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-rw-r--r--docs/articles/web_only/mesotrione_parent_2023.html707
1 files changed, 357 insertions, 350 deletions
diff --git a/docs/articles/web_only/mesotrione_parent_2023.html b/docs/articles/web_only/mesotrione_parent_2023.html
index 13b62fe3..c9409d05 100644
--- a/docs/articles/web_only/mesotrione_parent_2023.html
+++ b/docs/articles/web_only/mesotrione_parent_2023.html
@@ -1,5 +1,5 @@
<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en-GB">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<meta charset="utf-8">
@@ -20,7 +20,7 @@
<a class="navbar-brand me-2" href="../../index.html">mkin</a>
- <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.10</small>
+ <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.11</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -30,7 +30,7 @@
<div id="navbar" class="collapse navbar-collapse ms-3">
<ul class="navbar-nav me-auto">
<li class="nav-item"><a class="nav-link" href="../../reference/index.html">Reference</a></li>
-<li class="nav-item dropdown">
+<li class="active nav-item dropdown">
<button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
<ul class="dropdown-menu" aria-labelledby="dropdown-articles">
<li><a class="dropdown-item" href="../../articles/mkin.html">Introduction to mkin</a></li>
@@ -43,7 +43,7 @@
<li><h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6></li>
<li><a class="dropdown-item" href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
<li><a class="dropdown-item" href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></li>
- <li><a class="dropdown-item" href="../../articles/web_only/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
+ <li><a class="dropdown-item" href="../../articles/web_only/mesotrione_parent_2023.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a></li>
<li><a class="dropdown-item" href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></li>
<li><a class="dropdown-item" href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a></li>
<li><a class="dropdown-item" href="../../articles/web_only/multistart.html">Short demo of the multistart method</a></li>
@@ -83,8 +83,8 @@
<h4 data-toc-skip class="author">Johannes
Ranke</h4>
- <h4 data-toc-skip class="date">Last change 13 May 2025
-(rebuilt 2025-05-14)</h4>
+ <h4 data-toc-skip class="date">Last change 12 September 2025
+(rebuilt 2025-09-12)</h4>
<small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/mesotrione_parent_2023.rmd" class="external-link"><code>vignettes/web_only/mesotrione_parent_2023.rmd</code></a></small>
<div class="d-none name"><code>mesotrione_parent_2023.rmd</code></div>
@@ -103,7 +103,7 @@ parameters. Because in some other case studies, the SFORB
parameterisation of biexponential decline has shown some advantages over
the DFOP parameterisation, SFORB was included in the list of tested
models as well.</p>
-<p>The mkin package is used in version 1.2.10, which is contains the
+<p>The mkin package is used in version 1.2.11, which is contains the
functions that were used for the evaluations. The <code>saemix</code>
package is used as a backend for fitting the NLHM, but is also loaded to
make the convergence plot function available.</p>
@@ -1393,8 +1393,9 @@ with the saemix package), is ill-defined in all fits.</p>
</tbody>
</table>
<p>For obtaining fits with only well-defined random effects, we update
-the set of fits, excluding random effects that were ill-defined
-according to the <code>illparms</code> function.</p>
+the set of fits, excluding random effects for degradation parameters
+that were ill-defined according to the <code>illparms</code>
+function.</p>
<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">f_saem_2</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_1</span>, no_random_effect <span class="op">=</span> <span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">)</span><span class="op">)</span></span>
<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
@@ -1432,7 +1433,8 @@ according to the <code>illparms</code> function.</p>
</tr>
</tbody>
</table>
-<p>The updated fits terminate without errors.</p>
+<p>The updated fits terminate without errors, and the only
+ill-defined</p>
<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
<table class="table">
@@ -1487,7 +1489,7 @@ significant pH effect could be found.</p>
</h3>
<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">sfo_pH</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_sep_const</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span>, no_random_effect <span class="op">=</span> <span class="st">"meso_0"</span>, covariates <span class="op">=</span> <span class="va">pH</span>,</span>
-<span> covariate_models <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">log_k_meso</span> <span class="op">~</span> <span class="va">pH</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<span> covariate_models <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">log_k_meso</span> <span class="op">~</span> <span class="va">pH</span><span class="op">)</span>, center_covariates <span class="op">=</span> <span class="st">"median"</span><span class="op">)</span></span></code></pre></div>
<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">sfo_pH</span><span class="op">)</span><span class="op">$</span><span class="va">confint_trans</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></code></pre></div>
<table class="table">
@@ -1506,9 +1508,9 @@ significant pH effect could be found.</p>
</tr>
<tr class="even">
<td align="left">log_k_meso</td>
-<td align="right">-6.66</td>
-<td align="right">-7.97</td>
-<td align="right">-5.35</td>
+<td align="right">-3.29</td>
+<td align="right">-3.46</td>
+<td align="right">-3.11</td>
</tr>
<tr class="odd">
<td align="left">beta_pH(log_k_meso)</td>
@@ -1585,7 +1587,7 @@ meso 8.89035 29.5331</code></pre>
</h3>
<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">fomc_pH</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_sep_const</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span>, no_random_effect <span class="op">=</span> <span class="st">"meso_0"</span>, covariates <span class="op">=</span> <span class="va">pH</span>,</span>
-<span> covariate_models <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">log_alpha</span> <span class="op">~</span> <span class="va">pH</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<span> covariate_models <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">log_alpha</span> <span class="op">~</span> <span class="va">pH</span><span class="op">)</span>, center_covariates <span class="op">=</span> <span class="st">"median"</span><span class="op">)</span></span></code></pre></div>
