From e5aea600fd957955fc3e8e41dbc93bf4f65af803 Mon Sep 17 00:00:00 2001
From: Ranke Johannes <johannes.ranke@agroscope.admin.ch>
Date: Thu, 19 Dec 2024 10:59:44 +0100
Subject: Support observed data as tibble

Closes #14
---
 vignettes/FOCUS_L.html | 435 +++++++++++++++++++++++++------------------------
 1 file changed, 221 insertions(+), 214 deletions(-)

(limited to 'vignettes/FOCUS_L.html')

diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html
index bd1fa16e..c341f104 100644
--- a/vignettes/FOCUS_L.html
+++ b/vignettes/FOCUS_L.html
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@@ -364,25 +364,33 @@ code > span.er { color: #a61717; background-color: #e3d2d2; }
 <h1 class="title toc-ignore">Example evaluation of FOCUS Laboratory Data
 L1 to L3</h1>
 <h4 class="author">Johannes Ranke</h4>
-<h4 class="date">Last change 18 May 2023 (rebuilt 2023-05-18)</h4>
+<h4 class="date">Last change 18 May 2023 (rebuilt 2024-12-19)</h4>
 
 
 <div id="TOC">
 <ul>
-<li><a href="#laboratory-data-l1">Laboratory Data L1</a></li>
-<li><a href="#laboratory-data-l2">Laboratory Data L2</a>
+<li><a href="#laboratory-data-l1" id="toc-laboratory-data-l1">Laboratory
+Data L1</a></li>
+<li><a href="#laboratory-data-l2" id="toc-laboratory-data-l2">Laboratory
+Data L2</a>
 <ul>
-<li><a href="#sfo-fit-for-l2">SFO fit for L2</a></li>
-<li><a href="#fomc-fit-for-l2">FOMC fit for L2</a></li>
-<li><a href="#dfop-fit-for-l2">DFOP fit for L2</a></li>
+<li><a href="#sfo-fit-for-l2" id="toc-sfo-fit-for-l2">SFO fit for
+L2</a></li>
+<li><a href="#fomc-fit-for-l2" id="toc-fomc-fit-for-l2">FOMC fit for
+L2</a></li>
+<li><a href="#dfop-fit-for-l2" id="toc-dfop-fit-for-l2">DFOP fit for
+L2</a></li>
 </ul></li>
-<li><a href="#laboratory-data-l3">Laboratory Data L3</a>
+<li><a href="#laboratory-data-l3" id="toc-laboratory-data-l3">Laboratory
+Data L3</a>
 <ul>
-<li><a href="#fit-multiple-models">Fit multiple models</a></li>
-<li><a href="#accessing-mmkin-objects">Accessing mmkin objects</a></li>
+<li><a href="#fit-multiple-models" id="toc-fit-multiple-models">Fit
+multiple models</a></li>
+<li><a href="#accessing-mmkin-objects" id="toc-accessing-mmkin-objects">Accessing mmkin objects</a></li>
 </ul></li>
-<li><a href="#laboratory-data-l4">Laboratory Data L4</a></li>
-<li><a href="#references">References</a></li>
+<li><a href="#laboratory-data-l4" id="toc-laboratory-data-l4">Laboratory
+Data L4</a></li>
+<li><a href="#references" id="toc-references">References</a></li>
 </ul>
 </div>
 
@@ -390,13 +398,13 @@ L1 to L3</h1>
 <h1>Laboratory Data L1</h1>
 <p>The following code defines example dataset L1 from the FOCUS kinetics
 report, p. 284:</p>
-<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(<span class="st">&quot;mkin&quot;</span>, <span class="at">quietly =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L1 <span class="ot">=</span> <span class="fu">data.frame</span>(</span>
-<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>  <span class="at">t =</span> <span class="fu">rep</span>(<span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">3</span>, <span class="dv">5</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">21</span>, <span class="dv">30</span>), <span class="at">each =</span> <span class="dv">2</span>),</span>
-<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a>  <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">88.3</span>, <span class="fl">91.4</span>, <span class="fl">85.6</span>, <span class="fl">84.5</span>, <span class="fl">78.9</span>, <span class="fl">77.6</span>,</span>
-<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>             <span class="fl">72.0</span>, <span class="fl">71.9</span>, <span class="fl">50.3</span>, <span class="fl">59.4</span>, <span class="fl">47.0</span>, <span class="fl">45.1</span>,</span>
-<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>             <span class="fl">27.7</span>, <span class="fl">27.3</span>, <span class="fl">10.0</span>, <span class="fl">10.4</span>, <span class="fl">2.9</span>, <span class="fl">4.0</span>))</span>
-<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L1_mkin <span class="ot">&lt;-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L1)</span></code></pre></div>
+<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">library</span>(<span class="st">&quot;mkin&quot;</span>, <span class="at">quietly =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a>FOCUS_2006_L1 <span class="ot">=</span> <span class="fu">data.frame</span>(</span>
+<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a>  <span class="at">t =</span> <span class="fu">rep</span>(<span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">3</span>, <span class="dv">5</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">21</span>, <span class="dv">30</span>), <span class="at">each =</span> <span class="dv">2</span>),</span>
+<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a>  <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">88.3</span>, <span class="fl">91.4</span>, <span class="fl">85.6</span>, <span class="fl">84.5</span>, <span class="fl">78.9</span>, <span class="fl">77.6</span>,</span>
+<span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a>             <span class="fl">72.0</span>, <span class="fl">71.9</span>, <span class="fl">50.3</span>, <span class="fl">59.4</span>, <span class="fl">47.0</span>, <span class="fl">45.1</span>,</span>
+<span id="cb1-6"><a href="#cb1-6" tabindex="-1"></a>             <span class="fl">27.7</span>, <span class="fl">27.3</span>, <span class="fl">10.0</span>, <span class="fl">10.4</span>, <span class="fl">2.9</span>, <span class="fl">4.0</span>))</span>
+<span id="cb1-7"><a href="#cb1-7" tabindex="-1"></a>FOCUS_2006_L1_mkin <span class="ot">&lt;-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L1)</span></code></pre></div>
 <p>Here we use the assumptions of simple first order (SFO), the case of
 declining rate constant over time (FOMC) and the case of two different
 phases of the kinetics (DFOP). For a more detailed discussion of the
@@ -406,19 +414,19 @@ like <code>&quot;SFO&quot;</code> for parent only degradation models. The
 following two lines fit the model and produce the summary report of the
 model fit. This covers the numerical analysis given in the FOCUS
 report.</p>
-<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>m.L1.SFO <span class="ot">&lt;-</span> <span class="fu">mkinfit</span>(<span class="st">&quot;SFO&quot;</span>, FOCUS_2006_L1_mkin, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(m.L1.SFO)</span></code></pre></div>
-<pre><code>## mkin version used for fitting:    1.2.4 
