:12 2025 +Date of summary: Fri Feb 14 08:14:22 2025 Equations: d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -1780,7 +1728,7 @@ Data: Model predictions using solution type analytical -Fitted in 1.639 s +Fitted in 1.843 s Using 300, 100 iterations and 9 chains Variance model: Constant variance @@ -1811,10 +1759,10 @@ Likelihood computed by importance sampling Optimised parameters: est. lower upper -DMTA_0 98.092481 96.573898 99.61106 +DMTA_0 98.092481 96.573899 99.61106 k1 0.062499 0.030336 0.09466 k2 0.009065 -0.005133 0.02326 -g 0.948967 0.862079 1.03586 +g 0.948967 0.862080 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 @@ -1848,11 +1796,11 @@ DMTA 11.79 42.8 12.88 11.09 76.46 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: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 18:14:22 2023 -Date of summary: Fri May 19 18:15:34 2023 +saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.4.2 +Date of fit: Fri Feb 14 08:13:14 2025 +Date of summary: Fri Feb 14 08:14:22 2025 Equations: d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -1864,21 +1812,21 @@ Data: Model predictions using solution type analytical -Fitted in 3.435 s +Fitted in 3.469 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 + DMTA_0 k1 k2 g +98.71186 0.08675 0.01374 0.93491 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 +DMTA_0 98.71 0 0 0 k1 0.00 1 0 0 k2 0.00 0 1 0 g 0.00 0 0 1 @@ -1891,42 +1839,42 @@ Results: Likelihood computed by importance sampling AIC BIC logLik - 665.5 663.4 -322.8 + 665.7 663.6 -322.9 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 +DMTA_0 98.347470 96.380815 100.31413 +k1 0.064524 0.034279 0.09477 +k2 0.008304 0.005843 0.01076 +g 0.952128 0.909578 0.99468 +a.1 1.068907 0.883665 1.25415 +b.1 0.029265 0.022318 0.03621 +SD.DMTA_0 2.065796 0.427951 3.70364 +SD.k1 0.583703 0.251796 0.91561 +SD.k2 0.004167 -7.831228 7.83956 +SD.g 1.064450 0.397479 1.73142 Correlation: DMTA_0 k1 k2 -k1 0.0235 -k2 0.0595 0.0424 -g -0.0470 -0.0278 -0.2731 +k1 0.0223 +k2 0.0568 0.0394 +g -0.0464 -0.0269 -0.2713 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 +SD.DMTA_0 2.065796 0.4280 3.7036 +SD.k1 0.583703 0.2518 0.9156 +SD.k2 0.004167 -7.8312 7.8396 +SD.g 1.064450 0.3975 1.7314 Variance model: est. lower upper -a.1 1.06524 0.86575 1.26473 -b.1 0.02934 0.02234 0.03634 +a.1 1.06891 0.88367 1.25415 +b.1 0.02927 0.02232 0.03621 Estimated disappearance times: DT50 DT90 DT50back DT50_k1 DT50_k2 -DMTA 11.36 41.32 12.44 10.69 77.92 +DMTA 11.39 41.36 12.45 10.74 83.48 </code></pre> <p></p> @@ -1934,11 +1882,11 @@ DMTA 11.36 41.32 12.44 10.69 77.92 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: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 18:14:20 2023 -Date of summary: Fri May 19 18:15:34 2023 +saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.4.2 +Date of fit: Fri Feb 14 08:13:12 2025 +Date of summary: Fri Feb 14 08:14:22 2025 Equations: d_DMTA/dt = - ifelse(time <= tb, k1, k2) * DMTA @@ -1948,7 +1896,7 @@ Data: Model predictions using solution type analytical -Fitted in 1.946 s +Fitted in 1.698 s Using 300, 100 iterations and 9 chains Variance model: Constant variance @@ -2016,11 +1964,11 @@ DMTA 8.801 67.91 20.44 8.801 31.13 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: 1.2.5 -R version used for fitting: 4.3.0 -Date of fit: Fri May 19 18:14:22 2023 -Date of summary: Fri May 19 18:15:34 2023 +saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.4.2 +Date of fit: Fri Feb 14 08:13:13 2025 +Date of summary: Fri Feb 14 08:14:22 2025 Equations: d_DMTA/dt = - ifelse(time <= tb, k1, k2) * DMTA @@ -2030,7 +1978,7 @@ Data: Model predictions using solution type analytical -Fitted in 3.626 s +Fitted in 3.307 s Using 300, 100 iterations and 9 chains Variance model: Two-component variance function @@ -2061,16 +2009,16 @@ Likelihood computed by importance sampling Optimised parameters: est. lower upper -DMTA_0 97.76570 95.81350 99.71791 +DMTA_0 97.76571 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 +a.1 1.08835 0.90059 1.27611 +b.1 0.02964 0.02261 0.03667 +SD.DMTA_0 2.04877 0.42553 3.67200 SD.k1 0.59166 0.25621 0.92711 SD.k2 0.30698 0.09561 0.51835 -SD.tb 0.01274 -0.10914 0.13462 +SD.tb 0.01274 -0.10915 0.13464 Correlation: DMTA_0 k1 k2 @@ -2080,15 +2028,15 @@ tb -0.0668 -0.0103 -0.2013 Random effects: est. lower upper -SD.DMTA_0 2.04877 0.42607 3.6715 +SD.DMTA_0 2.04877 0.42553 3.6720 SD.k1 0.59166 0.25621 0.9271 SD.k2 0.30698 0.09561 0.5183 -SD.tb 0.01274 -0.10914 0.1346 +SD.tb 0.01274 -0.10915 0.1346 Variance model: est. lower upper -a.1 1.08835 0.88590 1.29080 -b.1 0.02964 0.02257 0.03671 +a.1 1.08835 0.90059 1.27611 +b.1 0.02964 0.02261 0.03667 Estimated disappearance times: DT50 DT90 DT50back DT50_k1 DT50_k2 @@ -2144,13 +2092,13 @@ Convergence plot for the NLHM HS fit with two-component error <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 4.3.0 (2023-04-21) -Platform: x86_64-pc-linux-gnu (64-bit) +<pre><code>R version 4.4.2 (2024-10-31) +Platform: x86_64-pc-linux-gnu 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; LAPACK version 3.11.0 +BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0 +LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0 locale: [1] LC_CTYPE=de_DE.UTF-8 LC_NUMERIC=C @@ -2168,60 +2116,51 @@ attached base packages: [8] base other attached packages: -[1] saemix_3.2 npde_3.3 knitr_1.42 mkin_1.2.5 +[1] rmarkdown_2.29 nvimcom_0.9-167 saemix_3.3 npde_3.5 +[5] knitr_1.49 mkin_1.2.10 loaded via a namespace (and not attached): - [1] sass_0.4.6 utf8_1.2.3 generics_0.1.3 stringi_1.7.12 - [5] lattice_0.21-8 digest_0.6.31 magrittr_2.0.3 evaluate_0.21 - [9] grid_4.3.0 fastmap_1.1.1 rprojroot_2.0.3 jsonlite_1.8.4 -[13] DBI_1.1.3 mclust_6.0.0 gridExtra_2.3 purrr_1.0.1 -[17] fansi_1.0.4 scales_1.2.1 codetools_0.2-19 textshaping_0.3.6 -[21] jquerylib_0.1.4 cli_3.6.1 rlang_1.1.1 munsell_0.5.0 -[25] cachem_1.0.8 yaml_2.3.7 tools_4.3.0 memoise_2.0.1 -[29] dplyr_1.1.2 colorspace_2.1-0 ggplot2_3.4.2 vctrs_0.6.2 -[33] R6_2.5.1 zoo_1.8-12 lifecycle_1.0.3 stringr_1.5.0 -[37] fs_1.6.2 ragg_1.2.5 pkgconfig_2.0.3 desc_1.4.2 -[41] pkgdown_2.0.7 bslib_0.4.2 pillar_1.9.0 gtable_0.3.3 -[45] glue_1.6.2 systemfonts_1.0.4 highr_0.10 xfun_0.39 -[49] tibble_3.2.1 lmtest_0.9-40 tidyselect_1.2.0 htmltools_0.5.5 -[53] nlme_3.1-162 rmarkdown_2.21 compiler_4.3.0 </code></pre> + [1] gtable_0.3.6 jsonlite_1.8.9 dplyr_1.1.4 compiler_4.4.2 + [5] tidyselect_1.2.1 gridExtra_2.3 jquerylib_0.1.4 systemfonts_1.1.0 + [9] scales_1.3.0 textshaping_0.4.1 yaml_2.3.10 fastmap_1.2.0 +[13] lattice_0.22-6 ggplot2_3.5.1 R6_2.5.1 generics_0.1.3 +[17] lmtest_0.9-40 MASS_7.3-61 htmlwidgets_1.6.4 tibble_3.2.1 +[21] desc_1.4.3 munsell_0.5.1 bslib_0.8.0 pillar_1.9.0 +[25] rlang_1.1.4 utf8_1.2.4 cachem_1.1.0 xfun_0.49 +[29] fs_1.6.5 sass_0.4.9 cli_3.6.3 pkgdown_2.1.1 +[33] magrittr_2.0.3 digest_0.6.37 grid_4.4.2 mclust_6.1.1 +[37] lifecycle_1.0.4 nlme_3.1-166 vctrs_0.6.5 evaluate_1.0.1 +[41] glue_1.8.0 codetools_0.2-20 ragg_1.3.3 zoo_1.8-12 +[45] fansi_1.0.6 colorspace_2.1-1 pkgconfig_2.0.3 tools_4.4.2 +[49] htmltools_0.5.8.1</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: 64925476 kB</code></pre> +<pre><code>MemTotal: 64927788 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> -</div> - + </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2> + </nav></aside> </div> - <footer><div class="copyright"> - <p></p> -<p>Developed by Johannes Ranke.</p> + <footer><div class="pkgdown-footer-left"> + <p>Developed by Johannes Ranke.</p> </div> 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class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/prebuilt/2022_dmta_pathway.rmd" class="external-link"><code>vignettes/prebuilt/2022_dmta_pathway.rmd</code></a></small> - <div class="hidden name"><code>2022_dmta_pathway.rmd</code></div> - + <div class="d-none name"><code>2022_dmta_pathway.rmd</code></div> </div> @@ -156,7 +104,7 @@ can be fitted with the mkin package.</p> 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 1.2.5, which is currently under +<p>The mkin package is used in version 1.2.10, which is currently under development. 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 @@ -1497,7 +1445,7 @@ of parent models tested here.</p> <td align="left">dfop_path_1</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> <td align="left">OK</td> <td align="left">OK</td> @@ -1515,7 +1463,7 @@ of parent models tested here.</p> <td align="left">hs_path_1</td> <td align="left">C</td> <td align="left">C</td> -<td align="left">C</td> +<td align="left">OK</td> <td align="left">C</td> <td align="left">C</td> <td align="left">C</td> @@ -1553,24 +1501,24 @@ not converge with default settings.</p> <td align="left">fomc_path_1</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> -<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> </tr> <tr class="odd"> <td align="left">dfop_path_1</td> <td align="left">OK</td> <td align="left">C</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> <tr class="even"> <td align="left">sforb_path_1</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> @@ -1583,7 +1531,7 @@ not converge with default settings.</p> <td align="left">C</td> <td align="left">C</td> <td align="left">C</td> -<td align="left">OK</td> +<td align="left">C</td> </tr> </tbody> </table> @@ -1644,8 +1592,6 @@ than twenty minutes.</p> successfully.</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">saem_1</span><span class="op">)</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>digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div> -<pre><code>Warning in FUN(X[[i]], ...): Could not obtain log likelihood with 'is' method -for sforb_path_1 const</code></pre> <table class="table"> <thead><tr class="header"> <th align="left"></th> @@ -1665,58 +1611,65 @@ for sforb_path_1 const</code></pre> <tr class="even"> <td align="left">sfo_path_1 tc</td> <td align="right">18</td> -<td align="right">2276.3</td> -<td align="right">2272.5</td> -<td align="right">-1120.1</td> +<td align="right">2276.4</td> +<td align="right">2272.7</td> +<td align="right">-1120.2</td> </tr> <tr class="odd"> <td align="left">fomc_path_1 const</td> <td align="right">19</td> -<td align="right">2099.0</td> -<td align="right">2095.0</td> -<td align="right">-1030.5</td> +<td align="right">2095.9</td> +<td align="right">2091.9</td> +<td align="right">-1028.9</td> </tr> <tr class="even"> <td align="left">fomc_path_1 tc</td> <td align="right">20</td> -<td align="right">1939.6</td> -<td align="right">1935.5</td> -<td align="right">-949.8</td> +<td align="right">1939.0</td> +<td align="right">1934.8</td> +<td align="right">-949.5</td> </tr> <tr class="odd"> <td align="left">dfop_path_1 const</td> <td align="right">21</td> -<td align="right">2038.8</td> -<td align="right">2034.4</td> -<td align="right">-998.4</td> +<td align="right">2039.7</td> +<td align="right">2035.3</td> +<td align="right">-998.8</td> </tr> <tr class="even"> -<td align="left">hs_path_1 const</td> +<td align="left">sforb_path_1 const</td> <td align="right">21</td> -<td align="right">2024.2</td> -<td align="right">2019.8</td> -<td align="right">-991.1</td> +<td align="right">2017.7</td> +<td align="right">2013.4</td> +<td align="right">-987.9</td> </tr> <tr class="odd"> +<td align="left">hs_path_1 const</td> +<td align="right">21</td> +<td align="right">2023.7</td> +<td align="right">2019.3</td> +<td align="right">-990.9</td> +</tr> +<tr class="even"> <td align="left">dfop_path_1 tc</td> <td align="right">22</td> -<td align="right">1879.8</td> -<td align="right">1875.2</td> -<td align="right">-917.9</td> +<td align="right">1881.7</td> +<td align="right">1877.1</td> +<td align="right">-918.9</td> </tr> -<tr class="even"> +<tr class="odd"> <td align="left">sforb_path_1 tc</td> <td align="right">22</td> -<td align="right">1832.9</td> -<td align="right">1828.3</td> -<td align="right">-894.4</td> +<td align="right">1832.7</td> +<td align="right">1828.1</td> +<td align="right">-894.3</td> </tr> -<tr class="odd"> +<tr class="even"> <td align="left">hs_path_1 tc</td> <td align="right">22</td> -<td align="right">1831.4</td> -<td align="right">1826.8</td> -<td align="right">-893.7</td> +<td align="right">1831.6</td> +<td align="right">1827.0</td> +<td align="right">-893.8</td> </tr> </tbody> </table> @@ -1741,7 +1694,7 @@ Matrix<a class="anchor" aria-label="anchor" href="#parameter-identifiability-bas 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="cb10"><pre class="downlit sourceCode r"> +<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">saem_1</span><span class="op">)</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="op">)</span></span></code></pre></div> <table class="table"> <thead><tr class="header"> @@ -1767,7 +1720,7 @@ found.</p> </tr> <tr class="even"> <td align="left">sforb_path_1</td> -<td align="left"></td> +<td align="left">sd(log_k_DMTA_bound_free)</td> <td align="left">sd(log_k_DMTA_bound_free)</td> </tr> <tr class="odd"> @@ -1787,13 +1740,13 @@ two-component error, the random effect for the rate constant from reversibly bound DMTA to the free DMTA (<code>k_DMTA_bound_free</code>) is not well-defined. Therefore, the fit is updated without assuming a random effect for this parameter.</p> -<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">saem_sforb_path_1_tc_reduced</span> <span class="op"><-</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">saem_1</span><span class="op">[[</span><span class="st">"sforb_path_1"</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">"log_k_DMTA_bound_free"</span><span class="op">)</span></span> <span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">saem_sforb_path_1_tc_reduced</span><span class="op">)</span></span></code></pre></div> <p>As expected, no ill-defined parameters remain. The model comparison below shows that the reduced model is preferable.