Improve docs of multistart method

author: Johannes Ranke <jranke@uni-bremen.de> 2023-04-17 19:39:09 +0200
committer: Johannes Ranke <jranke@uni-bremen.de> 2023-04-17 19:39:09 +0200
commit: 004fce2520d6889d82226e21bc443426e81d93f2 (patch)
tree: a5b18bd357c506059ff2149f1c6602ea71df6d99
parent: 57ca408fda3fb8e42e0d5f3d9fc808d54268fa08 (diff)
22 files changed, 99 insertions, 186 deletions
diff --git a/R/multistart.R b/R/multistart.R
index bdfbfe63..aeea2d81 100644
--- a/R/multistart.R
+++ b/R/multistart.R
@@ -45,13 +45,12 @@
 #'
 #' f_saem_reduced <- update(f_saem_full, no_random_effect = "log_k2")
 #' illparms(f_saem_reduced)
-#' # On Windows, we need to create a cluster first. When working with
-#' # such a cluster, we need to export the mmkin object to the cluster
-#' # nodes, as it is referred to when updating the saem object on the nodes.
+#' # On Windows, we need to create a PSOCK cluster first and refer to it
+#' # in the call to multistart()
 #' library(parallel)
 #' cl <- makePSOCKcluster(12)
 #' f_saem_reduced_multi <- multistart(f_saem_reduced, n = 16, cluster = cl)
-#' parplot(f_saem_reduced_multi, lpos = "topright")
+#' parplot(f_saem_reduced_multi, lpos = "topright", ylim = c(0.5, 2))
 #' stopCluster(cl)
 #' }
 multistart <- function(object, n = 50,
@@ -103,9 +102,7 @@ multistart.saem.mmkin <- function(object, n = 50, cores = 1,
     res <- parallel::mclapply(1:n, fit_function,
       mc.cores = cores, mc.preschedule = FALSE)
   } else {
-    res <- parallel::parLapplyLB(cluster, 1:n, fit_function,
-      mc.preschedule = FALSE
-    )
+    res <- parallel::parLapplyLB(cluster, 1:n, fit_function)
   }
   attr(res, "orig") <- object
   attr(res, "start_parms") <- start_parms
diff --git a/docs/dev/articles/index.html b/docs/dev/articles/index.html
index a82eb999..2aacc53a 100644
--- a/docs/dev/articles/index.html
+++ b/docs/dev/articles/index.html
@@ -81,11 +81,6 @@
     <li>
       <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
     </li>
-    <li class="divider">
-    <li class="dropdown-header">Code coverage report</li>
-    <li>
-      <a href="../coverage/coverage.html"></a>
-    </li>
   </ul></li>
 <li>
   <a href="../news/index.html">News</a>
diff --git a/docs/dev/articles/web_only/multistart.html b/docs/dev/articles/web_only/multistart.html
index d3d9d76d..b5635df2 100644
--- a/docs/dev/articles/web_only/multistart.html
+++ b/docs/dev/articles/web_only/multistart.html
@@ -127,15 +127,13 @@
 
       
 
-      </header><div class="row">
+      </header><script src="multistart_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
   <div class="col-md-9 contents">
     <div class="page-header toc-ignore">
       <h1 data-toc-skip>Short demo of the multistart method</h1>
-                        <h4 data-toc-skip class="author">Johannes
-Ranke</h4>
+                        <h4 data-toc-skip class="author">Johannes Ranke</h4>
             
-            <h4 data-toc-skip class="date">Last change 26 September 2022
-(rebuilt 2023-04-16)</h4>
+            <h4 data-toc-skip class="date">Last change 17 April 2023 (rebuilt 2023-04-17)</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/multistart.rmd" class="external-link"><code>vignettes/web_only/multistart.rmd</code></a></small>
       <div class="hidden name"><code>multistart.rmd</code></div>
@@ -144,8 +142,7 @@ Ranke</h4>
 
     
     
-<p>The dimethenamid data from 2018 from seven soils is used as example
-data in this vignette.</p>
+<p>The dimethenamid data from 2018 from seven soils is used as example data in this vignette.</p>
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><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"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span><span class="op">)</span></span>
 <span><span class="va">dmta_ds</span> <span class="op">&lt;-</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="fl">1</span><span class="op">:</span><span class="fl">7</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span>
@@ -157,54 +154,41 @@ data in this vignette.</p>
 <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">dmta_ds</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="va">ds</span><span class="op">$</span><span class="va">title</span><span class="op">)</span></span>
 <span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span>, <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span>
 <span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="cn">NULL</span></span></code></pre></div>
-<p>First, we check the DFOP model with the two-component error model and
-random effects for all degradation parameters.</p>
+<p>First, we check the DFOP model with the two-component error model and random effects for all degradation parameters.</p>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">f_mmkin</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">dmta_ds</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, cores <span class="op">=</span> <span class="fl">7</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
 <span><span class="va">f_saem_full</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">)</span></span>
 <span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_full</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## [1] "sd(log_k2)"</span></span></code></pre>
-<p>We see that not all variability parameters are identifiable. The
-<code>illparms</code> function tells us that the confidence interval for
-the standard deviation of ‘log_k2’ includes zero. We check this
-assessment using multiple runs with different starting values.</p>
+<p>We see that not all variability parameters are identifiable. The <code>illparms</code> function tells us that the confidence interval for the standard deviation of ‘log_k2’ includes zero. We check this assessment using multiple runs with different starting values.</p>
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">f_saem_full_multi</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/multistart.html">multistart</a></span><span class="op">(</span><span class="va">f_saem_full</span>, n <span class="op">=</span> <span class="fl">16</span>, cores <span class="op">=</span> <span class="fl">16</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_full_multi</span><span class="op">)</span></span></code></pre></div>
+<code class="sourceCode R"><span><span class="va">f_saem_full_multi</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/multistart.html">multistart</a></span><span class="op">(</span><span class="va">f_saem_full</span>, n <span class="op">=</span> <span class="fl">16</span>, cores <span class="op">=</span> <span class="fl">8</span><span class="op">)</span></span>
+<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_full_multi</span>, lpos <span class="op">=</span> <span class="st">"topleft"</span><span class="op">)</span></span></code></pre></div>
 <p><img src="multistart_files/figure-html/unnamed-chunk-3-1.png" width="700"></p>
-<p>This confirms that the variance of k2 is the most problematic
-parameter, so we reduce the parameter distribution model by removing the
-intersoil variability for k2.</p>
+<p>This confirms that the variance of k2 is the most problematic parameter, so we reduce the parameter distribution model by removing the intersoil variability for k2.</p>
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">f_saem_reduced</span> <span class="op">&lt;-</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_full</span>, no_random_effect <span class="op">=</span> <span class="st">"log_k2"</span><span class="op">)</span></span>
 <span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_reduced</span><span class="op">)</span></span>
-<span><span class="va">f_saem_reduced_multi</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/multistart.html">multistart</a></span><span class="op">(</span><span class="va">f_saem_reduced</span>, n <span class="op">=</span> <span class="fl">16</span>, cores <span class="op">=</span> <span class="fl">16</span><span class="op">)</span></span>
-<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_reduced_multi</span>, lpos <span class="op">=</span> <span class="st">"topright"</span><span class="op">)</span></span></code></pre></div>
+<span><span class="va">f_saem_reduced_multi</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/multistart.html">multistart</a></span><span class="op">(</span><span class="va">f_saem_reduced</span>, n <span class="op">=</span> <span class="fl">16</span>, cores <span class="op">=</span> <span class="fl">8</span><span class="op">)</span></span>
+<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_reduced_multi</span>, lpos <span class="op">=</span> <span class="st">"topright"</span>, ylim <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">0.5</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <p><img src="multistart_files/figure-html/unnamed-chunk-4-1.png" width="700"></p>
-<p>The results confirm that all remaining parameters can be determined
-with sufficient certainty.</p>
-<p>We can also analyse the log-likelihoods obtained in the multiple
-runs:</p>
+<p>The results confirm that all remaining parameters can be determined with sufficient certainty.</p>
+<p>We can also analyse the log-likelihoods obtained in the multiple runs:</p>
 <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../../reference/llhist.html">llhist</a></span><span class="op">(</span><span class="va">f_saem_reduced_multi</span><span class="op">)</span></span></code></pre></div>
 <p><img src="multistart_files/figure-html/unnamed-chunk-5-1.png" width="700"></p>
-<p>The parameter histograms can be further improved by excluding the
-result with the low likelihood.</p>
+<p>We can use the <code>anova</code> method to compare the models.</p>
 <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_reduced_multi</span>, lpos <span class="op">=</span> <span class="st">"topright"</span>, llmin <span class="op">=</span> <span class="op">-</span><span class="fl">326</span>, ylim <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">0.5</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<p><img src="multistart_files/figure-html/unnamed-chunk-6-1.png" width="700"></p>
-<p>We can use the <code>anova</code> method to compare the models,
-including a likelihood ratio test if the models are nested.</p>
-<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><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_full</span>, <span class="fu"><a href="../../reference/multistart.html">best</a></span><span class="op">(</span><span class="va">f_saem_reduced_multi</span><span class="op">)</span>, test <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
+<code class="sourceCode R"><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_full</span>, <span class="fu"><a href="../../reference/multistart.html">best</a></span><span class="op">(</span><span class="va">f_saem_full_multi</span><span class="op">)</span>,</span>
+<span>  <span class="va">f_saem_reduced</span>, <span class="fu"><a href="../../reference/multistart.html">best</a></span><span class="op">(</span><span class="va">f_saem_reduced_multi</span><span class="op">)</span>, test <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
 <pre><code><span><span class="co">## Data: 155 observations of 1 variable(s) grouped in 6 datasets</span></span>
 <span><span class="co">## </span></span>
-<span><span class="co">##                            npar    AIC    BIC     Lik Chisq Df Pr(&gt;Chisq)</span></span>
-<span><span class="co">## best(f_saem_reduced_multi)    9 663.69 661.82 -322.85                    </span></span>
-<span><span class="co">## f_saem_full                  10 669.77 667.69 -324.89     0  1          1</span></span></code></pre>
-<p>While AIC and BIC are lower for the reduced model, the likelihood
-ratio test does not indicate a significant difference between the
-fits.</p>
+<span><span class="co">##                            npar    AIC    BIC     Lik  Chisq Df Pr(&gt;Chisq)</span></span>
+<span><span class="co">## f_saem_reduced                9 663.74 661.87 -322.87                     </span></span>
+<span><span class="co">## best(f_saem_reduced_multi)    9 663.60 661.72 -322.80 0.1476  0           </span></span>
+<span><span class="co">## f_saem_full                  10 670.35 668.26 -325.17 0.0000  1          1</span></span>
+<span><span class="co">## best(f_saem_full_multi)      10 665.61 663.53 -322.80 4.7372  0</span></span></code></pre>
+<p>The reduced model results in lower AIC and BIC values, so it is clearly preferable. Using multiple starting values gives a large improvement in case of the full model, because it is less well-defined, which impedes convergence. For the reduced model, using multiple starting values only results in a small improvement of the model fit.</p>
   </div>
 
