diff options
| author | Johannes Ranke <jranke@uni-bremen.de> | 2023-04-20 19:53:28 +0200 |
|---|---|---|
| committer | Johannes Ranke <jranke@uni-bremen.de> | 2023-04-20 20:03:32 +0200 |
| commit | 9ae42bd20bc2543a94cf1581ba9820c2f9e3afbd (patch) | |
| tree | b3539a9689f5930b8444a5fc459781b825e00fa4 | |
| parent | ad0efc2d16a84c674307ad2df9d44153b44a9cf8 (diff) | |
Fix and rebuild documentation, see NEWS
I had to fix the two pathway vignettes, as they did not work with
the released version any more. So they and the multistart vignette
which got some small fixes as well were rebuilt.
Complete rebuild of the online docs with the released version. The
documentation of the 'hierarchial_kinetics' format had to be fixed
as well.
269 files changed, 15174 insertions, 1784 deletions
diff --git a/DESCRIPTION b/DESCRIPTION index 8a9836da..54df9c7e 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,8 +1,8 @@ Package: mkin Type: Package Title: Kinetic Evaluation of Chemical Degradation Data -Version: 1.2.3 -Date: 2023-04-16 +Version: 1.2.3.1 +Date: 2023-04-20 Authors@R: c( person("Johannes", "Ranke", role = c("aut", "cre", "cph"), email = "johannes.ranke@jrwb.de", @@ -1,3 +1,7 @@ +# mkin 1.2.3.1 + +- Small fixes to get the online docs right (example code in R/hierarchical_kinetics, cluster setup in cyantraniliprole and dmta pathway vignettes, graphics and model comparison in multistart vignette), rebuild online docs + # mkin 1.2.3 - 'R/{endpoints,parms,plot.mixed.mmkin,summary.saem.mmkin}.R': Calculate parameters and endpoints and plot population curves for specific covariate values, or specific percentiles of covariate values used in saem fits. diff --git a/docs/404.html b/docs/404.html index 8e7e8f45..4d888396 100644 --- a/docs/404.html +++ b/docs/404.html @@ -32,14 +32,14 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="https://pkgdown.jrwb.de/mkin/index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.1</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3.1</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"> <li> - <a href="https://pkgdown.jrwb.de/mkin/reference/index.html">Functions and data</a> + <a href="https://pkgdown.jrwb.de/mkin/reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="https://pkgdown.jrwb.de/mkin/#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -51,6 +51,9 @@ <li> <a href="https://pkgdown.jrwb.de/mkin/articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="https://pkgdown.jrwb.de/mkin/articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -58,22 +61,31 @@ <a href="https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li class="divider"> </li> +<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="https://pkgdown.jrwb.de/mkin/articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + </li> +<li class="dropdown-header">Performance</li> + <li> + <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -81,6 +93,15 @@ <li> <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Miscellaneous</li> + <li> + <a href="https://pkgdown.jrwb.de/mkin/articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul> </li> <li> @@ -130,7 +151,7 @@ Content not found. Please use links in the navbar. <div class="pkgdown"> <p></p> -<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer> diff --git a/docs/articles/FOCUS_D.html b/docs/articles/FOCUS_D.html index 3c8ad547..0d2f56f5 100644 --- a/docs/articles/FOCUS_D.html +++ b/docs/articles/FOCUS_D.html @@ -33,14 +33,14 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"> <li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -52,6 +52,9 @@ <li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -59,22 +62,31 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li class="divider"> </li> +<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + </li> +<li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -82,6 +94,15 @@ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul> </li> <li> @@ -105,13 +126,16 @@ - </header><script src="FOCUS_D_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> - <h1 data-toc-skip>Example evaluation of FOCUS Example Dataset D</h1> - <h4 data-toc-skip class="author">Johannes Ranke</h4> + <h1 data-toc-skip>Example evaluation of FOCUS Example Dataset +D</h1> + <h4 data-toc-skip class="author">Johannes +Ranke</h4> - <h4 data-toc-skip class="date">Last change 31 January 2019 (rebuilt 2022-11-17)</h4> + <h4 data-toc-skip class="date">Last change 31 January 2019 +(rebuilt 2023-04-20)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/FOCUS_D.rmd" class="external-link"><code>vignettes/FOCUS_D.rmd</code></a></small> <div class="hidden name"><code>FOCUS_D.rmd</code></div> @@ -120,7 +144,12 @@ -<p>This is just a very simple vignette showing how to fit a degradation model for a parent compound with one transformation product using <code>mkin</code>. After loading the library we look at the data. We have observed concentrations in the column named <code>value</code> at the times specified in column <code>time</code> for the two observed variables named <code>parent</code> and <code>m1</code>.</p> +<p>This is just a very simple vignette showing how to fit a degradation +model for a parent compound with one transformation product using +<code>mkin</code>. After loading the library we look at the data. We +have observed concentrations in the column named <code>value</code> at +the times specified in column <code>time</code> for the two observed +variables named <code>parent</code> and <code>m1</code>.</p> <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span>, quietly <span class="op">=</span> <span class="cn">TRUE</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">FOCUS_2006_D</span><span class="op">)</span></span></code></pre></div> @@ -169,8 +198,14 @@ <span><span class="co">## 42 m1 100 33.13</span></span> <span><span class="co">## 43 m1 120 25.15</span></span> <span><span class="co">## 44 m1 120 33.31</span></span></code></pre> -<p>Next we specify the degradation model: The parent compound degrades with simple first-order kinetics (SFO) to one metabolite named m1, which also degrades with SFO kinetics.</p> -<p>The call to mkinmod returns a degradation model. The differential equations represented in R code can be found in the character vector <code>$diffs</code> of the <code>mkinmod</code> object. If a C compiler (gcc) is installed and functional, the differential equation model will be compiled from auto-generated C code.</p> +<p>Next we specify the degradation model: The parent compound degrades +with simple first-order kinetics (SFO) to one metabolite named m1, which +also degrades with SFO kinetics.</p> +<p>The call to mkinmod returns a degradation model. The differential +equations represented in R code can be found in the character vector +<code>$diffs</code> of the <code>mkinmod</code> object. If a C compiler +(gcc) is installed and functional, the differential equation model will +be compiled from auto-generated C code.</p> <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span><span class="op">)</span>, m1 <span class="op">=</span> <span class="fu"><a href="../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre> @@ -180,26 +215,32 @@ <span><span class="co">## "d_parent = - k_parent * parent" </span></span> <span><span class="co">## m1 </span></span> <span><span class="co">## "d_m1 = + f_parent_to_m1 * k_parent * parent - k_m1 * m1"</span></span></code></pre> -<p>We do the fitting without progress report (<code>quiet = TRUE</code>).</p> +<p>We do the fitting without progress report +(<code>quiet = TRUE</code>).</p> <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">fit</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_D</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE): Observations with value</span></span> -<span><span class="co">## of zero were removed from the data</span></span></code></pre> -<p>A plot of the fit including a residual plot for both observed variables is obtained using the <code>plot_sep</code> method for <code>mkinfit</code> objects, which shows separate graphs for all compounds and their residuals.</p> +<pre><code><span><span class="co">## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE): Observations with</span></span> +<span><span class="co">## value of zero were removed from the data</span></span></code></pre> +<p>A plot of the fit including a residual plot for both observed +variables is obtained using the <code>plot_sep</code> method for +<code>mkinfit</code> objects, which shows separate graphs for all +compounds and their residuals.</p> <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">fit</span>, lpos <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">"topright"</span>, <span class="st">"bottomright"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_D_files/figure-html/plot-1.png" width="768"></p> -<p>Confidence intervals for the parameter estimates are obtained using the <code>mkinparplot</code> function.</p> +<p>Confidence intervals for the parameter estimates are obtained using +the <code>mkinparplot</code> function.</p> <div class="sourceCode" id="cb10"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="../reference/mkinparplot.html">mkinparplot</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_D_files/figure-html/plot_2-1.png" width="768"></p> -<p>A comprehensive report of the results is obtained using the <code>summary</code> method for <code>mkinfit</code> objects.</p> +<p>A comprehensive report of the results is obtained using the +<code>summary</code> method for <code>mkinfit</code> objects.</p> <div class="sourceCode" id="cb11"><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">fit</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## mkin version used for fitting: 1.2.0 </span></span> -<span><span class="co">## R version used for fitting: 4.2.2 </span></span> -<span><span class="co">## Date of fit: Thu Nov 17 14:04:21 2022 </span></span> -<span><span class="co">## Date of summary: Thu Nov 17 14:04:21 2022 </span></span> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.3 </span></span> +<span><span class="co">## R version used for fitting: 4.2.3 </span></span> +<span><span class="co">## Date of fit: Thu Apr 20 07:37:14 2023 </span></span> +<span><span class="co">## Date of summary: Thu Apr 20 07:37:14 2023 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - k_parent * parent</span></span> @@ -207,7 +248,7 @@ <span><span class="co">## </span></span> <span><span class="co">## Model predictions using solution type analytical </span></span> <span><span class="co">## </span></span> -<span><span class="co">## Fitted using 401 model solutions performed in 0.154 s</span></span> +<span><span class="co">## Fitted using 401 model solutions performed in 0.047 s</span></span> <span><span class="co">## </span></span> <span><span class="co">## Error model: Constant variance </span></span> <span><span class="co">## </span></span> @@ -340,7 +381,7 @@ <div class="pkgdown"> <p></p> -<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer> diff --git a/docs/articles/FOCUS_D_files/figure-html/plot-1.png b/docs/articles/FOCUS_D_files/figure-html/plot-1.png Binary files differindex f0b51c1f..c0832a1a 100644 --- a/docs/articles/FOCUS_D_files/figure-html/plot-1.png +++ b/docs/articles/FOCUS_D_files/figure-html/plot-1.png diff --git a/docs/articles/FOCUS_D_files/figure-html/plot_2-1.png b/docs/articles/FOCUS_D_files/figure-html/plot_2-1.png Binary files differindex f6180470..02cfcfb4 100644 --- a/docs/articles/FOCUS_D_files/figure-html/plot_2-1.png +++ b/docs/articles/FOCUS_D_files/figure-html/plot_2-1.png diff --git a/docs/articles/FOCUS_L.html b/docs/articles/FOCUS_L.html index f02febc4..e47ed9d7 100644 --- a/docs/articles/FOCUS_L.html +++ b/docs/articles/FOCUS_L.html @@ -33,14 +33,14 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"> <li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -52,6 +52,9 @@ <li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -59,22 +62,31 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li class="divider"> </li> +<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + </li> +<li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -82,6 +94,15 @@ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul> </li> <li> @@ -105,13 +126,16 @@ - </header><script src="FOCUS_L_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> - <h1 data-toc-skip>Example evaluation of FOCUS Laboratory Data L1 to L3</h1> - <h4 data-toc-skip class="author">Johannes Ranke</h4> + <h1 data-toc-skip>Example evaluation of FOCUS Laboratory Data L1 +to L3</h1> + <h4 data-toc-skip class="author">Johannes +Ranke</h4> - <h4 data-toc-skip class="date">Last change 18 May 2022 (rebuilt 2022-11-17)</h4> + <h4 data-toc-skip class="date">Last change 18 May 2022 +(rebuilt 2023-04-20)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/FOCUS_L.rmd" class="external-link"><code>vignettes/FOCUS_L.rmd</code></a></small> <div class="hidden name"><code>FOCUS_L.rmd</code></div> @@ -123,7 +147,8 @@ <div class="section level2"> <h2 id="laboratory-data-l1">Laboratory Data L1<a class="anchor" aria-label="anchor" href="#laboratory-data-l1"></a> </h2> -<p>The following code defines example dataset L1 from the FOCUS kinetics report, p. 284:</p> +<p>The following code defines example dataset L1 from the FOCUS kinetics +report, p. 284:</p> <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="st"><a href="https://pkgdown.jrwb.de/mkin/">"mkin"</a></span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> <span><span class="va">FOCUS_2006_L1</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span> @@ -132,22 +157,29 @@ <span> <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> <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 class="op">)</span><span class="op">)</span></span> <span><span class="va">FOCUS_2006_L1_mkin</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">FOCUS_2006_L1</span><span class="op">)</span></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 models, please see the FOCUS kinetics report.</p> -<p>Since mkin version 0.9-32 (July 2014), we can use shorthand notation like <code>"SFO"</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> +<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 +models, please see the FOCUS kinetics report.</p> +<p>Since mkin version 0.9-32 (July 2014), we can use shorthand notation +like <code>"SFO"</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="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">m.L1.SFO</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_L1_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> <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">m.L1.SFO</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## mkin version used for fitting: 1.2.0 </span></span> -<span><span class="co">## R version used for fitting: 4.2.2 </span></span> -<span><span class="co">## Date of fit: Thu Nov 17 14:04:25 2022 </span></span> -<span><span class="co">## Date of summary: Thu Nov 17 14:04:25 2022 </span></span> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.3 </span></span> +<span><span class="co">## R version used for fitting: 4.2.3 </span></span> +<span><span class="co">## Date of fit: Thu Apr 20 07:37:15 2023 </span></span> +<span><span class="co">## Date of summary: Thu Apr 20 07:37:15 2023 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - k_parent * parent</span></span> <span><span class="co">## </span></span> <span><span class="co">## Model predictions using solution type analytical </span></span> <span><span class="co">## </span></span> -<span><span class="co">## Fitted using 133 model solutions performed in 0.033 s</span></span> +<span><span class="co">## Fitted using 133 model solutions performed in 0.011 s</span></span> <span><span class="co">## </span></span> <span><span class="co">## Error model: Constant variance </span></span> <span><span class="co">## </span></span> @@ -221,7 +253,8 @@ <span><span class="co">## 21 parent 10.4 12.416 -2.0163</span></span> <span><span class="co">## 30 parent 2.9 5.251 -2.3513</span></span> <span><span class="co">## 30 parent 4.0 5.251 -1.2513</span></span></code></pre> -<p>A plot of the fit is obtained with the plot function for mkinfit objects.</p> +<p>A plot of the fit is obtained with the plot function for mkinfit +objects.</p> <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L1.SFO</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>, main <span class="op">=</span> <span class="st">"FOCUS L1 - SFO"</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-4-1.png" width="576"></p> @@ -241,19 +274,19 @@ <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">m.L1.FOMC</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre> <pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre> -<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is</span></span> -<span><span class="co">## doubtful</span></span></code></pre> -<pre><code><span><span class="co">## mkin version used for fitting: 1.2.0 </span></span> -<span><span class="co">## R version used for fitting: 4.2.2 </span></span> -<span><span class="co">## Date of fit: Thu Nov 17 14:04:25 2022 </span></span> -<span><span class="co">## Date of summary: Thu Nov 17 14:04:25 2022 </span></span> +<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result</span></span> +<span><span class="co">## is doubtful</span></span></code></pre> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.3 </span></span> +<span><span class="co">## R version used for fitting: 4.2.3 </span></span> +<span><span class="co">## Date of fit: Thu Apr 20 07:37:16 2023 </span></span> +<span><span class="co">## Date of summary: Thu Apr 20 07:37:16 2023 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span></span> <span><span class="co">## </span></span> <span><span class="co">## Model predictions using solution type analytical </span></span> <span><span class="co">## </span></span> -<span><span class="co">## Fitted using 369 model solutions performed in 0.08 s</span></span> +<span><span class="co">## Fitted using 369 model solutions performed in 0.025 s</span></span> <span><span class="co">## </span></span> <span><span class="co">## Error model: Constant variance </span></span> <span><span class="co">## </span></span> @@ -316,14 +349,40 @@ <span><span class="co">## Estimated disappearance times:</span></span> <span><span class="co">## DT50 DT90 DT50back</span></span> <span><span class="co">## parent 7.25 24.08 7.25</span></span></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 that are ill-defined as a consequence.</p> -<p>And in fact, due to the higher number of parameters, and the lower number of degrees of freedom of the fit, the <span class="math inline">\(\chi^2\)</span> error level is actually higher for the FOMC model (3.6%) than for the SFO model (3.4%). Additionally, the parameters <code>log_alpha</code> and <code>log_beta</code> internally fitted in the model have excessive confidence intervals, that span more than 25 orders of magnitude (!) when backtransformed to the scale of <code>alpha</code> and <code>beta</code>. Also, the t-test for significant difference from zero does not indicate such a significant difference, with p-values greater than 0.1, and finally, the parameter correlation of <code>log_alpha</code> and <code>log_beta</code> is 1.000, clearly indicating that the model is overparameterised.</p> -<p>The <span class="math inline">\(\chi^2\)</span> error levels reported in Appendix 3 and Appendix 7 to the FOCUS kinetics report are rounded to integer percentages and partly deviate by one percentage point from the results calculated by mkin. The reason for this is not known. However, mkin gives the same <span class="math inline">\(\chi^2\)</span> error levels as the kinfit package and the calculation routines of the kinfit package have been extensively compared to the results obtained by the KinGUI software, as documented in the kinfit package vignette. KinGUI was the first widely used standard package in this field. Also, the calculation of <span class="math inline">\(\chi^2\)</span> error levels was compared with KinGUII, CAKE and DegKin manager in a project sponsored by the German Umweltbundesamt <span class="citation">(Ranke 2014)</span>.</p> +<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 +that are ill-defined as a consequence.</p> +<p>And in fact, due to the higher number of parameters, and the lower +number of degrees of freedom of the fit, the <span class="math inline">\(\chi^2\)</span> error level is actually higher for +the FOMC model (3.6%) than for the SFO model (3.4%). Additionally, the +parameters <code>log_alpha</code> and <code>log_beta</code> internally +fitted in the model have excessive confidence intervals, that span more +than 25 orders of magnitude (!) when backtransformed to the scale of +<code>alpha</code> and <code>beta</code>. Also, the t-test for +significant difference from zero does not indicate such a significant +difference, with p-values greater than 0.1, and finally, the parameter +correlation of <code>log_alpha</code> and <code>log_beta</code> is +1.000, clearly indicating that the model is overparameterised.</p> +<p>The <span class="math inline">\(\chi^2\)</span> error levels reported +in Appendix 3 and Appendix 7 to the FOCUS kinetics report are rounded to +integer percentages and partly deviate by one percentage point from the +results calculated by mkin. The reason for this is not known. However, +mkin gives the same <span class="math inline">\(\chi^2\)</span> error +levels as the kinfit package and the calculation routines of the kinfit +package have been extensively compared to the results obtained by the +KinGUI software, as documented in the kinfit package vignette. KinGUI +was the first widely used standard package in this field. Also, the +calculation of <span class="math inline">\(\chi^2\)</span> error levels +was compared with KinGUII, CAKE and DegKin manager in a project +sponsored by the German Umweltbundesamt <span class="citation">(Ranke +2014)</span>.</p> </div> <div class="section level2"> <h2 id="laboratory-data-l2">Laboratory Data L2<a class="anchor" aria-label="anchor" href="#laboratory-data-l2"></a> </h2> -<p>The following code defines example dataset L2 from the FOCUS kinetics report, p. 287:</p> +<p>The following code defines example dataset L2 from the FOCUS kinetics +report, p. 287:</p> <div class="sourceCode" id="cb14"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">FOCUS_2006_L2</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span> <span> t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">28</span><span class="op">)</span>, each <span class="op">=</span> <span class="fl">2</span><span class="op">)</span>,</span> @@ -334,15 +393,28 @@ <div class="section level3"> <h3 id="sfo-fit-for-l2">SFO fit for L2<a class="anchor" aria-label="anchor" href="#sfo-fit-for-l2"></a> </h3> -<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> +<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="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">m.L2.SFO</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_L2_mkin</span>, quiet<span class="op">=</span><span class="cn">TRUE</span><span class="op">)</span></span> <span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">m.L2.SFO</span>, show_residuals <span class="op">=</span> <span class="cn">TRUE</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span>,</span> <span> main <span class="op">=</span> <span class="st">"FOCUS L2 - SFO"</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-8-1.png" width="672"></p> -<p>The <span class="math inline">\(\chi^2\)</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 residual plot.</p> -<p>In the FOCUS kinetics report, it is stated that there is no apparent systematic error observed from the residual plot up to the measured DT90 (approximately at day 5), and there is an underestimation beyond that point.</p> -<p>We may add that it is difficult to judge the random nature of the residuals just from the three samplings at days 0, 1 and 3. Also, it is not clear <em>a priori</em> why a consistent underestimation after the approximate DT90 should be irrelevant. However, this can be rationalised by the fact that the FOCUS fate models generally only implement SFO kinetics.</p> +<p>The <span class="math inline">\(\chi^2\)</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 residual +plot.</p> +<p>In the FOCUS kinetics report, it is stated that there is no apparent +systematic error observed from the residual plot up to the measured DT90 +(approximately at day 5), and there is an underestimation beyond that +point.</p> +<p>We may add that it is difficult to judge the random nature of the +residuals just from the three samplings at days 0, 1 and 3. Also, it is +not clear <em>a priori</em> why a consistent underestimation after the +approximate DT90 should be irrelevant. However, this can be rationalised +by the fact that the FOCUS fate models generally only implement SFO +kinetics.</p> </div> <div class="section level3"> <h3 id="fomc-fit-for-l2">FOMC fit for L2<a class="anchor" aria-label="anchor" href="#fomc-fit-for-l2"></a> @@ -355,17 +427,17 @@ <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-9-1.png" width="672"></p> <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">m.L2.FOMC</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## mkin version used for fitting: 1.2.0 </span></span> -<span><span class="co">## R version used for fitting: 4.2.2 </span></span> -<span><span class="co">## Date of fit: Thu Nov 17 14:04:26 2022 </span></span> -<span><span class="co">## Date of summary: Thu Nov 17 14:04:26 2022 </span></span> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.3 </span></span> +<span><span class="co">## R version used for fitting: 4.2.3 </span></span> +<span><span class="co">## Date of fit: Thu Apr 20 07:37:16 2023 </span></span> +<span><span class="co">## Date of summary: Thu Apr 20 07:37:16 2023 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span></span> <span><span class="co">## </span></span> <span><span class="co">## Model predictions using solution type analytical </span></span> <span><span class="co">## </span></span> -<span><span class="co">## Fitted using 239 model solutions performed in 0.048 s</span></span> +<span><span class="co">## Fitted using 239 model solutions performed in 0.015 s</span></span> <span><span class="co">## </span></span> <span><span class="co">## Error model: Constant variance </span></span> <span><span class="co">## </span></span> @@ -423,7 +495,10 @@ <span><span class="co">## Estimated disappearance times:</span></span> <span><span class="co">## DT50 DT90 DT50back</span></span> <span><span class="co">## parent 0.8092 5.356 1.612</span></span></code></pre> -<p>The error level at which the <span class="math inline">\(\chi^2\)</span> test passes is much lower in this case. Therefore, the FOMC model provides a better description of the data, as less experimental error has to be assumed in order to explain the data.</p> +<p>The error level at which the <span class="math inline">\(\chi^2\)</span> test passes is much lower in this +case. Therefore, the FOMC model provides a better description of the +data, as less experimental error has to be assumed in order to explain +the data.</p> </div> <div class="section level3"> <h3 id="dfop-fit-for-l2">DFOP fit for L2<a class="anchor" aria-label="anchor" href="#dfop-fit-for-l2"></a> @@ -436,10 +511,10 @@ <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-10-1.png" width="672"></p> <div class="sourceCode" id="cb20"><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">m.L2.DFOP</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## mkin version used for fitting: 1.2.0 </span></span> -<span><span class="co">## R version used for fitting: 4.2.2 </span></span> -<span><span class="co">## Date of fit: Thu Nov 17 14:04:27 2022 </span></span> -<span><span class="co">## Date of summary: Thu Nov 17 14:04:27 2022 </span></span> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.3 </span></span> +<span><span class="co">## R version used for fitting: 4.2.3 </span></span> +<span><span class="co">## Date of fit: Thu Apr 20 07:37:16 2023 </span></span> +<span><span class="co">## Date of summary: Thu Apr 20 07:37:16 2023 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span></span> @@ -448,7 +523,7 @@ <span><span class="co">## </span></span> <span><span class="co">## Model predictions using solution type analytical </span></span> <span><span class="co">## </span></span> -<span><span class="co">## Fitted using 581 model solutions performed in 0.128 s</span></span> +<span><span class="co">## Fitted using 581 model solutions performed in 0.04 s</span></span> <span><span class="co">## </span></span> <span><span class="co">## Error model: Constant variance </span></span> <span><span class="co">## </span></span> @@ -511,13 +586,15 @@ <span><span class="co">## Estimated disappearance times:</span></span> <span><span class="co">## DT50 DT90 DT50back DT50_k1 DT50_k2</span></span> <span><span class="co">## parent 0.5335 5.311 1.599 0.03084 2.058</span></span></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> +<p>Here, the DFOP model is clearly the best-fit model for dataset L2 +based on the chi^2 error level criterion.</p> </div> </div> <div class="section level2"> <h2 id="laboratory-data-l3">Laboratory Data L3<a class="anchor" aria-label="anchor" href="#laboratory-data-l3"></a> </h2> -<p>The following code defines example dataset L3 from the FOCUS kinetics report, p. 290.</p> +<p>The following code defines example dataset L3 from the FOCUS kinetics +report, p. 290.</p> <div class="sourceCode" id="cb22"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">FOCUS_2006_L3</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span> <span> t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">30</span>, <span class="fl">60</span>, <span class="fl">91</span>, <span class="fl">120</span><span class="op">)</span>,</span> @@ -526,26 +603,35 @@ <div class="section level3"> <h3 id="fit-multiple-models">Fit multiple models<a class="anchor" aria-label="anchor" href="#fit-multiple-models"></a> </h3> -<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> +<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="downlit sourceCode r"> <code class="sourceCode R"><span><span class="co"># Only use one core here, not to offend the CRAN checks</span></span> <span><span class="va">mm.L3</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, cores <span class="op">=</span> <span class="fl">1</span>,</span> <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="st">"FOCUS L3"</span> <span class="op">=</span> <span class="va">FOCUS_2006_L3_mkin</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> <span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">mm.L3</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-12-1.png" width="700"></p> -<p>The <span class="math inline">\(\chi^2\)</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 the <span class="math inline">\(\chi^2\)</span> test passes of 7%. Fitting the four parameter DFOP model further reduces the <span class="math inline">\(\chi^2\)</span> error level considerably.</p> +<p>The <span class="math inline">\(\chi^2\)</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 the <span class="math inline">\(\chi^2\)</span> test passes of 7%. Fitting the +four parameter DFOP model further reduces the <span class="math inline">\(\chi^2\)</span> error level considerably.</p> </div> <div class="section level3"> <h3 id="accessing-mmkin-objects">Accessing mmkin objects<a class="anchor" aria-label="anchor" href="#accessing-mmkin-objects"></a> </h3> -<p>The objects returned by mmkin are arranged like a matrix, with models 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> +<p>The objects returned by mmkin are arranged like a matrix, with models +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="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">mm.L3</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## mkin version used for fitting: 1.2.0 </span></span> -<span><span class="co">## R version used for fitting: 4.2.2 </span></span> -<span><span class="co">## Date of fit: Thu Nov 17 14:04:27 2022 </span></span> -<span><span class="co">## Date of summary: Thu Nov 17 14:04:28 2022 </span></span> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.3 </span></span> +<span><span class="co">## R version used for fitting: 4.2.3 </span></span> +<span><span class="co">## Date of fit: Thu Apr 20 07:37:17 2023 </span></span> +<span><span class="co">## Date of summary: Thu Apr 20 07:37:17 2023 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span></span> @@ -554,7 +640,7 @@ <span><span class="co">## </span></span> <span><span class="co">## Model predictions using solution type analytical </span></span> <span><span class="co">## </span></span> -<span><span class="co">## Fitted using 376 model solutions performed in 0.078 s</span></span> +<span><span class="co">## Fitted using 376 model solutions performed in 0.024 s</span></span> <span><span class="co">## </span></span> <span><span class="co">## Error model: Constant variance </span></span> <span><span class="co">## </span></span> @@ -631,20 +717,30 @@ <div class="sourceCode" id="cb26"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">mm.L3</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, show_errmin <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-13-1.png" width="700"></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 model based on the <span class="math inline">\(\chi^2\)</span> error level criterion for laboratory data L3.</p> -<p>This is also an example where the standard t-test for the parameter <code>g_ilr</code> is misleading, as it tests for a significant difference from zero. In this case, zero appears to be the correct value for this parameter, and the confidence interval for the backtransformed parameter <code>g</code> is quite narrow.</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 +model based on the <span class="math inline">\(\chi^2\)</span> error +level criterion for laboratory data L3.</p> +<p>This is also an example where the standard t-test for the parameter +<code>g_ilr</code> is misleading, as it tests for a significant +difference from zero. In this case, zero appears to be the correct value +for this parameter, and the confidence interval for the backtransformed +parameter <code>g</code> is quite narrow.</p> </div> </div> <div class="section level2"> <h2 id="laboratory-data-l4">Laboratory Data L4<a class="anchor" aria-label="anchor" href="#laboratory-data-l4"></a> </h2> -<p>The following code defines example dataset L4 from the FOCUS kinetics report, p. 293:</p> +<p>The following code defines example dataset L4 from the FOCUS kinetics +report, p. 293:</p> <div class="sourceCode" id="cb27"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">FOCUS_2006_L4</span> <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span> <span> t <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">3</span>, <span class="fl">7</span>, <span class="fl">14</span>, <span class="fl">30</span>, <span class="fl">60</span>, <span class="fl">91</span>, <span class="fl">120</span><span class="op">)</span>,</span> <span> parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">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 class="op">)</span><span class="op">)</span></span> <span><span class="va">FOCUS_2006_L4_mkin</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mkin_wide_to_long.html">mkin_wide_to_long</a></span><span class="op">(</span><span class="va">FOCUS_2006_L4</span><span class="op">)</span></span></code></pre></div> -<p>Fits of the SFO and FOMC models, plots and summaries are produced below:</p> +<p>Fits of the SFO and FOMC models, plots and summaries are produced +below:</p> <div class="sourceCode" id="cb28"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="co"># Only use one core here, not to offend the CRAN checks</span></span> <span><span class="va">mm.L4</span> <span class="op"><-</span> <span class="fu"><a href="../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span><span class="op">)</span>, cores <span class="op">=</span> <span class="fl">1</span>,</span> @@ -652,20 +748,24 @@ <span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> <span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">mm.L4</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-15-1.png" width="700"></p> -<p>The <span class="math inline">\(\chi^2\)</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">\(\chi^2\)</span> test passes is slightly lower for the FOMC model. However, the difference appears negligible.</p> +<p>The <span class="math inline">\(\chi^2\)</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">\(\chi^2\)</span> test +passes is slightly lower for the FOMC model. However, the difference +appears negligible.</p> <div class="sourceCode" id="cb29"><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">mm.L4</span><span class="op">[[</span><span class="st">"SFO"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## mkin version used for fitting: 1.2.0 </span></span> -<span><span class="co">## R version used for fitting: 4.2.2 </span></span> -<span><span class="co">## Date of fit: Thu Nov 17 14:04:28 2022 </span></span> -<span><span class="co">## Date of summary: Thu Nov 17 14:04:29 2022 </span></span> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.3 </span></span> +<span><span class="co">## R version used for fitting: 4.2.3 </span></span> +<span><span class="co">## Date of fit: Thu Apr 20 07:37:17 2023 </span></span> +<span><span class="co">## Date of summary: Thu Apr 20 07:37:17 2023 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - k_parent * parent</span></span> <span><span class="co">## </span></span> <span><span class="co">## Model predictions using solution type analytical </span></span> <span><span class="co">## </span></span> -<span><span class="co">## Fitted using 142 model solutions performed in 0.029 s</span></span> +<span><span class="co">## Fitted using 142 model solutions performed in 0.009 s</span></span> <span><span class="co">## </span></span> <span><span class="co">## Error model: Constant variance </span></span> <span><span class="co">## </span></span> @@ -720,17 +820,17 @@ <span><span class="co">## parent 106 352</span></span></code></pre> <div class="sourceCode" id="cb31"><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">mm.L4</span><span class="op">[[</span><span class="st">"FOMC"</span>, <span class="fl">1</span><span class="op">]</span><span class="op">]</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## mkin version used for fitting: 1.2.0 </span></span> -<span><span class="co">## R version used for fitting: 4.2.2 </span></span> -<span><span class="co">## Date of fit: Thu Nov 17 14:04:28 2022 </span></span> -<span><span class="co">## Date of summary: Thu Nov 17 14:04:29 2022 </span></span> +<pre><code><span><span class="co">## mkin version used for fitting: 1.2.3 </span></span> +<span><span class="co">## R version used for fitting: 4.2.3 </span></span> +<span><span class="co">## Date of fit: Thu Apr 20 07:37:17 2023 </span></span> +<span><span class="co">## Date of summary: Thu Apr 20 07:37:17 2023 </span></span> <span><span class="co">## </span></span> <span><span class="co">## Equations:</span></span> <span><span class="co">## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span></span> <span><span class="co">## </span></span> <span><span class="co">## Model predictions using solution type analytical </span></span> <span><span class="co">## </span></span> -<span><span class="co">## Fitted using 224 model solutions performed in 0.046 s</span></span> +<span><span class="co">## Fitted using 224 model solutions performed in 0.014 s</span></span> <span><span class="co">## </span></span> <span><span class="co">## Error model: Constant variance </span></span> <span><span class="co">## </span></span> @@ -792,9 +892,11 @@ <div class="section level2"> <h2 class="unnumbered" id="references">References<a class="anchor" aria-label="anchor" href="#references"></a> </h2> -<div id="refs" class="references hanging-indent"> -<div id="ref-ranke2014"> -<p>Ranke, Johannes. 2014. “Prüfung und Validierung von Modellierungssoftware als Alternative zu ModelMaker 4.0.” Umweltbundesamt Projektnummer 27452.</p> +<div id="refs" class="references csl-bib-body hanging-indent"> +<div id="ref-ranke2014" class="csl-entry"> +Ranke, Johannes. 