<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fomc_pH</span><span class="op">)</span><span class="op">$</span><span class="va">confint_trans</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></code></pre></div>
<table class="table">
@@ -1604,9 +1606,9 @@ meso 8.89035 29.5331</code></pre>
</tr>
<tr class="even">
<td align="left">log_alpha</td>
-<td align="right">-2.21</td>
-<td align="right">-3.49</td>
-<td align="right">-0.92</td>
+<td align="right">1.11</td>
+<td align="right">0.48</td>
+<td align="right">1.75</td>
</tr>
<tr class="odd">
<td align="left">beta_pH(log_alpha)</td>
@@ -1661,7 +1663,7 @@ ill-defined parameters remain.</p>
npar AIC BIC Lik Chisq Df Pr(&gt;Chisq)
f_saem_2[["FOMC", "const"]] 5 783.25 787.71 -386.63
-fomc_pH_2 6 767.49 772.83 -377.75 17.762 1 2.503e-05 ***
+fomc_pH_2 6 766.50 771.84 -377.25 18.753 1 1.488e-05 ***
fomc_pH 7 770.07 776.30 -378.04 0.000 1 1
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
@@ -1680,39 +1682,39 @@ results in the most preferable FOMC fit.</p>
<tbody>
<tr class="odd">
<td align="left">meso_0</td>
-<td align="right">93.05</td>
-<td align="right">90.98</td>
-<td align="right">95.13</td>
+<td align="right">93.20</td>
+<td align="right">91.10</td>
+<td align="right">95.29</td>
</tr>
<tr class="even">
<td align="left">log_alpha</td>
-<td align="right">-2.91</td>
-<td align="right">-4.18</td>
-<td align="right">-1.63</td>
+<td align="right">0.81</td>
+<td align="right">0.33</td>
+<td align="right">1.30</td>
</tr>
<tr class="odd">
<td align="left">beta_pH(log_alpha)</td>
-<td align="right">0.66</td>
-<td align="right">0.44</td>
-<td align="right">0.87</td>
+<td align="right">0.55</td>
+<td align="right">0.35</td>
+<td align="right">0.75</td>
</tr>
<tr class="even">
<td align="left">log_beta</td>
-<td align="right">3.95</td>
-<td align="right">3.29</td>
-<td align="right">4.62</td>
+<td align="right">3.85</td>
+<td align="right">3.22</td>
+<td align="right">4.47</td>
</tr>
<tr class="odd">
<td align="left">a.1</td>
-<td align="right">4.98</td>
-<td align="right">4.28</td>
-<td align="right">5.68</td>
+<td align="right">5.00</td>
+<td align="right">4.30</td>
+<td align="right">5.70</td>
</tr>
<tr class="even">
<td align="left">SD.log_beta</td>
-<td align="right">0.40</td>
-<td align="right">0.26</td>
-<td align="right">0.54</td>
+<td align="right">0.38</td>
+<td align="right">0.25</td>
+<td align="right">0.52</td>
</tr>
</tbody>
</table>
@@ -1727,7 +1729,7 @@ results in the most preferable FOMC fit.</p>
$distimes
DT50 DT90 DT50back
-meso 17.30248 82.91343 24.95943</code></pre>
+meso 16.86448 83.26704 25.06588</code></pre>
<div class="sourceCode" id="cb38"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fomc_pH_2</span>, covariates <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>pH <span class="op">=</span> <span class="fl">7</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
<pre><code>$covariates
@@ -1736,7 +1738,7 @@ User 7
$distimes
DT50 DT90 DT50back
-meso 6.986239 27.02927 8.136621</code></pre>
+meso 7.835768 31.46451 9.47176</code></pre>
</div>
<div class="section level3">
<h3 id="dfop">DFOP<a class="anchor" aria-label="anchor" href="#dfop"></a>
@@ -1805,7 +1807,9 @@ identifiable parameters <code>k2</code> and <code>g</code>.</p>
<div class="sourceCode" id="cb41"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">dfop_pH</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_sep_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, no_random_effect <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"meso_0"</span>, <span class="st">"log_k1"</span><span class="op">)</span>,</span>
<span> covariates <span class="op">=</span> <span class="va">pH</span>,</span>
-<span> covariate_models <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">log_k2</span> <span class="op">~</span> <span class="va">pH</span>, <span class="va">g_qlogis</span> <span class="op">~</span> <span class="va">pH</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<span> covariate_models <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">log_k2</span> <span class="op">~</span> <span class="va">pH</span>, <span class="va">g_qlogis</span> <span class="op">~</span> <span class="va">pH</span><span class="op">)</span>,</span>
+<span> center_covariates <span class="op">=</span> <span class="st">"median"</span></span>
+<span><span class="op">)</span></span></code></pre></div>
<p>The corresponding parameters for the influence of soil pH are
<code>beta_pH(log_k2)</code> for the influence of soil pH on
<code>k2</code>, and <code>beta_pH(g_qlogis)</code> for its influence on
@@ -1834,9 +1838,9 @@ identifiable parameters <code>k2</code> and <code>g</code>.</p>
</tr>
<tr class="odd">
<td align="left">log_k2</td>
-<td align="right">-11.48</td>
-<td align="right">-15.32</td>
-<td align="right">-7.64</td>
+<td align="right">-3.96</td>
+<td align="right">-4.47</td>
+<td align="right">-3.44</td>
</tr>
<tr class="even">
<td align="left">beta_pH(log_k2)</td>
@@ -1846,9 +1850,9 @@ identifiable parameters <code>k2</code> and <code>g</code>.</p>
</tr>
<tr class="odd">
<td align="left">g_qlogis</td>
-<td align="right">3.13</td>
-<td align="right">0.47</td>
-<td align="right">5.80</td>
+<td align="right">-0.12</td>
+<td align="right">-0.57</td>
+<td align="right">0.33</td>
</tr>
<tr class="even">
<td align="left">beta_pH(g_qlogis)</td>
@@ -1894,7 +1898,8 @@ corresponding covariate model.</p>
<div class="sourceCode" id="cb47"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">dfop_pH_3</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_sep_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, no_random_effect <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"meso_0"</span>, <span class="st">"log_k1"</span><span class="op">)</span>,</span>
<span> covariates <span class="op">=</span> <span class="va">pH</span>,</span>
-<span> covariate_models <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">log_k2</span> <span class="op">~</span> <span class="va">pH</span><span class="op">)</span><span class="op">)</span></span>
+<span> covariate_models <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">log_k2</span> <span class="op">~</span> <span class="va">pH</span><span class="op">)</span>,</span>
+<span> center_covariates <span class="op">=</span> <span class="st">"median"</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">dfop_pH_3</span><span class="op">)</span></span></code></pre></div>