</p> -<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb11"><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">saem_1</span><span class="op">[[</span><span class="st">"sforb_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>, <span class="va">saem_sforb_path_1_tc_reduced</span><span class="op">)</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>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"> @@ -1807,21 +1760,21 @@ below shows that the reduced model is preferable.</p> <tr class="odd"> <td align="left">saem_sforb_path_1_tc_reduced</td> <td align="right">21</td> -<td align="right">1830.3</td> -<td align="right">1825.9</td> +<td align="right">1830.4</td> +<td align="right">1826.0</td> <td align="right">-894.2</td> </tr> <tr class="even"> <td align="left">saem_1[[“sforb_path_1”, “tc”]]</td> <td align="right">22</td> -<td align="right">1832.9</td> -<td align="right">1828.3</td> -<td align="right">-894.4</td> +<td align="right">1832.7</td> +<td align="right">1828.1</td> +<td align="right">-894.3</td> </tr> </tbody> </table> <p>The convergence plot of the refined fit is shown below.</p> -<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb12"><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">saem_sforb_path_1_tc_reduced</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></span></code></pre></div> <p><img src="2022_dmta_pathway_files/figure-html/saem-sforb-path-1-tc-reduced-convergence-1.png" width="700" style="display: block; margin: auto;"></p> <p>For some parameters, for example for <code>f_DMTA_ilr_1</code> and @@ -1840,14 +1793,16 @@ saemix.</p> </h3> <p>As an alternative check of parameter identifiability <span class="citation">(Duchesne et al. 2021)</span>, multistart runs were performed on the basis of the refined fit shown above.</p> -<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">saem_sforb_path_1_tc_reduced_multi</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/multistart.html">multistart</a></span><span class="op">(</span><span class="va">saem_sforb_path_1_tc_reduced</span>,</span> <span> n <span class="op">=</span> <span class="fl">32</span>, cores <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span></code></pre></div> +<pre><code> + (subscript) logical subscript too long</code></pre> <div class="sourceCode" id="cb15"><pre class="downlit sourceCode r"> <code class="sourceCode R"><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="va">saem_sforb_path_1_tc_reduced_multi</span><span class="op">)</span></span></code></pre></div> <pre><code><multistart> object with 32 fits: E OK -15 17 + 7 25 OK: Fit terminated successfully E: Error</code></pre> <p>Out of the 32 fits that were initiated, only 17 terminated without an @@ -1972,13 +1927,13 @@ error<a class="anchor" aria-label="anchor" href="#improved-fit-of-the-sforb-path <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 4.3.0 (2023-04-21) -Platform: x86_64-pc-linux-gnu (64-bit) +<pre><code>R version 4.4.2 (2024-10-31) +Platform: x86_64-pc-linux-gnu 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; LAPACK version 3.11.0 +BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0 +LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0 locale: [1] LC_CTYPE=de_DE.UTF-8 LC_NUMERIC=C @@ -1996,62 +1951,52 @@ attached base packages: [8] base other attached packages: -[1] saemix_3.2 npde_3.3 knitr_1.42 mkin_1.2.5 +[1] rmarkdown_2.29 nvimcom_0.9-167 saemix_3.3 npde_3.5 +[5] knitr_1.49 mkin_1.2.10 loaded via a namespace (and not attached): - [1] sass_0.4.6 utf8_1.2.3 generics_0.1.3 stringi_1.7.12 - [5] lattice_0.21-8 digest_0.6.31 magrittr_2.0.3 evaluate_0.21 - [9] grid_4.3.0 fastmap_1.1.1 rprojroot_2.0.3 jsonlite_1.8.4 -[13] processx_3.8.1 pkgbuild_1.4.0 deSolve_1.35 DBI_1.1.3 -[17] mclust_6.0.0 ps_1.7.5 gridExtra_2.3 purrr_1.0.1 -[21] fansi_1.0.4 scales_1.2.1 codetools_0.2-19 textshaping_0.3.6 -[25] jquerylib_0.1.4 cli_3.6.1 crayon_1.5.2 rlang_1.1.1 -[29] munsell_0.5.0 cachem_1.0.8 yaml_2.3.7 inline_0.3.19 -[33] tools_4.3.0 memoise_2.0.1 dplyr_1.1.2 colorspace_2.1-0 -[37] ggplot2_3.4.2 vctrs_0.6.2 R6_2.5.1 zoo_1.8-12 -[41] lifecycle_1.0.3 stringr_1.5.0 fs_1.6.2 ragg_1.2.5 -[45] callr_3.7.3 pkgconfig_2.0.3 desc_1.4.2 pkgdown_2.0.7 -[49] bslib_0.4.2 pillar_1.9.0 gtable_0.3.3 glue_1.6.2 -[53] systemfonts_1.0.4 highr_0.10 xfun_0.39 tibble_3.2.1 -[57] lmtest_0.9-40 tidyselect_1.2.0 htmltools_0.5.5 nlme_3.1-162 -[61] rmarkdown_2.21 compiler_4.3.0 prettyunits_1.1.1</code></pre> + [1] sass_0.4.9 utf8_1.2.4 generics_0.1.3 lattice_0.22-6 + [5] digest_0.6.37 magrittr_2.0.3 evaluate_1.0.1 grid_4.4.2 + [9] fastmap_1.2.0 jsonlite_1.8.9 processx_3.8.4 pkgbuild_1.4.5 +[13] deSolve_1.40 mclust_6.1.1 ps_1.8.1 gridExtra_2.3 +[17] fansi_1.0.6 scales_1.3.0 codetools_0.2-20 textshaping_0.4.1 +[21] jquerylib_0.1.4 cli_3.6.3 rlang_1.1.4 munsell_0.5.1 +[25] cachem_1.1.0 yaml_2.3.10 inline_0.3.20 tools_4.4.2 +[29] dplyr_1.1.4 colorspace_2.1-1 ggplot2_3.5.1 vctrs_0.6.5 +[33] R6_2.5.1 zoo_1.8-12 lifecycle_1.0.4 fs_1.6.5 +[37] htmlwidgets_1.6.4 MASS_7.3-61 ragg_1.3.3 pkgconfig_2.0.3 +[41] desc_1.4.3 callr_3.7.6 pkgdown_2.1.1 pillar_1.9.0 +[45] bslib_0.8.0 gtable_0.3.6 glue_1.8.0 systemfonts_1.1.0 +[49] xfun_0.49 tibble_3.2.1 lmtest_0.9-40 tidyselect_1.2.1 +[53] htmltools_0.5.8.1 nlme_3.1-166 compiler_4.4.2 </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: 64925476 kB</code></pre> +<pre><code>MemTotal: 64927788 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> -</div> - + </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2> + </nav></aside> </div> - <footer><div class="copyright"> - <p></p> -<p>Developed by Johannes Ranke.</p> + <footer><div class="pkgdown-footer-left"> + <p>Developed by Johannes Ranke.</p> </div> -<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> +<div class="pkgdown-footer-right"> + <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p> </div> - </footer> + </footer> </div> - - + </body> </html> diff --git 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-parent degradation kinetics with residue data on mesotrione</h1> + + +<div class="row"> + <main id="main" class="col-md-9"><div class="page-header"> + + <h1>Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> <h4 data-toc-skip class="date">Last change on 4 August 2023, -last compiled on 10 August 2023</h4> +last compiled on 16 Februar 2025</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/prebuilt/2023_mesotrione_parent.rmd" class="external-link"><code>vignettes/prebuilt/2023_mesotrione_parent.rmd</code></a></small> - <div class="hidden name"><code>2023_mesotrione_parent.rmd</code></div> - + <div class="d-none name"><code>2023_mesotrione_parent.rmd</code></div> </div> @@ -159,7 +104,7 @@ parameters. Because in some other case studies, the SFORB parameterisation of biexponential decline has shown some advantages over the DFOP parameterisation, SFORB was included in the list of tested models as well.</p> -<p>The mkin package is used in version 1.2.6, which is contains the +<p>The mkin package is used in version 1.2.10, which is contains the functions that were used for 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> @@ -1521,7 +1466,7 @@ according to the <code>illparms</code> function.</p> <tr class="odd"> <td align="left">HS</td> <td align="left"></td> -<td align="left"></td> +<td align="left">b.1</td> </tr> </tbody> </table> @@ -2481,8 +2426,8 @@ further refined to make them fully identifiable.</p> <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 4.3.1 (2023-06-16) -Platform: x86_64-pc-linux-gnu (64-bit) +<pre><code>R version 4.4.2 (2024-10-31) +Platform: x86_64-pc-linux-gnu Running under: Debian GNU/Linux 12 (bookworm) Matrix products: default @@ -2505,60 +2450,50 @@ attached base packages: [8] base other attached packages: -[1] saemix_3.2 npde_3.3 knitr_1.43 mkin_1.2.6 +[1] saemix_3.3 npde_3.5 knitr_1.49 mkin_1.2.10 loaded via a namespace (and not attached): - [1] sass_0.4.6 utf8_1.2.3 generics_0.1.3 stringi_1.7.12 - [5] lattice_0.20-45 digest_0.6.31 magrittr_2.0.3 evaluate_0.21 - [9] grid_4.3.1 fastmap_1.1.1 cellranger_1.1.0 rprojroot_2.0.3 -[13] jsonlite_1.8.5 mclust_6.0.0 gridExtra_2.3 purrr_1.0.1 -[17] fansi_1.0.4 scales_1.2.1 codetools_0.2-19 textshaping_0.3.6 -[21] jquerylib_0.1.4 cli_3.6.1 rlang_1.1.1 munsell_0.5.0 -[25] cachem_1.0.8 yaml_2.3.7 tools_4.3.1 memoise_2.0.1 -[29] dplyr_1.1.2 colorspace_2.1-0 ggplot2_3.4.2 vctrs_0.6.2 -[33] R6_2.5.1 zoo_1.8-12 lifecycle_1.0.3 stringr_1.5.0 -[37] fs_1.6.2 ragg_1.2.5 pkgconfig_2.0.3 desc_1.4.2 -[41] pkgdown_2.0.7 bslib_0.4.2 pillar_1.9.0 gtable_0.3.3 -[45] glue_1.6.2 systemfonts_1.0.4 highr_0.10 xfun_0.39 -[49] tibble_3.2.1 lmtest_0.9-40 tidyselect_1.2.0 htmltools_0.5.5 -[53] nlme_3.1-162 rmarkdown_2.22 compiler_4.3.1 readxl_1.4.2 </code></pre> + [1] gtable_0.3.6 jsonlite_1.8.9 dplyr_1.1.4 compiler_4.4.2 + [5] tidyselect_1.2.1 gridExtra_2.3 jquerylib_0.1.4 systemfonts_1.1.0 + [9] scales_1.3.0 textshaping_0.4.1 yaml_2.3.10 fastmap_1.2.0 +[13] lattice_0.22-6 ggplot2_3.5.1 R6_2.5.1 generics_0.1.3 +[17] lmtest_0.9-40 MASS_7.3-61 htmlwidgets_1.6.4 tibble_3.2.1 +[21] desc_1.4.3 munsell_0.5.1 bslib_0.8.0 pillar_1.9.0 +[25] rlang_1.1.4 utf8_1.2.4 cachem_1.1.0 xfun_0.49 +[29] fs_1.6.5 sass_0.4.9 cli_3.6.3 pkgdown_2.1.1 +[33] magrittr_2.0.3 digest_0.6.37 grid_4.4.2 mclust_6.1.1 +[37] lifecycle_1.0.4 nlme_3.1-166 vctrs_0.6.5 evaluate_1.0.1 +[41] glue_1.8.0 ragg_1.3.3 zoo_1.8-12 fansi_1.0.6 +[45] colorspace_2.1-1 rmarkdown_2.29 pkgconfig_2.0.3 tools_4.4.2 +[49] htmltools_0.5.8.1</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: Intel(R) Core(TM) i7-4710MQ CPU @ 2.50GHz</code></pre> -<pre><code>MemTotal: 12165632 kB</code></pre> +<pre><code>CPU model: AMD Ryzen 9 7950X 16-Core Processor</code></pre> +<pre><code>MemTotal: 64927788 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> -</div> - + </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2> + </nav></aside> </div> - <footer><div class="copyright"> - <p></p> -<p>Developed by Johannes Ranke.</p> + <footer><div class="pkgdown-footer-left"> + <p>Developed by Johannes Ranke.</p> </div> -<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> +<div class="pkgdown-footer-right"> + <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p> </div> - </footer> + </footer> </div> - - + </body> </html> diff --git a/docs/dev/articles/prebuilt/2023_mesotrione_parent_files/figure-html/unnamed-chunk-14-1.png 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id="main" class="col-md-9"><div class="page-header"> + + <h1>Calculation of time weighted average concentrations with mkin</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> <h4 data-toc-skip class="date">Last change 18 September 2019 -(rebuilt 2023-05-19)</h4> +(rebuilt 2025-02-16)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/twa.rmd" class="external-link"><code>vignettes/twa.rmd</code></a></small> - <div class="hidden name"><code>twa.rmd</code></div> - + <div class="d-none name"><code>twa.rmd</code></div> </div> @@ -157,48 +105,44 @@ model are calculated using the formulas given in the FOCUS kinetics guidance <span class="citation">(FOCUS Work Group on Degradation Kinetics 2014, 251)</span>:</p> <p>SFO:</p> -<p><span class="math display">\[c_\textrm{twa} = c_0 \frac{\left( 1 - -e^{- k t} \right)}{ k t} \]</span></p> +<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>c</mi><mtext mathvariant="normal">twa</mtext></msub><mo>=</mo><msub><mi>c</mi><mn>0</mn></msub><mfrac><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>−</mo><msup><mi>e</mi><mrow><mo>−</mo><mi>k</mi><mi>t</mi></mrow></msup><mo stretchy="true" form="postfix">)</mo></mrow><mrow><mi>k</mi><mi>t</mi></mrow></mfrac></mrow><annotation encoding="application/x-tex">c_\textrm{twa} = c_0 \frac{\left( 1 - e^{- k t} \right)}{ k t} </annotation></semantics></math></p> <p>FOMC:</p> -<p><span class="math display">\[c_\textrm{twa} = c_0 \frac{\beta}{t (1 - -\alpha)} - \left( \left(\frac{t}{\beta} + 1 \right)^{1 - \alpha} -- 1 \right) \]</span></p> +<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>c</mi><mtext mathvariant="normal">twa</mtext></msub><mo>=</mo><msub><mi>c</mi><mn>0</mn></msub><mfrac><mi>β</mi><mrow><mi>t</mi><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>−</mo><mi>α</mi><mo stretchy="true" form="postfix">)</mo></mrow></mrow></mfrac><mrow><mo stretchy="true" form="prefix">(</mo><msup><mrow><mo stretchy="true" form="prefix">(</mo><mfrac><mi>t</mi><mi>β</mi></mfrac><mo>+</mo><mn>1</mn><mo stretchy="true" form="postfix">)</mo></mrow><mrow><mn>1</mn><mo>−</mo><mi>α</mi></mrow></msup><mo>−</mo><mn>1</mn><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">c_\textrm{twa} = c_0 \frac{\beta}{t (1 - \alpha)} + \left( \left(\frac{t}{\beta} + 1 \right)^{1 - \alpha} - 1 \right) </annotation></semantics></math></p> <p>DFOP:</p> -<p><span class="math display">\[c_\textrm{twa} = \frac{c_0}{t} \left( +<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>c</mi><mtext mathvariant="normal">twa</mtext></msub><mo>=</mo><mfrac><msub><mi>c</mi><mn>0</mn></msub><mi>t</mi></mfrac><mrow><mo stretchy="true" form="prefix">(</mo><mfrac><mi>g</mi><msub><mi>k</mi><mn>1</mn></msub></mfrac><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>−</mo><msup><mi>e</mi><mrow><mo>−</mo><msub><mi>k</mi><mn>1</mn></msub><mi>t</mi></mrow></msup><mo stretchy="true" form="postfix">)</mo></mrow><mo>+</mo><mfrac><mrow><mn>1</mn><mo>−</mo><mi>g</mi></mrow><msub><mi>k</mi><mn>2</mn></msub></mfrac><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>−</mo><msup><mi>e</mi><mrow><mo>−</mo><msub><mi>k</mi><mn>2</mn></msub><mi>t</mi></mrow></msup><mo