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+ <h1>Create an nlme model for an mmkin row object</h1>
+
+ <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='no'>model</span>,
+ <span class='kw'>data</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/sys.parent.html'>sys.frame</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/sys.parent.html'>sys.parent</a></span>()),
+ <span class='no'>fixed</span>,
+ <span class='kw'>random</span> <span class='kw'>=</span> <span class='no'>fixed</span>,
+ <span class='no'>groups</span>,
+ <span class='no'>start</span>,
+ <span class='kw'>correlation</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
+ <span class='kw'>weights</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
+ <span class='no'>subset</span>,
+ <span class='kw'>method</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"ML"</span>, <span class='st'>"REML"</span>),
+ <span class='kw'>na.action</span> <span class='kw'>=</span> <span class='no'>na.fail</span>,
+ <span class='no'>naPattern</span>,
+ <span class='kw'>control</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(),
+ <span class='kw'>verbose</span> <span class='kw'>=</span> <span class='fl'>FALSE</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>
+ </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.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='no'>ds</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span>(<span class='no'>experimental_data_for_UBA_2019</span>[<span class='fl'>6</span>:<span class='fl'>10</span>],
+ <span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span>(<span class='no'>x</span>$<span class='no'>data</span>[<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"name"</span>, <span class='st'>"time"</span>, <span class='st'>"value"</span>)], <span class='no'>name</span> <span class='kw'>==</span> <span class='st'>"parent"</span>))
+<span class='no'>f</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span>(<span class='st'>"SFO"</span>, <span class='no'>ds</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>)
+<span class='fu'><a href='https://rdrr.io/r/base/library.html'>library</a></span>(<span class='no'>nlme</span>)
+<span class='no'>f_nlme</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span>(<span class='no'>f</span>)
+<span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span>(<span class='no'>f</span>, <span class='kw'>random</span> <span class='kw'>=</span> <span class='no'>parent_0</span> ~ <span class='fl'>1</span>)</div><div class='output co'>#&gt; Nonlinear mixed-effects model fit by maximum likelihood
+#&gt; Model: value ~ deg_func(name, time, parent_0, log_k_parent_sink)
+#&gt; Data: structure(list(ds = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L), .Label = c("1", "2", "3", "4", "5"), class = c("ordered", "factor")), name = c("parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent"), time = c(0, 0, 3, 3, 6, 6, 10, 10, 20, 20, 34, 34, 55, 55, 90, 90, 112, 112, 132, 132, 0, 0, 3, 3, 7, 7, 14, 14, 30, 30, 60, 60, 90, 90, 120, 120, 180, 180, 0, 0, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 0, 0, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 91, 91, 120, 120, 0, 0, 8, 8, 14, 14, 21, 21, 41, 41, 63, 63, 91, 91, 120, 120), value = c(97.2, 96.4, 71.1, 69.2, 58.1, 56.6, 44.4, 43.4, 33.3, 29.2, 17.6, 18, 10.5, 9.3, 4.5, 4.7, 3, 3.4, 2.3, 2.7, 93.6, 92.3, 87, 82.2, 74, 73.9, 64.2, 69.5, 54, 54.6, 41.1, 38.4, 32.5, 35.5, 28.1, 29, 26.5, 27.6, 91.9, 90.8, 64.9, 66.2, 43.5, 44.1, 18.3, 18.1, 10.2, 10.8, 4.9, 3.3, 1.6, 1.5, 1.1, 0.9, 99.8, 98.3, 77.1, 77.2, 59, 58.1, 27.4, 29.2, 19.1, 29.6, 10.1, 18.2, 4.5, 9.1, 2.3, 2.9, 2, 1.8, 2, 2.2, 96.1, 94.3, 73.9, 73.9, 69.4, 73.1, 65.6, 65.3, 55.9, 54.4, 47, 49.3, 44.7, 46.7, 42.1, 41.3)), row.names = c(NA, -90L), class = c("nfnGroupedData", "nfGroupedData", "groupedData", "data.frame"), formula = value ~ time | ds, FUN = function (x) max(x, na.rm = TRUE), order.groups = FALSE)