<pre><code>[1] "sd(g_qlogis)"</code></pre>
<p>As the random effect for <code>g</code> is again ill-defined, the fit
@@ -1913,7 +1918,7 @@ on the likelihood ratio test.</p>
npar AIC BIC Lik
f_saem_2[["DFOP", "const"]] 7 782.94 789.18 -384.47
dfop_pH_4 7 767.35 773.58 -376.68
-dfop_pH_2 8 765.14 772.26 -374.57
+dfop_pH_2 8 765.06 772.18 -374.53
dfop_pH_3 8 769.00 776.12 -376.50
dfop_pH 9 769.10 777.11 -375.55</code></pre>
<div class="sourceCode" id="cb52"><pre class="downlit sourceCode r">
@@ -1922,7 +1927,7 @@ dfop_pH 9 769.10 777.11 -375.55</code></pre>
npar AIC BIC Lik Chisq Df Pr(&gt;Chisq)
dfop_pH_4 7 767.35 773.58 -376.68
-dfop_pH_2 8 765.14 772.26 -374.57 4.2153 1 0.04006 *
+dfop_pH_2 8 765.06 772.18 -374.53 4.2909 1 0.03832 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
<p>When focussing on parameter identifiability using the test if the
@@ -1944,7 +1949,7 @@ most preferable model.</p>
$distimes
DT50 DT90 DT50back DT50_k1 DT50_k2
-meso 18.36876 73.51841 22.13125 4.191901 23.98672</code></pre>
+meso 18.46687 74.91602 22.55197 4.568623 24.64483</code></pre>
<div class="sourceCode" id="cb57"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">dfop_pH_2</span>, covariates <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>pH <span class="op">=</span> <span class="fl">7</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
<pre><code>$covariates
@@ -1953,7 +1958,7 @@ User 7
$distimes
DT50 DT90 DT50back DT50_k1 DT50_k2
-meso 8.346428 28.34437 8.532507 4.191901 8.753618</code></pre>
+meso 8.370528 28.37824 8.542701 4.568623 8.785409</code></pre>
</div>
<div class="section level3">
<h3 id="sforb">SFORB<a class="anchor" aria-label="anchor" href="#sforb"></a>
@@ -1961,7 +1966,8 @@ meso 8.346428 28.34437 8.532507 4.191901 8.753618</code></pre>
<div class="sourceCode" id="cb59"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">sforb_pH</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_sep_const</span><span class="op">[</span><span class="st">"SFORB"</span>, <span class="op">]</span>, no_random_effect <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"meso_free_0"</span>, <span class="st">"log_k_meso_free_bound"</span><span class="op">)</span>,</span>
<span> covariates <span class="op">=</span> <span class="va">pH</span>,</span>
-<span> covariate_models <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">log_k_meso_free</span> <span class="op">~</span> <span class="va">pH</span>, <span class="va">log_k_meso_bound_free</span> <span class="op">~</span> <span class="va">pH</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<span> covariate_models <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">log_k_meso_free</span> <span class="op">~</span> <span class="va">pH</span>, <span class="va">log_k_meso_bound_free</span> <span class="op">~</span> <span class="va">pH</span><span class="op">)</span>,</span>
+<span> center_covariates <span class="op">=</span> <span class="st">"median"</span><span class="op">)</span></span></code></pre></div>
<div class="sourceCode" id="cb60"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">sforb_pH</span><span class="op">)</span><span class="op">$</span><span class="va">confint_trans</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></code></pre></div>
<table class="table">
@@ -1980,9 +1986,9 @@ meso 8.346428 28.34437 8.532507 4.191901 8.753618</code></pre>
</tr>
<tr class="even">
<td align="left">log_k_meso_free</td>
-<td align="right">-5.37</td>
-<td align="right">-6.94</td>
-<td align="right">-3.81</td>
+<td align="right">-2.94</td>
+<td align="right">-3.23</td>
+<td align="right">-2.65</td>
</tr>
<tr class="odd">
<td align="left">beta_pH(log_k_meso_free)</td>
@@ -1998,9 +2004,9 @@ meso 8.346428 28.34437 8.532507 4.191901 8.753618</code></pre>
</tr>
<tr class="odd">
<td align="left">log_k_meso_bound_free</td>
-<td align="right">-9.98</td>
-<td align="right">-19.22</td>
-<td align="right">-0.74</td>
+<td align="right">-2.91</td>
+<td align="right">-4.30</td>
+<td align="right">-1.52</td>
</tr>
<tr class="even">
<td align="left">beta_pH(log_k_meso_bound_free)</td>
@@ -2075,14 +2081,14 @@ not fully identifiable, the second model is selected.</p>
<tr class="odd">
<td align="left">meso_free_0</td>
<td align="right">93.32</td>
-<td align="right">91.16</td>
-<td align="right">95.48</td>
+<td align="right">91.17</td>
+<td align="right">95.47</td>
</tr>
<tr class="even">
<td align="left">log_k_meso_free</td>
-<td align="right">-6.15</td>
-<td align="right">-7.43</td>
-<td align="right">-4.86</td>
+<td align="right">-3.02</td>
+<td align="right">-3.27</td>
+<td align="right">-2.78</td>
</tr>
<tr class="odd">
<td align="left">beta_pH(log_k_meso_free)</td>
@@ -2093,19 +2099,19 @@ not fully identifiable, the second model is selected.</p>
<tr class="even">
<td align="left">log_k_meso_free_bound</td>
<td align="right">-3.80</td>
-<td align="right">-5.20</td>
-<td align="right">-2.40</td>
+<td align="right">-5.18</td>
+<td align="right">-2.42</td>
</tr>
<tr class="odd">
<td align="left">log_k_meso_bound_free</td>
<td align="right">-2.95</td>
-<td align="right">-4.26</td>
-<td align="right">-1.64</td>
+<td align="right">-4.24</td>
+<td align="right">-1.65</td>
</tr>
<tr class="even">
<td align="left">a.1</td>
<td align="right">5.08</td>
-<td align="right">4.38</td>
+<td align="right">4.37</td>
<td align="right">5.79</td>
</tr>
<tr class="odd">
@@ -2160,7 +2166,8 @@ meso 7.932495 36.93311 11.11797 5.205671 18.26</code></pre>
<div class="sourceCode" id="cb72"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">hs_pH</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_sep_const</span><span class="op">[</span><span class="st">"HS"</span>, <span class="op">]</span>, no_random_effect <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"meso_0"</span><span class="op">)</span>,</span>
<span> covariates <span class="op">=</span> <span class="va">pH</span>,</span>
-<span> covariate_models <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">log_k1</span> <span class="op">~</span> <span class="va">pH</span>, <span class="va">log_k2</span> <span class="op">~</span> <span class="va">pH</span>, <span class="va">log_tb</span> <span class="op">~</span> <span class="va">pH</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<span> covariate_models <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">log_k1</span> <span class="op">~</span> <span class="va">pH</span>, <span class="va">log_k2</span> <span class="op">~</span> <span class="va">pH</span>, <span class="va">log_tb</span> <span class="op">~</span> <span class="va">pH</span><span class="op">)</span>,</span>