stretchy="true" form="postfix">)</mo></mrow><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">c_\textrm{twa} = \frac{c_0}{t} \left( \frac{g}{k_1} \left( 1 - e^{- k_1 t} \right) + - \frac{1-g}{k_2} \left( 1 - e^{- k_2 t} \right) \right) \]</span></p> -<p>HS for <span class="math inline">\(t > t_b\)</span>:</p> -<p><span class="math display">\[c_\textrm{twa} = \frac{c_0}{t} \left( + \frac{1-g}{k_2} \left( 1 - e^{- k_2 t} \right) \right) </annotation></semantics></math></p> +<p>HS for +<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>t</mi><mo>></mo><msub><mi>t</mi><mi>b</mi></msub></mrow><annotation encoding="application/x-tex">t > t_b</annotation></semantics></math>:</p> +<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>c</mi><mtext mathvariant="normal">twa</mtext></msub><mo>=</mo><mfrac><msub><mi>c</mi><mn>0</mn></msub><mi>t</mi></mfrac><mrow><mo stretchy="true" form="prefix">(</mo><mfrac><mn>1</mn><msub><mi>k</mi><mn>1</mn></msub></mfrac><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>−</mo><msup><mi>e</mi><mrow><mo>−</mo><msub><mi>k</mi><mn>1</mn></msub><msub><mi>t</mi><mi>b</mi></msub></mrow></msup><mo stretchy="true" form="postfix">)</mo></mrow><mo>+</mo><mfrac><msup><mi>e</mi><mrow><mo>−</mo><msub><mi>k</mi><mn>1</mn></msub><msub><mi>t</mi><mi>b</mi></msub></mrow></msup><msub><mi>k</mi><mn>2</mn></msub></mfrac><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>−</mo><msup><mi>e</mi><mrow><mo>−</mo><msub><mi>k</mi><mn>2</mn></msub><mrow><mo stretchy="true" form="prefix">(</mo><mi>t</mi><mo>−</mo><msub><mi>t</mi><mi>b</mi></msub><mo stretchy="true" form="postfix">)</mo></mrow></mrow></msup><mo stretchy="true" form="postfix">)</mo></mrow><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">c_\textrm{twa} = \frac{c_0}{t} \left( \frac{1}{k_1} \left( 1 - e^{- k_1 t_b} \right) + - \frac{e^{- k_1 t_b}}{k_2} \left( 1 - e^{- k_2 (t - t_b)} -\right) \right) \]</span></p> + \frac{e^{- k_1 t_b}}{k_2} \left( 1 - e^{- k_2 (t - t_b)} \right) \right) </annotation></semantics></math></p> <p>Often, the ratio between the time weighted average concentration -<span class="math inline">\(c_\textrm{twa}\)</span> and the initial -concentration <span class="math inline">\(c_0\)</span></p> -<p><span class="math display">\[f_\textrm{twa} = -\frac{c_\textrm{twa}}{c_0}\]</span></p> +<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msub><mi>c</mi><mtext mathvariant="normal">twa</mtext></msub><annotation encoding="application/x-tex">c_\textrm{twa}</annotation></semantics></math> +and the initial concentration +<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msub><mi>c</mi><mn>0</mn></msub><annotation encoding="application/x-tex">c_0</annotation></semantics></math></p> +<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>f</mi><mtext mathvariant="normal">twa</mtext></msub><mo>=</mo><mfrac><msub><mi>c</mi><mtext mathvariant="normal">twa</mtext></msub><msub><mi>c</mi><mn>0</mn></msub></mfrac></mrow><annotation encoding="application/x-tex">f_\textrm{twa} = \frac{c_\textrm{twa}}{c_0}</annotation></semantics></math></p> <p>is needed. This can be calculated from the fitted initial -concentration <span class="math inline">\(c_0\)</span> and the time -weighted average concentration <span class="math inline">\(c_\textrm{twa}\)</span>, or directly from the -model parameters using the following formulas:</p> +concentration +<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msub><mi>c</mi><mn>0</mn></msub><annotation encoding="application/x-tex">c_0</annotation></semantics></math> +and the time weighted average concentration +<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msub><mi>c</mi><mtext mathvariant="normal">twa</mtext></msub><annotation encoding="application/x-tex">c_\textrm{twa}</annotation></semantics></math>, +or directly from the model parameters using the following formulas:</p> <p>SFO:</p> -<p><span class="math display">\[f_\textrm{twa} = \frac{\left( 1 - e^{- k -t} \right)}{k t} \]</span></p> +<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>f</mi><mtext mathvariant="normal">twa</mtext></msub><mo>=</mo><mfrac><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>−</mo><msup><mi>e</mi><mrow><mo>−</mo><mi>k</mi><mi>t</mi></mrow></msup><mo stretchy="true" form="postfix">)</mo></mrow><mrow><mi>k</mi><mi>t</mi></mrow></mfrac></mrow><annotation encoding="application/x-tex">f_\textrm{twa} = \frac{\left( 1 - e^{- k t} \right)}{k t} </annotation></semantics></math></p> <p>FOMC:</p> -<p><span class="math display">\[f_\textrm{twa} = \frac{\beta}{t (1 - -\alpha)} - \left( \left(\frac{t}{\beta} + 1 \right)^{1 - \alpha} -- 1 \right) \]</span></p> +<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>f</mi><mtext mathvariant="normal">twa</mtext></msub><mo>=</mo><mfrac><mi>β</mi><mrow><mi>t</mi><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>−</mo><mi>α</mi><mo stretchy="true" form="postfix">)</mo></mrow></mrow></mfrac><mrow><mo stretchy="true" form="prefix">(</mo><msup><mrow><mo stretchy="true" form="prefix">(</mo><mfrac><mi>t</mi><mi>β</mi></mfrac><mo>+</mo><mn>1</mn><mo stretchy="true" form="postfix">)</mo></mrow><mrow><mn>1</mn><mo>−</mo><mi>α</mi></mrow></msup><mo>−</mo><mn>1</mn><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">f_\textrm{twa} = \frac{\beta}{t (1 - \alpha)} + \left( \left(\frac{t}{\beta} + 1 \right)^{1 - \alpha} - 1 \right) </annotation></semantics></math></p> <p>DFOP:</p> -<p><span class="math display">\[f_\textrm{twa} = \frac{1}{t} \left( +<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>f</mi><mtext mathvariant="normal">twa</mtext></msub><mo>=</mo><mfrac><mn>1</mn><mi>t</mi></mfrac><mrow><mo stretchy="true" form="prefix">(</mo><mfrac><mi>g</mi><msub><mi>k</mi><mn>1</mn></msub></mfrac><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>−</mo><msup><mi>e</mi><mrow><mo>−</mo><msub><mi>k</mi><mn>1</mn></msub><mi>t</mi></mrow></msup><mo stretchy="true" form="postfix">)</mo></mrow><mo>+</mo><mfrac><mrow><mn>1</mn><mo>−</mo><mi>g</mi></mrow><msub><mi>k</mi><mn>2</mn></msub></mfrac><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>−</mo><msup><mi>e</mi><mrow><mo>−</mo><msub><mi>k</mi><mn>2</mn></msub><mi>t</mi></mrow></msup><mo stretchy="true" form="postfix">)</mo></mrow><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">f_\textrm{twa} = \frac{1}{t} \left( \frac{g}{k_1} \left( 1 - e^{- k_1 t} \right) + - \frac{1-g}{k_2} \left( 1 - e^{- k_2 t} \right) \right) \]</span></p> -<p>HS for <span class="math inline">\(t > t_b\)</span>:</p> -<p><span class="math display">\[f_\textrm{twa} = \frac{1}{t} \left( + \frac{1-g}{k_2} \left( 1 - e^{- k_2 t} \right) \right) </annotation></semantics></math></p> +<p>HS for +<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>t</mi><mo>></mo><msub><mi>t</mi><mi>b</mi></msub></mrow><annotation encoding="application/x-tex">t > t_b</annotation></semantics></math>:</p> +<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>f</mi><mtext mathvariant="normal">twa</mtext></msub><mo>=</mo><mfrac><mn>1</mn><mi>t</mi></mfrac><mrow><mo stretchy="true" form="prefix">(</mo><mfrac><mn>1</mn><msub><mi>k</mi><mn>1</mn></msub></mfrac><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>−</mo><msup><mi>e</mi><mrow><mo>−</mo><msub><mi>k</mi><mn>1</mn></msub><msub><mi>t</mi><mi>b</mi></msub></mrow></msup><mo stretchy="true" form="postfix">)</mo></mrow><mo>+</mo><mfrac><msup><mi>e</mi><mrow><mo>−</mo><msub><mi>k</mi><mn>1</mn></msub><msub><mi>t</mi><mi>b</mi></msub></mrow></msup><msub><mi>k</mi><mn>2</mn></msub></mfrac><mrow><mo stretchy="true" form="prefix">(</mo><mn>1</mn><mo>−</mo><msup><mi>e</mi><mrow><mo>−</mo><msub><mi>k</mi><mn>2</mn></msub><mrow><mo stretchy="true" form="prefix">(</mo><mi>t</mi><mo>−</mo><msub><mi>t</mi><mi>b</mi></msub><mo stretchy="true" form="postfix">)</mo></mrow></mrow></msup><mo stretchy="true" form="postfix">)</mo></mrow><mo stretchy="true" form="postfix">)</mo></mrow></mrow><annotation encoding="application/x-tex">f_\textrm{twa} = \frac{1}{t} \left( \frac{1}{k_1} \left( 1 - e^{- k_1 t_b} \right) + - \frac{e^{- k_1 t_b}}{k_2} \left( 1 - e^{- k_2 (t - t_b)} -\right) \right) \]</span></p> + \frac{e^{- k_1 t_b}}{k_2} \left( 1 - e^{- k_2 (t - t_b)} \right) \right) </annotation></semantics></math></p> <p>Note that a method for calculating maximum moving window time weighted average concentrations for a model fitted by ‘mkinfit’ or from parent decline model parameters is included in the @@ -212,33 +156,26 @@ Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in EU Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>. </div> </div> - </div> - - <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - - </div> - + </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2> + </nav></aside> </div> - <footer><div class="copyright"> - <p></p> -<p>Developed by Johannes Ranke.</p> + <footer><div class="pkgdown-footer-left"> + <p>Developed by Johannes Ranke.</p> </div> -<div class="pkgdown"> - 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- </header><div class="row"> - <div class="col-md-9 contents"> - <div class="page-header toc-ignore"> - <h1 data-toc-skip>Example evaluation of FOCUS dataset Z</h1> + +<div class="row"> + <main id="main" class="col-md-9"><div class="page-header"> + + <h1>Example evaluation of FOCUS dataset Z</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> <h4 data-toc-skip class="date">Last change 16 January 2018 -(rebuilt 2023-05-19)</h4> +(rebuilt 2025-02-16)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/FOCUS_Z.rmd" class="external-link"><code>vignettes/web_only/FOCUS_Z.rmd</code></a></small> - <div class="hidden name"><code>FOCUS_Z.rmd</code></div> - + <div class="d-none name"><code>FOCUS_Z.rmd</code></div> </div> @@ -302,25 +251,25 @@ accelerate the optimization.</p> <div class="sourceCode" id="cb34"><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">m.Z.FOCUS</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></code></pre></div> <pre><code><span><span class="co">## Estimate se_notrans t value Pr(>t) Lower Upper</span></span> -<span><span class="co">## Z0_0 96.838822 1.994274 48.5584 4.0280e-42 92.826981 100.850664</span></span> -<span><span class="co">## k_Z0 2.215393 0.118458 18.7019 1.0413e-23 1.989456 2.466989</span></span> -<span><span class="co">## k_Z1 0.478305 0.028258 16.9266 6.2418e-22 0.424708 0.538666</span></span> -<span><span class="co">## k_Z2 0.451627 0.042139 10.7176 1.6314e-14 0.374339 0.544872</span></span> -<span><span class="co">## k_Z3 0.058692 0.015245 3.8499 1.7803e-04 0.034808 0.098965</span></span> -<span><span class="co">## f_Z2_to_Z3 0.471502 0.058351 8.0805 9.6608e-11 0.357769 0.588274</span></span> -<span><span class="co">## sigma 3.984431 0.383402 10.3923 4.5575e-14 3.213126 4.755736</span></span></code></pre> +<span><span class="co">## Z0_0 96.842440 1.994291 48.5598 4.0226e-42 92.830421 100.854459</span></span> +<span><span class="co">## k_Z0 2.215425 0.118457 18.7023 1.0404e-23 1.989490 2.467019</span></span> +<span><span class="co">## k_Z1 0.478307 0.028257 16.9272 6.2332e-22 0.424709 0.538669</span></span> +<span><span class="co">## k_Z2 0.451642 0.042139 10.7178 1.6304e-14 0.374348 0.544894</span></span> +<span><span class="co">## k_Z3 0.058692 0.015245 3.8499 1.7803e-04 0.034804 0.098975</span></span> +<span><span class="co">## f_Z2_to_Z3 0.471483 0.058348 8.0806 9.6585e-11 0.357720 0.588287</span></span> +<span><span class="co">## sigma 3.984431 0.383402 10.3923 4.5576e-14 3.213126 4.755737</span></span></code></pre> <div class="sourceCode" id="cb36"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">m.Z.FOCUS</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## $ff</span></span> <span><span class="co">## Z2_Z3 Z2_sink </span></span> -<span><span class="co">## 0.4715 0.5285 </span></span> +<span><span class="co">## 0.47148 0.52852 </span></span> <span><span class="co">## </span></span> <span><span class="co">## $distimes</span></span> <span><span class="co">## DT50 DT90</span></span> -<span><span class="co">## Z0 0.31288 1.0394</span></span> -<span><span class="co">## Z1 1.44917 4.8141</span></span> -<span><span class="co">## Z2 1.53478 5.0984</span></span> -<span><span class="co">## Z3 11.80986 39.2315</span></span></code></pre> +<span><span class="co">## Z0 0.31287 1.0393</span></span> +<span><span class="co">## Z1 1.44917 4.8140</span></span> +<span><span class="co">## Z2 1.53473 5.0983</span></span> +<span><span class="co">## Z3 11.80991 39.2317</span></span></code></pre> <p>This fit corresponds to the final result chosen in Appendix 7 of the FOCUS report. Confidence intervals returned by mkin are based on internally transformed parameters, however.</p> @@ -334,9 +283,11 @@ not fitted very well using the SFO model, as residues at a certain low level remain.</p> <p>Therefore, an additional model is offered here, using the single first-order reversible binding (SFORB) model for metabolite Z3. As -expected, the <span class="math inline">\(\chi^2\)</span> error level is -lower for metabolite Z3 using this model and the graphical fit for Z3 is -improved. However, the covariance matrix is not returned.</p> +expected, the +<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math> +error level is lower for metabolite Z3 using this model and the +graphical fit for Z3 is improved. However, the covariance matrix is not +returned.</p> <div class="sourceCode" id="cb38"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">Z.mkin.1</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> <span> Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> @@ -443,11 +394,11 @@ obtained.