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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/master/R/nlme.mmkin.R'><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>

    <pre class="usage"><span class='co'># S3 method for mmkin</span>
<span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span><span class='op'>(</span>
  <span class='va'>model</span>,
  data <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/sys.parent.html'>sys.frame</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/sys.parent.html'>sys.parent</a></span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span>,
  <span class='va'>fixed</span>,
  random <span class='op'>=</span> <span class='va'>fixed</span>,
  <span class='va'>groups</span>,
  <span class='va'>start</span>,
  correlation <span class='op'>=</span> <span class='cn'>NULL</span>,
  weights <span class='op'>=</span> <span class='cn'>NULL</span>,
  <span class='va'>subset</span>,
  method <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"ML"</span>, <span class='st'>"REML"</span><span class='op'>)</span>,
  na.action <span class='op'>=</span> <span class='va'>na.fail</span>,
  <span class='va'>naPattern</span>,
  control <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='op'>)</span>,
  verbose <span class='op'>=</span> <span class='cn'>FALSE</span>
<span class='op'>)</span>

<span class='co'># S3 method for nlme.mmkin</span>
<span class='fu'><a href='https://rdrr.io/r/base/print.html'>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'>max</a></span><span class='op'>(</span><span class='fl'>3</span>, <span class='fu'><a href='https://rdrr.io/r/base/options.html'>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 class='co'># S3 method for nlme.mmkin</span>
<span class='fu'><a href='https://rdrr.io/r/stats/update.html'>update</a></span><span class='op'>(</span><span class='va'>object</span>, <span class='va'>...</span><span class='op'>)</span></pre>

    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
    <table class="ref-arguments">
    <colgroup><col class="name" /><col class="desc" /></colgroup>
    <tr>
      <th>model</th>
      <td><p>An <a href='mmkin.html'>mmkin</a> row object.</p></td>
    </tr>
    <tr>
      <th>data</th>
      <td><p>Ignored, data are taken from the mmkin model</p></td>
    </tr>
    <tr>
      <th>fixed</th>
      <td><p>Ignored, all degradation parameters fitted in the
mmkin model are used as fixed parameters</p></td>
    </tr>
    <tr>
      <th>random</th>
      <td><p>If not specified, correlated random effects are set up
for all optimised degradation model parameters using the log-Cholesky
parameterization <a href='https://rdrr.io/pkg/nlme/man/pdLogChol.html'>nlme::pdLogChol</a> that is also the default of
the generic <a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a> method.</p></td>
    </tr>
    <tr>
      <th>groups</th>
      <td><p>See the documentation of nlme</p></td>
    </tr>
    <tr>
      <th>start</th>
      <td><p>If not specified, mean values of the fitted degradation
parameters taken from the mmkin object are used</p></td>
    </tr>
    <tr>
      <th>correlation</th>
      <td><p>See the documentation of nlme</p></td>
    </tr>
    <tr>
      <th>weights</th>
      <td><p>passed to nlme</p></td>
    </tr>
    <tr>
      <th>subset</th>
      <td><p>passed to nlme</p></td>
    </tr>
    <tr>
      <th>method</th>
      <td><p>passed to nlme</p></td>
    </tr>
    <tr>
      <th>na.action</th>
      <td><p>passed to nlme</p></td>
    </tr>
    <tr>
      <th>naPattern</th>
      <td><p>passed to nlme</p></td>
    </tr>
    <tr>
      <th>control</th>
      <td><p>passed to nlme</p></td>
    </tr>
    <tr>
      <th>verbose</th>
      <td><p>passed to nlme</p></td>
    </tr>
    <tr>
      <th>x</th>
      <td><p>An nlme.mmkin object to print</p></td>
    </tr>
    <tr>
      <th>digits</th>
      <td><p>Number of digits to use for printing</p></td>
    </tr>
    <tr>
      <th>...</th>
      <td><p>Update specifications passed to update.nlme</p></td>
    </tr>
    <tr>
      <th>object</th>
      <td><p>An nlme.mmkin object to update</p></td>
    </tr>
    </table>

    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>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>
    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>

    <p>As the object inherits from <a href='https://rdrr.io/pkg/nlme/man/nlme.html'>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'>nlme::intervals()</a></code>, <code><a href='https://rdrr.io/pkg/nlme/man/anova.lme.html'>nlme::anova.lme()</a></code> and <code><a href='https://rdrr.io/pkg/nlme/man/coef.lme.html'>nlme::coef.lme()</a></code>.</p>
    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>

