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have been obtained by fitting the same model to a list of datasets." />
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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'>nlme</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>, <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 <code><a href='mmkin.html'>mmkin</a></code> 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, all fixed effects are complemented
with uncorrelated random effects</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>...</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</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></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'><-</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'><-</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>
</div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.0195</span></div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.00985</span></div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.011</span></div><div class='input'><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'><-</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>
<span class='va'>f_nlme_dfop</span> <span class='op'><-</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/AIC.html'>AIC</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'>#> df AIC
#> f_nlme_sfo 5 625.0539
#> f_nlme_dfop 9 495.1270</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'>#> Nonlinear mixed-effects model fit by maximum likelihood
#> Model: value ~ (mkin::get_deg_func())(name, time, parent_0, log_k1, log_k2, g_ilr)
#> Data: "Not shown"
#> Log-likelihood: -238.5635
#> Fixed: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_ilr ~ 1)
#> parent_0 log_k1 log_k2 g_ilr
#> 94.17015133 -1.80015306 -4.14738870 0.02290935
#>
#> Random effects:
#> Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_ilr ~ 1)
#> Level: ds
#> Structure: Diagonal
#> parent_0 log_k1 log_k2 g_ilr Residual
#> StdDev: 2.488249 0.8447273 1.32965 0.3289311 2.321364
#>
#> Number of Observations: 90
#> Number of Groups: 5 </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'>#> $distimes
#> DT50 DT90 DT50back DT50_k1 DT50_k2
#> parent 10.79857 100.7937 30.34193 4.193938 43.85443
#> </div><div class='input'><span class='co'># \dontrun{</span>
<span class='va'>f_nlme_2</span> <span class='op'><-</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>, start <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span>parent_0 <span class='op'>=</span> <span class='fl'>100</span>, log_k_parent <span class='op'>=</span> <span class='fl'>0.1</span><span class='op'>)</span><span class='op'>)</span>
<span class='fu'><a href='https://rdrr.io/r/stats/update.html'>update</a></span><span class='op'>(</span><span class='va'>f_nlme_2</span>, random <span class='op'>=</span> <span class='va'>parent_0</span> <span class='op'>~</span> <span class='fl'>1</span><span class='op'>)</span>
</div><div class='output co'>#> Nonlinear mixed-effects model fit by maximum likelihood
#> Model: value ~ (mkin::get_deg_func())(name, time, parent_0, log_k_parent)
#> Data: "Not shown"
#> Log-likelihood: -404.3729
#> Fixed: list(parent_0 ~ 1, log_k_parent ~ 1)
#> parent_0 log_k_parent
#> 75.933480 -3.555983
#>
#> Random effects:
#> Formula: parent_0 ~ 1 | ds
#> parent_0 Residual
#> StdDev: 0.002416792 21.63027
#>
#> Number of Observations: 90
#> Number of Groups: 5 </div><div class='input'> <span class='va'>ds_2</span> <span class='op'><-</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'><-</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='mkinsub.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='mkinsub.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'><-</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='mkinsub.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='mkinsub.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_fomc_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='mkinsub.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"FOMC"</span>, <span class='st'>"A1"</span><span class='op'>)</span>,
A1 <span class='op'>=</span> <span class='fu'><a href='mkinsub.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'>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='mkinsub.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='mkinsub.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'><-</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'>"FOMC-SFO"</span> <span class='op'>=</span> <span class='va'>m_fomc_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><span class='op'>)</span>
<span class='fu'><a href='https://rdrr.io/r/graphics/plot.default.html'>plot</a></span><span class='op'>(</span><span class='va'>f_2</span><span class='op'>[</span><span class='st'>"SFO-SFO"</span>, <span class='fl'>3</span><span class='op'>:</span><span class='fl'>4</span><span class='op'>]</span><span class='op'>)</span> <span class='co'># Separate fits for datasets 3 and 4</span>
</div><div class='img'><img src='nlme.mmkin-1.png' alt='' width='700' height='433' /></div><div class='input'>
<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'>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 class='co'># plot(f_nlme_sfo_sfo) # not feasible with pkgdown figures</span>
<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='fl'>3</span><span class='op'>:</span><span class='fl'>4</span><span class='op'>)</span> <span class='co'># Global mixed model: Fits for datasets 3 and 4</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</span>
<span class='va'>f_nlme_sfo_sfo_ff</span> <span class='op'><-</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-ff"</span>, <span class='op'>]</span><span class='op'>)</span>
<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_ff</span>, <span class='fl'>3</span><span class='op'>:</span><span class='fl'>4</span><span class='op'>)</span> <span class='co'># chi2 different due to different df attribution</span>
</div><div class='img'><img src='nlme.mmkin-3.png' alt='' width='700' height='433' /></div><div class='input'>
<span class='co'># For more parameters, we need to increase pnlsMaxIter and the tolerance</span>
<span class='co'># to get convergence</span>
<span class='va'>f_nlme_fomc_sfo</span> <span class='op'><-</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'>"FOMC-SFO"</span>, <span class='op'>]</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>pnlsMaxIter <span class='op'>=</span> <span class='fl'>100</span>, tolerance <span class='op'>=</span> <span class='fl'>1e-4</span><span class='op'>)</span>, verbose <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span>
</div><div class='output co'>#>
#> **Iteration 1
#> LME step: Loglik: -394.1603, nlminb iterations: 3
#> reStruct parameters:
#> ds1 ds2 ds3 ds4 ds5
#> -0.2079793 0.8563830 1.7454105 1.0917354 1.2756825
#> Beginning PNLS step: .. completed fit_nlme() step.