2014. <span>“<span class="nocase">Prüfung und +Validierung von Modellierungssoftware als Alternative zu ModelMaker +4.0</span>.”</span> Umweltbundesamt Projektnummer 27452. </div> </div> </div> @@ -817,7 +919,7 @@ <div class="pkgdown"> <p></p> -<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer> diff --git a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-10-1.png b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-10-1.png Binary files differindex b2bff18f..11706305 100644 --- a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-10-1.png +++ b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-10-1.png diff --git a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-12-1.png b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-12-1.png Binary files differindex d613c035..daa488a3 100644 --- a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-12-1.png +++ b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-12-1.png diff --git a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-13-1.png b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-13-1.png Binary files differindex 8387a272..5caea744 100644 --- a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-13-1.png +++ b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-13-1.png diff --git a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-15-1.png b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-15-1.png Binary files differindex 74f0fc48..0dc9d57d 100644 --- a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-15-1.png +++ b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-15-1.png diff --git a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-4-1.png b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-4-1.png Binary files differindex 1c56cb20..13344b25 100644 --- a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-4-1.png +++ b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-4-1.png diff --git a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-5-1.png b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-5-1.png Binary files differindex 4247131e..ec234b6e 100644 --- a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-5-1.png +++ b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-5-1.png diff --git a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-6-1.png b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-6-1.png Binary files differindex b6130527..c3f55dd6 100644 --- a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-6-1.png +++ b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-6-1.png diff --git a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-8-1.png b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-8-1.png Binary files differindex dea51d58..d3551b47 100644 --- a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-8-1.png +++ b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-8-1.png diff --git a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-9-1.png b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-9-1.png Binary files differindex 05460304..5f8afc00 100644 --- a/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-9-1.png +++ b/docs/articles/FOCUS_L_files/figure-html/unnamed-chunk-9-1.png diff --git a/docs/articles/index.html b/docs/articles/index.html index f4f6d557..991f8994 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -17,13 +17,13 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.1</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3.1</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -34,6 +34,8 @@ <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -41,22 +43,29 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -64,6 +73,14 @@ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -96,6 +113,12 @@ <dd> </dd><dt><a href="mkin.html">Introduction to mkin</a></dt> <dd> + </dd><dt><a href="prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a></dt> + <dd> + </dd><dt><a href="prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a></dt> + <dd> + </dd><dt><a href="prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a></dt> + <dd> </dd><dt><a href="twa.html">Calculation of time weighted average concentrations with mkin</a></dt> <dd> </dd><dt><a href="web_only/FOCUS_Z.html">Example evaluation of FOCUS dataset Z</a></dt> @@ -122,7 +145,7 @@ </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/articles/mkin.html b/docs/articles/mkin.html index da499501..88c63bef 100644 --- a/docs/articles/mkin.html +++ b/docs/articles/mkin.html @@ -33,14 +33,14 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"> <li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -52,6 +52,9 @@ <li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -59,22 +62,31 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li class="divider"> </li> +<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + </li> +<li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -82,6 +94,15 @@ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul> </li> <li> @@ -105,13 +126,15 @@ - </header><script src="mkin_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> <h1 data-toc-skip>Introduction to mkin</h1> - <h4 data-toc-skip class="author">Johannes Ranke</h4> + <h4 data-toc-skip class="author">Johannes +Ranke</h4> - <h4 data-toc-skip class="date">Last change 15 February 2021 (rebuilt 2022-11-17)</h4> + <h4 data-toc-skip class="date">Last change 15 February 2021 +(rebuilt 2023-04-20)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/mkin.rmd" class="external-link"><code>vignettes/mkin.rmd</code></a></small> <div class="hidden name"><code>mkin.rmd</code></div> @@ -120,11 +143,21 @@ -<p><a href="https://www.jrwb.de" class="external-link">Wissenschaftlicher Berater, Kronacher Str. 12, 79639 Grenzach-Wyhlen, Germany</a><br> Privatdozent at the University of Freiburg</p> +<p><a href="https://www.jrwb.de" class="external-link">Wissenschaftlicher Berater, Kronacher +Str. 12, 79639 Grenzach-Wyhlen, Germany</a><br> Privatdozent at the +University of Freiburg</p> <div class="section level2"> <h2 id="abstract">Abstract<a class="anchor" aria-label="anchor" href="#abstract"></a> </h2> -<p>In the regulatory evaluation of chemical substances like plant protection products (pesticides), biocides and other chemicals, degradation data play an important role. For the evaluation of pesticide degradation experiments, detailed guidance has been developed, based on nonlinear optimisation. The <code>R</code> add-on package <code>mkin</code> implements fitting some of the models recommended in this guidance from within R and calculates some statistical measures for data series within one or more compartments, for parent and metabolites.</p> +<p>In the regulatory evaluation of chemical substances like plant +protection products (pesticides), biocides and other chemicals, +degradation data play an important role. For the evaluation of pesticide +degradation experiments, detailed guidance has been developed, based on +nonlinear optimisation. The <code>R</code> add-on package +<code>mkin</code> implements fitting some of the models recommended in +this guidance from within R and calculates some statistical measures for +data series within one or more compartments, for parent and +metabolites.</p> <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="st"><a href="https://pkgdown.jrwb.de/mkin/">"mkin"</a></span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> <span><span class="co"># Define the kinetic model</span></span> @@ -159,95 +192,248 @@ <div class="section level2"> <h2 id="background">Background<a class="anchor" aria-label="anchor" href="#background"></a> </h2> -<p>The <code>mkin</code> package <span class="citation">(Ranke 2021)</span> implements the approach to degradation kinetics recommended in the kinetics report provided by the FOrum for Co-ordination of pesticide fate models and their USe <span class="citation">(FOCUS Work Group on Degradation Kinetics 2006, 2014)</span>. It covers data series describing the decline of one compound, data series with transformation products (commonly termed metabolites) and data series for more than one compartment. It is possible to include back reactions. Therefore, equilibrium reactions and equilibrium partitioning can be specified, although this often leads to an overparameterisation of the model.</p> -<p>When the first <code>mkin</code> code was published in 2010, the most commonly used tools for fitting more complex kinetic degradation models to experimental data were KinGUI <span class="citation">(Schäfer et al. 2007)</span>, a MATLAB based tool with a graphical user interface that was specifically tailored to the task and included some output as proposed by the FOCUS Kinetics Workgroup, and ModelMaker, a general purpose compartment based tool providing infrastructure for fitting dynamic simulation models based on differential equations to data.</p> -<p>The ‘mkin’ code was first uploaded to the BerliOS development platform. When this was taken down, the version control history was imported into the R-Forge site (see <em>e.g.</em> <a href="https://cgit.jrwb.de/mkin/commit/?id=30cbb4092f6d2d3beff5800603374a0d009ad770" class="external-link">the initial commit on 11 May 2010</a>), where the code is still being updated.</p> -<p>At that time, the R package <code>FME</code> (Flexible Modelling Environment) <span class="citation">(Soetaert and Petzoldt 2010)</span> was already available, and provided a good basis for developing a package specifically tailored to the task. The remaining challenge was to make it as easy as possible for the users (including the author of this vignette) to specify the system of differential equations and to include the output requested by the FOCUS guidance, such as the <span class="math inline">\(\chi^2\)</span> error level as defined in this guidance.</p> -<p>Also, <code>mkin</code> introduced using analytical solutions for parent only kinetics for improved optimization speed. Later, Eigenvalue based solutions were introduced to <code>mkin</code> for the case of linear differential equations (<em>i.e.</em> where the FOMC or DFOP models were not used for the parent compound), greatly improving the optimization speed for these cases. This, has become somehow obsolete, as the use of compiled code described below gives even faster execution times.</p> -<p>The possibility to specify back-reactions and a biphasic model (SFORB) for metabolites were present in <code>mkin</code> from the very beginning.</p> +<p>The <code>mkin</code> package <span class="citation">(J. Ranke +2021)</span> implements the approach to degradation kinetics recommended +in the kinetics report provided by the FOrum for Co-ordination of +pesticide fate models and their USe <span class="citation">(FOCUS Work +Group on Degradation Kinetics 2006, 2014)</span>. It covers data series +describing the decline of one compound, data series with transformation +products (commonly termed metabolites) and data series for more than one +compartment. It is possible to include back reactions. Therefore, +equilibrium reactions and equilibrium partitioning can be specified, +although this often leads to an overparameterisation of the model.</p> +<p>When the first <code>mkin</code> code was published in 2010, the most +commonly used tools for fitting more complex kinetic degradation models +to experimental data were KinGUI <span class="citation">(Schäfer et al. +2007)</span>, a MATLAB based tool with a graphical user interface that +was specifically tailored to the task and included some output as +proposed by the FOCUS Kinetics Workgroup, and ModelMaker, a general +purpose compartment based tool providing infrastructure for fitting +dynamic simulation models based on differential equations to data.</p> +<p>The ‘mkin’ code was first uploaded to the BerliOS development +platform. When this was taken down, the version control history was +imported into the R-Forge site (see <em>e.g.</em> <a href="https://cgit.jrwb.de/mkin/commit/?id=30cbb4092f6d2d3beff5800603374a0d009ad770" class="external-link">the +initial commit on 11 May 2010</a>), where the code is still being +updated.</p> +<p>At that time, the R package <code>FME</code> (Flexible Modelling +Environment) <span class="citation">(Soetaert and Petzoldt 2010)</span> +was already available, and provided a good basis for developing a +package specifically tailored to the task. The remaining challenge was +to make it as easy as possible for the users (including the author of +this vignette) to specify the system of differential equations and to +include the output requested by the FOCUS guidance, such as the <span class="math inline">\(\chi^2\)</span> error level as defined in this +guidance.</p> +<p>Also, <code>mkin</code> introduced using analytical solutions for +parent only kinetics for improved optimization speed. Later, Eigenvalue +based solutions were introduced to <code>mkin</code> for the case of +linear differential equations (<em>i.e.</em> where the FOMC or DFOP +models were not used for the parent compound), greatly improving the +optimization speed for these cases. This, has become somehow obsolete, +as the use of compiled code described below gives even faster execution +times.</p> +<p>The possibility to specify back-reactions and a biphasic model +(SFORB) for metabolites were present in <code>mkin</code> from the very +beginning.</p> <div class="section level3"> <h3 id="derived-software-tools">Derived software tools<a class="anchor" aria-label="anchor" href="#derived-software-tools"></a> </h3> -<p>Soon after the publication of <code>mkin</code>, two derived tools were published, namely KinGUII (developed at Bayer Crop Science) and CAKE (commissioned to Tessella by Syngenta), which added a graphical user interface (GUI), and added fitting by iteratively reweighted least squares (IRLS) and characterisation of likely parameter distributions by Markov Chain Monte Carlo (MCMC) sampling.</p> -<p>CAKE focuses on a smooth use experience, sacrificing some flexibility in the model definition, originally allowing only two primary metabolites in parallel. The current version 3.4 of CAKE released in May 2020 uses a scheme for up to six metabolites in a flexible arrangement and supports biphasic modelling of metabolites, but does not support back-reactions (non-instantaneous equilibria).</p> -<p>KinGUI offers an even more flexible widget for specifying complex kinetic models. Back-reactions (non-instantaneous equilibria) were supported early on, but until 2014, only simple first-order models could be specified for transformation products. Starting with KinGUII version 2.1, biphasic modelling of metabolites was also available in KinGUII.</p> -<p>A further graphical user interface (GUI) that has recently been brought to a decent degree of maturity is the browser based GUI named <code>gmkin</code>. Please see its <a href="https://pkgdown.jrwb.de/gmkin/" class="external-link">documentation page</a> and <a href="https://pkgdown.jrwb.de/gmkin/articles/gmkin_manual.html" class="external-link">manual</a> for further information.</p> -<p>A comparison of scope, usability and numerical results obtained with these tools has been recently been published by <span class="citation">Ranke, Wöltjen, and Meinecke (2018)</span>.</p> +<p>Soon after the publication of <code>mkin</code>, two derived tools +were published, namely KinGUII (developed at Bayer Crop Science) and +CAKE (commissioned to Tessella by Syngenta), which added a graphical +user interface (GUI), and added fitting by iteratively reweighted least +squares (IRLS) and characterisation of likely parameter distributions by +Markov Chain Monte Carlo (MCMC) sampling.</p> +<p>CAKE focuses on a smooth use experience, sacrificing some flexibility +in the model definition, originally allowing only two primary +metabolites in parallel. The current version 3.4 of CAKE released in May +2020 uses a scheme for up to six metabolites in a flexible arrangement +and supports biphasic modelling of metabolites, but does not support +back-reactions (non-instantaneous equilibria).</p> +<p>KinGUI offers an even more flexible widget for specifying complex +kinetic models. Back-reactions (non-instantaneous equilibria) were +supported early on, but until 2014, only simple first-order models could +be specified for transformation products. Starting with KinGUII version +2.1, biphasic modelling of metabolites was also available in +KinGUII.</p> +<p>A further graphical user interface (GUI) that has recently been +brought to a decent degree of maturity is the browser based GUI named +<code>gmkin</code>. Please see its <a href="https://pkgdown.jrwb.de/gmkin/" class="external-link">documentation page</a> and <a href="https://pkgdown.jrwb.de/gmkin/articles/gmkin_manual.html" class="external-link">manual</a> +for further information.</p> +<p>A comparison of scope, usability and numerical results obtained with +these tools has been recently been published by <span class="citation">Johannes Ranke, Wöltjen, and Meinecke +(2018)</span>.</p> </div> </div> <div class="section level2"> <h2 id="unique-features">Unique features<a class="anchor" aria-label="anchor" href="#unique-features"></a> </h2> -<p>Currently, the main unique features available in <code>mkin</code> are</p> +<p>Currently, the main unique features available in <code>mkin</code> +are</p> <ul> -<li>the <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html">speed increase</a> by using compiled code when a compiler is present,</li> -<li>parallel model fitting on multicore machines using the <a href="https://pkgdown.jrwb.de/mkin/reference/mmkin.html"><code>mmkin</code> function</a>,</li> -<li>the estimation of parameter confidence intervals based on transformed parameters (see below) and</li> -<li>the possibility to use the <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">two-component error model</a> +<li>the <a href="https://pkgdown.jrwb.de/mkin/articles/web_only/compiled_models.html">speed +increase</a> by using compiled code when a compiler is present,</li> +<li>parallel model fitting on multicore machines using the <a href="https://pkgdown.jrwb.de/mkin/reference/mmkin.html"><code>mmkin</code> +function</a>,</li> +<li>the estimation of parameter confidence intervals based on +transformed parameters (see below) and</li> +<li>the possibility to use the <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">two-component +error model</a> </li> </ul> -<p>The iteratively reweighted least squares fitting of different variances for each variable as introduced by <span class="citation">Gao et al. (2011)</span> has been available in mkin since <a href="https://pkgdown.jrwb.de/mkin/news/index.html#mkin-0-9-22-2013-10-26">version 0.9-22</a>. With <a href="https://pkgdown.jrwb.de/mkin/news/index.html#mkin-0-9-49-5-2019-07-04">release 0.9.49.5</a>, the IRLS algorithm has been complemented by direct or step-wise maximisation of the likelihood function, which makes it possible not only to fit the variance by variable error model but also a <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">two-component error model</a> inspired by error models developed in analytical chemistry <span class="citation">(Ranke and Meinecke 2019)</span>.</p> +<p>The iteratively reweighted least squares fitting of different +variances for each variable as introduced by <span class="citation">Gao +et al. (2011)</span> has been available in mkin since <a href="https://pkgdown.jrwb.de/mkin/news/index.html#mkin-0-9-22-2013-10-26">version +0.9-22</a>. With <a href="https://pkgdown.jrwb.de/mkin/news/index.html#mkin-0-9-49-5-2019-07-04">release +0.9.49.5</a>, the IRLS algorithm has been complemented by direct or +step-wise maximisation of the likelihood function, which makes it +possible not only to fit the variance by variable error model but also a +<a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">two-component +error model</a> inspired by error models developed in analytical +chemistry <span class="citation">(Johannes Ranke and Meinecke +2019)</span>.</p> </div> <div class="section level2"> <h2 id="internal-parameter-transformations">Internal parameter transformations<a class="anchor" aria-label="anchor" href="#internal-parameter-transformations"></a> </h2> -<p>For rate constants, the log transformation is used, as proposed by Bates and Watts <span class="citation">(1988, 77, 149)</span>. Approximate intervals are constructed for the transformed rate constants <span class="citation">(compare Bates and Watts 1988, 135)</span>, <em>i.e.</em> for their logarithms. Confidence intervals for the rate constants are then obtained using the appropriate backtransformation using the exponential function.</p> -<p>In the first version of <code>mkin</code> allowing for specifying models using formation fractions, a home-made reparameterisation was used in order to ensure that the sum of formation fractions would not exceed unity.</p> -<p>This method is still used in the current version of KinGUII (v2.1 from April 2014), with a modification that allows for fixing the pathway to sink to zero. CAKE uses penalties in the objective function in order to enforce this constraint.</p> -<p>In 2012, an alternative reparameterisation of the formation fractions was proposed together with René Lehmann <span class="citation">(Ranke and Lehmann 2012)</span>, based on isometric logratio transformation (ILR). The aim was to improve the validity of the linear approximation of the objective function during the parameter estimation procedure as well as in the subsequent calculation of parameter confidence intervals. In the current version of mkin, a logit transformation is used for parameters that are bound between 0 and 1, such as the g parameter of the DFOP model.</p> +<p>For rate constants, the log transformation is used, as proposed by +Bates and Watts <span class="citation">(1988, 77, 149)</span>. +Approximate intervals are constructed for the transformed rate constants +<span class="citation">(compare Bates and Watts 1988, 135)</span>, +<em>i.e.</em> for their logarithms. Confidence intervals for the rate +constants are then obtained using the appropriate backtransformation +using the exponential function.</p> +<p>In the first version of <code>mkin</code> allowing for specifying +models using formation fractions, a home-made reparameterisation was +used in order to ensure that the sum of formation fractions would not +exceed unity.</p> +<p>This method is still used in the current version of KinGUII (v2.1 +from April 2014), with a modification that allows for fixing the pathway +to sink to zero. CAKE uses penalties in the objective function in order +to enforce this constraint.</p> +<p>In 2012, an alternative reparameterisation of the formation fractions +was proposed together with René Lehmann <span class="citation">(J. Ranke +and Lehmann 2012)</span>, based on isometric logratio transformation +(ILR). The aim was to improve the validity of the linear approximation +of the objective function during the parameter estimation procedure as +well as in the subsequent calculation of parameter confidence intervals. +In the current version of mkin, a logit transformation is used for +parameters that are bound between 0 and 1, such as the g parameter of +the DFOP model.</p> <div class="section level3"> <h3 id="confidence-intervals-based-on-transformed-parameters">Confidence intervals based on transformed parameters<a class="anchor" aria-label="anchor" href="#confidence-intervals-based-on-transformed-parameters"></a> </h3> -<p>In the first attempt at providing improved parameter confidence intervals introduced to <code>mkin</code> in 2013, confidence intervals obtained from FME on the transformed parameters were simply all backtransformed one by one to yield asymmetric confidence intervals for the backtransformed parameters.</p> -<p>However, while there is a 1:1 relation between the rate constants in the model and the transformed parameters fitted in the model, the parameters obtained by the isometric logratio transformation are calculated from the set of formation fractions that quantify the paths to each of the compounds formed from a specific parent compound, and no such 1:1 relation exists.</p> -<p>Therefore, parameter confidence intervals for formation fractions obtained with this method only appear valid for the case of a single transformation product, where currently the logit transformation is used for the formation fraction.</p> -<p>The confidence intervals obtained by backtransformation for the cases where a 1:1 relation between transformed and original parameter exist are considered by the author of this vignette to be more accurate than those obtained using a re-estimation of the Hessian matrix after backtransformation, as implemented in the FME package.</p> +<p>In the first attempt at providing improved parameter confidence +intervals introduced to <code>mkin</code> in 2013, confidence intervals +obtained from FME on the transformed parameters were simply all +backtransformed one by one to yield asymmetric confidence intervals for +the backtransformed parameters.</p> +<p>However, while there is a 1:1 relation between the rate constants in +the model and the transformed parameters fitted in the model, the +parameters obtained by the isometric logratio transformation are +calculated from the set of formation fractions that quantify the paths +to each of the compounds formed from a specific parent compound, and no +such 1:1 relation exists.</p> +<p>Therefore, parameter confidence intervals for formation fractions +obtained with this method only appear valid for the case of a single +transformation product, where currently the logit transformation is used +for the formation fraction.</p> +<p>The confidence intervals obtained by backtransformation for the cases +where a 1:1 relation between transformed and original parameter exist +are considered by the author of this vignette to be more accurate than +those obtained using a re-estimation of the Hessian matrix after +backtransformation, as implemented in the FME package.</p> </div> <div class="section level3"> <h3 id="parameter-t-test-based-on-untransformed-parameters">Parameter t-test based on untransformed parameters<a class="anchor" aria-label="anchor" href="#parameter-t-test-based-on-untransformed-parameters"></a> </h3> -<p>The standard output of many nonlinear regression software packages includes the results from a test for significant difference from zero for all parameters. Such a test is also recommended to check the validity of rate constants in the FOCUS guidance <span class="citation">(FOCUS Work Group on Degradation Kinetics 2014, 96ff)</span>.</p> -<p>It has been argued that the precondition for this test, <em>i.e.</em> normal distribution of the estimator for the parameters, is not fulfilled in the case of nonlinear regression <span class="citation">(Ranke and Lehmann 2015)</span>. However, this test is commonly used by industry, consultants and national authorities in order to decide on the reliability of parameter estimates, based on the FOCUS guidance mentioned above. Therefore, the results of this one-sided t-test are included in the summary output from <code>mkin</code>.</p> -<p>As it is not reasonable to test for significant difference of the transformed parameters (<em>e.g.</em> <span class="math inline">\(log(k)\)</span>) from zero, the t-test is calculated based on the model definition before parameter transformation, <em>i.e.</em> in a similar way as in packages that do not apply such an internal parameter transformation. A note is included in the <code>mkin</code> output, pointing to the fact that the t-test is based on the unjustified assumption of normal distribution of the parameter estimators.</p> +<p>The standard output of many nonlinear regression software packages +includes the results from a test for significant difference from zero +for all parameters. Such a test is also recommended to check the +validity of rate constants in the FOCUS guidance <span class="citation">(FOCUS Work Group on Degradation Kinetics 2014, +96ff)</span>.</p> +<p>It has been argued that the precondition for this test, <em>i.e.</em> +normal distribution of the estimator for the parameters, is not +fulfilled in the case of nonlinear regression <span class="citation">(J. +Ranke and Lehmann 2015)</span>. However, this test is commonly used by +industry, consultants and national authorities in order to decide on the +reliability of parameter estimates, based on the FOCUS guidance +mentioned above. Therefore, the results of this one-sided t-test are +included in the summary output from <code>mkin</code>.</p> +<p>As it is not reasonable to test for significant difference of the +transformed parameters (<em>e.g.</em> <span class="math inline">\(log(k)\)</span>) from zero, the t-test is +calculated based on the model definition before parameter +transformation, <em>i.e.</em> in a similar way as in packages that do +not apply such an internal parameter transformation. A note is included +in the <code>mkin</code> output, pointing to the fact that the t-test is +based on the unjustified assumption of normal distribution of the +parameter estimators.</p> </div> </div> <div class="section level2"> <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a> </h2> <!-- vim: set foldmethod=syntax: --> -<div id="refs" class="references hanging-indent"> -<div id="ref-bates1988"> -<p>Bates, D., and D. Watts. 1988. <em>Nonlinear Regression and Its Applications</em>. Wiley-Interscience.</p> +<div id="refs" class="references csl-bib-body hanging-indent"> +<div id="ref-bates1988" class="csl-entry"> +Bates, D., and D. Watts. 1988. <em>Nonlinear Regression and Its +Applications</em>. Wiley-Interscience. </div> -<div id="ref-FOCUS2006"> -<p>FOCUS Work Group on Degradation Kinetics. 2006. <em>Guidance Document on Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration. Report of the Focus Work Group on Degradation Kinetics</em>. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> +<div id="ref-FOCUS2006" class="csl-entry"> +FOCUS Work Group on Degradation Kinetics. 2006. <em>Guidance Document on +Estimating Persistence and Degradation Kinetics from Environmental Fate +Studies on Pesticides in EU Registration. Report of the FOCUS Work Group +on Degradation Kinetics</em>. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>. </div> -<div id="ref-FOCUSkinetics2014"> -<p>———. 2014. <em>Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> +<div id="ref-FOCUSkinetics2014" class="csl-entry"> +———. 2014. <em>Generic Guidance for Estimating Persistence and +Degradation Kinetics from Environmental Fate Studies on Pesticides in EU +Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>. </div> -<div id="ref-gao11"> -<p>Gao, Z., J. W. Green, J. Vanderborght, and W. Schmitt. 2011. “Improving Uncertainty Analysis in Kinetic Evaluations Using Iteratively Reweighted Least Squares.” Journal. <em>Environmental Science and Technology</em> 45: 4429–37.</p> +<div id="ref-gao11" class="csl-entry"> +Gao, Z., J. W. Green, J. Vanderborght, and W. Schmitt. 2011. +<span>“Improving Uncertainty Analysis in Kinetic Evaluations Using +Iteratively Reweighted Least Squares.”</span> Journal. <em>Environmental +Science and Technology</em> 45: 4429–37. </div> -<div id="ref-pkg:mkin"> -<p>Ranke, J. 2021. <em>‘mkin‘: Kinetic Evaluation of Chemical Degradation Data</em>. <a href="https://CRAN.R-project.org/package=mkin" class="external-link">https://CRAN.R-project.org/package=mkin</a>.</p> +<div id="ref-pkg:mkin" class="csl-entry"> +Ranke, J. 2021. <em>‘<span class="nocase">mkin</span>‘: +<span>K</span>inetic Evaluation of Chemical Degradation Data</em>. <a href="https://CRAN.R-project.org/package=mkin" class="external-link">https://CRAN.R-project.org/package=mkin</a>. </div> -<div id="ref-ranke2012"> -<p>Ranke, J., and R. Lehmann. 2012. “Parameter Reliability in Kinetic Evaluation of Environmental Metabolism Data - Assessment and the Influence of Model Specification.” In <em>SETAC World 20-24 May</em>. Berlin. <a href="https://jrwb.de/posters/Poster_SETAC_2012_Kinetic_parameter_uncertainty_model_parameterization_Lehmann_Ranke.pdf" class="external-link">https://jrwb.de/posters/Poster_SETAC_2012_Kinetic_parameter_uncertainty_model_parameterization_Lehmann_Ranke.pdf</a>.</p> +<div id="ref-ranke2012" class="csl-entry"> +Ranke, J., and R. Lehmann. 2012. <span>“Parameter Reliability in Kinetic +Evaluation of Environmental Metabolism Data - Assessment and the +Influence of Model Specification.”</span> In <em>SETAC World 20-24 +May</em>. Berlin. <a href="https://jrwb.de/posters/Poster_SETAC_2012_Kinetic_parameter_uncertainty_model_parameterization_Lehmann_Ranke.pdf" class="external-link">https://jrwb.de/posters/Poster_SETAC_2012_Kinetic_parameter_uncertainty_model_parameterization_Lehmann_Ranke.pdf</a>. </div> -<div id="ref-ranke2015"> -<p>———. 2015. “To T-Test or Not to T-Test, That Is the Question.” In <em>XV Symposium on Pesticide Chemistry 2-4 September 2015</em>. Piacenza. <a href="https://jrwb.de/posters/piacenza_2015.pdf" class="external-link">https://jrwb.de/posters/piacenza_2015.pdf</a>.</p> +<div id="ref-ranke2015" class="csl-entry"> +———. 2015. <span>“To t-Test or Not to t-Test, That Is the +Question.”</span> In <em>XV Symposium on Pesticide Chemistry 2-4 +September 2015</em>. Piacenza. <a href="https://jrwb.de/posters/piacenza_2015.pdf" class="external-link">https://jrwb.de/posters/piacenza_2015.pdf</a>. </div> -<div id="ref-ranke2019"> -<p>Ranke, Johannes, and Stefan Meinecke. 2019. “Error Models for the Kinetic Evaluation of Chemical Degradation Data.” <em>Environments</em> 6 (12). <a href="https://doi.org/10.3390/environments6120124" class="external-link">https://doi.org/10.3390/environments6120124</a>.</p> +<div id="ref-ranke2019" class="csl-entry"> +Ranke, Johannes, and Stefan Meinecke. 2019. <span>“Error Models for the +Kinetic Evaluation of Chemical Degradation Data.”</span> +<em>Environments</em> 6 (12). <a href="https://doi.org/10.3390/environments6120124" class="external-link">https://doi.org/10.3390/environments6120124</a>. </div> -<div id="ref-ranke2018"> -<p>Ranke, Johannes, Janina Wöltjen, and Stefan Meinecke. 2018. “Comparison of Software Tools for Kinetic Evaluation of Chemical Degradation Data.” <em>Environmental Sciences Europe</em> 30 (1): 17. <a href="https://doi.org/10.1186/s12302-018-0145-1" class="external-link">https://doi.org/10.1186/s12302-018-0145-1</a>.</p> +<div id="ref-ranke2018" class="csl-entry"> +Ranke, Johannes, Janina Wöltjen, and Stefan Meinecke. 2018. +<span>“Comparison of Software Tools for Kinetic Evaluation of Chemical +Degradation Data.”</span> <em>Environmental Sciences Europe</em> 30 (1): +17. <a href="https://doi.org/10.1186/s12302-018-0145-1" class="external-link">https://doi.org/10.1186/s12302-018-0145-1</a>. </div> -<div id="ref-schaefer2007"> -<p>Schäfer, D., B. Mikolasch, P. Rainbird, and B. Harvey. 2007. “KinGUI: A New Kinetic Software Tool for Evaluations According to FOCUS Degradation Kinetics.” In <em>Proceedings of the Xiii Symposium Pesticide Chemistry</em>, edited by Del Re A. A. M., Capri E., Fragoulis G., and Trevisan M., 916–23. Piacenza.</p> +<div id="ref-schaefer2007" class="csl-entry"> +Schäfer, D., B. Mikolasch, P. Rainbird, and B. Harvey. 2007. +<span>“<span>KinGUI</span>: A New Kinetic Software Tool for Evaluations +According to <span>FOCUS</span> Degradation Kinetics.”</span> In +<em>Proceedings of the XIII Symposium Pesticide Chemistry</em>, edited +by Del Re A. A. M., Capri E., Fragoulis G., and Trevisan M., 916–23. +Piacenza. </div> -<div id="ref-soetaert2010"> -<p>Soetaert, Karline, and Thomas Petzoldt. 2010. “Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME.” <em>Journal of Statistical Software</em> 33 (3): 1–28. <a href="https://doi.org/10.18637/jss.v033.i03" class="external-link">https://doi.org/10.18637/jss.v033.i03</a>.</p> +<div id="ref-soetaert2010" class="csl-entry"> +Soetaert, Karline, and Thomas Petzoldt. 2010. <span>“Inverse Modelling, +Sensitivity and Monte Carlo Analysis in <span>R</span> Using Package +<span>FME</span>.”</span> <em>Journal of Statistical Software</em> 33 +(3): 1–28. <a href="https://doi.org/10.18637/jss.v033.i03" class="external-link">https://doi.org/10.18637/jss.v033.i03</a>. </div> </div> </div> @@ -270,7 +456,7 @@ <div class="pkgdown"> <p></p> -<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer> diff --git a/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png b/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png Binary files differindex 63246387..7ba861ea 100644 --- a/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png +++ b/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png diff --git a/docs/articles/prebuilt/2022_cyan_pathway.html b/docs/articles/prebuilt/2022_cyan_pathway.html new file mode 100644 index 00000000..cd63fa3c --- /dev/null +++ b/docs/articles/prebuilt/2022_cyan_pathway.html @@ -0,0 +1,5657 @@ +<!DOCTYPE html> +<!-- Generated by pkgdown: do not edit by hand --><html lang="en"> +<head> +<meta http-equiv="Content-Type" content="text/html; charset=UTF-8"> +<meta charset="utf-8"> +<meta http-equiv="X-UA-Compatible" content="IE=edge"> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<title>Testing hierarchical pathway kinetics with residue data on cyantraniliprole • mkin</title> +<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"> +<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../../bootstrap-toc.css"> +<script src="../../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"> +<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"> +<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../../pkgdown.css" rel="stylesheet"> +<script src="../../pkgdown.js"></script><meta property="og:title" content="Testing hierarchical pathway kinetics with residue data on cyantraniliprole"> +<meta property="og:description" content="mkin"> +<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> +<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> +<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> +<![endif]--> +</head> +<body data-spy="scroll" data-target="#toc"> + + + <div class="container template-article"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <div class="container"> + <div class="navbar-header"> + <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> + <span class="sr-only">Toggle navigation</span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + </button> + <span class="navbar-brand"> + <a class="navbar-link" href="../../index.html">mkin</a> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3.1</span> + </span> + </div> + + <div id="navbar" class="navbar-collapse collapse"> + <ul class="nav navbar-nav"> +<li> + <a href="../../reference/index.html">Reference</a> +</li> +<li class="dropdown"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> + Articles + + <span class="caret"></span> + </a> + <ul class="dropdown-menu" role="menu"> +<li> + <a href="../../articles/mkin.html">Introduction to mkin</a> + </li> + <li class="divider"> + </li> +<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> + <li> + <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> + </li> + <li> + <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> + </li> + <li> + <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li class="divider"> + </li> +<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> + <li> + <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> + </li> + <li> + <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> + </li> + <li> + <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> + </li> + <li> + <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> + </li> + <li> + <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + </li> +<li class="dropdown-header">Performance</li> + <li> + <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + </li> + <li> + <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + <li class="divider"> + </li> +<li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> + </ul> +</li> +<li> + <a href="../../news/index.html">News</a> +</li> + </ul> +<ul class="nav navbar-nav navbar-right"> +<li> + <a href="https://github.com/jranke/mkin/" class="external-link"> + <span class="fab fa-github fa-lg"></span> + + </a> +</li> + </ul> +</div> +<!--/.nav-collapse --> + </div> +<!--/.container --> +</div> +<!