    <div class='dont-index'><p><code><a href='nlme_function.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>

    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
    <pre class="examples"><div class='input'><span class='va'>ds</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>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 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'>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'>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 class='va'>f</span> <span class='op'>&lt;-</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'>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 class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://svn.r-project.org/R-packages/trunk/nlme/'>nlme</a></span><span class='op'>)</span>
<span class='va'>f_nlme_sfo</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>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>
</div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 1, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='input'>
<span class='co'># \dontrun{</span>

  <span class='va'>f_nlme_dfop</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>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 class='fu'><a href='https://rdrr.io/r/stats/anova.html'>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>
</div><div class='output co'>#&gt;             Model df      AIC      BIC    logLik   Test  L.Ratio p-value
#&gt; f_nlme_sfo      1  6 622.0677 637.0666 -305.0338                        
#&gt; f_nlme_dfop     2 15 487.0134 524.5105 -228.5067 1 vs 2 153.0543  &lt;.0001</div><div class='input'>  <span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>f_nlme_dfop</span><span class='op'>)</span>
</div><div class='output co'>#&gt; Kinetic nonlinear mixed-effects model fit by maximum likelihood
#&gt; 
#&gt; Structural model:
#&gt; d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
#&gt;            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
#&gt;            * parent
#&gt; 
#&gt; Data:
#&gt; 90 observations of 1 variable(s) grouped in 5 datasets
#&gt; 
#&gt; Log-likelihood: -228.5067
#&gt; 
#&gt; Fixed effects:
#&gt;  list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) 
#&gt; parent_0   log_k1   log_k2 g_qlogis 
#&gt; 94.18273 -1.82135 -4.16872  0.08949 
#&gt; 
#&gt; Random effects:
#&gt;  Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)
#&gt;  Level: ds
#&gt;  Structure: General positive-definite, Log-Cholesky parametrization
#&gt;          StdDev    Corr                
#&gt; parent_0 2.4656397 prnt_0 log_k1 log_k2
#&gt; log_k1   0.7950788  0.240              
#&gt; log_k2   1.2605419  0.150  0.984       
#&gt; g_qlogis 0.5013272 -0.075  0.843  0.834
#&gt; Residual 2.3308100                     
#&gt; </div><div class='input'>  <span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_nlme_dfop</span><span class='op'>)</span>
</div><div class='img'><img src='nlme.mmkin-1.png' alt='' width='700' height='433' /></div><div class='input'>  <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>
</div><div class='output co'>#&gt; $distimes
#&gt;            DT50     DT90 DT50back  DT50_k1  DT50_k2
#&gt; parent 10.57119 101.0652 30.42366 4.283776 44.80015
#&gt; </div><div class='input'>
  <span class='va'>ds_2</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span><span class='op'>(</span><span class='va'>experimental_data_for_UBA_2019</span><span class='op'>[</span><span class='fl'>6</span><span class='op'>:</span><span class='fl'>10</span><span class='op'>]</span>,
   <span class='kw'>function</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span> <span class='va'>x</span><span class='op'>$</span><span class='va'>data</span><span class='op'>[</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>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 class='va'>m_sfo_sfo</span> <span class='op'>&lt;-</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>,
    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 class='va'>m_sfo_sfo_ff</span> <span class='op'>&lt;-</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>,
    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 class='va'>m_dfop_sfo</span> <span class='op'>&lt;-</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>,
    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 class='va'>f_2</span> <span class='op'>&lt;-</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'>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 class='st'>"SFO-SFO-ff"</span> <span class='op'>=</span> <span class='va'>m_sfo_sfo_ff</span>,
   <span class='st'>"DFOP-SFO"</span> <span class='op'>=</span> <span class='va'>m_dfop_sfo</span><span class='op'>)</span>,
    <span class='va'>ds_2</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span>

  <span class='va'>f_nlme_sfo_sfo</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>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>
</div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 1, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 2, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='input'>  <span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_nlme_sfo_sfo</span><span class='op'>)</span>
</div><div class='img'><img src='nlme.mmkin-2.png' alt='' width='700' height='433' /></div><div class='input'>
  <span class='co'># With formation fractions this does not coverge with defaults</span>
  <span class='co'># f_nlme_sfo_sfo_ff &lt;- nlme(f_2["SFO-SFO-ff", ])</span>
  <span class='co'>#plot(f_nlme_sfo_sfo_ff)</span>

