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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." /> +<meta name="twitter:card" content="summary" /> + + + + +<!-- mathjax --> +<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script> +<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script> + +<!--[if lt IE 9]> +<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> +<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> +<![endif]--> + + + + </head> + + <body> + <div class="container template-reference-topic"> + <header> + <div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <div class="container"> + <div class="navbar-header"> + <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> + <span class="sr-only">Toggle navigation</span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + </button> + <span class="navbar-brand"> + <a class="navbar-link" href="../index.html">mkin</a> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.9.49.10</span> + </span> + </div> + + <div id="navbar" class="navbar-collapse collapse"> + <ul class="nav navbar-nav"> + <li> + <a href="../reference/index.html">Functions and data</a> +</li> +<li class="dropdown"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + Articles + + <span class="caret"></span> + </a> + <ul class="dropdown-menu" role="menu"> + <li> + <a href="../articles/mkin.html">Introduction to mkin</a> + </li> + <li> + <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> + </li> + <li> + <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> + </li> + <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + </li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> + </ul> +</li> +<li> + <a href="../news/index.html">News</a> +</li> + </ul> + <ul class="nav navbar-nav navbar-right"> + + </ul> + + </div><!--/.nav-collapse --> + </div><!--/.container --> +</div><!--/.navbar --> + + + + </header> + +<div class="row"> + <div class="col-md-9 contents"> + <div class="page-header"> + <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'><-</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'><-</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'><-</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'>#> Nonlinear mixed-effects model fit by maximum likelihood +#> Model: value ~ deg_func(name, time, parent_0, log_k_parent_sink) +#> 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) +#> Log-likelihood: -394.4901 +#> Fixed: list(parent_0 ~ 1, log_k_parent_sink ~ 1) +#> parent_0 log_k_parent_sink +#> 73.985522 -3.869079 +#> +#> Random effects: +#> Formula: parent_0 ~ 1 | ds +#> parent_0 Residual +#> StdDev: 18.6134 18.22029 +#> +#> Number of Observations: 90 +#> Number of Groups: 5 </div><div class='input'><span class='no'>f_nlme</span> <span class='kw'><-</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'>#> Nonlinear mixed-effects model fit by maximum likelihood +#> Model: value ~ deg_func(name, time, parent_0, log_k_parent_sink) +#> 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) +#> Log-likelihood: -404.3729 +#> Fixed: list(parent_0 ~ 1, log_k_parent_sink ~ 1) +#> parent_0 log_k_parent_sink +#> 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></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> + + + <footer> + <div class="copyright"> + <p>Developed by Johannes Ranke.</p> +</div> + +<div class="pkgdown"> + <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.4.1.</p> +</div> + + </footer> + </div> + + + + + </body> +</html> + + |