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</div><!--/.container --> -</div><!--/.navbar --> - - - - </header><div class="row"> - <div class="col-md-9 contents"> - <div class="page-header"> - <h1>Fit nonlinear mixed models with SAEM</h1> - <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/saem.R" class="external-link"><code>R/saem.R</code></a></small> - <div class="hidden name"><code>saem.Rd</code></div> - </div> - - <div class="ref-description"> - <p>This function uses <code><a href="https://rdrr.io/pkg/saemix/man/saemix.html" class="external-link">saemix::saemix()</a></code> as a backend for fitting nonlinear mixed -effects models created from <a href="mmkin.html">mmkin</a> row objects using the Stochastic Approximation -Expectation Maximisation algorithm (SAEM).</p> - </div> - - <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">saem</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span> -<span></span> -<span><span class="co"># S3 method for mmkin</span></span> -<span><span class="fu">saem</span><span class="op">(</span></span> -<span> <span class="va">object</span>,</span> -<span> transformations <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"mkin"</span>, <span class="st">"saemix"</span><span class="op">)</span>,</span> -<span> error_model <span class="op">=</span> <span class="st">"auto"</span>,</span> -<span> degparms_start <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">numeric</a></span><span class="op">(</span><span class="op">)</span>,</span> -<span> test_log_parms <span class="op">=</span> <span class="cn">TRUE</span>,</span> -<span> conf.level <span class="op">=</span> <span class="fl">0.6</span>,</span> -<span> solution_type <span class="op">=</span> <span class="st">"auto"</span>,</span> -<span> covariance.model <span class="op">=</span> <span class="st">"auto"</span>,</span> -<span> omega.init <span class="op">=</span> <span class="st">"auto"</span>,</span> -<span> covariates <span class="op">=</span> <span class="cn">NULL</span>,</span> -<span> covariate_models <span class="op">=</span> <span class="cn">NULL</span>,</span> -<span> no_random_effect <span class="op">=</span> <span class="cn">NULL</span>,</span> -<span> error.init <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">)</span>,</span> -<span> nbiter.saemix <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">300</span>, <span class="fl">100</span><span class="op">)</span>,</span> -<span> control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>displayProgress <span class="op">=</span> <span class="cn">FALSE</span>, print <span class="op">=</span> <span class="cn">FALSE</span>, nbiter.saemix <span class="op">=</span> <span class="va">nbiter.saemix</span>,</span> -<span> save <span class="op">=</span> <span class="cn">FALSE</span>, save.graphs <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> -<span> verbose <span class="op">=</span> <span class="cn">FALSE</span>,</span> -<span> quiet <span class="op">=</span> <span class="cn">FALSE</span>,</span> -<span> <span class="va">...</span></span> -<span><span class="op">)</span></span> -<span></span> -<span><span class="co"># S3 method for saem.mmkin</span></span> -<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">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" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">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> -<span></span> -<span><span class="fu">saemix_model</span><span class="op">(</span></span> -<span> <span class="va">object</span>,</span> -<span> solution_type <span class="op">=</span> <span class="st">"auto"</span>,</span> -<span> transformations <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"mkin"</span>, <span class="st">"saemix"</span><span class="op">)</span>,</span> -<span> error_model <span class="op">=</span> <span class="st">"auto"</span>,</span> -<span> degparms_start <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">numeric</a></span><span class="op">(</span><span class="op">)</span>,</span> -<span> covariance.model <span class="op">=</span> <span class="st">"auto"</span>,</span> -<span> no_random_effect <span class="op">=</span> <span class="cn">NULL</span>,</span> -<span> omega.init <span class="op">=</span> <span class="st">"auto"</span>,</span> -<span> covariates <span class="op">=</span> <span class="cn">NULL</span>,</span> -<span> covariate_models <span class="op">=</span> <span class="cn">NULL</span>,</span> -<span> error.init <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">numeric</a></span><span class="op">(</span><span class="op">)</span>,</span> -<span> test_log_parms <span class="op">=</span> <span class="cn">FALSE</span>,</span> -<span> conf.level <span class="op">=</span> <span class="fl">0.6</span>,</span> -<span> verbose <span class="op">=</span> <span class="cn">FALSE</span>,</span> -<span> <span class="va">...</span></span> -<span><span class="op">)</span></span> -<span></span> -<span><span class="fu">saemix_data</span><span class="op">(</span><span class="va">object</span>, covariates <span class="op">=</span> <span class="cn">NULL</span>, verbose <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> - </div> - - <div id="arguments"> - <h2>Arguments</h2> - <dl><dt>object</dt> -<dd><p>An <a href="mmkin.html">mmkin</a> row object containing several fits of the same -<a href="mkinmod.html">mkinmod</a> model to different datasets</p></dd> - - -<dt>...</dt> -<dd><p>Further parameters passed to <a href="https://rdrr.io/pkg/saemix/man/saemixModel.html" class="external-link">saemix::saemixModel</a>.</p></dd> - - -<dt>transformations</dt> -<dd><p>Per default, all parameter transformations are done -in mkin. If this argument is set to 'saemix', parameter transformations -are done in 'saemix' for the supported cases, i.e. (as of version 1.1.2) -SFO, FOMC, DFOP and HS without fixing <code>parent_0</code>, and SFO or DFOP with -one SFO metabolite.</p></dd> - - -<dt>error_model</dt> -<dd><p>Possibility to override the error model used in the mmkin object</p></dd> - - -<dt>degparms_start</dt> -<dd><p>Parameter values given as a named numeric vector will -be used to override the starting values obtained from the 'mmkin' object.</p></dd> - - -<dt>test_log_parms</dt> -<dd><p>If TRUE, an attempt is made to use more robust starting -values for population parameters fitted as log parameters in mkin (like -rate constants) by only considering rate constants that pass the t-test -when calculating mean degradation parameters using <a href="mean_degparms.html">mean_degparms</a>.