-## R version used for fitting:       4.3.0 
-## Date of fit:     Thu May 18 11:38:11 2023 
-## Date of summary: Thu May 18 11:38:11 2023 
+<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a>m.L1.SFO <span class="ot">&lt;-</span> <span class="fu">mkinfit</span>(<span class="st">&quot;SFO&quot;</span>, FOCUS_2006_L1_mkin, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a><span class="fu">summary</span>(m.L1.SFO)</span></code></pre></div>
+<pre><code>## mkin version used for fitting:    1.2.6 
+## R version used for fitting:       4.4.2 
+## Date of fit:     Thu Dec 19 10:28:47 2024 
+## Date of summary: Thu Dec 19 10:28:47 2024 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 133 model solutions performed in 0.011 s
+## Fitted using 133 model solutions performed in 0.02 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -494,34 +502,33 @@ report.</p>
 ##    30   parent      4.0     5.251  -1.2513</code></pre>
 <p>A plot of the fit is obtained with the plot function for mkinfit
 objects.</p>
-<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(m.L1.SFO, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>, <span class="at">main =</span> <span class="st">&quot;FOCUS L1 - SFO&quot;</span>)</span></code></pre></div>
-<p><img 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" /><!-- --></p>
+<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="fu">plot</span>(m.L1.SFO, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>, <span class="at">main =</span> <span class="st">&quot;FOCUS L1 - SFO&quot;</span>)</span></code></pre></div>
+<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
 <p>The residual plot can be easily obtained by</p>
-<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="fu">mkinresplot</span>(m.L1.SFO, <span class="at">ylab =</span> <span class="st">&quot;Observed&quot;</span>, <span class="at">xlab =</span> <span class="st">&quot;Time&quot;</span>)</span></code></pre></div>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
+<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="fu">mkinresplot</span>(m.L1.SFO, <span class="at">ylab =</span> <span class="st">&quot;Observed&quot;</span>, <span class="at">xlab =</span> <span class="st">&quot;Time&quot;</span>)</span></code></pre></div>
+<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
 <p>For comparison, the FOMC model is fitted as well, and the <span class="math inline"><em>χ</em><sup>2</sup></span> error level is
 checked.</p>
-<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>m.L1.FOMC <span class="ot">&lt;-</span> <span class="fu">mkinfit</span>(<span class="st">&quot;FOMC&quot;</span>, FOCUS_2006_L1_mkin, <span class="at">quiet=</span><span class="cn">TRUE</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a>m.L1.FOMC <span class="ot">&lt;-</span> <span class="fu">mkinfit</span>(<span class="st">&quot;FOMC&quot;</span>, FOCUS_2006_L1_mkin, <span class="at">quiet=</span><span class="cn">TRUE</span>)</span></code></pre></div>
 <pre><code>## Warning in mkinfit(&quot;FOMC&quot;, FOCUS_2006_L1_mkin, quiet = TRUE): Optimisation did not converge:
 ## false convergence (8)</code></pre>
-<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(m.L1.FOMC, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>, <span class="at">main =</span> <span class="st">&quot;FOCUS L1 - FOMC&quot;</span>)</span></code></pre></div>
-<p><img 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" /><!-- --></p>
-<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(m.L1.FOMC, <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a><span class="fu">plot</span>(m.L1.FOMC, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>, <span class="at">main =</span> <span class="st">&quot;FOCUS L1 - FOMC&quot;</span>)</span></code></pre></div>
+<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
+<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a><span class="fu">summary</span>(m.L1.FOMC, <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
 <pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre>
-<pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre>
-<pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result
-## is doubtful</code></pre>
-<pre><code>## mkin version used for fitting:    1.2.4 
-## R version used for fitting:       4.3.0 
-## Date of fit:     Thu May 18 11:38:11 2023 
-## Date of summary: Thu May 18 11:38:11 2023 
+<pre><code>## Warning in cov2cor(ans$covar): diag(V) had non-positive or NA entries; the
+## non-finite result may be dubious</code></pre>
+<pre><code>## mkin version used for fitting:    1.2.6 
+## R version used for fitting:       4.4.2 
+## Date of fit:     Thu Dec 19 10:28:47 2024 
+## Date of summary: Thu Dec 19 10:28:47 2024 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 369 model solutions performed in 0.025 s
+## Fitted using 342 model solutions performed in 0.045 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -549,32 +556,32 @@ checked.</p>
 ## 
 ## Results:
 ## 
-##        AIC      BIC   logLik
-##   95.88781 99.44929 -43.9439
+##        AIC      BIC    logLik
+##   95.88782 99.44931 -43.94391
 ## 
 ## Optimised, transformed parameters with symmetric confidence intervals:
-##           Estimate Std. Error  Lower  Upper
-## parent_0     92.47     1.2820 89.720 95.220
-## log_alpha    13.78        NaN    NaN    NaN
-## log_beta     16.13        NaN    NaN    NaN
-## sigma         2.78     0.4598  1.794  3.766
+##           Estimate Std. Error Lower Upper
+## parent_0     92.46     1.2820 89.71 95.21
+## log_alpha    15.08        NaN   NaN   NaN
+## log_beta     17.43        NaN   NaN   NaN
+## sigma         2.78     0.4569  1.80  3.76
 ## 
 ## Parameter correlation:
 ##            parent_0 log_alpha log_beta     sigma
-## parent_0  1.0000000       NaN      NaN 0.0001671
+## parent_0   1.000000       NaN      NaN -0.000772
 ## log_alpha       NaN         1      NaN       NaN
 ## log_beta        NaN       NaN        1       NaN
-## sigma     0.0001671       NaN      NaN 1.0000000
+## sigma     -0.000772       NaN      NaN  1.000000
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
 ## t-test (unrealistically) based on the assumption of normal distribution
 ## for estimators of untransformed parameters.
-##           Estimate t value Pr(&gt;t)  Lower  Upper
-## parent_0 9.247e+01      NA     NA 89.720 95.220
-## alpha    9.658e+05      NA     NA     NA     NA
-## beta     1.010e+07      NA     NA     NA     NA
-## sigma    2.780e+00      NA     NA  1.794  3.766
+##           Estimate t value Pr(&gt;t) Lower Upper
+## parent_0 9.246e+01      NA     NA 89.71 95.21