--/.navbar --> + + + + </header><div class="row"> + <div class="col-md-9 contents"> + <div class="page-header toc-ignore"> + <h1 data-toc-skip>Testing hierarchical pathway kinetics with +residue data on cyantraniliprole</h1> + <h4 data-toc-skip class="author">Johannes +Ranke</h4> + + <h4 data-toc-skip class="date">Last change on 20 April 2023, +last compiled on 20 April 2023</h4> + + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/prebuilt/2022_cyan_pathway.rmd" class="external-link"><code>vignettes/prebuilt/2022_cyan_pathway.rmd</code></a></small> + <div class="hidden name"><code>2022_cyan_pathway.rmd</code></div> + + </div> + + + +<div class="section level2"> +<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a> +</h2> +<p>The purpose of this document is to test demonstrate how nonlinear +hierarchical models (NLHM) based on the parent degradation models SFO, +FOMC, DFOP and HS, with serial formation of two or more metabolites can +be fitted with the mkin package.</p> +<p>It was assembled in the course of work package 1.2 of Project Number +173340 (Application of nonlinear hierarchical models to the kinetic +evaluation of chemical degradation data) of the German Environment +Agency carried out in 2022 and 2023.</p> +<p>The mkin package is used in version 1.2.3 which is currently under +development. The newly introduced functionality that is used here is a +simplification of excluding random effects for a set of fits based on a +related set of fits with a reduced model, and the documentation of the +starting parameters of the fit, so that all starting parameters of +<code>saem</code> fits are now listed in the summary. 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> +<p>This document is processed with the <code>knitr</code> package, which +also provides the <code>kable</code> function that is used to improve +the display of tabular data in R markdown documents. For parallel +processing, the <code>parallel</code> package is used.</p> +<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span><span class="op">)</span></span> +<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://yihui.org/knitr/" class="external-link">knitr</a></span><span class="op">)</span></span> +<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">saemix</span><span class="op">)</span></span> +<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">parallel</span><span class="op">)</span></span> +<span><span class="va">n_cores</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># We need to start a new cluster after defining a compiled model that is</span></span> +<span><span class="co"># saved as a DLL to the user directory, therefore we define a function</span></span> +<span><span class="co"># This is used again after defining the pathway model</span></span> +<span><span class="va">start_cluster</span> <span class="op"><-</span> <span class="kw">function</span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span> <span class="op">{</span></span> +<span> <span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.info.html" class="external-link">Sys.info</a></span><span class="op">(</span><span class="op">)</span><span class="op">[</span><span class="st">"sysname"</span><span class="op">]</span> <span class="op">==</span> <span class="st">"Windows"</span><span class="op">)</span> <span class="op">{</span></span> +<span> <span class="va">ret</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makePSOCKcluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span> +<span> <span class="op">}</span> <span class="kw">else</span> <span class="op">{</span></span> +<span> <span class="va">ret</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makeForkCluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span> +<span> <span class="op">}</span></span> +<span> <span class="kw"><a href="https://rdrr.io/r/base/function.html" class="external-link">return</a></span><span class="op">(</span><span class="va">ret</span><span class="op">)</span></span> +<span><span class="op">}</span></span> +<span><span class="va">cl</span> <span class="op"><-</span> <span class="fu">start_cluster</span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span></code></pre></div> +<div class="section level3"> +<h3 id="test-data">Test data<a class="anchor" aria-label="anchor" href="#test-data"></a> +</h3> +<p>The example data are taken from the final addendum to the DAR from +2014 and are distributed with the mkin package. Residue data and time +step normalisation factors are read in using the function +<code>read_spreadsheet</code> from the mkin package. This function also +performs the time step normalisation.</p> +<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">data_file</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.file.html" class="external-link">system.file</a></span><span class="op">(</span></span> +<span> <span class="st">"testdata"</span>, <span class="st">"cyantraniliprole_soil_efsa_2014.xlsx"</span>,</span> +<span> package <span class="op">=</span> <span class="st">"mkin"</span><span class="op">)</span></span> +<span><span class="va">cyan_ds</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/read_spreadsheet.html">read_spreadsheet</a></span><span class="op">(</span><span class="va">data_file</span>, parent_only <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> +<p>The following tables show the covariate data and the 5 datasets that +were read in from the spreadsheet file.</p> +<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">pH</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/attr.html" class="external-link">attr</a></span><span class="op">(</span><span class="va">cyan_ds</span>, <span class="st">"covariates"</span><span class="op">)</span></span> +<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="va">pH</span>, caption <span class="op">=</span> <span class="st">"Covariate data"</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<caption>Covariate data</caption> +<thead><tr class="header"> +<th align="left"></th> +<th align="right">pH</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">Nambsheim</td> +<td align="right">7.90</td> +</tr> +<tr class="even"> +<td align="left">Tama</td> +<td align="right">6.20</td> +</tr> +<tr class="odd"> +<td align="left">Gross-Umstadt</td> +<td align="right">7.04</td> +</tr> +<tr class="even"> +<td align="left">Sassafras</td> +<td align="right">4.62</td> +</tr> +<tr class="odd"> +<td align="left">Lleida</td> +<td align="right">8.05</td> +</tr> +</tbody> +</table> +<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="kw">for</span> <span class="op">(</span><span class="va">ds_name</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">cyan_ds</span><span class="op">)</span><span class="op">)</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> +<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="fu"><a href="../../reference/mkin_long_to_wide.html">mkin_long_to_wide</a></span><span class="op">(</span><span class="va">cyan_ds</span><span class="op">[[</span><span class="va">ds_name</span><span class="op">]</span><span class="op">]</span><span class="op">)</span>,</span> +<span> caption <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="va">ds_name</span><span class="op">)</span>,</span> +<span> booktabs <span class="op">=</span> <span class="cn">TRUE</span>, row.names <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">)</span></span> +<span> <span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="st">"\n\\clearpage\n"</span><span class="op">)</span></span> +<span><span class="op">}</span></span></code></pre></div> +<table class="table"> +<caption>Dataset Nambsheim</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">cyan</th> +<th align="right">JCZ38</th> +<th align="right">J9C38</th> +<th align="right">JSE76</th> +<th align="right">J9Z38</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">105.79</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">3.210424</td> +<td align="right">77.26</td> +<td align="right">7.92</td> +<td align="right">11.94</td> +<td align="right">5.58</td> +<td align="right">9.12</td> +</tr> +<tr class="odd"> +<td align="right">7.490988</td> +<td align="right">57.13</td> +<td align="right">15.46</td> +<td align="right">16.58</td> +<td align="right">12.59</td> +<td align="right">11.74</td> +</tr> +<tr class="even"> +<td align="right">17.122259</td> +<td align="right">37.74</td> +<td align="right">15.98</td> +<td align="right">13.36</td> +<td align="right">26.05</td> +<td align="right">10.77</td> +</tr> +<tr class="odd"> +<td align="right">23.543105</td> +<td align="right">31.47</td> +<td align="right">6.05</td> +<td align="right">14.49</td> +<td align="right">34.71</td> +<td align="right">4.96</td> +</tr> +<tr class="even"> +<td align="right">43.875788</td> +<td align="right">16.74</td> +<td align="right">6.07</td> +<td align="right">7.57</td> +<td align="right">40.38</td> +<td align="right">6.52</td> +</tr> +<tr class="odd"> +<td align="right">67.418893</td> +<td align="right">8.85</td> +<td align="right">10.34</td> +<td align="right">6.39</td> +<td align="right">30.71</td> +<td align="right">8.90</td> +</tr> +<tr class="even"> +<td align="right">107.014116</td> +<td align="right">5.19</td> +<td align="right">9.61</td> +<td align="right">1.95</td> +<td align="right">20.41</td> +<td align="right">12.93</td> +</tr> +<tr class="odd"> +<td align="right">129.487080</td> +<td align="right">3.45</td> +<td align="right">6.18</td> +<td align="right">1.36</td> +<td align="right">21.78</td> +<td align="right">6.99</td> +</tr> +<tr class="even"> +<td align="right">195.835832</td> +<td align="right">2.15</td> +<td align="right">9.13</td> +<td align="right">0.95</td> +<td align="right">16.29</td> +<td align="right">7.69</td> +</tr> +<tr class="odd"> +<td align="right">254.693596</td> +<td align="right">1.92</td> +<td align="right">6.92</td> +<td align="right">0.20</td> +<td align="right">13.57</td> +<td align="right">7.16</td> +</tr> +<tr class="even"> +<td align="right">321.042348</td> +<td align="right">2.26</td> +<td align="right">7.02</td> +<td align="right">NA</td> +<td align="right">11.12</td> +<td align="right">8.66</td> +</tr> +<tr class="odd"> +<td align="right">383.110535</td> +<td align="right">NA</td> +<td align="right">5.05</td> +<td align="right">NA</td> +<td align="right">10.64</td> +<td align="right">5.56</td> +</tr> +<tr class="even"> +<td align="right">0.000000</td> +<td align="right">105.57</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">3.210424</td> +<td align="right">78.88</td> +<td align="right">12.77</td> +<td align="right">11.94</td> +<td align="right">5.47</td> +<td align="right">9.12</td> +</tr> +<tr class="even"> +<td align="right">7.490988</td> +<td align="right">59.94</td> +<td align="right">15.27</td> +<td align="right">16.58</td> +<td align="right">13.60</td> +<td align="right">11.74</td> +</tr> +<tr class="odd"> +<td align="right">17.122259</td> +<td align="right">39.67</td> +<td align="right">14.26</td> +<td align="right">13.36</td> +<td align="right">29.44</td> +<td align="right">10.77</td> +</tr> +<tr class="even"> +<td align="right">23.543105</td> +<td align="right">30.21</td> +<td align="right">16.07</td> +<td align="right">14.49</td> +<td align="right">35.90</td> +<td align="right">4.96</td> +</tr> +<tr class="odd"> +<td align="right">43.875788</td> +<td align="right">18.06</td> +<td align="right">9.44</td> +<td align="right">7.57</td> +<td align="right">42.30</td> +<td align="right">6.52</td> +</tr> +<tr class="even"> +<td align="right">67.418893</td> +<td align="right">8.54</td> +<td align="right">5.78</td> +<td align="right">6.39</td> +<td align="right">34.70</td> +<td align="right">8.90</td> +</tr> +<tr class="odd"> +<td align="right">107.014116</td> +<td align="right">7.26</td> +<td align="right">4.54</td> +<td align="right">1.95</td> +<td align="right">23.33</td> +<td align="right">12.93</td> +</tr> +<tr class="even"> +<td align="right">129.487080</td> +<td align="right">3.60</td> +<td align="right">4.22</td> +<td align="right">1.36</td> +<td align="right">23.56</td> +<td align="right">6.99</td> +</tr> +<tr class="odd"> +<td align="right">195.835832</td> +<td align="right">2.84</td> +<td align="right">3.05</td> +<td align="right">0.95</td> +<td align="right">16.21</td> +<td align="right">7.69</td> +</tr> +<tr class="even"> +<td align="right">254.693596</td> +<td align="right">2.00</td> +<td align="right">2.90</td> +<td align="right">0.20</td> +<td align="right">15.53</td> +<td align="right">7.16</td> +</tr> +<tr class="odd"> +<td align="right">321.042348</td> +<td align="right">1.79</td> +<td align="right">0.94</td> +<td align="right">NA</td> +<td align="right">9.80</td> +<td align="right">8.66</td> +</tr> +<tr class="even"> +<td align="right">383.110535</td> +<td align="right">NA</td> +<td align="right">1.82</td> +<td align="right">NA</td> +<td align="right">9.49</td> +<td align="right">5.56</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset Tama</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">cyan</th> +<th align="right">JCZ38</th> +<th align="right">J9Z38</th> +<th align="right">JSE76</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">106.14</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">2.400833</td> +<td align="right">93.47</td> +<td align="right">6.46</td> +<td align="right">2.85</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">5.601943</td> +<td align="right">88.39</td> +<td align="right">10.86</td> +<td align="right">4.65</td> +<td align="right">3.85</td> +</tr> +<tr class="even"> +<td align="right">12.804442</td> +<td align="right">72.29</td> +<td align="right">11.97</td> +<td align="right">4.91</td> +<td align="right">11.24</td> +</tr> +<tr class="odd"> +<td align="right">17.606108</td> +<td align="right">65.79</td> +<td align="right">13.11</td> +<td align="right">6.63</td> +<td align="right">13.79</td> +</tr> +<tr class="even"> +<td align="right">32.811382</td> +<td align="right">53.16</td> +<td align="right">11.24</td> +<td align="right">8.90</td> +<td align="right">23.40</td> +</tr> +<tr class="odd"> +<td align="right">50.417490</td> +<td align="right">44.01</td> +<td align="right">11.34</td> +<td align="right">9.98</td> +<td align="right">29.56</td> +</tr> +<tr class="even"> +<td align="right">80.027761</td> +<td align="right">33.23</td> +<td align="right">8.82</td> +<td align="right">11.31</td> +<td align="right">35.63</td> +</tr> +<tr class="odd"> +<td align="right">96.833591</td> +<td align="right">40.68</td> +<td align="right">5.94</td> +<td align="right">8.32</td> +<td align="right">29.09</td> +</tr> +<tr class="even"> +<td align="right">146.450803</td> +<td align="right">20.65</td> +<td align="right">4.49</td> +<td align="right">8.72</td> +<td align="right">36.88</td> +</tr> +<tr class="odd"> +<td align="right">190.466072</td> +<td align="right">17.71</td> +<td align="right">4.66</td> +<td align="right">11.10</td> +<td align="right">40.97</td> +</tr> +<tr class="even"> +<td align="right">240.083284</td> +<td align="right">14.86</td> +<td align="right">2.27</td> +<td align="right">11.62</td> +<td align="right">40.11</td> +</tr> +<tr class="odd"> +<td align="right">286.499386</td> +<td align="right">12.02</td> +<td align="right">NA</td> +<td align="right">10.73</td> +<td align="right">42.58</td> +</tr> +<tr class="even"> +<td align="right">0.000000</td> +<td align="right">109.11</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">2.400833</td> +<td align="right">96.84</td> +<td align="right">5.52</td> +<td align="right">2.04</td> +<td align="right">2.02</td> +</tr> +<tr class="even"> +<td align="right">5.601943</td> +<td align="right">85.29</td> +<td align="right">9.65</td> +<td align="right">2.99</td> +<td align="right">4.39</td> +</tr> +<tr class="odd"> +<td align="right">12.804442</td> +<td align="right">73.68</td> +<td align="right">12.48</td> +<td align="right">5.05</td> +<td align="right">11.47</td> +</tr> +<tr class="even"> +<td align="right">17.606108</td> +<td align="right">64.89</td> +<td align="right">12.44</td> +<td align="right">6.29</td> +<td align="right">15.00</td> +</tr> +<tr class="odd"> +<td align="right">32.811382</td> +<td align="right">52.27</td> +<td align="right">10.86</td> +<td align="right">7.65</td> +<td align="right">23.30</td> +</tr> +<tr class="even"> +<td align="right">50.417490</td> +<td align="right">42.61</td> +<td align="right">10.54</td> +<td align="right">9.37</td> +<td align="right">31.06</td> +</tr> +<tr class="odd"> +<td align="right">80.027761</td> +<td align="right">34.29</td> +<td align="right">10.02</td> +<td align="right">9.04</td> +<td align="right">37.87</td> +</tr> +<tr class="even"> +<td align="right">96.833591</td> +<td align="right">30.50</td> +<td align="right">6.34</td> +<td align="right">8.14</td> +<td align="right">33.97</td> +</tr> +<tr class="odd"> +<td align="right">146.450803</td> +<td align="right">19.21</td> +<td align="right">6.29</td> +<td align="right">8.52</td> +<td align="right">26.15</td> +</tr> +<tr class="even"> +<td align="right">190.466072</td> +<td align="right">17.55</td> +<td align="right">5.81</td> +<td align="right">9.89</td> +<td align="right">32.08</td> +</tr> +<tr class="odd"> +<td align="right">240.083284</td> +<td align="right">13.22</td> +<td align="right">5.99</td> +<td align="right">10.79</td> +<td align="right">40.66</td> +</tr> +<tr class="even"> +<td align="right">286.499386</td> +<td align="right">11.09</td> +<td align="right">6.05</td> +<td align="right">8.82</td> +<td align="right">42.90</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset Gross-Umstadt</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">cyan</th> +<th align="right">JCZ38</th> +<th align="right">J9Z38</th> +<th align="right">JSE76</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.0000000</td> +<td align="right">103.03</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">2.1014681</td> +<td align="right">87.85</td> +<td align="right">4.79</td> +<td align="right">3.26</td> +<td align="right">0.62</td> +</tr> +<tr class="odd"> +<td align="right">4.9034255</td> +<td align="right">77.35</td> +<td align="right">8.05</td> +<td align="right">9.89</td> +<td align="right">1.32</td> +</tr> +<tr class="even"> +<td align="right">10.5073404</td> +<td align="right">69.33</td> +<td align="right">9.74</td> +<td align="right">12.32</td> +<td align="right">4.74</td> +</tr> +<tr class="odd"> +<td align="right">21.0146807</td> +<td align="right">55.65</td> +<td align="right">14.57</td> +<td align="right">13.59</td> +<td align="right">9.84</td> +</tr> +<tr class="even"> +<td align="right">31.5220211</td> +<td align="right">49.03</td> +<td align="right">14.66</td> +<td align="right">16.71</td> +<td align="right">12.32</td> +</tr> +<tr class="odd"> +<td align="right">42.0293615</td> +<td align="right">41.86</td> +<td align="right">15.97</td> +<td align="right">13.64</td> +<td align="right">15.53</td> +</tr> +<tr class="even"> +<td align="right">63.0440422</td> +<td align="right">34.88</td> +<td align="right">18.20</td> +<td align="right">14.12</td> +<td align="right">22.02</td> +</tr> +<tr class="odd"> +<td align="right">84.0587230</td> +<td align="right">28.26</td> +<td align="right">15.64</td> +<td align="right">14.06</td> +<td align="right">25.60</td> +</tr> +<tr class="even"> +<td align="right">0.0000000</td> +<td align="right">104.05</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">2.1014681</td> +<td align="right">85.25</td> +<td align="right">2.68</td> +<td align="right">7.32</td> +<td align="right">0.69</td> +</tr> +<tr class="even"> +<td align="right">4.9034255</td> +<td align="right">77.22</td> +<td align="right">7.28</td> +<td align="right">8.37</td> +<td align="right">1.45</td> +</tr> +<tr class="odd"> +<td align="right">10.5073404</td> +<td align="right">65.23</td> +<td align="right">10.73</td> +<td align="right">10.93</td> +<td align="right">4.74</td> +</tr> +<tr class="even"> +<td align="right">21.0146807</td> +<td align="right">57.78</td> +<td align="right">12.29</td> +<td align="right">14.80</td> +<td align="right">9.05</td> +</tr> +<tr class="odd"> +<td align="right">31.5220211</td> +<td align="right">54.83</td> +<td align="right">14.05</td> +<td align="right">12.01</td> +<td align="right">11.05</td> +</tr> +<tr class="even"> +<td align="right">42.0293615</td> +<td align="right">45.17</td> +<td align="right">12.12</td> +<td align="right">17.89</td> +<td align="right">15.71</td> +</tr> +<tr class="odd"> +<td align="right">63.0440422</td> +<td align="right">34.83</td> +<td align="right">12.90</td> +<td align="right">15.86</td> +<td align="right">22.52</td> +</tr> +<tr class="even"> +<td align="right">84.0587230</td> +<td align="right">26.59</td> +<td align="right">14.28</td> +<td align="right">14.91</td> +<td align="right">28.48</td> +</tr> +<tr class="odd"> +<td align="right">0.0000000</td> +<td align="right">104.62</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">0.8145225</td> +<td align="right">97.21</td> +<td align="right">NA</td> +<td align="right">4.00</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">1.9005525</td> +<td align="right">89.64</td> +<td align="right">3.59</td> +<td align="right">5.24</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">4.0726125</td> +<td align="right">87.90</td> +<td align="right">4.10</td> +<td align="right">9.58</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">8.1452251</td> +<td align="right">86.90</td> +<td align="right">5.96</td> +<td align="right">9.45</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">12.2178376</td> +<td align="right">74.74</td> +<td align="right">7.83</td> +<td align="right">15.03</td> +<td align="right">5.33</td> +</tr> +<tr class="odd"> +<td align="right">16.2904502</td> +<td align="right">74.13</td> +<td align="right">8.84</td> +<td align="right">14.41</td> +<td align="right">5.10</td> +</tr> +<tr class="even"> +<td align="right">24.4356753</td> +<td align="right">65.26</td> +<td align="right">11.84</td> +<td align="right">18.33</td> +<td align="right">6.71</td> +</tr> +<tr class="odd"> +<td align="right">32.5809004</td> +<td align="right">57.70</td> +<td align="right">12.74</td> +<td align="right">19.93</td> +<td align="right">9.74</td> +</tr> +<tr class="even"> +<td align="right">0.0000000</td> +<td align="right">101.94</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">0.8145225</td> +<td align="right">99.94</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">1.9005525</td> +<td align="right">94.87</td> +<td align="right">NA</td> +<td align="right">4.56</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">4.0726125</td> +<td align="right">86.96</td> +<td align="right">6.75</td> +<td align="right">6.90</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">8.1452251</td> +<td align="right">80.51</td> +<td align="right">10.68</td> +<td align="right">7.43</td> +<td align="right">2.58</td> +</tr> +<tr class="odd"> +<td align="right">12.2178376</td> +<td align="right">78.38</td> +<td align="right">10.35</td> +<td align="right">9.46</td> +<td align="right">3.69</td> +</tr> +<tr class="even"> +<td align="right">16.2904502</td> +<td align="right">70.05</td> +<td align="right">13.73</td> +<td align="right">9.27</td> +<td align="right">7.18</td> +</tr> +<tr class="odd"> +<td align="right">24.4356753</td> +<td align="right">61.28</td> +<td align="right">12.57</td> +<td align="right">13.28</td> +<td align="right">13.19</td> +</tr> +<tr class="even"> +<td align="right">32.5809004</td> +<td align="right">52.85</td> +<td align="right">12.67</td> +<td align="right">12.95</td> +<td align="right">13.69</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset Sassafras</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">cyan</th> +<th align="right">JCZ38</th> +<th align="right">J9Z38</th> +<th align="right">JSE76</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">102.17</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">2.216719</td> +<td align="right">95.49</td> +<td align="right">1.11</td> +<td align="right">0.10</td> +<td align="right">0.83</td> +</tr> +<tr class="odd"> +<td align="right">5.172343</td> +<td align="right">83.35</td> +<td align="right">6.43</td> +<td align="right">2.89</td> +<td align="right">3.30</td> +</tr> +<tr class="even"> +<td align="right">11.083593</td> +<td align="right">78.18</td> +<td align="right">10.00</td> +<td align="right">5.59</td> +<td align="right">0.81</td> +</tr> +<tr class="odd"> +<td align="right">22.167186</td> +<td align="right">70.44</td> +<td align="right">17.21</td> +<td align="right">4.23</td> +<td align="right">1.09</td> +</tr> +<tr class="even"> +<td align="right">33.250779</td> +<td align="right">68.00</td> +<td align="right">20.45</td> +<td align="right">5.86</td> +<td align="right">1.17</td> +</tr> +<tr class="odd"> +<td align="right">44.334371</td> +<td align="right">59.64</td> +<td align="right">24.64</td> +<td align="right">3.17</td> +<td align="right">2.72</td> +</tr> +<tr class="even"> +<td align="right">66.501557</td> +<td align="right">50.73</td> +<td align="right">27.50</td> +<td align="right">6.19</td> +<td align="right">1.27</td> +</tr> +<tr class="odd"> +<td align="right">88.668742</td> +<td align="right">45.65</td> +<td align="right">32.77</td> +<td align="right">5.69</td> +<td align="right">4.54</td> +</tr> +<tr class="even"> +<td align="right">0.000000</td> +<td align="right">100.43</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">2.216719</td> +<td align="right">95.34</td> +<td align="right">3.21</td> +<td align="right">0.14</td> +<td align="right">0.46</td> +</tr> +<tr class="even"> +<td align="right">5.172343</td> +<td align="right">84.38</td> +<td align="right">5.73</td> +<td align="right">4.75</td> +<td align="right">0.62</td> +</tr> +<tr class="odd"> +<td align="right">11.083593</td> +<td align="right">78.50</td> +<td align="right">11.89</td> +<td align="right">3.99</td> +<td align="right">0.73</td> +</tr> +<tr class="even"> +<td align="right">22.167186</td> +<td align="right">71.17</td> +<td align="right">17.28</td> +<td align="right">4.39</td> +<td align="right">0.66</td> +</tr> +<tr class="odd"> +<td align="right">33.250779</td> +<td align="right">59.41</td> +<td align="right">18.73</td> +<td align="right">11.85</td> +<td align="right">2.65</td> +</tr> +<tr class="even"> +<td align="right">44.334371</td> +<td align="right">64.57</td> +<td align="right">22.93</td> +<td align="right">5.13</td> +<td align="right">2.01</td> +</tr> +<tr class="odd"> +<td align="right">66.501557</td> +<td align="right">49.08</td> +<td align="right">33.39</td> +<td align="right">5.67</td> +<td align="right">3.63</td> +</tr> +<tr class="even"> +<td align="right">88.668742</td> +<td align="right">40.41</td> +<td align="right">39.60</td> +<td align="right">5.93</td> +<td align="right">6.17</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset Lleida</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">cyan</th> +<th align="right">JCZ38</th> +<th align="right">J9Z38</th> +<th align="right">JSE76</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">102.71</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">2.821051</td> +<td align="right">79.11</td> +<td align="right">5.70</td> +<td align="right">8.07</td> +<td align="right">0.97</td> +</tr> +<tr class="odd"> +<td align="right">6.582451</td> +<td align="right">70.03</td> +<td align="right">7.17</td> +<td align="right">11.31</td> +<td align="right">4.72</td> +</tr> +<tr class="even"> +<td align="right">14.105253</td> +<td align="right">50.93</td> +<td align="right">10.25</td> +<td align="right">14.84</td> +<td align="right">9.95</td> +</tr> +<tr class="odd"> +<td align="right">28.210505</td> +<td align="right">33.43</td> +<td align="right">10.40</td> +<td align="right">14.82</td> +<td align="right">24.06</td> +</tr> +<tr class="even"> +<td align="right">42.315758</td> +<td align="right">24.69</td> +<td align="right">9.75</td> +<td align="right">16.38</td> +<td align="right">29.38</td> +</tr> +<tr class="odd"> +<td align="right">56.421010</td> +<td align="right">22.99</td> +<td align="right">10.06</td> +<td align="right">15.51</td> +<td align="right">29.25</td> +</tr> +<tr class="even"> +<td align="right">84.631516</td> +<td align="right">14.63</td> +<td align="right">5.63</td> +<td align="right">14.74</td> +<td align="right">31.04</td> +</tr> +<tr class="odd"> +<td align="right">112.842021</td> +<td align="right">12.43</td> +<td align="right">4.17</td> +<td align="right">13.53</td> +<td align="right">33.28</td> +</tr> +<tr class="even"> +<td align="right">0.000000</td> +<td align="right">99.31</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">2.821051</td> +<td align="right">82.07</td> +<td align="right">6.55</td> +<td align="right">5.60</td> +<td align="right">1.12</td> +</tr> +<tr class="even"> +<td align="right">6.582451</td> +<td align="right">70.65</td> +<td align="right">7.61</td> +<td align="right">8.01</td> +<td align="right">3.21</td> +</tr> +<tr class="odd"> +<td align="right">14.105253</td> +<td align="right">53.52</td> +<td align="right">11.48</td> +<td align="right">10.82</td> +<td align="right">12.24</td> +</tr> +<tr class="even"> +<td align="right">28.210505</td> +<td align="right">35.60</td> +<td align="right">11.19</td> +<td align="right">15.43</td> +<td align="right">23.53</td> +</tr> +<tr class="odd"> +<td align="right">42.315758</td> +<td align="right">34.26</td> +<td align="right">11.09</td> +<td align="right">13.26</td> +<td align="right">27.42</td> +</tr> +<tr class="even"> +<td align="right">56.421010</td> +<td align="right">21.79</td> +<td align="right">4.80</td> +<td align="right">18.30</td> +<td align="right">30.20</td> +</tr> +<tr class="odd"> +<td align="right">84.631516</td> +<td align="right">14.06</td> +<td align="right">6.30</td> +<td align="right">16.35</td> +<td align="right">32.32</td> +</tr> +<tr class="even"> +<td align="right">112.842021</td> +<td align="right">11.51</td> +<td align="right">5.57</td> +<td align="right">12.64</td> +<td align="right">32.51</td> +</tr> +</tbody> +</table> +</div> +</div> +<div class="section level2"> +<h2 id="parent-only-evaluations">Parent only evaluations<a class="anchor" aria-label="anchor" href="#parent-only-evaluations"></a> +</h2> +<p>As the pathway fits have very long run times, evaluations of the +parent data are performed first, in order to determine for each +hierarchical parent degradation model which random effects on the +degradation model parameters are ill-defined.</p> +<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">cyan_sep_const</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span>, <span class="st">"SFORB"</span>, <span class="st">"HS"</span><span class="op">)</span>,</span> +<span> <span class="va">cyan_ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, cores <span class="op">=</span> <span class="va">n_cores</span><span class="op">)</span></span> +<span><span class="va">cyan_sep_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">cyan_sep_const</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span> +<span><span class="va">cyan_saem_full</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mhmkin.html">mhmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">cyan_sep_const</span>, <span class="va">cyan_sep_tc</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">cyan_saem_full</span><span class="op">)</span> <span class="op">|></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"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">SFO</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">FOMC</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">DFOP</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">SFORB</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">HS</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +</tbody> +</table> +<p>All fits converged successfully.</p> +<div class="sourceCode" id="cb6"><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">cyan_saem_full</span><span class="op">)</span> <span class="op">|></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"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">SFO</td> +<td align="left">sd(cyan_0)</td> +<td align="left">sd(cyan_0)</td> +</tr> +<tr class="even"> +<td align="left">FOMC</td> +<td align="left">sd(log_beta)</td> +<td align="left">sd(cyan_0)</td> +</tr> +<tr class="odd"> +<td align="left">DFOP</td> +<td align="left">sd(cyan_0)</td> +<td align="left">sd(cyan_0), sd(log_k1)</td> +</tr> +<tr class="even"> +<td align="left">SFORB</td> +<td align="left">sd(cyan_free_0)</td> +<td align="left">sd(cyan_free_0), sd(log_k_cyan_free_bound)</td> +</tr> +<tr class="odd"> +<td align="left">HS</td> +<td align="left">sd(cyan_0)</td> +<td align="left">sd(cyan_0)</td> +</tr> +</tbody> +</table> +<p>In almost all models, the random effect for the initial concentration +of the parent compound is ill-defined. For the biexponential models DFOP +and SFORB, the random effect of one additional parameter is ill-defined +when the two-component error model is used.</p> +<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">cyan_saem_full</span><span class="op">)</span> <span class="op">|></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">1</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="right">npar</th> +<th align="right">AIC</th> +<th align="right">BIC</th> +<th align="right">Lik</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">SFO const</td> +<td align="right">5</td> +<td align="right">833.9</td> +<td align="right">832.0</td> +<td align="right">-412.0</td> +</tr> +<tr class="even"> +<td align="left">SFO tc</td> +<td align="right">6</td> +<td align="right">831.6</td> +<td align="right">829.3</td> +<td align="right">-409.8</td> +</tr> +<tr class="odd"> +<td align="left">FOMC const</td> +<td align="right">7</td> +<td align="right">709.1</td> +<td align="right">706.4</td> +<td align="right">-347.6</td> +</tr> +<tr class="even"> +<td align="left">FOMC tc</td> +<td align="right">8</td> +<td align="right">689.2</td> +<td align="right">686.1</td> +<td align="right">-336.6</td> +</tr> +<tr class="odd"> +<td align="left">DFOP const</td> +<td align="right">9</td> +<td align="right">703.0</td> +<td align="right">699.5</td> +<td align="right">-342.5</td> +</tr> +<tr class="even"> +<td align="left">SFORB const</td> +<td align="right">9</td> +<td align="right">701.3</td> +<td align="right">697.8</td> +<td align="right">-341.7</td> +</tr> +<tr class="odd"> +<td align="left">HS const</td> +<td align="right">9</td> +<td align="right">718.6</td> +<td align="right">715.1</td> +<td align="right">-350.3</td> +</tr> +<tr class="even"> +<td align="left">DFOP tc</td> +<td align="right">10</td> +<td align="right">703.1</td> +<td align="right">699.2</td> +<td align="right">-341.6</td> +</tr> +<tr class="odd"> +<td align="left">SFORB tc</td> +<td align="right">10</td> +<td align="right">700.1</td> +<td align="right">696.2</td> +<td align="right">-340.1</td> +</tr> +<tr class="even"> +<td align="left">HS tc</td> +<td align="right">10</td> +<td align="right">716.7</td> +<td align="right">712.8</td> +<td align="right">-348.3</td> +</tr> +</tbody> +</table> +<p>Model comparison based on AIC and BIC indicates that the +two-component error model is preferable for all parent models with the +exception of DFOP. The lowest AIC and BIC values are are obtained with +the FOMC model, followed by SFORB and DFOP.</p> +<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">stopCluster</a></span><span class="op">(</span><span class="va">cl</span><span class="op">)</span></span></code></pre></div> +</div> +<div class="section level2"> +<h2 id="pathway-fits">Pathway fits<a class="anchor" aria-label="anchor" href="#pathway-fits"></a> +</h2> +<div class="section level3"> +<h3 id="evaluations-with-pathway-established-previously">Evaluations with pathway established previously<a class="anchor" aria-label="anchor" href="#evaluations-with-pathway-established-previously"></a> +</h3> +<p>To test the technical feasibility of coupling the relevant parent +degradation models with different transformation pathway models, a list +of <code>mkinmod</code> models is set up below. As in the EU evaluation, +parallel formation of metabolites JCZ38 and J9Z38 and secondary +formation of metabolite JSE76 from JCZ38 is used.</p> +<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="kw">if</span> <span class="op">(</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/files2.html" class="external-link">dir.exists</a></span><span class="op">(</span><span class="st">"cyan_dlls"</span><span class="op">)</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/files2.html" class="external-link">dir.create</a></span><span class="op">(</span><span class="st">"cyan_dlls"</span><span class="op">)</span></span> +<span><span class="va">cyan_path_1</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span> +<span> sfo_path_1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> +<span> cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</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">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span> +<span> JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span> +<span> J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> name <span class="op">=</span> <span class="st">"sfo_path_1"</span>, dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span> +<span> fomc_path_1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> +<span> cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"FOMC"</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">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span> +<span> JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span> +<span> J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> name <span class="op">=</span> <span class="st">"fomc_path_1"</span>, dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span> +<span> dfop_path_1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> +<span> cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</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">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span> +<span> JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span> +<span> J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> name <span class="op">=</span> <span class="st">"dfop_path_1"</span>, dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span> +<span> sforb_path_1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> +<span> cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</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">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span> +<span> JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span> +<span> J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> name <span class="op">=</span> <span class="st">"sforb_path_1"</span>, dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span> +<span> hs_path_1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> +<span> cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"HS"</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">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span> +<span> JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span> +<span> J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> name <span class="op">=</span> <span class="st">"hs_path_1"</span>, dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span><span class="op">)</span></span> +<span><span class="va">cl_path_1</span> <span class="op"><-</span> <span class="fu">start_cluster</span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span></code></pre></div> +<p>To obtain suitable starting values for the NLHM fits, separate +pathway fits are performed for all datasets.