   <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
diff --git a/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-3-1.png b/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-3-1.png
index 8f514227..7ad9fa28 100644
--- a/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-3-1.png
+++ b/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-3-1.png
diff --git a/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-4-1.png b/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-4-1.png
index 8135d270..f36995ae 100644
--- a/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-4-1.png
+++ b/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-4-1.png
diff --git a/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-5-1.png b/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-5-1.png
index f0270537..152554f5 100644
--- a/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-5-1.png
+++ b/docs/dev/articles/web_only/multistart_files/figure-html/unnamed-chunk-5-1.png
diff --git a/docs/dev/authors.html b/docs/dev/authors.html
index 7c11d186..3c95e7e0 100644
--- a/docs/dev/authors.html
+++ b/docs/dev/authors.html
@@ -81,11 +81,6 @@
     <li>
       <a href="articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
     </li>
-    <li class="divider">
-    <li class="dropdown-header">Code coverage report</li>
-    <li>
-      <a href="coverage/coverage.html"></a>
-    </li>
   </ul></li>
 <li>
   <a href="news/index.html">News</a>
diff --git a/docs/dev/index.html b/docs/dev/index.html
index 317e3380..f892841f 100644
--- a/docs/dev/index.html
+++ b/docs/dev/index.html
@@ -115,12 +115,6 @@
     <li>
       <a href="articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
     </li>
-    <li class="divider">
-    </li>
-<li class="dropdown-header">Code coverage report</li>
-    <li>
-      <a href="coverage/coverage.html"></a>
-    </li>
   </ul>
 </li>
 <li>
@@ -251,21 +245,12 @@
 <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
 </h2>
 <table class="table">
-<tr>
-<td>
-Ranke J, Wöltjen J, Schmidt J, and Comets E (2021) Taking kinetic evaluations of degradation data to the next level with nonlinear mixed-effects models. <i>Environments</i> <b>8</b> (8) 71 <a href="https://doi.org/10.3390/environments8080071" class="external-link">doi:10.3390/environments8080071</a>
-</td>
-</tr>
-<tr>
-<td>
-Ranke J, Meinecke S (2019) Error Models for the Kinetic Evaluation of Chemical Degradation Data <i>Environments</i> <b>6</b> (12) 124 <a href="https://doi.org/10.3390/environments6120124" class="external-link">doi:10.3390/environments6120124</a>
-</td>
-</tr>
-<tr>
-<td>
-Ranke J, Wöltjen J, Meinecke S (2018) Comparison of software tools for kinetic evaluation of chemical degradation data <i>Environmental Sciences Europe</i> <b>30</b> 17 <a href="https://doi.org/10.1186/s12302-018-0145-1" class="external-link">doi:10.1186/s12302-018-0145-1</a>
-</td>
-</tr>
+<tr><td>Ranke J, Wöltjen J, Schmidt J, and Comets E (2021) Taking kinetic evaluations of degradation data to the next level with nonlinear mixed-effects models. <i>Environments</i> <b>8</b> (8) 71 <a href="https://doi.org/10.3390/environments8080071" class="external-link">doi:10.3390/environments8080071</a>
+</td></tr>
+<tr><td>Ranke J, Meinecke S (2019) Error Models for the Kinetic Evaluation of Chemical Degradation Data <i>Environments</i> <b>6</b> (12) 124 <a href="https://doi.org/10.3390/environments6120124" class="external-link">doi:10.3390/environments6120124</a>
+</td></tr>
+<tr><td>Ranke J, Wöltjen J, Meinecke S (2018) Comparison of software tools for kinetic evaluation of chemical degradation data <i>Environmental Sciences Europe</i> <b>30</b> 17 <a href="https://doi.org/10.1186/s12302-018-0145-1" class="external-link">doi:10.1186/s12302-018-0145-1</a>
+</td></tr>
 </table>
 </div>
 <div class="section level2">
diff --git a/docs/dev/news/index.html b/docs/dev/news/index.html
index 2c169609..70eeeed6 100644
--- a/docs/dev/news/index.html
+++ b/docs/dev/news/index.html
@@ -81,11 +81,6 @@
     <li>
       <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
     </li>
-    <li class="divider">
-    <li class="dropdown-header">Code coverage report</li>
-    <li>
-      <a href="../coverage/coverage.html"></a>
-    </li>
   </ul></li>
 <li>
   <a href="../news/index.html">News</a>
@@ -176,8 +171,7 @@
 </div>
     <div class="section level2">
 <h2 class="page-header" data-toc-text="1.0.5" id="mkin-105-2021-09-15">mkin 1.0.5 (2021-09-15)<a class="anchor" aria-label="anchor" href="#mkin-105-2021-09-15"></a></h2>
-<ul><li>‘dimethenamid_2018’: Correct the data for the Borstel soil. The five observations from Staudenmaier (2013) that were previously stored as “Borstel 2” are actually just a subset of the 16 observations in “Borstel 1” which is now simply “Borstel”</li>
-</ul></div>
+<ul><li>‘dimethenamid_2018’: Correct the data for the Borstel soil. The five observations from Staudenmaier (2013) that were previously stored as “Borstel 2” are actually just a subset of the 16 observations in “Borstel 1” which is now simply “Borstel”</li></ul></div>
     <div class="section level2">
 <h2 class="page-header" data-toc-text="1.0.4" id="mkin-104-2021-04-20">mkin 1.0.4 (2021-04-20)<a class="anchor" aria-label="anchor" href="#mkin-104-2021-04-20"></a></h2>
 <ul><li><p>All plotting functions setting graphical parameters: Use on.exit() for resetting graphical parameters</p></li>
@@ -186,12 +180,10 @@
 </ul></div>
     <div class="section level2">
 <h2 class="page-header" data-toc-text="1.0.3" id="mkin-103-2021-02-15">mkin 1.0.3 (2021-02-15)<a class="anchor" aria-label="anchor" href="#mkin-103-2021-02-15"></a></h2>
-<ul><li>Review and update README, the ‘Introduction to mkin’ vignette and some of the help pages</li>
-</ul></div>
+<ul><li>Review and update README, the ‘Introduction to mkin’ vignette and some of the help pages</li></ul></div>
     <div class="section level2">
 <h2 class="page-header" data-toc-text="1.0.2" id="mkin-102-unreleased">mkin 1.0.2 (Unreleased)<a class="anchor" aria-label="anchor" href="#mkin-102-unreleased"></a></h2>
-<ul><li>‘mkinfit’: Keep model names stored in ‘mkinmod’ objects, avoiding their loss in ‘gmkin’</li>
-</ul></div>
+<ul><li>‘mkinfit’: Keep model names stored in ‘mkinmod’ objects, avoiding their loss in ‘gmkin’</li></ul></div>
     <div class="section level2">
 <h2 class="page-header" data-toc-text="1.0.1" id="mkin-101-2021-02-10">mkin 1.0.1 (2021-02-10)<a class="anchor" aria-label="anchor" href="#mkin-101-2021-02-10"></a></h2>
 <ul><li><p>‘confint.mmkin’, ‘nlme.mmkin’, ‘transform_odeparms’: Fix example code in dontrun sections that failed with current defaults</p></li>
@@ -246,8 +238,7 @@
 </ul></div>
     <div class="section level2">
 <h2 class="page-header" data-toc-text="0.9.49.11" id="mkin-094911-2020-04-20">mkin 0.9.49.11 (2020-04-20)<a class="anchor" aria-label="anchor" href="#mkin-094911-2020-04-20"></a></h2>
-<ul><li>Increase a test tolerance to make it pass on all CRAN check machines</li>
-</ul></div>
+<ul><li>Increase a test tolerance to make it pass on all CRAN check machines</li></ul></div>
     <div class="section level2">
 <h2 class="page-header" data-toc-text="0.9.49.10" id="mkin-094910-2020-04-18">mkin 0.9.49.10 (2020-04-18)<a class="anchor" aria-label="anchor" href="#mkin-094910-2020-04-18"></a></h2>
 <ul><li><p>‘nlme.mmkin’: An nlme method for mmkin row objects and an associated S3 class with print, plot, anova and endpoint methods</p></li>
@@ -362,8 +353,7 @@
 </ul></div>
     <div class="section level2">
 <h2 class="page-header" data-toc-text="0.9.46" id="mkin-0946-2017-07-24">mkin 0.9.46 (2017-07-24)<a class="anchor" aria-label="anchor" href="#mkin-0946-2017-07-24"></a></h2>
-<ul><li>Remove <code>test_FOMC_ill-defined.R</code> as it is too platform dependent</li>
-</ul></div>
+<ul><li>Remove <code>test_FOMC_ill-defined.R</code> as it is too platform dependent</li></ul></div>
     <div class="section level2">
 <h2 class="page-header" data-toc-text="0.9.45.2" id="mkin-09452-2017-07-24">mkin 0.9.45.2 (2017-07-24)<a class="anchor" aria-label="anchor" href="#mkin-09452-2017-07-24"></a></h2>
 <ul><li><p>Rename <code>twa</code> to <code>max_twa_parent</code> to avoid conflict with <code>twa</code> from my <code>pfm</code> package</p></li>
@@ -375,8 +365,7 @@
 <h2 class="page-header" data-toc-text="0.9.45.1" id="mkin-09451-2016-12-20">mkin 0.9.45.1 (2016-12-20)<a class="anchor" aria-label="anchor" href="#mkin-09451-2016-12-20"></a></h2>
 <div class="section level3">
 <h3 id="new-features-0-9-45-1">New features<a class="anchor" aria-label="anchor" href="#new-features-0-9-45-1"></a></h3>
-<ul><li>A <code>twa</code> function, calculating maximum time weighted average concentrations for the parent (SFO, FOMC and DFOP).</li>
-</ul></div>