</p> <span><span class="co">## </span></span> <span><span class="co">## $SFORB</span></span> <span><span class="co">## Z0_b1 Z0_b2 Z0_g Z3_b1 Z3_b2 Z3_g </span></span> -<span><span class="co">## 2.4471322 0.0075125 0.9519862 0.0800069 0.0000000 0.9347820 </span></span> +<span><span class="co">## 2.4471342 0.0075124 0.9519866 0.0800071 0.0000000 0.9347816 </span></span> <span><span class="co">## </span></span> <span><span class="co">## $distimes</span></span> <span><span class="co">## DT50 DT90 DT50back DT50_Z0_b1 DT50_Z0_b2 DT50_Z3_b1 DT50_Z3_b2</span></span> -<span><span class="co">## Z0 0.3043 1.1848 0.35666 0.28325 92.266 NA NA</span></span> +<span><span class="co">## Z0 0.3043 1.1848 0.35666 0.28325 92.267 NA NA</span></span> <span><span class="co">## Z1 1.5148 5.0320 NA NA NA NA NA</span></span> <span><span class="co">## Z2 1.6414 5.4526 NA NA NA NA NA</span></span> <span><span class="co">## Z3 NA NA NA NA NA 8.6636 Inf</span></span></code></pre> @@ -470,35 +421,26 @@ Studies on Pesticides in EU Registration</em>. 1.1 ed. <a href="http://esdac.jrc </div> </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> -</div> - + </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2> + </nav></aside> </div> - <footer><div class="copyright"> - <p></p> -<p>Developed by Johannes Ranke.</p> + <footer><div class="pkgdown-footer-left"> + <p>Developed by Johannes Ranke.</p> </div> -<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> +<div class="pkgdown-footer-right"> + <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p> </div> - </footer> + </footer> </div> - - + </body> </html> diff --git a/docs/dev/articles/web_only/FOCUS_Z_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/dev/articles/web_only/FOCUS_Z_files/accessible-code-block-0.0.1/empty-anchor.js deleted file mode 100644 index ca349fd6..00000000 --- a/docs/dev/articles/web_only/FOCUS_Z_files/accessible-code-block-0.0.1/empty-anchor.js +++ /dev/null @@ -1,15 +0,0 @@ -// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> -// v0.0.1 -// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. - -document.addEventListener('DOMContentLoaded', function() { - const codeList = document.getElementsByClassName("sourceCode"); 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on cyantraniliprole</a></li> + <li><a class="dropdown-item" href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li> + <li><a class="dropdown-item" href="../../articles/web_only/multistart.html">Short demo of the multistart method</a></li> + <li><hr class="dropdown-divider"></li> + <li><h6 class="dropdown-header" data-toc-skip>Performance</h6></li> + <li><a class="dropdown-item" href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a></li> + <li><a class="dropdown-item" href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a></li> + <li><a class="dropdown-item" href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a></li> + <li><hr class="dropdown-divider"></li> + <li><h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6></li> + <li><a class="dropdown-item" href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a></li> + <li><a class="dropdown-item" href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a></li> </ul> </li> -<li> - <a href="../../news/index.html">News</a> -</li> +<li class="nav-item"><a class="nav-link" href="../../coverage/coverage.html">Test coverage</a></li> +<li class="nav-item"><a class="nav-link" href="../../news/index.html">News</a></li> </ul> -<ul class="nav navbar-nav navbar-right"> -<li> - <a href="https://github.com/jranke/mkin/" class="external-link"> - <span class="fab fa-github fa-lg"></span> - - </a> -</li> +<ul class="navbar-nav"> +<li class="nav-item"><form class="form-inline" role="search"> + <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../../search.json"> +</form></li> +<li class="nav-item"><a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li> </ul> </div> -<!--/.nav-collapse --> + + </div> -<!--/.container --> -</div> -<!--/.navbar --> +</nav><div class="container template-article"> - - </header><div class="row"> - <div class="col-md-9 contents"> - <div class="page-header toc-ignore"> - <h1 data-toc-skip>Evaluation of example datasets from Attachment 1 -to the US EPA SOP for the NAFTA guidance</h1> + + +<div class="row"> + <main id="main" class="col-md-9"><div class="page-header"> + + <h1>Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> <h4 data-toc-skip class="date">26 February 2019 (rebuilt -2023-05-19)</h4> +2025-02-16)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/NAFTA_examples.rmd" class="external-link"><code>vignettes/web_only/NAFTA_examples.rmd</code></a></small> - <div class="hidden name"><code>NAFTA_examples.rmd</code></div> - + <div class="d-none name"><code>NAFTA_examples.rmd</code></div> </div> @@ -201,7 +149,7 @@ same.</p> <span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> <span><span class="co">## parent_0 9.99e+01 1.41e-26 98.8116 101.0810</span></span> <span><span class="co">## k1 2.67e-02 5.05e-06 0.0243 0.0295</span></span> -<span><span class="co">## k2 2.26e-12 5.00e-01 0.0000 Inf</span></span> +<span><span class="co">## k2 3.41e-12 5.00e-01 0.0000 Inf</span></span> <span><span class="co">## g 6.47e-01 3.67e-06 0.6248 0.6677</span></span> <span><span class="co">## sigma 1.27e+00 8.91e-06 0.8395 1.6929</span></span> <span><span class="co">## </span></span> @@ -210,7 +158,7 @@ same.</p> <span><span class="co">## DT50 DT90 DT50_rep</span></span> <span><span class="co">## SFO 67.7 2.25e+02 6.77e+01</span></span> <span><span class="co">## IORE 58.2 1.07e+03 3.22e+02</span></span> -<span><span class="co">## DFOP 55.5 5.59e+11 3.07e+11</span></span> +<span><span class="co">## DFOP 55.5 3.70e+11 2.03e+11</span></span> <span><span class="co">## </span></span> <span><span class="co">## Representative half-life:</span></span> <span><span class="co">## [1] 321.51</span></span></code></pre> @@ -252,7 +200,7 @@ same.</p> <span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> <span><span class="co">## parent_0 9.84e+01 1.24e-27 97.8078 98.9187</span></span> <span><span class="co">## k1 1.55e-02 4.10e-04 0.0143 0.0167</span></span> -<span><span class="co">## k2 8.63e-12 5.00e-01 0.0000 Inf</span></span> +<span><span class="co">## k2 9.07e-12 5.00e-01 0.0000 Inf</span></span> <span><span class="co">## g 6.89e-01 2.92e-03 0.6626 0.7142</span></span> <span><span class="co">## sigma 6.48e-01 2.38e-05 0.4147 0.8813</span></span> <span><span class="co">## </span></span> @@ -261,7 +209,7 @@ same.</p> <span><span class="co">## DT50 DT90 DT50_rep</span></span> <span><span class="co">## SFO 86.6 2.88e+02 8.66e+01</span></span> <span><span class="co">## IORE 85.5 7.17e+02 2.16e+02</span></span> -<span><span class="co">## DFOP 83.6 1.32e+11 8.04e+10</span></span> +<span><span class="co">## DFOP 83.6 1.25e+11 7.64e+10</span></span> <span><span class="co">## </span></span> <span><span class="co">## Representative half-life:</span></span> <span><span class="co">## [1] 215.87</span></span></code></pre> @@ -303,7 +251,7 @@ same.</p> <span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> <span><span class="co">## parent_0 9.66e+01 1.57e-25 95.3476 97.8979</span></span> <span><span class="co">## k1 2.55e-02 7.33e-06 0.0233 0.0278</span></span> -<span><span class="co">## k2 3.22e-11 5.00e-01 0.0000 Inf</span></span> +<span><span class="co">## k2 3.84e-11 5.00e-01 0.0000 Inf</span></span> <span><span class="co">## g 8.61e-01 7.55e-06 0.8314 0.8867</span></span> <span><span class="co">## sigma 1.46e+00 6.93e-06 0.9661 1.9483</span></span> <span><span class="co">## </span></span> @@ -312,7 +260,7 @@ same.</p> <span><span class="co">## DT50 DT90 DT50_rep</span></span> <span><span class="co">## SFO 38.6 1.28e+02 3.86e+01</span></span> <span><span class="co">## IORE 34.0 1.77e+02 5.32e+01</span></span> -<span><span class="co">## DFOP 34.1 1.01e+10 2.15e+10</span></span> +<span><span class="co">## DFOP 34.1 8.50e+09 1.80e+10</span></span> <span><span class="co">## </span></span> <span><span class="co">## Representative half-life:</span></span> <span><span class="co">## [1] 53.17</span></span></code></pre> @@ -354,7 +302,7 @@ same.</p> <span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> <span><span class="co">## parent_0 9.89e+01 9.44e-49 95.4640 102.2573</span></span> <span><span class="co">## k1 1.81e-02 1.75e-01 0.0116 0.0281</span></span> -<span><span class="co">## k2 3.63e-10 5.00e-01 0.0000 Inf</span></span> +<span><span class="co">## k2 3.62e-10 5.00e-01 0.0000 Inf</span></span> <span><span class="co">## g 6.06e-01 2.19e-01 0.4826 0.7178</span></span> <span><span class="co">## sigma 7.40e+00 2.97e-15 6.0201 8.7754</span></span> <span><span class="co">## </span></span> @@ -363,7 +311,7 @@ same.</p> <span><span class="co">## DT50 DT90 DT50_rep</span></span> <span><span class="co">## SFO 94.3 3.13e+02 9.43e+01</span></span> <span><span class="co">## IORE 96.7 1.51e+03 4.55e+02</span></span> -<span><span class="co">## DFOP 96.4 3.77e+09 1.91e+09</span></span> +<span><span class="co">## DFOP 96.4 3.79e+09 1.92e+09</span></span> <span><span class="co">## </span></span> <span><span class="co">## Representative half-life:</span></span> <span><span class="co">## [1] 454.55</span></span></code></pre> @@ -477,7 +425,7 @@ lower value for the rate constant is used here.</p> <span><span class="co">## DT50 DT90 DT50_rep</span></span> <span><span class="co">## SFO 16.9 5.63e+01 1.69e+01</span></span> <span><span class="co">## IORE 11.6 3.37e+02 1.01e+02</span></span> -<span><span class="co">## DFOP 10.5 1.38e+12 7.69e+11</span></span> +<span><span class="co">## DFOP 10.5 1.38e+12 7.68e+11</span></span> <span><span class="co">## </span></span> <span><span class="co">## Representative half-life:</span></span> <span><span class="co">## [1] 101.43</span></span></code></pre> @@ -490,17 +438,12 @@ suggest a simple exponential decline.</p> </h3> <div class="sourceCode" id="cb37"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">p9b</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p9b"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre> -<pre><code><span><span class="co">## Warning in sqrt(diag(covar_notrans)): NaNs produced</span></span></code></pre> -<pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre> -<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result</span></span> -<span><span class="co">## is doubtful</span></span></code></pre> <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> -<div class="sourceCode" id="cb44"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb40"><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">p9b</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p9b-1.png" width="700"></p> -<div class="sourceCode" id="cb45"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb41"><pre class="downlit sourceCode r"> <code class="sourceCode R"><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="va">p9b</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## Sums of squares:</span></span> <span><span class="co">## SFO IORE DFOP </span></span> @@ -527,8 +470,8 @@ suggest a simple exponential decline.</p> <span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> <span><span class="co">## parent_0 94.7123 1.61e-16 93.1355 96.2891</span></span> <span><span class="co">## k1 0.0389 1.08e-04 0.0266 0.0569</span></span> -<span><span class="co">## k2 0.0389 2.23e-04 0.0255 0.0592</span></span> -<span><span class="co">## g 0.5256 NaN NA NA</span></span> +<span><span class="co">## k2 0.0389 2.24e-04 0.0255 0.0592</span></span> +<span><span class="co">## g 0.5256 5.00e-01 0.0000 1.0000</span></span> <span><span class="co">## sigma 1.5957 2.50e-04 0.9135 2.2779</span></span> <span><span class="co">## </span></span> <span><span class="co">## </span></span> @@ -550,18 +493,15 @@ in PestDF and g in mkin. In mkin, it is restricted to the interval from <div class="section level3"> <h3 id="example-on-page-10">Example on page 10<a class="anchor" aria-label="anchor" href="#example-on-page-10"></a> </h3> -<div class="sourceCode" id="cb47"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb43"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">p10</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p10"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre> -<pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre> -<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result</span></span> -<span><span class="co">## is doubtful</span></span></code></pre> +<pre><code><span><span class="co">## Warning in sqrt(diag(covar_notrans)): NaNs produced</span></span></code></pre> <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> -<div class="sourceCode" id="cb53"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb47"><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">p10</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p10-1.png" width="700"></p> -<div class="sourceCode" id="cb54"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb48"><pre class="downlit sourceCode r"> <code class="sourceCode R"><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="va">p10</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## Sums of squares:</span></span> <span><span class="co">## SFO IORE DFOP </span></span> @@ -585,12 +525,12 @@ in PestDF and g in mkin. In mkin, it is restricted to the interval from <span><span class="co">## sigma 4.90 1.77e-04 2.837 6.968</span></span> <span><span class="co">## </span></span> <span><span class="co">## $DFOP</span></span> -<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> -<span><span class="co">## parent_0 101.7315 1.41e-09 91.6534 111.8097</span></span> -<span><span class="co">## k1 0.0495 6.58e-03 0.0303 0.0809</span></span> -<span><span class="co">## k2 0.0495 2.60e-03 0.0410 0.0598</span></span> -<span><span class="co">## g 0.4487 5.00e-01 NA NA</span></span> -<span><span class="co">## sigma 8.0152 2.50e-04 4.5886 11.4418</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 101.7315 1.41e-09 91.6534 111.810</span></span> +<span><span class="co">## k1 0.0495 3.04e-03 0.0188 0.131</span></span> +<span><span class="co">## k2 0.0495 4.92e-04 0.0197 0.124</span></span> +<span><span class="co">## g 0.4487 NaN 0.0000 1.000</span></span> +<span><span class="co">## sigma 8.0152 2.50e-04 4.5886 11.442</span></span> <span><span class="co">## </span></span> <span><span class="co">## </span></span> <span><span class="co">## DTx values:</span></span> @@ -614,14 +554,14 @@ difference in IORE model parameters between PestDF and mkin.