</p></dd> - - -<dt>conf.level</dt> -<dd><p>Possibility to adjust the required confidence level -for parameter that are tested if requested by 'test_log_parms'.</p></dd> - - -<dt>solution_type</dt> -<dd><p>Possibility to specify the solution type in case the -automatic choice is not desired</p></dd> - - -<dt>covariance.model</dt> -<dd><p>Will be passed to <code><a href="https://rdrr.io/pkg/saemix/man/saemixModel.html" class="external-link">saemix::saemixModel()</a></code>. Per -default, uncorrelated random effects are specified for all degradation -parameters.</p></dd> - - -<dt>omega.init</dt> -<dd><p>Will be passed to <code><a href="https://rdrr.io/pkg/saemix/man/saemixModel.html" class="external-link">saemix::saemixModel()</a></code>. If using -mkin transformations and the default covariance model with optionally -excluded random effects, the variances of the degradation parameters -are estimated using <a href="mean_degparms.html">mean_degparms</a>, with testing of untransformed -log parameters for significant difference from zero. If not using -mkin transformations or a custom covariance model, the default -initialisation of <a href="https://rdrr.io/pkg/saemix/man/saemixModel.html" class="external-link">saemix::saemixModel</a> is used for omega.init.</p></dd> - - -<dt>covariates</dt> -<dd><p>A data frame with covariate data for use in -'covariate_models', with dataset names as row names.</p></dd> - - -<dt>covariate_models</dt> -<dd><p>A list containing linear model formulas with one explanatory -variable, i.e. of the type 'parameter ~ covariate'. Covariates must be available -in the 'covariates' data frame.</p></dd> - - -<dt>no_random_effect</dt> -<dd><p>Character vector of degradation parameters for -which there should be no variability over the groups. Only used -if the covariance model is not explicitly specified.</p></dd> - - -<dt>error.init</dt> -<dd><p>Will be passed to <code><a href="https://rdrr.io/pkg/saemix/man/saemixModel.html" class="external-link">saemix::saemixModel()</a></code>.</p></dd> - - -<dt>nbiter.saemix</dt> -<dd><p>Convenience option to increase the number of -iterations</p></dd> - - -<dt>control</dt> -<dd><p>Passed to <a href="https://rdrr.io/pkg/saemix/man/saemix.html" class="external-link">saemix::saemix</a>.</p></dd> - - -<dt>verbose</dt> -<dd><p>Should we print information about created objects of -type <a href="https://rdrr.io/pkg/saemix/man/SaemixModel-class.html" class="external-link">saemix::SaemixModel</a> and <a href="https://rdrr.io/pkg/saemix/man/SaemixData-class.html" class="external-link">saemix::SaemixData</a>?</p></dd> - - -<dt>quiet</dt> -<dd><p>Should we suppress the messages saemix prints at the beginning -and the end of the optimisation process?</p></dd> - - -<dt>x</dt> -<dd><p>An saem.mmkin object to print</p></dd> - - -<dt>digits</dt> -<dd><p>Number of digits to use for printing</p></dd> - -</dl></div> - <div id="value"> - <h2>Value</h2> - - -<p>An S3 object of class 'saem.mmkin', containing the fitted -<a href="https://rdrr.io/pkg/saemix/man/SaemixObject-class.html" class="external-link">saemix::SaemixObject</a> as a list component named 'so'. The -object also inherits from 'mixed.mmkin'.</p> - - -<p>An <a href="https://rdrr.io/pkg/saemix/man/SaemixModel-class.html" class="external-link">saemix::SaemixModel</a> object.</p> - - -<p>An <a href="https://rdrr.io/pkg/saemix/man/SaemixData-class.html" class="external-link">saemix::SaemixData</a> object.</p> - </div> - <div id="details"> - <h2>Details</h2> - <p>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 using <a href="mkinfit.html">mkinfit</a>.</p> -<p>Starting values for the fixed effects (population mean parameters, argument -psi0 of <code><a href="https://rdrr.io/pkg/saemix/man/saemixModel.html" class="external-link">saemix::saemixModel()</a></code> are the mean values of the parameters found -using <a href="mmkin.html">mmkin</a>.</p> - </div> - <div id="see-also"> - <h2>See also</h2> - <div class="dont-index"><p><a href="summary.saem.mmkin.html">summary.saem.mmkin</a> <a href="plot.mixed.mmkin.html">plot.mixed.mmkin</a></p></div> - </div> - - <div id="ref-examples"> - <h2>Examples</h2> - <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span> -<span class="r-in"><span><span class="va">ds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">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></span> -<span class="r-in"><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" class="external-link">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" class="external-link">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="op">)</span></span></span> -<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">)</span></span></span> -<span class="r-in"><span><span class="va">f_mmkin_parent_p0_fixed</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">ds</span>,</span></span> -<span class="r-in"><span> state.ini <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, fixed_initials <span class="op">=</span> <span class="st">"parent"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> -<span class="r-in"><span><span class="va">f_saem_p0_fixed</span> <span class="op"><-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin_parent_p0_fixed</span><span class="op">)</span></span></span> -<span class="r-in"><span></span></span> -<span class="r-in"><span><span class="va">f_mmkin_parent</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" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</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><span class="op">)</span></span></span> -<span class="r-in"><span><span class="va">f_saem_sfo</span> <span class="op"><-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span></span></span> -<span class="r-in"><span><span class="va">f_saem_fomc</span> <span class="op"><-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span><span class="op">)</span></span></span> -<span class="r-in"><span><span class="va">f_saem_dfop</span> <span class="op"><-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span></span></span> -<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_sfo</span>, <span class="va">f_saem_fomc</span>, <span class="va">f_saem_dfop</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> Data: 90 observations of 1 variable(s) grouped