+<ul><li>A <code>twa</code> function, calculating maximum time weighted average concentrations for the parent (SFO, FOMC and DFOP).</li></ul></div>
 </div>
     <div class="section level2">
 <h2 class="page-header" data-toc-text="0.9.45" id="mkin-0945-2016-12-08">mkin 0.9.45 (2016-12-08)<a class="anchor" aria-label="anchor" href="#mkin-0945-2016-12-08"></a></h2>
@@ -391,8 +380,7 @@
 <h2 class="page-header" data-toc-text="0.9.44" id="mkin-0944-2016-06-29">mkin 0.9.44 (2016-06-29)<a class="anchor" aria-label="anchor" href="#mkin-0944-2016-06-29"></a></h2>
 <div class="section level3">
 <h3 id="bug-fixes-0-9-44">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-44"></a></h3>
-<ul><li>The test <code>test_FOMC_ill-defined</code> failed on several architectures, so the test is now skipped</li>
-</ul></div>
+<ul><li>The test <code>test_FOMC_ill-defined</code> failed on several architectures, so the test is now skipped</li></ul></div>
 </div>
     <div class="section level2">
 <h2 class="page-header" data-toc-text="0.9.43" id="mkin-0943-2016-06-28">mkin 0.9.43 (2016-06-28)<a class="anchor" aria-label="anchor" href="#mkin-0943-2016-06-28"></a></h2>
@@ -426,8 +414,7 @@
 <h2 class="page-header" data-toc-text="0.9.42" id="mkin-0942-2016-03-25">mkin 0.9.42 (2016-03-25)<a class="anchor" aria-label="anchor" href="#mkin-0942-2016-03-25"></a></h2>
 <div class="section level3">
 <h3 id="major-changes-0-9-42">Major changes<a class="anchor" aria-label="anchor" href="#major-changes-0-9-42"></a></h3>
-<ul><li>Add the argument <code>from_max_mean</code> to <code>mkinfit</code>, for fitting only the decline from the maximum observed value for models with a single observed variable</li>
-</ul></div>
+<ul><li>Add the argument <code>from_max_mean</code> to <code>mkinfit</code>, for fitting only the decline from the maximum observed value for models with a single observed variable</li></ul></div>
 <div class="section level3">
 <h3 id="minor-changes-0-9-42">Minor changes<a class="anchor" aria-label="anchor" href="#minor-changes-0-9-42"></a></h3>
 <ul><li><p>Add plots to <code>compiled_models</code> vignette</p></li>
@@ -447,21 +434,18 @@
 <div class="section level3">
 <h3 id="bug-fixes-0-9-41">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-41"></a></h3>
 <ul><li>
-<code><a href="../reference/summary.mkinfit.html">print.summary.mkinfit()</a></code>: Avoid an error that occurred when printing summaries generated with mkin versions before 0.9-36</li>
-</ul></div>
+<code><a href="../reference/summary.mkinfit.html">print.summary.mkinfit()</a></code>: Avoid an error that occurred when printing summaries generated with mkin versions before 0.9-36</li></ul></div>
 </div>
     <div class="section level2">
 <h2 class="page-header" data-toc-text="0.9-40" id="mkin-09-40-2015-07-21">mkin 0.9-40 (2015-07-21)<a class="anchor" aria-label="anchor" href="#mkin-09-40-2015-07-21"></a></h2>
 <div class="section level3">
 <h3 id="bug-fixes-0-9-40">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-40"></a></h3>
 <ul><li>
-<code><a href="../reference/endpoints.html">endpoints()</a></code>: For DFOP and SFORB models, where <code><a href="https://rdrr.io/r/stats/optimize.html" class="external-link">optimize()</a></code> is used, make use of the fact that the DT50 must be between DT50_k1 and DT50_k2 (DFOP) or DT50_b1 and DT50_b2 (SFORB), as <code><a href="https://rdrr.io/r/stats/optimize.html" class="external-link">optimize()</a></code> sometimes did not find the minimum. Likewise for finding DT90 values. Also fit on the log scale to make the function more efficient.</li>
-</ul></div>
+<code><a href="../reference/endpoints.html">endpoints()</a></code>: For DFOP and SFORB models, where <code><a href="https://rdrr.io/r/stats/optimize.html" class="external-link">optimize()</a></code> is used, make use of the fact that the DT50 must be between DT50_k1 and DT50_k2 (DFOP) or DT50_b1 and DT50_b2 (SFORB), as <code><a href="https://rdrr.io/r/stats/optimize.html" class="external-link">optimize()</a></code> sometimes did not find the minimum. Likewise for finding DT90 values. Also fit on the log scale to make the function more efficient.</li></ul></div>
 <div class="section level3">
 <h3 id="internal-changes-0-9-40">Internal changes<a class="anchor" aria-label="anchor" href="#internal-changes-0-9-40"></a></h3>
 <ul><li>
-<code>DESCRIPTION</code>, <code>NAMESPACE</code>, <code>R/*.R</code>: Import (from) stats, graphics and methods packages, and qualify some function calls for non-base packages installed with R to avoid NOTES made by R CMD check –as-cran with upcoming R versions.</li>
-</ul></div>
+<code>DESCRIPTION</code>, <code>NAMESPACE</code>, <code>R/*.R</code>: Import (from) stats, graphics and methods packages, and qualify some function calls for non-base packages installed with R to avoid NOTES made by R CMD check –as-cran with upcoming R versions.</li></ul></div>
 </div>
     <div class="section level2">
 <h2 class="page-header" data-toc-text="0.9-39" id="mkin-09-39-2015-06-26">mkin 0.9-39 (2015-06-26)<a class="anchor" aria-label="anchor" href="#mkin-09-39-2015-06-26"></a></h2>
@@ -473,8 +457,7 @@
 <div class="section level3">
 <h3 id="bug-fixes-0-9-39">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-39"></a></h3>
 <ul><li>
-<code><a href="../reference/mkinparplot.html">mkinparplot()</a></code>: Fix the x axis scaling for rate constants and formation fractions that got confused by the introduction of the t-values of transformed parameters.</li>
-</ul></div>
+<code><a href="../reference/mkinparplot.html">mkinparplot()</a></code>: Fix the x axis scaling for rate constants and formation fractions that got confused by the introduction of the t-values of transformed parameters.</li></ul></div>
 </div>
     <div class="section level2">
 <h2 class="page-header" data-toc-text="0.9-38" id="mkin-09-38-2015-06-24">mkin 0.9-38 (2015-06-24)<a class="anchor" aria-label="anchor" href="#mkin-09-38-2015-06-24"></a></h2>
@@ -486,8 +469,7 @@
 <div class="section level3">
 <h3 id="bug-fixes-0-9-38">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-38"></a></h3>
 <ul><li>
-<code><a href="../reference/mkinmod.html">mkinmod()</a></code>: When generating the C code for the derivatives, only declare the time variable when it is needed and remove the ‘-W-no-unused-variable’ compiler flag as the C compiler used in the CRAN checks on Solaris does not know it.</li>
-</ul></div>
+<code><a href="../reference/mkinmod.html">mkinmod()</a></code>: When generating the C code for the derivatives, only declare the time variable when it is needed and remove the ‘-W-no-unused-variable’ compiler flag as the C compiler used in the CRAN checks on Solaris does not know it.</li></ul></div>
 </div>
     <div class="section level2">
 <h2 class="page-header" data-toc-text="0.9-36" id="mkin-09-36-2015-06-21">mkin 0.9-36 (2015-06-21)<a class="anchor" aria-label="anchor" href="#mkin-09-36-2015-06-21"></a></h2>
@@ -500,15 +482,13 @@
 </ul></div>
 <div class="section level3">
 <h3 id="minor-changes-0-9-36">Minor changes<a class="anchor" aria-label="anchor" href="#minor-changes-0-9-36"></a></h3>
-<ul><li>Added a simple showcase vignette with an evaluation of FOCUS example dataset D</li>
-</ul></div>
+<ul><li>Added a simple showcase vignette with an evaluation of FOCUS example dataset D</li></ul></div>
 </div>
     <div class="section level2">
 <h2 class="page-header" data-toc-text="0.9-35" id="mkin-09-35-2015-05-15">mkin 0.9-35 (2015-05-15)<a class="anchor" aria-label="anchor" href="#mkin-09-35-2015-05-15"></a></h2>
 <div class="section level3">
 <h3 id="major-changes-0-9-35">Major changes<a class="anchor" aria-label="anchor" href="#major-changes-0-9-35"></a></h3>
-<ul><li>Switch from RUnit to testthat for testing</li>
-</ul></div>
+<ul><li>Switch from RUnit to testthat for testing</li></ul></div>
 <div class="section level3">
 <h3 id="bug-fixes-0-9-35">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-35"></a></h3>
 <ul><li><p><code><a href="../reference/mkinparplot.html">mkinparplot()</a></code>: Avoid warnings that occurred when not all confidence intervals were available in the summary of the fit</p></li>
@@ -590,15 +570,13 @@
 <h2 class="page-header" data-toc-text="0.9-31" id="mkin-09-31-2014-07-14">mkin 0.9-31 (2014-07-14)<a class="anchor" aria-label="anchor" href="#mkin-09-31-2014-07-14"></a></h2>
 <div class="section level3">
 <h3 id="bug-fixes-0-9-31">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-31"></a></h3>
-<ul><li>The internal renaming of optimised parameters in Version 0.9-30 led to errors in the determination of the degrees of freedom for the chi2 error level calulations in <code><a href="../reference/mkinerrmin.html">mkinerrmin()</a></code> used by the summary function.</li>
-</ul></div>