+## alpha    3.555e+06      NA     NA    NA    NA
+## beta     3.719e+07      NA     NA    NA    NA
+## sigma    2.780e+00      NA     NA  1.80  3.76
 ## 
 ## FOCUS Chi2 error levels in percent:
 ##          err.min n.optim df
@@ -583,7 +590,7 @@ checked.</p>
 ## 
 ## Estimated disappearance times:
 ##        DT50  DT90 DT50back
-## parent 7.25 24.08     7.25</code></pre>
+## parent 7.25 24.09     7.25</code></pre>
 <p>We get a warning that the default optimisation algorithm
 <code>Port</code> did not converge, which is an indication that the
 model is overparameterised, <em>i.e.</em> contains too many parameters
@@ -618,21 +625,21 @@ sponsored by the German Umweltbundesamt <span class="citation">(Ranke
 <h1>Laboratory Data L2</h1>
 <p>The following code defines example dataset L2 from the FOCUS kinetics
 report, p. 287:</p>
-<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L2 <span class="ot">=</span> <span class="fu">data.frame</span>(</span>
-<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>  <span class="at">t =</span> <span class="fu">rep</span>(<span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">28</span>), <span class="at">each =</span> <span class="dv">2</span>),</span>
-<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a>  <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">96.1</span>, <span class="fl">91.8</span>, <span class="fl">41.4</span>, <span class="fl">38.7</span>,</span>
-<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a>             <span class="fl">19.3</span>, <span class="fl">22.3</span>, <span class="fl">4.6</span>, <span class="fl">4.6</span>,</span>
-<span id="cb14-5"><a href="#cb14-5" aria-hidden="true" tabindex="-1"></a>             <span class="fl">2.6</span>, <span class="fl">1.2</span>, <span class="fl">0.3</span>, <span class="fl">0.6</span>))</span>
-<span id="cb14-6"><a href="#cb14-6" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L2_mkin <span class="ot">&lt;-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L2)</span></code></pre></div>
+<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" tabindex="-1"></a>FOCUS_2006_L2 <span class="ot">=</span> <span class="fu">data.frame</span>(</span>
+<span id="cb13-2"><a href="#cb13-2" tabindex="-1"></a>  <span class="at">t =</span> <span class="fu">rep</span>(<span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">28</span>), <span class="at">each =</span> <span class="dv">2</span>),</span>
+<span id="cb13-3"><a href="#cb13-3" tabindex="-1"></a>  <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">96.1</span>, <span class="fl">91.8</span>, <span class="fl">41.4</span>, <span class="fl">38.7</span>,</span>
+<span id="cb13-4"><a href="#cb13-4" tabindex="-1"></a>             <span class="fl">19.3</span>, <span class="fl">22.3</span>, <span class="fl">4.6</span>, <span class="fl">4.6</span>,</span>
+<span id="cb13-5"><a href="#cb13-5" tabindex="-1"></a>             <span class="fl">2.6</span>, <span class="fl">1.2</span>, <span class="fl">0.3</span>, <span class="fl">0.6</span>))</span>
+<span id="cb13-6"><a href="#cb13-6" tabindex="-1"></a>FOCUS_2006_L2_mkin <span class="ot">&lt;-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L2)</span></code></pre></div>
 <div id="sfo-fit-for-l2" class="section level2">
 <h2>SFO fit for L2</h2>
 <p>Again, the SFO model is fitted and the result is plotted. The
 residual plot can be obtained simply by adding the argument
 <code>show_residuals</code> to the plot command.</p>
-<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>m.L2.SFO <span class="ot">&lt;-</span> <span class="fu">mkinfit</span>(<span class="st">&quot;SFO&quot;</span>, FOCUS_2006_L2_mkin, <span class="at">quiet=</span><span class="cn">TRUE</span>)</span>
-<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(m.L2.SFO, <span class="at">show_residuals =</span> <span class="cn">TRUE</span>, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>,</span>
-<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a>     <span class="at">main =</span> <span class="st">&quot;FOCUS L2 - SFO&quot;</span>)</span></code></pre></div>
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" /><!-- --></p>
+<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" tabindex="-1"></a>m.L2.SFO <span class="ot">&lt;-</span> <span class="fu">mkinfit</span>(<span class="st">&quot;SFO&quot;</span>, FOCUS_2006_L2_mkin, <span class="at">quiet=</span><span class="cn">TRUE</span>)</span>
+<span id="cb14-2"><a href="#cb14-2" tabindex="-1"></a><span class="fu">plot</span>(m.L2.SFO, <span class="at">show_residuals =</span> <span class="cn">TRUE</span>, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>,</span>
+<span id="cb14-3"><a href="#cb14-3" tabindex="-1"></a>     <span class="at">main =</span> <span class="st">&quot;FOCUS L2 - SFO&quot;</span>)</span></code></pre></div>
+<p><img role="img" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAJACAMAAABlpiR1AAADAFBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7////isF19AAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nO2dCXjUVNuGT6GF0pZCKZQdadn3HQqCsi8CH4jghwion4hSZZNdVgUVBFFAtKAIUkSEgqxFKCIIgig/IqKgFhDcoZSlSPeeP8nMtNOZyTSZ5HROMs99XWSbk/d9md7XZHImySEUAI4h3i4AAHdAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQQFXANBAddAUMA1EBRwDQSV4xli45Cwtndks+CmwzflWl7LXdUxPDBy1Hfi8mRC4sV5PCEThFnKnHYRIY2Hfp0XZyEhsXkrp/9To3Trl9Nd5bPf0V1uHwOCymEvyb9jrIt9UsWX/uxpWSv+BnUU9GJ5a8sFtjj2gib6S6+1znROV2BHN7l9DQgqhyBJTKzEn3QkISEPvTA4hJDHxJc6EVJu2OzBxQnZ4Sjo/YQMXvfB2FKk2AlrHHtB65Kgtw48RMirzukK7Ogmt68BQeUQJNltXUwkpO5PwvynOoQkUvoxIQ0vC6u7CGnuIGh6AOkqrnxEyMvWne0E/ZOQqZTeDSU9nLIV3FE+t88BQeWwk6Q1IQelhf3C4ZnSjoTsk1b/27xlZkFBrxLSNltYSd+w4bh1ZztBvyDkQ2HWiNRxylZwR/ncPgcElUOQZOx7Ij9c9yO1rBtrEr9kGk7K27UreIiPIqT2lC1Jdic0doJmJidnUHo7hHR3TldgR/ncPgcElSPvROWtb4UTFOtG4ezoVLLwYWfXrqCgx6pK+5QdfND2eoGzeIGcEYRsdk5XYEfZ3Hr+B40BBJUjX5JThDxg3fgAIV8LR+MOdu0cupnubBzVNEDcbYX1dQdBbw4kZLx1eddzAiutK/Y7yuZm8R/lGwgqR/73wOT8w2wtQv6hYaSKZS0zPT3XUVCRjC+fFE69cywrBQX9vyhS/DXbyizRwd52OW07yuf2OSCoHHYnKq0sHeaUHiKkBaXtCbH0IbUgAf/S+YSsEVfWEDKPnlu40HJyJByPf7fsXEDQ9YGkYt7B317QgjvK5/Y5IKgcdpLsI6T+BWH+S31C9lL6ASHtbwiru/3IvZRuJaS/2Kg/IVvoT4TcL56M57YlwdmWne0F3elH6v3mMlvBHeVz+xwQVA47SehwQkIfmftIaUKGU0kiUnPikkeLk2KCMjerC77OniV8rFa7IX6okvZvbV3VlZCHrfsKgg6Xzsjf+zenBiETxaWtzukK7Cif2+eAoHLYS3K7h/WkpcdtcfVyJ8tawOvi2uFwy1r5w8LKr5WtLRvftO67MO9ny9+P2pYaO6crsKOb3L4GBJXDXhKau3FYk+AmwzZa+zdzVgysWbrlk79Y1m5Max8e3n7aDWnlzuKO1UpGdVubZdvVTtB1bgQtsKO73D4GBAVcA0EB10BQwDUQFHANBAVcA0EB10BQwDUQFHANBPWACwNGxhbeCugBBPWAKadoJ2/X4CtAUA+4m/vbg4U0OTRQySbL7/VZ16yreQsplzyuzWxAUE/YPOCvQlooEvRWU2k21XaZkrDwa3S9ZZTGnNdaoWmAoB6w+3mHCzcyrf/y15QI+nL9mtL28lZBxYWnP81