in 5 datasets</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> npar AIC BIC Lik</span> -<span class="r-out co"><span class="r-pr">#></span> f_saem_sfo 5 624.33 622.38 -307.17</span> -<span class="r-out co"><span class="r-pr">#></span> f_saem_fomc 7 467.85 465.11 -226.92</span> -<span class="r-out co"><span class="r-pr">#></span> f_saem_dfop 9 493.76 490.24 -237.88</span> -<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_sfo</span>, <span class="va">f_saem_dfop</span>, test <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> Data: 90 observations of 1 variable(s) grouped in 5 datasets</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> npar AIC BIC Lik Chisq Df Pr(>Chisq) </span> -<span class="r-out co"><span class="r-pr">#></span> f_saem_sfo 5 624.33 622.38 -307.17 </span> -<span class="r-out co"><span class="r-pr">#></span> f_saem_dfop 9 493.76 490.24 -237.88 138.57 4 < 2.2e-16 ***</span> -<span class="r-out co"><span class="r-pr">#></span> ---</span> -<span class="r-out co"><span class="r-pr">#></span> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span> -<span class="r-in"><span><span class="fu"><a href="illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_dfop</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> [1] "sd(g_qlogis)"</span> -<span class="r-in"><span><span class="va">f_saem_dfop_red</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_dfop</span>, no_random_effect <span class="op">=</span> <span class="st">"g_qlogis"</span><span class="op">)</span></span></span> -<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_dfop</span>, <span class="va">f_saem_dfop_red</span>, test <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> Data: 90 observations of 1 variable(s) grouped in 5 datasets</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> npar AIC BIC Lik Chisq Df Pr(>Chisq)</span> -<span class="r-out co"><span class="r-pr">#></span> f_saem_dfop_red 8 488.68 485.55 -236.34 </span> -<span class="r-out co"><span class="r-pr">#></span> f_saem_dfop 9 493.76 490.24 -237.88 0 1 1</span> -<span class="r-in"><span></span></span> -<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_sfo</span>, <span class="va">f_saem_fomc</span>, <span class="va">f_saem_dfop</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> Data: 90 observations of 1 variable(s) grouped in 5 datasets</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> npar AIC BIC Lik</span> -<span class="r-out co"><span class="r-pr">#></span> f_saem_sfo 5 624.33 622.38 -307.17</span> -<span class="r-out co"><span class="r-pr">#></span> f_saem_fomc 7 467.85 465.11 -226.92</span> -<span class="r-out co"><span class="r-pr">#></span> f_saem_dfop 9 493.76 490.24 -237.88</span> -<span class="r-in"><span><span class="co"># The returned saem.mmkin object contains an SaemixObject, therefore we can use</span></span></span> -<span class="r-in"><span><span class="co"># functions from saemix</span></span></span> -<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">saemix</span><span class="op">)</span></span></span> -<span class="r-msg co"><span class="r-pr">#></span> Loading required package: npde</span> -<span class="r-msg co"><span class="r-pr">#></span> Package saemix, version 3.2</span> -<span class="r-msg co"><span class="r-pr">#></span> please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr</span> -<span class="r-msg co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> Attaching package: ‘saemix’</span> -<span class="r-msg co"><span class="r-pr">#></span> The following objects are masked from ‘package:npde’:</span> -<span class="r-msg co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> kurtosis, skewness</span> -<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/compare.saemix.html" class="external-link">compare.saemix</a></span><span class="op">(</span><span class="va">f_saem_sfo</span><span class="op">$</span><span class="va">so</span>, <span class="va">f_saem_fomc</span><span class="op">$</span><span class="va">so</span>, <span class="va">f_saem_dfop</span><span class="op">$</span><span class="va">so</span><span class="op">)</span></span></span> -<span class="r-msg co"><span class="r-pr">#></span> Likelihoods calculated by importance sampling</span> -<span class="r-out co"><span class="r-pr">#></span> AIC BIC</span> -<span class="r-out co"><span class="r-pr">#></span> 1 624.3316 622.3788</span> -<span class="r-out co"><span class="r-pr">#></span> 2 467.8472 465.1132</span> -<span class="r-out co"><span class="r-pr">#></span> 3 493.7592 490.2441</span> -<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_fomc</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></span></span> -<span class="r-plt img"><img src="saem-1.png" alt="" width="700" height="433"></span> -<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_fomc</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"individual.fit"</span><span class="op">)</span></span></span> -<span class="r-plt img"><img src="saem-2.png" alt="" width="700" height="433"></span> -<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_fomc</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"npde"</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> Simulating data using nsim = 1000 simulated datasets</span> -<span class="r-out co"><span class="r-pr">#></span> Computing WRES and npde .</span> -<span class="r-msg co"><span class="r-pr">#></span> Please use npdeSaemix to obtain VPC and npde</span> -<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_fomc</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"vpc"</span><span class="op">)</span></span></span> -<span class="r-plt img"><img src="saem-3.png" alt="" width="700" height="433"></span> -<span class="r-in"><span></span></span> -<span class="r-in"><span><span class="va">f_mmkin_parent_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_mmkin_parent</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span> -<span class="r-in"><span><span class="va">f_saem_fomc_tc</span> <span class="op"><-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin_parent_tc</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span><span class="op">)</span></span></span> -<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_fomc</span>, <span class="va">f_saem_fomc_tc</span>, test <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> Data: 90 observations of 1 variable(s) grouped in 5 