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- <h1 data-toc-skip>Example evaluations of the dimethenamid data
-from 2018</h1>
+
+
+<div class="row">
+ <main id="main" class="col-md-9"><div class="page-header">
+
+ <h1>Example evaluations of the dimethenamid data from 2018</h1>
<h4 data-toc-skip class="author">Johannes
Ranke</h4>
<h4 data-toc-skip class="date">Last change 1 July 2022,
-built on 19 May 2023</h4>
+built on 16 Feb 2025</h4>
<small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/dimethenamid_2018.rmd" class="external-link"><code>vignettes/web_only/dimethenamid_2018.rmd</code></a></small>
- <div class="hidden name"><code>dimethenamid_2018.rmd</code></div>
-
+ <div class="d-none name"><code>dimethenamid_2018.rmd</code></div>
</div>
@@ -264,7 +212,7 @@ Status of individual fits:
dataset
model Calke Borstel Flaach BBA 2.2 BBA 2.3 Elliot
- DFOP OK OK C OK C OK
+ DFOP OK OK OK OK C OK
C: Optimisation did not converge:
iteration limit reached without convergence (10)
@@ -320,7 +268,7 @@ indicates that this difference is significant as the p-value is below
<pre><code> Model df AIC BIC logLik Test L.Ratio p-value
f_parent_nlme_sfo_const 1 5 796.60 811.82 -393.30
f_parent_nlme_sfo_tc 2 6 798.60 816.86 -393.30 1 vs 2 0.00 0.998
-f_parent_nlme_dfop_tc 3 10 671.91 702.34 -325.96 2 vs 3 134.69 &lt;.0001</code></pre>
+f_parent_nlme_dfop_tc 3 10 671.91 702.34 -325.95 2 vs 3 134.69 &lt;.0001</code></pre>
<p>In addition to these fits, attempts were also made to include
correlations between random effects by using the log Cholesky
parameterisation of the matrix specifying them. The code used for these
@@ -409,11 +357,11 @@ DMTA_0 97.99583 96.50079 99.4909
k1 0.06377 0.03432 0.0932
k2 0.00848 0.00444 0.0125
g 0.95701 0.91313 1.0009
-a.1 1.82141 1.65122 1.9916
-SD.DMTA_0 1.64787 0.45772 2.8380
+a.1 1.82141 1.60516 2.0377
+SD.DMTA_0 1.64787 0.45729 2.8384
SD.k1 0.57439 0.24731 0.9015
-SD.k2 0.03296 -2.50195 2.5679
-SD.g 1.10266 0.32369 1.8816</code></pre>
+SD.k2 0.03296 -2.50524 2.5712
+SD.g 1.10266 0.32354 1.8818</code></pre>
<p>While the other parameters converge to credible values, the variance
of k2 (<code>omega2.k2</code>) converges to a very small value. The
printout of the <code>saem.mmkin</code> model shows that the estimated
@@ -447,17 +395,17 @@ Likelihood computed by importance sampling
666 664 -323
Fitted parameters:
- estimate lower upper
-DMTA_0 98.27617 96.3088 100.2436
-k1 0.06437 0.0337 0.0950
-k2 0.00880 0.0063 0.0113
-g 0.95249 0.9100 0.9949
-a.1 1.06161 0.8625 1.2607
-b.1 0.02967 0.0226 0.0367
-SD.DMTA_0 2.06075 0.4187 3.7028
-SD.k1 0.59357 0.2561 0.9310
-SD.k2 0.00292 -10.2960 10.3019
-SD.g 1.05725 0.3808 1.7337</code></pre>
+ estimate lower upper
+DMTA_0 98.24165 96.29190 100.1914
+k1 0.06421 0.03352 0.0949
+k2 0.00866 0.00617 0.0111
+g 0.95340 0.91218 0.9946
+a.1 1.06463 0.87979 1.2495
+b.1 0.02964 0.02266 0.0366
+SD.DMTA_0 2.03611 0.40361 3.6686
+SD.k1 0.59534 0.25692 0.9338
+SD.k2 0.00042 -73.00540 73.0062
+SD.g 1.04234 0.37189 1.7128</code></pre>
<p>Doubling the number of iterations in the first phase of the algorithm
leads to a slightly lower likelihood, and therefore to slightly higher
AIC and BIC values. With even more iterations, the algorithm stops with
@@ -489,8 +437,8 @@ comparison function of the saemix package:</p>
SFO const 796.38 795.34
SFO tc 798.38 797.13
DFOP const 705.75 703.88
-DFOP tc 665.65 663.57
-DFOP tc more iterations 665.88 663.80</code></pre>
+DFOP tc 665.67 663.59
+DFOP tc more iterations 665.85 663.76</code></pre>
<p>In order to check the influence of the likelihood calculation
algorithms implemented in saemix, the likelihood from Gaussian
quadrature is added to the best fit, and the AIC values obtained from
@@ -505,7 +453,7 @@ the three methods are compared.</p>
<span><span class="op">)</span></span>
<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">AIC_parent_saemix_methods</span><span class="op">)</span></span></code></pre></div>
