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object. An mmkin row object is essentially a list of mkinfit objects that
have been obtained by fitting the same model to a list of datasets."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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<h1>Create an nlme model for an mmkin row object</h1>
<small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/nlme.mmkin.R" class="external-link"><code>R/nlme.mmkin.R</code></a></small>
<div class="hidden name"><code>nlme.mmkin.Rd</code></div>
</div>
<div class="ref-description">
<p>This functions sets up a nonlinear mixed effects model for an mmkin row
object. An mmkin row object is essentially a list of mkinfit objects that
have been obtained by fitting the same model to a list of datasets.</p>
</div>
<div id="ref-usage">
<div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for mmkin</span></span>
<span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span></span>
<span> <span class="va">model</span>,</span>
<span> data <span class="op">=</span> <span class="st">"auto"</span>,</span>
<span> fixed <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="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">as.list</a></span><span class="op">(</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="fu"><a href="mean_degparms.html">mean_degparms</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>, <span class="kw">function</span><span class="op">(</span><span class="va">el</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/eval.html" class="external-link">eval</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/parse.html" class="external-link">parse</a></span><span class="op">(</span>text <span class="op">=</span></span>
<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="va">el</span>, <span class="fl">1</span>, sep <span class="op">=</span> <span class="st">"~"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span><span class="op">)</span>,</span>
<span> random <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/pdDiag.html" class="external-link">pdDiag</a></span><span class="op">(</span><span class="va">fixed</span><span class="op">)</span>,</span>
<span> <span class="va">groups</span>,</span>
<span> start <span class="op">=</span> <span class="fu"><a href="mean_degparms.html">mean_degparms</a></span><span class="op">(</span><span class="va">model</span>, random <span class="op">=</span> <span class="cn">TRUE</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span>
<span> correlation <span class="op">=</span> <span class="cn">NULL</span>,</span>
<span> weights <span class="op">=</span> <span class="cn">NULL</span>,</span>
<span> <span class="va">subset</span>,</span>
<span> method <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">"ML"</span>, <span class="st">"REML"</span><span class="op">)</span>,</span>
<span> na.action <span class="op">=</span> <span class="va">na.fail</span>,</span>
<span> <span class="va">naPattern</span>,</span>
<span> control <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>,</span>
<span> verbose <span class="op">=</span> <span class="cn">FALSE</span></span>
<span><span class="op">)</span></span>
<span></span>
<span><span class="co"># S3 method for nlme.mmkin</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">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></span>
<span></span>
<span><span class="co"># S3 method for nlme.mmkin</span></span>
<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">object</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
</div>
<div id="arguments">
<h2>Arguments</h2>
<dl><dt>model</dt>
<dd><p>An <a href="mmkin.html">mmkin</a> row object.</p></dd>
<dt>data</dt>
<dd><p>Ignored, data are taken from the mmkin model</p></dd>
<dt>fixed</dt>
<dd><p>Ignored, all degradation parameters fitted in the
mmkin model are used as fixed parameters</p></dd>
<dt>random</dt>
<dd><p>If not specified, no correlations between random effects are
set up for the optimised degradation model parameters. This is
achieved by using the <a href="https://rdrr.io/pkg/nlme/man/pdDiag.html" class="external-link">nlme::pdDiag</a> method.</p></dd>
<dt>groups</dt>
<dd><p>See the documentation of nlme</p></dd>
<dt>start</dt>
<dd><p>If not specified, mean values of the fitted degradation
parameters taken from the mmkin object are used</p></dd>
<dt>correlation</dt>
<dd><p>See the documentation of nlme</p></dd>
<dt>weights</dt>
<dd><p>passed to nlme</p></dd>
<dt>subset</dt>
<dd><p>passed to nlme</p></dd>
<dt>method</dt>
<dd><p>passed to nlme</p></dd>
<dt>na.action</dt>
<dd><p>passed to nlme</p></dd>
<dt>naPattern</dt>
<dd><p>passed to nlme</p></dd>
<dt>control</dt>
<dd><p>passed to nlme</p></dd>
<dt>verbose</dt>
<dd><p>passed to nlme</p></dd>
<dt>x</dt>
<dd><p>An nlme.mmkin object to print</p></dd>