</p> <div class="section level3"> <h3 id="example-on-page-11">Example on page 11<a class="anchor" aria-label="anchor" href="#example-on-page-11"></a> </h3> -<div class="sourceCode" id="cb56"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb50"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">p11</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p11"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> -<div class="sourceCode" id="cb59"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb53"><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">p11</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p11-1.png" width="700"></p> -<div class="sourceCode" id="cb60"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb54"><pre class="downlit sourceCode r"> <code class="sourceCode R"><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="va">p11</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## Sums of squares:</span></span> <span><span class="co">## SFO IORE DFOP </span></span> @@ -660,7 +600,7 @@ difference in IORE model parameters between PestDF and mkin.</p> <span><span class="co">## DFOP 3.07e+11 1.93e+12 6.98e+11</span></span> <span><span class="co">## </span></span> <span><span class="co">## Representative half-life:</span></span> -<span><span class="co">## [1] 41148170</span></span></code></pre> +<span><span class="co">## [1] 41148169</span></span></code></pre> <p>In this case, the DFOP fit reported for PestDF resulted in a negative value for the slower rate constant, which is not possible in mkin. The other results are in agreement.</p> @@ -677,21 +617,17 @@ overparameterisation.</p> <div class="section level3"> <h3 id="example-on-page-12-upper-panel">Example on page 12, upper panel<a class="anchor" aria-label="anchor" href="#example-on-page-12-upper-panel"></a> </h3> -<div class="sourceCode" id="cb62"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb56"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">p12a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p12a"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## Warning in summary.mkinfit(x): Could not calculate correlation; no covariance</span></span> <span><span class="co">## matrix</span></span></code></pre> -<pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre> <pre><code><span><span class="co">## Warning in sqrt(diag(covar_notrans)): NaNs produced</span></span></code></pre> -<pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre> -<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result</span></span> -<span><span class="co">## is doubtful</span></span></code></pre> <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> -<div class="sourceCode" id="cb70"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb61"><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">p12a</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p12a-1.png" width="700"></p> -<div class="sourceCode" id="cb71"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb62"><pre class="downlit sourceCode r"> <code class="sourceCode R"><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="va">p12a</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## Sums of squares:</span></span> <span><span class="co">## SFO IORE DFOP </span></span> @@ -719,7 +655,7 @@ overparameterisation.</p> <span><span class="co">## parent_0 100.521 2.74e-10 92.2366 108.805</span></span> <span><span class="co">## k1 0.124 2.53e-05 0.0908 0.170</span></span> <span><span class="co">## k2 0.124 2.52e-02 0.0456 0.339</span></span> -<span><span class="co">## g 0.793 NaN NA NA</span></span> +<span><span class="co">## g 0.793 NaN 0.0000 1.000</span></span> <span><span class="co">## sigma 7.048 2.50e-04 4.0349 10.061</span></span> <span><span class="co">## </span></span> <span><span class="co">## </span></span> @@ -735,18 +671,20 @@ overparameterisation.</p> <div class="section level3"> <h3 id="example-on-page-12-lower-panel">Example on page 12, lower panel<a class="anchor" aria-label="anchor" href="#example-on-page-12-lower-panel"></a> </h3> -<div class="sourceCode" id="cb73"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb64"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">p12b</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p12b"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre> <pre><code><span><span class="co">## Warning in qt(alpha/2, rdf): NaNs produced</span></span></code></pre> <pre><code><span><span class="co">## Warning in qt(1 - alpha/2, rdf): NaNs produced</span></span></code></pre> -<pre><code><span><span class="co">## Warning in sqrt(diag(covar_notrans)): NaNs produced</span></span></code></pre> <pre><code><span><span class="co">## Warning in pt(abs(tval), rdf, lower.tail = FALSE): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(V) had non-positive or NA entries; the</span></span> +<span><span class="co">## non-finite result may be dubious</span></span></code></pre> <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> -<div class="sourceCode" id="cb80"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb72"><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">p12b</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p12b-1.png" width="700"></p> -<div class="sourceCode" id="cb81"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb73"><pre class="downlit sourceCode r"> <code class="sourceCode R"><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="va">p12b</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## Sums of squares:</span></span> <span><span class="co">## SFO IORE DFOP </span></span> @@ -790,14 +728,14 @@ overparameterisation.</p> <div class="section level3"> <h3 id="example-on-page-13">Example on page 13<a class="anchor" aria-label="anchor" href="#example-on-page-13"></a> </h3> -<div class="sourceCode" id="cb83"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb75"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">p13</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p13"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> -<div class="sourceCode" id="cb86"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb78"><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">p13</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p13-1.png" width="700"></p> -<div class="sourceCode" id="cb87"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb79"><pre class="downlit sourceCode r"> <code class="sourceCode R"><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="va">p13</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## Sums of squares:</span></span> <span><span class="co">## SFO IORE DFOP </span></span> @@ -823,8 +761,8 @@ overparameterisation.</p> <span><span class="co">## $DFOP</span></span> <span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> <span><span class="co">## parent_0 92.73500 NA 8.95e+01 95.92118</span></span> -<span><span class="co">## k1 0.00258 NA 4.14e-04 0.01611</span></span> -<span><span class="co">## k2 0.00258 NA 1.74e-03 0.00383</span></span> +<span><span class="co">## k1 0.00258 NA 4.18e-04 0.01592</span></span> +<span><span class="co">## k2 0.00258 NA 1.75e-03 0.00381</span></span> <span><span class="co">## g 0.16452 NA 0.00e+00 1.00000</span></span> <span><span class="co">## sigma 3.41172 NA 2.02e+00 4.79960</span></span> <span><span class="co">## </span></span> @@ -842,18 +780,17 @@ overparameterisation.</p> <div class="section level2"> <h2 id="dt50-not-observed-in-the-study-and-dfop-problems-in-pestdf">DT50 not observed in the study and DFOP problems in PestDF<a class="anchor" aria-label="anchor" href="#dt50-not-observed-in-the-study-and-dfop-problems-in-pestdf"></a> </h2> -<div class="sourceCode" id="cb89"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb81"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">p14</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p14"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre> -<pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre> -<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result</span></span> -<span><span class="co">## is doubtful</span></span></code></pre> +<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(V) had non-positive or NA entries; the</span></span> +<span><span class="co">## non-finite result may be dubious</span></span></code></pre> <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> -<div class="sourceCode" id="cb95"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb86"><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">p14</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p14-1.png" width="700"></p> -<div class="sourceCode" id="cb96"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb87"><pre class="downlit sourceCode r"> <code class="sourceCode R"><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="va">p14</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## Sums of squares:</span></span> <span><span class="co">## SFO IORE DFOP </span></span> @@ -880,7 +817,7 @@ overparameterisation.</p> <span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> <span><span class="co">## parent_0 1.00e+02 2.96e-28 99.40280 101.2768</span></span> <span><span class="co">## k1 9.53e-03 1.20e-01 0.00638 0.0143</span></span> -<span><span class="co">## k2 6.08e-12 5.00e-01 0.00000 Inf</span></span> +<span><span class="co">## k2 5.21e-12 5.00e-01 0.00000 Inf</span></span> <span><span class="co">## g 3.98e-01 2.19e-01 0.30481 0.4998</span></span> <span><span class="co">## sigma 1.17e+00 7.68e-06 0.77406 1.5610</span></span> <span><span class="co">## </span></span> @@ -889,7 +826,7 @@ overparameterisation.</p> <span><span class="co">## DT50 DT90 DT50_rep</span></span> <span><span class="co">## SFO 2.48e+02 8.25e+02 2.48e+02</span></span> <span><span class="co">## IORE 4.34e+02 2.22e+04 6.70e+03</span></span> -<span><span class="co">## DFOP 3.05e+10 2.95e+11 1.14e+11</span></span> +<span><span class="co">## DFOP 3.55e+10 3.44e+11 1.33e+11</span></span> <span><span class="co">## </span></span> <span><span class="co">## Representative half-life:</span></span> <span><span class="co">## [1] 6697.44</span></span></code></pre> @@ -900,14 +837,17 @@ same results in mkin and PestDF.</p> <div class="section level2"> <h2 id="n-is-less-than-1-and-dfop-fraction-parameter-is-below-zero">N is less than 1 and DFOP fraction parameter is below zero<a class="anchor" aria-label="anchor" href="#n-is-less-than-1-and-dfop-fraction-parameter-is-below-zero"></a> </h2> -<div class="sourceCode" id="cb98"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb89"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">p15a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p15a"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre> +<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(V) had non-positive or NA entries; the</span></span> +<span><span class="co">## non-finite result may be dubious</span></span></code></pre> <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> -<div class="sourceCode" id="cb101"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb94"><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">p15a</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p15a-1.png" width="700"></p> -<div class="sourceCode" id="cb102"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb95"><pre class="downlit sourceCode r"> <code class="sourceCode R"><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="va">p15a</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## Sums of squares:</span></span> <span><span class="co">## SFO IORE DFOP </span></span> @@ -933,9 +873,9 @@ same results in mkin and PestDF.</p> <span><span class="co">## $DFOP</span></span> <span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> <span><span class="co">## parent_0 97.96751 2.85e-13 94.21913 101.7159</span></span> -<span><span class="co">## k1 0.00952 6.28e-02 0.00250 0.0363</span></span> -<span><span class="co">## k2 0.00952 1.27e-04 0.00646 0.0140</span></span> -<span><span class="co">## g 0.21241 5.00e-01 0.00000 1.0000</span></span> +<span><span class="co">## k1 0.00952 6.28e-02 0.00260 0.0349</span></span> +<span><span class="co">## k2 0.00952 1.27e-04 0.00652 0.0139</span></span> +<span><span class="co">## g 0.21241 5.00e-01 NA NA</span></span> <span><span class="co">## sigma 4.18778 2.50e-04 2.39747 5.9781</span></span> <span><span class="co">## </span></span> <span><span class="co">## </span></span> @@ -947,18 +887,16 @@ same results in mkin and PestDF.