  <span class='co'># With the log-Cholesky parameterization, this converges in 11</span>
  <span class='co'># iterations and around 100 seconds, but without tweaking control</span>
  <span class='co'># parameters (with pdDiag, increasing the tolerance and pnlsMaxIter was</span>
  <span class='co'># necessary)</span>
  <span class='va'>f_nlme_dfop_sfo</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>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 class='op'>)</span>
</div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 1, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 2, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 3, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 4, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='input'>
  <span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_nlme_dfop_sfo</span><span class='op'>)</span>
</div><div class='img'><img src='nlme.mmkin-3.png' alt='' width='700' height='433' /></div><div class='input'>
  <span class='fu'><a href='https://rdrr.io/r/stats/anova.html'>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>
</div><div class='output co'>#&gt;                 Model df       AIC       BIC    logLik   Test  L.Ratio p-value
#&gt; f_nlme_dfop_sfo     1 28  811.7199  899.5222 -377.8599                        
#&gt; f_nlme_sfo_sfo      2 15 1075.1934 1122.2304 -522.5967 1 vs 2 289.4736  &lt;.0001</div><div class='input'>
  <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>
</div><div class='output co'>#&gt; $ff
#&gt; parent_sink   parent_A1     A1_sink 
#&gt;   0.6512742   0.3487258   1.0000000 
#&gt; 
#&gt; $distimes
#&gt;             DT50      DT90
#&gt; parent  18.03144  59.89916
#&gt; A1     102.72949 341.25997
#&gt; </div><div class='input'>  <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>
</div><div class='output co'>#&gt; $ff
#&gt;   parent_A1 parent_sink 
#&gt;   0.2762167   0.7237833 
#&gt; 
#&gt; $distimes
#&gt;             DT50     DT90 DT50back  DT50_k1  DT50_k2
#&gt; parent  11.15024 133.9652 40.32755 4.688015 62.16017
#&gt; A1     235.83191 783.4167       NA       NA       NA
#&gt; </div><div class='input'>
  <span class='kw'>if</span> <span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/length.html'>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'>&gt;</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 class='co'># Attempts to fit metabolite kinetics with the tc error model are possible,</span>
    <span class='co'># but need tweeking of control values and sometimes do not converge</span>