+<span> center_covariates <span class="op">=</span> <span class="st">"median"</span><span class="op">)</span></span></code></pre></div>
<div class="sourceCode" id="cb73"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">hs_pH</span><span class="op">)</span><span class="op">$</span><span class="va">confint_trans</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>digits <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></code></pre></div>
<table class="table">
@@ -2179,9 +2186,9 @@ meso 7.932495 36.93311 11.11797 5.205671 18.26</code></pre>
</tr>
<tr class="even">
<td align="left">log_k1</td>
-<td align="right">-5.81</td>
-<td align="right">-7.27</td>
-<td align="right">-4.36</td>
+<td align="right">-3.08</td>
+<td align="right">-3.28</td>
+<td align="right">-2.89</td>
</tr>
<tr class="odd">
<td align="left">beta_pH(log_k1)</td>
@@ -2191,9 +2198,9 @@ meso 7.932495 36.93311 11.11797 5.205671 18.26</code></pre>
</tr>
<tr class="even">
<td align="left">log_k2</td>
-<td align="right">-6.80</td>
-<td align="right">-8.76</td>
-<td align="right">-4.83</td>
+<td align="right">-3.68</td>
+<td align="right">-3.91</td>
+<td align="right">-3.46</td>
</tr>
<tr class="odd">
<td align="left">beta_pH(log_k2)</td>
@@ -2203,9 +2210,9 @@ meso 7.932495 36.93311 11.11797 5.205671 18.26</code></pre>
</tr>
<tr class="even">
<td align="left">log_tb</td>
-<td align="right">3.25</td>
-<td align="right">1.25</td>
-<td align="right">5.25</td>
+<td align="right">2.68</td>
+<td align="right">2.38</td>
+<td align="right">2.97</td>
</tr>
<tr class="odd">
<td align="left">beta_pH(log_tb)</td>
@@ -2281,9 +2288,9 @@ Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
</tr>
<tr class="even">
<td align="left">log_k1</td>
-<td align="right">-5.68</td>
-<td align="right">-7.09</td>
-<td align="right">-4.27</td>
+<td align="right">-3.05</td>
+<td align="right">-3.25</td>
+<td align="right">-2.86</td>
</tr>
<tr class="odd">
<td align="left">beta_pH(log_k1)</td>
@@ -2293,9 +2300,9 @@ Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
</tr>
<tr class="even">
<td align="left">log_k2</td>
-<td align="right">-6.61</td>
-<td align="right">-8.34</td>
-<td align="right">-4.88</td>
+<td align="right">-3.74</td>
+<td align="right">-3.94</td>
+<td align="right">-3.54</td>
</tr>
<tr class="odd">
<td align="left">beta_pH(log_k2)</td>
@@ -2368,10 +2375,10 @@ compared with each other.</p>
npar AIC BIC Lik
sfo_pH 5 783.09 787.54 -386.54
-fomc_pH_2 6 767.49 772.83 -377.75
+fomc_pH_2 6 766.50 771.84 -377.25
dfop_pH_4 7 767.35 773.58 -376.68
sforb_pH_2 7 770.94 777.17 -378.47
-dfop_pH_2 8 765.14 772.26 -374.57
+dfop_pH_2 8 765.06 772.18 -374.53
hs_pH_2 10 766.47 775.37 -373.23</code></pre>
<p>The DFOP model with pH influence on <code>k2</code> and
<code>g</code> and a random effect only on <code>k2</code> is finally
@@ -2386,7 +2393,7 @@ refer to the Appendix for a detailed listing.</p>
$distimes
DT50 DT90 DT50back DT50_k1 DT50_k2
-meso 18.36876 73.51841 22.13125 4.191901 23.98672</code></pre>
+meso 18.46687 74.91602 22.55197 4.568623 24.64483</code></pre>
<div class="sourceCode" id="cb89"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">dfop_pH_2</span>, covariates <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>pH <span class="op">=</span> <span class="fl">7</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
<pre><code>$covariates
@@ -2395,7 +2402,7 @@ User 7
$distimes
DT50 DT90 DT50back DT50_k1 DT50_k2
-meso 8.346428 28.34437 8.532507 4.191901 8.753618</code></pre>
+meso 8.370528 28.37824 8.542701 4.568623 8.785409</code></pre>
</div>
</div>
<div class="section level2">
@@ -2418,11 +2425,11 @@ further refined to make them fully identifiable.</p>
Hierarchical SFO fit with constant variance
</caption>
<pre><code>
-saemix version used for fitting: 3.3
-mkin version used for pre-fitting: 1.2.10
-R version used for fitting: 4.5.0
-Date of fit: Wed May 14 05:12:46 2025
-Date of summary: Wed May 14 05:13:35 2025
+saemix version used for fitting: 3.4
+mkin version used for pre-fitting: 1.2.11
+R version used for fitting: 4.5.1
+Date of fit: Fri Sep 12 22:15:01 2025
+Date of summary: Fri Sep 12 22:15:50 2025
Equations:
d_meso/dt = - k_meso * meso
@@ -2432,7 +2439,7 @@ Data:
Model predictions using solution type analytical
-Fitted in 0.71 s
+Fitted in 0.602 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
@@ -2495,11 +2502,11 @@ meso 16.41 54.5
Hierarchical FOMC fit with constant variance
</caption>
<pre><code>
-saemix version used for fitting: 3.3
-mkin version used for pre-fitting: 1.2.10
-R version used for fitting: 4.5.0
-Date of fit: Wed May 14 05:12:46 2025
-Date of summary: Wed May 14 05:13:35 2025
+saemix version used for fitting: 3.4
+mkin version used for pre-fitting: 1.2.11
+R version used for fitting: 4.5.1
+Date of fit: Fri Sep 12 22:15:02 2025
+Date of summary: Fri Sep 12 22:15:50 2025
Equations:
d_meso/dt = - (alpha/beta) * 1/((time/beta) + 1) * meso
@@ -2509,7 +2516,7 @@ Data:
Model predictions using solution type analytical
-Fitted in 0.842 s
+Fitted in 0.983 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
@@ -2578,11 +2585,11 @@ meso 14.8 74.64 22.47
Hierarchical DFOP fit with constant variance
</caption>
<pre><code>
-saemix version used for fitting: 3.3
-mkin version used for pre-fitting: 1.2.10
-R version used for fitting: 4.5.0
-Date of fit: Wed May 14 05:12:47 2025
-Date of summary: Wed May 14 05:13:35 2025
+saemix version used for fitting: 3.4
+mkin version used for pre-fitting: 1.2.11
+R version used for fitting: 4.5.1
+Date of fit: Fri Sep 12 22:15:02 2025
+Date of summary: Fri Sep 12 22:15:50 2025
Equations:
d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -2594,7 +2601,7 @@ Data:
Model predictions using solution type analytical
-Fitted in 1.168 s
+Fitted in 1.202 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
@@ -2669,11 +2676,11 @@ meso 16.04 63.75 19.19 3.931 20.8
Hierarchical SFORB fit with constant variance
</caption>
<pre><code>
-saemix version used for fitting: 3.3
-mkin version used for pre-fitting: 1.2.10
-R version used for fitting: 4.5.0
-Date of fit: Wed May 14 05:12:47 2025
-Date of summary: Wed May 14 05:13:35 2025
+saemix version used for fitting: 3.4
+mkin version used for pre-fitting: 1.2.11
+R version used for fitting: 4.5.1
+Date of fit: Fri Sep 12 22:15:02 2025
+Date of summary: Fri Sep 12 22:15:50 2025