#> PNLS step: RSS = 643.8803
#> fixed effects: 94.17379 -5.473193 -0.6970236 -0.2025091 2.103883
#> iterations: 100
#> Convergence crit. (must all become <= tolerance = 0.0001):
#> fixed reStruct
#> 0.7960134 0.1447728
#>
#> **Iteration 2
#> LME step: Loglik: -396.3824, nlminb iterations: 7
#> reStruct parameters:
#> ds1 ds2 ds3 ds4 ds5
#> -1.712404e-01 -2.432655e-05 1.842120e+00 1.073975e+00 1.322925e+00
#> Beginning PNLS step: .. completed fit_nlme() step.
#> PNLS step: RSS = 643.8035
#> fixed effects: 94.17385 -5.473487 -0.6970404 -0.2025137 2.103871
#> iterations: 100
#> Convergence crit. (must all become <= tolerance = 0.0001):
#> fixed reStruct
#> 5.382757e-05 1.236667e-03
#>
#> **Iteration 3
#> LME step: Loglik: -396.3825, nlminb iterations: 7
#> reStruct parameters:
#> ds1 ds2 ds3 ds4 ds5
#> -0.1712499044 -0.0001499831 1.8420971364 1.0739799123 1.3229167796
#> Beginning PNLS step: .. completed fit_nlme() step.
#> PNLS step: RSS = 643.7948
#> fixed effects: 94.17386 -5.473521 -0.6970422 -0.2025144 2.10387
#> iterations: 100
#> Convergence crit. (must all become <= tolerance = 0.0001):
#> fixed reStruct
#> 6.072817e-06 1.400857e-04
#>
#> **Iteration 4
#> LME step: Loglik: -396.3825, nlminb iterations: 7
#> reStruct parameters:
#> ds1 ds2 ds3 ds4 ds5
#> -0.1712529502 -0.0001641277 1.8420957542 1.0739797181 1.3229173076
#> Beginning PNLS step: .. completed fit_nlme() step.
#> PNLS step: RSS = 643.7936
#> fixed effects: 94.17386 -5.473526 -0.6970426 -0.2025146 2.103869
#> iterations: 100
#> Convergence crit. (must all become <= tolerance = 0.0001):
#> fixed reStruct
#> 1.027451e-06 2.275704e-05 </div><div class='input'> <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'>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>,
control <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>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>, verbose <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span>
</div><div class='output co'>#>
#> **Iteration 1
#> LME step: Loglik: -404.9582, nlminb iterations: 1
#> reStruct parameters:
#> ds1 ds2 ds3 ds4 ds5 ds6
#> -0.4114355 0.9798697 1.6990037 0.7293315 0.3354323 1.7113046
#> Beginning PNLS step: .. completed fit_nlme() step.
#> PNLS step: RSS = 630.3644
#> fixed effects: 93.82269 -5.455991 -0.6788957 -1.862196 -4.199671 0.05532828
#> iterations: 120
#> Convergence crit. (must all become <= tolerance = 0.0005):
#> fixed reStruct
#> 0.7885368 0.5822683
#>
#> **Iteration 2
#> LME step: Loglik: -407.7755, nlminb iterations: 11
#> reStruct parameters:
#> ds1 ds2 ds3 ds4 ds5 ds6
#> -0.371224133 0.003056179 1.789939402 0.724671158 0.301602977 1.754200729
#> Beginning PNLS step: .. completed fit_nlme() step.