+<ul><li>The internal renaming of optimised parameters in Version 0.9-30 led to errors in the determination of the degrees of freedom for the chi2 error level calulations in <code><a href="../reference/mkinerrmin.html">mkinerrmin()</a></code> used by the summary function.</li></ul></div>
 </div>
     <div class="section level2">
 <h2 class="page-header" data-toc-text="0.9-30" id="mkin-09-30-2014-07-11">mkin 0.9-30 (2014-07-11)<a class="anchor" aria-label="anchor" href="#mkin-09-30-2014-07-11"></a></h2>
 <div class="section level3">
 <h3 id="new-features-0-9-30">New features<a class="anchor" aria-label="anchor" href="#new-features-0-9-30"></a></h3>
-<ul><li>It is now possible to use formation fractions in combination with turning off the sink in <code><a href="../reference/mkinmod.html">mkinmod()</a></code>.</li>
-</ul></div>
+<ul><li>It is now possible to use formation fractions in combination with turning off the sink in <code><a href="../reference/mkinmod.html">mkinmod()</a></code>.</li></ul></div>
 <div class="section level3">
 <h3 id="major-changes-0-9-30">Major changes<a class="anchor" aria-label="anchor" href="#major-changes-0-9-30"></a></h3>
 <ul><li><p>The original and the transformed parameters now have different names (e.g. <code>k_parent</code> and <code>log_k_parent</code>. They also differ in how many they are when we have formation fractions but no pathway to sink.</p></li>
diff --git a/docs/dev/pkgdown.yml b/docs/dev/pkgdown.yml
index b7825ae1..a9ab0c60 100644
--- a/docs/dev/pkgdown.yml
+++ b/docs/dev/pkgdown.yml
@@ -1,4 +1,4 @@
-pandoc: 2.17.1.1
+pandoc: 2.9.2.1
 pkgdown: 2.0.7
 pkgdown_sha: ~
 articles:
@@ -16,7 +16,7 @@ articles:
   dimethenamid_2018: web_only/dimethenamid_2018.html
   multistart: web_only/multistart.html
   saem_benchmarks: web_only/saem_benchmarks.html
-last_built: 2023-04-16T08:48Z
+last_built: 2023-04-17T17:35Z
 urls:
   reference: https://pkgdown.jrwb.de/mkin/reference
   article: https://pkgdown.jrwb.de/mkin/articles
diff --git a/docs/dev/reference/Rplot001.png b/docs/dev/reference/Rplot001.png
index 5de2bdc7..278fd2e2 100644
--- a/docs/dev/reference/Rplot001.png
+++ b/docs/dev/reference/Rplot001.png
diff --git a/docs/dev/reference/Rplot002.png b/docs/dev/reference/Rplot002.png
index 556ca0a7..4b646f22 100644
--- a/docs/dev/reference/Rplot002.png
+++ b/docs/dev/reference/Rplot002.png
diff --git a/docs/dev/reference/index.html b/docs/dev/reference/index.html
index 84bbbdc4..85dc7c6e 100644
--- a/docs/dev/reference/index.html
+++ b/docs/dev/reference/index.html
@@ -81,11 +81,6 @@
     <li>
       <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
     </li>
-    <li class="divider">
-    <li class="dropdown-header">Code coverage report</li>
-    <li>
-      <a href="../coverage/coverage.html"></a>
-    </li>
   </ul></li>
 <li>
   <a href="../news/index.html">News</a>
diff --git a/docs/dev/reference/mkinpredict.html b/docs/dev/reference/mkinpredict.html
index 01c15a19..7d8e7c26 100644
--- a/docs/dev/reference/mkinpredict.html
+++ b/docs/dev/reference/mkinpredict.html
@@ -394,11 +394,12 @@ as these always return mapped output.</p></dd>
 <span class="r-in"><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>, m1 <span class="op">=</span> <span class="fl">0</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">20</span>, by <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>,</span></span>
 <span class="r-in"><span>      solution_type <span class="op">=</span> <span class="st">"analytical"</span>, use_compiled <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">[</span><span class="fl">201</span>,<span class="op">]</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> Loading required package: rbenchmark</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>               test relative elapsed</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2 deSolve_compiled      1.0   0.002</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4       analytical      1.0   0.002</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1            eigen      4.0   0.008</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3          deSolve     30.5   0.061</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2 deSolve_compiled     1.00   0.004</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1            eigen     4.00   0.016</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4       analytical     4.25   0.017</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3          deSolve    40.75   0.163</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
 <span class="r-in"><span>  <span class="co"># Predict from a fitted model</span></span></span>
diff --git a/docs/dev/reference/multistart-1.png b/docs/dev/reference/multistart-1.png
index c7937d67..ee0306d6 100644
--- a/docs/dev/reference/multistart-1.png
+++ b/docs/dev/reference/multistart-1.png
diff --git a/docs/dev/reference/multistart-2.png b/docs/dev/reference/multistart-2.png
index e1983f12..69a178e3 100644
--- a/docs/dev/reference/multistart-2.png
+++ b/docs/dev/reference/multistart-2.png
diff --git a/docs/dev/reference/multistart.html b/docs/dev/reference/multistart.html
index 3cdede7b..36767560 100644
--- a/docs/dev/reference/multistart.html
+++ b/docs/dev/reference/multistart.html
@@ -222,15 +222,13 @@ doi: 10.1186/s12859-021-04373-4.</p>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">f_saem_reduced</span> <span class="op">&lt;-</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_full</span>, no_random_effect <span class="op">=</span> <span class="st">"log_k2"</span><span class="op">)</span></span></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_reduced</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="co"># On Windows, we need to create a cluster first. When working with</span></span></span>
-<span class="r-in"><span><span class="co"># such a cluster, we need to export the mmkin object to the cluster</span></span></span>
-<span class="r-in"><span><span class="co"># nodes, as it is referred to when updating the saem object on the nodes.</span></span></span>
+<span class="r-in"><span><span class="co"># On Windows, we need to create a PSOCK cluster first and refer to it</span></span></span>
+<span class="r-in"><span><span class="co"># in the call to multistart()</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">parallel</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">cl</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makePSOCKcluster</a></span><span class="op">(</span><span class="fl">12</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">f_saem_reduced_multi</span> <span class="op">&lt;-</span> <span class="fu">multistart</span><span class="op">(</span><span class="va">f_saem_reduced</span>, n <span class="op">=</span> <span class="fl">16</span>, cluster <span class="op">=</span> <span class="va">cl</span><span class="op">)</span></span></span>
-<span class="r-err co"><span class="r-pr">#&gt;</span> <span class="error">Error in checkForRemoteErrors(val):</span> 16 nodes produced errors; first error: unused argument (mc.preschedule = FALSE)</span>
-<span class="r-in"><span><span class="fu"><a href="parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_reduced_multi</span>, lpos <span class="op">=</span> <span class="st">"topright"</span><span class="op">)</span></span></span>
-<span class="r-err co"><span class="r-pr">#&gt;</span> <span class="error">Error in parplot(f_saem_reduced_multi, lpos = "topright"):</span> object 'f_saem_reduced_multi' not found</span>
+<span class="r-in"><span><span class="fu"><a href="parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_reduced_multi</span>, lpos <span class="op">=</span> <span class="st">"topright"</span>, ylim <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">0.5</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span></span>
+<span class="r-plt img"><img src="multistart-2.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">stopCluster</a></span><span class="op">(</span><span class="va">cl</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
diff --git a/docs/dev/sitemap.xml b/docs/dev/sitemap.xml
index b70dc782..b3542d0b 100644
--- a/docs/dev/sitemap.xml
+++ b/docs/dev/sitemap.xml
@@ -55,9 +55,6 @@
     <loc>https://pkgdown.jrwb.de/mkin/authors.html</loc>
   </url>
   <url>
-    <loc>https://pkgdown.jrwb.de/mkin/coverage/coverage.html</loc>
-  </url>
-  <url>
     <loc>https://pkgdown.jrwb.de/mkin/index.html</loc>
   </url>
   <url>
diff --git a/inst/rmarkdown/templates/hierarchical_kinetics/skeleton/.skeleton.Rmd.swp b/inst/rmarkdown/templates/hierarchical_kinetics/skeleton/.skeleton.Rmd.swp
new file mode 100644
index 00000000..2c5bfed8
--- /dev/null
+++ b/inst/rmarkdown/templates/hierarchical_kinetics/skeleton/.skeleton.Rmd.swp
diff --git a/man/multistart.Rd b/man/multistart.Rd
index 5a5f7b44..0df29bfa 100644
--- a/man/multistart.Rd
+++ b/man/multistart.Rd
@@ -82,13 +82,12 @@ illparms(f_saem_full)
 