<dt>digits</dt>
<dd><p>Number of digits to use for printing</p></dd>
<dt>...</dt>
<dd><p>Update specifications passed to update.nlme</p></dd>
<dt>object</dt>
<dd><p>An nlme.mmkin object to update</p></dd>
</dl></div>
<div id="value">
<h2>Value</h2>
<p>Upon success, a fitted 'nlme.mmkin' object, which is an nlme object
with additional elements. It also inherits from 'mixed.mmkin'.</p>
</div>
<div id="details">
<h2>Details</h2>
<p>Note that the convergence of the nlme algorithms depends on the quality
of the data. In degradation kinetics, we often only have few datasets
(e.g. data for few soils) and complicated degradation models, which may
make it impossible to obtain convergence with nlme.</p>
</div>
<div id="note">
<h2>Note</h2>
<p>As the object inherits from <a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme::nlme</a>, there is a wealth of
methods that will automatically work on 'nlme.mmkin' objects, such as
<code><a href="https://rdrr.io/pkg/nlme/man/intervals.html" class="external-link">nlme::intervals()</a></code>, <code><a href="https://rdrr.io/pkg/nlme/man/anova.lme.html" class="external-link">nlme::anova.lme()</a></code> and <code><a href="https://rdrr.io/pkg/nlme/man/coef.lme.html" class="external-link">nlme::coef.lme()</a></code>.</p>
</div>
<div id="see-also">
<h2>See also</h2>
<div class="dont-index"><p><code><a href="nlme.html">nlme_function()</a></code>, <a href="plot.mixed.mmkin.html">plot.mixed.mmkin</a>, <a href="summary.nlme.mmkin.html">summary.nlme.mmkin</a></p></div>
</div>
<div id="ref-examples">
<h2>Examples</h2>
<div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">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="va">experimental_data_for_UBA_2019</span><span class="op">[</span><span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span>,</span></span>
<span class="r-in"><span> <span class="kw">function</span><span class="op">(</span><span class="va">x</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">x</span><span class="op">$</span><span class="va">data</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">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">]</span>, <span class="va">name</span> <span class="op">==</span> <span class="st">"parent"</span><span class="op">)</span><span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
<span class="r-in"><span> <span class="va">f</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span>
<span class="r-in"><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://svn.r-project.org/R-packages/trunk/nlme/" class="external-link">nlme</a></span><span class="op">)</span></span></span>
<span class="r-in"><span> <span class="va">f_nlme_sfo</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span></span></span>
<span class="r-in"><span> <span class="va">f_nlme_dfop</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span></span></span>
<span class="r-in"><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_nlme_sfo</span>, <span class="va">f_nlme_dfop</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#></span> Model df AIC BIC logLik Test L.Ratio p-value</span>
<span class="r-out co"><span class="r-pr">#></span> f_nlme_sfo 1 5 625.0539 637.5529 -307.5269 </span>
<span class="r-out co"><span class="r-pr">#></span> f_nlme_dfop 2 9 495.1270 517.6253 -238.5635 1 vs 2 137.9269 <.0001</span>
<span class="r-in"><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">f_nlme_dfop</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#></span> Kinetic nonlinear mixed-effects model fit by maximum likelihood</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Structural model:</span>
<span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span>
<span class="r-out co"><span class="r-pr">#></span> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span>
<span class="r-out co"><span class="r-pr">#></span> * parent</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Data:</span>
<span class="r-out co"><span class="r-pr">#></span> 90 observations of 1 variable(s) grouped in 5 datasets</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Log-likelihood: -238.6</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Fixed effects:</span>
<span class="r-out co"><span class="r-pr">#></span> list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) </span>
<span class="r-out co"><span class="r-pr">#></span> parent_0 log_k1 log_k2 g_qlogis </span>
<span class="r-out co"><span class="r-pr">#></span> 94.1702 -1.8002 -4.1474 0.0324 </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Random effects:</span>
<span class="r-out co"><span class="r-pr">#></span> Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)</span>
<span class="r-out co"><span class="r-pr">#></span> Level: ds</span>
<span class="r-out co"><span class="r-pr">#></span> Structure: Diagonal</span>