ueeunGB2LNTgQVBXP1zlBt9R/ou9gi1PLI2s/n3N02NBZ4j+6ICpyirRGJRulF3t9Qmnrw9Kik6Ant4mC3miz3BJMWpi69FLDuyP/KNL/FNdAUHVs7XX3nrwL2w81u3pn6IKjZc5T8d+n9W6ld1wvLokvDbS8uHYEvRD1ubQoCdrnHokz0v6/i4I+snOTRVBp4dbT3fd/M6Po/1/cAkHVkVvnsf55K7Nrdu7cqO/R+ykV/02dSenKx8UlKgpqefFmxcxXXrQsOh/1RUE3PEUtguYt0ME3dvaa7PIBYz4IBFXJohK/5C+/SmnaraP9BEGFf1OEQ3vsCHGJioJaXqQD9jW/aFmUBO1ZVUJ6Lp4k6MDKNSsEi3cW5y3sey255eXZrxf9f41LIKhKhobnf7Z9W/9q+gMJNkH3NkjN6LQ2T1DLi/Sjzl2s7Vx/ggoIH5y557MsC8Jn9H/SLnfL3fxCkfxv+AeCquNo5f4b8tdia1UdS22C0vm1oybl5AlqeZHeCVpnbedO0HTyK7UKuuk9Suc26PR3EfxnjAAEVUVOq7Wft8oqvB3QCwiqijWtc+nwahe9XYYPAUEB10BQwDUQFHANBAVcA0EB10BQwDUQFHANBAVcA0EB10BQwDUQFHCNzoI+3woABXRUerWWzoJGrz0JQOHU+c5Lgh4vvA0AtCkEBTzDkaCnF8+OxyW+oCDcCJoztvrkl7o1TtI3DTA63Ai66t5UYbqijb5pgNHhRtB7E8VpbtSP+uYBBocbQWtekmY99+mbBxgcbgRt/7k0q/O9vnmAweFG0BVd0oTpmua+OY4fv+QcTmRPsnx+bgTNfqLWvDcH1MJXUM44U7o7c2q7eToKN4JSenTO+LV4EhZvfNucfY5Xp8u/xo+gGUt7tX8G3aC8AUGt3Gw+eO+xVyJ2u2wNvAYEtTJ9lDg9XiXTRWPgPSColWYnpVnLE/rmARqBoFYiL9Lft1HaY7++eYBGIKiVPpvo8TY0PeI3ffMAjUBQK5/WPHsr5M7/huibBmgFgtqIq3xfYIXHbuubBmgFguaRerj5B/omAdqBoHaMx4gV3AFB7YgdpW8SoB0Iasfhe/VNArTDUNBvv7EuGEbQ5LL6JgHaYSjocNtwO4YRlFYobPBqUNQ4CZq7bfLETTlu98nNdtqUUeAnbMvNuwYU9P7P9M0CNOMo6I1OHRYv7dz6H9kdksjeGn71FwlLt8fXDoyan0tppY8nh/1K17UKavS+sLnayu6kzJDrNJoQclPaxTiCjlmhbxagGUdBn4oR73mY9l/ZHZJIqae2T/abS+mgsCU7x5ONgqCtB2xJWx4wJ2GC39uCoGGPHnrN/1l6/aHef1vunzCOoMtj9M0CNCMJmp5i43rpn8XZ7yF/5W1yuMg8iTwkTKeUvk0fihUW6k0VBG2aS++EzxfWnooQBBVv6+l/ryEP8Qe66JsFaEYS9IEwG2X9LPNiZfI2dSi4QxKJF6bfE/GytNRv3wuYLAg6jdIT5FhycnIcuUKriaPcT442pKB/VNQ3C9CM4yG+3O/i9Eao7M05SeSo2EDQ9GhTvyp9qoiCLqX0Y2LhDK22mHIhaMa1FDevyjw8LMzN/X3AGzgKOvER4Xw8e/STsjtYPkF/JF+mBMQIp1LRoqBvUnqQXLU2qLaEel/QKy9E+hFSsvZ0ueFUZQRtf0RVGsAcR0HvDqo/bUaT3rdkd0giDwvTF4Ju7CdJlKZVtgp6teQaYfPsbnwIeiqo+phlceuXPxcVdtp1CxlBn1ylJg1gj3NH/ZGFrxx0s0MSCYzZM6PYC/TXgCFHdrQLfiBZEpROK7EgYYrfMjtBn2h00tJhWvSCdu5z17KQ9Uh31y1kBF0yQU0awB7VvyQlkU/6lq37inCmvqlecNtdH4TNsQiau6RxUEPxvD5P0M8jS1s+iIte0NB429IRmV8vZQRN6KUmDWCPB4KeVJuj6AVtndefOa+d6xYygl6qriYNYI85BY3367Pm2LnzX60fVDzedQsZQXNLy3/7Bt7AnILS3V2kTi+/rgkyDeSeUd8Ktx3zhWpBs37NUJvDK/2gKWcTE89cd9iYmvc8s9pfuN5txFqVeQBbTH3Bcs4vDs8J+TrvgWb+Mnd3vDLVgzyAHSYVdPfgfmvpqnASuEDmeZ8h77revr2fqjyANeYUdDNp80BATMj8vbMC1rhuISfoT7XU5AHMMaegLcZQ+g55VVia0cJ1CzlBs4P+VZMIsMacggbtofSadFXL3mDXLeQEpU2+VZMIsMacgtZZROlXZJ2wtKy+6xaygv73QzWJAGvMKeirgRNerNqmyv7kHeEzXbeQFXTeLDWJAGvMKWjWrCoVnskcRQjpK/OVUlbQzYPUJAKsMaegVs6tl/1dSFbQs/U8SASYcbZEFHPKzZbP761bPmQFzQrGA+644tIF9rjpueFOUNoWF9WDfPgT9Jll+mYChoY/QVc/pm8mYGj4E/RkE30zAUPDn6AZ+LET5MOfoLTFV/qmAkaGQ0FHva1vKmBkOBR0JR4EDvLgUNDjLfVNBYwMh4KmBWHUeGCDQ0FpE9X3rQLTwqOgj63WNxcwMDwKumyMvrmAgeFR0CNt9c0FDAyPgqYGZcq/CHwLHgWl9ZUWBUwPl4IOW6tvMmBcuBR0yVh9kwHjwqWgBzGoLLDCpaA3Q5wHewS+CZeC0to/6psNGBY+BX04Tt9swLB4ayAv94K++rzabMCkeGsgL/eC7u+sKhswL94ayMu9oNfKyDz3Fvga3hrIy72gtNZZNemAefFI0CvfF9K48IG8ChF0ZKzCsoDJ8UDQTysRQrstd9e48IG8ChH03eEKywImR72gcf6j4wid7edu2NfCB/IqRNBz9ygsC5gc9YI2nEiThZUpjd21LnQgr0IEzY24orAuYG7UCxqUIAmaIPPweRsuB/L6frSNgDfc7/7gRoV1AXOjXtCWcyVBX2pa+D43fs4puOHau6usBL7jftfXY9y/DnwE9YKuDZh/jFxdU2Kpu9Zx7c/Qv/sRUvpNmQaFHOLp13iEGBDx4Cx+ebjw7bLkdHdd6ctIp79pz6rv7J3iv951i8IEzQp1/HoAfBJP+kHvnPj44DW3jSNnUPo3EceLna5yIK88uu1SWBgwNWx+SQrfKpwQkTvCUkJp1y0KFXTeNDUJgVlRL2g/G24aD3wgg2aVPiAszfCso57SAx0UFgZMjXpBHxcZGFHsOTeNz0U0fu3zBVXe/3xGwEeuWxQq6L/BeI4t8PwQf6dnG3etfx5TTuqpb/SxTINCBaVtDyusDJgZj7+DHiJX3bbPvHh8zzG5q0GVCDppgaK6gLnxWNB1pTRdslm4oJ/01hIfmAT1gm6QmFdZ20XvhQt6rQxu7QQeCBooERR9TlPewgWlDU5pygBMAZ93dUo85faSU+AbcCzoB0P0TQmMiEpBy9uhKa8CQS9W0pQBmAKVgsbGxr4ZUnXC0pn1qu/WlFeBoLTaL5pSADOg/hD/zP3iIBzZvZ/WlFeJoI9hRC+gXtCqlruMtlfRlFeJoJv6a0oBzIAHglquQV5eQ1NeJYKmlMGAST6PekGfKiNeqbm7DPtDPO2QqCkHMAHqBU3tTMo1Lke63tGUV5Gg8/EMMZ/Hk37QgwsnLP5CY15Fgp5soDELMDwcd9RTmlv5gr5ZgeFQKeh7ifQ9G5ryKhIUHU1ApaAhQ2lZG5ryKhMUHU0+D9eHeHQ0AQ8FPb9X423rygRFR5PPo17QKz0m063FSXmZRycrRKGg6GjyddQLOrDKdtq818Ue7m47LhyFgqKjyddRL2i51fRPkkg3VtCUV6Gg6GjyddQLWnYTfT8wje4K0ZRXoaDoaPJ11Avas8OBJgPonb6tNeVVKig6mnwc9YKerkhCT9G6JQp7uJeWgbzyQUeTj+NBN9Pdk9cp3fyz29YaB/KyZfo0dn/7/QoLBKaEzTA0WgfysnAoqsvT94cPVFggMCVshqHRPJCXyJ8RnwnTdcX+UFghMCNshqHRPJCXyOJnpFkEnlbvy7AZhkbzQF4iYyw9TA82VFghMCNshqHRPJCXyMw50uzZUncVlghMCKNhaLQO5CXyTY3XWke0WVKl3VaFJQITwmgYGpmBvA6SPBYVmjG3buD4Dc+WbLr6YYUlAhPCZhgaG9tlH3Kr4BN0d6tjMX2f+6rR9rKpiioEZoTNMDR5Ox2Qe0WBoJMsH7Ivzuz7oaJkwIyoFTR138eXhaPvHz8c6uum8bbhFki34TLjaisQ9NkV0mzxpDj8Hu+7qBT0x+rC4X3MdzXEEyA3jfcEkVbRAqRBdLTrFgoEXT1ImvXZcLsMRp3zWVQK2j887sxHFaq237T/hNsHN5xv1fYnqvEQ/2/tVzJp+tyG6XSwtjtIgYFRKWgF8XvhIvJroc0zp4W8o1FQenlAWIuyg3+ndEsPhUUC06FSUCKOe7RV0R2eh6r3+UuboJSmnLohztLK/a2kNTAhagUVfxjaruwW5Bv/La9VUBvDV6hpDUwEQ0Ep/WjCT3IvqRM0sbma1sBEMBXUDeoEza33pfaUwIioFTQgMDAwgEhDJWnKq05Q+uajmrIBw6JS0Fl2aMqrUtCbYThN8k34fjZTPk+9rG9+YBCMIujpGhi50ycxiqC0w3Z9CwDGwDCCbuilbwHAGBhG0IxKsn2qwMQYRlA6A09i9EWMI+jl8toGvgGGxDiC0v/gojsfxECC7mui5DYoYC4MJChtJ3OPPTAxRhJ0T6McfYsA/GMkQfER6oMYStAEfIT6HIYSlN67Rd8qAPcYS9C9+Aj1NYwlKO24Wd8yAO8YTFB8hPoaBhMUH6G+htEE3dcwS99CAN+wE1SfcZKc6OPwXNLcbZPGbcDV9qaFkaD6jJPkil8q/G6/euv+9kuW9Wj2p4fRAO+wEVSfcZJcM3Oo/VrMaPEKkrkYTMmssBFUl3GSZLgbZf88nXBpFKW0UDyE2aSwEVSXcZLk2FEnf/jOzADLPFLuqwQwOGwE1WWcJFn6v5q/XEkyMzX0X8/DAZ5hI6gu4yTJcrn8pbzlaQ99sfjVxJgRnkcDXMPoLF6PcZLkeXFQ3uLNGgFt2pcKL/yJusCYMOsHdTlO0tV3V1kJfEdlPHvS62+yLU4b/MVrrxx4Fp+gZoWVoNkX06R5WoGHfn0/2kbAG+riFeRMJdtJEb6Dmhw2gmbNLkVKTRV/31kns5+mQzyly9pkSHOcxZsdNoK+5j8pfqL/E5SZoLkDplsW0A9qctgIWneGMNlAdjATlKbU3C/Nnx0l/pI050Ft0QC3sBE0eI84HR6ZxkxQerjyX+LsVufoJcu7N8dv8WaFjaBtpOco/RPxLDtB6axe0nMccj+ZNO5DXM1kWtgIupyM259O6Z7iI6cxEzSr00ytIQD/MOpmmh9KkoTZriqEmaA0ud5bmmMA3mHVD5pxQbqiI+uzWNev6yAovVAFT102PUa75aMA31Q4rkMUwDOGFpTurvSzHmEAvxhbULqyXrIucQCvGFxQ+kIzjPBlaowuKF0YdUGnSIBHDC8ofasmhv8wMcYXlMZVlblzFJgAEwhKt1TEYN2mxQyC0k8jZH4OAIbHFILSX5oOxyX15sQcgtK0J5rjZN6UmERQSpdV3K1zRMADphGUHo183N3j9IAxMY+g9N9pVT7QPSjwMiYSlNJjDfv9xiAs8CKmEpSmzy7/Mk7nTYW5BKX019EVF2awCQ28gdkEpfSbbnW3YCgQ02A+QSnd36beOx4c6Oc2rzUAty/zhhkFpfTkiHLjVJ4upVYu9eCo2sXj2BQEPMWcglL6y3PlhuxSM2JN3wjxq+vkEhjlhi/MKiilN1d3rDjhW8XNg1dKs5IYKIwvzCuoQNKcyHpTjyp77Ij/EWkW/grLgtxydM7499MLb+ZjmFpQSnP/b26LCk9suVp4y7IvidMs//2sS5Ih+/HaL74xsNYPXkrPLSYXVOTyW/3KNhn3SSG3fz4d9DOlOd3LFE1NzizvKn56rm2W660COMUHBBXI/vq1PqF1Hn3zWJp8m07F6rUJLv110RVVkPafrXm497Q/6nzvrQI4xTcEFcn5YV1M61INHp6/Lcn1t9KDI/ot8l4P/z2te2zaOz2i9T6vVeBtziydt835L2O4wWS1kXlm4/R+kYH1+k18O+EHrn61r95VnB7w99VP0NwxIeGh4bWdLjs33GCyepD+wyeLR/dqUKp8y/+MefHdXV9f5kDV6k3Fp5gvD/rG24V4iSUlY87+tqFslON24w0mqyP/fLPj7TlP9m1VPTCoRuueQ8fMXLxq86fHzl5KySz6WiIfqz55QbfGHb3Vi+BtKj8sTn8s7uiL8QaTZULqxROfblw5f9JTQ3pGN7wnLCAgrEatVtHd+w8ZMXrstOkLFy5bterDzZv3JiYePHny5KkLAn+kiOh3zt1j2+nXZm65W/mSbhGNRcBeaVZmqcP2oh1M9uvuNvxfVxOvyMlM+TXp5LHEnZs/WLVs4avTpo0bPfqRIUN6de/epVWrVs2jBKqEifhJA+qR0mFWKkcVoFmrQuiQ94Y0C7q3e/duNcp391GKNe7eXZAzyPGDq2gHk01NtFH7CzXxeOd2ipU/LxTg9MlC+DLvDUkcWza6W6V7tyX6KFE19iZeoKuKOz4LzluDyUbj0bOOXNsZ56un8AJflqr28lt9g/7ruN1bg8lCUFCQxBplKwRNc7obomgHk80HggIHci+ecvFDn7d+SYKgQBEQFHANBAVc4y1Bu+Z1HeYTTPyYwTA0SwxadjHnP66n+Csdx0VnQdNSnNnY7SIzqh9mFnrW/5iF/tmfWeiLw19iFvpAbRd/XQ+5qdQonQV1xc7+7GJHyl21op2lE5mFzvJnFprGrGQW+lx9ZqHlgaByQFBHIKh6IKgjEFQ1ENQRCKocCCoHBHUEgqoHgjoCQVUDQR2BoMqBoHJAUEfMKugvDIfjmpvKLPSRT5iFzp3MLDTd8hWz0DfnMwstTxEICoDnQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVzDXtD4NmW6KB94Sw3bpAdFjWIQOXGHNGNRuiU0i9JXtAupt1gcUU//sm2h2b3jsjAXdLffmC29g6+wCL0kIlbgkP6Bc9pKlxuxKN0amkHp88nzu6f7z2ZRdl5oZu+4PMwF7dKb0rvVX2AROqYbi6j0t5X3Ecki/UvPC61/6Rmh44TppFLZ+pedH5rRO+4O1oKmkDXC9JlIFrF7j2YRlSZ07BgoWsSgdFtoBqVfIOJj8OPJRf3LzgvN6h13B2tBz5JjwnSZn9MDInWgbq+Wwc1WMQhMa4sWsSldCs2g9PQkcUi7iaXS9C87LzTDd1wW1oIeIOeEaRy5pn/onBLhy7aPIkv0j2yxiE3pUmhWpW/wn8LqHRdDM3zHZWEtaCI5L0zXE3ejf3lIxiZxeKiRoQyGkZMsYlO6FJpN6VdHkMez2JRtCc3wHZeFtaBniHiPzPKSzBJsI0n6B5UsYlN67fz7kXQufU9E5HbKpmxraAtM3nFZWAt63W+9MB1bi0Hof06KwxntJI4DSOiAZBGb0qXQLErfUzxGetQ2g7JtoRm+47Iw72bqOojSrKhpDCInkg+F6dM1GIS2fMwxKd367UH30rOqjLAu6V52XmiG77gszAVNKP7i0WFhLG5fz24XMX/PuGJbGIS2CMqkdCk0g9I/I1PXiaTpX3ZeaIbvuCzsf+rc0rZMNzY/dd6dUL90h70sIlu/KLIo3RJa/9JjLYPjiYdfvcvOD83uHZcFF4sAroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGsgKOAaCAq4BoICroGggGt0FnT7QgAUsPhf7wgaPWIaAIVT4TsvCXpc33jApDSFoIBnICjgGggKuAaCAq6BoIBrICjgGggKuAaCAq4xvqCfL5i7J1evYIA3jC7o3QGNZs5r0ylZn2iAO4pW0NwUG610EnTSsCwh7MSh+kQD3FG0gh4Ks+H3ph7xKI24LE7vhCq95gUYDG8d4kPe1SVMZoBlHsliCEXAAQYXlJb/TZzeDU3VJxzgDaMLOv6xHGE6fYg+0QB3GF3Q1J4tXnntvjb/6BMNcIfRBaU0YcaU+By9ggHeML6gwNRAUMA1EBRwDQQFXANBAddAUMA1xhf0xOuLPtcrFuAOowuaPqzW81Ob9LqpTzTAHUYXdPpD6ZRmjxmpTzTAHUYXtJJ0GdPt0Lv6hAO8YXBBcbmd2TG4oLTcn+I0PfS2PuEAbxhd0JinxRvm5g3UJxrgDqMLerNThzdW9mzyhz7RAHeoFPQtOzTl1a0fNDd+fExclk7BAHeoFLS8HYXukpP0t+xr+CUJKILNIX7UIWGyOJSQGltkWkBQoAiPBU2e465xLKWxZOS2naOL7XXdwlnQ7NWDu0/6XWE1wFdQL2juh1MnCzxQzl1jQdAmo8Wl5+513cJJ0NT2vbckzow4oLAc4COoF/RFUi+gSnTl4PXuGguCBu0Ql3aH2m//nOSxyGGXOcOlBjVwvgPsUS9o5AT67gP0btvd7hoLgrZYLi691MB1C6dP0OYnpFmzbxTWA3wD9YKW3EVPVaV0ezt3jSO6je4ZfkH4OhAxwXULJ0FrWn6s7LFfYT0MSDqMj2/uUC9ordfpbb+r9IsQN423LYnpXbfEJnqStJO5isNJ0J5bxWlmpV8V1qM77wb7+fs9kOGt9MA16gUdFxZLm8YkDalf2B45GfSfRLk71p0E3VEnidKM5/6jsBzd2VhsCqVHwlp5Kz9wjXpBbw16iB4JIAGbNeV17mZ6J6LPsHseStEUVQPVpCc4XvT72VsFAJd42A+asl/jodhFR/31hA9/0BZUCwGWL79hS7xXAnCB0S8W0Y0S26VZaW2XGAC9US9oPxua8nInaMP7xOkhv2teq+Ds8pd2ZHstO6+oF/RxkYERxZ7TlJc7Qb8uft9Xf88K8NqzxHMnVR07u1OLS97KzyueHuLv9GyjKS93gtJv6xb3C33Za+nfa3dLmL4e7bUCOMXj76CHyFUtefkT1Mt02idOc2ue83YhnOGxoOtKaRqcCII6UPOSNOu5z7tlcId6QTdIzKvcWVNeCOpAu8PSrN4ZL9fBG+oFDZQIitZ2LIKgDrzRI1OYxjXBoHkFQT8oJ2QNixo4sNM9+AB1AIJyQu708BYtolpd9nYdvMHypjl3QFAH1q2tv3IAAApSSURBVLa+IUwXytyA4LuoFDQ2NvbNkKoTls6sV93dBcuFA0EduO9TcZp7z3lvF+I1vnt97lbnC3LVH+KfuT9dmGb3flpTNa4EvXLWh6/G9PVuptzx1Z6f3aXJBcft6gWtGi/NtlfRVI+zoAcbVG9UZm6mpqgGpu0X0qy+0r+H2Vjd8sEypZo+0dZxuweCWsYpXl6jkB0yrrm7ttNJ0BOVhWPcH31iFJZjOl7vLR7eNjX01W6m1uWW3Mw61Kjcjw7b1Qv6VJldwnR3GbeH+CsvRPoRUrL2dLnHIjoJOkjacCtM0w+oBiZzSOPFax6t/q236/AWIVPE6W8B2x22qxc0tTMp17gc6XrHTeNTQdXHLItbv/y5qLDTMvU4ClorSZp19d0b4/dN+t8Kd2+quSm1zn6Wjyf9oAcXTlj8hdvGnftY75XLeqS76xZOgja0dFFHH1FYDzAXFaLFc+91wZ85bGfTUR8ab1s6UtZ+++dhNvxKhYWFX6H0sTDrvGSgOD9fsqzDdsx9Y17Cv1jQS4PuCXD6+6sT9L1E+p4NN41b553rzCt4+/yNFCshy1JSxAsgM1Ks83PVnj3510c1Vqc4bMfcN+bHK0x8fuyLbV5w3N5YnaAhQ2lZG24ax/v1WXPs3Pmv1g8qHu+6hXM3U/K4upV64wDvs5zuElyhaqzTbeqMfovf3UV6AJNf1wSZBvglCTiS5qoHx0NBz++9Xkj7lLOJiWfkG0FQoAj1gl7pMZluLU7Ky/QfKQSCAkWoF3Rgle20ea+LPUx22zHgE/WClltN/ySJdGMFTXkhKFCEekHLbqLvB6bRXe6eblc4EBQoQr2gPTscaDKA3unbWlNeCAoUoV7Q0xVJ6Clat8QuTXkhKFCEB91Md09ep3SzxscUQlCgCI/6Qa98rzkvBAWK8EDQTysRQrst15YXggJFqBc0zn90HKGz/VZpygtBgSLUC9pwIk0WVqY01pQXggJFqBc0KEESNCFYU14IChShXtCWcyVBX2qqKS8EBYpQL+jagPnHyNU1JZZqygtBgSI8OItfHk4IKTld2/2xEBQowpN+0DsnPj6odawBCAoUoVbQ1H0fX6Y0948fDvXVlBeCAkWoFPTH6sLhfcx3NcTbOTTlhaBAESoF7R8ed+ajClXbb9p/QtszBiAoUIRKQSssEiaLiPYhiSEoUIRKQcnHwmSrx09bPhRlo9giBc0PP9l11DFPcwFToFZQ8Tb37R4LmnPxgpVgBZ+g02q989lbNed6mgyYgaIVNB8Fh/jjkTeF6fXq2m4fBcaGY0FnzJNm01/Sng0YFrWCBgQGBgYQaagkTXkVCDrmbWm2dKKmRMDYqBR0lh2a8ioQdOkoaTb8HU2JgLHheJykfyqK9+XFV3b3JHFgdjgWlB6rFz2yTaNv9E0MjAXPgtLMQx8ccR45B/gSXAsKAAQFXANBAddAUMA1fAt67etkfdMCo8FOUJVDIbrgUt/wNmEDr6hNDMwEI0HVD4XozJ2oxVk0Y369NFWZgblgI6gHQyE6s2qwNOsbpyYzMBlsBPVgKERnnl0hzZZMUpMZmIyiHQoxHwWCTloozeZpuyoFGBs2gsoOhZiHAkETWmQK0/SGh9RkBiaDjaCeDIXoRO6DXQ7/dbDjMDWJgdngeSjEnFXRlTq87zR6I/AlmPWDYihEoAd8/5IEfB4ICrimaAU92spG8df1iAdMDxtBd0zOx3575v+dtNLoSzXxgM/CRtDEtiSwthXXLaKPq4kHfBZGh/jsTh3dN4CgQBGsvoO+BUGBHrAS9De5HnorEBQowlvdTBAUKIKpoNOvyr4EQYEimApKzsu+BEGBIiAo4BoICriGqaAH/pV9CYICReAsHnCN1wSdvsqJl3sNZ0bXR5mFHtiPWejhXdiF7juIWehHBjr/cT2lqpcEfXe0M51DGjAjoDaz0BXLMQtd349Z6AZhlZiFjirl4q/rIeNueUdQV+zszy52pNwTJLTD8CH6Wf7MQtOYlcxCn6vPLLQ8EFQOCOoIBFUPBHUEgqoGgjoCQZUDQeWAoI5AUPVAUEcgqGogqCMQVDkQVA4I6ohZBd37ILvYddg9qHnFFGahs4OZhabjVjML/UtjZqHlKQJBs2+wi83wWfdpd9jFZlh2ajq72N4YWaAIBAXAcyAo4BoICrgGggKugaCAayAo4BoICrgGggKugaCAayAo4BoICrgGggKuYS9ofJsyXb5lEnmbNKrYKAaRE3dIMxalW0KzKH1Fu5B6i7Moi7Jtodm947IwF3S335gtvYOZXBW3JCJWgMGYnzltpTEiWJRuDc2g9Pnk+d3T/WezKDsvNLN3XB7mgnbpTend6i+wCB3TjUVU+tvK+4hkkf6l54XWv/SM0HHCdFKpbP3Lzg/N6B13B2tBU8gaYfpMJIvYvUeziEoTOnYMFC1iULotNIPSL5D9wjSeXNS/7LzQrN5xd7AW9Cw5JkyX+WUwiF23V8vgZqsYBKa1RYvYlC6FZlB6epJ4pfLEUmn6l50XmuE7LgtrQQ+Qc8I0jlzTP3ROifBl20eRJfpHtljEpnQpNKvSN/hPYfWOi6EZvuOysBY0UXra7XqSon/ojE0XhOnIUAYDe0sWsSldCs2m9KsjyONZbMq2hGb4jsvCWtAz5CthurwkswTbSJL+QSWL2JReO38QSZ1L3xMRuZ2yKdsa2gKTd1wW1oJe91svTMfWYhD6n5O5wnQn+Vv/0JJFbEqXQrMofU/xmDRxzqBsW2iG77gszLuZug6iNCtqGoPIieRDYfp0DQahLR9zTEq3fnvQvfSsKiOsS7qXnRea4TsuC3NBE4q/eHRYGIvnK2S3i5i/Z1yxLQxCWwRlUroUmkHpn5Gp60TS9C87LzTDd1wW9j91bmlbphubnzrvTqhfusNeFpGtXxRZlG4JrX/pscTC3/qXnR+a3TsuCy4WAVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFDANRAUcA0EBVwDQQHXQFC9GWx9UAxZQEPe83YxxgeC6s3x+Pj46vcKk3N0aKK3izE+EJQFjYd6uwLTAEFZYBW0rHCIr/ZG+6Cot//sV/Ye8dma61oFNXrfu7UZDAjKAntBA+Z81s+v2hsJbQNT6fKAOQkT/N72cnWGAoKywF7QIZSeJ+Mp3U2+vxM+X9j6VIR3izMWEJQF9oIuojSbbBQtPX2CHEtOTo4jTAaGNCkQlAX2gi4RBY2XBP3Y2gF1xsvlGQkIygIZQQ+Sq14uzHhAUBbICHq1pDiM5uyiH5HVwEBQFsgISqeVWJAwxW+Zl6szFBCUBXKC5i5pHNQw1svFGQsICrgGggKugaCAayAo4BoICrgGggKugaCAayAo4BoICrgGggKugaCAayAo4BoICrgGggKugaCAayAo4BoICrgGggKugaCAayAo4BoICrgGggKugaCAayAo4BoICrjm/wHRgtMjAeXcqQAAAABJRU5ErkJggg==" /><!-- --></p>
 <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error
 level of 14% suggests that the model does not fit very well. This is
 also obvious from the plots of the fit, in which we have included the
@@ -652,22 +659,22 @@ kinetics.</p>
 <h2>FOMC fit for L2</h2>
 <p>For comparison, the FOMC model is fitted as well, and the <span class="math inline"><em>χ</em><sup>2</sup></span> error level is
 checked.</p>