 f_saem_reduced <- update(f_saem_full, no_random_effect = "log_k2")
 illparms(f_saem_reduced)
-# On Windows, we need to create a cluster first. When working with
-# such a cluster, we need to export the mmkin object to the cluster
-# nodes, as it is referred to when updating the saem object on the nodes.
+# On Windows, we need to create a PSOCK cluster first and refer to it
+# in the call to multistart()
 library(parallel)
 cl <- makePSOCKcluster(12)
 f_saem_reduced_multi <- multistart(f_saem_reduced, n = 16, cluster = cl)
-parplot(f_saem_reduced_multi, lpos = "topright")
+parplot(f_saem_reduced_multi, lpos = "topright", ylim = c(0.5, 2))
 stopCluster(cl)
 }
 }
diff --git a/vignettes/web_only/multistart.html b/vignettes/web_only/multistart.html
index 5568ad2c..93f08ca3 100644
--- a/vignettes/web_only/multistart.html
+++ b/vignettes/web_only/multistart.html
@@ -299,8 +299,8 @@ pre code {
   border-radius: 4px;
 }
 
-.tabset-dropdown > .nav-tabs > li.active:before {
-  content: "";
+.tabset-dropdown > .nav-tabs > li.active:before, .tabset-dropdown > .nav-tabs.nav-tabs-open:before {
+  content: "\e259";
   font-family: 'Glyphicons Halflings';
   display: inline-block;
   padding: 10px;
@@ -308,16 +308,9 @@ pre code {
 }
 