</p> +<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">f_sep_1_const</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span></span> +<span> <span class="va">cyan_path_1</span>,</span> +<span> <span class="va">cyan_ds</span>,</span> +<span> error_model <span class="op">=</span> <span class="st">"const"</span>,</span> +<span> cluster <span class="op">=</span> <span class="va">cl_path_1</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">TRUE</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_sep_1_const</span><span class="op">)</span> <span class="op">|></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"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">Nambsheim</th> +<th align="left">Tama</th> +<th align="left">Gross-Umstadt</th> +<th align="left">Sassafras</th> +<th align="left">Lleida</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">sfo_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">fomc_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">dfop_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">sforb_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">hs_path_1</td> +<td align="left">C</td> +<td align="left">C</td> +<td align="left">C</td> +<td align="left">C</td> +<td align="left">C</td> +</tr> +</tbody> +</table> +<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">f_sep_1_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_sep_1_const</span>, error_model <span class="op">=</span> <span class="st">"tc"</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_sep_1_tc</span><span class="op">)</span> <span class="op">|></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"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">Nambsheim</th> +<th align="left">Tama</th> +<th align="left">Gross-Umstadt</th> +<th align="left">Sassafras</th> +<th align="left">Lleida</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">sfo_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">fomc_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +</tr> +<tr class="odd"> +<td align="left">dfop_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">sforb_path_1</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">hs_path_1</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +</tbody> +</table> +<p>Most separate fits converged successfully. The biggest convergence +problems are seen when using the HS model with constant variance.</p> +<p>For the hierarchical pathway fits, those random effects that could +not be quantified in the corresponding parent data analyses are +excluded.</p> +<p>In the code below, the output of the <code>illparms</code> function +for the parent only fits is used as an argument +<code>no_random_effect</code> to the <code>mhmkin</code> function. The +possibility to do so was introduced in mkin version <code>1.2.2</code> +which is currently under development.</p> +<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">f_saem_1</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mhmkin.html">mhmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">f_sep_1_const</span>, <span class="va">f_sep_1_tc</span><span class="op">)</span>,</span> +<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">cyan_saem_full</span><span class="op">)</span>,</span> +<span> cluster <span class="op">=</span> <span class="va">cl_path_1</span><span class="op">)</span></span></code></pre></div> +<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">)</span> <span class="op">|></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"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">sfo_path_1</td> +<td align="left">Fth, FO</td> +<td align="left">Fth, FO</td> +</tr> +<tr class="even"> +<td align="left">fomc_path_1</td> +<td align="left">OK</td> +<td align="left">Fth, FO</td> +</tr> +<tr class="odd"> +<td align="left">dfop_path_1</td> +<td align="left">Fth, FO</td> +<td align="left">Fth, FO</td> +</tr> +<tr class="even"> +<td align="left">sforb_path_1</td> +<td align="left">Fth, FO</td> +<td align="left">Fth, FO</td> +</tr> +<tr class="odd"> +<td align="left">hs_path_1</td> +<td align="left">Fth, FO</td> +<td align="left">Fth, FO</td> +</tr> +</tbody> +</table> +<p>The status information from the individual fits shows that all fits +completed successfully. The matrix entries Fth and FO indicate that the +Fisher Information Matrix could not be inverted for the fixed effects +(theta) and the random effects (Omega), respectively. For the affected +fits, ill-defined parameters cannot be determined using the +<code>illparms</code> function, because it relies on the Fisher +Information Matrix.</p> +<div class="sourceCode" id="cb14"><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_1</span><span class="op">)</span> <span class="op">|></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"> +<colgroup> +<col width="18%"> +<col width="77%"> +<col width="4%"> +</colgroup> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">sfo_path_1</td> +<td align="left">NA</td> +<td align="left">NA</td> +</tr> +<tr class="even"> +<td align="left">fomc_path_1</td> +<td align="left">sd(log_k_J9Z38), sd(f_cyan_ilr_2), +sd(f_JCZ38_qlogis)</td> +<td align="left">NA</td> +</tr> +<tr class="odd"> +<td align="left">dfop_path_1</td> +<td align="left">NA</td> +<td align="left">NA</td> +</tr> +<tr class="even"> +<td align="left">sforb_path_1</td> +<td align="left">NA</td> +<td align="left">NA</td> +</tr> +<tr class="odd"> +<td align="left">hs_path_1</td> +<td align="left">NA</td> +<td align="left">NA</td> +</tr> +</tbody> +</table> +<p>The model comparison below suggests that the pathway fits using DFOP +or SFORB for the parent compound provide the best fit.</p> +<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">)</span> <span class="op">|></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">1</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="right">npar</th> +<th align="right">AIC</th> +<th align="right">BIC</th> +<th align="right">Lik</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">sfo_path_1 const</td> +<td align="right">16</td> +<td align="right">2692.8</td> +<td align="right">2686.6</td> +<td align="right">-1330.4</td> +</tr> +<tr class="even"> +<td align="left">sfo_path_1 tc</td> +<td align="right">17</td> +<td align="right">2657.7</td> +<td align="right">2651.1</td> +<td align="right">-1311.9</td> +</tr> +<tr class="odd"> +<td align="left">fomc_path_1 const</td> +<td align="right">18</td> +<td align="right">2427.8</td> +<td align="right">2420.8</td> +<td align="right">-1195.9</td> +</tr> +<tr class="even"> +<td align="left">fomc_path_1 tc</td> +<td align="right">19</td> +<td align="right">2423.4</td> +<td align="right">2416.0</td> +<td align="right">-1192.7</td> +</tr> +<tr class="odd"> +<td align="left">dfop_path_1 const</td> +<td align="right">20</td> +<td align="right">2403.2</td> +<td align="right">2395.4</td> +<td align="right">-1181.6</td> +</tr> +<tr class="even"> +<td align="left">sforb_path_1 const</td> +<td align="right">20</td> +<td align="right">2401.4</td> +<td align="right">2393.6</td> +<td align="right">-1180.7</td> +</tr> +<tr class="odd"> +<td align="left">hs_path_1 const</td> +<td align="right">20</td> +<td align="right">2427.3</td> +<td align="right">2419.5</td> +<td align="right">-1193.7</td> +</tr> +<tr class="even"> +<td align="left">dfop_path_1 tc</td> +<td align="right">20</td> +<td align="right">2398.0</td> +<td align="right">2390.2</td> +<td align="right">-1179.0</td> +</tr> +<tr class="odd"> +<td align="left">sforb_path_1 tc</td> +<td align="right">20</td> +<td align="right">2399.8</td> +<td align="right">2392.0</td> +<td align="right">-1179.9</td> +</tr> +<tr class="even"> +<td align="left">hs_path_1 tc</td> +<td align="right">21</td> +<td align="right">2422.3</td> +<td align="right">2414.1</td> +<td align="right">-1190.2</td> +</tr> +</tbody> +</table> +<p>For these two parent model, successful fits are shown below. Plots of +the fits with the other parent models are shown in the Appendix.</p> +<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">[[</span><span class="st">"dfop_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-7-1.png" alt="DFOP pathway fit with two-component error" width="700"><p class="caption"> +DFOP pathway fit with two-component error +</p> +</div> +<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">[[</span><span class="st">"sforb_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-8-1.png" alt="SFORB pathway fit with two-component error" width="700"><p class="caption"> +SFORB pathway fit with two-component error +</p> +</div> +<p>A closer graphical analysis of these Figures shows that the residues +of transformation product JCZ38 in the soils Tama and Nambsheim observed +at later time points are strongly and systematically underestimated.</p> +<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">stopCluster</a></span><span class="op">(</span><span class="va">cl_path_1</span><span class="op">)</span></span></code></pre></div> +</div> +<div class="section level3"> +<h3 id="alternative-pathway-fits">Alternative pathway fits<a class="anchor" aria-label="anchor" href="#alternative-pathway-fits"></a> +</h3> +<p>To improve the fit for JCZ38, a back-reaction from JSE76 to JCZ38 was +introduced in an alternative version of the transformation pathway, in +analogy to the back-reaction from K5A78 to K5A77. Both pairs of +transformation products are pairs of an organic acid with its +corresponding amide (Addendum 2014, p. 109). As FOMC provided the best +fit for the parent, and the biexponential models DFOP and SFORB provided +the best initial pathway fits, these three parent models are used in the +alternative pathway fits.</p> +<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">cyan_path_2</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span> +<span> fomc_path_2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> +<span> cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"FOMC"</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">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span> +<span> JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span> +<span> J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JCZ38"</span><span class="op">)</span>,</span> +<span> name <span class="op">=</span> <span class="st">"fomc_path_2"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>,</span> +<span> overwrite <span class="op">=</span> <span class="cn">TRUE</span></span> +<span> <span class="op">)</span>,</span> +<span> dfop_path_2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> +<span> cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</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">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span> +<span> JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span> +<span> J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JCZ38"</span><span class="op">)</span>,</span> +<span> name <span class="op">=</span> <span class="st">"dfop_path_2"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>,</span> +<span> overwrite <span class="op">=</span> <span class="cn">TRUE</span></span> +<span> <span class="op">)</span>,</span> +<span> sforb_path_2 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> +<span> cyan <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</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">"JCZ38"</span>, <span class="st">"J9Z38"</span><span class="op">)</span><span class="op">)</span>,</span> +<span> JCZ38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JSE76"</span><span class="op">)</span>,</span> +<span> J9Z38 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> JSE76 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"JCZ38"</span><span class="op">)</span>,</span> +<span> name <span class="op">=</span> <span class="st">"sforb_path_2"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> dll_dir <span class="op">=</span> <span class="st">"cyan_dlls"</span>,</span> +<span> overwrite <span class="op">=</span> <span class="cn">TRUE</span></span> +<span> <span class="op">)</span></span> +<span><span class="op">)</span></span> +<span></span> +<span><span class="va">cl_path_2</span> <span class="op"><-</span> <span class="fu">start_cluster</span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span> +<span><span class="va">f_sep_2_const</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span></span> +<span> <span class="va">cyan_path_2</span>,</span> +<span> <span class="va">cyan_ds</span>,</span> +<span> error_model <span class="op">=</span> <span class="st">"const"</span>,</span> +<span> cluster <span class="op">=</span> <span class="va">cl_path_2</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span></span> +<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_2_const</span><span class="op">)</span> <span class="op">|></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"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">Nambsheim</th> +<th align="left">Tama</th> +<th align="left">Gross-Umstadt</th> +<th align="left">Sassafras</th> +<th align="left">Lleida</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">fomc_path_2</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">dfop_path_2</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">sforb_path_2</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +</tr> +</tbody> +</table> +<p>Using constant variance, separate fits converge with the exception of +the fits to the Sassafras soil data.</p> +<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">f_sep_2_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_sep_2_const</span>, error_model <span class="op">=</span> <span class="st">"tc"</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_sep_2_tc</span><span class="op">)</span> <span class="op">|></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"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">Nambsheim</th> +<th align="left">Tama</th> +<th align="left">Gross-Umstadt</th> +<th align="left">Sassafras</th> +<th align="left">Lleida</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">fomc_path_2</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">dfop_path_2</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">sforb_path_2</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +</tbody> +</table> +<p>Using the two-component error model, all separate fits converge with +the exception of the alternative pathway fit with DFOP used for the +parent and the Sassafras dataset.</p> +<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">f_saem_2</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mhmkin.html">mhmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">f_sep_2_const</span>, <span class="va">f_sep_2_tc</span><span class="op">)</span>,</span> +<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">cyan_saem_full</span><span class="op">[</span><span class="fl">2</span><span class="op">:</span><span class="fl">4</span>, <span class="op">]</span><span class="op">)</span>,</span> +<span> cluster <span class="op">=</span> <span class="va">cl_path_2</span><span class="op">)</span></span></code></pre></div> +<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><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">|></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"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">fomc_path_2</td> +<td align="left">OK</td> +<td align="left">FO</td> +</tr> +<tr class="even"> +<td align="left">dfop_path_2</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">sforb_path_2</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +</tbody> +</table> +<p>The hierarchical fits for the alternative pathway completed +successfully.</p> +<div class="sourceCode" id="cb23"><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">|></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"> +<colgroup> +<col width="14%"> +<col width="42%"> +<col width="42%"> +</colgroup> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">fomc_path_2</td> +<td align="left">sd(f_JCZ38_qlogis), sd(f_JSE76_qlogis)</td> +<td align="left">NA</td> +</tr> +<tr class="even"> +<td align="left">dfop_path_2</td> +<td align="left">sd(f_JCZ38_qlogis), sd(f_JSE76_qlogis)</td> +<td align="left">sd(f_JCZ38_qlogis), sd(f_JSE76_qlogis)</td> +</tr> +<tr class="odd"> +<td align="left">sforb_path_2</td> +<td align="left">sd(f_JCZ38_qlogis), sd(f_JSE76_qlogis)</td> +<td align="left">sd(f_JCZ38_qlogis), sd(f_JSE76_qlogis)</td> +</tr> +</tbody> +</table> +<p>In both fits, the random effects for the formation fractions for the +pathways from JCZ38 to JSE76, and for the reverse pathway from JSE76 to +JCZ38 are ill-defined.</p> +<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">)</span> <span class="op">|></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">1</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="right">npar</th> +<th align="right">AIC</th> +<th align="right">BIC</th> +<th align="right">Lik</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">fomc_path_2 const</td> +<td align="right">20</td> +<td align="right">2308.3</td> +<td align="right">2300.5</td> +<td align="right">-1134.2</td> +</tr> +<tr class="even"> +<td align="left">fomc_path_2 tc</td> +<td align="right">21</td> +<td align="right">2248.3</td> +<td align="right">2240.1</td> +<td align="right">-1103.2</td> +</tr> +<tr class="odd"> +<td align="left">dfop_path_2 const</td> +<td align="right">22</td> +<td align="right">2289.6</td> +<td align="right">2281.0</td> +<td align="right">-1122.8</td> +</tr> +<tr class="even"> +<td align="left">sforb_path_2 const</td> +<td align="right">22</td> +<td align="right">2284.1</td> +<td align="right">2275.5</td> +<td align="right">-1120.0</td> +</tr> +<tr class="odd"> +<td align="left">dfop_path_2 tc</td> +<td align="right">22</td> +<td align="right">2234.4</td> +<td align="right">2225.8</td> +<td align="right">-1095.2</td> +</tr> +<tr class="even"> +<td align="left">sforb_path_2 tc</td> +<td align="right">22</td> +<td align="right">2240.4</td> +<td align="right">2231.8</td> +<td align="right">-1098.2</td> +</tr> +</tbody> +</table> +<p>The variants using the biexponential models DFOP and SFORB for the +parent compound and the two-component error model give the lowest AIC +and BIC values and are plotted below. Compared with the original +pathway, the AIC and BIC values indicate a large improvement. This is +confirmed by the plots, which show that the metabolite JCZ38 is fitted +much better with this model.</p> +<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">[[</span><span class="st">"fomc_path_2"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-13-1.png" alt="FOMC pathway fit with two-component error, alternative pathway" width="700"><p class="caption"> +FOMC pathway fit with two-component error, alternative pathway +</p> +</div> +<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">[[</span><span class="st">"dfop_path_2"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-14-1.png" alt="DFOP pathway fit with two-component error, alternative pathway" width="700"><p class="caption"> +DFOP pathway fit with two-component error, alternative pathway +</p> +</div> +<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">[[</span><span class="st">"sforb_path_2"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-15-1.png" alt="SFORB pathway fit with two-component error, alternative pathway" width="700"><p class="caption"> +SFORB pathway fit with two-component error, alternative pathway +</p> +</div> +</div> +<div class="section level3"> +<h3 id="refinement-of-alternative-pathway-fits">Refinement of alternative pathway fits<a class="anchor" aria-label="anchor" href="#refinement-of-alternative-pathway-fits"></a> +</h3> +<p>All ill-defined random effects that were identified in the parent +only fits and in the above pathway fits, are excluded for the final +evaluations below. For this purpose, a list of character vectors is +created below that can be indexed by row and column indices, and which +contains the degradation parameter names for which random effects should +be excluded for each of the hierarchical fits contained in +<code>f_saem_2</code>.</p> +<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">no_ranef</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="op">)</span>, nrow <span class="op">=</span> <span class="fl">3</span>, ncol <span class="op">=</span> <span class="fl">2</span>, dimnames <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/dimnames.html" class="external-link">dimnames</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">)</span><span class="op">)</span></span> +<span><span class="va">no_ranef</span><span class="op">[[</span><span class="st">"fomc_path_2"</span>, <span class="st">"const"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"log_beta"</span>, <span class="st">"f_JCZ38_qlogis"</span>, <span class="st">"f_JSE76_qlogis"</span><span class="op">)</span></span> +<span><span class="va">no_ranef</span><span class="op">[[</span><span class="st">"fomc_path_2"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"cyan_0"</span>, <span class="st">"f_JCZ38_qlogis"</span>, <span class="st">"f_JSE76_qlogis"</span><span class="op">)</span></span> +<span><span class="va">no_ranef</span><span class="op">[[</span><span class="st">"dfop_path_2"</span>, <span class="st">"const"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"cyan_0"</span>, <span class="st">"f_JCZ38_qlogis"</span>, <span class="st">"f_JSE76_qlogis"</span><span class="op">)</span></span> +<span><span class="va">no_ranef</span><span class="op">[[</span><span class="st">"dfop_path_2"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"cyan_0"</span>, <span class="st">"log_k1"</span>, <span class="st">"f_JCZ38_qlogis"</span>, <span class="st">"f_JSE76_qlogis"</span><span class="op">)</span></span> +<span><span class="va">no_ranef</span><span class="op">[[</span><span class="st">"sforb_path_2"</span>, <span class="st">"const"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"cyan_free_0"</span>,</span> +<span> <span class="st">"f_JCZ38_qlogis"</span>, <span class="st">"f_JSE76_qlogis"</span><span class="op">)</span></span> +<span><span class="va">no_ranef</span><span class="op">[[</span><span class="st">"sforb_path_2"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"cyan_free_0"</span>, <span class="st">"log_k_cyan_free_bound"</span>,</span> +<span> <span class="st">"f_JCZ38_qlogis"</span>, <span class="st">"f_JSE76_qlogis"</span><span class="op">)</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/parallel/clusterApply.html" class="external-link">clusterExport</a></span><span class="op">(</span><span class="va">cl_path_2</span>, <span class="st">"no_ranef"</span><span class="op">)</span></span> +<span></span> +<span><span class="va">f_saem_3</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_2</span>,</span> +<span> no_random_effect <span class="op">=</span> <span class="va">no_ranef</span>,</span> +<span> cluster <span class="op">=</span> <span class="va">cl_path_2</span><span class="op">)</span></span></code></pre></div> +<div class="sourceCode" id="cb29"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_saem_3</span><span class="op">)</span> <span class="op">|></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"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">fomc_path_2</td> +<td align="left">E</td> +<td align="left">Fth</td> +</tr> +<tr class="even"> +<td align="left">dfop_path_2</td> +<td align="left">Fth</td> +<td align="left">Fth</td> +</tr> +<tr class="odd"> +<td align="left">sforb_path_2</td> +<td align="left">Fth</td> +<td align="left">Fth</td> +</tr> +</tbody> +</table> +<p>With the exception of the FOMC pathway fit with constant variance, +all updated fits completed successfully. However, the Fisher Information +Matrix for the fixed effects (Fth) could not be inverted, so no +confidence intervals for the optimised parameters are available.</p> +<div class="sourceCode" id="cb30"><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_3</span><span class="op">)</span> <span class="op">|></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"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">fomc_path_2</td> +<td align="left">E</td> +<td align="left"></td> +</tr> +<tr class="even"> +<td align="left">dfop_path_2</td> +<td align="left"></td> +<td align="left"></td> +</tr> +<tr class="odd"> +<td align="left">sforb_path_2</td> +<td align="left"></td> +<td align="left"></td> +</tr> +</tbody> +</table> +<div class="sourceCode" id="cb31"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_3</span><span class="op">)</span> <span class="op">|></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">1</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="right">npar</th> +<th align="right">AIC</th> +<th align="right">BIC</th> +<th align="right">Lik</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">fomc_path_2 tc</td> +<td align="right">19</td> +<td align="right">2250.9</td> +<td align="right">2243.5</td> +<td align="right">-1106.5</td> +</tr> +<tr class="even"> +<td align="left">dfop_path_2 const</td> +<td align="right">20</td> +<td align="right">2281.7</td> +<td align="right">2273.9</td> +<td align="right">-1120.8</td> +</tr> +<tr class="odd"> +<td align="left">sforb_path_2 const</td> +<td align="right">20</td> +<td align="right">2279.5</td> +<td align="right">2271.7</td> +<td align="right">-1119.7</td> +</tr> +<tr class="even"> +<td align="left">dfop_path_2 tc</td> +<td align="right">20</td> +<td align="right">2231.5</td> +<td align="right">2223.7</td> +<td align="right">-1095.8</td> +</tr> +<tr class="odd"> +<td align="left">sforb_path_2 tc</td> +<td align="right">20</td> +<td align="right">2235.7</td> +<td align="right">2227.9</td> +<td align="right">-1097.9</td> +</tr> +</tbody> +</table> +<p>While the AIC and BIC values of the best fit (DFOP pathway fit with +two-component error) are lower than in the previous fits with the +alternative pathway, the practical value of these refined evaluations is +limited as no confidence intervals are obtained.</p> +<div class="sourceCode" id="cb32"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">stopCluster</a></span><span class="op">(</span><span class="va">cl_path_2</span><span class="op">)</span></span></code></pre></div> +</div> +</div> +<div class="section level2"> +<h2 id="conclusion">Conclusion<a class="anchor" aria-label="anchor" href="#conclusion"></a> +</h2> +<p>It was demonstrated that a relatively complex transformation pathway +with parallel formation of two primary metabolites and one secondary +metabolite can be fitted even if the data in the individual datasets are +quite different and partly only cover the formation phase.</p> +<p>The run times of the pathway fits were several hours, limiting the +practical feasibility of iterative refinements based on ill-defined +parameters and of alternative checks of parameter identifiability based +on multistart runs.</p> +</div> +<div class="section level2"> +<h2 id="acknowledgements">Acknowledgements<a class="anchor" aria-label="anchor" href="#acknowledgements"></a> +</h2> +<p>The helpful comments by Janina Wöltjen of the German Environment +Agency are gratefully acknowledged.</p> +</div> +<div class="section level2"> +<h2 id="appendix">Appendix<a class="anchor" aria-label="anchor" href="#appendix"></a> +</h2> +<div class="section level3"> +<h3 id="plots-of-fits-that-were-not-refined-further">Plots of fits that were not refined further<a class="anchor" aria-label="anchor" href="#plots-of-fits-that-were-not-refined-further"></a> +</h3> +<div class="sourceCode" id="cb33"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">[[</span><span class="st">"sfo_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-20-1.png" alt="SFO pathway fit with two-component error" width="700"><p class="caption"> +SFO pathway fit with two-component error +</p> +</div> +<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">[[</span><span class="st">"fomc_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-21-1.png" alt="FOMC pathway fit with two-component error" width="700"><p class="caption"> +FOMC pathway fit with two-component error +</p> +</div> +<div class="sourceCode" id="cb35"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">[[</span><span class="st">"sforb_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_cyan_pathway_files/figure-html/unnamed-chunk-22-1.png" alt="HS pathway fit with two-component error" width="700"><p class="caption"> +HS pathway fit with two-component error +</p> +</div> +</div> +<div class="section level3"> +<h3 id="hierarchical-fit-listings">Hierarchical fit listings<a class="anchor" aria-label="anchor" href="#hierarchical-fit-listings"></a> +</h3> +<div class="section level4"> +<h4 id="pathway-1">Pathway 1<a class="anchor" aria-label="anchor" href="#pathway-1"></a> +</h4> +<caption> +Hierarchical SFO path 1 fit with constant variance +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 07:44:55 2023 +Date of summary: Thu Apr 20 20:01:30 2023 + +Equations: +d_cyan/dt = - k_cyan * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * k_cyan * cyan - k_JCZ38 * JCZ38 +d_J9Z38/dt = + f_cyan_to_J9Z38 * k_cyan * cyan - k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 431.793 s +Using 300, 100 iterations and 10 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + cyan_0 log_k_cyan log_k_JCZ38 log_k_J9Z38 log_k_JSE76 + 95.3304 -3.8459 -3.1305 -5.0678 -5.3196 + f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis + 0.8158 22.5404 10.4289 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_cyan log_k_JCZ38 log_k_J9Z38 log_k_JSE76 +cyan_0 4.797 0.0000 0.000 0.000 0.0000 +log_k_cyan 0.000 0.9619 0.000 0.000 0.0000 +log_k_JCZ38 0.000 0.0000 2.139 0.000 0.0000 +log_k_J9Z38 0.000 0.0000 0.000 1.639 0.0000 +log_k_JSE76 0.000 0.0000 0.000 0.000 0.7894 +f_cyan_ilr_1 0.000 0.0000 0.000 0.000 0.0000 +f_cyan_ilr_2 0.000 0.0000 0.000 0.000 0.0000 +f_JCZ38_qlogis 0.000 0.0000 0.000 0.000 0.0000 + f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis +cyan_0 0.0000 0.000 0.00 +log_k_cyan 0.0000 0.000 0.00 +log_k_JCZ38 0.0000 0.000 0.00 +log_k_J9Z38 0.0000 0.000 0.00 +log_k_JSE76 0.0000 0.000 0.00 +f_cyan_ilr_1 0.7714 0.000 0.00 +f_cyan_ilr_2 0.0000 8.684 0.00 +f_JCZ38_qlogis 0.0000 0.000 13.48 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2693 2687 -1330 + +Optimised parameters: + est. lower upper +cyan_0 95.0946 NA NA +log_k_cyan -3.8544 NA NA +log_k_JCZ38 -3.0402 NA NA +log_k_J9Z38 -5.0109 NA NA +log_k_JSE76 -5.2857 NA NA +f_cyan_ilr_1 0.8069 NA NA +f_cyan_ilr_2 16.6623 NA NA +f_JCZ38_qlogis 1.3602 NA NA +a.1 4.8326 NA NA +SD.log_k_cyan 0.5842 NA NA +SD.log_k_JCZ38 1.2680 NA NA +SD.log_k_J9Z38 0.3626 NA NA +SD.log_k_JSE76 0.5244 NA NA +SD.f_cyan_ilr_1 0.2752 NA NA +SD.f_cyan_ilr_2 2.3556 NA NA +SD.f_JCZ38_qlogis 0.2400 NA NA + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_cyan 0.5842 NA NA +SD.log_k_JCZ38 1.2680 NA NA +SD.log_k_J9Z38 0.3626 NA NA +SD.log_k_JSE76 0.5244 NA NA +SD.f_cyan_ilr_1 0.2752 NA NA +SD.f_cyan_ilr_2 2.3556 NA NA +SD.f_JCZ38_qlogis 0.2400 NA NA + +Variance model: + est. lower upper +a.1 4.833 NA NA + +Backtransformed parameters: + est. lower upper +cyan_0 95.094581 NA NA +k_cyan 0.021186 NA NA +k_JCZ38 0.047825 NA NA +k_J9Z38 0.006665 NA NA +k_JSE76 0.005063 NA NA +f_cyan_to_JCZ38 0.757885 NA NA +f_cyan_to_J9Z38 0.242115 NA NA +f_JCZ38_to_JSE76 0.795792 NA NA + +Resulting formation fractions: + ff +cyan_JCZ38 7.579e-01 +cyan_J9Z38 2.421e-01 +cyan_sink 5.877e-10 +JCZ38_JSE76 7.958e-01 +JCZ38_sink 2.042e-01 + +Estimated disappearance times: + DT50 DT90 +cyan 32.72 108.68 +JCZ38 14.49 48.15 +J9Z38 103.99 345.46 +JSE76 136.90 454.76 + +</code></pre> +<p></p> +<caption> +Hierarchical SFO path 1 fit with two-component error +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 07:44:53 2023 +Date of summary: Thu Apr 20 20:01:30 2023 + +Equations: +d_cyan/dt = - k_cyan * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * k_cyan * cyan - k_JCZ38 * JCZ38 +d_J9Z38/dt = + f_cyan_to_J9Z38 * k_cyan * cyan - k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 429.526 s +Using 300, 100 iterations and 10 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + cyan_0 log_k_cyan log_k_JCZ38 log_k_J9Z38 log_k_JSE76 + 96.0039 -3.8907 -3.1276 -5.0069 -4.9367 + f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis + 0.7937 20.0030 15.1336 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_cyan log_k_JCZ38 log_k_J9Z38 log_k_JSE76 +cyan_0 4.859 0.000 0.00 0.00 0.0000 +log_k_cyan 0.000 0.962 0.00 0.00 0.0000 +log_k_JCZ38 0.000 0.000 2.04 0.00 0.0000 +log_k_J9Z38 0.000 0.000 0.00 1.72 0.0000 +log_k_JSE76 0.000 0.000 0.00 0.00 0.9076 +f_cyan_ilr_1 0.000 0.000 0.00 0.00 0.0000 +f_cyan_ilr_2 0.000 0.000 0.00 0.00 0.0000 +f_JCZ38_qlogis 0.000 0.000 0.00 0.00 0.0000 + f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis +cyan_0 0.0000 0.000 0.00 +log_k_cyan 0.0000 0.000 0.00 +log_k_JCZ38 0.0000 0.000 0.00 +log_k_J9Z38 0.0000 0.000 0.00 +log_k_JSE76 0.0000 0.000 0.00 +f_cyan_ilr_1 0.7598 0.000 0.00 +f_cyan_ilr_2 0.0000 7.334 0.00 +f_JCZ38_qlogis 0.0000 0.000 11.78 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2658 2651 -1312 + +Optimised parameters: + est. lower upper +cyan_0 94.72923 NA NA +log_k_cyan -3.91670 NA NA +log_k_JCZ38 -3.12917 NA NA +log_k_J9Z38 -5.06070 NA NA +log_k_JSE76 -5.09254 NA NA +f_cyan_ilr_1 0.81116 NA NA +f_cyan_ilr_2 39.97850 NA NA +f_JCZ38_qlogis 3.09728 NA NA +a.1 3.95044 NA NA +b.1 0.07998 NA NA +SD.log_k_cyan 0.58855 NA NA +SD.log_k_JCZ38 1.29753 NA NA +SD.log_k_J9Z38 0.62851 NA NA +SD.log_k_JSE76 0.37235 NA NA +SD.f_cyan_ilr_1 0.37346 NA NA +SD.f_cyan_ilr_2 1.41667 NA NA +SD.f_JCZ38_qlogis 1.81467 NA NA + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_cyan 0.5886 NA NA +SD.log_k_JCZ38 1.2975 NA NA +SD.log_k_J9Z38 0.6285 NA NA +SD.log_k_JSE76 0.3724 NA NA +SD.f_cyan_ilr_1 0.3735 NA NA +SD.f_cyan_ilr_2 1.4167 NA NA +SD.f_JCZ38_qlogis 1.8147 NA NA + +Variance model: + est. lower upper +a.1 3.95044 NA NA +b.1 0.07998 NA NA + +Backtransformed parameters: + est. lower upper +cyan_0 94.729229 NA NA +k_cyan 0.019907 NA NA +k_JCZ38 0.043754 NA NA +k_J9Z38 0.006341 NA NA +k_JSE76 0.006142 NA NA +f_cyan_to_JCZ38 0.758991 NA NA +f_cyan_to_J9Z38 0.241009 NA NA +f_JCZ38_to_JSE76 0.956781 NA NA + +Resulting formation fractions: + ff +cyan_JCZ38 0.75899 +cyan_J9Z38 0.24101 +cyan_sink 0.00000 +JCZ38_JSE76 0.95678 +JCZ38_sink 0.04322 + +Estimated disappearance times: + DT50 DT90 +cyan 34.82 115.67 +JCZ38 15.84 52.63 +J9Z38 109.31 363.12 +JSE76 112.85 374.87 + +</code></pre> +<p></p> +<caption> +Hierarchical FOMC path 1 fit with constant variance +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 07:45:50 2023 +Date of summary: Thu Apr 20 20:01:30 2023 + +Equations: +d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * (alpha/beta) * 1/((time/beta) + 1) * + cyan - k_JCZ38 * JCZ38 +d_J9Z38/dt = + f_cyan_to_J9Z38 * (alpha/beta) * 1/((time/beta) + 1) * + cyan - k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 477.996 s +Using 300, 100 iterations and 10 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 + 101.2314 -3.3680 -5.1108 -5.9416 0.7144 + f_cyan_ilr_2 f_JCZ38_qlogis log_alpha log_beta + 7.3870 15.7604 -0.1791 2.9811 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 +cyan_0 5.416 0.000 0.0 0.000 0.0000 +log_k_JCZ38 0.000 2.439 0.0 0.000 0.0000 +log_k_J9Z38 0.000 0.000 1.7 0.000 0.0000 +log_k_JSE76 0.000 0.000 0.0 1.856 0.0000 +f_cyan_ilr_1 0.000 0.000 0.0 0.000 0.7164 +f_cyan_ilr_2 0.000 0.000 0.0 0.000 0.0000 +f_JCZ38_qlogis 0.000 0.000 0.0 0.000 0.0000 +log_alpha 0.000 0.000 0.0 0.000 0.0000 +log_beta 0.000 0.000 0.0 0.000 0.0000 + f_cyan_ilr_2 f_JCZ38_qlogis log_alpha log_beta +cyan_0 0.00 0.00 0.0000 0.0000 +log_k_JCZ38 0.00 0.00 0.0000 0.0000 +log_k_J9Z38 0.00 0.00 0.0000 0.0000 +log_k_JSE76 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_1 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_2 12.33 0.00 0.0000 0.0000 +f_JCZ38_qlogis 0.00 20.42 0.0000 0.0000 +log_alpha 0.00 0.00 0.4144 0.0000 +log_beta 0.00 0.00 0.0000 0.5077 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2428 2421 -1196 + +Optimised parameters: + est. lower upper +cyan_0 101.0225 98.306270 103.7387 +log_k_JCZ38 -3.3786 -4.770657 -1.9866 +log_k_J9Z38 -5.2603 -5.902085 -4.6186 +log_k_JSE76 -6.1427 -7.318336 -4.9671 +f_cyan_ilr_1 0.7437 0.421215 1.0663 +f_cyan_ilr_2 0.9108 0.267977 1.5537 +f_JCZ38_qlogis 2.0487 0.524897 3.5724 +log_alpha -0.2268 -0.618049 0.1644 +log_beta 2.8986 2.700701 3.0964 +a.1 3.4058 3.169913 3.6416 +SD.cyan_0 2.5279 0.454190 4.6016 +SD.log_k_JCZ38 1.5636 0.572824 2.5543 +SD.log_k_J9Z38 0.5316 -0.004405 1.0677 +SD.log_k_JSE76 0.9903 0.106325 1.8742 +SD.f_cyan_ilr_1 0.3464 0.112066 0.5807 +SD.f_cyan_ilr_2 0.2804 -0.393900 0.9546 +SD.f_JCZ38_qlogis 0.9416 -0.152986 2.0362 +SD.log_alpha 0.4273 0.161044 0.6936 + +Correlation: + cyan_0 l__JCZ3 l__J9Z3 l__JSE7 f_cy__1 f_cy__2 f_JCZ38 log_lph +log_k_JCZ38 -0.0156 +log_k_J9Z38 -0.0493 0.0073 +log_k_JSE76 -0.0329 0.0018 0.0069 +f_cyan_ilr_1 -0.0086 0.0180 -0.1406 0.0012 +f_cyan_ilr_2 -0.2629 0.0779 0.2826 0.0274 0.0099 +f_JCZ38_qlogis 0.0713 -0.0747 -0.0505 0.1169 -0.1022 -0.4893 +log_alpha -0.0556 0.0120 0.0336 0.0193 0.0036 0.0840 -0.0489 +log_beta -0.2898 0.0460 0.1305 0.0768 0.0190 0.4071 -0.1981 0.2772 + +Random effects: + est. lower upper +SD.cyan_0 2.5279 0.454190 4.6016 +SD.log_k_JCZ38 1.5636 0.572824 2.5543 +SD.log_k_J9Z38 0.5316 -0.004405 1.0677 +SD.log_k_JSE76 0.9903 0.106325 1.8742 +SD.f_cyan_ilr_1 0.3464 0.112066 0.5807 +SD.f_cyan_ilr_2 0.2804 -0.393900 0.9546 +SD.f_JCZ38_qlogis 0.9416 -0.152986 2.0362 +SD.log_alpha 0.4273 0.161044 0.6936 + +Variance model: + est. lower upper +a.1 3.406 3.17 3.642 + +Backtransformed parameters: + est. lower upper +cyan_0 1.010e+02 9.831e+01 1.037e+02 +k_JCZ38 3.409e-02 8.475e-03 1.372e-01 +k_J9Z38 5.194e-03 2.734e-03 9.867e-03 +k_JSE76 2.149e-03 6.633e-04 6.963e-03 +f_cyan_to_JCZ38 6.481e-01 NA NA +f_cyan_to_J9Z38 2.264e-01 NA NA +f_JCZ38_to_JSE76 8.858e-01 6.283e-01 9.727e-01 +alpha 7.971e-01 5.390e-01 1.179e+00 +beta 1.815e+01 1.489e+01 2.212e+01 + +Resulting formation fractions: + ff +cyan_JCZ38 0.6481 +cyan_J9Z38 0.2264 +cyan_sink 0.1255 +JCZ38_JSE76 0.8858 +JCZ38_sink 0.1142 + +Estimated disappearance times: + DT50 DT90 DT50back +cyan 25.15 308.01 92.72 +JCZ38 20.33 67.54 NA +J9Z38 133.46 443.35 NA +JSE76 322.53 1071.42 NA + +</code></pre> +<p></p> +<caption> +Hierarchical FOMC path 1 fit with two-component error +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 07:45:45 2023 +Date of summary: Thu Apr 20 20:01:30 2023 + +Equations: +d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * (alpha/beta) * 1/((time/beta) + 1) * + cyan - k_JCZ38 * JCZ38 +d_J9Z38/dt = + f_cyan_to_J9Z38 * (alpha/beta) * 1/((time/beta) + 1) * + cyan - k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 480.648 s +Using 300, 100 iterations and 10 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 + 101.13827 -3.32493 -5.08921 -5.93478 0.71330 + f_cyan_ilr_2 f_JCZ38_qlogis log_alpha log_beta + 10.05989 12.79248 -0.09621 3.10646 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 +cyan_0 5.643 0.000 0.000 0.00 0.0000 +log_k_JCZ38 0.000 2.319 0.000 0.00 0.0000 +log_k_J9Z38 0.000 0.000 1.731 0.00 0.0000 +log_k_JSE76 0.000 0.000 0.000 1.86 0.0000 +f_cyan_ilr_1 0.000 0.000 0.000 0.00 0.7186 +f_cyan_ilr_2 0.000 0.000 0.000 0.00 0.0000 +f_JCZ38_qlogis 0.000 0.000 0.000 0.00 0.0000 +log_alpha 0.000 0.000 0.000 0.00 0.0000 +log_beta 0.000 0.000 0.000 0.00 0.0000 + f_cyan_ilr_2 f_JCZ38_qlogis log_alpha log_beta +cyan_0 0.00 0.00 0.0000 0.0000 +log_k_JCZ38 0.00 0.00 0.0000 0.0000 +log_k_J9Z38 0.00 0.00 0.0000 0.0000 +log_k_JSE76 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_1 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_2 12.49 0.00 0.0000 0.0000 +f_JCZ38_qlogis 0.00 20.19 0.0000 0.0000 +log_alpha 0.00 0.00 0.3142 0.0000 +log_beta 0.00 0.00 0.0000 0.7331 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2423 2416 -1193 + +Optimised parameters: + est. lower upper +cyan_0 100.57649 NA NA +log_k_JCZ38 -3.46250 NA NA +log_k_J9Z38 -5.24442 NA NA +log_k_JSE76 -5.75229 NA NA +f_cyan_ilr_1 0.68480 NA NA +f_cyan_ilr_2 0.61670 NA NA +f_JCZ38_qlogis 87.97407 NA NA +log_alpha -0.15699 NA NA +log_beta 3.01540 NA NA +a.1 3.11518 NA NA +b.1 0.04445 NA NA +SD.log_k_JCZ38 1.40732 NA NA +SD.log_k_J9Z38 0.56510 NA NA +SD.log_k_JSE76 0.72067 NA NA +SD.f_cyan_ilr_1 0.31199 NA NA +SD.f_cyan_ilr_2 0.36894 NA NA +SD.f_JCZ38_qlogis 6.92892 NA NA +SD.log_alpha 0.25662 NA NA +SD.log_beta 0.35845 NA NA + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_JCZ38 1.4073 NA NA +SD.log_k_J9Z38 0.5651 NA NA +SD.log_k_JSE76 0.7207 NA NA +SD.f_cyan_ilr_1 0.3120 NA NA +SD.f_cyan_ilr_2 0.3689 NA NA +SD.f_JCZ38_qlogis 6.9289 NA NA +SD.log_alpha 0.2566 NA NA +SD.log_beta 0.3585 NA NA + +Variance model: + est. lower upper +a.1 3.11518 NA NA +b.1 0.04445 NA NA + +Backtransformed parameters: + est. lower upper +cyan_0 1.006e+02 NA NA +k_JCZ38 3.135e-02 NA NA +k_J9Z38 5.277e-03 NA NA +k_JSE76 3.175e-03 NA NA +f_cyan_to_JCZ38 5.991e-01 NA NA +f_cyan_to_J9Z38 2.275e-01 NA NA +f_JCZ38_to_JSE76 1.000e+00 NA NA +alpha 8.547e-01 NA NA +beta 2.040e+01 NA NA + +Resulting formation fractions: + ff +cyan_JCZ38 0.5991 +cyan_J9Z38 0.2275 +cyan_sink 0.1734 +JCZ38_JSE76 1.0000 +JCZ38_sink 0.0000 + +Estimated disappearance times: + DT50 DT90 DT50back +cyan 25.50 281.29 84.68 +JCZ38 22.11 73.44 NA +J9Z38 131.36 436.35 NA +JSE76 218.28 725.11 NA + +</code></pre> +<p></p> +<caption> +Hierarchical DFOP path 1 fit with constant variance +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 07:46:41 2023 +Date of summary: Thu Apr 20 20:01:30 2023 + +Equations: +d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 - + g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * + exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38 +d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 - + g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * + exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 528.713 s +Using 300, 100 iterations and 10 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 + 102.0644 -3.4008 -5.0024 -5.8613 0.6855 + f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 g_qlogis + 1.2365 13.7245 -1.8641 -4.5063 -0.6468 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 +cyan_0 4.466 0.000 0.000 0.000 0.0000 +log_k_JCZ38 0.000 2.382 0.000 0.000 0.0000 +log_k_J9Z38 0.000 0.000 1.595 0.000 0.0000 +log_k_JSE76 0.000 0.000 0.000 1.245 0.0000 +f_cyan_ilr_1 0.000 0.000 0.000 0.000 0.6852 +f_cyan_ilr_2 0.000 0.000 0.000 0.000 0.0000 +f_JCZ38_qlogis 0.000 0.000 0.000 0.000 0.0000 +log_k1 0.000 0.000 0.000 0.000 0.0000 +log_k2 0.000 0.000 0.000 0.000 0.0000 +g_qlogis 0.000 0.000 0.000 0.000 0.0000 + f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 g_qlogis +cyan_0 0.00 0.00 0.0000 0.0000 0.000 +log_k_JCZ38 0.00 0.00 0.0000 0.0000 0.000 +log_k_J9Z38 0.00 0.00 0.0000 0.0000 0.000 +log_k_JSE76 0.00 0.00 0.0000 0.0000 0.000 +f_cyan_ilr_1 0.00 0.00 0.0000 0.0000 0.000 +f_cyan_ilr_2 1.28 0.00 0.0000 0.0000 0.000 +f_JCZ38_qlogis 0.00 16.11 0.0000 0.0000 0.000 +log_k1 0.00 0.00 0.9866 0.0000 0.000 +log_k2 0.00 0.00 0.0000 0.5953 0.000 +g_qlogis 0.00 0.00 0.0000 0.0000 1.583 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2403 2395 -1182 + +Optimised parameters: + est. lower upper +cyan_0 102.6079 NA NA +log_k_JCZ38 -3.4855 NA NA +log_k_J9Z38 -5.1686 NA NA +log_k_JSE76 -5.6697 NA NA +f_cyan_ilr_1 0.6714 NA NA +f_cyan_ilr_2 0.4986 NA NA +f_JCZ38_qlogis 55.4760 NA NA +log_k1 -1.8409 NA NA +log_k2 -4.4915 NA NA +g_qlogis -0.6403 NA NA +a.1 3.2387 NA NA +SD.log_k_JCZ38 1.4524 NA NA +SD.log_k_J9Z38 0.5151 NA NA +SD.log_k_JSE76 0.6514 NA NA +SD.f_cyan_ilr_1 0.3023 NA NA +SD.f_cyan_ilr_2 0.2959 NA NA +SD.f_JCZ38_qlogis 1.9984 NA NA +SD.log_k1 0.5188 NA NA +SD.log_k2 0.3894 NA NA +SD.g_qlogis 0.8579 NA NA + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_JCZ38 1.4524 NA NA +SD.log_k_J9Z38 0.5151 NA NA +SD.log_k_JSE76 0.6514 NA NA +SD.f_cyan_ilr_1 0.3023 NA NA +SD.f_cyan_ilr_2 0.2959 NA NA +SD.f_JCZ38_qlogis 1.9984 NA NA +SD.log_k1 0.5188 NA NA +SD.log_k2 0.3894 NA NA +SD.g_qlogis 0.8579 NA NA + +Variance model: + est. lower upper +a.1 3.239 NA NA + +Backtransformed parameters: + est. lower upper +cyan_0 1.026e+02 NA NA +k_JCZ38 3.064e-02 NA NA +k_J9Z38 5.692e-03 NA NA +k_JSE76 3.449e-03 NA NA +f_cyan_to_JCZ38 5.798e-01 NA NA +f_cyan_to_J9Z38 2.243e-01 NA NA +f_JCZ38_to_JSE76 1.000e+00 NA NA +k1 1.587e-01 NA NA +k2 1.120e-02 NA NA +g 3.452e-01 NA NA + +Resulting formation fractions: + ff +cyan_JCZ38 0.5798 +cyan_J9Z38 0.2243 +cyan_sink 0.1958 +JCZ38_JSE76 1.0000 +JCZ38_sink 0.0000 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +cyan 25.21 167.73 50.49 4.368 61.87 +JCZ38 22.62 75.15 NA NA NA +J9Z38 121.77 404.50 NA NA NA +JSE76 200.98 667.64 NA NA NA + +</code></pre> +<p></p> +<caption> +Hierarchical DFOP path 1 fit with two-component error +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 07:49:05 2023 +Date of summary: Thu Apr 20 20:01:30 2023 + +Equations: +d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 - + g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * + exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38 +d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 - + g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * + exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 673.139 s +Using 300, 100 iterations and 10 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 + 101.3964 -3.3626 -4.9792 -5.8727 0.6814 + f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 g_qlogis + 6.7799 13.7245 -1.9222 -4.5035 -0.7172 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 +cyan_0 5.317 0.000 0.000 0.000 0.0000 +log_k_JCZ38 0.000 2.272 0.000 0.000 0.0000 +log_k_J9Z38 0.000 0.000 1.633 0.000 0.0000 +log_k_JSE76 0.000 0.000 0.000 1.271 0.0000 +f_cyan_ilr_1 0.000 0.000 0.000 0.000 0.6838 +f_cyan_ilr_2 0.000 0.000 0.000 0.000 0.0000 +f_JCZ38_qlogis 0.000 0.000 0.000 0.000 0.0000 +log_k1 0.000 0.000 0.000 0.000 0.0000 +log_k2 0.000 0.000 0.000 0.000 0.0000 +g_qlogis 0.000 0.000 0.000 0.000 0.0000 + f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 g_qlogis +cyan_0 0.00 0.00 0.0000 0.0000 0.000 +log_k_JCZ38 0.00 0.00 0.0000 0.0000 0.000 +log_k_J9Z38 0.00 0.00 0.0000 0.0000 0.000 +log_k_JSE76 0.00 0.00 0.0000 0.0000 0.000 +f_cyan_ilr_1 0.00 0.00 0.0000 0.0000 0.000 +f_cyan_ilr_2 11.77 0.00 0.0000 0.0000 0.000 +f_JCZ38_qlogis 0.00 16.11 0.0000 0.0000 0.000 +log_k1 0.00 0.00 0.9496 0.0000 0.000 +log_k2 0.00 0.00 0.0000 0.5846 0.000 +g_qlogis 0.00 0.00 0.0000 0.0000 1.719 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2398 2390 -1179 + +Optimised parameters: + est. lower upper +cyan_0 100.8076 NA NA +log_k_JCZ38 -3.4684 NA NA +log_k_J9Z38 -5.0844 NA NA +log_k_JSE76 -5.5743 NA NA +f_cyan_ilr_1 0.6669 NA NA +f_cyan_ilr_2 0.7912 NA NA +f_JCZ38_qlogis 84.1825 NA NA +log_k1 -2.1671 NA NA +log_k2 -4.5447 NA NA +g_qlogis -0.5631 NA NA +a.1 2.9627 NA NA +b.1 0.0444 NA NA +SD.log_k_JCZ38 1.4044 NA NA +SD.log_k_J9Z38 0.6410 NA NA +SD.log_k_JSE76 0.5391 NA NA +SD.f_cyan_ilr_1 0.3203 NA NA +SD.f_cyan_ilr_2 0.5038 NA NA +SD.f_JCZ38_qlogis 3.5865 NA NA +SD.log_k2 0.3119 NA NA +SD.g_qlogis 0.8276 NA NA + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_JCZ38 1.4044 NA NA +SD.log_k_J9Z38 0.6410 NA NA +SD.log_k_JSE76 0.5391 NA NA +SD.f_cyan_ilr_1 0.3203 NA NA +SD.f_cyan_ilr_2 0.5038 NA NA +SD.f_JCZ38_qlogis 3.5865 NA NA +SD.log_k2 0.3119 NA NA +SD.g_qlogis 0.8276 NA NA + +Variance model: + est. lower upper +a.1 2.9627 NA NA +b.1 0.0444 NA NA + +Backtransformed parameters: + est. lower upper +cyan_0 1.008e+02 NA NA +k_JCZ38 3.117e-02 NA NA +k_J9Z38 6.193e-03 NA NA +k_JSE76 3.794e-03 NA NA +f_cyan_to_JCZ38 6.149e-01 NA NA +f_cyan_to_J9Z38 2.395e-01 NA NA +f_JCZ38_to_JSE76 1.000e+00 NA NA +k1 1.145e-01 NA NA +k2 1.062e-02 NA NA +g 3.628e-01 NA NA + +Resulting formation fractions: + ff +cyan_JCZ38 0.6149 +cyan_J9Z38 0.2395 +cyan_sink 0.1456 +JCZ38_JSE76 1.0000 +JCZ38_sink 0.0000 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +cyan 26.26 174.32 52.47 6.053 65.25 +JCZ38 22.24 73.88 NA NA NA +J9Z38 111.93 371.82 NA NA NA +JSE76 182.69 606.88 NA NA NA + +</code></pre> +<p></p> +<caption> +Hierarchical SFORB path 1 fit with constant variance +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 07:46:35 2023 +Date of summary: Thu Apr 20 20:01:30 2023 + +Equations: +d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound * + cyan_free + k_cyan_bound_free * cyan_bound +d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free * + cyan_bound +d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38 + * JCZ38 +d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38 + * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 531.17 s +Using 300, 100 iterations and 10 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + cyan_free_0 log_k_cyan_free log_k_cyan_free_bound + 102.0643 -2.8987 -2.7077 +log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 + -3.4717 -3.4008 -5.0024 + log_k_JSE76 f_cyan_ilr_1 f_cyan_ilr_2 + -5.8613 0.6855 1.2366 + f_JCZ38_qlogis + 13.7418 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_free_0 log_k_cyan_free log_k_cyan_free_bound +cyan_free_0 4.466 0.0000 0.000 +log_k_cyan_free 0.000 0.6158 0.000 +log_k_cyan_free_bound 0.000 0.0000 1.463 +log_k_cyan_bound_free 0.000 0.0000 0.000 +log_k_JCZ38 0.000 0.0000 0.000 +log_k_J9Z38 0.000 0.0000 0.000 +log_k_JSE76 0.000 0.0000 0.000 +f_cyan_ilr_1 0.000 0.0000 0.000 +f_cyan_ilr_2 0.000 0.0000 0.000 +f_JCZ38_qlogis 0.000 0.0000 0.000 + log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76 +cyan_free_0 0.000 0.000 0.000 0.000 +log_k_cyan_free 0.000 0.000 0.000 0.000 +log_k_cyan_free_bound 0.000 0.000 0.000 0.000 +log_k_cyan_bound_free 1.058 0.000 0.000 0.000 +log_k_JCZ38 0.000 2.382 0.000 0.000 +log_k_J9Z38 0.000 0.000 1.595 0.000 +log_k_JSE76 0.000 0.000 0.000 1.245 +f_cyan_ilr_1 0.000 0.000 0.000 0.000 +f_cyan_ilr_2 0.000 0.000 0.000 0.000 +f_JCZ38_qlogis 0.000 0.000 0.000 0.000 + f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis +cyan_free_0 0.0000 0.00 0.00 +log_k_cyan_free 0.0000 0.00 0.00 +log_k_cyan_free_bound 0.0000 0.00 0.00 +log_k_cyan_bound_free 0.0000 0.00 0.00 +log_k_JCZ38 0.0000 0.00 0.00 +log_k_J9Z38 0.0000 0.00 0.00 +log_k_JSE76 0.0000 0.00 0.00 +f_cyan_ilr_1 0.6852 0.00 0.00 +f_cyan_ilr_2 0.0000 1.28 0.00 +f_JCZ38_qlogis 0.0000 0.00 16.14 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2401 2394 -1181 + +Optimised parameters: + est. lower upper +cyan_free_0 102.7803 NA NA +log_k_cyan_free -2.8068 NA NA +log_k_cyan_free_bound -2.5714 NA NA +log_k_cyan_bound_free -3.4426 NA NA +log_k_JCZ38 -3.4994 NA NA +log_k_J9Z38 -5.1148 NA NA +log_k_JSE76 -5.6335 NA NA +f_cyan_ilr_1 0.6597 NA NA +f_cyan_ilr_2 0.5132 NA NA +f_JCZ38_qlogis 37.2090 NA NA +a.1 3.2367 NA NA +SD.log_k_cyan_free 0.3161 NA NA +SD.log_k_cyan_free_bound 0.8103 NA NA +SD.log_k_cyan_bound_free 0.5554 NA NA +SD.log_k_JCZ38 1.4858 NA NA +SD.log_k_J9Z38 0.5859 NA NA +SD.log_k_JSE76 0.6195 NA NA +SD.f_cyan_ilr_1 0.3118 NA NA +SD.f_cyan_ilr_2 0.3344 NA NA +SD.f_JCZ38_qlogis 0.5518 NA NA + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_cyan_free 0.3161 NA NA +SD.log_k_cyan_free_bound 0.8103 NA NA +SD.log_k_cyan_bound_free 0.5554 NA NA +SD.log_k_JCZ38 1.4858 NA NA +SD.log_k_J9Z38 0.5859 NA NA +SD.log_k_JSE76 0.6195 NA NA +SD.f_cyan_ilr_1 0.3118 NA NA +SD.f_cyan_ilr_2 0.3344 NA NA +SD.f_JCZ38_qlogis 0.5518 NA NA + +Variance model: + est. lower upper +a.1 3.237 NA NA + +Backtransformed parameters: + est. lower upper +cyan_free_0 1.028e+02 NA NA +k_cyan_free 6.040e-02 NA NA +k_cyan_free_bound 7.643e-02 NA NA +k_cyan_bound_free 3.198e-02 NA NA +k_JCZ38 3.022e-02 NA NA +k_J9Z38 6.007e-03 NA NA +k_JSE76 3.576e-03 NA NA +f_cyan_free_to_JCZ38 5.787e-01 NA NA +f_cyan_free_to_J9Z38 2.277e-01 NA NA +f_JCZ38_to_JSE76 1.000e+00 NA NA + +Estimated Eigenvalues of SFORB model(s): +cyan_b1 cyan_b2 cyan_g +0.15646 0.01235 0.33341 + +Resulting formation fractions: + ff +cyan_free_JCZ38 0.5787 +cyan_free_J9Z38 0.2277 +cyan_free_sink 0.1936 +cyan_free 1.0000 +JCZ38_JSE76 1.0000 +JCZ38_sink 0.0000 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 +cyan 24.48 153.7 46.26 4.43 56.15 +JCZ38 22.94 76.2 NA NA NA +J9Z38 115.39 383.3 NA NA NA +JSE76 193.84 643.9 NA NA NA + +</code></pre> +<p></p> +<caption> +Hierarchical SFORB path 1 fit with two-component error +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 07:49:08 2023 +Date of summary: Thu Apr 20 20:01:30 2023 + +Equations: +d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound * + cyan_free + k_cyan_bound_free * cyan_bound +d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free * + cyan_bound +d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38 + * JCZ38 +d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38 + * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 675.659 s +Using 300, 100 iterations and 10 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + cyan_free_0 log_k_cyan_free log_k_cyan_free_bound + 101.3964 -2.9881 -2.7949 +log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 + -3.4376 -3.3626 -4.9792 + log_k_JSE76 f_cyan_ilr_1 f_cyan_ilr_2 + -5.8727 0.6814 6.8139 + f_JCZ38_qlogis + 13.7419 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_free_0 log_k_cyan_free log_k_cyan_free_bound +cyan_free_0 5.317 0.0000 0.000 +log_k_cyan_free 0.000 0.7301 0.000 +log_k_cyan_free_bound 0.000 0.0000 1.384 +log_k_cyan_bound_free 0.000 0.0000 0.000 +log_k_JCZ38 0.000 0.0000 0.000 +log_k_J9Z38 0.000 0.0000 0.000 +log_k_JSE76 0.000 0.0000 0.000 +f_cyan_ilr_1 0.000 0.0000 0.000 +f_cyan_ilr_2 0.000 0.0000 0.000 +f_JCZ38_qlogis 0.000 0.0000 0.000 + log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76 +cyan_free_0 0.000 0.000 0.000 0.000 +log_k_cyan_free 0.000 0.000 0.000 0.000 +log_k_cyan_free_bound 0.000 0.000 0.000 0.000 +log_k_cyan_bound_free 1.109 0.000 0.000 0.000 +log_k_JCZ38 0.000 2.272 0.000 0.000 +log_k_J9Z38 0.000 0.000 1.633 0.000 +log_k_JSE76 0.000 0.000 0.000 1.271 +f_cyan_ilr_1 0.000 0.000 0.000 0.000 +f_cyan_ilr_2 0.000 0.000 0.000 0.000 +f_JCZ38_qlogis 0.000 0.000 0.000 0.000 + f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis +cyan_free_0 0.0000 0.00 0.00 +log_k_cyan_free 0.0000 0.00 0.00 +log_k_cyan_free_bound 0.0000 0.00 0.00 +log_k_cyan_bound_free 0.0000 0.00 0.00 +log_k_JCZ38 0.0000 0.00 0.00 +log_k_J9Z38 0.0000 0.00 0.00 +log_k_JSE76 0.0000 0.00 0.00 +f_cyan_ilr_1 0.6838 0.00 0.00 +f_cyan_ilr_2 0.0000 11.84 0.00 +f_JCZ38_qlogis 0.0000 0.00 16.14 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2400 2392 -1180 + +Optimised parameters: + est. lower upper +cyan_free_0 100.69983 NA NA +log_k_cyan_free -3.11584 NA NA +log_k_cyan_free_bound -3.15216 NA NA +log_k_cyan_bound_free -3.65986 NA NA +log_k_JCZ38 -3.47811 NA NA +log_k_J9Z38 -5.08835 NA NA +log_k_JSE76 -5.55514 NA NA +f_cyan_ilr_1 0.66764 NA NA +f_cyan_ilr_2 0.78329 NA NA +f_JCZ38_qlogis 25.35245 NA NA +a.1 2.99088 NA NA +b.1 0.04346 NA NA +SD.log_k_cyan_free 0.48797 NA NA +SD.log_k_cyan_bound_free 0.27243 NA NA +SD.log_k_JCZ38 1.42450 NA NA +SD.log_k_J9Z38 0.63496 NA NA +SD.log_k_JSE76 0.55951 NA NA +SD.f_cyan_ilr_1 0.32687 NA NA +SD.f_cyan_ilr_2 0.48056 NA NA +SD.f_JCZ38_qlogis 0.43818 NA NA + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_cyan_free 0.4880 NA NA +SD.log_k_cyan_bound_free 0.2724 NA NA +SD.log_k_JCZ38 1.4245 NA NA +SD.log_k_J9Z38 0.6350 NA NA +SD.log_k_JSE76 0.5595 NA NA +SD.f_cyan_ilr_1 0.3269 NA NA +SD.f_cyan_ilr_2 0.4806 NA NA +SD.f_JCZ38_qlogis 0.4382 NA NA + +Variance model: + est. lower upper +a.1 2.99088 NA NA +b.1 0.04346 NA NA + +Backtransformed parameters: + est. lower upper +cyan_free_0 1.007e+02 NA NA +k_cyan_free 4.434e-02 NA NA +k_cyan_free_bound 4.276e-02 NA NA +k_cyan_bound_free 2.574e-02 NA NA +k_JCZ38 3.087e-02 NA NA +k_J9Z38 6.168e-03 NA NA +k_JSE76 3.868e-03 NA NA +f_cyan_free_to_JCZ38 6.143e-01 NA NA +f_cyan_free_to_J9Z38 2.389e-01 NA NA +f_JCZ38_to_JSE76 1.000e+00 NA NA + +Estimated Eigenvalues of SFORB model(s): +cyan_b1 cyan_b2 cyan_g +0.10161 0.01123 0.36636 + +Resulting formation fractions: + ff +cyan_free_JCZ38 6.143e-01 +cyan_free_J9Z38 2.389e-01 +cyan_free_sink 1.468e-01 +cyan_free 1.000e+00 +JCZ38_JSE76 1.000e+00 +JCZ38_sink 9.763e-12 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 +cyan 25.91 164.4 49.49 6.822 61.72 +JCZ38 22.46 74.6 NA NA NA +J9Z38 112.37 373.3 NA NA NA +JSE76 179.22 595.4 NA NA NA + +</code></pre> +<p></p> +<caption> +Hierarchical HS path 1 fit with constant variance +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 07:46:30 2023 +Date of summary: Thu Apr 20 20:01:30 2023 + +Equations: +d_cyan/dt = - ifelse(time <= tb, k1, k2) * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * ifelse(time <= tb, k1, k2) * cyan - + k_JCZ38 * JCZ38 +d_J9Z38/dt = + f_cyan_to_J9Z38 * ifelse(time <= tb, k1, k2) * cyan - + k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 525.846 s +Using 300, 100 iterations and 10 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 + 102.8738 -3.4490 -4.9348 -5.5989 0.6469 + f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 log_tb + 1.2854 9.7193 -2.9084 -4.1810 1.7813 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 +cyan_0 5.409 0.00 0.00 0.000 0.0000 +log_k_JCZ38 0.000 2.33 0.00 0.000 0.0000 +log_k_J9Z38 0.000 0.00 1.59 0.000 0.0000 +log_k_JSE76 0.000 0.00 0.00 1.006 0.0000 +f_cyan_ilr_1 0.000 0.00 0.00 0.000 0.6371 +f_cyan_ilr_2 0.000 0.00 0.00 0.000 0.0000 +f_JCZ38_qlogis 0.000 0.00 0.00 0.000 0.0000 +log_k1 0.000 0.00 0.00 0.000 0.0000 +log_k2 0.000 0.00 0.00 0.000 0.0000 +log_tb 0.000 0.00 0.00 0.000 0.0000 + f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 log_tb +cyan_0 0.000 0.00 0.0000 0.0000 0.0000 +log_k_JCZ38 0.000 0.00 0.0000 0.0000 0.0000 +log_k_J9Z38 0.000 0.00 0.0000 0.0000 0.0000 +log_k_JSE76 0.000 0.00 0.0000 0.0000 0.0000 +f_cyan_ilr_1 0.000 0.00 0.0000 0.0000 0.0000 +f_cyan_ilr_2 2.167 0.00 0.0000 0.0000 0.0000 +f_JCZ38_qlogis 0.000 10.22 0.0000 0.0000 0.0000 +log_k1 0.000 0.00 0.7003 0.0000 0.0000 +log_k2 0.000 0.00 0.0000 0.8928 0.0000 +log_tb 0.000 0.00 0.0000 0.0000 0.6774 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2427 2420 -1194 + +Optimised parameters: + est. lower upper +cyan_0 101.84849 NA NA +log_k_JCZ38 -3.47365 NA NA +log_k_J9Z38 -5.10562 NA NA +log_k_JSE76 -5.60318 NA NA +f_cyan_ilr_1 0.66127 NA NA +f_cyan_ilr_2 0.60283 NA NA +f_JCZ38_qlogis 45.06408 NA NA +log_k1 -3.10124 NA NA +log_k2 -4.39028 NA NA +log_tb 2.32256 NA NA +a.1 3.32683 NA NA +SD.log_k_JCZ38 1.41427 NA NA +SD.log_k_J9Z38 0.54767 NA NA +SD.log_k_JSE76 0.62147 NA NA +SD.f_cyan_ilr_1 0.30189 NA NA +SD.f_cyan_ilr_2 0.34960 NA NA +SD.f_JCZ38_qlogis 0.04644 NA NA +SD.log_k1 0.39534 NA NA +SD.log_k2 0.43468 NA NA +SD.log_tb 0.60781 NA NA + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_JCZ38 1.41427 NA NA +SD.log_k_J9Z38 0.54767 NA NA +SD.log_k_JSE76 0.62147 NA NA +SD.f_cyan_ilr_1 0.30189 NA NA +SD.f_cyan_ilr_2 0.34960 NA NA +SD.f_JCZ38_qlogis 0.04644 NA NA +SD.log_k1 0.39534 NA NA +SD.log_k2 0.43468 NA NA +SD.log_tb 0.60781 NA NA + +Variance model: + est. lower upper +a.1 3.327 NA NA + +Backtransformed parameters: + est. lower upper +cyan_0 1.018e+02 NA NA +k_JCZ38 3.100e-02 NA NA +k_J9Z38 6.063e-03 NA NA +k_JSE76 3.686e-03 NA NA +f_cyan_to_JCZ38 5.910e-01 NA NA +f_cyan_to_J9Z38 2.320e-01 NA NA +f_JCZ38_to_JSE76 1.000e+00 NA NA +k1 4.499e-02 NA NA +k2 1.240e-02 NA NA +tb 1.020e+01 NA NA + +Resulting formation fractions: + ff +cyan_JCZ38 0.591 +cyan_J9Z38 0.232 +cyan_sink 0.177 +JCZ38_JSE76 1.000 +JCZ38_sink 0.000 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +cyan 29.09 158.91 47.84 15.41 55.91 +JCZ38 22.36 74.27 NA NA NA +J9Z38 114.33 379.80 NA NA NA +JSE76 188.04 624.66 NA NA NA + +</code></pre> +<p></p> +<caption> +Hierarchical HS path 1 fit with two-component error +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 07:46:19 2023 +Date of summary: Thu Apr 20 20:01:30 2023 + +Equations: +d_cyan/dt = - ifelse(time <= tb, k1, k2) * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * ifelse(time <= tb, k1, k2) * cyan - + k_JCZ38 * JCZ38 +d_J9Z38/dt = + f_cyan_to_J9Z38 * ifelse(time <= tb, k1, k2) * cyan - + k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 514.968 s +Using 300, 100 iterations and 10 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 + 101.168 -3.358 -4.941 -5.794 0.676 + f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 log_tb + 5.740 13.863 -3.147 -4.262 2.173 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 +cyan_0 5.79 0.000 0.000 0.000 0.0000 +log_k_JCZ38 0.00 2.271 0.000 0.000 0.0000 +log_k_J9Z38 0.00 0.000 1.614 0.000 0.0000 +log_k_JSE76 0.00 0.000 0.000 1.264 0.0000 +f_cyan_ilr_1 0.00 0.000 0.000 0.000 0.6761 +f_cyan_ilr_2 0.00 0.000 0.000 0.000 0.0000 +f_JCZ38_qlogis 0.00 0.000 0.000 0.000 0.0000 +log_k1 0.00 0.000 0.000 0.000 0.0000 +log_k2 0.00 0.000 0.000 0.000 0.0000 +log_tb 0.00 0.000 0.000 0.000 0.0000 + f_cyan_ilr_2 f_JCZ38_qlogis log_k1 log_k2 log_tb +cyan_0 0.000 0.00 0.0000 0.0000 0.000 +log_k_JCZ38 0.000 0.00 0.0000 0.0000 0.000 +log_k_J9Z38 0.000 0.00 0.0000 0.0000 0.000 +log_k_JSE76 0.000 0.00 0.0000 0.0000 0.000 +f_cyan_ilr_1 0.000 0.00 0.0000 0.0000 0.000 +f_cyan_ilr_2 9.572 0.00 0.0000 0.0000 0.000 +f_JCZ38_qlogis 0.000 19.19 0.0000 0.0000 0.000 +log_k1 0.000 0.00 0.8705 0.0000 0.000 +log_k2 0.000 0.00 0.0000 0.9288 0.000 +log_tb 0.000 0.00 0.0000 0.0000 1.065 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2422 2414 -1190 + +Optimised parameters: + est. lower upper +cyan_0 100.9521 NA NA +log_k_JCZ38 -3.4629 NA NA +log_k_J9Z38 -5.0346 NA NA +log_k_JSE76 -5.5722 NA NA +f_cyan_ilr_1 0.6560 NA NA +f_cyan_ilr_2 0.7983 NA NA +f_JCZ38_qlogis 42.7949 NA NA +log_k1 -3.1721 NA NA +log_k2 -4.4039 NA NA +log_tb 2.3994 NA NA +a.1 3.0586 NA NA +b.1 0.0380 NA NA +SD.log_k_JCZ38 1.3754 NA NA +SD.log_k_J9Z38 0.6703 NA NA +SD.log_k_JSE76 0.5876 NA NA +SD.f_cyan_ilr_1 0.3272 NA NA +SD.f_cyan_ilr_2 0.5300 NA NA +SD.f_JCZ38_qlogis 6.4465 NA NA +SD.log_k1 0.4135 NA NA +SD.log_k2 0.4182 NA NA +SD.log_tb 0.6035 NA NA + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_JCZ38 1.3754 NA NA +SD.log_k_J9Z38 0.6703 NA NA +SD.log_k_JSE76 0.5876 NA NA +SD.f_cyan_ilr_1 0.3272 NA NA +SD.f_cyan_ilr_2 0.5300 NA NA +SD.f_JCZ38_qlogis 6.4465 NA NA +SD.log_k1 0.4135 NA NA +SD.log_k2 0.4182 NA NA +SD.log_tb 0.6035 NA NA + +Variance model: + est. lower upper +a.1 3.059 NA NA +b.1 0.038 NA NA + +Backtransformed parameters: + est. lower upper +cyan_0 1.010e+02 NA NA +k_JCZ38 3.134e-02 NA NA +k_J9Z38 6.509e-03 NA NA +k_JSE76 3.802e-03 NA NA +f_cyan_to_JCZ38 6.127e-01 NA NA +f_cyan_to_J9Z38 2.423e-01 NA NA +f_JCZ38_to_JSE76 1.000e+00 NA NA +k1 4.191e-02 NA NA +k2 1.223e-02 NA NA +tb 1.102e+01 NA NA + +Resulting formation fractions: + ff +cyan_JCZ38 0.6127 +cyan_J9Z38 0.2423 +cyan_sink 0.1449 +JCZ38_JSE76 1.0000 +JCZ38_sink 0.0000 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +cyan 29.94 161.54 48.63 16.54 56.68 +JCZ38 22.12 73.47 NA NA NA +J9Z38 106.50 353.77 NA NA NA +JSE76 182.30 605.60 NA NA NA + +</code></pre> +<p></p> +</div> +<div class="section level4"> +<h4 id="pathway-2">Pathway 2<a class="anchor" aria-label="anchor" href="#pathway-2"></a> +</h4> +<caption> +Hierarchical FOMC path 2 fit with constant variance +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 07:58:00 2023 +Date of summary: Thu Apr 20 20:01:30 2023 + +Equations: +d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * (alpha/beta) * 1/((time/beta) + 1) * + cyan - k_JCZ38 * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 +d_J9Z38/dt = + f_cyan_to_J9Z38 * (alpha/beta) * 1/((time/beta) + 1) * + cyan - k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 522.351 s +Using 300, 100 iterations and 10 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 + 101.8173 -1.8998 -5.1449 -2.5415 0.6705 + f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_alpha log_beta + 4.4669 16.1281 13.3327 -0.2314 2.8738 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 +cyan_0 5.742 0.000 0.000 0.00 0.0000 +log_k_JCZ38 0.000 1.402 0.000 0.00 0.0000 +log_k_J9Z38 0.000 0.000 1.718 0.00 0.0000 +log_k_JSE76 0.000 0.000 0.000 3.57 0.0000 +f_cyan_ilr_1 0.000 0.000 0.000 0.00 0.5926 +f_cyan_ilr_2 0.000 0.000 0.000 0.00 0.0000 +f_JCZ38_qlogis 0.000 0.000 0.000 0.00 0.0000 +f_JSE76_qlogis 0.000 0.000 0.000 0.00 0.0000 +log_alpha 0.000 0.000 0.000 0.00 0.0000 +log_beta 0.000 0.000 0.000 0.00 0.0000 + f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_alpha log_beta +cyan_0 0.00 0.00 0.00 0.0000 0.0000 +log_k_JCZ38 0.00 0.00 0.00 0.0000 0.0000 +log_k_J9Z38 0.00 0.00 0.00 0.0000 0.0000 +log_k_JSE76 0.00 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_1 0.00 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_2 10.56 0.00 0.00 0.0000 0.0000 +f_JCZ38_qlogis 0.00 12.04 0.00 0.0000 0.0000 +f_JSE76_qlogis 0.00 0.00 15.26 0.0000 0.0000 +log_alpha 0.00 0.00 0.00 0.4708 0.0000 +log_beta 0.00 0.00 0.00 0.0000 0.4432 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2308 2301 -1134 + +Optimised parameters: + est. lower upper +cyan_0 101.9586 99.22024 104.69700 +log_k_JCZ38 -2.4861 -3.17661 -1.79560 +log_k_J9Z38 -5.3926 -6.08842 -4.69684 +log_k_JSE76 -3.1193 -4.12904 -2.10962 +f_cyan_ilr_1 0.7368 0.42085 1.05276 +f_cyan_ilr_2 0.6196 0.06052 1.17861 +f_JCZ38_qlogis 4.8970 -4.68003 14.47398 +f_JSE76_qlogis 4.4066 -1.02087 9.83398 +log_alpha -0.3021 -0.68264 0.07838 +log_beta 2.7438 2.57970 2.90786 +a.1 2.9008 2.69920 3.10245 +SD.cyan_0 2.7081 0.64216 4.77401 +SD.log_k_JCZ38 0.7043 0.19951 1.20907 +SD.log_k_J9Z38 0.6248 0.05790 1.19180 +SD.log_k_JSE76 1.0750 0.33157 1.81839 +SD.f_cyan_ilr_1 0.3429 0.11688 0.56892 +SD.f_cyan_ilr_2 0.4774 0.09381 0.86097 +SD.f_JCZ38_qlogis 1.5565 -7.83970 10.95279 +SD.f_JSE76_qlogis 1.6871 -1.25577 4.63000 +SD.log_alpha 0.4216 0.15913 0.68405 + +Correlation: + cyan_0 l__JCZ3 l__J9Z3 l__JSE7 f_cy__1 f_cy__2 f_JCZ38 f_JSE76 +log_k_JCZ38 -0.0167 +log_k_J9Z38 -0.0307 0.0057 +log_k_JSE76 -0.0032 0.1358 0.0009 +f_cyan_ilr_1 -0.0087 0.0206 -0.1158 -0.0009 +f_cyan_ilr_2 -0.1598 0.0690 0.1770 0.0002 -0.0007 +f_JCZ38_qlogis 0.0966 -0.1132 -0.0440 0.0182 -0.1385 -0.4583 +f_JSE76_qlogis -0.0647 0.1157 0.0333 -0.0026 0.1110 0.3620 -0.8586 +log_alpha -0.0389 0.0113 0.0209 0.0021 0.0041 0.0451 -0.0605 0.0412 +log_beta -0.2508 0.0533 0.0977 0.0098 0.0220 0.2741 -0.2934 0.1999 + log_lph +log_k_JCZ38 +log_k_J9Z38 +log_k_JSE76 +f_cyan_ilr_1 +f_cyan_ilr_2 +f_JCZ38_qlogis +f_JSE76_qlogis +log_alpha +log_beta 0.2281 + +Random effects: + est. lower upper +SD.cyan_0 2.7081 0.64216 4.7740 +SD.log_k_JCZ38 0.7043 0.19951 1.2091 +SD.log_k_J9Z38 0.6248 0.05790 1.1918 +SD.log_k_JSE76 1.0750 0.33157 1.8184 +SD.f_cyan_ilr_1 0.3429 0.11688 0.5689 +SD.f_cyan_ilr_2 0.4774 0.09381 0.8610 +SD.f_JCZ38_qlogis 1.5565 -7.83970 10.9528 +SD.f_JSE76_qlogis 1.6871 -1.25577 4.6300 +SD.log_alpha 0.4216 0.15913 0.6840 + +Variance model: + est. lower upper +a.1 2.901 2.699 3.102 + +Backtransformed parameters: + est. lower upper +cyan_0 101.95862 99.220240 1.047e+02 +k_JCZ38 0.08323 0.041727 1.660e-01 +k_J9Z38 0.00455 0.002269 9.124e-03 +k_JSE76 0.04419 0.016098 1.213e-01 +f_cyan_to_JCZ38 0.61318 NA NA +f_cyan_to_J9Z38 0.21630 NA NA +f_JCZ38_to_JSE76 0.99259 0.009193 1.000e+00 +f_JSE76_to_JCZ38 0.98795 0.264857 9.999e-01 +alpha 0.73924 0.505281 1.082e+00 +beta 15.54568 13.193194 1.832e+01 + +Resulting formation fractions: + ff +cyan_JCZ38 0.613182 +cyan_J9Z38 0.216298 +cyan_sink 0.170519 +JCZ38_JSE76 0.992586 +JCZ38_sink 0.007414 +JSE76_JCZ38 0.987950 +JSE76_sink 0.012050 + +Estimated disappearance times: + DT50 DT90 DT50back +cyan 24.157 334.68 100.7 +JCZ38 8.328 27.66 NA +J9Z38 152.341 506.06 NA +JSE76 15.687 52.11 NA + +</code></pre> +<p></p> +<caption> +Hierarchical FOMC path 2 fit with two-component error +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 07:57:52 2023 +Date of summary: Thu Apr 20 20:01:30 2023 + +Equations: +d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * (alpha/beta) * 1/((time/beta) + 1) * + cyan - k_JCZ38 * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 +d_J9Z38/dt = + f_cyan_to_J9Z38 * (alpha/beta) * 1/((time/beta) + 1) * + cyan - k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 514.301 s +Using 300, 100 iterations and 10 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 + 101.9028 -1.9055 -5.0249 -2.5646 0.6807 + f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_alpha log_beta + 4.8883 16.0676 9.3923 -0.1346 3.0364 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 +cyan_0 6.321 0.000 0.000 0.000 0.0000 +log_k_JCZ38 0.000 1.392 0.000 0.000 0.0000 +log_k_J9Z38 0.000 0.000 1.561 0.000 0.0000 +log_k_JSE76 0.000 0.000 0.000 3.614 0.0000 +f_cyan_ilr_1 0.000 0.000 0.000 0.000 0.6339 +f_cyan_ilr_2 0.000 0.000 0.000 0.000 0.0000 +f_JCZ38_qlogis 0.000 0.000 0.000 0.000 0.0000 +f_JSE76_qlogis 0.000 0.000 0.000 0.000 0.0000 +log_alpha 0.000 0.000 0.000 0.000 0.0000 +log_beta 0.000 0.000 0.000 0.000 0.0000 + f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_alpha log_beta +cyan_0 0.00 0.00 0.00 0.0000 0.0000 +log_k_JCZ38 0.00 0.00 0.00 0.0000 0.0000 +log_k_J9Z38 0.00 0.00 0.00 0.0000 0.0000 +log_k_JSE76 0.00 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_1 0.00 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_2 10.41 0.00 0.00 0.0000 0.0000 +f_JCZ38_qlogis 0.00 12.24 0.00 0.0000 0.0000 +f_JSE76_qlogis 0.00 0.00 15.13 0.0000 0.0000 +log_alpha 0.00 0.00 0.00 0.3701 0.0000 +log_beta 0.00 0.00 0.00 0.0000 0.5662 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2248 2240 -1103 + +Optimised parameters: + est. lower upper +cyan_0 101.55545 9.920e+01 1.039e+02 +log_k_JCZ38 -2.37354 -2.928e+00 -1.819e+00 +log_k_J9Z38 -5.14736 -5.960e+00 -4.335e+00 +log_k_JSE76 -3.07802 -4.243e+00 -1.913e+00 +f_cyan_ilr_1 0.71263 3.655e-01 1.060e+00 +f_cyan_ilr_2 0.95202 2.701e-01 1.634e+00 +f_JCZ38_qlogis 3.58473 1.251e+00 5.919e+00 +f_JSE76_qlogis 19.03623 -1.037e+07 1.037e+07 +log_alpha -0.15297 -4.490e-01 1.431e-01 +log_beta 2.99230 2.706e+00 3.278e+00 +a.1 2.04816 NA NA +b.1 0.06886 NA NA +SD.log_k_JCZ38 0.56174 NA NA +SD.log_k_J9Z38 0.86509 NA NA +SD.log_k_JSE76 1.28450 NA NA +SD.f_cyan_ilr_1 0.38705 NA NA +SD.f_cyan_ilr_2 0.54153 NA NA +SD.f_JCZ38_qlogis 1.65311 NA NA +SD.f_JSE76_qlogis 7.51468 NA NA +SD.log_alpha 0.31586 NA NA +SD.log_beta 0.24696 NA NA + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_JCZ38 0.5617 NA NA +SD.log_k_J9Z38 0.8651 NA NA +SD.log_k_JSE76 1.2845 NA NA +SD.f_cyan_ilr_1 0.3870 NA NA +SD.f_cyan_ilr_2 0.5415 NA NA +SD.f_JCZ38_qlogis 1.6531 NA NA +SD.f_JSE76_qlogis 7.5147 NA NA +SD.log_alpha 0.3159 NA NA +SD.log_beta 0.2470 NA NA + +Variance model: + est. lower upper +a.1 2.04816 NA NA +b.1 0.06886 NA NA + +Backtransformed parameters: + est. lower upper +cyan_0 1.016e+02 99.20301 103.9079 +k_JCZ38 9.315e-02 0.05349 0.1622 +k_J9Z38 5.815e-03 0.00258 0.0131 +k_JSE76 4.605e-02 0.01436 0.1477 +f_cyan_to_JCZ38 6.438e-01 NA NA +f_cyan_to_J9Z38 2.350e-01 NA NA +f_JCZ38_to_JSE76 9.730e-01 0.77745 0.9973 +f_JSE76_to_JCZ38 1.000e+00 0.00000 1.0000 +alpha 8.582e-01 0.63824 1.1538 +beta 1.993e+01 14.97621 26.5262 + +Resulting formation fractions: + ff +cyan_JCZ38 6.438e-01 +cyan_J9Z38 2.350e-01 +cyan_sink 1.212e-01 +JCZ38_JSE76 9.730e-01 +JCZ38_sink 2.700e-02 +JSE76_JCZ38 1.000e+00 +JSE76_sink 5.403e-09 + +Estimated disappearance times: + DT50 DT90 DT50back +cyan 24.771 271.70 81.79 +JCZ38 7.441 24.72 NA +J9Z38 119.205 395.99 NA +JSE76 15.052 50.00 NA + +</code></pre> +<p></p> +<caption> +Hierarchical DFOP path 2 fit with constant variance +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 07:58:43 2023 +Date of summary: Thu Apr 20 20:01:30 2023 + +Equations: +d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 - + g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * + exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38 + + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 +d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 - + g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * + exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 565.562 s +Using 300, 100 iterations and 10 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 + 102.4358 -2.3107 -5.3123 -3.7120 0.6753 + f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 + 1.1462 12.4095 12.3630 -1.9317 -4.4557 + g_qlogis + -0.5648 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 +cyan_0 4.594 0.0000 0.000 0.0 0.0000 +log_k_JCZ38 0.000 0.7966 0.000 0.0 0.0000 +log_k_J9Z38 0.000 0.0000 1.561 0.0 0.0000 +log_k_JSE76 0.000 0.0000 0.000 0.8 0.0000 +f_cyan_ilr_1 0.000 0.0000 0.000 0.0 0.6349 +f_cyan_ilr_2 0.000 0.0000 0.000 0.0 0.0000 +f_JCZ38_qlogis 0.000 0.0000 0.000 0.0 0.0000 +f_JSE76_qlogis 0.000 0.0000 0.000 0.0 0.0000 +log_k1 0.000 0.0000 0.000 0.0 0.0000 +log_k2 0.000 0.0000 0.000 0.0 0.0000 +g_qlogis 0.000 0.0000 0.000 0.0 0.0000 + f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 +cyan_0 0.000 0.00 0.0 0.000 0.0000 +log_k_JCZ38 0.000 0.00 0.0 0.000 0.0000 +log_k_J9Z38 0.000 0.00 0.0 0.000 0.0000 +log_k_JSE76 0.000 0.00 0.0 0.000 0.0000 +f_cyan_ilr_1 0.000 0.00 0.0 0.000 0.0000 +f_cyan_ilr_2 1.797 0.00 0.0 0.000 0.0000 +f_JCZ38_qlogis 0.000 13.85 0.0 0.000 0.0000 +f_JSE76_qlogis 0.000 0.00 14.1 0.000 0.0000 +log_k1 0.000 0.00 0.0 1.106 0.0000 +log_k2 0.000 0.00 0.0 0.000 0.6141 +g_qlogis 0.000 0.00 0.0 0.000 0.0000 + g_qlogis +cyan_0 0.000 +log_k_JCZ38 0.000 +log_k_J9Z38 0.000 +log_k_JSE76 0.000 +f_cyan_ilr_1 0.000 +f_cyan_ilr_2 0.000 +f_JCZ38_qlogis 0.000 +f_JSE76_qlogis 0.000 +log_k1 0.000 +log_k2 0.000 +g_qlogis 1.595 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2290 2281 -1123 + +Optimised parameters: + est. lower upper +cyan_0 102.6903 101.44420 103.9365 +log_k_JCZ38 -2.4018 -2.98058 -1.8230 +log_k_J9Z38 -5.1865 -5.92931 -4.4437 +log_k_JSE76 -3.0784 -4.25226 -1.9045 +f_cyan_ilr_1 0.7157 0.37625 1.0551 +f_cyan_ilr_2 0.7073 0.20136 1.2132 +f_JCZ38_qlogis 4.6797 0.43240 8.9269 +f_JSE76_qlogis 5.0080 -1.01380 11.0299 +log_k1 -1.9620 -2.62909 -1.2949 +log_k2 -4.4894 -4.94958 -4.0292 +g_qlogis -0.4658 -1.34443 0.4129 +a.1 2.7158 2.52576 2.9059 +SD.log_k_JCZ38 0.5818 0.15679 1.0067 +SD.log_k_J9Z38 0.7421 0.16751 1.3167 +SD.log_k_JSE76 1.2841 0.43247 2.1356 +SD.f_cyan_ilr_1 0.3748 0.13040 0.6192 +SD.f_cyan_ilr_2 0.4550 0.08396 0.8261 +SD.f_JCZ38_qlogis 2.0862 -0.73390 4.9062 +SD.f_JSE76_qlogis 1.9585 -3.14773 7.0647 +SD.log_k1 0.7389 0.25761 1.2201 +SD.log_k2 0.5132 0.18143 0.8450 +SD.g_qlogis 0.9870 0.35773 1.6164 + +Correlation: + cyan_0 l__JCZ3 l__J9Z3 l__JSE7 f_cy__1 f_cy__2 f_JCZ38 f_JSE76 +log_k_JCZ38 -0.0170 +log_k_J9Z38 -0.0457 0.0016 +log_k_JSE76 -0.0046 0.1183 0.0005 +f_cyan_ilr_1 0.0079 0.0072 -0.0909 0.0003 +f_cyan_ilr_2 -0.3114 0.0343 0.1542 0.0023 -0.0519 +f_JCZ38_qlogis 0.0777 -0.0601 -0.0152 0.0080 -0.0520 -0.2524 +f_JSE76_qlogis -0.0356 0.0817 0.0073 0.0051 0.0388 0.1959 -0.6236 +log_k1 0.0848 -0.0028 0.0010 -0.0010 -0.0014 -0.0245 0.0121 -0.0177 +log_k2 0.0274 -0.0001 0.0075 0.0000 -0.0023 -0.0060 0.0000 -0.0130 +g_qlogis 0.0159 0.0002 -0.0095 0.0002 0.0029 -0.0140 -0.0001 0.0149 + log_k1 log_k2 +log_k_JCZ38 +log_k_J9Z38 +log_k_JSE76 +f_cyan_ilr_1 +f_cyan_ilr_2 +f_JCZ38_qlogis +f_JSE76_qlogis +log_k1 +log_k2 0.0280 +g_qlogis -0.0278 -0.0310 + +Random effects: + est. lower upper +SD.log_k_JCZ38 0.5818 0.15679 1.0067 +SD.log_k_J9Z38 0.7421 0.16751 1.3167 +SD.log_k_JSE76 1.2841 0.43247 2.1356 +SD.f_cyan_ilr_1 0.3748 0.13040 0.6192 +SD.f_cyan_ilr_2 0.4550 0.08396 0.8261 +SD.f_JCZ38_qlogis 2.0862 -0.73390 4.9062 +SD.f_JSE76_qlogis 1.9585 -3.14773 7.0647 +SD.log_k1 0.7389 0.25761 1.2201 +SD.log_k2 0.5132 0.18143 0.8450 +SD.g_qlogis 0.9870 0.35773 1.6164 + +Variance model: + est. lower upper +a.1 2.716 2.526 2.906 + +Backtransformed parameters: + est. lower upper +cyan_0 1.027e+02 1.014e+02 103.93649 +k_JCZ38 9.056e-02 5.076e-02 0.16154 +k_J9Z38 5.591e-03 2.660e-03 0.01175 +k_JSE76 4.603e-02 1.423e-02 0.14890 +f_cyan_to_JCZ38 6.184e-01 NA NA +f_cyan_to_J9Z38 2.248e-01 NA NA +f_JCZ38_to_JSE76 9.908e-01 6.064e-01 0.99987 +f_JSE76_to_JCZ38 9.934e-01 2.662e-01 0.99998 +k1 1.406e-01 7.214e-02 0.27393 +k2 1.123e-02 7.086e-03 0.01779 +g 3.856e-01 2.068e-01 0.60177 + +Resulting formation fractions: + ff +cyan_JCZ38 0.618443 +cyan_J9Z38 0.224770 +cyan_sink 0.156787 +JCZ38_JSE76 0.990803 +JCZ38_sink 0.009197 +JSE76_JCZ38 0.993360 +JSE76_sink 0.006640 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +cyan 21.674 161.70 48.68 4.931 61.74 +JCZ38 7.654 25.43 NA NA NA +J9Z38 123.966 411.81 NA NA NA +JSE76 15.057 50.02 NA NA NA + +</code></pre> +<p></p> +<caption> +Hierarchical DFOP path 2 fit with two-component error +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 08:01:24 2023 +Date of summary: Thu Apr 20 20:01:30 2023 + +Equations: +d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 - + g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * + exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38 + + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 +d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 - + g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * + exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 726.501 s +Using 300, 100 iterations and 10 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 + 101.7523 -1.5948 -5.0119 -2.2723 0.6719 + f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 + 5.1681 12.8238 12.4130 -2.0057 -4.5526 + g_qlogis + -0.5805 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 +cyan_0 5.627 0.000 0.000 0.000 0.0000 +log_k_JCZ38 0.000 2.327 0.000 0.000 0.0000 +log_k_J9Z38 0.000 0.000 1.664 0.000 0.0000 +log_k_JSE76 0.000 0.000 0.000 4.566 0.0000 +f_cyan_ilr_1 0.000 0.000 0.000 0.000 0.6519 +f_cyan_ilr_2 0.000 0.000 0.000 0.000 0.0000 +f_JCZ38_qlogis 0.000 0.000 0.000 0.000 0.0000 +f_JSE76_qlogis 0.000 0.000 0.000 0.000 0.0000 +log_k1 0.000 0.000 0.000 0.000 0.0000 +log_k2 0.000 0.000 0.000 0.000 0.0000 +g_qlogis 0.000 0.000 0.000 0.000 0.0000 + f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 +cyan_0 0.0 0.00 0.00 0.0000 0.0000 +log_k_JCZ38 0.0 0.00 0.00 0.0000 0.0000 +log_k_J9Z38 0.0 0.00 0.00 0.0000 0.0000 +log_k_JSE76 0.0 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_1 0.0 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_2 10.1 0.00 0.00 0.0000 0.0000 +f_JCZ38_qlogis 0.0 13.99 0.00 0.0000 0.0000 +f_JSE76_qlogis 0.0 0.00 14.15 0.0000 0.0000 +log_k1 0.0 0.00 0.00 0.8452 0.0000 +log_k2 0.0 0.00 0.00 0.0000 0.5968 +g_qlogis 0.0 0.00 0.00 0.0000 0.0000 + g_qlogis +cyan_0 0.000 +log_k_JCZ38 0.000 +log_k_J9Z38 0.000 +log_k_JSE76 0.000 +f_cyan_ilr_1 0.000 +f_cyan_ilr_2 0.000 +f_JCZ38_qlogis 0.000 +f_JSE76_qlogis 0.000 +log_k1 0.000 +log_k2 0.000 +g_qlogis 1.691 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2234 2226 -1095 + +Optimised parameters: + est. lower upper +cyan_0 101.10667 9.903e+01 103.18265 +log_k_JCZ38 -2.49437 -3.297e+00 -1.69221 +log_k_J9Z38 -5.08171 -5.875e+00 -4.28846 +log_k_JSE76 -3.20072 -4.180e+00 -2.22163 +f_cyan_ilr_1 0.71059 3.639e-01 1.05727 +f_cyan_ilr_2 1.15398 2.981e-01 2.00984 +f_JCZ38_qlogis 3.18027 1.056e+00 5.30452 +f_JSE76_qlogis 5.61578 -2.505e+01 36.28077 +log_k1 -2.38875 -2.517e+00 -2.26045 +log_k2 -4.67246 -4.928e+00 -4.41715 +g_qlogis -0.28231 -1.135e+00 0.57058 +a.1 2.08190 1.856e+00 2.30785 +b.1 0.06114 5.015e-02 0.07214 +SD.log_k_JCZ38 0.84622 2.637e-01 1.42873 +SD.log_k_J9Z38 0.84564 2.566e-01 1.43464 +SD.log_k_JSE76 1.04385 3.242e-01 1.76351 +SD.f_cyan_ilr_1 0.38568 1.362e-01 0.63514 +SD.f_cyan_ilr_2 0.68046 7.166e-02 1.28925 +SD.f_JCZ38_qlogis 1.25244 -4.213e-02 2.54700 +SD.f_JSE76_qlogis 0.28202 -1.515e+03 1515.87968 +SD.log_k2 0.25749 7.655e-02 0.43843 +SD.g_qlogis 0.94535 3.490e-01 1.54174 + +Correlation: + cyan_0 l__JCZ3 l__J9Z3 l__JSE7 f_cy__1 f_cy__2 f_JCZ38 f_JSE76 +log_k_JCZ38 -0.0086 +log_k_J9Z38 -0.0363 -0.0007 +log_k_JSE76 0.0015 0.1210 -0.0017 +f_cyan_ilr_1 -0.0048 0.0095 -0.0572 0.0030 +f_cyan_ilr_2 -0.4788 0.0328 0.1143 0.0027 -0.0316 +f_JCZ38_qlogis 0.0736 -0.0664 -0.0137 0.0145 -0.0444 -0.2175 +f_JSE76_qlogis -0.0137 0.0971 0.0035 0.0009 0.0293 0.1333 -0.6767 +log_k1 0.2345 -0.0350 -0.0099 -0.0113 -0.0126 -0.1652 0.1756 -0.2161 +log_k2 0.0440 -0.0133 0.0199 -0.0040 -0.0097 -0.0119 0.0604 -0.1306 +g_qlogis 0.0438 0.0078 -0.0123 0.0029 0.0046 -0.0363 -0.0318 0.0736 + log_k1 log_k2 +log_k_JCZ38 +log_k_J9Z38 +log_k_JSE76 +f_cyan_ilr_1 +f_cyan_ilr_2 +f_JCZ38_qlogis +f_JSE76_qlogis +log_k1 +log_k2 0.3198 +g_qlogis -0.1666 -0.0954 + +Random effects: + est. lower upper +SD.log_k_JCZ38 0.8462 2.637e-01 1.4287 +SD.log_k_J9Z38 0.8456 2.566e-01 1.4346 +SD.log_k_JSE76 1.0439 3.242e-01 1.7635 +SD.f_cyan_ilr_1 0.3857 1.362e-01 0.6351 +SD.f_cyan_ilr_2 0.6805 7.166e-02 1.2893 +SD.f_JCZ38_qlogis 1.2524 -4.213e-02 2.5470 +SD.f_JSE76_qlogis 0.2820 -1.515e+03 1515.8797 +SD.log_k2 0.2575 7.655e-02 0.4384 +SD.g_qlogis 0.9453 3.490e-01 1.5417 + +Variance model: + est. lower upper +a.1 2.08190 1.85595 2.30785 +b.1 0.06114 0.05015 0.07214 + +Backtransformed parameters: + est. lower upper +cyan_0 1.011e+02 9.903e+01 103.18265 +k_JCZ38 8.255e-02 3.701e-02 0.18411 +k_J9Z38 6.209e-03 2.809e-03 0.01373 +k_JSE76 4.073e-02 1.530e-02 0.10843 +f_cyan_to_JCZ38 6.608e-01 NA NA +f_cyan_to_J9Z38 2.419e-01 NA NA +f_JCZ38_to_JSE76 9.601e-01 7.419e-01 0.99506 +f_JSE76_to_JCZ38 9.964e-01 1.322e-11 1.00000 +k1 9.174e-02 8.070e-02 0.10430 +k2 9.349e-03 7.243e-03 0.01207 +g 4.299e-01 2.432e-01 0.63890 + +Resulting formation fractions: + ff +cyan_JCZ38 0.660808 +cyan_J9Z38 0.241904 +cyan_sink 0.097288 +JCZ38_JSE76 0.960085 +JCZ38_sink 0.039915 +JSE76_JCZ38 0.996373 +JSE76_sink 0.003627 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +cyan 24.359 186.18 56.05 7.555 74.14 +JCZ38 8.397 27.89 NA NA NA +J9Z38 111.631 370.83 NA NA NA +JSE76 17.017 56.53 NA NA NA + +</code></pre> +<p></p> +<caption> +Hierarchical SFORB path 2 fit with constant variance +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 07:58:46 2023 +Date of summary: Thu Apr 20 20:01:30 2023 + +Equations: +d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound * + cyan_free + k_cyan_bound_free * cyan_bound +d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free * + cyan_bound +d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38 + * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 +d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38 + * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 568.562 s +Using 300, 100 iterations and 10 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + cyan_free_0 log_k_cyan_free log_k_cyan_free_bound + 102.4394 -2.7673 -2.8942 +log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 + -3.6201 -2.3107 -5.3123 + log_k_JSE76 f_cyan_ilr_1 f_cyan_ilr_2 + -3.7120 0.6754 1.1448 + f_JCZ38_qlogis f_JSE76_qlogis + 13.2672 13.3538 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_free_0 log_k_cyan_free log_k_cyan_free_bound +cyan_free_0 4.589 0.0000 0.00 +log_k_cyan_free 0.000 0.4849 0.00 +log_k_cyan_free_bound 0.000 0.0000 1.62 +log_k_cyan_bound_free 0.000 0.0000 0.00 +log_k_JCZ38 0.000 0.0000 0.00 +log_k_J9Z38 0.000 0.0000 0.00 +log_k_JSE76 0.000 0.0000 0.00 +f_cyan_ilr_1 0.000 0.0000 0.00 +f_cyan_ilr_2 0.000 0.0000 0.00 +f_JCZ38_qlogis 0.000 0.0000 0.00 +f_JSE76_qlogis 0.000 0.0000 0.00 + log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76 +cyan_free_0 0.000 0.0000 0.000 0.0 +log_k_cyan_free 0.000 0.0000 0.000 0.0 +log_k_cyan_free_bound 0.000 0.0000 0.000 0.0 +log_k_cyan_bound_free 1.197 0.0000 0.000 0.0 +log_k_JCZ38 0.000 0.7966 0.000 0.0 +log_k_J9Z38 0.000 0.0000 1.561 0.0 +log_k_JSE76 0.000 0.0000 0.000 0.8 +f_cyan_ilr_1 0.000 0.0000 0.000 0.0 +f_cyan_ilr_2 0.000 0.0000 0.000 0.0 +f_JCZ38_qlogis 0.000 0.0000 0.000 0.0 +f_JSE76_qlogis 0.000 0.0000 0.000 0.0 + f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis +cyan_free_0 0.0000 0.000 0.00 0.00 +log_k_cyan_free 0.0000 0.000 0.00 0.00 +log_k_cyan_free_bound 0.0000 0.000 0.00 0.00 +log_k_cyan_bound_free 0.0000 0.000 0.00 0.00 +log_k_JCZ38 0.0000 0.000 0.00 0.00 +log_k_J9Z38 0.0000 0.000 0.00 0.00 +log_k_JSE76 0.0000 0.000 0.00 0.00 +f_cyan_ilr_1 0.6349 0.000 0.00 0.00 +f_cyan_ilr_2 0.0000 1.797 0.00 0.00 +f_JCZ38_qlogis 0.0000 0.000 13.84 0.00 +f_JSE76_qlogis 0.0000 0.000 0.00 14.66 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2284 2275 -1120 + +Optimised parameters: + est. lower upper +cyan_free_0 102.7730 1.015e+02 1.041e+02 +log_k_cyan_free -2.8530 -3.167e+00 -2.539e+00 +log_k_cyan_free_bound -2.7326 -3.543e+00 -1.922e+00 +log_k_cyan_bound_free -3.5582 -4.126e+00 -2.990e+00 +log_k_JCZ38 -2.3810 -2.921e+00 -1.841e+00 +log_k_J9Z38 -5.2301 -5.963e+00 -4.497e+00 +log_k_JSE76 -3.0286 -4.286e+00 -1.771e+00 +f_cyan_ilr_1 0.7081 3.733e-01 1.043e+00 +f_cyan_ilr_2 0.5847 7.846e-03 1.162e+00 +f_JCZ38_qlogis 9.5676 -1.323e+03 1.342e+03 +f_JSE76_qlogis 3.7042 7.254e-02 7.336e+00 +a.1 2.7222 2.532e+00 2.913e+00 +SD.log_k_cyan_free 0.3338 1.086e-01 5.589e-01 +SD.log_k_cyan_free_bound 0.8888 3.023e-01 1.475e+00 +SD.log_k_cyan_bound_free 0.6220 2.063e-01 1.038e+00 +SD.log_k_JCZ38 0.5221 1.334e-01 9.108e-01 +SD.log_k_J9Z38 0.7104 1.371e-01 1.284e+00 +SD.log_k_JSE76 1.3837 4.753e-01 2.292e+00 +SD.f_cyan_ilr_1 0.3620 1.248e-01 5.992e-01 +SD.f_cyan_ilr_2 0.4259 8.145e-02 7.704e-01 +SD.f_JCZ38_qlogis 3.5332 -1.037e+05 1.037e+05 +SD.f_JSE76_qlogis 1.6990 -2.771e-01 3.675e+00 + +Correlation: + cyn_f_0 lg_k_c_ lg_k_cyn_f_ lg_k_cyn_b_ l__JCZ3 l__J9Z3 +log_k_cyan_free 0.2126 +log_k_cyan_free_bound 0.0894 0.0871 +log_k_cyan_bound_free 0.0033 0.0410 0.0583 +log_k_JCZ38 -0.0708 -0.0280 -0.0147 0.0019 +log_k_J9Z38 -0.0535 -0.0138 0.0012 0.0148 0.0085 +log_k_JSE76 -0.0066 -0.0030 -0.0021 -0.0005 0.1090 0.0010 +f_cyan_ilr_1 -0.0364 -0.0157 -0.0095 -0.0015 0.0458 -0.0960 +f_cyan_ilr_2 -0.3814 -0.1104 -0.0423 0.0146 0.1540 0.1526 +f_JCZ38_qlogis 0.2507 0.0969 0.0482 -0.0097 -0.2282 -0.0363 +f_JSE76_qlogis -0.1648 -0.0710 -0.0443 -0.0087 0.2002 0.0226 + l__JSE7 f_cy__1 f_cy__2 f_JCZ38 +log_k_cyan_free +log_k_cyan_free_bound +log_k_cyan_bound_free +log_k_JCZ38 +log_k_J9Z38 +log_k_JSE76 +f_cyan_ilr_1 0.0001 +f_cyan_ilr_2 0.0031 0.0586 +f_JCZ38_qlogis 0.0023 -0.1867 -0.6255 +f_JSE76_qlogis 0.0082 0.1356 0.4519 -0.7951 + +Random effects: + est. lower upper +SD.log_k_cyan_free 0.3338 1.086e-01 5.589e-01 +SD.log_k_cyan_free_bound 0.8888 3.023e-01 1.475e+00 +SD.log_k_cyan_bound_free 0.6220 2.063e-01 1.038e+00 +SD.log_k_JCZ38 0.5221 1.334e-01 9.108e-01 +SD.log_k_J9Z38 0.7104 1.371e-01 1.284e+00 +SD.log_k_JSE76 1.3837 4.753e-01 2.292e+00 +SD.f_cyan_ilr_1 0.3620 1.248e-01 5.992e-01 +SD.f_cyan_ilr_2 0.4259 8.145e-02 7.704e-01 +SD.f_JCZ38_qlogis 3.5332 -1.037e+05 1.037e+05 +SD.f_JSE76_qlogis 1.6990 -2.771e-01 3.675e+00 + +Variance model: + est. lower upper +a.1 2.722 2.532 2.913 + +Backtransformed parameters: + est. lower upper +cyan_free_0 1.028e+02 1.015e+02 104.06475 +k_cyan_free 5.767e-02 4.213e-02 0.07894 +k_cyan_free_bound 6.505e-02 2.892e-02 0.14633 +k_cyan_bound_free 2.849e-02 1.614e-02 0.05028 +k_JCZ38 9.246e-02 5.390e-02 0.15859 +k_J9Z38 5.353e-03 2.572e-03 0.01114 +k_JSE76 4.838e-02 1.376e-02 0.17009 +f_cyan_free_to_JCZ38 6.011e-01 5.028e-01 0.83792 +f_cyan_free_to_J9Z38 2.208e-01 5.028e-01 0.83792 +f_JCZ38_to_JSE76 9.999e-01 0.000e+00 1.00000 +f_JSE76_to_JCZ38 9.760e-01 5.181e-01 0.99935 + +Estimated Eigenvalues of SFORB model(s): +cyan_b1 cyan_b2 cyan_g +0.13942 0.01178 0.35948 + +Resulting formation fractions: + ff +cyan_free_JCZ38 6.011e-01 +cyan_free_J9Z38 2.208e-01 +cyan_free_sink 1.780e-01 +cyan_free 1.000e+00 +JCZ38_JSE76 9.999e-01 +JCZ38_sink 6.996e-05 +JSE76_JCZ38 9.760e-01 +JSE76_sink 2.403e-02 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 +cyan 23.390 157.60 47.44 4.971 58.82 +JCZ38 7.497 24.90 NA NA NA +J9Z38 129.482 430.13 NA NA NA +JSE76 14.326 47.59 NA NA NA + +</code></pre> +<p></p> +<caption> +Hierarchical SFORB path 2 fit with two-component error +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 08:01:30 2023 +Date of summary: Thu Apr 20 20:01:30 2023 + +Equations: +d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound * + cyan_free + k_cyan_bound_free * cyan_bound +d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free * + cyan_bound +d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38 + * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 +d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38 + * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 732.212 s +Using 300, 100 iterations and 10 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + cyan_free_0 log_k_cyan_free log_k_cyan_free_bound + 101.751 -2.837 -3.016 +log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 + -3.660 -2.299 -5.313 + log_k_JSE76 f_cyan_ilr_1 f_cyan_ilr_2 + -3.699 0.672 5.873 + f_JCZ38_qlogis f_JSE76_qlogis + 13.216 13.338 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_free_0 log_k_cyan_free log_k_cyan_free_bound +cyan_free_0 5.629 0.000 0.000 +log_k_cyan_free 0.000 0.446 0.000 +log_k_cyan_free_bound 0.000 0.000 1.449 +log_k_cyan_bound_free 0.000 0.000 0.000 +log_k_JCZ38 0.000 0.000 0.000 +log_k_J9Z38 0.000 0.000 0.000 +log_k_JSE76 0.000 0.000 0.000 +f_cyan_ilr_1 0.000 0.000 0.000 +f_cyan_ilr_2 0.000 0.000 0.000 +f_JCZ38_qlogis 0.000 0.000 0.000 +f_JSE76_qlogis 0.000 0.000 0.000 + log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76 +cyan_free_0 0.000 0.0000 0.000 0.0000 +log_k_cyan_free 0.000 0.0000 0.000 0.0000 +log_k_cyan_free_bound 0.000 0.0000 0.000 0.0000 +log_k_cyan_bound_free 1.213 0.0000 0.000 0.0000 +log_k_JCZ38 0.000 0.7801 0.000 0.0000 +log_k_J9Z38 0.000 0.0000 1.575 0.0000 +log_k_JSE76 0.000 0.0000 0.000 0.8078 +f_cyan_ilr_1 0.000 0.0000 0.000 0.0000 +f_cyan_ilr_2 0.000 0.0000 0.000 0.0000 +f_JCZ38_qlogis 0.000 0.0000 0.000 0.0000 +f_JSE76_qlogis 0.000 0.0000 0.000 0.0000 + f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis +cyan_free_0 0.0000 0.00 0.00 0.00 +log_k_cyan_free 0.0000 0.00 0.00 0.00 +log_k_cyan_free_bound 0.0000 0.00 0.00 0.00 +log_k_cyan_bound_free 0.0000 0.00 0.00 0.00 +log_k_JCZ38 0.0000 0.00 0.00 0.00 +log_k_J9Z38 0.0000 0.00 0.00 0.00 +log_k_JSE76 0.0000 0.00 0.00 0.00 +f_cyan_ilr_1 0.6519 0.00 0.00 0.00 +f_cyan_ilr_2 0.0000 10.78 0.00 0.00 +f_JCZ38_qlogis 0.0000 0.00 13.96 0.00 +f_JSE76_qlogis 0.0000 0.00 0.00 14.69 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2240 2232 -1098 + +Optimised parameters: + est. lower upper +cyan_free_0 101.10205 98.99221 103.2119 +log_k_cyan_free -3.16929 -3.61395 -2.7246 +log_k_cyan_free_bound -3.38259 -3.63022 -3.1350 +log_k_cyan_bound_free -3.81075 -4.13888 -3.4826 +log_k_JCZ38 -2.42057 -3.00756 -1.8336 +log_k_J9Z38 -5.07501 -5.85138 -4.2986 +log_k_JSE76 -3.12442 -4.21277 -2.0361 +f_cyan_ilr_1 0.70577 0.35788 1.0537 +f_cyan_ilr_2 1.14824 0.15810 2.1384 +f_JCZ38_qlogis 3.52245 0.43257 6.6123 +f_JSE76_qlogis 5.65140 -21.22295 32.5257 +a.1 2.07062 1.84329 2.2980 +b.1 0.06227 0.05124 0.0733 +SD.log_k_cyan_free 0.49468 0.18566 0.8037 +SD.log_k_cyan_bound_free 0.28972 0.07188 0.5076 +SD.log_k_JCZ38 0.58852 0.16800 1.0090 +SD.log_k_J9Z38 0.82500 0.24730 1.4027 +SD.log_k_JSE76 1.19201 0.40313 1.9809 +SD.f_cyan_ilr_1 0.38534 0.13640 0.6343 +SD.f_cyan_ilr_2 0.72463 0.10076 1.3485 +SD.f_JCZ38_qlogis 1.38223 -0.20997 2.9744 +SD.f_JSE76_qlogis 2.07989 -72.53027 76.6901 + +Correlation: + cyn_f_0 lg_k_c_ lg_k_cyn_f_ lg_k_cyn_b_ l__JCZ3 l__J9Z3 +log_k_cyan_free 0.1117 +log_k_cyan_free_bound 0.1763 0.1828 +log_k_cyan_bound_free 0.0120 0.0593 0.5030 +log_k_JCZ38 -0.0459 -0.0230 -0.0931 -0.0337 +log_k_J9Z38 -0.0381 -0.0123 -0.0139 0.0237 0.0063 +log_k_JSE76 -0.0044 -0.0038 -0.0175 -0.0072 0.1120 0.0003 +f_cyan_ilr_1 -0.0199 -0.0087 -0.0407 -0.0233 0.0268 -0.0552 +f_cyan_ilr_2 -0.4806 -0.1015 -0.2291 -0.0269 0.1156 0.1113 +f_JCZ38_qlogis 0.1805 0.0825 0.3085 0.0963 -0.1674 -0.0314 +f_JSE76_qlogis -0.1586 -0.0810 -0.3560 -0.1563 0.2025 0.0278 + l__JSE7 f_cy__1 f_cy__2 f_JCZ38 +log_k_cyan_free +log_k_cyan_free_bound +log_k_cyan_bound_free +log_k_JCZ38 +log_k_J9Z38 +log_k_JSE76 +f_cyan_ilr_1 0.0024 +f_cyan_ilr_2 0.0087 0.0172 +f_JCZ38_qlogis -0.0016 -0.1047 -0.4656 +f_JSE76_qlogis 0.0119 0.1034 0.4584 -0.8137 + +Random effects: + est. lower upper +SD.log_k_cyan_free 0.4947 0.18566 0.8037 +SD.log_k_cyan_bound_free 0.2897 0.07188 0.5076 +SD.log_k_JCZ38 0.5885 0.16800 1.0090 +SD.log_k_J9Z38 0.8250 0.24730 1.4027 +SD.log_k_JSE76 1.1920 0.40313 1.9809 +SD.f_cyan_ilr_1 0.3853 0.13640 0.6343 +SD.f_cyan_ilr_2 0.7246 0.10076 1.3485 +SD.f_JCZ38_qlogis 1.3822 -0.20997 2.9744 +SD.f_JSE76_qlogis 2.0799 -72.53027 76.6901 + +Variance model: + est. lower upper +a.1 2.07062 1.84329 2.2980 +b.1 0.06227 0.05124 0.0733 + +Backtransformed parameters: + est. lower upper +cyan_free_0 1.011e+02 9.899e+01 103.21190 +k_cyan_free 4.203e-02 2.695e-02 0.06557 +k_cyan_free_bound 3.396e-02 2.651e-02 0.04350 +k_cyan_bound_free 2.213e-02 1.594e-02 0.03073 +k_JCZ38 8.887e-02 4.941e-02 0.15984 +k_J9Z38 6.251e-03 2.876e-03 0.01359 +k_JSE76 4.396e-02 1.481e-02 0.13054 +f_cyan_free_to_JCZ38 6.590e-01 5.557e-01 0.95365 +f_cyan_free_to_J9Z38 2.429e-01 5.557e-01 0.95365 +f_JCZ38_to_JSE76 9.713e-01 6.065e-01 0.99866 +f_JSE76_to_JCZ38 9.965e-01 6.067e-10 1.00000 + +Estimated Eigenvalues of SFORB model(s): +cyan_b1 cyan_b2 cyan_g +0.08749 0.01063 0.40855 + +Resulting formation fractions: + ff +cyan_free_JCZ38 0.65905 +cyan_free_J9Z38 0.24291 +cyan_free_sink 0.09805 +cyan_free 1.00000 +JCZ38_JSE76 0.97132 +JCZ38_sink 0.02868 +JSE76_JCZ38 0.99650 +JSE76_sink 0.00350 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 +cyan 24.91 167.16 50.32 7.922 65.19 +JCZ38 7.80 25.91 NA NA NA +J9Z38 110.89 368.36 NA NA NA +JSE76 15.77 52.38 NA NA NA + +</code></pre> +<p></p> +</div> +<div class="section level4"> +<h4 id="pathway-2-refined-fits">Pathway 2, refined fits<a class="anchor" aria-label="anchor" href="#pathway-2-refined-fits"></a> +</h4> +<caption> +Hierarchical FOMC path 2 fit with reduced random effects, two-component +error +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 08:15:01 2023 +Date of summary: Thu Apr 20 20:01:31 2023 + +Equations: +d_cyan/dt = - (alpha/beta) * 1/((time/beta) + 1) * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * (alpha/beta) * 1/((time/beta) + 1) * + cyan - k_JCZ38 * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 +d_J9Z38/dt = + f_cyan_to_J9Z38 * (alpha/beta) * 1/((time/beta) + 1) * + cyan - k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 808.728 s +Using 300, 100 iterations and 10 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 + 101.9028 -1.9055 -5.0249 -2.5646 0.6807 + f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_alpha log_beta + 4.8883 16.0676 9.3923 -0.1346 3.0364 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 +cyan_0 6.321 0.000 0.000 0.000 0.0000 +log_k_JCZ38 0.000 1.392 0.000 0.000 0.0000 +log_k_J9Z38 0.000 0.000 1.561 0.000 0.0000 +log_k_JSE76 0.000 0.000 0.000 3.614 0.0000 +f_cyan_ilr_1 0.000 0.000 0.000 0.000 0.6339 +f_cyan_ilr_2 0.000 0.000 0.000 0.000 0.0000 +f_JCZ38_qlogis 0.000 0.000 0.000 0.000 0.0000 +f_JSE76_qlogis 0.000 0.000 0.000 0.000 0.0000 +log_alpha 0.000 0.000 0.000 0.000 0.0000 +log_beta 0.000 0.000 0.000 0.000 0.0000 + f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_alpha log_beta +cyan_0 0.00 0.00 0.00 0.0000 0.0000 +log_k_JCZ38 0.00 0.00 0.00 0.0000 0.0000 +log_k_J9Z38 0.00 0.00 0.00 0.0000 0.0000 +log_k_JSE76 0.00 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_1 0.00 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_2 10.41 0.00 0.00 0.0000 0.0000 +f_JCZ38_qlogis 0.00 12.24 0.00 0.0000 0.0000 +f_JSE76_qlogis 0.00 0.00 15.13 0.0000 0.0000 +log_alpha 0.00 0.00 0.00 0.3701 0.0000 +log_beta 0.00 0.00 0.00 0.0000 0.5662 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2251 2244 -1106 + +Optimised parameters: + est. lower upper +cyan_0 101.05768 NA NA +log_k_JCZ38 -2.73252 NA NA +log_k_J9Z38 -5.07399 NA NA +log_k_JSE76 -3.52863 NA NA +f_cyan_ilr_1 0.72176 NA NA +f_cyan_ilr_2 1.34610 NA NA +f_JCZ38_qlogis 2.08337 NA NA +f_JSE76_qlogis 1590.31880 NA NA +log_alpha -0.09336 NA NA +log_beta 3.10191 NA NA +a.1 2.08557 1.85439 2.31675 +b.1 0.06998 0.05800 0.08197 +SD.log_k_JCZ38 1.20053 0.43329 1.96777 +SD.log_k_J9Z38 0.85854 0.26708 1.45000 +SD.log_k_JSE76 0.62528 0.16061 1.08995 +SD.f_cyan_ilr_1 0.35190 0.12340 0.58039 +SD.f_cyan_ilr_2 0.85385 0.15391 1.55378 +SD.log_alpha 0.28971 0.08718 0.49225 +SD.log_beta 0.31614 0.05938 0.57290 + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_JCZ38 1.2005 0.43329 1.9678 +SD.log_k_J9Z38 0.8585 0.26708 1.4500 +SD.log_k_JSE76 0.6253 0.16061 1.0900 +SD.f_cyan_ilr_1 0.3519 0.12340 0.5804 +SD.f_cyan_ilr_2 0.8538 0.15391 1.5538 +SD.log_alpha 0.2897 0.08718 0.4923 +SD.log_beta 0.3161 0.05938 0.5729 + +Variance model: + est. lower upper +a.1 2.08557 1.854 2.31675 +b.1 0.06998 0.058 0.08197 + +Backtransformed parameters: + est. lower upper +cyan_0 1.011e+02 NA NA +k_JCZ38 6.506e-02 NA NA +k_J9Z38 6.257e-03 NA NA +k_JSE76 2.935e-02 NA NA +f_cyan_to_JCZ38 6.776e-01 NA NA +f_cyan_to_J9Z38 2.442e-01 NA NA +f_JCZ38_to_JSE76 8.893e-01 NA NA +f_JSE76_to_JCZ38 1.000e+00 NA NA +alpha 9.109e-01 NA NA +beta 2.224e+01 NA NA + +Resulting formation fractions: + ff +cyan_JCZ38 0.67761 +cyan_J9Z38 0.24417 +cyan_sink 0.07822 +JCZ38_JSE76 0.88928 +JCZ38_sink 0.11072 +JSE76_JCZ38 1.00000 +JSE76_sink 0.00000 + +Estimated disappearance times: + DT50 DT90 DT50back +cyan 25.36 256.37 77.18 +JCZ38 10.65 35.39 NA +J9Z38 110.77 367.98 NA +JSE76 23.62 78.47 NA + +</code></pre> +<p></p> +<caption> +Hierarchical DFOP path 2 fit with reduced random effects, constant +variance +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 08:16:32 2023 +Date of summary: Thu Apr 20 20:01:31 2023 + +Equations: +d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 - + g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * + exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38 + + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 +d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 - + g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * + exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 900.061 s +Using 300, 100 iterations and 10 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 + 102.4358 -2.3107 -5.3123 -3.7120 0.6753 + f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 + 1.1462 12.4095 12.3630 -1.9317 -4.4557 + g_qlogis + -0.5648 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 +cyan_0 4.594 0.0000 0.000 0.0 0.0000 +log_k_JCZ38 0.000 0.7966 0.000 0.0 0.0000 +log_k_J9Z38 0.000 0.0000 1.561 0.0 0.0000 +log_k_JSE76 0.000 0.0000 0.000 0.8 0.0000 +f_cyan_ilr_1 0.000 0.0000 0.000 0.0 0.6349 +f_cyan_ilr_2 0.000 0.0000 0.000 0.0 0.0000 +f_JCZ38_qlogis 0.000 0.0000 0.000 0.0 0.0000 +f_JSE76_qlogis 0.000 0.0000 0.000 0.0 0.0000 +log_k1 0.000 0.0000 0.000 0.0 0.0000 +log_k2 0.000 0.0000 0.000 0.0 0.0000 +g_qlogis 0.000 0.0000 0.000 0.0 0.0000 + f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 +cyan_0 0.000 0.00 0.0 0.000 0.0000 +log_k_JCZ38 0.000 0.00 0.0 0.000 0.0000 +log_k_J9Z38 0.000 0.00 0.0 0.000 0.0000 +log_k_JSE76 0.000 0.00 0.0 0.000 0.0000 +f_cyan_ilr_1 0.000 0.00 0.0 0.000 0.0000 +f_cyan_ilr_2 1.797 0.00 0.0 0.000 0.0000 +f_JCZ38_qlogis 0.000 13.85 0.0 0.000 0.0000 +f_JSE76_qlogis 0.000 0.00 14.1 0.000 0.0000 +log_k1 0.000 0.00 0.0 1.106 0.0000 +log_k2 0.000 0.00 0.0 0.000 0.6141 +g_qlogis 0.000 0.00 0.0 0.000 0.0000 + g_qlogis +cyan_0 0.000 +log_k_JCZ38 0.000 +log_k_J9Z38 0.000 +log_k_JSE76 0.000 +f_cyan_ilr_1 0.000 +f_cyan_ilr_2 0.000 +f_JCZ38_qlogis 0.000 +f_JSE76_qlogis 0.000 +log_k1 0.000 +log_k2 0.000 +g_qlogis 1.595 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2282 2274 -1121 + +Optimised parameters: + est. lower upper +cyan_0 102.5254 NA NA +log_k_JCZ38 -2.9358 NA NA +log_k_J9Z38 -5.1424 NA NA +log_k_JSE76 -3.6458 NA NA +f_cyan_ilr_1 0.6957 NA NA +f_cyan_ilr_2 0.6635 NA NA +f_JCZ38_qlogis 4984.8163 NA NA +f_JSE76_qlogis 1.9415 NA NA +log_k1 -1.9456 NA NA +log_k2 -4.4705 NA NA +g_qlogis -0.5117 NA NA +a.1 2.7455 2.55392 2.9370 +SD.log_k_JCZ38 1.3163 0.47635 2.1563 +SD.log_k_J9Z38 0.7162 0.16133 1.2711 +SD.log_k_JSE76 0.6457 0.15249 1.1390 +SD.f_cyan_ilr_1 0.3424 0.11714 0.5677 +SD.f_cyan_ilr_2 0.4524 0.09709 0.8077 +SD.log_k1 0.7353 0.25445 1.2161 +SD.log_k2 0.5137 0.18206 0.8453 +SD.g_qlogis 0.9857 0.35651 1.6148 + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_JCZ38 1.3163 0.47635 2.1563 +SD.log_k_J9Z38 0.7162 0.16133 1.2711 +SD.log_k_JSE76 0.6457 0.15249 1.1390 +SD.f_cyan_ilr_1 0.3424 0.11714 0.5677 +SD.f_cyan_ilr_2 0.4524 0.09709 0.8077 +SD.log_k1 0.7353 0.25445 1.2161 +SD.log_k2 0.5137 0.18206 0.8453 +SD.g_qlogis 0.9857 0.35651 1.6148 + +Variance model: + est. lower upper +a.1 2.745 2.554 2.937 + +Backtransformed parameters: + est. lower upper +cyan_0 1.025e+02 NA NA +k_JCZ38 5.309e-02 NA NA +k_J9Z38 5.844e-03 NA NA +k_JSE76 2.610e-02 NA NA +f_cyan_to_JCZ38 6.079e-01 NA NA +f_cyan_to_J9Z38 2.272e-01 NA NA +f_JCZ38_to_JSE76 1.000e+00 NA NA +f_JSE76_to_JCZ38 8.745e-01 NA NA +k1 1.429e-01 NA NA +k2 1.144e-02 NA NA +g 3.748e-01 NA NA + +Resulting formation fractions: + ff +cyan_JCZ38 0.6079 +cyan_J9Z38 0.2272 +cyan_sink 0.1649 +JCZ38_JSE76 1.0000 +JCZ38_sink 0.0000 +JSE76_JCZ38 0.8745 +JSE76_sink 0.1255 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +cyan 22.29 160.20 48.22 4.85 60.58 +JCZ38 13.06 43.37 NA NA NA +J9Z38 118.61 394.02 NA NA NA +JSE76 26.56 88.22 NA NA NA + +</code></pre> +<p></p> +<caption> +Hierarchical DFOP path 2 fit with reduced random effects, two-component +error +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 08:16:47 2023 +Date of summary: Thu Apr 20 20:01:31 2023 + +Equations: +d_cyan/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * cyan +d_JCZ38/dt = + f_cyan_to_JCZ38 * ((k1 * g * exp(-k1 * time) + k2 * (1 - + g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * + exp(-k2 * time))) * cyan - k_JCZ38 * JCZ38 + + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 +d_J9Z38/dt = + f_cyan_to_J9Z38 * ((k1 * g * exp(-k1 * time) + k2 * (1 - + g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * + exp(-k2 * time))) * cyan - k_J9Z38 * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 914.763 s +Using 300, 100 iterations and 10 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 + 101.7523 -1.5948 -5.0119 -2.2723 0.6719 + f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 + 5.1681 12.8238 12.4130 -2.0057 -4.5526 + g_qlogis + -0.5805 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_0 log_k_JCZ38 log_k_J9Z38 log_k_JSE76 f_cyan_ilr_1 +cyan_0 5.627 0.000 0.000 0.000 0.0000 +log_k_JCZ38 0.000 2.327 0.000 0.000 0.0000 +log_k_J9Z38 0.000 0.000 1.664 0.000 0.0000 +log_k_JSE76 0.000 0.000 0.000 4.566 0.0000 +f_cyan_ilr_1 0.000 0.000 0.000 0.000 0.6519 +f_cyan_ilr_2 0.000 0.000 0.000 0.000 0.0000 +f_JCZ38_qlogis 0.000 0.000 0.000 0.000 0.0000 +f_JSE76_qlogis 0.000 0.000 0.000 0.000 0.0000 +log_k1 0.000 0.000 0.000 0.000 0.0000 +log_k2 0.000 0.000 0.000 0.000 0.0000 +g_qlogis 0.000 0.000 0.000 0.000 0.0000 + f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis log_k1 log_k2 +cyan_0 0.0 0.00 0.00 0.0000 0.0000 +log_k_JCZ38 0.0 0.00 0.00 0.0000 0.0000 +log_k_J9Z38 0.0 0.00 0.00 0.0000 0.0000 +log_k_JSE76 0.0 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_1 0.0 0.00 0.00 0.0000 0.0000 +f_cyan_ilr_2 10.1 0.00 0.00 0.0000 0.0000 +f_JCZ38_qlogis 0.0 13.99 0.00 0.0000 0.0000 +f_JSE76_qlogis 0.0 0.00 14.15 0.0000 0.0000 +log_k1 0.0 0.00 0.00 0.8452 0.0000 +log_k2 0.0 0.00 0.00 0.0000 0.5968 +g_qlogis 0.0 0.00 0.00 0.0000 0.0000 + g_qlogis +cyan_0 0.000 +log_k_JCZ38 0.000 +log_k_J9Z38 0.000 +log_k_JSE76 0.000 +f_cyan_ilr_1 0.000 +f_cyan_ilr_2 0.000 +f_JCZ38_qlogis 0.000 +f_JSE76_qlogis 0.000 +log_k1 0.000 +log_k2 0.000 +g_qlogis 1.691 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2232 2224 -1096 + +Optimised parameters: + est. lower upper +cyan_0 101.20051 NA NA +log_k_JCZ38 -2.93542 NA NA +log_k_J9Z38 -5.03151 NA NA +log_k_JSE76 -3.67679 NA NA +f_cyan_ilr_1 0.67290 NA NA +f_cyan_ilr_2 0.99787 NA NA +f_JCZ38_qlogis 348.32484 NA NA +f_JSE76_qlogis 1.87846 NA NA +log_k1 -2.32738 NA NA +log_k2 -4.61295 NA NA +g_qlogis -0.38342 NA NA +a.1 2.06184 1.83746 2.28622 +b.1 0.06329 0.05211 0.07447 +SD.log_k_JCZ38 1.29042 0.47468 2.10617 +SD.log_k_J9Z38 0.84235 0.25903 1.42566 +SD.log_k_JSE76 0.56930 0.13934 0.99926 +SD.f_cyan_ilr_1 0.35183 0.12298 0.58068 +SD.f_cyan_ilr_2 0.77269 0.17908 1.36631 +SD.log_k2 0.28549 0.09210 0.47888 +SD.g_qlogis 0.93830 0.34568 1.53093 + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_JCZ38 1.2904 0.4747 2.1062 +SD.log_k_J9Z38 0.8423 0.2590 1.4257 +SD.log_k_JSE76 0.5693 0.1393 0.9993 +SD.f_cyan_ilr_1 0.3518 0.1230 0.5807 +SD.f_cyan_ilr_2 0.7727 0.1791 1.3663 +SD.log_k2 0.2855 0.0921 0.4789 +SD.g_qlogis 0.9383 0.3457 1.5309 + +Variance model: + est. lower upper +a.1 2.06184 1.83746 2.28622 +b.1 0.06329 0.05211 0.07447 + +Backtransformed parameters: + est. lower upper +cyan_0 1.012e+02 NA NA +k_JCZ38 5.311e-02 NA NA +k_J9Z38 6.529e-03 NA NA +k_JSE76 2.530e-02 NA NA +f_cyan_to_JCZ38 6.373e-01 NA NA +f_cyan_to_J9Z38 2.461e-01 NA NA +f_JCZ38_to_JSE76 1.000e+00 NA NA +f_JSE76_to_JCZ38 8.674e-01 NA NA +k1 9.755e-02 NA NA +k2 9.922e-03 NA NA +g 4.053e-01 NA NA + +Resulting formation fractions: + ff +cyan_JCZ38 0.6373 +cyan_J9Z38 0.2461 +cyan_sink 0.1167 +JCZ38_JSE76 1.0000 +JCZ38_sink 0.0000 +JSE76_JCZ38 0.8674 +JSE76_sink 0.1326 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +cyan 24.93 179.68 54.09 7.105 69.86 +JCZ38 13.05 43.36 NA NA NA +J9Z38 106.16 352.67 NA NA NA +JSE76 27.39 91.00 NA NA NA + +</code></pre> +<p></p> +<caption> +Hierarchical SFORB path 2 fit with reduced random effects, constant +variance +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 08:16:33 2023 +Date of summary: Thu Apr 20 20:01:31 2023 + +Equations: +d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound * + cyan_free + k_cyan_bound_free * cyan_bound +d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free * + cyan_bound +d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38 + * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 +d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38 + * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 901.179 s +Using 300, 100 iterations and 10 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + cyan_free_0 log_k_cyan_free log_k_cyan_free_bound + 102.4394 -2.7673 -2.8942 +log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 + -3.6201 -2.3107 -5.3123 + log_k_JSE76 f_cyan_ilr_1 f_cyan_ilr_2 + -3.7120 0.6754 1.1448 + f_JCZ38_qlogis f_JSE76_qlogis + 13.2672 13.3538 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_free_0 log_k_cyan_free log_k_cyan_free_bound +cyan_free_0 4.589 0.0000 0.00 +log_k_cyan_free 0.000 0.4849 0.00 +log_k_cyan_free_bound 0.000 0.0000 1.62 +log_k_cyan_bound_free 0.000 0.0000 0.00 +log_k_JCZ38 0.000 0.0000 0.00 +log_k_J9Z38 0.000 0.0000 0.00 +log_k_JSE76 0.000 0.0000 0.00 +f_cyan_ilr_1 0.000 0.0000 0.00 +f_cyan_ilr_2 0.000 0.0000 0.00 +f_JCZ38_qlogis 0.000 0.0000 0.00 +f_JSE76_qlogis 0.000 0.0000 0.00 + log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76 +cyan_free_0 0.000 0.0000 0.000 0.0 +log_k_cyan_free 0.000 0.0000 0.000 0.0 +log_k_cyan_free_bound 0.000 0.0000 0.000 0.0 +log_k_cyan_bound_free 1.197 0.0000 0.000 0.0 +log_k_JCZ38 0.000 0.7966 0.000 0.0 +log_k_J9Z38 0.000 0.0000 1.561 0.0 +log_k_JSE76 0.000 0.0000 0.000 0.8 +f_cyan_ilr_1 0.000 0.0000 0.000 0.0 +f_cyan_ilr_2 0.000 0.0000 0.000 0.0 +f_JCZ38_qlogis 0.000 0.0000 0.000 0.0 +f_JSE76_qlogis 0.000 0.0000 0.000 0.0 + f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis +cyan_free_0 0.0000 0.000 0.00 0.00 +log_k_cyan_free 0.0000 0.000 0.00 0.00 +log_k_cyan_free_bound 0.0000 0.000 0.00 0.00 +log_k_cyan_bound_free 0.0000 0.000 0.00 0.00 +log_k_JCZ38 0.0000 0.000 0.00 0.00 +log_k_J9Z38 0.0000 0.000 0.00 0.00 +log_k_JSE76 0.0000 0.000 0.00 0.00 +f_cyan_ilr_1 0.6349 0.000 0.00 0.00 +f_cyan_ilr_2 0.0000 1.797 0.00 0.00 +f_JCZ38_qlogis 0.0000 0.000 13.84 0.00 +f_JSE76_qlogis 0.0000 0.000 0.00 14.66 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2279 2272 -1120 + +Optimised parameters: + est. lower upper +cyan_free_0 102.5621 NA NA +log_k_cyan_free -2.8531 NA NA +log_k_cyan_free_bound -2.6916 NA NA +log_k_cyan_bound_free -3.5032 NA NA +log_k_JCZ38 -2.9436 NA NA +log_k_J9Z38 -5.1140 NA NA +log_k_JSE76 -3.6472 NA NA +f_cyan_ilr_1 0.6887 NA NA +f_cyan_ilr_2 0.6874 NA NA +f_JCZ38_qlogis 4063.6389 NA NA +f_JSE76_qlogis 1.9556 NA NA +a.1 2.7460 2.55451 2.9376 +SD.log_k_cyan_free 0.3131 0.09841 0.5277 +SD.log_k_cyan_free_bound 0.8850 0.29909 1.4710 +SD.log_k_cyan_bound_free 0.6167 0.20391 1.0295 +SD.log_k_JCZ38 1.3555 0.49101 2.2200 +SD.log_k_J9Z38 0.7200 0.16166 1.2783 +SD.log_k_JSE76 0.6252 0.14619 1.1042 +SD.f_cyan_ilr_1 0.3386 0.11447 0.5627 +SD.f_cyan_ilr_2 0.4699 0.09810 0.8417 + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_cyan_free 0.3131 0.09841 0.5277 +SD.log_k_cyan_free_bound 0.8850 0.29909 1.4710 +SD.log_k_cyan_bound_free 0.6167 0.20391 1.0295 +SD.log_k_JCZ38 1.3555 0.49101 2.2200 +SD.log_k_J9Z38 0.7200 0.16166 1.2783 +SD.log_k_JSE76 0.6252 0.14619 1.1042 +SD.f_cyan_ilr_1 0.3386 0.11447 0.5627 +SD.f_cyan_ilr_2 0.4699 0.09810 0.8417 + +Variance model: + est. lower upper +a.1 2.746 2.555 2.938 + +Backtransformed parameters: + est. lower upper +cyan_free_0 1.026e+02 NA NA +k_cyan_free 5.767e-02 NA NA +k_cyan_free_bound 6.777e-02 NA NA +k_cyan_bound_free 3.010e-02 NA NA +k_JCZ38 5.267e-02 NA NA +k_J9Z38 6.012e-03 NA NA +k_JSE76 2.606e-02 NA NA +f_cyan_free_to_JCZ38 6.089e-01 NA NA +f_cyan_free_to_J9Z38 2.299e-01 NA NA +f_JCZ38_to_JSE76 1.000e+00 NA NA +f_JSE76_to_JCZ38 8.761e-01 NA NA + +Estimated Eigenvalues of SFORB model(s): +cyan_b1 cyan_b2 cyan_g + 0.1434 0.0121 0.3469 + +Resulting formation fractions: + ff +cyan_free_JCZ38 0.6089 +cyan_free_J9Z38 0.2299 +cyan_free_sink 0.1612 +cyan_free 1.0000 +JCZ38_JSE76 1.0000 +JCZ38_sink 0.0000 +JSE76_JCZ38 0.8761 +JSE76_sink 0.1239 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 +cyan 23.94 155.06 46.68 4.832 57.28 +JCZ38 13.16 43.71 NA NA NA +J9Z38 115.30 383.02 NA NA NA +JSE76 26.59 88.35 NA NA NA + +</code></pre> +<p></p> +<caption> +Hierarchical SFORB path 2 fit with reduced random effects, two-component +error +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 08:16:19 2023 +Date of summary: Thu Apr 20 20:01:31 2023 + +Equations: +d_cyan_free/dt = - k_cyan_free * cyan_free - k_cyan_free_bound * + cyan_free + k_cyan_bound_free * cyan_bound +d_cyan_bound/dt = + k_cyan_free_bound * cyan_free - k_cyan_bound_free * + cyan_bound +d_JCZ38/dt = + f_cyan_free_to_JCZ38 * k_cyan_free * cyan_free - k_JCZ38 + * JCZ38 + f_JSE76_to_JCZ38 * k_JSE76 * JSE76 +d_J9Z38/dt = + f_cyan_free_to_J9Z38 * k_cyan_free * cyan_free - k_J9Z38 + * J9Z38 +d_JSE76/dt = + f_JCZ38_to_JSE76 * k_JCZ38 * JCZ38 - k_JSE76 * JSE76 + +Data: +433 observations of 4 variable(s) grouped in 5 datasets + +Model predictions using solution type deSolve + +Fitted in 887.343 s +Using 300, 100 iterations and 10 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + cyan_free_0 log_k_cyan_free log_k_cyan_free_bound + 101.751 -2.837 -3.016 +log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 + -3.660 -2.299 -5.313 + log_k_JSE76 f_cyan_ilr_1 f_cyan_ilr_2 + -3.699 0.672 5.873 + f_JCZ38_qlogis f_JSE76_qlogis + 13.216 13.338 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + cyan_free_0 log_k_cyan_free log_k_cyan_free_bound +cyan_free_0 5.629 0.000 0.000 +log_k_cyan_free 0.000 0.446 0.000 +log_k_cyan_free_bound 0.000 0.000 1.449 +log_k_cyan_bound_free 0.000 0.000 0.000 +log_k_JCZ38 0.000 0.000 0.000 +log_k_J9Z38 0.000 0.000 0.000 +log_k_JSE76 0.000 0.000 0.000 +f_cyan_ilr_1 0.000 0.000 0.000 +f_cyan_ilr_2 0.000 0.000 0.000 +f_JCZ38_qlogis 0.000 0.000 0.000 +f_JSE76_qlogis 0.000 0.000 0.000 + log_k_cyan_bound_free log_k_JCZ38 log_k_J9Z38 log_k_JSE76 +cyan_free_0 0.000 0.0000 0.000 0.0000 +log_k_cyan_free 0.000 0.0000 0.000 0.0000 +log_k_cyan_free_bound 0.000 0.0000 0.000 0.0000 +log_k_cyan_bound_free 1.213 0.0000 0.000 0.0000 +log_k_JCZ38 0.000 0.7801 0.000 0.0000 +log_k_J9Z38 0.000 0.0000 1.575 0.0000 +log_k_JSE76 0.000 0.0000 0.000 0.8078 +f_cyan_ilr_1 0.000 0.0000 0.000 0.0000 +f_cyan_ilr_2 0.000 0.0000 0.000 0.0000 +f_JCZ38_qlogis 0.000 0.0000 0.000 0.0000 +f_JSE76_qlogis 0.000 0.0000 0.000 0.0000 + f_cyan_ilr_1 f_cyan_ilr_2 f_JCZ38_qlogis f_JSE76_qlogis +cyan_free_0 0.0000 0.00 0.00 0.00 +log_k_cyan_free 0.0000 0.00 0.00 0.00 +log_k_cyan_free_bound 0.0000 0.00 0.00 0.00 +log_k_cyan_bound_free 0.0000 0.00 0.00 0.00 +log_k_JCZ38 0.0000 0.00 0.00 0.00 +log_k_J9Z38 0.0000 0.00 0.00 0.00 +log_k_JSE76 0.0000 0.00 0.00 0.00 +f_cyan_ilr_1 0.6519 0.00 0.00 0.00 +f_cyan_ilr_2 0.0000 10.78 0.00 0.00 +f_JCZ38_qlogis 0.0000 0.00 13.96 0.00 +f_JSE76_qlogis 0.0000 0.00 0.00 14.69 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 2236 2228 -1098 + +Optimised parameters: + est. lower upper +cyan_free_0 100.72760 NA NA +log_k_cyan_free -3.18281 NA NA +log_k_cyan_free_bound -3.37924 NA NA +log_k_cyan_bound_free -3.77107 NA NA +log_k_JCZ38 -2.92811 NA NA +log_k_J9Z38 -5.02759 NA NA +log_k_JSE76 -3.65835 NA NA +f_cyan_ilr_1 0.67390 NA NA +f_cyan_ilr_2 1.15106 NA NA +f_JCZ38_qlogis 827.82299 NA NA +f_JSE76_qlogis 1.83064 NA NA +a.1 2.06921 1.84443 2.29399 +b.1 0.06391 0.05267 0.07515 +SD.log_k_cyan_free 0.50518 0.18962 0.82075 +SD.log_k_cyan_bound_free 0.30991 0.08170 0.53813 +SD.log_k_JCZ38 1.26661 0.46578 2.06744 +SD.log_k_J9Z38 0.88272 0.27813 1.48730 +SD.log_k_JSE76 0.53050 0.12561 0.93538 +SD.f_cyan_ilr_1 0.35547 0.12461 0.58633 +SD.f_cyan_ilr_2 0.91446 0.20131 1.62761 + +Correlation is not available + +Random effects: + est. lower upper +SD.log_k_cyan_free 0.5052 0.1896 0.8207 +SD.log_k_cyan_bound_free 0.3099 0.0817 0.5381 +SD.log_k_JCZ38 1.2666 0.4658 2.0674 +SD.log_k_J9Z38 0.8827 0.2781 1.4873 +SD.log_k_JSE76 0.5305 0.1256 0.9354 +SD.f_cyan_ilr_1 0.3555 0.1246 0.5863 +SD.f_cyan_ilr_2 0.9145 0.2013 1.6276 + +Variance model: + est. lower upper +a.1 2.06921 1.84443 2.29399 +b.1 0.06391 0.05267 0.07515 + +Backtransformed parameters: + est. lower upper +cyan_free_0 1.007e+02 NA NA +k_cyan_free 4.147e-02 NA NA +k_cyan_free_bound 3.407e-02 NA NA +k_cyan_bound_free 2.303e-02 NA NA +k_JCZ38 5.350e-02 NA NA +k_J9Z38 6.555e-03 NA NA +k_JSE76 2.578e-02 NA NA +f_cyan_free_to_JCZ38 6.505e-01 NA NA +f_cyan_free_to_J9Z38 2.508e-01 NA NA +f_JCZ38_to_JSE76 1.000e+00 NA NA +f_JSE76_to_JCZ38 8.618e-01 NA NA + +Estimated Eigenvalues of SFORB model(s): +cyan_b1 cyan_b2 cyan_g +0.08768 0.01089 0.39821 + +Resulting formation fractions: + ff +cyan_free_JCZ38 0.65053 +cyan_free_J9Z38 0.25082 +cyan_free_sink 0.09864 +cyan_free 1.00000 +JCZ38_JSE76 1.00000 +JCZ38_sink 0.00000 +JSE76_JCZ38 0.86184 +JSE76_sink 0.13816 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_cyan_b1 DT50_cyan_b2 +cyan 25.32 164.79 49.61 7.906 63.64 +JCZ38 12.96 43.04 NA NA NA +J9Z38 105.75 351.29 NA NA NA +JSE76 26.89 89.33 NA NA NA + +</code></pre> +<p></p> +</div> +</div> +<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 4.2.3 (2023-03-15) +Platform: x86_64-pc-linux-gnu (64-bit) +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 + +locale: + [1] LC_CTYPE=de_DE.UTF-8 LC_NUMERIC=C + [3] LC_TIME=de_DE.UTF-8 LC_COLLATE=de_DE.UTF-8 + [5] LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=de_DE.UTF-8 + [7] LC_PAPER=de_DE.UTF-8 LC_NAME=C + [9] LC_ADDRESS=C LC_TELEPHONE=C +[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C + +attached base packages: +[1] parallel stats graphics grDevices utils datasets methods +[8] base + +other attached packages: +[1] saemix_3.2 npde_3.3 knitr_1.42 mkin_1.2.3 + +loaded via a namespace (and not attached): + [1] pillar_1.9.0 bslib_0.4.2 compiler_4.2.3 jquerylib_0.1.4 + [5] tools_4.2.3 mclust_6.0.0 digest_0.6.31 tibble_3.2.1 + [9] jsonlite_1.8.4 evaluate_0.20 memoise_2.0.1 lifecycle_1.0.3 +[13] nlme_3.1-162 gtable_0.3.3 lattice_0.21-8 pkgconfig_2.0.3 +[17] rlang_1.1.0 DBI_1.1.3 cli_3.6.1 yaml_2.3.7 +[21] pkgdown_2.0.7 xfun_0.38 fastmap_1.1.1 gridExtra_2.3 +[25] dplyr_1.1.1 stringr_1.5.0 generics_0.1.3 desc_1.4.2 +[29] fs_1.6.1 vctrs_0.6.1 sass_0.4.5 systemfonts_1.0.4 +[33] tidyselect_1.2.0 rprojroot_2.0.3 lmtest_0.9-40 grid_4.2.3 +[37] inline_0.3.19 glue_1.6.2 R6_2.5.1 textshaping_0.3.6 +[41] fansi_1.0.4 rmarkdown_2.21 purrr_1.0.1 ggplot2_3.4.2 +[45] magrittr_2.0.3 scales_1.2.1 htmltools_0.5.5 colorspace_2.1-0 +[49] ragg_1.2.5 utf8_1.2.3 stringi_1.7.12 munsell_0.5.0 +[53] cachem_1.0.7 zoo_1.8-12 </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: 64936316 kB</code></pre> +</div> +</div> + </div> + + <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> + + <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2> + </nav> +</div> + +</div> + + + + <footer><div class="copyright"> + <p></p> +<p>Developed by Johannes Ranke.</p> +</div> + +<div class="pkgdown"> + <p></p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> +</div> + + </footer> +</div> + + + + + + + </body> +</html> diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-13-1.png b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-13-1.png Binary files differnew file mode 100644 index 00000000..b969f2ff --- /dev/null +++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-13-1.png diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-14-1.png b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-14-1.png Binary files differnew file mode 100644 index 00000000..60393da3 --- /dev/null +++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-14-1.png diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-15-1.png b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-15-1.png Binary files differnew file mode 100644 index 00000000..b9a410f7 --- /dev/null +++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-15-1.png diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-20-1.png b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-20-1.png Binary files differnew file mode 100644 index 00000000..cf921dab --- /dev/null +++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-20-1.png diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-21-1.png b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-21-1.png Binary files differnew file mode 100644 index 00000000..ff732730 --- /dev/null +++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-21-1.png diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-22-1.png b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-22-1.png Binary files differnew file mode 100644 index 00000000..e30011bc --- /dev/null +++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-22-1.png diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-7-1.png b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-7-1.png Binary files differnew file mode 100644 index 00000000..4aad76df --- /dev/null +++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-7-1.png diff --git a/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-8-1.png b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-8-1.png Binary files differnew file mode 100644 index 00000000..e30011bc --- /dev/null +++ b/docs/articles/prebuilt/2022_cyan_pathway_files/figure-html/unnamed-chunk-8-1.png diff --git a/docs/articles/prebuilt/2022_dmta_parent.html b/docs/articles/prebuilt/2022_dmta_parent.html new file mode 100644 index 00000000..378a7e8e --- /dev/null +++ b/docs/articles/prebuilt/2022_dmta_parent.html @@ -0,0 +1,2223 @@ +<!DOCTYPE html> +<!-- Generated by pkgdown: do not edit by hand --><html lang="en"> +<head> +<meta http-equiv="Content-Type" content="text/html; charset=UTF-8"> +<meta charset="utf-8"> +<meta http-equiv="X-UA-Compatible" content="IE=edge"> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<title>Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P • mkin</title> +<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"> +<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../../bootstrap-toc.css"> +<script src="../../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"> +<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"> +<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../../pkgdown.css" rel="stylesheet"> +<script src="../../pkgdown.js"></script><meta property="og:title" content="Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P"> +<meta property="og:description" content="mkin"> +<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> +<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> +<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> +<![endif]--> +</head> +<body data-spy="scroll" data-target="#toc"> + + + <div class="container template-article"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <div class="container"> + <div class="navbar-header"> + <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> + <span class="sr-only">Toggle navigation</span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + </button> + <span class="navbar-brand"> + <a class="navbar-link" href="../../index.html">mkin</a> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> + </span> + </div> + + <div id="navbar" class="navbar-collapse collapse"> + <ul class="nav navbar-nav"> +<li> + <a href="../../reference/index.html">Reference</a> +</li> +<li class="dropdown"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> + Articles + + <span class="caret"></span> + </a> + <ul class="dropdown-menu" role="menu"> +<li> + <a href="../../articles/mkin.html">Introduction to mkin</a> + </li> + <li class="divider"> + </li> +<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> + <li> + <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> + </li> + <li> + <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> + </li> + <li> + <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li class="divider"> + </li> +<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> + <li> + <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> + </li> + <li> + <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> + </li> + <li> + <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> + </li> + <li> + <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> + </li> + <li> + <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + </li> +<li class="dropdown-header">Performance</li> + <li> + <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + </li> + <li> + <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + <li class="divider"> + </li> +<li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> + </ul> +</li> +<li> + <a href="../../news/index.html">News</a> +</li> + </ul> +<ul class="nav navbar-nav navbar-right"> +<li> + <a href="https://github.com/jranke/mkin/" class="external-link"> + <span class="fab fa-github fa-lg"></span> + + </a> +</li> + </ul> +</div> +<!--/.nav-collapse --> + </div> +<!--/.container --> +</div> +<!