-<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>m.L2.FOMC <span class="ot">&lt;-</span> <span class="fu">mkinfit</span>(<span class="st">&quot;FOMC&quot;</span>, FOCUS_2006_L2_mkin, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(m.L2.FOMC, <span class="at">show_residuals =</span> <span class="cn">TRUE</span>,</span>
-<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a>     <span class="at">main =</span> <span class="st">&quot;FOCUS L2 - FOMC&quot;</span>)</span></code></pre></div>
-<p><img 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" /><!-- --></p>
-<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(m.L2.FOMC, <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
-<pre><code>## mkin version used for fitting:    1.2.4 
-## R version used for fitting:       4.3.0 
-## Date of fit:     Thu May 18 11:38:11 2023 
-## Date of summary: Thu May 18 11:38:11 2023 
+<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" tabindex="-1"></a>m.L2.FOMC <span class="ot">&lt;-</span> <span class="fu">mkinfit</span>(<span class="st">&quot;FOMC&quot;</span>, FOCUS_2006_L2_mkin, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb15-2"><a href="#cb15-2" tabindex="-1"></a><span class="fu">plot</span>(m.L2.FOMC, <span class="at">show_residuals =</span> <span class="cn">TRUE</span>,</span>
+<span id="cb15-3"><a href="#cb15-3" tabindex="-1"></a>     <span class="at">main =</span> <span class="st">&quot;FOCUS L2 - FOMC&quot;</span>)</span></code></pre></div>
+<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
+<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" tabindex="-1"></a><span class="fu">summary</span>(m.L2.FOMC, <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
+<pre><code>## mkin version used for fitting:    1.2.6 
+## R version used for fitting:       4.4.2 
+## Date of fit:     Thu Dec 19 10:28:48 2024 
+## Date of summary: Thu Dec 19 10:28:48 2024 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 239 model solutions performed in 0.015 s
+## Fitted using 239 model solutions performed in 0.029 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -702,10 +709,10 @@ checked.</p>
 ## 
 ## Parameter correlation:
 ##             parent_0  log_alpha   log_beta      sigma
-## parent_0   1.000e+00 -1.151e-01 -2.085e-01 -7.828e-09
-## log_alpha -1.151e-01  1.000e+00  9.741e-01 -1.602e-07
-## log_beta  -2.085e-01  9.741e-01  1.000e+00 -1.372e-07
-## sigma     -7.828e-09 -1.602e-07 -1.372e-07  1.000e+00
+## parent_0   1.000e+00 -1.151e-01 -2.085e-01 -7.436e-09
+## log_alpha -1.151e-01  1.000e+00  9.741e-01 -1.617e-07
+## log_beta  -2.085e-01  9.741e-01  1.000e+00 -1.386e-07
+## sigma     -7.436e-09 -1.617e-07 -1.386e-07  1.000e+00
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -733,15 +740,15 @@ order to explain the data.</p>
 <div id="dfop-fit-for-l2" class="section level2">
 <h2>DFOP fit for L2</h2>
 <p>Fitting the four parameter DFOP model further reduces the <span class="math inline"><em>χ</em><sup>2</sup></span> error level.</p>
-<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a>m.L2.DFOP <span class="ot">&lt;-</span> <span class="fu">mkinfit</span>(<span class="st">&quot;DFOP&quot;</span>, FOCUS_2006_L2_mkin, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(m.L2.DFOP, <span class="at">show_residuals =</span> <span class="cn">TRUE</span>, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>,</span>
-<span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a>     <span class="at">main =</span> <span class="st">&quot;FOCUS L2 - DFOP&quot;</span>)</span></code></pre></div>
-<p><img 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" /><!-- --></p>
-<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(m.L2.DFOP, <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
-<pre><code>## mkin version used for fitting:    1.2.4 
-## R version used for fitting:       4.3.0 
-## Date of fit:     Thu May 18 11:38:11 2023 
-## Date of summary: Thu May 18 11:38:11 2023 
+<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" tabindex="-1"></a>m.L2.DFOP <span class="ot">&lt;-</span> <span class="fu">mkinfit</span>(<span class="st">&quot;DFOP&quot;</span>, FOCUS_2006_L2_mkin, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb18-2"><a href="#cb18-2" tabindex="-1"></a><span class="fu">plot</span>(m.L2.DFOP, <span class="at">show_residuals =</span> <span class="cn">TRUE</span>, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>,</span>
+<span id="cb18-3"><a href="#cb18-3" tabindex="-1"></a>     <span class="at">main =</span> <span class="st">&quot;FOCUS L2 - DFOP&quot;</span>)</span></code></pre></div>
+<p><img role="img" 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" /><!-- --></p>
+<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" tabindex="-1"></a><span class="fu">summary</span>(m.L2.DFOP, <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
+<pre><code>## mkin version used for fitting:    1.2.6 
+## R version used for fitting:       4.4.2 
+## Date of fit:     Thu Dec 19 10:28:48 2024 
+## Date of summary: Thu Dec 19 10:28:48 2024 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -750,7 +757,7 @@ order to explain the data.</p>
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 581 model solutions performed in 0.041 s
+## Fitted using 581 model solutions performed in 0.08 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -781,18 +788,18 @@ order to explain the data.</p>
 ## Optimised, transformed parameters with symmetric confidence intervals:
 ##          Estimate Std. Error      Lower     Upper
 ## parent_0   93.950  9.998e-01    91.5900   96.3100
-## log_k1      3.112  1.842e+03 -4353.0000 4359.0000
+## log_k1      3.113  1.849e+03 -4369.0000 4375.0000
 ## log_k2     -1.088  6.285e-02    -1.2370   -0.9394
 ## g_qlogis   -0.399  9.946e-02    -0.6342   -0.1638
 ## sigma       1.414  2.886e-01     0.7314    2.0960
 ## 
 ## Parameter correlation:
 ##            parent_0     log_k1     log_k2   g_qlogis      sigma
-## parent_0  1.000e+00  6.783e-07 -3.390e-10  2.665e-01 -2.967e-10
-## log_k1    6.783e-07  1.000e+00  1.116e-04 -2.196e-04 -1.031e-05
-## log_k2   -3.390e-10  1.116e-04  1.000e+00 -7.903e-01  2.917e-09
-## g_qlogis  2.665e-01 -2.196e-04 -7.903e-01  1.000e+00 -4.408e-09
-## sigma    -2.967e-10 -1.031e-05  2.917e-09 -4.408e-09  1.000e+00
+## parent_0  1.000e+00  6.765e-07 -9.004e-10  2.665e-01 -1.109e-09
+## log_k1    6.765e-07  1.000e+00  1.112e-04 -2.187e-04 -1.027e-05
+## log_k2   -9.004e-10  1.112e-04  1.000e+00 -7.903e-01  9.553e-09
+## g_qlogis  2.665e-01 -2.187e-04 -7.903e-01  1.000e+00 -1.545e-08
+## sigma    -1.109e-09 -1.027e-05  9.553e-09 -1.545e-08  1.000e+00
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -800,7 +807,7 @@ order to explain the data.</p>