 .tabset-dropdown > .nav-tabs.nav-tabs-open > li.active:before {
-  content: "";
-  border: none;
-}
-
-.tabset-dropdown > .nav-tabs.nav-tabs-open:before {
-  content: "";
+  content: "\e258";
   font-family: 'Glyphicons Halflings';
-  display: inline-block;
-  padding: 10px;
-  border-right: 1px solid #ddd;
+  border: none;
 }
 
 .tabset-dropdown > .nav-tabs > li.active {
@@ -364,7 +357,7 @@ pre code {
 
 <h1 class="title toc-ignore">Short demo of the multistart method</h1>
 <h4 class="author">Johannes Ranke</h4>
-<h4 class="date">Last change 26 September 2022 (rebuilt 2022-10-26)</h4>
+<h4 class="date">Last change 17 April 2023 (rebuilt 2023-04-17)</h4>
 
 </div>
 
@@ -386,31 +379,30 @@ f_saem_full &lt;- saem(f_mmkin)
 illparms(f_saem_full)</code></pre>
 <pre><code>## [1] &quot;sd(log_k2)&quot;</code></pre>
 <p>We see that not all variability parameters are identifiable. The <code>illparms</code> function tells us that the confidence interval for the standard deviation of ‘log_k2’ includes zero. We check this assessment using multiple runs with different starting values.</p>
-<pre class="r"><code>f_saem_full_multi &lt;- multistart(f_saem_full, n = 16, cores = 16)
-parhist(f_saem_full_multi)</code></pre>
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width="672" /></p>
+<pre class="r"><code>f_saem_full_multi &lt;- multistart(f_saem_full, n = 16, cores = 8)
+parplot(f_saem_full_multi, lpos = &quot;topleft&quot;)</code></pre>
+<p><img 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GC9bNky04qoZ2eszjIYDN27dy/+LNq6dev58+cfPnxYoVC88HCzPHtUoMzGyUWLFhkLjY2NLf5djAOtnZ2dc3Nzix9Va3c8s/xUWdH1X8cvodr8pTOeh8GDB2dnZxu3p6amFl8FuTIt8LV5kZdugb9z5w6XMiQkJCcnp3hi4+CFIUOGmHKO6iME8GC6qt4Bja/NTp06ZdxYIoBftmwZ98+YmJgSh3Nzv9E/HY/NGMAXFRVx67pJpdISN6bBgwdzh3///feV+Y4vNGPGDC7Do0ePlpfG+MIyMjLSLIWCyUo8FeXl5Rl/EU1Y6m/NmjVcv1+xWHz79u0aqG+dlpmZyc0G1LJlS4PBUGLvoUOHzPtYU8dxUyHY2tqW2U/e2OUnKyvL5CKs6HG8MooH8F999VXpBBcvXuT2yuVy085bPTtjdZlSqZw0aRIXEhTH5/NDQ0Pnzp174MABpVJZ5rFmefaoQOnYJjExUSKREJGDg0OJ0IIzb948roiFCxcaN9bmHc8sP1XWdf3X2UuoNv/fuWmhiKhr164lXgSzLKtUKr28vLgELwzga/kiLx3A/+9//+NSbtq0qXTpfn5+tra2TZo0ecEZaTAwiR3UHltbW+5Dfn5+eWmMy6oZw3Ujrv+8n59f8XeKZnH06FFuQpQRI0ZwDV9GxvjfXHPRG6dREYvF5aUx7jLj5HlQfQUFBYMGDeLGbkybNu27776r/LGxsbFDhgyZMWMGy7I8Hm/Lli1t27atsZrWUdu3b+f+9ufMmVN6APPLL7/MTWPZQPz0009Xr169cuWKSCQqvdfY6VGr1dZuvayAs7PzzJkzS2/v1q0b98pVoVCcO3eu1uv1/+3dfZBVZR0H8LPCMjAgMqC8CPmGCSEYgsaLZb5AoAgiKJgWjvmSI6VUOKFIQ42AgchYCIiYUUFMWCCyCIFKYyxCgBCIA/Ie8bKCsLwJC8v2x5nO7OzLZWGXe+/Z/Xz+unPP2XvPvefZc8/3nOf5PZyFWrVqvf766//5z39eeumlLl26RIPF8vPzV65cOXbs2F69ejVp0uTJJ5/873//W873Ksu5R2LTp08/fvx4EASDBg0qsQf14MGDw1EbYb/uUKqOeOX5qYqRtG1Cydzv0bS1I0eOLH4to1atWuEA+LJIn0a+adOm4k9u37798OHDJS6qmgR4kufo0aPhg/ByXYmuvPLKcAL2IrXojxw5Mm/evCAI+vXrV2LtovKIpn9/6KGHiizq3bt3eEly+fLl4SD5cgpfLUh4Xp6Xlxc+SBDySbIjR47ceeed4USDQRCUvREePXp02LBhrVq1Chtw/fr1Z86cee+9956vDU1j0U2PEqvUZGRkdOnSJblblErt2rXr0KFDeBuksFOnTs2fPz8q2Elx3bt3j86ni4h6LlTI4Zrz7dJLL/3Zz362cOHCgwcPZmdnjxo1qnv37tEZQm5u7sSJE9u1a1fOyzFlOfdILKq5c88995S4QqNGjcJe1jk5OVu3bg2fTMkR75x/qmIqDZtQ0vZ7fn5+2DKbNm367W9/u8R17rvvvjK+WsobeadOncJrECNGjPjhD3+4fPny06dPl/FvqyBF7EieL774InyQoPByEAT333//smXL1q9fv3bt2mjeizlz5nz55ZdBoVviFSU3Nzfs5FOtWrWPPvpoxYoVRVZo1KjR9u3bgyCYPn16WCe/PKLzzgQzmkSLzvlsgwr3yiuvnDx5MiMjIzMzMy8vb9KkSQ899FA0wrA0s2bNevrpp8PiLkEQ9O7de+LEiY0bNz7/25uOwkx1wQUXRJ36ivjKV76S3C1KvRMnTmRnZ69du/azzz7bunXr9u3bN27cGF3Co0RRndEEi8L5k4iLzMzMTp06derUaciQISdPnly8ePGMGTOmTp2an5+fk5PTt2/fDRs2NGjQ4NxevIznHglEt3ATTLQWLdq1a1f4OCVHvHP7qaoE0qcJJW2/79+/P+ynGU2iVFyTJk1q1qwZ3lpPLOWN/LrrrvvVr341bNiw06dPT548efLkyRdddFHHjh07duz4ne98p2PHjsW7GFRlvguSJyzAnpGRkXiu0X79+oX/pTNmzIieDPvPN2/evH379hW7VTNnzgwPbfn5+cOHD3++mDC9B4Umii+PqKTHnj17SlsnWlS/fv3yvyMVIjwleu2114YPHx4EwenTp5944on8/PzS1j906FD//v379OkTpvd27dq9//77s2bNqrLpPQiCLVu2BEFQv379sDZkcVXqy8nLyxsxYkSjRo1uu+22p59+evz48VlZWevWrcvPzw9r/6Z6A9NXkyZNSlt02WWXhQ92796drM2hgmVmZnbt2vWNN974+OOPw1m+9+/f/9JLL53zC5bx3COBsJNw7dq1E1xVb9q0afjg4MGD4YOUHPHO9qeqUkptE0rafo9aWtT2isvIyEiwtLB0aORDhw5dunTpHXfcEfY/zc3NXbBgwS9/+cubbrqpSZMmI0eOdHU7IsCTJBs2bDhw4EAQBC1atEg8CUc4S1xQKMAfOnQoLF8UjZCvQFH/+TPatGnT8uXLy/l20XCgbdu2lbhCQUHBjh07giC49NJL69atW863o6KEp0SPPfbY4MGDW7VqFQTBxx9/XNoUXwcPHvzmN78ZXnWqX7/+73//+xUrVtx6661J3eL0Ew72PnDgQGlnk2EpiqqgoKDgvvvue/7553NzczMzM3v16jVixIh33nnnk08+OXr0aHZ29s0335zqbUxfCa5+Rrn9nGcL43ybM2dO165du3btGo4qSqBNmzbjxo0LHxfvHFdGZT/3SCCMNEePHk1QmCZqllE/u5Qc8c7qpyqm0rwJJW2/R9l4165dCVbbu3dvWV4tTRp5hw4d5s2bt3///jlz5gwePLhz585hmM/JyRk6dGiXLl2q2tWo0gjwJEl0+/q2224748phP/ktW7aEgXn27NnhFJcVHuC3b9/+4YcfBkFQp06do0ePllbssW/fvuH65S9lFw0K+Ne//lXiCp9++mk42ipak3QwcODAxx57LAiCzMzMiRMnhk8OGzas+A/n8ePHe/XqtXbt2iAIunXr9sknnzz00EOVfiBiWTRq1CgIgvz8/NLONqLeLpVeVlZWWD24TZs2W7Zsefvtt5977rm77rqrVatWKl+c0ebNm0tbFPWcv+KKK5K0NZyljIyMRYsWLVq0KPzxTSwq9vn555+f29ud1blHaaJOH9HQ3+KiZhmtnJIjXtl/quIrzZtQ0vZ73bp1w/4FCUp+7N27t4zlkNOqkdeuXbtnz55jxoxZsmTJ/v37p02bFt78//DDD6dOnXoOL1j5CPAkw+7du3/729+Gjx9//PEzrn/vvfeGvXHCUnbhncyWLVtWeOHuadOmFRQUBEHQu3fvqIRpcdHA+xkzZpw6dao873jFFVe0bNkyCILs7OyoD1Jh0VRJ0WRypINwosHQzTffHE7iffjw4UGDBhVZ8xe/+EV4VvHAAw/MnTu3SnULTyyaSve9994rcYXFixcnb2tS6v333w8fjB07tlmzZsVXKPHm1ewZAAANiElEQVTgQGjBggVRTakioulLbrrppiRuEWfhmmuuCR+89dZbZxyaGxXWCiedOltne+5Rmmjum2jWriL2798fbmr9+vWjAckpOeKV/acqvtK8CSVzv4eNbefOndEkmkXMmjWrjC+V8kY+adKkX//611GPiUjt2rUfeOCBKLeXvyds5SDAc94dOXKkf//+ubm5QRDccccdX//618/4JxdffPHtt98eBMFf/vKX/fv3//3vfw/OQ/m6oNCF1QceeCDBaj169Aj7C+Xk5CxatKicbxoWBT1+/Pjrr79eZNHJkydfe+21IAiqV69eWiFQ0sGYMWPCCgUzZ8589913o+ePHTs2ZcqUIAiaN28+ZcqU0kaFVU39+vULH4wdOza8cFbY/Pnzw24LVUGUP0ssqrRv376FCxcmd4vi5PPPP3/11VeLP7906dLZs2cHQXDNNdeUWBKZdNCiRYuwYvamTZsGDx6coEPswYMHoynQyl5MO3IO5x6l+e53vxt2jRk7duyhQ4eKrzB69OjwPmfh/lbpcMQr7acq1tK8CSVzv/fs2TN88NxzzxV/r+PHj48YMaKML5XyRv63v/1tyJAhP/3pT0vsAhDdYzvnUpSVTRLmmqeyimaPaNu2bYkrnD59esGCBddee224Wt26dXfs2FFknUcffTRcumLFisLPv/nmm+Hz3//+98MHn376aeEVXnzxxfD5GTNmJNjIKPbPnj27yKJoQNTFF1988uTJxB/2wQcfDFf+3ve+l3jNM9q3b1841qhOnTqFP/Xp06ejyY0ff/zxcr4LFSJq5MOHDy+yKLr+ctVVVx07dix8MpofdcyYMUnf2Bjo06dP9H994sSJ6Plly5YV7qowZcqUFG5kEkyYMCH8pD/60Y+KLFq2bFnbtm2jr2Lz5s2Fl37xxRez/+/LL79M/C7du3cPX2TPnj0V/AFSIeqdFARB9erVX3311cJL582bF53YvfXWW9HzVfkbS1sfffRRtWrVwq+6Xbt2K1euLL7O4sWLo9rpN9xww6lTp6JFFXLukaBhhJM7Xn311YWffOaZZ8JXa9++/fbt2wu/16hRo8LKuw0aNDhw4EDhv0raEe9sf6pKFKP2n+ZNKGn7/dixY1Efrp49ex48eDBatGfPnuj2eBAErVu3LvyHKW/kYYeIG264IXrm2WefDdfs0aPHvn37Cq988ODBqC7M3Llzz+WbqnTcHaIC7NixIxxzFcnLy/v8889XrVoVFc+48MIL586dW/b5JO65554nnnjixIkTYZG56667Lux5XoGi8nX9+/c/453S+++/PxwAP2vWrKNHj9auXfuc37dBgwavvPLKD37wgyNHjtx8882PPPLIDTfckJubO2PGjHDu1iuvvLLsF01JlUceeeTNN9/Mzs7esmXLiBEjXnjhhaBQaYM//OEPWVlZiV9hypQpCSbEqpQmT568atWqbdu2/fGPf1yyZMktt9zSuHHjVatWffDBB3l5eQ8++GD4X1arVq1Ub+n51bNnz6FDhx44cGD8+PHr16/v1atX/fr1t2zZsnLlyvDspEWLFhs2bAiC4Jlnnnn00UdvvfXWmjVrBkGwcePG3r17hy+yc+fOMpYXrmQuuOCCU6dODRw4cMKECZ07d65Ro8ayZctWrVoVThp8yy23RFVLAt9YWurQocPUqVMHDBhw+vTpVatWtW/f/qqrrrr22mvr1auXl5e3d+/ezZs3R7NvNmvW7M9//nOU1gorz7nH2TaMoUOH/ulPf9q9e/fKlSvbtGnzrW996/rrr9+zZ092dnZYojwIgpEjRxYpcpYOR7wSf6riLs2bUNL2e61atUaOHDlgwIAgCN55553mzZt37tz5a1/72ieffPLPf/4zNzf3kksuueCCC8pYxy61jfzHP/7xG2+8kZOTk5WV1bx5827dul199dXVq1fftGnTnDlzwpv/3bt3jy4zVXWpvoJAjEWXMM+oQ4cOq1evLvFFSrsDX1BQcPfdd0evMGLEiCJLy3kH/uTJk2HxjyAIlixZcsYPe+LEiej2TjhyvpzGjBlT4lWDli1bFulrQAoluK1RUFDw73//O9yJNWrUWL9+fUFBQbdu3cp++C3tn6Jy27p1azSCMVKtWrXx48dHg9xmzZqV6s0872bPnl3iCU29evWmTp1aZJjfzp07w7+KxnMWfrI0MbqfVhbRHfi5c+feeOONJf5P3XnnnYcPHy78V1X5G0tzixYtCm8DJtCjR4/idz4r5NwjQcMo8eZkQUHB5s2bS9vgmjVr/uY3vynxjZJzxDvbn6oSxa79p20TKkjuL924ceMyMzOLb3zDhg2XLl0afkVluQNfkMRGXvwOfEFBwXvvvVe4iEMR3bt3P3ToUEV8YZWBMfCcL/Xq1WvZsuXDDz88d+7cpUuXnsPws8KD3iu8/vzChQtzcnKCILj88ss7dep0xvVr1KgRdRMqfy36IAgGDx68YsWKxx9/vHnz5jVr1mzQoEGHDh3GjRu3cuXKCu9rwHnSpk2bn/zkJ0EQ5OXlPfnkk8H/50QlgSuuuGLNmjUvvvjiddddV6tWrUsuueSuu+76xz/+MXDgwHCoYVDKyPBK5u677964ceMTTzzRrl27Cy+88KKLLmrXrt3w4cM3bdo0YMCAG2+8cfTo0U2aNMnMzPzqV7+qNH1hrVu3Xrp06aRJk2699daGDRvWqFGjadOmffr0efvtt7OysqL5jUhzt99+++rVq7OysgYOHNi2bdsmTZrUqFGjYcOG3/jGN8JJFtesWXNWHfdC5T/3KM1VV121evXqqVOn9uzZs2nTpjVq1KhXr17btm1//vOfb9iwIRoBV0Q6HPGK/1RVDunchJK53wcNGrRu3bqnnnqqRYsWtWvXrlOnTqtWrYYMGbJ27dqoNF0ZpbaR33bbbdu2bRs9enTXrl2vueaaWrVqNW7cuFOnTg8//PDy5cvffffdBHPUVzUZBcVKDgBA8j311FNhvd89e/aEk9MAVFaOeFVTkvd7mzZt1q1b17p165SUidXIzxN34AFIhtWrV19//fXXX3/95MmTiy89derUX//61yAILrvsMj/zQNw54lVNVWq/V6kPm1YEeACSoVWrVp999tnq1asnTJiQl5dXZOnvfve7Xbt2BedhvAxA8jniVU1Var9XqQ+bVqoNHz481dsAQOVXrVq1nJycZcuW7d27d/ny5VdeeWXdunWPHz++Zs2al19++fnnnw+CoEGDBtOnTy/PLA8A6cARr2pKt/0+ceLEnJychg0bno/yB+n2YauQVFfRg/g550ksXnjhhVRvO6TSyZMnu3TpUto/SN26dT/44INUbyNAxTirI55Ti0ojrfZ7iVXoU/VhqSjmgYez1q1bt2bNmp3DH7Zt27bCNwZipHr16vPnz582bdqoUaM2bNhQ8P8qqo0aNerbt++zzz57bv9ZAGnorI54Ti0qjSq13/2sp4Qq9ACkwLFjx7Zs2XLs2LGGDRtefvnlGRkZqd4igPPFEa9qSvl+X7hw4ZIlSw4dOvTyyy+f7/dK+YetOgR4AAAAiAFV6AEAACAGBHgAAACIAQEeAAAAYkCABwAAgBgQ4AEAACAGBHgAAACIAQEeAAAAYkCABwAAgBgQ4AEAACAGBHgAAACIAQEeAAAAYkCABwAAgBgQ4AEAACAGBHgAAACIAQEeAAAAYkCABwAAgBgQ4AEAACAGBHgAAACIAQEeAAAAYkCABwAAgBgQ4AEAACAGBHgAAACIAQEeAAAAYkCABwAAgBgQ4AEAACAGBHgAAACIAQEeAAAAYkCABwAAgBgQ4AEAACAGBHgAAACIAQEeAAAAYkCABwAAgBgQ4AEAACAGBHgAAACIAQEeAAAAYkCABwAAgBgQ4AEAACAGBHgAAACIAQEeAAAAYkCABwAAgBgQ4AEAACAGBHgAAACIAQEeAAAAYkCABwAAgBgQ4AEAACAGBHgAAACIAQEeAAAAYkCABwAAgBgQ4AEAACAGBHgAAACIAQEeAAAAYkCABwAAgBgQ4AEAACAGBHgAAACIAQEeAAAAYkCABwAAgBgQ4AEAACAGBHgAAACIAQEeAAAAYkCABwAAgBgQ4AEAACAGBHgAAACIAQEeAAAAYkCABwAAgBgQ4AEAACAGBHgAAACIAQEeAAAAYkCABwAAgBgQ4AEAACAGBHgAAACIAQEeAAAAYkCABwAAgBgQ4AEAACAGBHgAAACIAQEeAAAAYkCABwAAgBgQ4AEAACAGBHgAAACIAQEeAAAAYkCABwAAgBgQ4AEAACAGBHgAAACIAQEeAAAAYkCABwAAgBgQ4AEAACAGBHgAAACIAQEeAAAAYkCABwAAgBgQ4AEAACAGBHgAAACIAQEeAAAAYkCABwAAgBgQ4AEAACAGBHgAAACIAQEeAAAAYkCABwAAgBgQ4AEAACAGBHgAAACIAQEeAAAAYkCABwAAgBgQ4AEAACAGBHgAAACIAQEeAAAAYkCABwAAgBgQ4AEAACAGBHgAAACIAQEeAAAAYkCABwAAgBgQ4AEAACAGBHgAAACIAQEeAAAAYkCABwAAgBgQ4AEAACAGBHgAAACIAQEeAAAAYkCABwAAgBgQ4AEAACAGBHgAAACIAQEeAAAAYuB/zccOOBqxZHIAAAAASUVORK5CYII=" width="672" /></p>
 <p>This confirms that the variance of k2 is the most problematic parameter, so we reduce the parameter distribution model by removing the intersoil variability for k2.</p>
 <pre class="r"><code>f_saem_reduced &lt;- update(f_saem_full, no_random_effect = &quot;log_k2&quot;)
-illparms(f_saem_reduced)</code></pre>
-<pre><code>## character(0)</code></pre>
-<pre class="r"><code>f_saem_reduced_multi &lt;- multistart(f_saem_reduced, n = 16, cores = 16)
-parhist(f_saem_reduced_multi, lpos = &quot;topright&quot;)</code></pre>
-<p><img 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" width="672" /></p>
+illparms(f_saem_reduced)
+f_saem_reduced_multi &lt;- multistart(f_saem_reduced, n = 16, cores = 8)
+parplot(f_saem_reduced_multi, lpos = &quot;topright&quot;, ylim = c(0.5, 2))</code></pre>
+<p><img src="data:image/png;base64,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" width="672" /></p>
 <p>The results confirm that all remaining parameters can be determined with sufficient certainty.</p>
 <p>We can also analyse the log-likelihoods obtained in the multiple runs:</p>
 <pre class="r"><code>llhist(f_saem_reduced_multi)</code></pre>
-<p><img 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" width="672" /></p>
-<p>The parameter histograms can be further improved by excluding the result with the low likelihood.</p>
-<pre class="r"><code>parhist(f_saem_reduced_multi, lpos = &quot;topright&quot;, llmin = -326, ylim = c(0.5, 2))</code></pre>
-<p><img 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" width="672" /></p>
-<p>We can use the <code>anova</code> method to compare the models, including a likelihood ratio test if the models are nested.</p>
-<pre class="r"><code>anova(f_saem_full, best(f_saem_reduced_multi), test = TRUE)</code></pre>
+<p><img 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" width="672" /></p>
+<p>We can use the <code>anova</code> method to compare the models.</p>
+<pre class="r"><code>anova(f_saem_full, best(f_saem_full_multi),
+  f_saem_reduced, best(f_saem_reduced_multi), test = TRUE)</code></pre>
 <pre><code>## Data: 155 observations of 1 variable(s) grouped in 6 datasets
 ## 
-##                            npar    AIC    BIC     Lik Chisq Df Pr(&gt;Chisq)
-## best(f_saem_reduced_multi)    9 663.81 661.93 -322.90                    
-## f_saem_full                  10 668.27 666.19 -324.13     0  1          1</code></pre>
-<p>While AIC and BIC are lower for the reduced model, the likelihood ratio test does not indicate a significant difference between the fits.</p>
+##                            npar    AIC    BIC     Lik  Chisq Df Pr(&gt;Chisq)
+## f_saem_reduced                9 663.74 661.87 -322.87                     
+## best(f_saem_reduced_multi)    9 663.60 661.72 -322.80 0.1476  0           
+## f_saem_full                  10 670.35 668.26 -325.17 0.0000  1          1
+## best(f_saem_full_multi)      10 665.61 663.53 -322.80 4.7372  0</code></pre>
+<p>The reduced model results in lower AIC and BIC values, so it is clearly preferable. Using multiple starting values gives a large improvement in case of the full model, because it is less well-defined, which impedes convergence. For the reduced model, using multiple starting values only results in a small improvement of the model fit.</p>
 