datasets</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> npar AIC BIC Lik Chisq Df Pr(>Chisq)</span> -<span class="r-out co"><span class="r-pr">#></span> f_saem_fomc 7 467.85 465.11 -226.92 </span> -<span class="r-out co"><span class="r-pr">#></span> f_saem_fomc_tc 8 469.83 466.71 -226.92 0.015 1 0.9027</span> -<span class="r-in"><span></span></span> -<span class="r-in"><span><span class="va">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="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span></span> -<span class="r-in"><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><span class="op">)</span></span></span> -<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"><span><span class="va">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="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span></span> -<span class="r-in"><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><span class="op">)</span></span></span> -<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"><span><span class="va">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="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span></span> -<span class="r-in"><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><span class="op">)</span></span></span> -<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> -<span class="r-in"><span><span class="co"># The following fit uses analytical solutions for SFO-SFO and DFOP-SFO,</span></span></span> -<span class="r-in"><span><span class="co"># and compiled ODEs for FOMC that are much slower</span></span></span> -<span class="r-in"><span><span class="va">f_mmkin</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" class="external-link">list</a></span><span class="op">(</span></span></span> -<span class="r-in"><span> <span class="st">"SFO-SFO"</span> <span class="op">=</span> <span class="va">sfo_sfo</span>, <span class="st">"FOMC-SFO"</span> <span class="op">=</span> <span class="va">fomc_sfo</span>, <span class="st">"DFOP-SFO"</span> <span class="op">=</span> <span class="va">dfop_sfo</span><span class="op">)</span>,</span></span> -<span class="r-in"><span> <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> -<span class="r-in"><span><span class="co"># saem fits of SFO-SFO and DFOP-SFO to these data take about five seconds</span></span></span> -<span class="r-in"><span><span class="co"># each on this system, as we use analytical solutions written for saemix.</span></span></span> -<span class="r-in"><span><span class="co"># When using the analytical solutions written for mkin this took around</span></span></span> -<span class="r-in"><span><span class="co"># four minutes</span></span></span> -<span class="r-in"><span><span class="va">f_saem_sfo_sfo</span> <span class="op"><-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">[</span><span class="st">"SFO-SFO"</span>, <span class="op">]</span><span class="op">)</span></span></span> -<span class="r-in"><span><span class="va">f_saem_dfop_sfo</span> <span class="op"><-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span><span class="op">)</span></span></span> -<span class="r-in"><span><span class="co"># We can use print, plot and summary methods to check the results</span></span></span> -<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> Kinetic nonlinear mixed-effects model fit by SAEM</span> -<span class="r-out co"><span class="r-pr">#></span> Structural model:</span> -<span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span> -<span class="r-out co"><span class="r-pr">#></span> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span> -<span class="r-out co"><span class="r-pr">#></span> * parent</span> -<span class="r-out co"><span class="r-pr">#></span> d_A1/dt = + f_parent_to_A1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span> -<span class="r-out co"><span class="r-pr">#></span> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span> -<span class="r-out co"><span class="r-pr">#></span> exp(-k2 * time))) * parent - k_A1 * A1</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Data:</span> -<span class="r-out co"><span class="r-pr">#></span> 170 observations of 2 variable(s) grouped in 5 datasets</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Likelihood computed by importance sampling</span> -<span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span> -<span class="r-out co"><span class="r-pr">#></span> 839.2 834.1 -406.6</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> estimate lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 93.70402 91.04104 96.3670</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_A1 -5.83760 -7.66452 -4.0107</span> -<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.95718 -1.35955 -0.5548</span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 -2.35514 -3.39402 -1.3163</span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 -3.79634 -5.64009 -1.9526</span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.02108 -0.66463 0.6225</span> -<span class="r-out co"><span class="r-pr">#></span> a.1 1.88191 1.66491 2.0989</span> -<span class="r-out co"><span class="r-pr">#></span> SD.parent_0 2.81628 0.78922 4.8433</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k_A1 1.78751 0.42105 3.1540</span> -<span class="r-out co"><span class="r-pr">#></span> SD.f_parent_qlogis 0.45016 0.16116 0.7391</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 1.06923 0.31676 1.8217</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 2.03768 0.70938 3.3660</span> -<span class="r-out co"><span class="r-pr">#></span> SD.g_qlogis 0.44024 -0.09262 0.9731</span> -<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span><span class="op">)</span></span></span> -<span class="r-plt img"><img src="saem-4.png" alt="" width="700" height="433"></span> -<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span>, data <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> saemix version used for fitting: 3.2 </span> -<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.2.3 </span> -<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.3 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Sun Apr 16 08:32:32 2023 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Sun Apr 16 08:32:32 2023 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Equations:</span> -<span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span> -<span class="r-out co"><span