<pre><code> is gq lin
-665.65 665.68 665.11 </code></pre>
+665.67 665.74 665.13 </code></pre>
<p>The AIC values based on importance sampling and Gaussian quadrature
are very similar. Using linearisation is known to be less accurate, but
still gives a similar value.</p>
@@ -530,7 +478,7 @@ using defaults for the fit.</p>
<span><span class="op">)</span></span>
<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">AIC_parent_saemix_methods_defaults</span><span class="op">)</span></span></code></pre></div>
<pre><code> is gq lin
-669.77 669.36 670.95 </code></pre>
+670.09 669.37 671.29 </code></pre>
</div>
</div>
<div class="section level3">
@@ -578,15 +526,15 @@ iterations second phase, 15 chains).</p>
<td align="left">DFOP</td>
<td align="left">const</td>
<td align="right">NA</td>
-<td align="right">709.26</td>
+<td align="right">704.95</td>
<td align="right">705.75</td>
</tr>
<tr class="even">
<td align="left">DFOP</td>
<td align="left">tc</td>
<td align="right">671.91</td>
-<td align="right">665.11</td>
-<td align="right">665.65</td>
+<td align="right">665.13</td>
+<td align="right">665.67</td>
</tr>
</tbody>
</table>
@@ -615,13 +563,13 @@ satisfactory precision.</p>
</h2>
<div class="sourceCode" id="cb32"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/sessionInfo.html" class="external-link">sessionInfo</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<pre><code>R version 4.3.0 (2023-04-21)
-Platform: x86_64-pc-linux-gnu (64-bit)
+<pre><code>R version 4.4.2 (2024-10-31)
+Platform: x86_64-pc-linux-gnu
Running under: Debian GNU/Linux 12 (bookworm)
Matrix products: default
-BLAS: /usr/lib/x86_64-linux-gnu/openblas-serial/libblas.so.3
-LAPACK: /usr/lib/x86_64-linux-gnu/openblas-serial/libopenblas-r0.3.21.so; LAPACK version 3.11.0
+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
locale:
[1] LC_CTYPE=de_DE.UTF-8 LC_NUMERIC=C
@@ -638,23 +586,22 @@ attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
-[1] saemix_3.2 npde_3.3 nlme_3.1-162 mkin_1.2.5 knitr_1.42
+[1] nlme_3.1-166 mkin_1.2.10 knitr_1.49
loaded via a namespace (and not attached):
- [1] sass_0.4.6 utf8_1.2.3 generics_0.1.3 stringi_1.7.12
- [5] lattice_0.21-8 digest_0.6.31 magrittr_2.0.3 evaluate_0.21
- [9] grid_4.3.0 fastmap_1.1.1 rprojroot_2.0.3 jsonlite_1.8.4
-[13] DBI_1.1.3 mclust_6.0.0 gridExtra_2.3 purrr_1.0.1
-[17] fansi_1.0.4 scales_1.2.1 codetools_0.2-19 textshaping_0.3.6
-[21] jquerylib_0.1.4 cli_3.6.1 rlang_1.1.1 munsell_0.5.0
-[25] cachem_1.0.8 yaml_2.3.7 tools_4.3.0 parallel_4.3.0
-[29] memoise_2.0.1 dplyr_1.1.2 colorspace_2.1-0 ggplot2_3.4.2
-[33] vctrs_0.6.2 R6_2.5.1 zoo_1.8-12 lifecycle_1.0.3
-[37] stringr_1.5.0 fs_1.6.2 ragg_1.2.5 pkgconfig_2.0.3
-[41] desc_1.4.2 pkgdown_2.0.7 bslib_0.4.2 pillar_1.9.0
-[45] gtable_0.3.3 glue_1.6.2 systemfonts_1.0.4 highr_0.10
-[49] xfun_0.39 tibble_3.2.1 lmtest_0.9-40 tidyselect_1.2.0
-[53] htmltools_0.5.5 rmarkdown_2.21 compiler_4.3.0 </code></pre>
+ [1] gtable_0.3.6 jsonlite_1.8.9 dplyr_1.1.4 compiler_4.4.2
+ [5] tidyselect_1.2.1 parallel_4.4.2 gridExtra_2.3 jquerylib_0.1.4
+ [9] systemfonts_1.1.0 scales_1.3.0 textshaping_0.4.1 yaml_2.3.10
+[13] fastmap_1.2.0 lattice_0.22-6 ggplot2_3.5.1 R6_2.5.1
+[17] generics_0.1.3 lmtest_0.9-40 MASS_7.3-61 htmlwidgets_1.6.4
+[21] tibble_3.2.1 desc_1.4.3 munsell_0.5.1 bslib_0.8.0
+[25] pillar_1.9.0 rlang_1.1.4 utf8_1.2.4 cachem_1.1.0
+[29] xfun_0.49 fs_1.6.5 sass_0.4.9 cli_3.6.3
+[33] pkgdown_2.1.1 magrittr_2.0.3 digest_0.6.37 grid_4.4.2
+[37] npde_3.5 mclust_6.1.1 lifecycle_1.0.4 vctrs_0.6.5
+[41] evaluate_1.0.1 glue_1.8.0 ragg_1.3.3 zoo_1.8-12
+[45] saemix_3.3 fansi_1.0.6 colorspace_2.1-1 rmarkdown_2.29
+[49] pkgconfig_2.0.3 tools_4.4.2 htmltools_0.5.8.1</code></pre>
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<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
@@ -679,35 +626,26 @@ November 2017</span>.”</span> <a href="https://open.efsa.europa.eu/study-inven
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