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<meta property="og:description" content="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." />
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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="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></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'>#> Kinetic nonlinear mixed-effects model fit by maximum likelihood
#>
#> Structural model:
#> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
#> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
#> * parent
#>
#> Data:
#> 90 observations of 1 variable(s) grouped in 5 datasets
#>
#> Log-likelihood: -238.5635
#>
#> Fixed effects:
#> 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
#> </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'>#> $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'>#> Kinetic nonlinear mixed-effects model fit by maximum likelihood
#>
#> Structural model:
#> d_parent/dt = - k_parent * parent
#>
#> Data:
#> observations of 0 variable(s) grouped in 0 datasets
#>
#> Log-likelihood: -404.3729
#>
#> Fixed effects:
#> 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
#> </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_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'>"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'><-</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='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</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='op'>)</span>
</div><div class='img'><img src='nlme.mmkin-3.png' alt='' width='700' height='433' /></div><div class='input'>
<span class='co'># For the following fit we need to increase pnlsMaxIter and the tolerance</span>
<span class='co'># to get convergence</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'>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_nlme_dfop_sfo</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/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'>#> 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'><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'>></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'><-</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'>#> Kinetic nonlinear mixed-effects model fit by maximum likelihood
#>
#> Structural model:
#> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
#> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
#> * parent
#>
#> Data:
#> 90 observations of 1 variable(s) grouped in 5 datasets
#>
#> Log-likelihood: -238.4298
#>
#> Fixed effects:
#> list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_ilr ~ 1)
#> parent_0 log_k1 log_k2 g_ilr
#> 94.04774463 -1.82339924 -4.16715509 0.04020161
#>
#> 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: 2.473883 0.8499901 1.337187 0.3294411 1
#>
#> Variance function:
#> Structure: Constant plus proportion of variance covariate
#> Formula: ~fitted(.)
#> Parameter estimates:
#> const prop
#> 2.23222625 0.01262414 </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='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'>#> Kinetic nonlinear mixed-effects model fit by maximum likelihood
#>
#> Structural model:
#> d_parent/dt = - k_parent_sink * parent - k_parent_A1 * parent
#> d_A1/dt = + k_parent_A1 * parent - k_A1_sink * A1
#>
#> Data:
#> 170 observations of 2 variable(s) grouped in 5 datasets
#>
#> Log-likelihood: -472.976
#>
#> Fixed effects:
#> list(parent_0 ~ 1, log_k_parent_sink ~ 1, log_k_parent_A1 ~ 1, log_k_A1_sink ~ 1)
#> parent_0 log_k_parent_sink log_k_parent_A1 log_k_A1_sink
#> 87.975536 -3.669816 -4.164127 -4.645073
#>
#> Random effects:
#> Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1, log_k_parent_A1 ~ 1, log_k_A1_sink ~ 1)
#> Level: ds
#> Structure: Diagonal
#> parent_0 log_k_parent_sink log_k_parent_A1 log_k_A1_sink Residual
#> StdDev: 3.992214 1.77702 1.054733 0.4821383 6.482585
#>
#> Variance function:
#> Structure: Different standard deviations per stratum
#> Formula: ~1 | name
#> Parameter estimates:
#> parent A1
#> 1.0000000 0.2050003 </div><div class='input'> <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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