--/.navbar --> + + + + </header><div class="row"> + <div class="col-md-9 contents"> + <div class="page-header toc-ignore"> + <h1 data-toc-skip>Testing hierarchical parent degradation kinetics +with residue data on dimethenamid and dimethenamid-P</h1> + <h4 data-toc-skip class="author">Johannes +Ranke</h4> + + <h4 data-toc-skip class="date">Last change on 5 January +2023, last compiled on 20 April 2023</h4> + + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/prebuilt/2022_dmta_parent.rmd" class="external-link"><code>vignettes/prebuilt/2022_dmta_parent.rmd</code></a></small> + <div class="hidden name"><code>2022_dmta_parent.rmd</code></div> + + </div> + + + +<div class="section level2"> +<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a> +</h2> +<p>The purpose of this document is to demonstrate how nonlinear +hierarchical models (NLHM) based on the parent degradation models SFO, +FOMC, DFOP and HS can be fitted with the mkin package.</p> +<p>It was assembled in the course of work package 1.1 of Project Number +173340 (Application of nonlinear hierarchical models to the kinetic +evaluation of chemical degradation data) of the German Environment +Agency carried out in 2022 and 2023.</p> +<p>The mkin package is used in version 1.2.3. It contains the test data +and the functions used in 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> +<p>This document is processed with the <code>knitr</code> package, which +also provides the <code>kable</code> function that is used to improve +the display of tabular data in R markdown documents. For parallel +processing, the <code>parallel</code> package is used.</p> +<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span><span class="op">)</span></span> +<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://yihui.org/knitr/" class="external-link">knitr</a></span><span class="op">)</span></span> +<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">saemix</span><span class="op">)</span></span> +<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">parallel</span><span class="op">)</span></span> +<span><span class="va">n_cores</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span></span> +<span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.info.html" class="external-link">Sys.info</a></span><span class="op">(</span><span class="op">)</span><span class="op">[</span><span class="st">"sysname"</span><span class="op">]</span> <span class="op">==</span> <span class="st">"Windows"</span><span class="op">)</span> <span class="op">{</span></span> +<span> <span class="va">cl</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makePSOCKcluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span> +<span><span class="op">}</span> <span class="kw">else</span> <span class="op">{</span></span> +<span> <span class="va">cl</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makeForkCluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span> +<span><span class="op">}</span></span></code></pre></div> +</div> +<div class="section level2"> +<h2 id="data">Data<a class="anchor" aria-label="anchor" href="#data"></a> +</h2> +<p>The test data are available in the mkin package as an object of class +<code>mkindsg</code> (mkin dataset group) under the identifier +<code>dimethenamid_2018</code>. The following preprocessing steps are +still necessary:</p> +<ul> +<li>The data available for the enantiomer dimethenamid-P (DMTAP) are +renamed to have the same substance name as the data for the racemic +mixture dimethenamid (DMTA). The reason for this is that no difference +between their degradation behaviour was identified in the EU risk +assessment.</li> +<li>The data for transformation products and unnecessary columns are +discarded</li> +<li>The observation times of each dataset are multiplied with the +corresponding normalisation factor also available in the dataset, in +order to make it possible to describe all datasets with a single set of +parameters that are independent of temperature</li> +<li>Finally, datasets observed in the same soil (<code>Elliot 1</code> +and <code>Elliot 2</code>) are combined, resulting in dimethenamid +(DMTA) data from six soils.</li> +</ul> +<p>The following commented R code performs this preprocessing.</p> +<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="co"># Apply a function to each of the seven datasets in the mkindsg object to create a list</span></span> +<span><span class="va">dmta_ds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span> +<span> <span class="va">ds_i</span> <span class="op"><-</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span><span class="op">[[</span><span class="va">i</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span> <span class="co"># Get a dataset</span></span> +<span> <span class="va">ds_i</span><span class="op">[</span><span class="va">ds_i</span><span class="op">$</span><span class="va">name</span> <span class="op">==</span> <span class="st">"DMTAP"</span>, <span class="st">"name"</span><span class="op">]</span> <span class="op"><-</span> <span class="st">"DMTA"</span> <span class="co"># Rename DMTAP to DMTA</span></span> +<span> <span class="va">ds_i</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">ds_i</span>, <span class="va">name</span> <span class="op">==</span> <span class="st">"DMTA"</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">)</span> <span class="co"># Select data</span></span> +<span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op"><-</span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">*</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">f_time_norm</span><span class="op">[</span><span class="va">i</span><span class="op">]</span> <span class="co"># Normalise time</span></span> +<span> <span class="va">ds_i</span> <span class="co"># Return the dataset</span></span> +<span><span class="op">}</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># Use dataset titles as names for the list elements</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="va">ds</span><span class="op">$</span><span class="va">title</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># Combine data for Elliot soil to obtain a named list with six elements</span></span> +<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span>, <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span> <span class="co">#</span></span> +<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="cn">NULL</span></span> +<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="cn">NULL</span></span></code></pre></div> +<p>The following tables show the 6 datasets.</p> +<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="kw">for</span> <span class="op">(</span><span class="va">ds_name</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span><span class="op">)</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="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mkin_long_to_wide.html">mkin_long_to_wide</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="va">ds_name</span><span class="op">]</span><span class="op">]</span><span class="op">)</span>,</span> +<span> caption <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="va">ds_name</span><span class="op">)</span>,</span> +<span> label <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="st">"tab:"</span>, <span class="va">ds_name</span><span class="op">)</span>, booktabs <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span> +<span> <span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="st">"\n\\clearpage\n"</span><span class="op">)</span></span> +<span><span class="op">}</span></span></code></pre></div> +<table class="table"> +<caption>Dataset Calke</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0</td> +<td align="right">95.8</td> +</tr> +<tr class="even"> +<td align="right">0</td> +<td align="right">98.7</td> +</tr> +<tr class="odd"> +<td align="right">14</td> +<td align="right">60.5</td> +</tr> +<tr class="even"> +<td align="right">30</td> +<td align="right">39.1</td> +</tr> +<tr class="odd"> +<td align="right">59</td> +<td align="right">15.2</td> +</tr> +<tr class="even"> +<td align="right">120</td> +<td align="right">4.8</td> +</tr> +<tr class="odd"> +<td align="right">120</td> +<td align="right">4.6</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset Borstel</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">100.5</td> +</tr> +<tr class="even"> +<td align="right">0.000000</td> +<td align="right">99.6</td> +</tr> +<tr class="odd"> +<td align="right">1.941295</td> +<td align="right">91.9</td> +</tr> +<tr class="even"> +<td align="right">1.941295</td> +<td align="right">91.3</td> +</tr> +<tr class="odd"> +<td align="right">6.794534</td> +<td align="right">81.8</td> +</tr> +<tr class="even"> +<td align="right">6.794534</td> +<td align="right">82.1</td> +</tr> +<tr class="odd"> +<td align="right">13.589067</td> +<td align="right">69.1</td> +</tr> +<tr class="even"> +<td align="right">13.589067</td> +<td align="right">68.0</td> +</tr> +<tr class="odd"> +<td align="right">27.178135</td> +<td align="right">51.4</td> +</tr> +<tr class="even"> +<td align="right">27.178135</td> +<td align="right">51.4</td> +</tr> +<tr class="odd"> +<td align="right">56.297565</td> +<td align="right">27.6</td> +</tr> +<tr class="even"> +<td align="right">56.297565</td> +<td align="right">26.8</td> +</tr> +<tr class="odd"> +<td align="right">86.387643</td> +<td align="right">15.7</td> +</tr> +<tr class="even"> +<td align="right">86.387643</td> +<td align="right">15.3</td> +</tr> +<tr class="odd"> +<td align="right">115.507073</td> +<td align="right">7.9</td> +</tr> +<tr class="even"> +<td align="right">115.507073</td> +<td align="right">8.1</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset Flaach</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.0000000</td> +<td align="right">96.5</td> +</tr> +<tr class="even"> +<td align="right">0.0000000</td> +<td align="right">96.8</td> +</tr> +<tr class="odd"> +<td align="right">0.0000000</td> +<td align="right">97.0</td> +</tr> +<tr class="even"> +<td align="right">0.6233856</td> +<td align="right">82.9</td> +</tr> +<tr class="odd"> +<td align="right">0.6233856</td> +<td align="right">86.7</td> +</tr> +<tr class="even"> +<td align="right">0.6233856</td> +<td align="right">87.4</td> +</tr> +<tr class="odd"> +<td align="right">1.8701567</td> +<td align="right">72.8</td> +</tr> +<tr class="even"> +<td align="right">1.8701567</td> +<td align="right">69.9</td> +</tr> +<tr class="odd"> +<td align="right">1.8701567</td> +<td align="right">71.9</td> +</tr> +<tr class="even"> +<td align="right">4.3636989</td> +<td align="right">51.4</td> +</tr> +<tr class="odd"> +<td align="right">4.3636989</td> +<td align="right">52.9</td> +</tr> +<tr class="even"> +<td align="right">4.3636989</td> +<td align="right">48.6</td> +</tr> +<tr class="odd"> +<td align="right">8.7273979</td> +<td align="right">28.5</td> +</tr> +<tr class="even"> +<td align="right">8.7273979</td> +<td align="right">27.3</td> +</tr> +<tr class="odd"> +<td align="right">8.7273979</td> +<td align="right">27.5</td> +</tr> +<tr class="even"> +<td align="right">13.0910968</td> +<td align="right">14.8</td> +</tr> +<tr class="odd"> +<td align="right">13.0910968</td> +<td align="right">13.4</td> +</tr> +<tr class="even"> +<td align="right">13.0910968</td> +<td align="right">14.4</td> +</tr> +<tr class="odd"> +<td align="right">17.4547957</td> +<td align="right">7.7</td> +</tr> +<tr class="even"> +<td align="right">17.4547957</td> +<td align="right">7.3</td> +</tr> +<tr class="odd"> +<td align="right">17.4547957</td> +<td align="right">8.1</td> +</tr> +<tr class="even"> +<td align="right">26.1821936</td> +<td align="right">2.0</td> +</tr> +<tr class="odd"> +<td align="right">26.1821936</td> +<td align="right">1.5</td> +</tr> +<tr class="even"> +<td align="right">26.1821936</td> +<td align="right">1.9</td> +</tr> +<tr class="odd"> +<td align="right">34.9095915</td> +<td align="right">1.3</td> +</tr> +<tr class="even"> +<td align="right">34.9095915</td> +<td align="right">1.0</td> +</tr> +<tr class="odd"> +<td align="right">34.9095915</td> +<td align="right">1.1</td> +</tr> +<tr class="even"> +<td align="right">43.6369893</td> +<td align="right">0.9</td> +</tr> +<tr class="odd"> +<td align="right">43.6369893</td> +<td align="right">0.7</td> +</tr> +<tr class="even"> +<td align="right">43.6369893</td> +<td align="right">0.7</td> +</tr> +<tr class="odd"> +<td align="right">52.3643872</td> +<td align="right">0.6</td> +</tr> +<tr class="even"> +<td align="right">52.3643872</td> +<td align="right">0.4</td> +</tr> +<tr class="odd"> +<td align="right">52.3643872</td> +<td align="right">0.5</td> +</tr> +<tr class="even"> +<td align="right">74.8062674</td> +<td align="right">0.4</td> +</tr> +<tr class="odd"> +<td align="right">74.8062674</td> +<td align="right">0.3</td> +</tr> +<tr class="even"> +<td align="right">74.8062674</td> +<td align="right">0.3</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset BBA 2.2</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.0000000</td> +<td align="right">98.09</td> +</tr> +<tr class="even"> +<td align="right">0.0000000</td> +<td align="right">98.77</td> +</tr> +<tr class="odd"> +<td align="right">0.7678922</td> +<td align="right">93.52</td> +</tr> +<tr class="even"> +<td align="right">0.7678922</td> +<td align="right">92.03</td> +</tr> +<tr class="odd"> +<td align="right">2.3036765</td> +<td align="right">88.39</td> +</tr> +<tr class="even"> +<td align="right">2.3036765</td> +<td align="right">87.18</td> +</tr> +<tr class="odd"> +<td align="right">5.3752452</td> +<td align="right">69.38</td> +</tr> +<tr class="even"> +<td align="right">5.3752452</td> +<td align="right">71.06</td> +</tr> +<tr class="odd"> +<td align="right">10.7504904</td> +<td align="right">45.21</td> +</tr> +<tr class="even"> +<td align="right">10.7504904</td> +<td align="right">46.81</td> +</tr> +<tr class="odd"> +<td align="right">16.1257355</td> +<td align="right">30.54</td> +</tr> +<tr class="even"> +<td align="right">16.1257355</td> +<td align="right">30.07</td> +</tr> +<tr class="odd"> +<td align="right">21.5009807</td> +<td align="right">21.60</td> +</tr> +<tr class="even"> +<td align="right">21.5009807</td> +<td align="right">20.41</td> +</tr> +<tr class="odd"> +<td align="right">32.2514711</td> +<td align="right">9.10</td> +</tr> +<tr class="even"> +<td align="right">32.2514711</td> +<td align="right">9.70</td> +</tr> +<tr class="odd"> +<td align="right">43.0019614</td> +<td align="right">6.58</td> +</tr> +<tr class="even"> +<td align="right">43.0019614</td> +<td align="right">6.31</td> +</tr> +<tr class="odd"> +<td align="right">53.7524518</td> +<td align="right">3.47</td> +</tr> +<tr class="even"> +<td align="right">53.7524518</td> +<td align="right">3.52</td> +</tr> +<tr class="odd"> +<td align="right">64.5029421</td> +<td align="right">3.40</td> +</tr> +<tr class="even"> +<td align="right">64.5029421</td> +<td align="right">3.67</td> +</tr> +<tr class="odd"> +<td align="right">91.3791680</td> +<td align="right">1.62</td> +</tr> +<tr class="even"> +<td align="right">91.3791680</td> +<td align="right">1.62</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset BBA 2.3</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.0000000</td> +<td align="right">99.33</td> +</tr> +<tr class="even"> +<td align="right">0.0000000</td> +<td align="right">97.44</td> +</tr> +<tr class="odd"> +<td align="right">0.6733938</td> +<td align="right">93.73</td> +</tr> +<tr class="even"> +<td align="right">0.6733938</td> +<td align="right">93.77</td> +</tr> +<tr class="odd"> +<td align="right">2.0201814</td> +<td align="right">87.84</td> +</tr> +<tr class="even"> +<td align="right">2.0201814</td> +<td align="right">89.82</td> +</tr> +<tr class="odd"> +<td align="right">4.7137565</td> +<td align="right">71.61</td> +</tr> +<tr class="even"> +<td align="right">4.7137565</td> +<td align="right">71.42</td> +</tr> +<tr class="odd"> +<td align="right">9.4275131</td> +<td align="right">45.60</td> +</tr> +<tr class="even"> +<td align="right">9.4275131</td> +<td align="right">45.42</td> +</tr> +<tr class="odd"> +<td align="right">14.1412696</td> +<td align="right">31.12</td> +</tr> +<tr class="even"> +<td align="right">14.1412696</td> +<td align="right">31.68</td> +</tr> +<tr class="odd"> +<td align="right">18.8550262</td> +<td align="right">23.20</td> +</tr> +<tr class="even"> +<td align="right">18.8550262</td> +<td align="right">24.13</td> +</tr> +<tr class="odd"> +<td align="right">28.2825393</td> +<td align="right">9.43</td> +</tr> +<tr class="even"> +<td align="right">28.2825393</td> +<td align="right">9.82</td> +</tr> +<tr class="odd"> +<td align="right">37.7100523</td> +<td align="right">7.08</td> +</tr> +<tr class="even"> +<td align="right">37.7100523</td> +<td align="right">8.64</td> +</tr> +<tr class="odd"> +<td align="right">47.1375654</td> +<td align="right">4.41</td> +</tr> +<tr class="even"> +<td align="right">47.1375654</td> +<td align="right">4.78</td> +</tr> +<tr class="odd"> +<td align="right">56.5650785</td> +<td align="right">4.92</td> +</tr> +<tr class="even"> +<td align="right">56.5650785</td> +<td align="right">5.08</td> +</tr> +<tr class="odd"> +<td align="right">80.1338612</td> +<td align="right">2.13</td> +</tr> +<tr class="even"> +<td align="right">80.1338612</td> +<td align="right">2.23</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset Elliot</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">97.5</td> +</tr> +<tr class="even"> +<td align="right">0.000000</td> +<td align="right">100.7</td> +</tr> +<tr class="odd"> +<td align="right">1.228478</td> +<td align="right">86.4</td> +</tr> +<tr class="even"> +<td align="right">1.228478</td> +<td align="right">88.5</td> +</tr> +<tr class="odd"> +<td align="right">3.685435</td> +<td align="right">69.8</td> +</tr> +<tr class="even"> +<td align="right">3.685435</td> +<td align="right">77.1</td> +</tr> +<tr class="odd"> +<td align="right">8.599349</td> +<td align="right">59.0</td> +</tr> +<tr class="even"> +<td align="right">8.599349</td> +<td align="right">54.2</td> +</tr> +<tr class="odd"> +<td align="right">17.198697</td> +<td align="right">31.3</td> +</tr> +<tr class="even"> +<td align="right">17.198697</td> +<td align="right">33.5</td> +</tr> +<tr class="odd"> +<td align="right">25.798046</td> +<td align="right">19.6</td> +</tr> +<tr class="even"> +<td align="right">25.798046</td> +<td align="right">20.9</td> +</tr> +<tr class="odd"> +<td align="right">34.397395</td> +<td align="right">13.3</td> +</tr> +<tr class="even"> +<td align="right">34.397395</td> +<td align="right">15.8</td> +</tr> +<tr class="odd"> +<td align="right">51.596092</td> +<td align="right">6.7</td> +</tr> +<tr class="even"> +<td align="right">51.596092</td> +<td align="right">8.7</td> +</tr> +<tr class="odd"> +<td align="right">68.794789</td> +<td align="right">8.8</td> +</tr> +<tr class="even"> +<td align="right">68.794789</td> +<td align="right">8.7</td> +</tr> +<tr class="odd"> +<td align="right">103.192184</td> +<td align="right">6.0</td> +</tr> +<tr class="even"> +<td align="right">103.192184</td> +<td align="right">4.4</td> +</tr> +<tr class="odd"> +<td align="right">146.188928</td> +<td align="right">3.3</td> +</tr> +<tr class="even"> +<td align="right">146.188928</td> +<td align="right">2.8</td> +</tr> +<tr class="odd"> +<td align="right">223.583066</td> +<td align="right">1.4</td> +</tr> +<tr class="even"> +<td align="right">223.583066</td> +<td align="right">1.8</td> +</tr> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">93.4</td> +</tr> +<tr class="even"> +<td align="right">0.000000</td> +<td align="right">103.2</td> +</tr> +<tr class="odd"> +<td align="right">1.228478</td> +<td align="right">89.2</td> +</tr> +<tr class="even"> +<td align="right">1.228478</td> +<td align="right">86.6</td> +</tr> +<tr class="odd"> +<td align="right">3.685435</td> +<td align="right">78.2</td> +</tr> +<tr class="even"> +<td align="right">3.685435</td> +<td align="right">78.1</td> +</tr> +<tr class="odd"> +<td align="right">8.599349</td> +<td align="right">55.6</td> +</tr> +<tr class="even"> +<td align="right">8.599349</td> +<td align="right">53.0</td> +</tr> +<tr class="odd"> +<td align="right">17.198697</td> +<td align="right">33.7</td> +</tr> +<tr class="even"> +<td align="right">17.198697</td> +<td align="right">33.2</td> +</tr> +<tr class="odd"> +<td align="right">25.798046</td> +<td align="right">20.9</td> +</tr> +<tr class="even"> +<td align="right">25.798046</td> +<td align="right">19.9</td> +</tr> +<tr class="odd"> +<td align="right">34.397395</td> +<td align="right">18.2</td> +</tr> +<tr class="even"> +<td align="right">34.397395</td> +<td align="right">12.7</td> +</tr> +<tr class="odd"> +<td align="right">51.596092</td> +<td align="right">7.8</td> +</tr> +<tr class="even"> +<td align="right">51.596092</td> +<td align="right">9.0</td> +</tr> +<tr class="odd"> +<td align="right">68.794789</td> +<td align="right">11.4</td> +</tr> +<tr class="even"> +<td align="right">68.794789</td> +<td align="right">9.0</td> +</tr> +<tr class="odd"> +<td align="right">103.192184</td> +<td align="right">3.9</td> +</tr> +<tr class="even"> +<td align="right">103.192184</td> +<td align="right">4.4</td> +</tr> +<tr class="odd"> +<td align="right">146.188928</td> +<td align="right">2.6</td> +</tr> +<tr class="even"> +<td align="right">146.188928</td> +<td align="right">3.4</td> +</tr> +<tr class="odd"> +<td align="right">223.583066</td> +<td align="right">2.0</td> +</tr> +<tr class="even"> +<td align="right">223.583066</td> +<td align="right">1.7</td> +</tr> +</tbody> +</table> +</div> +<div class="section level2"> +<h2 id="separate-evaluations">Separate evaluations<a class="anchor" aria-label="anchor" href="#separate-evaluations"></a> +</h2> +<p>In order to obtain suitable starting parameters for the NLHM fits, +separate fits of the four models to the data for each soil are generated +using the <code>mmkin</code> function from the <code>mkin</code> +package. In a first step, constant variance is assumed. Convergence is +checked with the <code>status</code> function.</p> +<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">deg_mods</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span>, <span class="st">"HS"</span><span class="op">)</span></span> +<span><span class="va">f_sep_const</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span></span> +<span> <span class="va">deg_mods</span>,</span> +<span> <span class="va">dmta_ds</span>,</span> +<span> error_model <span class="op">=</span> <span class="st">"const"</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span></span> +<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_sep_const</span><span class="op">)</span> <span class="op">|></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"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">Calke</th> +<th align="left">Borstel</th> +<th align="left">Flaach</th> +<th align="left">BBA 2.2</th> +<th align="left">BBA 2.3</th> +<th align="left">Elliot</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">SFO</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">FOMC</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">DFOP</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">HS</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +</tbody> +</table> +<p>In the table above, OK indicates convergence, and C indicates failure +to converge. All separate fits with constant variance converged, with +the sole exception of the HS fit to the BBA 2.2 data. To prepare for +fitting NLHM using the two-component error model, the separate fits are +updated assuming two-component error.</p> +<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">f_sep_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_sep_const</span>, error_model <span class="op">=</span> <span class="st">"tc"</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_sep_tc</span><span class="op">)</span> <span class="op">|></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"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">Calke</th> +<th align="left">Borstel</th> +<th align="left">Flaach</th> +<th align="left">BBA 2.2</th> +<th align="left">BBA 2.3</th> +<th align="left">Elliot</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">SFO</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">FOMC</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">DFOP</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">HS</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +</tbody> +</table> +<p>Using the two-component error model, the one fit that did not +converge with constant variance did converge, but other non-SFO fits +failed to converge.</p> +</div> +<div class="section level2"> +<h2 id="hierarchichal-model-fits">Hierarchichal model fits<a class="anchor" aria-label="anchor" href="#hierarchichal-model-fits"></a> +</h2> +<p>The following code fits eight versions of hierarchical models to the +data, using SFO, FOMC, DFOP and HS for the parent compound, and using +either constant variance or two-component error for the error model. The +default parameter distribution model in mkin allows for variation of all +degradation parameters across the assumed population of soils. In other +words, each degradation parameter is associated with a random effect as +a first step. The <code>mhmkin</code> function makes it possible to fit +all eight versions in parallel (given a sufficient number of computing +cores being available) to save execution time.</p> +<p>Convergence plots and summaries for these fits are shown in the +appendix.</p> +<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">f_saem</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mhmkin.html">mhmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">f_sep_const</span>, <span class="va">f_sep_tc</span><span class="op">)</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span></span></code></pre></div> +<p>The output of the <code>status</code> function shows that all fits +terminated successfully.</p> +<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">)</span> <span class="op">|></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"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">SFO</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">FOMC</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">DFOP</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">HS</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +</tbody> +</table> +<p>The AIC and BIC values show that the biphasic models DFOP and HS give +the best fits.</p> +<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">)</span> <span class="op">|></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">1</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="right">npar</th> +<th align="right">AIC</th> +<th align="right">BIC</th> +<th align="right">Lik</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">SFO const</td> +<td align="right">5</td> +<td align="right">796.3</td> +<td align="right">795.3</td> +<td align="right">-393.2</td> +</tr> +<tr class="even"> +<td align="left">SFO tc</td> +<td align="right">6</td> +<td align="right">798.3</td> +<td align="right">797.1</td> +<td align="right">-393.2</td> +</tr> +<tr class="odd"> +<td align="left">FOMC const</td> +<td align="right">7</td> +<td align="right">734.2</td> +<td align="right">732.7</td> +<td align="right">-360.1</td> +</tr> +<tr class="even"> +<td align="left">FOMC tc</td> +<td align="right">8</td> +<td align="right">720.4</td> +<td align="right">718.8</td> +<td align="right">-352.2</td> +</tr> +<tr class="odd"> +<td align="left">DFOP const</td> +<td align="right">9</td> +<td align="right">711.8</td> +<td align="right">710.0</td> +<td align="right">-346.9</td> +</tr> +<tr class="even"> +<td align="left">HS const</td> +<td align="right">9</td> +<td align="right">714.0</td> +<td align="right">712.1</td> +<td align="right">-348.0</td> +</tr> +<tr class="odd"> +<td align="left">DFOP tc</td> +<td align="right">10</td> +<td align="right">665.5</td> +<td align="right">663.4</td> +<td align="right">-322.8</td> +</tr> +<tr class="even"> +<td align="left">HS tc</td> +<td align="right">10</td> +<td align="right">667.1</td> +<td align="right">665.0</td> +<td align="right">-323.6</td> +</tr> +</tbody> +</table> +<p>The DFOP model is preferred here, as it has a better mechanistic +basis for batch experiments with constant incubation conditions. Also, +it shows the lowest AIC and BIC values in the first set of fits when +combined with the two-component error model. Therefore, the DFOP model +was selected for further refinements of the fits with the aim to make +the model fully identifiable.</p> +<div class="section level3"> +<h3 id="parameter-identifiability-based-on-the-fisher-information-matrix">Parameter identifiability based on the Fisher Information +Matrix<a class="anchor" aria-label="anchor" href="#parameter-identifiability-based-on-the-fisher-information-matrix"></a> +</h3> +<p>Using the <code>illparms</code> function, ill-defined statistical +model parameters such as standard deviations of the degradation +parameters in the population and error model parameters can be +found.</p> +<div class="sourceCode" id="cb9"><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</span><span class="op">)</span> <span class="op">|></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"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">SFO</td> +<td align="left"></td> +<td align="left">b.1</td> +</tr> +<tr class="even"> +<td align="left">FOMC</td> +<td align="left"></td> +<td align="left">sd(DMTA_0)</td> +</tr> +<tr class="odd"> +<td align="left">DFOP</td> +<td align="left">sd(k2)</td> +<td align="left">sd(k2)</td> +</tr> +<tr class="even"> +<td align="left">HS</td> +<td align="left"></td> +<td align="left">sd(tb)</td> +</tr> +</tbody> +</table> +<p>According to the <code>illparms</code> function, the fitted standard +deviation of the second kinetic rate constant <code>k2</code> is +ill-defined in both DFOP fits. This suggests that different values would +be obtained for this standard deviation when using different starting +values.</p> +<p>The thus identified overparameterisation is addressed by removing the +random effect for <code>k2</code> from the parameter model.</p> +<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">f_saem_dfop_tc_no_ranef_k2</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>,</span> +<span> no_random_effect <span class="op">=</span> <span class="st">"k2"</span><span class="op">)</span></span></code></pre></div> +<p>For the resulting fit, it is checked whether there are still +ill-defined parameters,</p> +<div class="sourceCode" id="cb11"><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_dfop_tc_no_ranef_k2</span><span class="op">)</span></span></code></pre></div> +<p>which is not the case. Below, the refined model is compared with the +previous best model. The model without random effect for <code>k2</code> +is a reduced version of the previous model. Therefore, the models are +nested and can be compared using the likelihood ratio test. This is +achieved with the argument <code>test = TRUE</code> to the +<code>anova</code> function.</p> +<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>, <span class="va">f_saem_dfop_tc_no_ranef_k2</span>, test <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op">|></span></span> +<span> <span class="fu"><a href="https://rdrr.io/pkg/knitr/man/kable.html" class="external-link">kable</a></span><span class="op">(</span>format.args <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>digits <span class="op">=</span> <span class="fl">4</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<colgroup> +<col width="37%"> +<col width="6%"> +<col width="8%"> +<col width="8%"> +<col width="9%"> +<col width="9%"> +<col width="4%"> +<col width="15%"> +</colgroup> +<thead><tr class="header"> +<th align="left"></th> +<th align="right">npar</th> +<th align="right">AIC</th> +<th align="right">BIC</th> +<th align="right">Lik</th> +<th align="right">Chisq</th> +<th align="right">Df</th> +<th align="right">Pr(>Chisq)</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">f_saem_dfop_tc_no_ranef_k2</td> +<td align="right">9</td> +<td align="right">663.8</td> +<td align="right">661.9</td> +<td align="right">-322.9</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="left">f_saem[[“DFOP”, “tc”]]</td> +<td align="right">10</td> +<td align="right">665.5</td> +<td align="right">663.4</td> +<td align="right">-322.8</td> +<td align="right">0.2809</td> +<td align="right">1</td> +<td align="right">0.5961</td> +</tr> +</tbody> +</table> +<p>The AIC and BIC criteria are lower after removal of the ill-defined +random effect for <code>k2</code>. The p value of the likelihood ratio +test is much greater than 0.05, indicating that the model with the +higher likelihood (here the model with random effects for all +degradation parameters <code>f_saem[["DFOP", "tc"]]</code>) does not fit +significantly better than the model with the lower likelihood (the +reduced model <code>f_saem_dfop_tc_no_ranef_k2</code>).</p> +<p>Therefore, AIC, BIC and likelihood ratio test suggest the use of the +reduced model.</p> +<p>The convergence of the fit is checked visually.</p> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-no-ranef-k2-1.png" alt="Convergence plot for the NLHM DFOP fit with two-component error and without a random effect on 'k2'" width="864"><p class="caption"> +Convergence plot for the NLHM DFOP fit with two-component error and +without a random effect on ‘k2’ +</p> +</div> +<p>All parameters appear to have converged to a satisfactory degree. The +final fit is plotted using the plot method from the mkin package.</p> +<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_parent_files/figure-html/plot-saem-dfop-tc-no-ranef-k2-1.png" alt="Plot of the final NLHM DFOP fit" width="864"><p class="caption"> +Plot of the final NLHM DFOP fit +</p> +</div> +<p>Finally, a summary report of the fit is produced.</p> +<div class="sourceCode" id="cb14"><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">f_saem_dfop_tc_no_ranef_k2</span><span class="op">)</span></span></code></pre></div> +<pre><code>saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 14:07:09 2023 +Date of summary: Thu Apr 20 14:07:10 2023 + +Equations: +d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Model predictions using solution type analytical + +Fitted in 4.175 s +Using 300, 100 iterations and 9 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + DMTA_0 k1 k2 g +98.759266 0.087034 0.009933 0.930827 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + DMTA_0 k1 k2 g +DMTA_0 98.76 0 0 0 +k1 0.00 1 0 0 +k2 0.00 0 1 0 +g 0.00 0 0 1 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 663.8 661.9 -322.9 + +Optimised parameters: + est. lower upper +DMTA_0 98.228939 96.285869 100.17201 +k1 0.064063 0.033477 0.09465 +k2 0.008297 0.005824 0.01077 +g 0.953821 0.914328 0.99331 +a.1 1.068479 0.869538 1.26742 +b.1 0.029424 0.022406 0.03644 +SD.DMTA_0 2.030437 0.404824 3.65605 +SD.k1 0.594692 0.256660 0.93272 +SD.g 1.006754 0.361327 1.65218 + +Correlation: + DMTA_0 k1 k2 +k1 0.0218 +k2 0.0556 0.0355 +g -0.0516 -0.0284 -0.2800 + +Random effects: + est. lower upper +SD.DMTA_0 2.0304 0.4048 3.6560 +SD.k1 0.5947 0.2567 0.9327 +SD.g 1.0068 0.3613 1.6522 + +Variance model: + est. lower upper +a.1 1.06848 0.86954 1.26742 +b.1 0.02942 0.02241 0.03644 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +DMTA 11.45 41.4 12.46 10.82 83.54</code></pre> +</div> +<div class="section level3"> +<h3 id="alternative-check-of-parameter-identifiability">Alternative check of parameter identifiability<a class="anchor" aria-label="anchor" href="#alternative-check-of-parameter-identifiability"></a> +</h3> +<p>The parameter check used in the <code>illparms</code> function is +based on a quadratic approximation of the likelihood surface near its +optimum, which is calculated using the Fisher Information Matrix (FIM). +An alternative way to check parameter identifiability <span class="citation">(Duchesne et al. 2021)</span> based on a multistart +approach has recently been implemented in mkin.</p> +<p>The graph below shows boxplots of the parameters obtained in 50 runs +of the saem algorithm with different parameter combinations, sampled +from the range of the parameters obtained for the individual datasets +fitted separately using nonlinear regression.</p> +<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">f_saem_dfop_tc_multi</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/multistart.html">multistart</a></span><span class="op">(</span><span class="va">f_saem</span><span class="op">[[</span><span class="st">"DFOP"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>, n <span class="op">=</span> <span class="fl">50</span>, cores <span class="op">=</span> <span class="fl">15</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/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">6.1</span>, <span class="fl">4.1</span>, <span class="fl">2.1</span>, <span class="fl">2.1</span><span class="op">)</span><span class="op">)</span></span> +<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_multi</span>, lpos <span class="op">=</span> <span class="st">"bottomright"</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.3</span>, <span class="fl">10</span><span class="op">)</span>, las <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_parent_files/figure-html/multistart-full-par-1.png" alt="Scaled parameters from the multistart runs, full model" width="960"><p class="caption"> +Scaled parameters from the multistart runs, full model +</p> +</div> +<p>The graph clearly confirms the lack of identifiability of the +variance of <code>k2</code> in the full model. The overparameterisation +of the model also indicates a lack of identifiability of the variance of +parameter <code>g</code>.</p> +<p>The parameter boxplots of the multistart runs with the reduced model +shown below indicate that all runs give similar results, regardless of +the starting parameters.</p> +<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">f_saem_dfop_tc_no_ranef_k2_multi</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/multistart.html">multistart</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2</span>,</span> +<span> n <span class="op">=</span> <span class="fl">50</span>, cores <span class="op">=</span> <span class="fl">15</span><span class="op">)</span></span></code></pre></div> +<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">6.1</span>, <span class="fl">4.1</span>, <span class="fl">2.1</span>, <span class="fl">2.1</span><span class="op">)</span><span class="op">)</span></span> +<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2_multi</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">2</span><span class="op">)</span>, las <span class="op">=</span> <span class="fl">2</span>,</span> +<span> lpos <span class="op">=</span> <span class="st">"bottomright"</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_parent_files/figure-html/multistart-reduced-par-1.png" alt="Scaled parameters from the multistart runs, reduced model" width="960"><p class="caption"> +Scaled parameters from the multistart runs, reduced model +</p> +</div> +<p>When only the parameters of the top 25% of the fits are shown (based +on a feature introduced in mkin 1.2.2 currently under development), the +scatter is even less as shown below.</p> +<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">6.1</span>, <span class="fl">4.1</span>, <span class="fl">2.1</span>, <span class="fl">2.1</span><span class="op">)</span><span class="op">)</span></span> +<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_dfop_tc_no_ranef_k2_multi</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">2</span><span class="op">)</span>, las <span class="op">=</span> <span class="fl">2</span>, llquant <span class="op">=</span> <span class="fl">0.25</span>,</span> +<span> lpos <span class="op">=</span> <span class="st">"bottomright"</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_parent_files/figure-html/multistart-reduced-par-llquant-1.png" alt="Scaled parameters from the multistart runs, reduced model, fits with the top 25\% likelihood values" width="960"><p class="caption"> +Scaled parameters from the multistart runs, reduced model, fits with the +top 25% likelihood values +</p> +</div> +</div> +</div> +<div class="section level2"> +<h2 id="conclusions">Conclusions<a class="anchor" aria-label="anchor" href="#conclusions"></a> +</h2> +<p>Fitting the four parent degradation models SFO, FOMC, DFOP and HS as +part of hierarchical model fits with two different error models and +normal distributions of the transformed degradation parameters works +without technical problems. The biphasic models DFOP and HS gave the +best fit to the data, but the default parameter distribution model was +not fully identifiable. Removing the random effect for the second +kinetic rate constant of the DFOP model resulted in a reduced model that +was fully identifiable and showed the lowest values for the model +selection criteria AIC and BIC. The reliability of the identification of +all model parameters was confirmed using multiple starting values.