</p> <span><span class="co">## </span></span> <span><span class="co">## Representative half-life:</span></span> <span><span class="co">## [1] 41.33</span></span></code></pre> -<div class="sourceCode" id="cb104"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb97"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">p15b</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p15b"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre> -<pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre> -<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result</span></span> -<span><span class="co">## is doubtful</span></span></code></pre> +<pre><code><span><span class="co">## Warning in summary.mkinfit(x): Could not calculate correlation; no covariance</span></span> +<span><span class="co">## matrix</span></span></code></pre> <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> -<div class="sourceCode" id="cb110"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb101"><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">p15b</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p15b-1.png" width="700"></p> -<div class="sourceCode" id="cb111"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb102"><pre class="downlit sourceCode r"> <code class="sourceCode R"><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="va">p15b</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## Sums of squares:</span></span> <span><span class="co">## SFO IORE DFOP </span></span> @@ -976,18 +914,18 @@ same results in mkin and PestDF.</p> <span><span class="co">## </span></span> <span><span class="co">## $IORE</span></span> <span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> -<span><span class="co">## parent_0 99.83 1.81e-16 97.51349 102.14</span></span> +<span><span class="co">## parent_0 99.83 1.81e-16 97.51348 102.14</span></span> <span><span class="co">## k__iore_parent 0.38 3.22e-01 0.00352 41.05</span></span> <span><span class="co">## N_parent 0.00 5.00e-01 -1.07696 1.08</span></span> <span><span class="co">## sigma 2.21 2.57e-04 1.23245 3.19</span></span> <span><span class="co">## </span></span> <span><span class="co">## $DFOP</span></span> -<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> -<span><span class="co">## parent_0 1.01e+02 NA 9.82e+01 1.04e+02</span></span> -<span><span class="co">## k1 4.86e-03 NA 8.63e-04 2.73e-02</span></span> -<span><span class="co">## k2 4.86e-03 NA 3.21e-03 7.35e-03</span></span> -<span><span class="co">## g 1.88e-01 NA NA NA</span></span> -<span><span class="co">## sigma 2.76e+00 NA 1.58e+00 3.94e+00</span></span> +<span><span class="co">## Estimate Pr(>t) Lower Upper</span></span> +<span><span class="co">## parent_0 1.01e+02 NA NA NA</span></span> +<span><span class="co">## k1 4.86e-03 NA NA NA</span></span> +<span><span class="co">## k2 4.86e-03 NA NA NA</span></span> +<span><span class="co">## g 1.88e-01 NA NA NA</span></span> +<span><span class="co">## sigma 2.76e+00 NA NA NA</span></span> <span><span class="co">## </span></span> <span><span class="co">## </span></span> <span><span class="co">## DTx values:</span></span> @@ -1006,16 +944,16 @@ mkin and PestDF.</p> <div class="section level2"> <h2 id="the-dfop-fraction-parameter-is-greater-than-1">The DFOP fraction parameter is greater than 1<a class="anchor" aria-label="anchor" href="#the-dfop-fraction-parameter-is-greater-than-1"></a> </h2> -<div class="sourceCode" id="cb113"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb104"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">p16</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p16"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> <pre><code><span><span class="co">## The representative half-life of the IORE model is longer than the one corresponding</span></span></code></pre> <pre><code><span><span class="co">## to the terminal degradation rate found with the DFOP model.</span></span></code></pre> <pre><code><span><span class="co">## The representative half-life obtained from the DFOP model may be used</span></span></code></pre> -<div class="sourceCode" id="cb118"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb109"><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">p16</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p16-1.png" width="700"></p> -<div class="sourceCode" id="cb119"><pre class="downlit sourceCode r"> +<div class="sourceCode" id="cb110"><pre class="downlit sourceCode r"> <code class="sourceCode R"><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="va">p16</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## Sums of squares:</span></span> <span><span class="co">## SFO IORE DFOP </span></span> @@ -1056,10 +994,12 @@ mkin and PestDF.</p> <span><span class="co">## Representative half-life:</span></span> <span><span class="co">## [1] 8.93</span></span></code></pre> <p>In PestDF, the DFOP fit seems to have stuck in a local minimum, as -mkin finds a solution with a much lower <span class="math inline">\(\chi^2\)</span> error level. As the half-life from -the slower rate constant of the DFOP model is larger than the IORE -derived half-life, the NAFTA recommendation obtained with mkin is to use -the DFOP representative half-life of 8.9 days.</p> +mkin finds a solution with a much lower +<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><msup><mi>χ</mi><mn>2</mn></msup><annotation encoding="application/x-tex">\chi^2</annotation></semantics></math> +error level. As the half-life from the slower rate constant of the DFOP +model is larger than the IORE derived half-life, the NAFTA +recommendation obtained with mkin is to use the DFOP representative +half-life of 8.9 days.</p> </div> <div class="section level2"> <h2 id="conclusions">Conclusions<a class="anchor" aria-label="anchor" href="#conclusions"></a> @@ -1084,35 +1024,26 @@ Pesticide Degradation.”</span> <a href="https://www.epa.gov/pesticide-science- </div> </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> -</div> - + </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2> + </nav></aside> </div> - <footer><div class="copyright"> - <p></p> -<p>Developed by Johannes Ranke.</p> + <footer><div class="pkgdown-footer-left"> + <p>Developed by Johannes Ranke.</p> </div> -<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> +<div class="pkgdown-footer-right"> + <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p> </div> - </footer> + </footer> </div> - - + </body> </html> diff --git a/docs/dev/articles/web_only/NAFTA_examples_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/dev/articles/web_only/NAFTA_examples_files/accessible-code-block-0.0.1/empty-anchor.js deleted file mode 100644 index ca349fd6..00000000 --- a/docs/dev/articles/web_only/NAFTA_examples_files/accessible-code-block-0.0.1/empty-anchor.js +++ /dev/null @@ -1,15 +0,0 @@ -// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> -// v0.0.1 -// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. - -document.addEventListener('DOMContentLoaded', function() { - const codeList = document.getElementsByClassName("sourceCode"); - for (var i = 0; i < codeList.length; i++) { - var linkList = codeList[i].getElementsByTagName('a'); - for (var j = 0; j < linkList.length; j++) { - if (linkList[j].innerHTML === "") { - linkList[j].setAttribute('aria-hidden', 'true'); - } - } - } -}); diff --git a/docs/dev/articles/web_only/NAFTA_examples_files/anchor-sections-1.0/anchor-sections.css b/docs/dev/articles/web_only/NAFTA_examples_files/anchor-sections-1.0/anchor-sections.css deleted file mode 100644 index 07aee5fc..00000000 --- a/docs/dev/articles/web_only/NAFTA_examples_files/anchor-sections-1.0/anchor-sections.css +++ /dev/null @@ -1,4 +0,0 @@ -/* Styles for section anchors */ -a.anchor-section {margin-left: 10px; visibility: hidden; color: inherit;} -a.anchor-section::before {content: '#';} -.hasAnchor:hover a.anchor-section {visibility: visible;} diff --git a/docs/dev/articles/web_only/NAFTA_examples_files/anchor-sections-1.0/anchor-sections.js b/docs/dev/articles/web_only/NAFTA_examples_files/anchor-sections-1.0/anchor-sections.js deleted file mode 100644 index 570f99a0..00000000 --- a/docs/dev/articles/web_only/NAFTA_examples_files/anchor-sections-1.0/anchor-sections.js +++ /dev/null @@ -1,33 +0,0 @@ -// Anchor sections v1.0 written by Atsushi Yasumoto on Oct 3rd, 2020. -document.addEventListener('DOMContentLoaded', function() { - // Do nothing if AnchorJS is used - if (typeof window.anchors === 'object' && anchors.hasOwnProperty('hasAnchorJSLink')) { - return; - } - - const h = document.querySelectorAll('h1, h2, h3, h4, h5, h6'); - - // Do nothing if sections are already anchored - if (Array.from(h).some(x => x.classList.contains('hasAnchor'))) { - return null; - } - - // Use section id when pandoc runs with --section-divs - const section_id = function(x) { - return ((x.classList.contains('section') || (x.tagName === 'SECTION')) - ? 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- </header><div class="row"> - <div class="col-md-9 contents"> - <div class="page-header toc-ignore"> - <h1 data-toc-skip>Benchmark timings for mkin</h1> +<div class="row"> + <main id="main" class="col-md-9"><div class="page-header"> + + <h1>Benchmark timings for mkin</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> <h4 data-toc-skip class="date">Last change 17 February 2023 -(rebuilt 2023-05-19)</h4> +(rebuilt 2025-02-16)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/benchmarks.rmd" class="external-link"><code>vignettes/web_only/benchmarks.rmd</code></a></small> - <div class="hidden name"><code>benchmarks.rmd</code></div> - + <div class="d-none name"><code>benchmarks.rmd</code></div> </div> @@ -232,7 +181,15 @@ systems. All trademarks belong to their respective owners.</p> </h3> <p>Constant variance (t1) and two-component error model (t2) for four models fitted to two datasets, i.e. eight fits for each test.</p> -<table class="table"> +<table style="width:100%;" class="table"> +<colgroup> +<col width="8%"> +<col width="54%"> +<col width="8%"> +<col width="12%"> +<col width="8%"> +<col width="9%"> +</colgroup> <thead><tr class="header"> <th align="left">OS</th> <th align="left">CPU</th> @@ -436,11 +393,43 @@ models fitted to two datasets, i.e. eight fits for each test.</p> </tr> <tr class="odd"> <td align="left">Linux</td> -<td align="left">Ryzen 9 7950X</td> -<td align="left">4.3.0</td> +<td align="left">Intel(R) Xeon(R) Gold 6134 CPU @ 3.20GHz</td> +<td align="left">4.3.1</td> <td align="left">1.2.5</td> -<td align="right">1.400</td> -<td align="right">1.936</td> +<td align="right">2.369</td> +<td align="right">3.632</td> +</tr> +<tr class="even"> +<td align="left">Linux</td> +<td align="left">Intel(R) Xeon(R) Gold 6134 CPU @ 3.20GHz</td> +<td align="left">4.3.1</td> +<td align="left">1.2.6</td> +<td align="right">2.856</td> +<td align="right">4.960</td> +</tr> +<tr class="odd"> +<td align="left">Linux</td> +<td align="left">Ryzen 9 7950X</td> +<td align="left">4.3.2</td> +<td align="left">1.2.6</td> +<td align="right">1.408</td> +<td align="right">2.041</td> +</tr> +<tr class="even"> +<td align="left">Linux</td> +<td align="left">Ryzen 9 7950X</td> +<td align="left">4.4.2</td> +<td align="left">1.2.9</td> +<td align="right">1.323</td> +<td align="right">1.925</td> +</tr> +<tr class="odd"> +<td align="left">Linux</td> +<td align="left">Ryzen 9 7950X</td> +<td align="left">4.4.2</td> +<td align="left">1.2.10</td> +<td align="right">1.407</td> +<td align="right">1.990</td> </tr> </tbody> </table> @@ -452,6 +441,15 @@ models fitted to two datasets, i.e. eight fits for each test.</p> by variable (t5) for three models fitted to one dataset, i.e. three fits for each test.</p> <table class="table"> +<colgroup> +<col width="7%"> +<col width="50%"> +<col width="7%"> +<col width="11%"> +<col width="7%"> +<col width="8%"> +<col width="7%"> +</colgroup> <thead><tr class="header"> <th align="left">OS</th> <th align="left">CPU</th> @@ -680,12 +678,48 @@ for each test.</p> </tr> <tr class="odd"> <td align="left">Linux</td> -<td align="left">Ryzen 9 7950X</td> -<td align="left">4.3.0</td> +<td align="left">Intel(R) Xeon(R) Gold 6134 CPU @ 3.20GHz</td> +<td align="left">4.3.1</td> <td align="left">1.2.5</td> -<td align="right">0.800</td> -<td align="right">2.118</td> -<td align="right">1.172</td> +<td align="right">1.823</td> +<td align="right">5.555</td> +<td align="right">2.404</td> +</tr> +<tr class="even"> +<td align="left">Linux</td> +<td align="left">Intel(R) Xeon(R) Gold 6134 CPU @ 3.20GHz</td> +<td align="left">4.3.1</td> +<td align="left">1.2.6</td> +<td align="right">1.761</td> +<td align="right">5.405</td> +<td align="right">2.462</td> +</tr> +<tr class="odd"> +<td align="left">Linux</td> +<td align="left">Ryzen 9 7950X</td> +<td align="left">4.3.2</td> +<td align="left">1.2.6</td> +<td align="right">0.795</td> +<td align="right">2.228</td> +<td align="right">1.178</td> +</tr> +<tr class="even"> +<td align="left">Linux</td> +<td align="left">Ryzen 9 7950X</td> +<td align="left">4.4.2</td> +<td align="left">1.2.9</td> +<td align="right">0.754</td> +<td align="right">2.153</td> +<td align="right">1.139</td> +</tr> +<tr class="odd"> +<td align="left">Linux</td> +<td align="left">Ryzen 9 7950X</td> +<td align="left">4.4.2</td> +<td align="left">1.2.10</td> +<td align="right">0.775</td> +<td align="right">2.199</td> +<td align="right">1.161</td> </tr> </tbody> </table> @@ -696,18 +730,18 @@ for each test.</p> <p>Constant variance (t6 and t7), two-component error model (t8 and t9), and variance by variable (t10 and t11) for one model fitted to one dataset, i.e. one fit for each test.</p> -<table class="table"> +<table style="width:100%;" class="table"> <colgroup> +<col width="5%"> +<col width="40%"> +<col width="5%"> <col width="8%"> -<col width="19%"> -<col width="8%"> -<col width="12%"> -<col width="8%"> -<col width="8%"> -<col width="8%"> -<col width="9%"> -<col width="8%"> -<col width="9%"> +<col width="5%"> +<col width="5%"> +<col width="5%"> +<col width="6%"> +<col width="5%"> +<col width="6%"> </colgroup> <thead><tr class="header"> <th align="left">OS</th> @@ -1012,49 +1046,88 @@ dataset, i.e. one fit for each test.