<span class="r-out co"><span class="r-pr">#></span> parent_0 log_k1 log_k2 g_qlogis Residual</span>
<span class="r-out co"><span class="r-pr">#></span> StdDev: 2.488 0.8447 1.33 0.4652 2.321</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_nlme_dfop</span><span class="op">)</span></span></span>
<span class="r-plt img"><img src="nlme.mmkin-1.png" alt="" width="700" height="433"></span>
<span class="r-in"><span> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">f_nlme_dfop</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#></span> $distimes</span>
<span class="r-out co"><span class="r-pr">#></span> DT50 DT90 DT50back DT50_k1 DT50_k2</span>
<span class="r-out co"><span class="r-pr">#></span> parent 10.79857 100.7937 30.34192 4.193937 43.85442</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-in"><span></span></span>
<span class="r-in"><span> <span class="va">ds_2</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">experimental_data_for_UBA_2019</span><span class="op">[</span><span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span>,</span></span>
<span class="r-in"><span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="va">x</span><span class="op">$</span><span class="va">data</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">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">]</span><span class="op">)</span></span></span>
<span class="r-in"><span> <span class="va">m_sfo_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span></span>
<span class="r-in"><span> A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"min"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
<span class="r-in"><span> <span class="va">m_sfo_sfo_ff</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span></span>
<span class="r-in"><span> A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
<span class="r-in"><span> <span class="va">m_dfop_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span></span>
<span class="r-in"><span> A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span> <span class="va">f_2</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"SFO-SFO"</span> <span class="op">=</span> <span class="va">m_sfo_sfo</span>,</span></span>
<span class="r-in"><span> <span class="st">"SFO-SFO-ff"</span> <span class="op">=</span> <span class="va">m_sfo_sfo_ff</span>,</span></span>
<span class="r-in"><span> <span class="st">"DFOP-SFO"</span> <span class="op">=</span> <span class="va">m_dfop_sfo</span><span class="op">)</span>,</span></span>
<span class="r-in"><span> <span class="va">ds_2</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span> <span class="va">f_nlme_sfo_sfo</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_2</span><span class="op">[</span><span class="st">"SFO-SFO"</span>, <span class="op">]</span><span class="op">)</span></span></span>
<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_nlme_sfo_sfo</span><span class="op">)</span></span></span>
<span class="r-plt img"><img src="nlme.mmkin-2.png" alt="" width="700" height="433"></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span> <span class="co"># With formation fractions this does not coverge with defaults</span></span></span>
<span class="r-in"><span> <span class="co"># f_nlme_sfo_sfo_ff <- nlme(f_2["SFO-SFO-ff", ])</span></span></span>
<span class="r-in"><span> <span class="co">#plot(f_nlme_sfo_sfo_ff)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span> <span class="co"># For the following, we need to increase pnlsMaxIter and the tolerance</span></span></span>
<span class="r-in"><span> <span class="co"># to get convergence</span></span></span>
<span class="r-in"><span> <span class="va">f_nlme_dfop_sfo</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_2</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span>,</span></span>
<span class="r-in"><span> control <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>pnlsMaxIter <span class="op">=</span> <span class="fl">120</span>, tolerance <span class="op">=</span> <span class="fl">5e-4</span><span class="op">)</span><span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_nlme_dfop_sfo</span><span class="op">)</span></span></span>
<span class="r-plt img"><img src="nlme.mmkin-3.png" alt="" width="700" height="433"></span>
<span class="r-in"><span></span></span>
<span class="r-in"><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_nlme_dfop_sfo</span>, <span class="va">f_nlme_sfo_sfo</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#></span> Model df AIC BIC logLik Test L.Ratio p-value</span>
<span class="r-out co"><span class="r-pr">#></span> f_nlme_dfop_sfo 1 13 843.8547 884.620 -408.9273 </span>
<span class="r-out co"><span class="r-pr">#></span> f_nlme_sfo_sfo 2 9 1085.1821 1113.404 -533.5910 1 vs 2 249.3274 <.0001</span>
<span class="r-in"><span></span></span>