 
 
diff --git a/vignettes/web_only/multistart.rmd b/vignettes/web_only/multistart.rmd
index 27a8a96a..ccf26b3d 100644
--- a/vignettes/web_only/multistart.rmd
+++ b/vignettes/web_only/multistart.rmd
@@ -1,7 +1,7 @@
 ---
 title: Short demo of the multistart method
 author: Johannes Ranke
-date: Last change 26 September 2022 (rebuilt `r Sys.Date()`)
+date: Last change 17 April 2023 (rebuilt `r Sys.Date()`)
 output:
   html_document
 vignette: >
@@ -39,8 +39,8 @@ of 'log_k2' includes zero. We check this assessment using multiple runs
 with different starting values.
 
 ```{r}
-f_saem_full_multi <- multistart(f_saem_full, n = 16, cores = 16)
-parplot(f_saem_full_multi)
+f_saem_full_multi <- multistart(f_saem_full, n = 16, cores = 8)
+parplot(f_saem_full_multi, lpos = "topleft")
 ```
 
 This confirms that the variance of k2 is the most problematic parameter, so we
@@ -50,8 +50,8 @@ for k2.
 ```{r}
 f_saem_reduced <- update(f_saem_full, no_random_effect = "log_k2")
 illparms(f_saem_reduced)
-f_saem_reduced_multi <- multistart(f_saem_reduced, n = 16, cores = 16)
-parplot(f_saem_reduced_multi, lpos = "topright")
+f_saem_reduced_multi <- multistart(f_saem_reduced, n = 16, cores = 8)
+parplot(f_saem_reduced_multi, lpos = "topright", ylim = c(0.5, 2))
 ```
 
 The results confirm that all remaining parameters can be determined with sufficient
@@ -63,20 +63,17 @@ We can also analyse the log-likelihoods obtained in the multiple runs:
 llhist(f_saem_reduced_multi)
 ```
 
-The parameter histograms can be further improved by excluding the result with
-the low likelihood.
+We can use the `anova` method to compare the models.
 
 ```{r}
-parplot(f_saem_reduced_multi, lpos = "topright", llmin = -326, ylim = c(0.5, 2))
+anova(f_saem_full, best(f_saem_full_multi),
+  f_saem_reduced, best(f_saem_reduced_multi), test = TRUE)
 ```
 
-We can use the `anova` method to compare the models, including a likelihood ratio
-test if the models are nested.
-
-```{r}
-anova(f_saem_full, best(f_saem_reduced_multi), test = TRUE)
-```
-
-While AIC and BIC are lower for the reduced model, the likelihood ratio test
-does not indicate a significant difference between the fits.
+The reduced model results in lower AIC and BIC values, so it
+is clearly preferable. Using multiple starting values gives
+a large improvement in case of the full model, because it is
+less well-defined, which impedes convergence. For the reduced
+model, using multiple starting values only results in a small
+improvement of the model fit.
author	Johannes Ranke <jranke@uni-bremen.de>	2023-04-17 19:39:09 +0200
committer	Johannes Ranke <jranke@uni-bremen.de>	2023-04-17 19:39:09 +0200
commit	004fce2520d6889d82226e21bc443426e81d93f2 (patch)
tree	a5b18bd357c506059ff2149f1c6602ea71df6d99
parent	57ca408fda3fb8e42e0d5f3d9fc808d54268fa08 (diff)