 ## for estimators of untransformed parameters.
 ##          Estimate   t value    Pr(&gt;t)   Lower   Upper
 ## parent_0  93.9500 9.397e+01 2.036e-12 91.5900 96.3100
-## k1        22.4800 5.553e-04 4.998e-01  0.0000     Inf
+## k1        22.4900 5.533e-04 4.998e-01  0.0000     Inf
 ## k2         0.3369 1.591e+01 4.697e-07  0.2904  0.3909
 ## g          0.4016 1.680e+01 3.238e-07  0.3466  0.4591
 ## sigma      1.4140 4.899e+00 8.776e-04  0.7314  2.0960
@@ -812,7 +819,7 @@ order to explain the data.</p>
 ## 
 ## Estimated disappearance times:
 ##          DT50  DT90 DT50back DT50_k1 DT50_k2
-## parent 0.5335 5.311    1.599 0.03084   2.058</code></pre>
+## parent 0.5335 5.311    1.599 0.03083   2.058</code></pre>
 <p>Here, the DFOP model is clearly the best-fit model for dataset L2
 based on the chi^2 error level criterion.</p>
 </div>
@@ -821,21 +828,21 @@ based on the chi^2 error level criterion.</p>
 <h1>Laboratory Data L3</h1>
 <p>The following code defines example dataset L3 from the FOCUS kinetics
 report, p. 290.</p>
-<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L3 <span class="ot">=</span> <span class="fu">data.frame</span>(</span>
-<span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a>  <span class="at">t =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">30</span>, <span class="dv">60</span>, <span class="dv">91</span>, <span class="dv">120</span>),</span>
-<span id="cb22-3"><a href="#cb22-3" aria-hidden="true" tabindex="-1"></a>  <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">97.8</span>, <span class="dv">60</span>, <span class="dv">51</span>, <span class="dv">43</span>, <span class="dv">35</span>, <span class="dv">22</span>, <span class="dv">15</span>, <span class="dv">12</span>))</span>
-<span id="cb22-4"><a href="#cb22-4" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L3_mkin <span class="ot">&lt;-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L3)</span></code></pre></div>
+<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" tabindex="-1"></a>FOCUS_2006_L3 <span class="ot">=</span> <span class="fu">data.frame</span>(</span>
+<span id="cb21-2"><a href="#cb21-2" tabindex="-1"></a>  <span class="at">t =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">30</span>, <span class="dv">60</span>, <span class="dv">91</span>, <span class="dv">120</span>),</span>
+<span id="cb21-3"><a href="#cb21-3" tabindex="-1"></a>  <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">97.8</span>, <span class="dv">60</span>, <span class="dv">51</span>, <span class="dv">43</span>, <span class="dv">35</span>, <span class="dv">22</span>, <span class="dv">15</span>, <span class="dv">12</span>))</span>
+<span id="cb21-4"><a href="#cb21-4" tabindex="-1"></a>FOCUS_2006_L3_mkin <span class="ot">&lt;-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L3)</span></code></pre></div>
 <div id="fit-multiple-models" class="section level2">
 <h2>Fit multiple models</h2>
 <p>As of mkin version 0.9-39 (June 2015), we can fit several models to
 one or more datasets in one call to the function <code>mmkin</code>. The
 datasets have to be passed in a list, in this case a named list holding
 only the L3 dataset prepared above.</p>
-<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Only use one core here, not to offend the CRAN checks</span></span>
-<span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a>mm.L3 <span class="ot">&lt;-</span> <span class="fu">mmkin</span>(<span class="fu">c</span>(<span class="st">&quot;SFO&quot;</span>, <span class="st">&quot;FOMC&quot;</span>, <span class="st">&quot;DFOP&quot;</span>), <span class="at">cores =</span> <span class="dv">1</span>,</span>
-<span id="cb23-3"><a href="#cb23-3" aria-hidden="true" tabindex="-1"></a>               <span class="fu">list</span>(<span class="st">&quot;FOCUS L3&quot;</span> <span class="ot">=</span> FOCUS_2006_L3_mkin), <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb23-4"><a href="#cb23-4" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(mm.L3)</span></code></pre></div>
-<p><img 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" /><!-- --></p>
+<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" tabindex="-1"></a><span class="co"># Only use one core here, not to offend the CRAN checks</span></span>
+<span id="cb22-2"><a href="#cb22-2" tabindex="-1"></a>mm.L3 <span class="ot">&lt;-</span> <span class="fu">mmkin</span>(<span class="fu">c</span>(<span class="st">&quot;SFO&quot;</span>, <span class="st">&quot;FOMC&quot;</span>, <span class="st">&quot;DFOP&quot;</span>), <span class="at">cores =</span> <span class="dv">1</span>,</span>
+<span id="cb22-3"><a href="#cb22-3" tabindex="-1"></a>               <span class="fu">list</span>(<span class="st">&quot;FOCUS L3&quot;</span> <span class="ot">=</span> FOCUS_2006_L3_mkin), <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb22-4"><a href="#cb22-4" tabindex="-1"></a><span class="fu">plot</span>(mm.L3)</span></code></pre></div>
+<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
 <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error
 level of 21% as well as the plot suggest that the SFO model does not fit
 very well. The FOMC model performs better, with an error level at which
@@ -850,11 +857,11 @@ as a row index and datasets as a column index.</p>
 <p>We can extract the summary and plot for <em>e.g.</em> the DFOP fit,
 using square brackets for indexing which will result in the use of the
 summary and plot functions working on mkinfit objects.</p>
-<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(mm.L3[[<span class="st">&quot;DFOP&quot;</span>, <span class="dv">1</span>]])</span></code></pre></div>
-<pre><code>## mkin version used for fitting:    1.2.4 
-## R version used for fitting:       4.3.0 
-## Date of fit:     Thu May 18 11:38:12 2023 
-## Date of summary: Thu May 18 11:38:12 2023 
+<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" tabindex="-1"></a><span class="fu">summary</span>(mm.L3[[<span class="st">&quot;DFOP&quot;</span>, <span class="dv">1</span>]])</span></code></pre></div>
+<pre><code>## mkin version used for fitting:    1.2.6 
+## R version used for fitting:       4.4.2 
+## Date of fit:     Thu Dec 19 10:28:48 2024 
+## Date of summary: Thu Dec 19 10:28:48 2024 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -863,7 +870,7 @@ summary and plot functions working on mkinfit objects.</p>
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 376 model solutions performed in 0.024 s
+## Fitted using 376 model solutions performed in 0.047 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -901,11 +908,11 @@ summary and plot functions working on mkinfit objects.</p>
 ## 
 ## Parameter correlation:
 ##            parent_0     log_k1     log_k2   g_qlogis      sigma
-## parent_0  1.000e+00  1.732e-01  2.282e-02  4.009e-01 -9.664e-08
-## log_k1    1.732e-01  1.000e+00  4.945e-01 -5.809e-01  7.147e-07
+## parent_0  1.000e+00  1.732e-01  2.282e-02  4.009e-01 -9.671e-08
+## log_k1    1.732e-01  1.000e+00  4.945e-01 -5.809e-01  7.148e-07
 ## log_k2    2.282e-02  4.945e-01  1.000e+00 -6.812e-01  1.022e-06
-## g_qlogis  4.009e-01 -5.809e-01 -6.812e-01  1.000e+00 -7.926e-07
-## sigma    -9.664e-08  7.147e-07  1.022e-06 -7.926e-07  1.000e+00
+## g_qlogis  4.009e-01 -5.809e-01 -6.812e-01  1.000e+00 -7.929e-07
+## sigma    -9.671e-08  7.148e-07  1.022e-06 -7.929e-07  1.000e+00
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -937,8 +944,8 @@ summary and plot functions working on mkinfit objects.</p>
 ##    60   parent     22.0     23.26 -1.25919
 ##    91   parent     15.0     15.18 -0.18181
 ##   120   parent     12.0     10.19  1.81395</code></pre>
-<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(mm.L3[[<span class="st">&quot;DFOP&quot;</span>, <span class="dv">1</span>]], <span class="at">show_errmin =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
+<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" tabindex="-1"></a><span class="fu">plot</span>(mm.L3[[<span class="st">&quot;DFOP&quot;</span>, <span class="dv">1</span>]], <span class="at">show_errmin =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