    <span class='va'>f_tc</span> <span class='op'>&lt;-</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'>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 class='va'>f_nlme_sfo_tc</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>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 class='va'>f_nlme_dfop_tc</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>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 class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>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 class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>f_nlme_dfop_tc</span><span class='op'>)</span>
  <span class='op'>}</span>
</div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 1, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 14, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 1, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 2, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 4, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 5, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 6, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 7, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 8, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 9, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 10, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 11, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 12, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 14, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 15, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 16, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 17, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 18, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#&gt; Kinetic nonlinear mixed-effects model fit by maximum likelihood
#&gt; 
#&gt; Structural model:
#&gt; d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
#&gt;            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
#&gt;            * parent
#&gt; 
#&gt; Data:
#&gt; 90 observations of 1 variable(s) grouped in 5 datasets
#&gt; 
#&gt; Log-likelihood: -228.3575
#&gt; 
#&gt; Fixed effects:
#&gt;  list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) 
#&gt; parent_0   log_k1   log_k2 g_qlogis 
#&gt;  93.6695  -1.9187  -4.4253   0.2215 
#&gt; 
#&gt; Random effects:
#&gt;  Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)
#&gt;  Level: ds
#&gt;  Structure: General positive-definite, Log-Cholesky parametrization
#&gt;          StdDev    Corr                
#&gt; parent_0 2.8574651 prnt_0 log_k1 log_k2
#&gt; log_k1   0.9689083  0.506              
#&gt; log_k2   1.5798002  0.446  0.997       
#&gt; g_qlogis 0.5761569 -0.457  0.247  0.263
#&gt; Residual 1.0000000                     
#&gt; 
#&gt; Variance function:
#&gt;  Structure: Constant plus proportion of variance covariate
#&gt;  Formula: ~fitted(.) 
#&gt;  Parameter estimates:
#&gt;     const      prop 
#&gt; 2.0376990 0.0221686 </div><div class='input'>
  <span class='va'>f_2_obs</span> <span class='op'>&lt;-</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'>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 class='st'>"DFOP-SFO"</span> <span class='op'>=</span> <span class='va'>m_dfop_sfo</span><span class='op'>)</span>,
    <span class='va'>ds_2</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span>, error_model <span class='op'>=</span> <span class='st'>"obs"</span><span class='op'>)</span>
  <span class='va'>f_nlme_sfo_sfo_obs</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>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>
</div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 1, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='input'>  <span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span><span class='op'>(</span><span class='va'>f_nlme_sfo_sfo_obs</span><span class='op'>)</span>
</div><div class='output co'>#&gt; Kinetic nonlinear mixed-effects model fit by maximum likelihood
#&gt; 
#&gt; Structural model:
#&gt; d_parent/dt = - k_parent_sink * parent - k_parent_A1 * parent
#&gt; d_A1/dt = + k_parent_A1 * parent - k_A1_sink * A1
#&gt; 
#&gt; Data:
#&gt; 170 observations of 2 variable(s) grouped in 5 datasets
#&gt; 
#&gt; Log-likelihood: -462.2203
#&gt; 
#&gt; Fixed effects:
#&gt;  list(parent_0 ~ 1, log_k_parent_sink ~ 1, log_k_parent_A1 ~ 1,      log_k_A1_sink ~ 1) 
#&gt;          parent_0 log_k_parent_sink   log_k_parent_A1     log_k_A1_sink 
#&gt;            88.682            -3.664            -4.164            -4.665 
#&gt; 
#&gt; Random effects:
#&gt;  Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1, log_k_parent_A1 ~ 1,      log_k_A1_sink ~ 1)
#&gt;  Level: ds
#&gt;  Structure: General positive-definite, Log-Cholesky parametrization
#&gt;                   StdDev    Corr                
#&gt; parent_0          4.9153305 prnt_0 lg_k__ l___A1
#&gt; log_k_parent_sink 1.8158570 0.956               
#&gt; log_k_parent_A1   1.0514548 0.821  0.907        
#&gt; log_k_A1_sink     0.4924122 0.035  0.315  0.533 
#&gt; Residual          6.3987599                     
#&gt; 
#&gt; Variance function:
#&gt;  Structure: Different standard deviations per stratum
#&gt;  Formula: ~1 | name 
#&gt;  Parameter estimates:
#&gt;    parent        A1 
#&gt; 1.0000000 0.2040647 </div><div class='input'>  <span class='va'>f_nlme_dfop_sfo_obs</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>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 class='op'>)</span>
</div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 1, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 2, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 3, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 4, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='input'>
  <span class='va'>f_2_tc</span> <span class='op'>&lt;-</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'>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 class='st'>"DFOP-SFO"</span> <span class='op'>=</span> <span class='va'>m_dfop_sfo</span><span class='op'>)</span>,
    <span class='va'>ds_2</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 class='co'># f_nlme_sfo_sfo_tc &lt;- nlme(f_2_tc["SFO-SFO", ]) # stops with error message</span>
  <span class='va'>f_nlme_dfop_sfo_tc</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span><span class='op'>(</span><span class='va'>f_2_tc</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='op'>]</span><span class='op'>)</span>
</div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 1, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 2, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 3, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 4, LME step: nlminb() did not converge (code = 1). Do increase 'msMaxIter'!</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 6, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 7, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 8, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 9, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 11, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 12, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 15, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Iteration 25, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='input'>  <span class='co'># We get warnings about false convergence in the LME step in several iterations</span>
  <span class='co'># but as the last such warning occurs in iteration 25 and we have 28 iterations</span>
  <span class='co'># we can ignore these</span>
  <span class='fu'><a href='https://rdrr.io/r/stats/anova.html'>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='va'>f_nlme_dfop_sfo_tc</span><span class='op'>)</span>
</div><div class='output co'>#&gt;                     Model df      AIC      BIC    logLik   Test L.Ratio p-value
#&gt; f_nlme_dfop_sfo         1 28 811.7199 899.5222 -377.8599                       
#&gt; f_nlme_dfop_sfo_obs     2 29 784.1304 875.0685 -363.0652 1 vs 2 29.5895  &lt;.0001
#&gt; f_nlme_dfop_sfo_tc      3 29 791.9981 882.9362 -366.9990                       </div><div class='input'>
<span class='co'># }</span>
</div></pre>
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