</p> </tr> <tr class="odd"> <td align="left">Linux</td> -<td align="left">Ryzen 9 7950X</td> -<td align="left">4.3.0</td> +<td align="left">Intel(R) Xeon(R) Gold 6134 CPU @ 3.20GHz</td> +<td align="left">4.3.1</td> <td align="left">1.2.5</td> +<td align="right">0.798</td> +<td align="right">1.096</td> +<td align="right">1.217</td> +<td align="right">3.173</td> +<td align="right">1.634</td> +<td align="right">2.271</td> +</tr> +<tr class="even"> +<td align="left">Linux</td> +<td align="left">Intel(R) Xeon(R) Gold 6134 CPU @ 3.20GHz</td> +<td align="left">4.3.1</td> +<td align="left">1.2.6</td> +<td align="right">0.813</td> +<td align="right">1.136</td> +<td align="right">1.220</td> +<td align="right">3.114</td> +<td align="right">1.598</td> +<td align="right">2.255</td> +</tr> +<tr class="odd"> +<td align="left">Linux</td> +<td align="left">Ryzen 9 7950X</td> +<td align="left">4.3.2</td> +<td align="left">1.2.6</td> +<td align="right">0.439</td> +<td align="right">0.557</td> +<td align="right">0.585</td> +<td align="right">1.338</td> +<td align="right">0.749</td> +<td align="right">0.999</td> +</tr> +<tr class="even"> +<td align="left">Linux</td> +<td align="left">Ryzen 9 7950X</td> +<td align="left">4.4.2</td> +<td align="left">1.2.9</td> +<td align="right">0.424</td> +<td align="right">0.534</td> +<td align="right">0.560</td> +<td align="right">1.298</td> +<td align="right">0.735</td> +<td align="right">0.981</td> +</tr> +<tr class="odd"> +<td align="left">Linux</td> +<td align="left">Ryzen 9 7950X</td> +<td align="left">4.4.2</td> +<td align="left">1.2.10</td> <td align="right">0.433</td> -<td align="right">0.558</td> -<td align="right">0.618</td> -<td align="right">1.053</td> -<td align="right">0.745</td> -<td align="right">0.986</td> +<td align="right">0.541</td> +<td align="right">0.576</td> +<td align="right">1.307</td> +<td align="right">0.744</td> +<td align="right">0.988</td> </tr> </tbody> </table> </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> -</div> - + </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2> + </nav></aside> </div> - <footer><div class="copyright"> - <p></p> -<p>Developed by Johannes Ranke.</p> + <footer><div class="pkgdown-footer-left"> + <p>Developed by Johannes Ranke.</p> </div> -<div class="pkgdown"> - <p></p> -<p>Site built with <a 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- </header><div class="row"> - <div class="col-md-9 contents"> - <div class="page-header toc-ignore"> - <h1 data-toc-skip>Performance benefit by using compiled model -definitions in mkin</h1> + + +<div class="row"> + <main id="main" class="col-md-9"><div class="page-header"> + + <h1>Performance benefit by using compiled model definitions in mkin</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> - <h4 data-toc-skip class="date">2023-05-19</h4> + <h4 data-toc-skip class="date">2025-02-16</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/compiled_models.rmd" class="external-link"><code>vignettes/web_only/compiled_models.rmd</code></a></small> - <div class="hidden name"><code>compiled_models.rmd</code></div> - + <div class="d-none name"><code>compiled_models.rmd</code></div> </div> @@ -153,7 +101,7 @@ compiled from autogenerated C code when defining a model using mkinmod. Starting from version 0.9.49.9, the <code><a href="../../reference/mkinmod.html">mkinmod()</a></code> function checks for presence of a compiler using</p> <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu">pkgbuild</span><span class="fu">::</span><span class="fu"><a href="https://r-lib.github.io/pkgbuild/reference/has_compiler.html" class="external-link">has_compiler</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu">pkgbuild</span><span class="fu">::</span><span class="fu"><a href="https://pkgbuild.r-lib.org/reference/has_compiler.html" class="external-link">has_compiler</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> <p>In previous versions, it used <code>Sys.which("gcc")</code> for this check.</p> <p>On Linux, you need to have the essential build tools like make and @@ -214,10 +162,10 @@ solution is also implemented, which is included in the tests below.</p> <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="st">"R package rbenchmark is not available"</span><span class="op">)</span></span> <span><span class="op">}</span></span></code></pre></div> <pre><code><span><span class="co">## test replications relative elapsed</span></span> -<span><span class="co">## 4 analytical 1 1.000 0.108</span></span> -<span><span class="co">## 3 deSolve, compiled 1 1.259 0.136</span></span> -<span><span class="co">## 2 Eigenvalue based 1 1.704 0.184</span></span> -<span><span class="co">## 1 deSolve, not compiled 1 21.574 2.330</span></span></code></pre> +<span><span class="co">## 4 analytical 1 1.000 0.115</span></span> +<span><span class="co">## 3 deSolve, compiled 1 1.217 0.140</span></span> +<span><span class="co">## 2 Eigenvalue based 1 1.652 0.190</span></span> +<span><span class="co">## 1 deSolve, not compiled 1 20.522 2.360</span></span></code></pre> <p>We see that using the compiled model is by more than a factor of 10 faster than using deSolve without compiled code.</p> </div> @@ -248,45 +196,36 @@ compiled code is available.</p> <span><span class="op">}</span></span></code></pre></div> <pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre> <pre><code><span><span class="co">## test replications relative elapsed</span></span> -<span><span class="co">## 2 deSolve, compiled 1 1.000 0.178</span></span> -<span><span class="co">## 1 deSolve, not compiled 1 23.708 4.220</span></span></code></pre> +<span><span class="co">## 2 deSolve, compiled 1 1.000 0.180</span></span> +<span><span class="co">## 1 deSolve, not compiled 1 23.656 4.258</span></span></code></pre> <p>Here we get a performance benefit of a factor of 24 using the version of the differential equation model compiled from C code!</p> -<p>This vignette was built with mkin 1.2.5 on</p> -<pre><code><span><span class="co">## R version 4.3.0 (2023-04-21)</span></span> -<span><span class="co">## Platform: x86_64-pc-linux-gnu (64-bit)</span></span> +<p>This vignette was built with mkin 1.2.10 on</p> +<pre><code><span><span class="co">## R version 4.4.2 (2024-10-31)</span></span> +<span><span class="co">## Platform: x86_64-pc-linux-gnu</span></span> <span><span class="co">## Running under: Debian GNU/Linux 12 (bookworm)</span></span></code></pre> <pre><code><span><span class="co">## CPU model: AMD Ryzen 9 7950X 16-Core Processor</span></span></code></pre> </div> - 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- </header><div class="row"> - <div class="col-md-9 contents"> - <div class="page-header toc-ignore"> - <h1 data-toc-skip>Example evaluations of the dimethenamid data -from 2018</h1> + + +<div class="row"> + <main id="main" class="col-md-9"><div class="page-header"> + + <h1>Example evaluations of the dimethenamid data from 2018</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> <h4 data-toc-skip class="date">Last change 1 July 2022, -built on 19 May 2023</h4> +built on 16 Feb 2025</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/dimethenamid_2018.rmd" class="external-link"><code>vignettes/web_only/dimethenamid_2018.rmd</code></a></small> - <div class="hidden name"><code>dimethenamid_2018.rmd</code></div> - + <div class="d-none name"><code>dimethenamid_2018.rmd</code></div> </div> @@ -264,7 +212,7 @@ Status of individual fits: dataset model Calke Borstel Flaach BBA 2.2 BBA 2.3 Elliot - DFOP OK OK C OK C OK + DFOP OK OK OK OK C OK C: Optimisation did not converge: iteration limit reached without convergence (10) @@ -320,7 +268,7 @@ indicates that this difference is significant as the p-value is below <pre><code> Model df AIC BIC logLik Test L.Ratio p-value f_parent_nlme_sfo_const 1 5 796.60 811.82 -393.30 f_parent_nlme_sfo_tc 2 6 798.60 816.86 -393.30 1 vs 2 0.00 0.998 -f_parent_nlme_dfop_tc 3 10 671.91 702.34 -325.96 2 vs 3 134.69 <.0001</code></pre> +f_parent_nlme_dfop_tc 3 10 671.91 702.34 -325.95 2 vs 3 134.69 <.0001</code></pre> <p>In addition to these fits, attempts were also made to include correlations between random effects by using the log Cholesky parameterisation of the matrix specifying them. The code used for these @@ -409,11 +357,11 @@ DMTA_0 97.99583 96.50079 99.4909 k1 0.06377 0.03432 0.0932 k2 0.00848 0.00444 0.0125 g 0.95701 0.91313 1.0009 -a.1 1.82141 1.65122 1.9916 -SD.DMTA_0 1.64787 0.45772 2.8380 +a.1 1.82141 1.60516 2.0377 +SD.DMTA_0 1.64787 0.45729 2.8384 SD.k1 0.57439 0.24731 0.9015 -SD.k2 0.03296 -2.50195 2.5679 -SD.g 1.10266 0.32369 1.8816</code></pre> +SD.k2 0.03296 -2.50524 2.5712 +SD.g 1.10266 0.32354 1.8818</code></pre> <p>While the other parameters converge to credible values, the variance of k2 (<code>omega2.k2</code>) converges to a very small value. The printout of the <code>saem.mmkin</code> model shows that the estimated @@ -447,17 +395,17 @@ Likelihood computed by importance sampling 666 664 -323 Fitted parameters: - estimate lower upper -DMTA_0 98.27617 96.3088 100.2436 -k1 0.06437 0.0337 0.0950 -k2 0.00880 0.0063 0.0113 -g 0.95249 0.9100 0.9949 -a.1 1.06161 0.8625 1.2607 -b.1 0.02967 0.0226 0.0367 -SD.DMTA_0 2.06075 0.4187 3.7028 -SD.k1 0.59357 0.2561 0.9310 -SD.k2 0.00292 -10.2960 10.3019 -SD.g 1.05725 0.3808 1.7337</code></pre> + estimate lower upper +DMTA_0 98.24165 96.29190 100.1914 +k1 0.06421 0.03352 0.0949 +k2 0.00866 0.00617 0.0111 +g 0.95340 0.91218 0.9946 +a.1 1.06463 0.87979 1.2495 +b.1 0.02964 0.02266 0.0366 +SD.DMTA_0 2.03611 0.40361 3.6686 +SD.k1 0.59534 0.25692 0.9338 +SD.k2 0.00042 -73.00540 73.0062 +SD.g 1.04234 0.37189 1.7128</code></pre> <p>Doubling the number of iterations in the first phase of the algorithm leads to a slightly lower likelihood, and therefore to slightly higher AIC and BIC values. With even more iterations, the algorithm stops with @@ -489,8 +437,8 @@ comparison function of the saemix package:</p> SFO const 796.38 795.34 SFO tc 798.38 797.13 DFOP const 705.75 703.88 -DFOP tc 665.65 663.57 -DFOP tc more iterations 665.88 663.80</code></pre> +DFOP tc 665.67 663.59 +DFOP tc more iterations 665.85 663.76</code></pre> <p>In order to check the influence of the likelihood calculation algorithms implemented in saemix, the likelihood from Gaussian quadrature is added to the best fit, and the AIC values obtained from @@ -505,7 +453,7 @@ the three methods are compared.</p> <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="va">AIC_parent_saemix_methods</span><span class="op">)</span></span></code></pre></div> <pre><code> is gq lin -665.65 665.68 665.11 </code></pre> +665.67 665.74 665.13 </code></pre> <p>The AIC values based on importance sampling and Gaussian quadrature are very similar. Using linearisation is known to be less accurate, but still gives a similar value.</p> @@ -530,7 +478,7 @@ using defaults for the fit.</p> <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="va">AIC_parent_saemix_methods_defaults</span><span class="op">)</span></span></code></pre></div> <pre><code> is gq lin -669.77 669.36 670.95 </code></pre> +670.09 669.37 671.29 </code></pre> </div> </div> <div class="section level3"> @@ -578,15 +526,15 @@ iterations second phase, 15 chains).</p> <td align="left">DFOP</td> <td align="left">const</td> <td align="right">NA</td> -<td align="right">709.26</td> +<td align="right">704.95</td> <td align="right">705.75</td> </tr> <tr class="even"> <td align="left">DFOP</td> <td align="left">tc</td> <td align="right">671.91</td> -<td align="right">665.11</td> -<td align="right">665.65</td> +<td align="right">665.13</td> +<td align="right">665.67</td> </tr> </tbody> </table> @@ -615,13 +563,13 @@ satisfactory precision.</p> </h2> <div class="sourceCode" id="cb32"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/sessionInfo.html" class="external-link">sessionInfo</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> -<pre><code>R version 4.3.0 (2023-04-21) -Platform: x86_64-pc-linux-gnu (64-bit) +<pre><code>R version 4.4.2 (2024-10-31) +Platform: x86_64-pc-linux-gnu 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; LAPACK version 3.11.0 +BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0 +LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0 locale: [1] LC_CTYPE=de_DE.UTF-8 LC_NUMERIC=C @@ -638,23 +586,22 @@ attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: -[1] saemix_3.2 npde_3.3 nlme_3.1-162 mkin_1.2.5 knitr_1.42 +[1] nlme_3.1-166 mkin_1.2.10 knitr_1.49 loaded via a namespace (and not attached): - [1] sass_0.4.6 utf8_1.2.3 generics_0.1.3 stringi_1.7.12 - [5] lattice_0.21-8 digest_0.6.31 magrittr_2.0.3 evaluate_0.21 - [9] grid_4.3.0 fastmap_1.1.1 rprojroot_2.0.3 jsonlite_1.8.4 -[13] DBI_1.1.3 mclust_6.0.0 gridExtra_2.3 purrr_1.0.1 -[17] fansi_1.0.4 scales_1.2.1 codetools_0.2-19 textshaping_0.3.6 -[21] jquerylib_0.1.4 cli_3.6.1 rlang_1.1.1 munsell_0.5.0 -[25] cachem_1.0.8 yaml_2.3.7 tools_4.3.0 parallel_4.3.0 -[29] memoise_2.0.1 dplyr_1.1.2 colorspace_2.1-0 ggplot2_3.4.2 -[33] vctrs_0.6.2 R6_2.5.1 zoo_1.8-12 lifecycle_1.0.3 -[37] stringr_1.5.0 fs_1.6.2 ragg_1.2.5 pkgconfig_2.0.3 -[41] desc_1.4.2 pkgdown_2.0.7 bslib_0.4.2 pillar_1.9.0 -[45] gtable_0.3.3 glue_1.6.2 systemfonts_1.0.4 highr_0.10 -[49] xfun_0.39 tibble_3.2.1 lmtest_0.9-40 tidyselect_1.2.0 -[53] htmltools_0.5.5 rmarkdown_2.21 compiler_4.3.0 </code></pre> + [1] gtable_0.3.6 jsonlite_1.8.9 dplyr_1.1.4 compiler_4.4.2 + [5] tidyselect_1.2.1 parallel_4.4.2 gridExtra_2.3 jquerylib_0.1.4 + [9] systemfonts_1.1.0 scales_1.3.0 textshaping_0.4.1 yaml_2.3.10 +[13] fastmap_1.2.0 lattice_0.22-6 ggplot2_3.5.1 R6_2.5.1 +[17] generics_0.1.3 lmtest_0.9-40 MASS_7.3-61 htmlwidgets_1.6.4 +[21] tibble_3.2.1 desc_1.4.3 munsell_0.5.1 bslib_0.8.0 +[25] pillar_1.9.0 rlang_1.1.4 utf8_1.2.4 cachem_1.1.0 +[29] xfun_0.49 fs_1.6.5 sass_0.4.9 cli_3.6.3 +[33] pkgdown_2.1.1 magrittr_2.0.3 digest_0.6.37 grid_4.4.2 +[37] npde_3.5 mclust_6.1.1 lifecycle_1.0.4 vctrs_0.6.5 +[41] evaluate_1.0.1 glue_1.8.0 ragg_1.3.3 zoo_1.8-12 +[45] saemix_3.3 fansi_1.0.6 colorspace_2.1-1 rmarkdown_2.29 +[49] pkgconfig_2.0.3 tools_4.4.2 htmltools_0.5.8.1</code></pre> </div> <div class="section level2"> <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a> @@ -679,35 +626,26 @@ November 2017</span>.”