Equations:
d_meso_free/dt = - k_meso_free * meso_free - k_meso_free_bound *
@@ -2686,7 +2693,7 @@ Data:
Model predictions using solution type analytical
-Fitted in 1.256 s
+Fitted in 1.557 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
@@ -2776,11 +2783,11 @@ meso 14.79 60.81 18.3 4.521 20.37
Hierarchical HS fit with constant variance
</caption>
<pre><code>
-saemix version used for fitting: 3.3
-mkin version used for pre-fitting: 1.2.10
-R version used for fitting: 4.5.0
-Date of fit: Wed May 14 05:12:48 2025
-Date of summary: Wed May 14 05:13:35 2025
+saemix version used for fitting: 3.4
+mkin version used for pre-fitting: 1.2.11
+R version used for fitting: 4.5.1
+Date of fit: Fri Sep 12 22:15:03 2025
+Date of summary: Fri Sep 12 22:15:50 2025
Equations:
d_meso/dt = - ifelse(time &lt;= tb, k1, k2) * meso
@@ -2790,7 +2797,7 @@ Data:
Model predictions using solution type analytical
-Fitted in 1.653 s
+Fitted in 1.61 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
@@ -2869,11 +2876,11 @@ meso 16 76 22.88 11.1 25.84
Hierarchichal SFO fit with pH influence
</caption>
<pre><code>
-saemix version used for fitting: 3.3
-mkin version used for pre-fitting: 1.2.10
-R version used for fitting: 4.5.0
-Date of fit: Wed May 14 05:13:00 2025
-Date of summary: Wed May 14 05:13:35 2025
+saemix version used for fitting: 3.4
+mkin version used for pre-fitting: 1.2.11
+R version used for fitting: 4.5.1
+Date of fit: Fri Sep 12 22:15:15 2025
+Date of summary: Fri Sep 12 22:15:50 2025
Equations:
d_meso/dt = - k_meso * meso
@@ -2883,7 +2890,7 @@ Data:
Model predictions using solution type analytical
-Fitted in 1.343 s
+Fitted in 0.924 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
@@ -2913,15 +2920,15 @@ Likelihood computed by importance sampling
Optimised parameters:
est. lower upper
meso_0 91.3481 89.2688 93.4275
-log_k_meso -6.6614 -7.9715 -5.3514
+log_k_meso -3.2854 -3.4590 -3.1118
beta_pH(log_k_meso) 0.5871 0.3684 0.8059
a.1 5.4750 4.7085 6.2415
SD.log_k_meso 0.3471 0.2258 0.4684
Correlation:
meso_0 lg_k_ms
-log_k_meso 0.0414
-beta_pH(log_k_meso) -0.0183 -0.9917
+log_k_meso 0.1797
+beta_pH(log_k_meso) -0.0183 -0.2379
Random effects:
est. lower upper
@@ -2932,9 +2939,9 @@ Variance model:
a.1 5.475 4.709 6.242
Backtransformed parameters:
- est. lower upper
-meso_0 91.348139 8.927e+01 93.427476
-k_meso 0.001279 3.452e-04 0.004741
+ est. lower upper
+meso_0 91.34814 89.26880 93.42748
+k_meso 0.03743 0.03146 0.04452
Covariates used for endpoints below:
pH
@@ -2950,11 +2957,11 @@ meso 18.52 61.52
Hierarchichal FOMC fit with pH influence
</caption>
<pre><code>
-saemix version used for fitting: 3.3
-mkin version used for pre-fitting: 1.2.10
-R version used for fitting: 4.5.0
-Date of fit: Wed May 14 05:13:03 2025
-Date of summary: Wed May 14 05:13:35 2025
+saemix version used for fitting: 3.4
+mkin version used for pre-fitting: 1.2.11
+R version used for fitting: 4.5.1
+Date of fit: Fri Sep 12 22:15:18 2025
+Date of summary: Fri Sep 12 22:15:50 2025
Equations:
d_meso/dt = - (alpha/beta) * 1/((time/beta) + 1) * meso
@@ -2964,7 +2971,7 @@ Data:
Model predictions using solution type analytical
-Fitted in 1.897 s
+Fitted in 1.901 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
@@ -2995,7 +3002,7 @@ Likelihood computed by importance sampling
Optimised parameters:
est. lower upper
meso_0 92.840646 90.750461 94.9308
-log_alpha -2.206602 -3.494546 -0.9187
+log_alpha 1.114051 0.475668 1.7524
beta_pH(log_alpha) 0.577505 0.369805 0.7852
log_beta 4.214099 3.438851 4.9893
a.1 5.027768 4.322028 5.7335
@@ -3004,9 +3011,9 @@ SD.log_beta 0.374640 0.009252 0.7400
Correlation:
meso_0 log_lph bt_H(_)
-log_alpha -0.0865
-beta_pH(log_alpha) -0.0789 -0.8704
-log_beta -0.3544 0.3302 0.1628
+log_alpha -0.3220
+beta_pH(log_alpha) -0.0789 0.1148
+log_beta -0.3544 0.9709 0.1628
Random effects:
est. lower upper
@@ -3018,10 +3025,10 @@ Variance model:
a.1 5.028 4.322 5.734
Backtransformed parameters:
- est. lower upper
-meso_0 92.8406 90.75046 94.9308
-alpha 0.1101 0.03036 0.3991
-beta 67.6332 31.15113 146.8404
+ est. lower upper
+meso_0 92.841 90.750 94.931
+alpha 3.047 1.609 5.769
+beta 67.633 31.151 146.840
Covariates used for endpoints below:
pH
@@ -3037,11 +3044,11 @@ meso 17.28 76.37 22.99
Refined hierarchichal FOMC fit with pH influence
</caption>
<pre><code>
-saemix version used for fitting: 3.3
-mkin version used for pre-fitting: 1.2.10
-R version used for fitting: 4.5.0
-Date of fit: Wed May 14 05:13:08 2025
-Date of summary: Wed May 14 05:13:35 2025
+saemix version used for fitting: 3.4
+mkin version used for pre-fitting: 1.2.11
+R version used for fitting: 4.5.1
+Date of fit: Fri Sep 12 22:15:21 2025
+Date of summary: Fri Sep 12 22:15:50 2025
Equations:
d_meso/dt = - (alpha/beta) * 1/((time/beta) + 1) * meso
@@ -3051,7 +3058,7 @@ Data:
Model predictions using solution type analytical
-Fitted in 4.184 s
+Fitted in 3.35 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
@@ -3077,44 +3084,44 @@ Results:
Likelihood computed by importance sampling
AIC BIC logLik
- 767.5 772.8 -377.7
+ 766.5 771.8 -377.3
Optimised parameters:
est. lower upper
-meso_0 93.0536 90.9771 95.1300
-log_alpha -2.9054 -4.1803 -1.6304
-beta_pH(log_alpha) 0.6590 0.4437 0.8744
-log_beta 3.9549 3.2860 4.6239
-a.1 4.9784 4.2815 5.6754
-SD.log_beta 0.4019 0.2632 0.5406
+meso_0 93.1950 91.1047 95.2853
+log_alpha 0.8125 0.3292 1.2957
+beta_pH(log_alpha) 0.5497 0.3490 0.7504
+log_beta 3.8464 3.2179 4.4750
+a.1 4.9972 4.2976 5.6968
+SD.log_beta 0.3829 0.2499 0.5159
Correlation:
meso_0 log_lph bt_H(_)
-log_alpha -0.0397
-beta_pH(log_alpha) -0.0899 -0.9146
-log_beta -0.3473 0.2038 0.1919
+log_alpha -0.3094
+beta_pH(log_alpha) -0.0779 0.0841
+log_beta -0.3493 0.9533 0.1434
Random effects:
est. lower upper
-SD.log_beta 0.4019 0.2632 0.5406
+SD.log_beta 0.3829 0.2499 0.5159
Variance model:
est. lower upper
-a.1 4.978 4.281 5.675
+a.1 4.997 4.298 5.697
Backtransformed parameters:
- est. lower upper
-meso_0 93.05359 90.97713 95.1300
-alpha 0.05473 0.01529 0.1958
-beta 52.19251 26.73597 101.8874
+ est. lower upper
+meso_0 93.195 91.10 95.285
+alpha 2.253 1.39 3.654
+beta 46.826 24.98 87.793
Covariates used for endpoints below:
pH
50% 5.75
Estimated disappearance times:
- DT50 DT90 DT50back
-meso 17.3 82.91 24.96
+ DT50 DT90 DT50back
+meso 16.86 83.27 25.07
</code></pre>
<p></p>
@@ -3122,11 +3129,11 @@ meso 17.3 82.91 24.96
Hierarchichal DFOP fit with pH influence
</caption>
<pre><code>
-saemix version used for fitting: 3.3
-mkin version used for pre-fitting: 1.2.10
-R version used for fitting: 4.5.0