class="r-pr">#></span> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span> -<span class="r-out co"><span class="r-pr">#></span> * parent</span> -<span class="r-out co"><span class="r-pr">#></span> d_A1/dt = + f_parent_to_A1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span> -<span class="r-out co"><span class="r-pr">#></span> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span> -<span class="r-out co"><span class="r-pr">#></span> exp(-k2 * time))) * parent - k_A1 * A1</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Data:</span> -<span class="r-out co"><span class="r-pr">#></span> 170 observations of 2 variable(s) grouped in 5 datasets</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type analytical </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted in 4.145 s</span> -<span class="r-out co"><span class="r-pr">#></span> Using 300, 100 iterations and 10 chains</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Variance model: Constant variance </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Starting values for degradation parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_A1 f_parent_qlogis log_k1 log_k2 </span> -<span class="r-out co"><span class="r-pr">#></span> 93.8102 -5.3734 -0.9711 -1.8799 -4.2708 </span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis </span> -<span class="r-out co"><span class="r-pr">#></span> 0.1356 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fixed degradation parameter values:</span> -<span class="r-out co"><span class="r-pr">#></span> None</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Starting values for random effects (square root of initial entries in omega):</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_A1 f_parent_qlogis log_k1 log_k2 g_qlogis</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 4.941 0.000 0.0000 0.000 0.000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_A1 0.000 2.551 0.0000 0.000 0.000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis 0.000 0.000 0.7251 0.000 0.000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 0.000 0.000 0.0000 1.449 0.000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 0.000 0.000 0.0000 0.000 2.228 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis 0.000 0.000 0.0000 0.000 0.000 0.7814</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Starting values for error model parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> a.1 </span> -<span class="r-out co"><span class="r-pr">#></span> 1 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Results:</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Likelihood computed by importance sampling</span> -<span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span> -<span class="r-out co"><span class="r-pr">#></span> 839.2 834.1 -406.6</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Optimised parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 93.70402 91.04104 96.3670</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_A1 -5.83760 -7.66452 -4.0107</span> -<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.95718 -1.35955 -0.5548</span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 -2.35514 -3.39402 -1.3163</span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 -3.79634 -5.64009 -1.9526</span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.02108 -0.66463 0.6225</span> -<span class="r-out co"><span class="r-pr">#></span> a.1 1.88191 1.66491 2.0989</span> -<span class="r-out co"><span class="r-pr">#></span> SD.parent_0 2.81628 0.78922 4.8433</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k_A1 1.78751 0.42105 3.1540</span> -<span class="r-out co"><span class="r-pr">#></span> SD.f_parent_qlogis 0.45016 0.16116 0.7391</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 1.06923 0.31676 1.8217</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 2.03768 0.70938 3.3660</span> -<span class="r-out co"><span class="r-pr">#></span> SD.g_qlogis 0.44024 -0.09262 0.9731</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Correlation: </span> -<span class="r-out co"><span class="r-pr">#></span> parnt_0 lg_k_A1 f_prnt_ log_k1 log_k2 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k_A1 -0.0147 </span> -<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.0269 0.0573 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 0.0263 -0.0011 -0.0040 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 0.0020 0.0065 -0.0002 -0.0776 </span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.0248 -0.0180 -0.0004 -0.0903 -0.0603</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Random effects:</span> -<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> SD.parent_0 2.8163 0.78922 4.8433</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k_A1 1.7875 0.42105 3.1540</span> -<span class="r-out co"><span class="r-pr">#></span> SD.f_parent_qlogis 0.4502 0.16116 0.7391</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 1.0692 0.31676 1.8217</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 2.0377 0.70938 3.3660</span> -<span class="r-out co"><span class="r-pr">#></span> SD.g_qlogis 0.4402 -0.09262 0.9731</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Variance model:</span> -<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> a.1 1.882 1.665 2.099</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Backtransformed parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 93.704015 9.104e+01 96.36699</span> -<span class="r-out co"><span class="r-pr">#></span> k_A1 0.002916 4.692e-04 0.01812</span> -<span class="r-out co"><span class="r-pr">#></span> f_parent_to_A1 0.277443 2.043e-01 0.36475</span> -<span class="r-out co"><span class="r-pr">#></span> k1 0.094880 3.357e-02 0.26813</span> -<span class="r-out co"><span class="r-pr">#></span> k2 0.022453 3.553e-03 0.14191</span> -<span class="r-out co"><span class="r-pr">#></span> g 0.494731 3.397e-01 0.65078</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Resulting formation fractions:</span> -<span class="r-out co"><span class="r-pr">#></span> ff</span> -<span class="r-out co"><span class="r-pr">#></span> parent_A1 0.2774</span> -<span class="r-out co"><span class="r-pr">#></span> parent_sink 0.7226</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Estimated disappearance times:</span> -<span class="r-out co"><span class="r-pr">#></span> DT50 DT90 DT50back DT50_k1 DT50_k2</span> -<span class="r-out co"><span