<span class="r-in"><span> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">f_nlme_sfo_sfo</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#></span> $ff</span>
<span class="r-out co"><span class="r-pr">#></span> parent_sink parent_A1 A1_sink </span>
<span class="r-out co"><span class="r-pr">#></span> 0.5912432 0.4087568 1.0000000 </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> $distimes</span>
<span class="r-out co"><span class="r-pr">#></span> DT50 DT90</span>
<span class="r-out co"><span class="r-pr">#></span> parent 19.13518 63.5657</span>
<span class="r-out co"><span class="r-pr">#></span> A1 66.02155 219.3189</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-in"><span> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">f_nlme_dfop_sfo</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#></span> $ff</span>
<span class="r-out co"><span class="r-pr">#></span> parent_A1 parent_sink </span>
<span class="r-out co"><span class="r-pr">#></span> 0.2768574 0.7231426 </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> $distimes</span>
<span class="r-out co"><span class="r-pr">#></span> DT50 DT90 DT50back DT50_k1 DT50_k2</span>
<span class="r-out co"><span class="r-pr">#></span> parent 11.07091 104.6320 31.49737 4.462383 46.20825</span>
<span class="r-out co"><span class="r-pr">#></span> A1 162.30550 539.1672 NA NA NA</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-in"><span></span></span>
<span class="r-in"><span> <span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="fu">findFunction</span><span class="op">(</span><span class="st">"varConstProp"</span><span class="op">)</span><span class="op">)</span> <span class="op">></span> <span class="fl">0</span><span class="op">)</span> <span class="op">{</span> <span class="co"># tc error model for nlme available</span></span></span>
<span class="r-in"><span> <span class="co"># Attempts to fit metabolite kinetics with the tc error model are possible,</span></span></span>
<span class="r-in"><span> <span class="co"># but need tweeking of control values and sometimes do not converge</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span> <span class="va">f_tc</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span>
<span class="r-in"><span> <span class="va">f_nlme_sfo_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_tc</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span></span></span>
<span class="r-in"><span> <span class="va">f_nlme_dfop_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span></span></span>
<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/stats/AIC.html" class="external-link">AIC</a></span><span class="op">(</span><span class="va">f_nlme_sfo</span>, <span class="va">f_nlme_sfo_tc</span>, <span class="va">f_nlme_dfop</span>, <span class="va">f_nlme_dfop_tc</span><span class="op">)</span></span></span>
<span class="r-in"><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">f_nlme_dfop_tc</span><span class="op">)</span></span></span>
<span class="r-in"><span> <span class="op">}</span></span></span>
<span class="r-out co"><span class="r-pr">#></span> Kinetic nonlinear mixed-effects model fit by maximum likelihood</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Structural model:</span>
<span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span>
<span class="r-out co"><span class="r-pr">#></span> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span>
<span class="r-out co"><span class="r-pr">#></span> * parent</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Data:</span>
<span class="r-out co"><span class="r-pr">#></span> 90 observations of 1 variable(s) grouped in 5 datasets</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Log-likelihood: -238.4</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Fixed effects:</span>
<span class="r-out co"><span class="r-pr">#></span> list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) </span>
<span class="r-out co"><span class="r-pr">#></span> parent_0 log_k1 log_k2 g_qlogis </span>
<span class="r-out co"><span class="r-pr">#></span> 94.04774 -1.82340 -4.16716 0.05685 </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Random effects:</span>
<span class="r-out co"><span class="r-pr">#></span> Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)</span>
<span class="r-out co"><span class="r-pr">#></span> Level: ds</span>
<span class="r-out co"><span class="r-pr">#></span> Structure: Diagonal</span>
<span class="r-out co"><span class="r-pr">#></span> parent_0 log_k1 log_k2 g_qlogis Residual</span>
<span class="r-out co"><span class="r-pr">#></span> StdDev: 2.474 0.85 1.337 0.4659 1</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Variance function:</span>
<span class="r-out co"><span class="r-pr">#></span> Structure: Constant plus proportion of variance covariate</span>