-Date of fit: Wed May 14 05:13:11 2025
-Date of summary: Wed May 14 05:13:35 2025
+saemix version used for fitting: 3.4
+mkin version used for pre-fitting: 1.2.11
+R version used for fitting: 4.5.1
+Date of fit: Fri Sep 12 22:15:25 2025
+Date of summary: Fri Sep 12 22:15:50 2025
Equations:
d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -3138,7 +3145,7 @@ Data:
Model predictions using solution type analytical
-Fitted in 2.18 s
+Fitted in 2.592 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
@@ -3168,24 +3175,24 @@ Likelihood computed by importance sampling
769.1 777.1 -375.5
Optimised parameters:
- est. lower upper
-meso_0 92.843344 90.8464 94.84028
-log_k1 -2.815685 -3.0888 -2.54261
-log_k2 -11.479779 -15.3203 -7.63923
-beta_pH(log_k2) 1.308417 0.6948 1.92203
-g_qlogis 3.133036 0.4657 5.80035
-beta_pH(g_qlogis) -0.565988 -1.0394 -0.09262
-a.1 4.955518 4.2597 5.65135
-SD.log_k2 0.758963 0.4685 1.04943
-SD.g_qlogis 0.005215 -9.9561 9.96656
+ est. lower upper
+meso_0 92.843344 90.8464 94.84028
+log_k1 -2.815685 -3.0888 -2.54261
+log_k2 -3.956384 -4.4741 -3.43868
+beta_pH(log_k2) 1.308417 0.6948 1.92203
+g_qlogis -0.121394 -0.5691 0.32627
+beta_pH(g_qlogis) -0.565988 -1.0394 -0.09262
+a.1 4.955518 4.2597 5.65135
+SD.log_k2 0.758963 0.4685 1.04943
+SD.g_qlogis 0.005215 -9.9561 9.96656
Correlation:
meso_0 log_k1 log_k2 b_H(_2) g_qlogs
log_k1 0.2706
-log_k2 -0.0571 0.1096
-beta_pH(log_k2) 0.0554 -0.1291 -0.9937
-g_qlogis -0.1125 -0.5062 -0.1305 0.1294
-beta_pH(g_qlogis) 0.1267 0.4226 0.0419 -0.0438 -0.9864
+log_k2 -0.0457 -0.0667
+beta_pH(log_k2) 0.0554 -0.1291 -0.5566
+g_qlogis 0.1004 -0.4462 -0.4397 0.5042
+beta_pH(g_qlogis) 0.1267 0.4226 0.0123 -0.0438 0.2029
Random effects:
est. lower upper
@@ -3197,11 +3204,11 @@ Variance model:
a.1 4.956 4.26 5.651
Backtransformed parameters:
- est. lower upper
-meso_0 9.284e+01 9.085e+01 9.484e+01
-k1 5.986e-02 4.556e-02 7.866e-02
-k2 1.034e-05 2.221e-07 4.812e-04
-g 9.582e-01 6.144e-01 9.970e-01
+ est. lower upper
+meso_0 92.84334 90.84641 94.84028
+k1 0.05986 0.04556 0.07866
+k2 0.01913 0.01140 0.03211
+g 0.46969 0.36145 0.58085
Covariates used for endpoints below:
pH
@@ -3217,11 +3224,11 @@ meso 20.23 88.45 26.62 11.58 36.23
Refined hierarchical DFOP fit with pH influence
</caption>
<pre><code>
-saemix version used for fitting: 3.3
-mkin version used for pre-fitting: 1.2.10
-R version used for fitting: 4.5.0
-Date of fit: Wed May 14 05:13:14 2025
-Date of summary: Wed May 14 05:13:35 2025
+saemix version used for fitting: 3.4
+mkin version used for pre-fitting: 1.2.11
+R version used for fitting: 4.5.1
+Date of fit: Fri Sep 12 22:15:30 2025
+Date of summary: Fri Sep 12 22:15:50 2025
Equations:
d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -3233,7 +3240,7 @@ Data:
Model predictions using solution type analytical
-Fitted in 2.424 s
+Fitted in 4.483 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
@@ -3260,41 +3267,41 @@ Results:
Likelihood computed by importance sampling
AIC BIC logLik
- 765.1 772.3 -374.6
+ 765.1 772.2 -374.5
Optimised parameters:
- est. lower upper
-meso_0 93.3333 91.2427 95.42394
-log_k1 -1.7997 -2.9124 -0.68698
-log_k2 -8.1810 -10.1819 -6.18008
-beta_pH(log_k2) 0.8064 0.4903 1.12257
-g_qlogis 3.3513 -1.1792 7.88182
-beta_pH(g_qlogis) -0.8672 -1.7661 0.03177
-a.1 4.9158 4.2277 5.60390
-SD.log_k2 0.3946 0.2565 0.53281
+ est. lower upper
+meso_0 93.1612 91.0766 95.24580
+log_k1 -1.8857 -2.8975 -0.87395
+log_k2 -3.5711 -3.8859 -3.25622
+beta_pH(log_k2) 0.8252 0.4952 1.15513
+g_qlogis -1.5326 -2.6994 -0.36574
+beta_pH(g_qlogis) -0.8365 -1.7163 0.04333
+a.1 4.9218 4.2328 5.61070
+SD.log_k2 0.4011 0.2604 0.54184
Correlation:
meso_0 log_k1 log_k2 b_H(_2) g_qlogs
-log_k1 0.1730
-log_k2 0.0442 0.5370
-beta_pH(log_k2) -0.0392 -0.4880 -0.9923
-g_qlogis -0.1536 0.1431 -0.1129 0.1432
-beta_pH(g_qlogis) 0.1504 -0.3151 -0.0196 -0.0212 -0.9798
+log_k1 0.1734
+log_k2 0.0553 0.6421
+beta_pH(log_k2) -0.0382 -0.5121 -0.5860
+g_qlogis 0.0616 -0.8501 -0.7251 0.4941
+beta_pH(g_qlogis) 0.1408 -0.3240 -0.2716 -0.0041 0.6264
Random effects:
est. lower upper
-SD.log_k2 0.3946 0.2565 0.5328
+SD.log_k2 0.4011 0.2604 0.5418
Variance model:
est. lower upper
-a.1 4.916 4.228 5.604
+a.1 4.922 4.233 5.611
Backtransformed parameters:
- est. lower upper
-meso_0 9.333e+01 9.124e+01 95.42394
-k1 1.654e-01 5.435e-02 0.50309
-k2 2.799e-04 3.785e-05 0.00207
-g 9.661e-01 2.352e-01 0.99962
+ est. lower upper
+meso_0 93.16118 91.07655 95.24580
+k1 0.15172 0.05516 0.41730
+k2 0.02813 0.02053 0.03853
+g 0.17762 0.06301 0.40957
Covariates used for endpoints below:
pH
@@ -3302,7 +3309,7 @@ Covariates used for endpoints below:
Estimated disappearance times:
DT50 DT90 DT50back DT50_k1 DT50_k2
-meso 18.37 73.52 22.13 4.192 23.99
+meso 18.47 74.92 22.55 4.569 24.64
</code></pre>
<p></p>
@@ -3310,11 +3317,11 @@ meso 18.37 73.52 22.13 4.192 23.99
Further refined hierarchical DFOP fit with pH influence
</caption>
<pre><code>
-saemix version used for fitting: 3.3
-mkin version used for pre-fitting: 1.2.10
-R version used for fitting: 4.5.0
-Date of fit: Wed May 14 05:13:23 2025
-Date of summary: Wed May 14 05:13:35 2025
+saemix version used for fitting: 3.4
+mkin version used for pre-fitting: 1.2.11
+R version used for fitting: 4.5.1
+Date of fit: Fri Sep 12 22:15:37 2025
+Date of summary: Fri Sep 12 22:15:50 2025
Equations:
d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
@@ -3326,7 +3333,7 @@ Data:
Model predictions using solution type analytical
-Fitted in 3.211 s
+Fitted in 3.213 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
@@ -3356,21 +3363,21 @@ Likelihood computed by importance sampling
767.4 773.6 -376.7
Optimised parameters:
- est. lower upper
-meso_0 93.3011 91.1905 95.4118
-log_k1 -2.1487 -2.7607 -1.5367
-log_k2 -8.1039 -10.4225 -5.7853
-beta_pH(log_k2) 0.7821 0.4126 1.1517
-g_qlogis -1.0373 -1.9337 -0.1409
-a.1 5.0095 4.3082 5.7108
-SD.log_k2 0.4622 0.3009 0.6235
+ est. lower upper
+meso_0 93.3011 91.1905 95.4118
+log_k1 -2.1487 -2.7607 -1.5367
+log_k2 -3.6066 -3.9305 -3.2828
+beta_pH(log_k2) 0.7821 0.4126 1.1517
+g_qlogis -1.0373 -1.9337 -0.1409
+a.1 5.0095 4.3082 5.7108
+SD.log_k2 0.4622 0.3009 0.6235
Correlation:
meso_0 log_k1 log_k2 b_H(_2)
log_k1 0.2179
-log_k2 0.0337 0.5791
-beta_pH(log_k2) -0.0326 -0.5546 -0.9932
-g_qlogis 0.0237 -0.8479 -0.6571 0.6123
+log_k2 0.0271 0.5067
+beta_pH(log_k2) -0.0326 -0.5546 -0.5485
+g_qlogis 0.0237 -0.8479 -0.6866 0.6123
Random effects:
est. lower upper
@@ -3381,11 +3388,11 @@ Variance model:
a.1 5.009 4.308 5.711