class="r-pr">#></span> parent 14.0 72.38 21.79 7.306 30.87</span> -<span class="r-out co"><span class="r-pr">#></span> A1 237.7 789.68 NA NA NA</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Data:</span> -<span class="r-out co"><span class="r-pr">#></span> ds name time observed predicted residual std standardized</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 0 97.2 95.70025 1.49975 1.882 0.79693</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 0 96.4 95.70025 0.69975 1.882 0.37183</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 3 71.1 71.44670 -0.34670 1.882 -0.18423</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 3 69.2 71.44670 -2.24670 1.882 -1.19384</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 6 58.1 56.59283 1.50717 1.882 0.80087</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 6 56.6 56.59283 0.00717 1.882 0.00381</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 10 44.4 44.56648 -0.16648 1.882 -0.08847</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 10 43.4 44.56648 -1.16648 1.882 -0.61984</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 20 33.3 29.76020 3.53980 1.882 1.88096</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 20 29.2 29.76020 -0.56020 1.882 -0.29767</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 34 17.6 19.39208 -1.79208 1.882 -0.95226</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 34 18.0 19.39208 -1.39208 1.882 -0.73971</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 55 10.5 10.55761 -0.05761 1.882 -0.03061</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 55 9.3 10.55761 -1.25761 1.882 -0.66826</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 90 4.5 3.84742 0.65258 1.882 0.34676</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 90 4.7 3.84742 0.85258 1.882 0.45304</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 112 3.0 2.03997 0.96003 1.882 0.51013</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 112 3.4 2.03997 1.36003 1.882 0.72268</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 132 2.3 1.14585 1.15415 1.882 0.61328</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 132 2.7 1.14585 1.55415 1.882 0.82583</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 3 4.3 4.86054 -0.56054 1.882 -0.29786</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 3 4.6 4.86054 -0.26054 1.882 -0.13844</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 6 7.0 7.74179 -0.74179 1.882 -0.39417</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 6 7.2 7.74179 -0.54179 1.882 -0.28789</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 10 8.2 9.94048 -1.74048 1.882 -0.92485</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 10 8.0 9.94048 -1.94048 1.882 -1.03112</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 20 11.0 12.19109 -1.19109 1.882 -0.63291</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 20 13.7 12.19109 1.50891 1.882 0.80180</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 34 11.5 13.10706 -1.60706 1.882 -0.85395</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 34 12.7 13.10706 -0.40706 1.882 -0.21630</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 55 14.9 13.06131 1.83869 1.882 0.97703</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 55 14.5 13.06131 1.43869 1.882 0.76448</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 90 12.1 11.54495 0.55505 1.882 0.29494</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 90 12.3 11.54495 0.75505 1.882 0.40122</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 112 9.9 10.31533 -0.41533 1.882 -0.22070</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 112 10.2 10.31533 -0.11533 1.882 -0.06128</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 132 8.8 9.20222 -0.40222 1.882 -0.21373</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 132 7.8 9.20222 -1.40222 1.882 -0.74510</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 0 93.6 90.82357 2.77643 1.882 1.47532</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 0 92.3 90.82357 1.47643 1.882 0.78453</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 3 87.0 84.73448 2.26552 1.882 1.20384</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 3 82.2 84.73448 -2.53448 1.882 -1.34675</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 7 74.0 77.65013 -3.65013 1.882 -1.93958</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 7 73.9 77.65013 -3.75013 1.882 -1.99272</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 14 64.2 67.60639 -3.40639 1.882 -1.81007</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 14 69.5 67.60639 1.89361 1.882 1.00621</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 30 54.0 52.53663 1.46337 1.882 0.77760</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 30 54.6 52.53663 2.06337 1.882 1.09642</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 60 41.1 39.42728 1.67272 1.882 0.88884</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 60 38.4 39.42728 -1.02728 1.882 -0.54587</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 90 32.5 33.76360 -1.26360 1.882 -0.67144</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 90 35.5 33.76360 1.73640 1.882 0.92268</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 120 28.1 30.39975 -2.29975 1.882 -1.22203</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 120 29.0 30.39975 -1.39975 1.882 -0.74379</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 180 26.5 25.62379 0.87621 1.882 0.46559</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 180 27.6 25.62379 1.97621 1.882 1.05010</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 3 3.9 2.70005 1.19995 1.882 0.63762</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 3 3.1 2.70005 0.39995 1.882 0.21252</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 7 6.9 5.83475 1.06525 1.882 0.56605</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 7 6.6 5.83475 0.76525 1.882 0.40663</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 14 10.4 10.26142 0.13858 1.882 0.07364</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 14 8.3 10.26142 -1.96142 1.882 -1.04225</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 30 14.4 16.82999 -2.42999 1.882 -1.29123</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 30 13.7 16.82999 -3.12999 1.882 -1.66319</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 60 22.1 22.32486 -0.22486 1.882 -0.11949</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 60 22.3 22.32486 -0.02486 1.882 -0.01321</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 90 27.5 24.45927 3.04073 1.882 1.61576</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 90 25.4 24.45927 0.94073 1.882 0.49988</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 120 28.0 25.54862 2.45138 1.882 1.30260</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 120 26.6 25.54862 1.05138 1.882 0.55868</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 180 25.8 26.82277 -1.02277 1.882 -0.54347</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 180 25.3 26.82277 -1.52277 1.882 -0.80916</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 0 91.9 91.16791 0.73209 1.882 0.38901</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 0 90.8 91.16791 -0.36791 1.882 -0.19550</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 1 64.9 67.58358 -2.68358 1.882 -1.42598</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 1 66.2 67.58358 -1.38358 1.882 -0.73520</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 3 43.5 41.62086 1.87914 1.882 0.99853</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 3 44.1 41.62086 2.47914 1.882 1.31735</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 8 18.3 19.60116 -1.30116 1.882 -0.69140</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 8 18.1 19.60116 -1.50116 1.882 -0.79768</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 14 10.2 10.63101 -0.43101 1.882 -0.22903</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 14 10.8 10.63101 0.16899 1.882 0.08980</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 27 4.9 3.12435 1.77565 1.882 0.94354</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 27 3.3 3.12435 0.17565 1.882 0.09334</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 48 1.6 0.43578 1.16422 1.882 0.61864</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 48 1.5 0.43578 1.06422 1.882 0.56550</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 70 1.1 0.05534 1.04466 1.882 0.55510</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 70 0.9 0.05534 0.84466 1.882 0.44883</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 1 9.6 7.63450 1.96550 1.882 1.04442</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 1 7.7 7.63450 0.06550 1.882 0.03481</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 3 15.0 15.52593 -0.52593 1.882 -0.27947</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 3 15.1 15.52593 -0.42593 1.882 -0.22633</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 8 21.2 20.32192 0.87808 1.882 0.46659</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 8 21.1 20.32192 0.77808 1.882 0.41345</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 14 19.7 20.09721 -0.39721 1.882 -0.21107</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 14 18.9 20.09721 -1.19721 1.882 -0.63617</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 27 17.5 16.37477 1.12523 1.882 0.59792</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 27 15.9 16.37477 -0.47477 1.882 -0.25228</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 48 9.5 10.13141 -0.63141 1.882 -0.33551</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 48 9.8 10.13141 -0.33141 1.882 -0.17610</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 70 6.2 5.81827 0.38173 1.882 0.20284</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 70 6.1 5.81827 0.28173 1.882 0.14970</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 0 99.8 97.48728 2.31272 1.882 1.22892</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 0 98.3 97.48728 0.81272 1.882 0.43186</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 1 77.1 79.29476 -2.19476 1.882 -1.16624</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 1 77.2 79.29476 -2.09476 1.882 -1.11310</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 3 59.0 55.67060 3.32940 1.882 1.76915</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 3 58.1 55.67060 2.42940 1.882 1.29092</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 8 27.4 31.57871 -4.17871 1.882 -2.22046</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 8 29.2 31.57871 -2.37871 1.882 -1.26398</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 14 19.1 22.51546 -3.41546 1.882 -1.81489</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 14 29.6 22.51546 7.08454 1.882 3.76454</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 27 10.1 14.09074 -3.99074 1.882 -2.12057</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 27 18.2 14.09074 4.10926 1.882 2.18355</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 48 4.5 6.95747 -2.45747 1.882 -1.30584</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 48 9.1 6.95747 2.14253 1.882 1.13848</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 70 2.3 3.32472 -1.02472 1.882 -0.54451</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 70 2.9 3.32472 -0.42472 1.882 -0.22569</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 91 2.0 1.64300 0.35700 1.882 0.18970</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 91 1.8 1.64300 0.15700 1.882 0.08343</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 120 2.0 0.62073 1.37927 1.882 0.73291</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 120 2.2 0.62073 1.57927 1.882 0.83918</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 1 4.2 3.64568 0.55432 1.882 0.29455</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 1 3.9 3.64568 0.25432 1.882 0.13514</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 3 7.4 8.30173 -0.90173 1.882 -0.47916</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 3 7.9 8.30173 -0.40173 1.882 -0.21347</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 8 14.5 12.71589 1.78411 1.882 0.94803</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 8 13.7 12.71589 0.98411 1.882 0.52293</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 14 14.2 13.90452 0.29548 1.882 0.15701</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 14 12.2 13.90452 -1.70452 1.882 -0.90574</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 27 13.7 14.15523 -0.45523 1.882 -0.24190</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 27 13.2 14.15523 -0.95523 1.882 -0.50759</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 48 13.6 13.31038 0.28962 1.882 0.15389</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 48 15.4 13.31038 