+<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
 <p>Here, a look to the model plot, the confidence intervals of the
 parameters and the correlation matrix suggest that the parameter
 estimates are reliable, and the DFOP model can be used as the best-fit
@@ -955,35 +962,35 @@ parameter <code>g</code> is quite narrow.</p>
 <h1>Laboratory Data L4</h1>
 <p>The following code defines example dataset L4 from the FOCUS kinetics
 report, p. 293:</p>
-<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L4 <span class="ot">=</span> <span class="fu">data.frame</span>(</span>
-<span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>  <span class="at">t =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">30</span>, <span class="dv">60</span>, <span class="dv">91</span>, <span class="dv">120</span>),</span>
-<span id="cb27-3"><a href="#cb27-3" aria-hidden="true" tabindex="-1"></a>  <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">96.6</span>, <span class="fl">96.3</span>, <span class="fl">94.3</span>, <span class="fl">88.8</span>, <span class="fl">74.9</span>, <span class="fl">59.9</span>, <span class="fl">53.5</span>, <span class="fl">49.0</span>))</span>
-<span id="cb27-4"><a href="#cb27-4" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L4_mkin <span class="ot">&lt;-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L4)</span></code></pre></div>
+<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" tabindex="-1"></a>FOCUS_2006_L4 <span class="ot">=</span> <span class="fu">data.frame</span>(</span>
+<span id="cb26-2"><a href="#cb26-2" tabindex="-1"></a>  <span class="at">t =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">30</span>, <span class="dv">60</span>, <span class="dv">91</span>, <span class="dv">120</span>),</span>
+<span id="cb26-3"><a href="#cb26-3" tabindex="-1"></a>  <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">96.6</span>, <span class="fl">96.3</span>, <span class="fl">94.3</span>, <span class="fl">88.8</span>, <span class="fl">74.9</span>, <span class="fl">59.9</span>, <span class="fl">53.5</span>, <span class="fl">49.0</span>))</span>
+<span id="cb26-4"><a href="#cb26-4" tabindex="-1"></a>FOCUS_2006_L4_mkin <span class="ot">&lt;-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L4)</span></code></pre></div>
 <p>Fits of the SFO and FOMC models, plots and summaries are produced
 below:</p>
-<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Only use one core here, not to offend the CRAN checks</span></span>
-<span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a>mm.L4 <span class="ot">&lt;-</span> <span class="fu">mmkin</span>(<span class="fu">c</span>(<span class="st">&quot;SFO&quot;</span>, <span class="st">&quot;FOMC&quot;</span>), <span class="at">cores =</span> <span class="dv">1</span>,</span>
-<span id="cb28-3"><a href="#cb28-3" aria-hidden="true" tabindex="-1"></a>               <span class="fu">list</span>(<span class="st">&quot;FOCUS L4&quot;</span> <span class="ot">=</span> FOCUS_2006_L4_mkin),</span>
-<span id="cb28-4"><a href="#cb28-4" aria-hidden="true" tabindex="-1"></a>               <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span>
-<span id="cb28-5"><a href="#cb28-5" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(mm.L4)</span></code></pre></div>
-<p><img src="data:image/png;base64,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" /><!-- --></p>
+<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" tabindex="-1"></a><span class="co"># Only use one core here, not to offend the CRAN checks</span></span>
+<span id="cb27-2"><a href="#cb27-2" tabindex="-1"></a>mm.L4 <span class="ot">&lt;-</span> <span class="fu">mmkin</span>(<span class="fu">c</span>(<span class="st">&quot;SFO&quot;</span>, <span class="st">&quot;FOMC&quot;</span>), <span class="at">cores =</span> <span class="dv">1</span>,</span>
+<span id="cb27-3"><a href="#cb27-3" tabindex="-1"></a>               <span class="fu">list</span>(<span class="st">&quot;FOCUS L4&quot;</span> <span class="ot">=</span> FOCUS_2006_L4_mkin),</span>
+<span id="cb27-4"><a href="#cb27-4" tabindex="-1"></a>               <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span>
+<span id="cb27-5"><a href="#cb27-5" tabindex="-1"></a><span class="fu">plot</span>(mm.L4)</span></code></pre></div>
+<p><img role="img" src="data:image/png;base64,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" /><!-- --></p>
 <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error
 level of 3.3% as well as the plot suggest that the SFO model fits very
 well. The error level at which the <span class="math inline"><em>χ</em><sup>2</sup></span> test passes is
 slightly lower for the FOMC model. However, the difference appears
 negligible.</p>
-<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(mm.L4[[<span class="st">&quot;SFO&quot;</span>, <span class="dv">1</span>]], <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
-<pre><code>## mkin version used for fitting:    1.2.4 
-## R version used for fitting:       4.3.0 
-## Date of fit:     Thu May 18 11:38:12 2023 
-## Date of summary: Thu May 18 11:38:12 2023 
+<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" tabindex="-1"></a><span class="fu">summary</span>(mm.L4[[<span class="st">&quot;SFO&quot;</span>, <span class="dv">1</span>]], <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
+<pre><code>## mkin version used for fitting:    1.2.6 
+## R version used for fitting:       4.4.2 
+## Date of fit:     Thu Dec 19 10:28:49 2024 
+## Date of summary: Thu Dec 19 10:28:49 2024 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 142 model solutions performed in 0.01 s
+## Fitted using 142 model solutions performed in 0.018 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -1036,18 +1043,18 @@ negligible.</p>
 ## Estimated disappearance times:
 ##        DT50 DT90
 ## parent  106  352</code></pre>
-<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(mm.L4[[<span class="st">&quot;FOMC&quot;</span>, <span class="dv">1</span>]], <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
-<pre><code>## mkin version used for fitting:    1.2.4 
-## R version used for fitting:       4.3.0 
-## Date of fit:     Thu May 18 11:38:12 2023 
-## Date of summary: Thu May 18 11:38:12 2023 
+<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" tabindex="-1"></a><span class="fu">summary</span>(mm.L4[[<span class="st">&quot;FOMC&quot;</span>, <span class="dv">1</span>]], <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
+<pre><code>## mkin version used for fitting:    1.2.6 
+## R version used for fitting:       4.4.2 
+## Date of fit:     Thu Dec 19 10:28:49 2024 
+## Date of summary: Thu Dec 19 10:28:49 2024 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 224 model solutions performed in 0.013 s
+## Fitted using 224 model solutions performed in 0.027 s
 ## 
 ## Error model: Constant variance 
 ## 
@@ -1082,10 +1089,10 @@ negligible.</p>
 ## 
 ## Parameter correlation:
 ##             parent_0  log_alpha   log_beta      sigma
-## parent_0   1.000e+00 -4.696e-01 -5.543e-01 -2.468e-07
-## log_alpha -4.696e-01  1.000e+00  9.889e-01  2.478e-08
-## log_beta  -5.543e-01  9.889e-01  1.000e+00  5.211e-08
-## sigma     -2.468e-07  2.478e-08  5.211e-08  1.000e+00
+## parent_0   1.000e+00 -4.696e-01 -5.543e-01 -2.456e-07
+## log_alpha -4.696e-01  1.000e+00  9.889e-01  2.169e-08
+## log_beta  -5.543e-01  9.889e-01  1.000e+00  4.910e-08
+## sigma     -2.456e-07  2.169e-08  4.910e-08  1.000e+00
 ## 
 ## Backtransformed parameters:
 ## Confidence intervals for internally transformed parameters are asymmetric.
@@ -1108,7 +1115,7 @@ negligible.</p>
 </div>
 <div id="references" class="section level1 unnumbered">
 <h1 class="unnumbered">References</h1>
-<div id="refs" class="references csl-bib-body hanging-indent">
+<div id="refs" class="references csl-bib-body hanging-indent" entry-spacing="0">
 <div id="ref-ranke2014" class="csl-entry">
 Ranke, Johannes. 2014. <span>“<span class="nocase">Prüfung und
 Validierung von Modellierungssoftware als Alternative zu ModelMaker
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