#> PNLS step: RSS = 630.3633
#> fixed effects: 93.82269 -5.455992 -0.6788958 -1.862196 -4.199671 0.05532831
#> iterations: 120
#> Convergence crit. (must all become <= tolerance = 0.0005):
#> fixed reStruct
#> 4.789774e-07 2.200661e-05 </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_2</span><span class='op'>[</span><span class='st'>"FOMC-SFO"</span>, <span class='fl'>3</span><span class='op'>:</span><span class='fl'>4</span><span class='op'>]</span><span class='op'>)</span>
</div><div class='img'><img src='nlme.mmkin-4.png' alt='' width='700' height='433' /></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_fomc_sfo</span>, <span class='fl'>3</span><span class='op'>:</span><span class='fl'>4</span><span class='op'>)</span>
</div><div class='img'><img src='nlme.mmkin-5.png' alt='' width='700' height='433' /></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_2</span><span class='op'>[</span><span class='st'>"DFOP-SFO"</span>, <span class='fl'>3</span><span class='op'>:</span><span class='fl'>4</span><span class='op'>]</span><span class='op'>)</span>
</div><div class='img'><img src='nlme.mmkin-6.png' alt='' width='700' height='433' /></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='fl'>3</span><span class='op'>:</span><span class='fl'>4</span><span class='op'>)</span>
</div><div class='img'><img src='nlme.mmkin-7.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_fomc_sfo</span>, <span class='va'>f_nlme_sfo_sfo</span><span class='op'>)</span>
</div><div class='output co'>#> Model df AIC BIC logLik Test L.Ratio p-value
#> f_nlme_dfop_sfo 1 13 843.8547 884.6201 -408.9273
#> f_nlme_fomc_sfo 2 11 818.5149 853.0087 -398.2575 1 vs 2 21.33975 <.0001
#> f_nlme_sfo_sfo 3 9 1085.1821 1113.4043 -533.5910 2 vs 3 270.66716 <.0001</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> <span class='co'># if we ignore FOMC</span>
</div><div class='output co'>#> Model df AIC BIC logLik Test L.Ratio p-value
#> f_nlme_dfop_sfo 1 13 843.8547 884.6201 -408.9273
#> f_nlme_sfo_sfo 2 9 1085.1821 1113.4043 -533.5910 1 vs 2 249.3274 <.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'>#> $ff
#> parent_sink parent_A1 A1_sink
#> 0.5912432 0.4087568 1.0000000
#>
#> $distimes
#> DT50 DT90
#> parent 19.13518 63.5657
#> A1 66.02155 219.3189
#> </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'>#> $ff
#> parent_A1 parent_sink
#> 0.2768574 0.7231426
#>
#> $distimes
#> DT50 DT90 DT50back DT50_k1 DT50_k2
#> parent 11.07091 104.6320 31.49738 4.462384 46.20825
#> A1 162.30536 539.1667 NA NA NA
#> </div><div class='input'>
<span class='kw'>if</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='co'># tc error model for nlme available</span>
<span class='co'># Attempts to fit metabolite kinetics with the tc error model</span>
<span class='co'>#f_2_tc <- mmkin(list("SFO-SFO" = m_sfo_sfo,</span>
<span class='co'># "SFO-SFO-ff" = m_sfo_sfo_ff,</span>
<span class='co'># "FOMC-SFO" = m_fomc_sfo,</span>
<span class='co'># "DFOP-SFO" = m_dfop_sfo),</span>
<span class='co'># ds_2, quiet = TRUE,</span>
<span class='co'># error_model = "tc")</span>
<span class='co'>#f_nlme_sfo_sfo_tc <- nlme(f_2_tc["SFO-SFO", ], control = list(maxIter = 100))</span>
<span class='co'>#f_nlme_dfop_sfo_tc <- nlme(f_2_tc["DFOP-SFO", ])</span>
<span class='co'>#f_nlme_dfop_sfo_tc <- update(f_nlme_dfop_sfo, weights = varConstProp(),</span>
<span class='co'># control = list(sigma = 1, msMaxIter = 100, pnlsMaxIter = 15))</span>
<span class='co'># Fitting metabolite kinetics with nlme.mmkin and the two-component</span>
<span class='co'># error model currently does not work, at least not with these data.</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'>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'><-</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'><-</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'>#> <span class='error'>Error in if (findFunction("varConstProp")) { f_tc <- mmkin(c("SFO", "DFOP"), ds, quiet = TRUE, error_model = "tc") f_nlme_sfo_tc <- nlme(f_tc["SFO", ]) f_nlme_dfop_tc <- nlme(f_tc["DFOP", ]) AIC(f_nlme_sfo, f_nlme_sfo_tc, f_nlme_dfop, f_nlme_dfop_tc) print(f_nlme_dfop_tc)}: argument is not interpretable as logical</span></div><div class='input'> <span class='va'>f_2_obs</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'>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'><-</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>
<span class='co'># The same with DFOP-SFO does not converge, apparently the variances of</span>
<span class='co'># parent and A1 are too similar in this case, so that the model is</span>
<span class='co'># overparameterised</span>
<span class='co'>#f_nlme_dfop_sfo_obs <- nlme(f_2_obs["DFOP-SFO", ], control = list(maxIter = 100))</span>
<span class='co'># }</span>
</div></pre>
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