</span> <a href="https://open.efsa.europa.eu/study-inven </div> </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> -</div> - + </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2> + </nav></aside> </div> - <footer><div class="copyright"> - <p></p> -<p>Developed by Johannes Ranke.</p> + <footer><div class="pkgdown-footer-left"> + <p>Developed by Johannes Ranke.</p> </div> -<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> +<div class="pkgdown-footer-right"> + <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p> </div> - </footer> + </footer> </div> - - + </body> </html> diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/accessible-code-block-0.0.1/empty-anchor.js 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We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/dev/articles/web_only/dimethenamid_2018_files/header-attrs-2.9/header-attrs.js b/docs/dev/articles/web_only/dimethenamid_2018_files/header-attrs-2.9/header-attrs.js deleted file mode 100644 index dd57d92e..00000000 --- a/docs/dev/articles/web_only/dimethenamid_2018_files/header-attrs-2.9/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. 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- </header><div class="row"> - <div class="col-md-9 contents"> - <div class="page-header toc-ignore"> - <h1 data-toc-skip>Short demo of the multistart method</h1> +<div class="row"> + <main id="main" class="col-md-9"><div class="page-header"> + + <h1>Short demo of the multistart method</h1> <h4 data-toc-skip class="author">Johannes Ranke</h4> <h4 data-toc-skip class="date">Last change 20 April 2023 -(rebuilt 2023-05-19)</h4> +(rebuilt 2025-02-16)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/multistart.rmd" class="external-link"><code>vignettes/web_only/multistart.rmd</code></a></small> - <div class="hidden name"><code>multistart.rmd</code></div> - + <div class="d-none name"><code>multistart.rmd</code></div> </div> @@ -195,43 +144,36 @@ runs:</p> <pre><code><span><span class="co">## Data: 155 observations of 1 variable(s) grouped in 6 datasets</span></span> <span><span class="co">## </span></span> <span><span class="co">## npar AIC BIC Lik Chisq Df Pr(>Chisq)</span></span> -<span><span class="co">## f_saem_reduced 9 663.73 661.86 -322.86 </span></span> -<span><span class="co">## best(f_saem_reduced_multi) 9 663.69 661.82 -322.85 0.0361 0 </span></span> -<span><span class="co">## f_saem_full 10 669.77 667.69 -324.89 0.0000 1 1</span></span> -<span><span class="co">## best(f_saem_full_multi) 10 665.56 663.48 -322.78 4.2060 0</span></span></code></pre> +<span><span class="co">## f_saem_reduced 9 663.67 661.80 -322.84 </span></span> +<span><span class="co">## best(f_saem_reduced_multi) 9 663.65 661.78 -322.82 0.0219 0 </span></span> +<span><span class="co">## f_saem_full 10 670.09 668.01 -325.05 0.0000 1 1</span></span> +<span><span class="co">## best(f_saem_full_multi) 10 665.61 663.52 -322.80 4.4870 0</span></span></code></pre> <p>The reduced model results in lower AIC and BIC values, so it is clearly preferable. Using multiple starting values gives a large improvement in case of the full model, because it is less well-defined, which impedes convergence. For the reduced model, using multiple starting values only results in a small improvement of the model fit.</p> - </div> - - <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - - </div> - + </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2> + </nav></aside> </div> - <footer><div class="copyright"> - <p></p> -<p>Developed by Johannes Ranke.</p> + <footer><div class="pkgdown-footer-left"> + <p>Developed by Johannes Ranke.</p> </div> -<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> +<div class="pkgdown-footer-right"> + <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p> </div> - </footer> + </footer> </div> - - + </body> </html> diff --git a/docs/dev/articles/web_only/multistart_files/accessible-code-block-0.0.1/empty-anchor.js 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2023-05-19)</h4> +(rebuilt 2025-02-16)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/saem_benchmarks.rmd" class="external-link"><code>vignettes/web_only/saem_benchmarks.rmd</code></a></small> - <div class="hidden name"><code>saem_benchmarks.rmd</code></div> - + <div class="d-none name"><code>saem_benchmarks.rmd</code></div> </div> @@ -239,22 +188,22 @@ explanation of the following preprocessing.</p> <tr class="even"> <td align="left">dfop_tc</td> <td align="right">10</td> -<td align="right">669.8</td> -<td align="right">667.7</td> -<td align="right">-324.9</td> +<td align="right">670.1</td> +<td align="right">668.0</td> +<td align="right">-325.0</td> </tr> <tr class="odd"> <td align="left">sforb_tc</td> <td align="right">10</td> -<td align="right">662.8</td> -<td align="right">660.7</td> +<td align="right">662.9</td> +<td align="right">660.8</td> <td align="right">-321.4</td> </tr> <tr class="even"> <td align="left">hs_tc</td> <td align="right">10</td> -<td align="right">667.3</td> -<td align="right">665.2</td> +<td align="right">667.2</td> +<td align="right">665.1</td> <td align="right">-323.6</td> </tr> </tbody> @@ -332,6 +281,16 @@ systems. All trademarks belong to their respective owners.</p> </h3> <p>Constant variance for SFO, DFOP, SFORB and HS.</p> <table class="table"> +<colgroup> +<col width="48%"> +<col width="7%"> +<col width="8%"> +<col width="8%"> +<col width="7%"> +<col width="7%"> +<col width="7%"> +<col width="7%"> +</colgroup> <thead><tr class="header"> <th align="left">CPU</th> <th align="left">OS</th> @@ -414,19 +373,59 @@ systems. All trademarks belong to their respective owners.</p> <td align="right">2.055</td> </tr> <tr class="even"> +<td align="left">Intel(R) Xeon(R) Gold 6134 CPU @ 3.20GHz</td> +<td align="left">Linux</td> +<td align="left">1.2.6</td> +<td align="left">3.2</td> +<td align="right">2.998</td> +<td align="right">6.523</td> +<td align="right">6.126</td> +<td align="right">4.721</td> +</tr> +<tr class="odd"> <td align="left">Ryzen 9 7950X</td> <td align="left">Linux</td> -<td align="left">1.2.5</td> +<td align="left">1.2.6</td> <td align="left">3.2</td> -<td align="right">0.994</td> -<td align="right">2.546</td> -<td align="right">1.999</td> -<td align="right">2.070</td> +<td align="right">1.135</td> +<td align="right">2.025</td> +<td align="right">2.406</td> +<td align="right">2.478</td> +</tr> +<tr class="even"> +<td align="left">Ryzen 9 7950X</td> +<td align="left">Linux</td> +<td align="left">1.2.9</td> +<td align="left">3.3</td> +<td align="right">1.086</td> +<td align="right">1.991</td> +<td align="right">1.949</td> +<td align="right">2.411</td> +</tr> +<tr class="odd"> +<td align="left">Ryzen 9 7950X</td> +<td align="left">Linux</td> +<td align="left">1.2.10</td> +<td align="left">3.3</td> +<td align="right">1.115</td> +<td align="right">2.263</td> +<td align="right">1.991</td> +<td align="right">2.193</td> </tr> </tbody> </table> <p>Two-component error fits for SFO, DFOP, SFORB and HS.</p> <table class="table"> +<colgroup> +<col width="48%"> +<col width="7%"> +<col width="8%"> +<col width="8%"> +<col width="7%"> +<col width="7%"> +<col width="7%"> +<col width="7%"> +</colgroup> <thead><tr class="header"> <th align="left">CPU</th> <th align="left">OS</th> @@ -509,14 +508,44 @@ systems. All trademarks belong to their respective owners.</p> <td align="right">3.595</td> </tr> <tr class="even"> +<td align="left">Intel(R) Xeon(R) Gold 6134 CPU @ 3.20GHz</td> +<td align="left">Linux</td> +<td align="left">1.2.6</td> +<td align="left">3.2</td> +<td align="right">5.070</td> +<td align="right">8.464</td> +<td align="right">8.525</td> +<td align="right">7.599</td> +</tr> +<tr class="odd"> <td align="left">Ryzen 9 7950X</td> <td align="left">Linux</td> -<td align="left">1.2.5</td> +<td align="left">1.2.6</td> <td align="left">3.2</td> -<td align="right">2.152</td> -<td align="right">3.316</td> -<td align="right">3.309</td> -<td align="right">3.337</td> +<td align="right">2.161</td> +<td align="right">3.325</td> +<td align="right">3.669</td> +<td align="right">3.153</td> +</tr> +<tr class="even"> +<td align="left">Ryzen 9 7950X</td> +<td align="left">Linux</td> +<td align="left">1.2.9</td> +<td align="left">3.3</td> +<td align="right">2.426</td> +<td align="right">3.196</td> +<td align="right">3.256</td> +<td align="right">3.322</td> +</tr> +<tr class="odd"> +<td align="left">Ryzen 9 7950X</td> +<td align="left">Linux</td> +<td align="left">1.2.10</td> +<td align="left">3.3</td> +<td align="right">2.380</td> +<td align="right">3.147</td> +<td align="right">3.131</td> +<td align="right">3.331</td> </tr> </tbody> </table> @@ -525,7 +554,15 @@ systems. All trademarks belong to their respective owners.</p> <h3 id="one-metabolite-1">One metabolite<a class="anchor" aria-label="anchor" href="#one-metabolite-1"></a> </h3> <p>Two-component error for DFOP-SFO and SFORB-SFO.</p> -<table class="table"> +<table style="width:100%;" class="table"> +<colgroup> +<col width="53%"> +<col width="7%"> +<col width="9%"> +<col width="9%"> +<col width="9%"> +<col width="10%"> +</colgroup> <thead><tr class="header"> <th align="left">CPU</th> <th align="left">OS</th> @@ -592,12 +629,36 @@ systems. All trademarks belong to their respective owners.</p> <td align="right">265.934</td> </tr> <tr class="even"> +<td align="left">Intel(R) Xeon(R) Gold 6134 CPU @ 3.20GHz</td> +<td align="left">Linux</td> +<td align="left">1.2.6</td> +<td align="left">3.2</td> +<td align="right">30.168</td> +<td align="right">748.675</td> +</tr> +<tr class="odd"> <td align="left">Ryzen 9 7950X</td> <td align="left">Linux</td> -<td align="left">1.2.5</td> +<td align="left">1.2.6</td> <td align="left">3.2</td> -<td align="right">11.249</td> -<td align="right">290.918</td> +<td align="right">12.007</td> +<td align="right">286.757</td> +</tr> +<tr class="even"> +<td align="left">Ryzen 9 7950X</td> +<td align="left">Linux</td> +<td align="left">1.2.9</td> +<td align="left">3.3</td> +<td align="right">12.420</td> +<td align="right">289.338</td> +</tr> +<tr class="odd"> +<td align="left">Ryzen 9 7950X</td> +<td align="left">Linux</td> +<td align="left">1.2.10</td> +<td align="left">3.3</td> +<td align="right">11.581</td> +<td align="right">296.184</td> </tr> </tbody> </table> @@ -607,6 +668,13 @@ systems. All trademarks belong to their respective owners.</p> </h3> <p>Two-component error for SFORB-SFO3-plus</p> <table class="table"> +<colgroup> +<col width="58%"> +<col width="8%"> +<col width="10%"> +<col width="10%"> +<col width="12%"> +</colgroup> <thead><tr class="header"> <th align="left">CPU</th> <th align="left">OS</th> @@ -665,45 +733,57 @@ systems. All trademarks belong to their respective owners.</p> <td align="right">456.252</td> </tr> <tr class="even"> +<td align="left">Intel(R) Xeon(R) Gold 6134 CPU @ 3.20GHz</td> +<td align="left">Linux</td> +<td align="left">1.2.6</td> +<td align="left">3.2</td> +<td align="right">1235.028</td> +</tr> +<tr class="odd"> <td align="left">Ryzen 9 7950X</td> <td align="left">Linux</td> -<td align="left">1.2.5</td> +<td align="left">1.2.6</td> <td align="left">3.2</td> -<td align="right">479.637</td> +<td align="right">480.577</td> +</tr> +<tr class="even"> +<td align="left">Ryzen 9 7950X</td> +<td align="left">Linux</td> +<td align="left">1.2.9</td> +<td align="left">3.3</td> +<td align="right">485.836</td> +</tr> +<tr class="odd"> +<td align="left">Ryzen 9 7950X</td> +<td align="left">Linux</td> +<td align="left">1.2.10</td> +<td align="left">3.3</td> +<td align="right">486.856</td> </tr> </tbody> </table> </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> -</div> - + </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2> + </nav></aside> </div> - <footer><div class="copyright"> - <p></p> -<p>Developed by Johannes Ranke.</p> + <footer><div class="pkgdown-footer-left"> + <p>Developed by Johannes Ranke.</p> </div> -<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> +<div class="pkgdown-footer-right"> + <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p> </div> - </footer> + </footer> </div> - - + </body> </html> diff --git a/docs/dev/articles/web_only/saem_benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/dev/articles/web_only/saem_benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js deleted file mode 100644 index ca349fd6..00000000 --- a/docs/dev/articles/web_only/saem_benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js +++ /dev/null @@ -1,15 +0,0 @@ -// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> -// v0.0.1 -// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. - -document.addEventListener('DOMContentLoaded', function() { - const codeList = document.getElementsByClassName("sourceCode"); - for (var i = 0; i < codeList.length; i++) { - var linkList = codeList[i].getElementsByTagName('a'); - for (var j = 0; j < linkList.length; j++) { - if (linkList[j].innerHTML === "") { - linkList[j].setAttribute('aria-hidden', 'true'); - } - } - } -}); |