2.08962 1.882 1.11037</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 70 10.4 11.85965 -1.45965 1.882 -0.77562</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 70 11.6 11.85965 -0.25965 1.882 -0.13797</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 91 10.0 10.36294 -0.36294 1.882 -0.19286</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 91 9.5 10.36294 -0.86294 1.882 -0.45855</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 120 9.1 8.43003 0.66997 1.882 0.35601</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 120 9.0 8.43003 0.56997 1.882 0.30287</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 0 96.1 93.95603 2.14397 1.882 1.13925</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 0 94.3 93.95603 0.34397 1.882 0.18278</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 8 73.9 77.70592 -3.80592 1.882 -2.02237</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 8 73.9 77.70592 -3.80592 1.882 -2.02237</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 14 69.4 70.04570 -0.64570 1.882 -0.34311</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 14 73.1 70.04570 3.05430 1.882 1.62298</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 21 65.6 64.01710 1.58290 1.882 0.84111</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 21 65.3 64.01710 1.28290 1.882 0.68170</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 41 55.9 54.98434 0.91566 1.882 0.48656</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 41 54.4 54.98434 -0.58434 1.882 -0.31050</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 63 47.0 49.87137 -2.87137 1.882 -1.52577</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 63 49.3 49.87137 -0.57137 1.882 -0.30361</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 91 44.7 45.06727 -0.36727 1.882 -0.19516</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 91 46.7 45.06727 1.63273 1.882 0.86759</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 120 42.1 40.76402 1.33598 1.882 0.70991</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 120 41.3 40.76402 0.53598 1.882 0.28481</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 8 3.3 4.14599 -0.84599 1.882 -0.44954</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 8 3.4 4.14599 -0.74599 1.882 -0.39640</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 14 3.9 6.08478 -2.18478 1.882 -1.16093</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 14 2.9 6.08478 -3.18478 1.882 -1.69231</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 21 6.4 7.59411 -1.19411 1.882 -0.63452</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 21 7.2 7.59411 -0.39411 1.882 -0.20942</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 41 9.1 9.78292 -0.68292 1.882 -0.36289</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 41 8.5 9.78292 -1.28292 1.882 -0.68171</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 63 11.7 10.93274 0.76726 1.882 0.40770</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 63 12.0 10.93274 1.06726 1.882 0.56711</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 91 13.3 11.93986 1.36014 1.882 0.72274</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 91 13.2 11.93986 1.26014 1.882 0.66961</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 120 14.3 12.79238 1.50762 1.882 0.80111</span> -<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 120 12.1 12.79238 -0.69238 1.882 -0.36791</span> -<span class="r-in"><span></span></span> -<span class="r-in"><span><span class="co"># The following takes about 6 minutes</span></span></span> -<span class="r-in"><span><span class="va">f_saem_dfop_sfo_deSolve</span> <span class="op"><-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span>,</span></span> -<span class="r-in"><span> nbiter.saemix <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">200</span>, <span class="fl">80</span><span class="op">)</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> DINTDY- T (=R1) illegal </span> -<span class="r-out co"><span class="r-pr">#></span> In above message, R1 = 70</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> T not in interval TCUR - HU (= R1) to TCUR (=R2) </span> -<span class="r-out co"><span class="r-pr">#></span> In above message, R1 = 53.1122, R2 = 56.6407</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> DINTDY- T (=R1) illegal </span> -<span class="r-out co"><span class="r-pr">#></span> In above message, R1 = 91</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> T not in interval TCUR - HU (= R1) to TCUR (=R2) </span> -<span class="r-out co"><span class="r-pr">#></span> In above message, R1 = 53.1122, R2 = 56.6407</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> DLSODA- Trouble in DINTDY. ITASK = I1, TOUT = R1</span> -<span class="r-out co"><span class="r-pr">#></span> In above message, I1 = 1</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> In above message, R1 = 91</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Error in deSolve::lsoda(y = odeini, times = outtimes, func = lsoda_func, : </span> -<span class="r-out co"><span class="r-pr">#></span> illegal input detected before taking any integration steps - see written message</span> -<span class="r-in"><span></span></span> -<span class="r-in"><span><span class="co">#anova(</span></span></span> -<span class="r-in"><span><span class="co"># f_saem_dfop_sfo,</span></span></span> -<span class="r-in"><span><span class="co"># f_saem_dfop_sfo_deSolve))</span></span></span> -<span class="r-in"><span></span></span> -<span class="r-in"><span><span class="co"># If the model supports it, we can also use eigenvalue based solutions, which</span></span></span> -<span class="r-in"><span><span class="co"># take a similar amount of time</span></span></span> -<span class="r-in"><span><span class="co">#f_saem_sfo_sfo_eigen <- saem(f_mmkin["SFO-SFO", ], solution_type = "eigen",</span></span></span> -<span class="r-in"><span><span class="co"># control = list(nbiter.saemix = c(200, 80), nbdisplay = 10))</span></span></span> -<span class="r-in"><span><span class="co"># }</span></span></span> -</code></pre></div> - </div> - </div> - <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> - </nav></div> -</div> - - - <footer><div class="copyright"> - <p></p><p>Developed by Johannes Ranke.</p> -</div> - -<div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> -</div> - - </footer></div> - - - - - - - </body></html> - |