+#&gt; Log-likelihood: -394.4901
+#&gt; Fixed: list(parent_0 ~ 1, log_k_parent_sink ~ 1)
+#&gt; parent_0 log_k_parent_sink
+#&gt; 73.985522 -3.869079
+#&gt;
+#&gt; Random effects:
+#&gt; Formula: parent_0 ~ 1 | ds
+#&gt; parent_0 Residual
+#&gt; StdDev: 18.6134 18.22029
+#&gt;
+#&gt; Number of Observations: 90
+#&gt; Number of Groups: 5 </div><div class='input'><span class='no'>f_nlme</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span>(<span class='no'>f</span>, <span class='kw'>start</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>parent_0</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>log_k_parent_sink</span> <span class='kw'>=</span> <span class='fl'>0.1</span>))
+<span class='fu'><a href='https://rdrr.io/r/stats/update.html'>update</a></span>(<span class='no'>f_nlme</span>, <span class='kw'>random</span> <span class='kw'>=</span> <span class='no'>parent_0</span> ~ <span class='fl'>1</span>)</div><div class='output co'>#&gt; Nonlinear mixed-effects model fit by maximum likelihood
+#&gt; Model: value ~ deg_func(name, time, parent_0, log_k_parent_sink)
+#&gt; Data: structure(list(ds = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L), .Label = c("1", "2", "3", "4", "5"), class = c("ordered", "factor")), name = c("parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent", "parent"), time = c(0, 0, 3, 3, 6, 6, 10, 10, 20, 20, 34, 34, 55, 55, 90, 90, 112, 112, 132, 132, 0, 0, 3, 3, 7, 7, 14, 14, 30, 30, 60, 60, 90, 90, 120, 120, 180, 180, 0, 0, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 0, 0, 1, 1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 91, 91, 120, 120, 0, 0, 8, 8, 14, 14, 21, 21, 41, 41, 63, 63, 91, 91, 120, 120), value = c(97.2, 96.4, 71.1, 69.2, 58.1, 56.6, 44.4, 43.4, 33.3, 29.2, 17.6, 18, 10.5, 9.3, 4.5, 4.7, 3, 3.4, 2.3, 2.7, 93.6, 92.3, 87, 82.2, 74, 73.9, 64.2, 69.5, 54, 54.6, 41.1, 38.4, 32.5, 35.5, 28.1, 29, 26.5, 27.6, 91.9, 90.8, 64.9, 66.2, 43.5, 44.1, 18.3, 18.1, 10.2, 10.8, 4.9, 3.3, 1.6, 1.5, 1.1, 0.9, 99.8, 98.3, 77.1, 77.2, 59, 58.1, 27.4, 29.2, 19.1, 29.6, 10.1, 18.2, 4.5, 9.1, 2.3, 2.9, 2, 1.8, 2, 2.2, 96.1, 94.3, 73.9, 73.9, 69.4, 73.1, 65.6, 65.3, 55.9, 54.4, 47, 49.3, 44.7, 46.7, 42.1, 41.3)), row.names = c(NA, -90L), class = c("nfnGroupedData", "nfGroupedData", "groupedData", "data.frame"), formula = value ~ time | ds, FUN = function (x) max(x, na.rm = TRUE), order.groups = FALSE)
+#&gt; Log-likelihood: -404.3729
+#&gt; Fixed: list(parent_0 ~ 1, log_k_parent_sink ~ 1)
+#&gt; parent_0 log_k_parent_sink
+#&gt; 75.933480 -3.555983
+#&gt;
+#&gt; Random effects:
+#&gt; Formula: parent_0 ~ 1 | ds
+#&gt; parent_0 Residual
+#&gt; StdDev: 0.002416792 21.63027
+#&gt;
+#&gt; Number of Observations: 90
+#&gt; Number of Groups: 5 </div></pre>
+ </div>
+ <div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
+ <h2>Contents</h2>
+ <ul class="nav nav-pills nav-stacked">
+ <li><a href="#arguments">Arguments</a></li>
+ <li><a href="#value">Value</a></li>
+ <li><a href="#see-also">See also</a></li>
+ <li><a href="#examples">Examples</a></li>
+ </ul>
+
+ </div>
+</div>
+
+
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+ <div class="copyright">
+ <p>Developed by Johannes Ranke.</p>
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