Backtransformed parameters:
- est. lower upper
-meso_0 9.330e+01 9.119e+01 95.411751
-k1 1.166e-01 6.325e-02 0.215084
-k2 3.024e-04 2.975e-05 0.003072
-g 2.617e-01 1.263e-01 0.464832
+ est. lower upper
+meso_0 93.30113 91.19050 95.41175
+k1 0.11664 0.06325 0.21508
+k2 0.02714 0.01963 0.03752
+g 0.26168 0.12635 0.46483
Covariates used for endpoints below:
pH
@@ -3401,11 +3408,11 @@ meso 17.09 73.67 22.18 5.943 25.54
Hierarchichal SFORB fit with pH influence
</caption>
<pre><code>
-saemix version used for fitting: 3.3
-mkin version used for pre-fitting: 1.2.10
-R version used for fitting: 4.5.0
-Date of fit: Wed May 14 05:13:26 2025
-Date of summary: Wed May 14 05:13:35 2025
+saemix version used for fitting: 3.4
+mkin version used for pre-fitting: 1.2.11
+R version used for fitting: 4.5.1
+Date of fit: Fri Sep 12 22:15:41 2025
+Date of summary: Fri Sep 12 22:15:50 2025
Equations:
d_meso_free/dt = - k_meso_free * meso_free - k_meso_free_bound *
@@ -3418,7 +3425,7 @@ Data:
Model predictions using solution type analytical
-Fitted in 2.649 s
+Fitted in 2.758 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
@@ -3455,24 +3462,24 @@ Likelihood computed by importance sampling
768.8 776.8 -375.4
Optimised parameters:
- est. lower upper
-meso_free_0 93.4204 91.3213 95.5195
-log_k_meso_free -5.3742 -6.9366 -3.8117
-beta_pH(log_k_meso_free) 0.4232 0.1769 0.6695
-log_k_meso_free_bound -3.4889 -4.9243 -2.0535
-log_k_meso_bound_free -9.9797 -19.2232 -0.7362
-beta_pH(log_k_meso_bound_free) 1.2290 -0.2107 2.6687
-a.1 4.9031 4.1795 5.6268
-SD.log_k_meso_free 0.3454 0.2252 0.4656
-SD.log_k_meso_bound_free 0.1277 -1.9459 2.2012
+ est. lower upper
+meso_free_0 93.4204 91.3213 95.5195
+log_k_meso_free -2.9408 -3.2344 -2.6471
+beta_pH(log_k_meso_free) 0.4232 0.1769 0.6695
+log_k_meso_free_bound -3.4889 -4.9243 -2.0535
+log_k_meso_bound_free -2.9130 -4.3047 -1.5212
+beta_pH(log_k_meso_bound_free) 1.2290 -0.2107 2.6687
+a.1 4.9031 4.1795 5.6268
+SD.log_k_meso_free 0.3454 0.2252 0.4656
+SD.log_k_meso_bound_free 0.1277 -1.9459 2.2012
Correlation:
ms_fr_0 lg_k_m_ b_H(___) lg_k_ms_f_ lg_k_ms_b_
-log_k_meso_free 0.1493
-beta_pH(log_k_meso_free) -0.0930 -0.9854
-log_k_meso_free_bound 0.2439 0.4621 -0.3492
-log_k_meso_bound_free 0.2188 0.1292 -0.0339 0.7287
-beta_pH(log_k_meso_bound_free) -0.2216 -0.0797 -0.0111 -0.6566 -0.9934
+log_k_meso_free 0.3460
+beta_pH(log_k_meso_free) -0.0930 -0.4206
+log_k_meso_free_bound 0.2439 0.7749 -0.3492
+log_k_meso_bound_free 0.1350 0.6400 -0.2912 0.9346
+beta_pH(log_k_meso_bound_free) -0.2216 -0.4778 -0.0111 -0.6566 -0.6498
Random effects:
est. lower upper
@@ -3484,11 +3491,11 @@ Variance model:
a.1 4.903 4.18 5.627
Backtransformed parameters:
- est. lower upper
-meso_free_0 9.342e+01 9.132e+01 95.51946
-k_meso_free 4.635e-03 9.716e-04 0.02211
-k_meso_free_bound 3.054e-02 7.268e-03 0.12829
-k_meso_bound_free 4.633e-05 4.482e-09 0.47894
+ est. lower upper
+meso_free_0 93.42040 91.321340 95.51946
+k_meso_free 0.05282 0.039384 0.07085
+k_meso_free_bound 0.03054 0.007268 0.12829
+k_meso_bound_free 0.05431 0.013505 0.21845
Covariates used for endpoints below:
pH
@@ -3512,11 +3519,11 @@ meso 16.42 75.2 22.64 6.185 27.08
Refined hierarchichal SFORB fit with pH influence
</caption>
<pre><code>
-saemix version used for fitting: 3.3
-mkin version used for pre-fitting: 1.2.10
-R version used for fitting: 4.5.0
-Date of fit: Wed May 14 05:13:30 2025
-Date of summary: Wed May 14 05:13:35 2025
+saemix version used for fitting: 3.4
+mkin version used for pre-fitting: 1.2.11
+R version used for fitting: 4.5.1
+Date of fit: Fri Sep 12 22:15:44 2025
+Date of summary: Fri Sep 12 22:15:50 2025
Equations:
d_meso_free/dt = - k_meso_free * meso_free - k_meso_free_bound *
@@ -3529,7 +3536,7 @@ Data:
Model predictions using solution type analytical
-Fitted in 3.186 s
+Fitted in 2.971 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
@@ -3567,20 +3574,20 @@ Likelihood computed by importance sampling
Optimised parameters:
est. lower upper
-meso_free_0 93.3196 91.1633 95.4760
-log_k_meso_free -6.1460 -7.4306 -4.8614
+meso_free_0 93.3196 91.1650 95.4743
+log_k_meso_free -3.0207 -3.2655 -2.7759
beta_pH(log_k_meso_free) 0.5435 0.3329 0.7542
-log_k_meso_free_bound -3.8001 -5.2027 -2.3975
-log_k_meso_bound_free -2.9462 -4.2565 -1.6359
-a.1 5.0825 4.3793 5.7856
+log_k_meso_free_bound -3.8001 -5.1809 -2.4193
+log_k_meso_bound_free -2.9462 -4.2411 -1.6513
+a.1 5.0825 4.3709 5.7940
SD.log_k_meso_free 0.3338 0.2175 0.4502
Correlation:
ms_fr_0 lg_k_m_ b_H(___ lg_k_ms_f_
-log_k_meso_free 0.1086
-beta_pH(log_k_meso_free) -0.0426 -0.9821
-log_k_meso_free_bound 0.2513 0.1717 -0.0409
-log_k_meso_bound_free 0.1297 0.1171 -0.0139 0.9224
+log_k_meso_free 0.3556
+beta_pH(log_k_meso_free) -0.0422 -0.2041
+log_k_meso_free_bound 0.2513 0.6866 -0.0400
+log_k_meso_bound_free 0.1292 0.5341 -0.0129 0.9214
Random effects:
est. lower upper
@@ -3588,14 +3595,14 @@ SD.log_k_meso_free 0.3338 0.2175 0.4502
Variance model:
est. lower upper
-a.1 5.082 4.379 5.786
+a.1 5.082 4.371 5.794
Backtransformed parameters:
- est. lower upper
-meso_free_0 93.319649 9.116e+01 95.47601
-k_meso_free 0.002142 5.928e-04 0.00774
-k_meso_free_bound 0.022369 5.502e-03 0.09095
-k_meso_bound_free 0.052539 1.417e-02 0.19478
+ est. lower upper
+meso_free_0 93.31965 91.165044 95.47425
+k_meso_free 0.04877 0.038177 0.06230
+k_meso_free_bound 0.02237 0.005623 0.08898
+k_meso_bound_free 0.05254 0.014392 0.19179
Covariates used for endpoints below:
pH
@@ -3619,11 +3626,11 @@ meso 16.87 73.16 22.02 7.12 26.34
Hierarchichal HS fit with pH influence
</caption>
<pre><code>
-saemix version used for fitting: 3.3
-mkin version used for pre-fitting: 1.2.10
-R version used for fitting: 4.5.0
-Date of fit: Wed May 14 05:13:32 2025
-Date of summary: Wed May 14 05:13:35 2025
+saemix version used for fitting: 3.4
+mkin version used for pre-fitting: 1.2.11
+R version used for fitting: 4.5.1
+Date of fit: Fri Sep 12 22:15:48 2025
+Date of summary: Fri Sep 12 22:15:50 2025
Equations:
d_meso/dt = - ifelse(time &lt;= tb, k1, k2) * meso
@@ -3633,7 +3640,7 @@ Data:
Model predictions using solution type analytical
-Fitted in 1.833 s
+Fitted in 2.254 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
@@ -3665,11 +3672,11 @@ Likelihood computed by importance sampling
Optimised parameters:
est. lower upper
meso_0 93.32599 91.4658 95.1862
-log_k1 -5.81463 -7.2710 -4.3583
+log_k1 -3.08497 -3.2830 -2.8870
beta_pH(log_k1) 0.47472 0.2334 0.7160
-log_k2 -6.79633 -8.7605 -4.8322
+log_k2 -3.68267 -3.9077 -3.4577