<span class="r-out co"><span class="r-pr">#></span> Formula: ~fitted(.) </span>
<span class="r-out co"><span class="r-pr">#></span> Parameter estimates:</span>
<span class="r-out co"><span class="r-pr">#></span> const prop </span>
<span class="r-out co"><span class="r-pr">#></span> 2.23222933 0.01262399 </span>
<span class="r-in"><span></span></span>
<span class="r-in"><span> <span class="va">f_2_obs</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_2</span>, error_model <span class="op">=</span> <span class="st">"obs"</span><span class="op">)</span></span></span>
<span class="r-in"><span> <span class="va">f_nlme_sfo_sfo_obs</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_2_obs</span><span class="op">[</span><span class="st">"SFO-SFO"</span>, <span class="op">]</span><span class="op">)</span></span></span>
<span class="r-in"><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">f_nlme_sfo_sfo_obs</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#></span> Kinetic nonlinear mixed-effects model fit by maximum likelihood</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Structural model:</span>
<span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - k_parent_sink * parent - k_parent_A1 * parent</span>
<span class="r-out co"><span class="r-pr">#></span> d_A1/dt = + k_parent_A1 * parent - k_A1_sink * A1</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Data:</span>
<span class="r-out co"><span class="r-pr">#></span> 170 observations of 2 variable(s) grouped in 5 datasets</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Log-likelihood: -473</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Fixed effects:</span>
<span class="r-out co"><span class="r-pr">#></span> list(parent_0 ~ 1, log_k_parent_sink ~ 1, log_k_parent_A1 ~ 1, log_k_A1_sink ~ 1) </span>
<span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_parent_sink log_k_parent_A1 log_k_A1_sink </span>
<span class="r-out co"><span class="r-pr">#></span> 87.976 -3.670 -4.164 -4.645 </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Random effects:</span>
<span class="r-out co"><span class="r-pr">#></span> Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1, log_k_parent_A1 ~ 1, log_k_A1_sink ~ 1)</span>
<span class="r-out co"><span class="r-pr">#></span> Level: ds</span>
<span class="r-out co"><span class="r-pr">#></span> Structure: Diagonal</span>
<span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_parent_sink log_k_parent_A1 log_k_A1_sink Residual</span>
<span class="r-out co"><span class="r-pr">#></span> StdDev: 3.992 1.777 1.055 0.4821 6.483</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Variance function:</span>
<span class="r-out co"><span class="r-pr">#></span> Structure: Different standard deviations per stratum</span>
<span class="r-out co"><span class="r-pr">#></span> Formula: ~1 | name </span>
<span class="r-out co"><span class="r-pr">#></span> Parameter estimates:</span>
<span class="r-out co"><span class="r-pr">#></span> parent A1 </span>
<span class="r-out co"><span class="r-pr">#></span> 1.0000000 0.2050005 </span>
<span class="r-in"><span> <span class="va">f_nlme_dfop_sfo_obs</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_2_obs</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span>,</span></span>
<span class="r-in"><span> control <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>pnlsMaxIter <span class="op">=</span> <span class="fl">120</span>, tolerance <span class="op">=</span> <span class="fl">5e-4</span><span class="op">)</span><span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span> <span class="va">f_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_2</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span>
<span class="r-in"><span> <span class="co"># f_nlme_sfo_sfo_tc <- nlme(f_2_tc["SFO-SFO", ]) # No convergence with 50 iterations</span></span></span>
<span class="r-in"><span> <span class="co"># f_nlme_dfop_sfo_tc <- nlme(f_2_tc["DFOP-SFO", ],</span></span></span>
<span class="r-in"><span> <span class="co"># control = list(pnlsMaxIter = 120, tolerance = 5e-4)) # Error in X[, fmap[[nm]]] <- gradnm</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><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_nlme_dfop_sfo</span>, <span class="va">f_nlme_dfop_sfo_obs</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#></span> Model df AIC BIC logLik Test L.Ratio p-value</span>
<span class="r-out co"><span class="r-pr">#></span> f_nlme_dfop_sfo 1 13 843.8547 884.620 -408.9273 </span>
<span class="r-out co"><span class="r-pr">#></span> f_nlme_dfop_sfo_obs 2 14 817.5338 861.435 -394.7669 1 vs 2 28.32084 <.0001</span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># }</span></span></span>
</code></pre></div>
</div>
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