beta_pH(log_k2) 0.54151 0.2124 0.8706
-log_tb 3.24674 1.2470 5.2465
+log_tb 2.67815 2.3846 2.9717
beta_pH(log_tb) -0.09889 -0.4258 0.2280
a.1 4.49487 3.7766 5.2132
SD.log_k1 0.37191 0.2370 0.5068
@@ -3678,12 +3685,12 @@ SD.log_tb 0.25353 -0.0664 0.5735
Correlation:
meso_0 log_k1 b_H(_1) log_k2 b_H(_2) log_tb
-log_k1 0.0744
-beta_pH(log_k1) -0.0452 -0.9915
-log_k2 0.0066 -0.0363 0.0376
-beta_pH(log_k2) -0.0071 0.0372 -0.0391 -0.9939
-log_tb -0.0238 -0.1483 0.1362 -0.3836 0.3696
-beta_pH(log_tb) 0.0097 0.1359 -0.1265 0.3736 -0.3653 -0.9905
+log_k1 0.2301
+beta_pH(log_k1) -0.0452 -0.2842
+log_k2 -0.0029 -0.0322 -0.0008
+beta_pH(log_k2) -0.0071 0.0000 -0.0391 -0.2655
+log_tb -0.1003 -0.2052 0.1180 -0.4197 0.1794
+beta_pH(log_tb) 0.0097 0.1125 -0.1265 0.1894 -0.3653 -0.3449
Random effects:
est. lower upper
@@ -3696,11 +3703,11 @@ Variance model:
a.1 4.495 3.777 5.213
Backtransformed parameters:
- est. lower upper
-meso_0 93.325994 9.147e+01 9.519e+01
-k1 0.002984 6.954e-04 1.280e-02
-k2 0.001118 1.568e-04 7.969e-03
-tb 25.706437 3.480e+00 1.899e+02
+ est. lower upper
+meso_0 93.32599 91.46575 95.18624
+k1 0.04573 0.03752 0.05574
+k2 0.02516 0.02009 0.03150
+tb 14.55810 10.85502 19.52445
Covariates used for endpoints below:
pH
@@ -3716,11 +3723,11 @@ meso 15.65 79.63 23.97 15.16 27.55
Refined hierarchichal HS fit with pH influence
</caption>
<pre><code>
-saemix version used for fitting: 3.3
-mkin version used for pre-fitting: 1.2.10
-R version used for fitting: 4.5.0
-Date of fit: Wed May 14 05:13:35 2025
-Date of summary: Wed May 14 05:13:35 2025
+saemix version used for fitting: 3.4
+mkin version used for pre-fitting: 1.2.11
+R version used for fitting: 4.5.1
+Date of fit: Fri Sep 12 22:15:50 2025
+Date of summary: Fri Sep 12 22:15:50 2025
Equations:
d_meso/dt = - ifelse(time &lt;= tb, k1, k2) * meso
@@ -3730,7 +3737,7 @@ Data:
Model predictions using solution type analytical
-Fitted in 1.852 s
+Fitted in 1.425 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
@@ -3762,9 +3769,9 @@ Likelihood computed by importance sampling
Optimised parameters:
est. lower upper
meso_0 93.3251 91.49823 95.1520
-log_k1 -5.6796 -7.08789 -4.2714
+log_k1 -3.0535 -3.24879 -2.8582
beta_pH(log_k1) 0.4567 0.22400 0.6894
-log_k2 -6.6083 -8.33839 -4.8781
+log_k2 -3.7439 -3.94307 -3.5447
beta_pH(log_k2) 0.4982 0.20644 0.7899
log_tb 2.7040 2.33033 3.0777
a.1 4.4452 3.73537 5.1551
@@ -3774,11 +3781,11 @@ SD.log_tb 0.5488 0.24560 0.8521
Correlation:
meso_0 log_k1 b_H(_1) log_k2 b_H(_2)
-log_k1 0.0740
-beta_pH(log_k1) -0.0453 -0.9912
-log_k2 0.0115 -0.0650 0.0661
-beta_pH(log_k2) -0.0116 0.0649 -0.0667 -0.9936
-log_tb -0.0658 -0.1135 0.0913 -0.1500 0.1210
+log_k1 0.2233
+beta_pH(log_k1) -0.0453 -0.2955
+log_k2 0.0028 -0.0420 0.0126
+beta_pH(log_k2) -0.0116 0.0112 -0.0667 -0.2097
+log_tb -0.0658 -0.1928 0.0913 -0.2843 0.1210
Random effects:
est. lower upper
@@ -3791,11 +3798,11 @@ Variance model:
a.1 4.445 3.735 5.155
Backtransformed parameters:
- est. lower upper
-meso_0 93.325134 9.150e+01 95.152036
-k1 0.003415 8.352e-04 0.013962
-k2 0.001349 2.392e-04 0.007611
-tb 14.939247 1.028e+01 21.707445
+ est. lower upper
+meso_0 93.32513 91.49823 95.15204
+k1 0.04719 0.03882 0.05737
+k2 0.02366 0.01939 0.02888
+tb 14.93925 10.28132 21.70744
Covariates used for endpoints below:
pH
@@ -3812,13 +3819,13 @@ meso 14.69 82.45 24.82 14.69 29.29
<div class="section level3">
<h3 id="session-info">Session info<a class="anchor" aria-label="anchor" href="#session-info"></a>
</h3>
-<pre><code>R version 4.5.0 (2025-04-11)
+<pre><code>R version 4.5.1 (2025-06-13)
Platform: x86_64-pc-linux-gnu
-Running under: Debian GNU/Linux 12 (bookworm)
+Running under: Debian GNU/Linux 13 (trixie)
Matrix products: default
-BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0
-LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0 LAPACK version 3.11.0
+BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.12.1
+LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.1; LAPACK version 3.12.0
locale:
[1] LC_CTYPE=de_DE.UTF-8 LC_NUMERIC=C
@@ -3836,29 +3843,29 @@ attached base packages:
[8] base
other attached packages:
-[1] rmarkdown_2.29 nvimcom_0.9-167 saemix_3.3 npde_3.5
-[5] knitr_1.49 mkin_1.2.10
+[1] rmarkdown_2.29 nvimcom_0.9-167 saemix_3.4 npde_3.5
+[5] knitr_1.50 mkin_1.2.11
loaded via a namespace (and not attached):
- [1] gtable_0.3.6 jsonlite_1.9.0 dplyr_1.1.4 compiler_4.5.0
- [5] tidyselect_1.2.1 gridExtra_2.3 jquerylib_0.1.4 systemfonts_1.2.1
- [9] scales_1.3.0 textshaping_1.0.0 readxl_1.4.4 yaml_2.3.10
-[13] fastmap_1.2.0 lattice_0.22-6 ggplot2_3.5.1 R6_2.6.1
-[17] generics_0.1.3 lmtest_0.9-40 MASS_7.3-65 htmlwidgets_1.6.4
-[21] tibble_3.2.1 desc_1.4.3 munsell_0.5.1 bslib_0.9.0
-[25] pillar_1.10.1 rlang_1.1.5 cachem_1.1.0 xfun_0.51
-[29] fs_1.6.5 sass_0.4.9 cli_3.6.4 pkgdown_2.1.1
-[33] magrittr_2.0.3 digest_0.6.37 grid_4.5.0 mclust_6.1.1
-[37] lifecycle_1.0.4 nlme_3.1-168 vctrs_0.6.5 evaluate_1.0.3
-[41] glue_1.8.0 cellranger_1.1.0 codetools_0.2-20 ragg_1.3.3
-[45] zoo_1.8-13 colorspace_2.1-1 tools_4.5.0 pkgconfig_2.0.3
-[49] htmltools_0.5.8.1</code></pre>
+ [1] gtable_0.3.6 jsonlite_2.0.0 dplyr_1.1.4 compiler_4.5.1
+ [5] tidyselect_1.2.1 dichromat_2.0-0.1 gridExtra_2.3 jquerylib_0.1.4
+ [9] systemfonts_1.2.3 scales_1.4.0 textshaping_1.0.1 readxl_1.4.5
+[13] yaml_2.3.10 fastmap_1.2.0 lattice_0.22-7 ggplot2_3.5.2
+[17] R6_2.6.1 generics_0.1.4 lmtest_0.9-40 MASS_7.3-65
+[21] htmlwidgets_1.6.4 tibble_3.3.0 desc_1.4.3 bslib_0.9.0
+[25] pillar_1.10.2 RColorBrewer_1.1-3 rlang_1.1.6 cachem_1.1.0
+[29] xfun_0.52 fs_1.6.6 sass_0.4.10 cli_3.6.5
+[33] pkgdown_2.1.3 magrittr_2.0.3 digest_0.6.37 grid_4.5.1
+[37] mclust_6.1.1 lifecycle_1.0.4 nlme_3.1-168 vctrs_0.6.5
+[41] evaluate_1.0.3 glue_1.8.0 cellranger_1.1.0 farver_2.1.2
+[45] codetools_0.2-20 ragg_1.4.0 zoo_1.8-14 colorspace_2.1-1
+[49] tools_4.5.1 pkgconfig_2.0.3 htmltools_0.5.8.1 </code></pre>
</div>
<div class="section level3">
<h3 id="hardware-info">Hardware info<a class="anchor" aria-label="anchor" href="#hardware-info"></a>
</h3>
<pre><code>CPU model: AMD Ryzen 9 7950X 16-Core Processor</code></pre>
-<pre><code>MemTotal: 64927780 kB</code></pre>
+<pre><code>MemTotal: 64933716 kB</code></pre>
</div>
</div>
</main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
@@ -3872,7 +3879,7 @@ loaded via a namespace (and not attached):
</div>
<div class="pkgdown-footer-right">
- <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
+ <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
</div>
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