</p> +</div> +<div class="section level2"> +<h2 id="acknowledgements">Acknowledgements<a class="anchor" aria-label="anchor" href="#acknowledgements"></a> +</h2> +<p>The helpful comments by Janina Wöltjen of the German Environment +Agency are gratefully acknowledged.</p> +</div> +<div class="section level2"> +<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a> +</h2> +<div id="refs" class="references csl-bib-body hanging-indent"> +<div id="ref-duchesne_2021" class="csl-entry"> +Duchesne, Ronan, Anissa Guillemin, Olivier Gandrillon, and Fabien +Crauste. 2021. <span>“Practical Identifiability in the Frame of +Nonlinear Mixed Effects Models: The Example of the in Vitro +Erythropoiesis.”</span> <em>BMC Bioinformatics</em> 22 (478). <a href="https://doi.org/10.1186/s12859-021-04373-4" class="external-link">https://doi.org/10.1186/s12859-021-04373-4</a>. +</div> +</div> +</div> +<div class="section level2"> +<h2 id="appendix">Appendix<a class="anchor" aria-label="anchor" href="#appendix"></a> +</h2> +<div class="section level3"> +<h3 id="hierarchical-model-fit-listings">Hierarchical model fit listings<a class="anchor" aria-label="anchor" href="#hierarchical-model-fit-listings"></a> +</h3> +<caption> +Hierarchical mkin fit of the SFO model with error model const +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 14:07:02 2023 +Date of summary: Thu Apr 20 14:08:16 2023 + +Equations: +d_DMTA/dt = - k_DMTA * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Model predictions using solution type analytical + +Fitted in 0.982 s +Using 300, 100 iterations and 9 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + DMTA_0 k_DMTA +97.2953 0.0566 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + DMTA_0 k_DMTA +DMTA_0 97.3 0 +k_DMTA 0.0 1 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 796.3 795.3 -393.2 + +Optimised parameters: + est. lower upper +DMTA_0 97.28130 95.71113 98.8515 +k_DMTA 0.05665 0.02909 0.0842 +a.1 2.66442 2.35579 2.9731 +SD.DMTA_0 1.54776 0.15447 2.9411 +SD.k_DMTA 0.60690 0.26248 0.9513 + +Correlation: + DMTA_0 +k_DMTA 0.0168 + +Random effects: + est. lower upper +SD.DMTA_0 1.5478 0.1545 2.9411 +SD.k_DMTA 0.6069 0.2625 0.9513 + +Variance model: + est. lower upper +a.1 2.664 2.356 2.973 + +Estimated disappearance times: + DT50 DT90 +DMTA 12.24 40.65 + +</code></pre> +<p></p> +<caption> +Hierarchical mkin fit of the SFO model with error model tc +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 14:07:03 2023 +Date of summary: Thu Apr 20 14:08:16 2023 + +Equations: +d_DMTA/dt = - k_DMTA * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Model predictions using solution type analytical + +Fitted in 2.398 s +Using 300, 100 iterations and 9 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + DMTA_0 k_DMTA +96.99175 0.05603 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + DMTA_0 k_DMTA +DMTA_0 96.99 0 +k_DMTA 0.00 1 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 798.3 797.1 -393.2 + +Optimised parameters: + est. lower upper +DMTA_0 97.271822 95.703157 98.84049 +k_DMTA 0.056638 0.029110 0.08417 +a.1 2.660081 2.230398 3.08976 +b.1 0.001665 -0.006911 0.01024 +SD.DMTA_0 1.545520 0.145035 2.94601 +SD.k_DMTA 0.606422 0.262274 0.95057 + +Correlation: + DMTA_0 +k_DMTA 0.0169 + +Random effects: + est. lower upper +SD.DMTA_0 1.5455 0.1450 2.9460 +SD.k_DMTA 0.6064 0.2623 0.9506 + +Variance model: + est. lower upper +a.1 2.660081 2.230398 3.08976 +b.1 0.001665 -0.006911 0.01024 + +Estimated disappearance times: + DT50 DT90 +DMTA 12.24 40.65 + +</code></pre> +<p></p> +<caption> +Hierarchical mkin fit of the FOMC model with error model const +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 14:07:02 2023 +Date of summary: Thu Apr 20 14:08:16 2023 + +Equations: +d_DMTA/dt = - (alpha/beta) * 1/((time/beta) + 1) * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Model predictions using solution type analytical + +Fitted in 1.398 s +Using 300, 100 iterations and 9 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + DMTA_0 alpha beta + 98.292 9.909 156.341 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + DMTA_0 alpha beta +DMTA_0 98.29 0 0 +alpha 0.00 1 0 +beta 0.00 0 1 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 734.2 732.7 -360.1 + +Optimised parameters: + est. lower upper +DMTA_0 98.3435 96.9033 99.784 +alpha 7.2007 2.5889 11.812 +beta 112.8746 34.8816 190.868 +a.1 2.0459 1.8054 2.286 +SD.DMTA_0 1.4795 0.2717 2.687 +SD.alpha 0.6396 0.1509 1.128 +SD.beta 0.6874 0.1587 1.216 + +Correlation: + DMTA_0 alpha +alpha -0.1125 +beta -0.1227 0.3632 + +Random effects: + est. lower upper +SD.DMTA_0 1.4795 0.2717 2.687 +SD.alpha 0.6396 0.1509 1.128 +SD.beta 0.6874 0.1587 1.216 + +Variance model: + est. lower upper +a.1 2.046 1.805 2.286 + +Estimated disappearance times: + DT50 DT90 DT50back +DMTA 11.41 42.53 12.8 + +</code></pre> +<p></p> +<caption> +Hierarchical mkin fit of the FOMC model with error model tc +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 14:07:04 2023 +Date of summary: Thu Apr 20 14:08:16 2023 + +Equations: +d_DMTA/dt = - (alpha/beta) * 1/((time/beta) + 1) * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Model predictions using solution type analytical + +Fitted in 3.044 s +Using 300, 100 iterations and 9 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: +DMTA_0 alpha beta +98.772 4.663 92.597 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + DMTA_0 alpha beta +DMTA_0 98.77 0 0 +alpha 0.00 1 0 +beta 0.00 0 1 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 720.4 718.8 -352.2 + +Optimised parameters: + est. lower upper +DMTA_0 98.99136 97.26011 100.72261 +alpha 5.86312 2.57485 9.15138 +beta 88.55571 29.20889 147.90254 +a.1 1.51063 1.24384 1.77741 +b.1 0.02824 0.02040 0.03609 +SD.DMTA_0 1.57436 -0.04867 3.19739 +SD.alpha 0.59871 0.17132 1.02611 +SD.beta 0.72994 0.22849 1.23139 + +Correlation: + DMTA_0 alpha +alpha -0.1363 +beta -0.1414 0.2542 + +Random effects: + est. lower upper +SD.DMTA_0 1.5744 -0.04867 3.197 +SD.alpha 0.5987 0.17132 1.026 +SD.beta 0.7299 0.22849 1.231 + +Variance model: + est. lower upper +a.1 1.51063 1.2438 1.77741 +b.1 0.02824 0.0204 0.03609 + +Estimated disappearance times: + DT50 DT90 DT50back +DMTA 11.11 42.6 12.82 + +</code></pre> +<p></p> +<caption> +Hierarchical mkin fit of the DFOP model with error model const +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 14:07:02 2023 +Date of summary: Thu Apr 20 14:08:16 2023 + +Equations: +d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Model predictions using solution type analytical + +Fitted in 1.838 s +Using 300, 100 iterations and 9 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + DMTA_0 k1 k2 g +98.64383 0.09211 0.02999 0.76814 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + DMTA_0 k1 k2 g +DMTA_0 98.64 0 0 0 +k1 0.00 1 0 0 +k2 0.00 0 1 0 +g 0.00 0 0 1 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 711.8 710 -346.9 + +Optimised parameters: + est. lower upper +DMTA_0 98.092481 96.573898 99.61106 +k1 0.062499 0.030336 0.09466 +k2 0.009065 -0.005133 0.02326 +g 0.948967 0.862079 1.03586 +a.1 1.821671 1.604774 2.03857 +SD.DMTA_0 1.677785 0.472066 2.88350 +SD.k1 0.634962 0.270788 0.99914 +SD.k2 1.033498 -0.205994 2.27299 +SD.g 1.710046 0.428642 2.99145 + +Correlation: + DMTA_0 k1 k2 +k1 0.0246 +k2 0.0491 0.0953 +g -0.0552 -0.0889 -0.4795 + +Random effects: + est. lower upper +SD.DMTA_0 1.678 0.4721 2.8835 +SD.k1 0.635 0.2708 0.9991 +SD.k2 1.033 -0.2060 2.2730 +SD.g 1.710 0.4286 2.9914 + +Variance model: + est. lower upper +a.1 1.822 1.605 2.039 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +DMTA 11.79 42.8 12.88 11.09 76.46 + +</code></pre> +<p></p> +<caption> +Hierarchical mkin fit of the DFOP model with error model tc +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 14:07:04 2023 +Date of summary: Thu Apr 20 14:08:16 2023 + +Equations: +d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Model predictions using solution type analytical + +Fitted in 3.297 s +Using 300, 100 iterations and 9 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + DMTA_0 k1 k2 g +98.759266 0.087034 0.009933 0.930827 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + DMTA_0 k1 k2 g +DMTA_0 98.76 0 0 0 +k1 0.00 1 0 0 +k2 0.00 0 1 0 +g 0.00 0 0 1 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 665.5 663.4 -322.8 + +Optimised parameters: + est. lower upper +DMTA_0 98.377019 96.447952 100.30609 +k1 0.064843 0.034607 0.09508 +k2 0.008895 0.006368 0.01142 +g 0.949696 0.903815 0.99558 +a.1 1.065241 0.865754 1.26473 +b.1 0.029340 0.022336 0.03634 +SD.DMTA_0 2.007754 0.387982 3.62753 +SD.k1 0.580473 0.250286 0.91066 +SD.k2 0.006105 -4.920337 4.93255 +SD.g 1.097149 0.412779 1.78152 + +Correlation: + DMTA_0 k1 k2 +k1 0.0235 +k2 0.0595 0.0424 +g -0.0470 -0.0278 -0.2731 + +Random effects: + est. lower upper +SD.DMTA_0 2.007754 0.3880 3.6275 +SD.k1 0.580473 0.2503 0.9107 +SD.k2 0.006105 -4.9203 4.9325 +SD.g 1.097149 0.4128 1.7815 + +Variance model: + est. lower upper +a.1 1.06524 0.86575 1.26473 +b.1 0.02934 0.02234 0.03634 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +DMTA 11.36 41.32 12.44 10.69 77.92 + +</code></pre> +<p></p> +<caption> +Hierarchical mkin fit of the HS model with error model const +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 14:07:03 2023 +Date of summary: Thu Apr 20 14:08:16 2023 + +Equations: +d_DMTA/dt = - ifelse(time <= tb, k1, k2) * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Model predictions using solution type analytical + +Fitted in 1.972 s +Using 300, 100 iterations and 9 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + DMTA_0 k1 k2 tb +97.82176 0.06931 0.02997 11.13945 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + DMTA_0 k1 k2 tb +DMTA_0 97.82 0 0 0 +k1 0.00 1 0 0 +k2 0.00 0 1 0 +tb 0.00 0 0 1 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 714 712.1 -348 + +Optimised parameters: + est. lower upper +DMTA_0 98.16102 96.47747 99.84456 +k1 0.07876 0.05261 0.10491 +k2 0.02227 0.01706 0.02747 +tb 13.99089 -7.40049 35.38228 +a.1 1.82305 1.60700 2.03910 +SD.DMTA_0 1.88413 0.56204 3.20622 +SD.k1 0.34292 0.10482 0.58102 +SD.k2 0.19851 0.01718 0.37985 +SD.tb 1.68168 0.58064 2.78272 + +Correlation: + DMTA_0 k1 k2 +k1 0.0142 +k2 0.0001 -0.0025 +tb 0.0165 -0.1256 -0.0301 + +Random effects: + est. lower upper +SD.DMTA_0 1.8841 0.56204 3.2062 +SD.k1 0.3429 0.10482 0.5810 +SD.k2 0.1985 0.01718 0.3798 +SD.tb 1.6817 0.58064 2.7827 + +Variance model: + est. lower upper +a.1 1.823 1.607 2.039 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +DMTA 8.801 67.91 20.44 8.801 31.13 + +</code></pre> +<p></p> +<caption> +Hierarchical mkin fit of the HS model with error model tc +</caption> +<pre><code> +saemix version used for fitting: 3.2 +mkin version used for pre-fitting: 1.2.3 +R version used for fitting: 4.2.3 +Date of fit: Thu Apr 20 14:07:04 2023 +Date of summary: Thu Apr 20 14:08:16 2023 + +Equations: +d_DMTA/dt = - ifelse(time <= tb, k1, k2) * DMTA + +Data: +155 observations of 1 variable(s) grouped in 6 datasets + +Model predictions using solution type analytical + +Fitted in 3.378 s +Using 300, 100 iterations and 9 chains + +Variance model: Two-component variance function + +Starting values for degradation parameters: + DMTA_0 k1 k2 tb +98.45190 0.07525 0.02576 19.19375 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + DMTA_0 k1 k2 tb +DMTA_0 98.45 0 0 0 +k1 0.00 1 0 0 +k2 0.00 0 1 0 +tb 0.00 0 0 1 + +Starting values for error model parameters: +a.1 b.1 + 1 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 667.1 665 -323.6 + +Optimised parameters: + est. lower upper +DMTA_0 97.76570 95.81350 99.71791 +k1 0.05855 0.03080 0.08630 +k2 0.02337 0.01664 0.03010 +tb 31.09638 29.38289 32.80987 +a.1 1.08835 0.88590 1.29080 +b.1 0.02964 0.02257 0.03671 +SD.DMTA_0 2.04877 0.42607 3.67147 +SD.k1 0.59166 0.25621 0.92711 +SD.k2 0.30698 0.09561 0.51835 +SD.tb 0.01274 -0.10914 0.13462 + +Correlation: + DMTA_0 k1 k2 +k1 0.0160 +k2 -0.0070 -0.0024 +tb -0.0668 -0.0103 -0.2013 + +Random effects: + est. lower upper +SD.DMTA_0 2.04877 0.42607 3.6715 +SD.k1 0.59166 0.25621 0.9271 +SD.k2 0.30698 0.09561 0.5183 +SD.tb 0.01274 -0.10914 0.1346 + +Variance model: + est. lower upper +a.1 1.08835 0.88590 1.29080 +b.1 0.02964 0.02257 0.03671 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +DMTA 11.84 51.71 15.57 11.84 29.66 + +</code></pre> +<p></p> +</div> +<div class="section level3"> +<h3 id="hierarchical-model-convergence-plots">Hierarchical model convergence plots<a class="anchor" aria-label="anchor" href="#hierarchical-model-convergence-plots"></a> +</h3> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_parent_files/figure-html/convergence-saem-sfo-const-1.png" alt="Convergence plot for the NLHM SFO fit with constant variance" width="864"><p class="caption"> +Convergence plot for the NLHM SFO fit with constant variance +</p> +</div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_parent_files/figure-html/convergence-saem-sfo-tc-1.png" alt="Convergence plot for the NLHM SFO fit with two-component error" width="864"><p class="caption"> +Convergence plot for the NLHM SFO fit with two-component error +</p> +</div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_parent_files/figure-html/convergence-saem-fomc-const-1.png" alt="Convergence plot for the NLHM FOMC fit with constant variance" width="864"><p class="caption"> +Convergence plot for the NLHM FOMC fit with constant variance +</p> +</div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_parent_files/figure-html/convergence-saem-fomc-tc-1.png" alt="Convergence plot for the NLHM FOMC fit with two-component error" width="864"><p class="caption"> +Convergence plot for the NLHM FOMC fit with two-component error +</p> +</div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_parent_files/figure-html/convergence-saem-dfop-const-1.png" alt="Convergence plot for the NLHM DFOP fit with constant variance" width="864"><p class="caption"> +Convergence plot for the NLHM DFOP fit with constant variance +</p> +</div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-1.png" alt="Convergence plot for the NLHM DFOP fit with two-component error" width="864"><p class="caption"> +Convergence plot for the NLHM DFOP fit with two-component error +</p> +</div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_parent_files/figure-html/convergence-saem-hs-const-1.png" alt="Convergence plot for the NLHM HS fit with constant variance" width="864"><p class="caption"> +Convergence plot for the NLHM HS fit with constant variance +</p> +</div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_parent_files/figure-html/convergence-saem-hs-tc-1.png" alt="Convergence plot for the NLHM HS fit with two-component error" width="864"><p class="caption"> +Convergence plot for the NLHM HS fit with two-component error +</p> +</div> +</div> +<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 4.2.3 (2023-03-15) +Platform: x86_64-pc-linux-gnu (64-bit) +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 + +locale: + [1] LC_CTYPE=de_DE.UTF-8 LC_NUMERIC=C + [3] LC_TIME=de_DE.UTF-8 LC_COLLATE=de_DE.UTF-8 + [5] LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=de_DE.UTF-8 + [7] LC_PAPER=de_DE.UTF-8 LC_NAME=C + [9] LC_ADDRESS=C LC_TELEPHONE=C +[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C + +attached base packages: +[1] parallel stats graphics grDevices utils datasets methods +[8] base + +other attached packages: +[1] saemix_3.2 npde_3.3 knitr_1.42 mkin_1.2.3 + +loaded via a namespace (and not attached): + [1] highr_0.10 pillar_1.9.0 bslib_0.4.2 compiler_4.2.3 + [5] jquerylib_0.1.4 tools_4.2.3 mclust_6.0.0 digest_0.6.31 + [9] tibble_3.2.1 jsonlite_1.8.4 evaluate_0.20 memoise_2.0.1 +[13] lifecycle_1.0.3 nlme_3.1-162 gtable_0.3.3 lattice_0.21-8 +[17] pkgconfig_2.0.3 rlang_1.1.0 DBI_1.1.3 cli_3.6.1 +[21] yaml_2.3.7 pkgdown_2.0.7 xfun_0.38 fastmap_1.1.1 +[25] gridExtra_2.3 dplyr_1.1.1 stringr_1.5.0 generics_0.1.3 +[29] desc_1.4.2 fs_1.6.1 vctrs_0.6.1 sass_0.4.5 +[33] systemfonts_1.0.4 tidyselect_1.2.0 rprojroot_2.0.3 lmtest_0.9-40 +[37] grid_4.2.3 glue_1.6.2 R6_2.5.1 textshaping_0.3.6 +[41] fansi_1.0.4 rmarkdown_2.21 purrr_1.0.1 ggplot2_3.4.2 +[45] magrittr_2.0.3 codetools_0.2-19 scales_1.2.1 htmltools_0.5.5 +[49] colorspace_2.1-0 ragg_1.2.5 utf8_1.2.3 stringi_1.7.12 +[53] munsell_0.5.0 cachem_1.0.7 zoo_1.8-12 </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: 64936316 kB</code></pre> +</div> +</div> + </div> + + <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> + + <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2> + </nav> +</div> + +</div> + + + + <footer><div class="copyright"> + <p></p> +<p>Developed by Johannes Ranke.</p> +</div> + +<div class="pkgdown"> + <p></p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> +</div> + + </footer> +</div> + + + + + + + </body> +</html> diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-const-1.png b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-const-1.png Binary files differnew file mode 100644 index 00000000..3f145074 --- /dev/null +++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-const-1.png diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-1.png b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-1.png Binary files differnew file mode 100644 index 00000000..e5457fc9 --- /dev/null +++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-1.png diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-no-ranef-k2-1.png b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-no-ranef-k2-1.png Binary files differnew file mode 100644 index 00000000..14707641 --- /dev/null +++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-dfop-tc-no-ranef-k2-1.png diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-fomc-const-1.png b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-fomc-const-1.png Binary files differnew file mode 100644 index 00000000..c7ed69a3 --- /dev/null +++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-fomc-const-1.png diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-fomc-tc-1.png b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-fomc-tc-1.png Binary files differnew file mode 100644 index 00000000..1a48524c --- /dev/null +++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-fomc-tc-1.png diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-hs-const-1.png b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-hs-const-1.png Binary files differnew file mode 100644 index 00000000..0f3b1184 --- /dev/null +++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-hs-const-1.png diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-hs-tc-1.png b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-hs-tc-1.png Binary files differnew file mode 100644 index 00000000..901a1579 --- /dev/null +++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-hs-tc-1.png diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-sfo-const-1.png b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-sfo-const-1.png Binary files differnew file mode 100644 index 00000000..a3e3a51f --- /dev/null +++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-sfo-const-1.png diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-sfo-tc-1.png b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-sfo-tc-1.png Binary files differnew file mode 100644 index 00000000..b85691eb --- /dev/null +++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/convergence-saem-sfo-tc-1.png diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-full-par-1.png b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-full-par-1.png Binary files differnew file mode 100644 index 00000000..a42950f0 --- /dev/null +++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-full-par-1.png diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-reduced-par-1.png b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-reduced-par-1.png Binary files differnew file mode 100644 index 00000000..caebc768 --- /dev/null +++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-reduced-par-1.png diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-reduced-par-llquant-1.png b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-reduced-par-llquant-1.png Binary files differnew file mode 100644 index 00000000..45ae57f1 --- /dev/null +++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/multistart-reduced-par-llquant-1.png diff --git a/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/plot-saem-dfop-tc-no-ranef-k2-1.png b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/plot-saem-dfop-tc-no-ranef-k2-1.png Binary files differnew file mode 100644 index 00000000..1f8eb9f0 --- /dev/null +++ b/docs/articles/prebuilt/2022_dmta_parent_files/figure-html/plot-saem-dfop-tc-no-ranef-k2-1.png diff --git a/docs/articles/prebuilt/2022_dmta_pathway.html b/docs/articles/prebuilt/2022_dmta_pathway.html new file mode 100644 index 00000000..c8323add --- /dev/null +++ b/docs/articles/prebuilt/2022_dmta_pathway.html @@ -0,0 +1,2053 @@ +<!DOCTYPE html> +<!-- Generated by pkgdown: do not edit by hand --><html lang="en"> +<head> +<meta http-equiv="Content-Type" content="text/html; charset=UTF-8"> +<meta charset="utf-8"> +<meta http-equiv="X-UA-Compatible" content="IE=edge"> +<meta name="viewport" content="width=device-width, initial-scale=1.0"> +<title>Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P • mkin</title> +<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"> +<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../../bootstrap-toc.css"> +<script src="../../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"> +<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"> +<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../../pkgdown.css" rel="stylesheet"> +<script src="../../pkgdown.js"></script><meta property="og:title" content="Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P"> +<meta property="og:description" content="mkin"> +<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> +<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> +<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> +<![endif]--> +</head> +<body data-spy="scroll" data-target="#toc"> + + + <div class="container template-article"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <div class="container"> + <div class="navbar-header"> + <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> + <span class="sr-only">Toggle navigation</span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + </button> + <span class="navbar-brand"> + <a class="navbar-link" href="../../index.html">mkin</a> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3.1</span> + </span> + </div> + + <div id="navbar" class="navbar-collapse collapse"> + <ul class="nav navbar-nav"> +<li> + <a href="../../reference/index.html">Reference</a> +</li> +<li class="dropdown"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> + Articles + + <span class="caret"></span> + </a> + <ul class="dropdown-menu" role="menu"> +<li> + <a href="../../articles/mkin.html">Introduction to mkin</a> + </li> + <li class="divider"> + </li> +<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> + <li> + <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> + </li> + <li> + <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> + </li> + <li> + <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li class="divider"> + </li> +<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> + <li> + <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> + </li> + <li> + <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> + </li> + <li> + <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> + </li> + <li> + <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> + </li> + <li> + <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + </li> +<li class="dropdown-header">Performance</li> + <li> + <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + </li> + <li> + <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + <li class="divider"> + </li> +<li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> + </ul> +</li> +<li> + <a href="../../news/index.html">News</a> +</li> + </ul> +<ul class="nav navbar-nav navbar-right"> +<li> + <a href="https://github.com/jranke/mkin/" class="external-link"> + <span class="fab fa-github fa-lg"></span> + + </a> +</li> + </ul> +</div> +<!--/.nav-collapse --> + </div> +<!--/.container --> +</div> +<!--/.navbar --> + + + + </header><div class="row"> + <div class="col-md-9 contents"> + <div class="page-header toc-ignore"> + <h1 data-toc-skip>Testing hierarchical pathway kinetics with +residue data on dimethenamid and dimethenamid-P</h1> + <h4 data-toc-skip class="author">Johannes +Ranke</h4> + + <h4 data-toc-skip class="date">Last change on 20 April 2023, +last compiled on 20 April 2023</h4> + + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/prebuilt/2022_dmta_pathway.rmd" class="external-link"><code>vignettes/prebuilt/2022_dmta_pathway.rmd</code></a></small> + <div class="hidden name"><code>2022_dmta_pathway.rmd</code></div> + + </div> + + + +<div class="section level2"> +<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a> +</h2> +<p>The purpose of this document is to test demonstrate how nonlinear +hierarchical models (NLHM) based on the parent degradation models SFO, +FOMC, DFOP and HS, with parallel formation of two or more metabolites +can be fitted with the mkin package.</p> +<p>It was assembled in the course of work package 1.2 of Project Number +173340 (Application of nonlinear hierarchical models to the kinetic +evaluation of chemical degradation data) of the German Environment +Agency carried out in 2022 and 2023.</p> +<p>The mkin package is used in version 1.2.3, which is currently under +development. It contains the test data, and the functions used in 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> +<p>This document is processed with the <code>knitr</code> package, which +also provides the <code>kable</code> function that is used to improve +the display of tabular data in R markdown documents. For parallel +processing, the <code>parallel</code> package is used.</p> +<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span><span class="op">)</span></span> +<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://yihui.org/knitr/" class="external-link">knitr</a></span><span class="op">)</span></span> +<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">saemix</span><span class="op">)</span></span> +<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">parallel</span><span class="op">)</span></span> +<span><span class="va">n_cores</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># We need to start a new cluster after defining a compiled model that is</span></span> +<span><span class="co"># saved as a DLL to the user directory, therefore we define a function</span></span> +<span><span class="co"># This is used again after defining the pathway model</span></span> +<span><span class="va">start_cluster</span> <span class="op"><-</span> <span class="kw">function</span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span> <span class="op">{</span></span> +<span> <span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Sys.info.html" class="external-link">Sys.info</a></span><span class="op">(</span><span class="op">)</span><span class="op">[</span><span class="st">"sysname"</span><span class="op">]</span> <span class="op">==</span> <span class="st">"Windows"</span><span class="op">)</span> <span class="op">{</span></span> +<span> <span class="va">ret</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makePSOCKcluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span> +<span> <span class="op">}</span> <span class="kw">else</span> <span class="op">{</span></span> +<span> <span class="va">ret</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makeForkCluster</a></span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span> +<span> <span class="op">}</span></span> +<span> <span class="kw"><a href="https://rdrr.io/r/base/function.html" class="external-link">return</a></span><span class="op">(</span><span class="va">ret</span><span class="op">)</span></span> +<span><span class="op">}</span></span></code></pre></div> +</div> +<div class="section level2"> +<h2 id="data">Data<a class="anchor" aria-label="anchor" href="#data"></a> +</h2> +<p>The test data are available in the mkin package as an object of class +<code>mkindsg</code> (mkin dataset group) under the identifier +<code>dimethenamid_2018</code>. The following preprocessing steps are +done in this document.</p> +<ul> +<li>The data available for the enantiomer dimethenamid-P (DMTAP) are +renamed to have the same substance name as the data for the racemic +mixture dimethenamid (DMTA). The reason for this is that no difference +between their degradation behaviour was identified in the EU risk +assessment.</li> +<li>Unnecessary columns are discarded</li> +<li>The observation times of each dataset are multiplied with the +corresponding normalisation factor also available in the dataset, in +order to make it possible to describe all datasets with a single set of +parameters that are independent of temperature</li> +<li>Finally, datasets observed in the same soil (<code>Elliot 1</code> +and <code>Elliot 2</code>) are combined, resulting in dimethenamid +(DMTA) data from six soils.</li> +</ul> +<p>The following commented R code performs this preprocessing.</p> +<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="co"># Apply a function to each of the seven datasets in the mkindsg object to create a list</span></span> +<span><span class="va">dmta_ds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span> +<span> <span class="va">ds_i</span> <span class="op"><-</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span><span class="op">[[</span><span class="va">i</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span> <span class="co"># Get a dataset</span></span> +<span> <span class="va">ds_i</span><span class="op">[</span><span class="va">ds_i</span><span class="op">$</span><span class="va">name</span> <span class="op">==</span> <span class="st">"DMTAP"</span>, <span class="st">"name"</span><span class="op">]</span> <span class="op"><-</span> <span class="st">"DMTA"</span> <span class="co"># Rename DMTAP to DMTA</span></span> +<span> <span class="va">ds_i</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">ds_i</span>, select <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">)</span> <span class="co"># Select data</span></span> +<span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op"><-</span> <span class="va">ds_i</span><span class="op">$</span><span class="va">time</span> <span class="op">*</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">f_time_norm</span><span class="op">[</span><span class="va">i</span><span class="op">]</span> <span class="co"># Normalise time</span></span> +<span> <span class="va">ds_i</span> <span class="co"># Return the dataset</span></span> +<span><span class="op">}</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># Use dataset titles as names for the list elements</span></span> +<span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="va">ds</span><span class="op">$</span><span class="va">title</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># Combine data for Elliot soil to obtain a named list with six elements</span></span> +<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span>, <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span> <span class="co">#</span></span> +<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="cn">NULL</span></span> +<span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="cn">NULL</span></span></code></pre></div> +<p>The following tables show the 6 datasets.</p> +<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="kw">for</span> <span class="op">(</span><span class="va">ds_name</span> <span class="kw">in</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span><span class="op">)</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> +<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="fu"><a href="../../reference/mkin_long_to_wide.html">mkin_long_to_wide</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="va">ds_name</span><span class="op">]</span><span class="op">]</span><span class="op">)</span>,</span> +<span> caption <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="va">ds_name</span><span class="op">)</span>,</span> +<span> booktabs <span class="op">=</span> <span class="cn">TRUE</span>, row.names <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">)</span></span> +<span> <span class="fu"><a href="https://rdrr.io/r/base/cat.html" class="external-link">cat</a></span><span class="op">(</span><span class="st">"\n\\clearpage\n"</span><span class="op">)</span></span> +<span><span class="op">}</span></span></code></pre></div> +<table class="table"> +<caption>Dataset Calke</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +<th align="right">M23</th> +<th align="right">M27</th> +<th align="right">M31</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0</td> +<td align="right">95.8</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">0</td> +<td align="right">98.7</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">14</td> +<td align="right">60.5</td> +<td align="right">4.1</td> +<td align="right">1.5</td> +<td align="right">2.0</td> +</tr> +<tr class="even"> +<td align="right">30</td> +<td align="right">39.1</td> +<td align="right">5.3</td> +<td align="right">2.4</td> +<td align="right">2.1</td> +</tr> +<tr class="odd"> +<td align="right">59</td> +<td align="right">15.2</td> +<td align="right">6.0</td> +<td align="right">3.2</td> +<td align="right">2.2</td> +</tr> +<tr class="even"> +<td align="right">120</td> +<td align="right">4.8</td> +<td align="right">4.3</td> +<td align="right">3.8</td> +<td align="right">1.8</td> +</tr> +<tr class="odd"> +<td align="right">120</td> +<td align="right">4.6</td> +<td align="right">4.1</td> +<td align="right">3.7</td> +<td align="right">2.1</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset Borstel</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +<th align="right">M23</th> +<th align="right">M27</th> +<th align="right">M31</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">100.5</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">0.000000</td> +<td align="right">99.6</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">1.941295</td> +<td align="right">91.9</td> +<td align="right">0.4</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">1.941295</td> +<td align="right">91.3</td> +<td align="right">0.5</td> +<td align="right">0.3</td> +<td align="right">0.1</td> +</tr> +<tr class="odd"> +<td align="right">6.794534</td> +<td align="right">81.8</td> +<td align="right">1.2</td> +<td align="right">0.8</td> +<td align="right">1.0</td> +</tr> +<tr class="even"> +<td align="right">6.794534</td> +<td align="right">82.1</td> +<td align="right">1.3</td> +<td align="right">0.9</td> +<td align="right">0.9</td> +</tr> +<tr class="odd"> +<td align="right">13.589067</td> +<td align="right">69.1</td> +<td align="right">2.8</td> +<td align="right">1.4</td> +<td align="right">2.0</td> +</tr> +<tr class="even"> +<td align="right">13.589067</td> +<td align="right">68.0</td> +<td align="right">2.0</td> +<td align="right">1.4</td> +<td align="right">2.5</td> +</tr> +<tr class="odd"> +<td align="right">27.178135</td> +<td align="right">51.4</td> +<td align="right">2.9</td> +<td align="right">2.7</td> +<td align="right">4.3</td> +</tr> +<tr class="even"> +<td align="right">27.178135</td> +<td align="right">51.4</td> +<td align="right">4.9</td> +<td align="right">2.6</td> +<td align="right">3.2</td> +</tr> +<tr class="odd"> +<td align="right">56.297565</td> +<td align="right">27.6</td> +<td align="right">12.2</td> +<td align="right">4.4</td> +<td align="right">4.3</td> +</tr> +<tr class="even"> +<td align="right">56.297565</td> +<td align="right">26.8</td> +<td align="right">12.2</td> +<td align="right">4.7</td> +<td align="right">4.8</td> +</tr> +<tr class="odd"> +<td align="right">86.387643</td> +<td align="right">15.7</td> +<td align="right">12.2</td> +<td align="right">5.4</td> +<td align="right">5.0</td> +</tr> +<tr class="even"> +<td align="right">86.387643</td> +<td align="right">15.3</td> +<td align="right">12.0</td> +<td align="right">5.2</td> +<td align="right">5.1</td> +</tr> +<tr class="odd"> +<td align="right">115.507073</td> +<td align="right">7.9</td> +<td align="right">10.4</td> +<td align="right">5.4</td> +<td align="right">4.3</td> +</tr> +<tr class="even"> +<td align="right">115.507073</td> +<td align="right">8.1</td> +<td align="right">11.6</td> +<td align="right">5.4</td> +<td align="right">4.4</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset Flaach</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +<th align="right">M23</th> +<th align="right">M27</th> +<th align="right">M31</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.0000000</td> +<td align="right">96.5</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">0.0000000</td> +<td align="right">96.8</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">0.0000000</td> +<td align="right">97.0</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">0.6233856</td> +<td align="right">82.9</td> +<td align="right">0.7</td> +<td align="right">1.1</td> +<td align="right">0.3</td> +</tr> +<tr class="odd"> +<td align="right">0.6233856</td> +<td align="right">86.7</td> +<td align="right">0.7</td> +<td align="right">1.1</td> +<td align="right">0.3</td> +</tr> +<tr class="even"> +<td align="right">0.6233856</td> +<td align="right">87.4</td> +<td align="right">0.2</td> +<td align="right">0.3</td> +<td align="right">0.1</td> +</tr> +<tr class="odd"> +<td align="right">1.8701567</td> +<td align="right">72.8</td> +<td align="right">2.2</td> +<td align="right">2.6</td> +<td align="right">0.7</td> +</tr> +<tr class="even"> +<td align="right">1.8701567</td> +<td align="right">69.9</td> +<td align="right">1.8</td> +<td align="right">2.4</td> +<td align="right">0.6</td> +</tr> +<tr class="odd"> +<td align="right">1.8701567</td> +<td align="right">71.9</td> +<td align="right">1.6</td> +<td align="right">2.3</td> +<td align="right">0.7</td> +</tr> +<tr class="even"> +<td align="right">4.3636989</td> +<td align="right">51.4</td> +<td align="right">4.1</td> +<td align="right">5.0</td> +<td align="right">1.3</td> +</tr> +<tr class="odd"> +<td align="right">4.3636989</td> +<td align="right">52.9</td> +<td align="right">4.2</td> +<td align="right">5.9</td> +<td align="right">1.2</td> +</tr> +<tr class="even"> +<td align="right">4.3636989</td> +<td align="right">48.6</td> +<td align="right">4.2</td> +<td align="right">4.8</td> +<td align="right">1.4</td> +</tr> +<tr class="odd"> +<td align="right">8.7273979</td> +<td align="right">28.5</td> +<td align="right">7.5</td> +<td align="right">8.5</td> +<td align="right">2.4</td> +</tr> +<tr class="even"> +<td align="right">8.7273979</td> +<td align="right">27.3</td> +<td align="right">7.1</td> +<td align="right">8.5</td> +<td align="right">2.1</td> +</tr> +<tr class="odd"> +<td align="right">8.7273979</td> +<td align="right">27.5</td> +<td align="right">7.5</td> +<td align="right">8.3</td> +<td align="right">2.3</td> +</tr> +<tr class="even"> +<td align="right">13.0910968</td> +<td align="right">14.8</td> +<td align="right">8.4</td> +<td align="right">9.3</td> +<td align="right">3.3</td> +</tr> +<tr class="odd"> +<td align="right">13.0910968</td> +<td align="right">13.4</td> +<td align="right">6.8</td> +<td align="right">8.7</td> +<td align="right">2.4</td> +</tr> +<tr class="even"> +<td align="right">13.0910968</td> +<td align="right">14.4</td> +<td align="right">8.0</td> +<td align="right">9.1</td> +<td align="right">2.6</td> +</tr> +<tr class="odd"> +<td align="right">17.4547957</td> +<td align="right">7.7</td> +<td align="right">7.2</td> +<td align="right">8.6</td> +<td align="right">4.0</td> +</tr> +<tr class="even"> +<td align="right">17.4547957</td> +<td align="right">7.3</td> +<td align="right">7.2</td> +<td align="right">8.5</td> +<td align="right">3.6</td> +</tr> +<tr class="odd"> +<td align="right">17.4547957</td> +<td align="right">8.1</td> +<td align="right">6.9</td> +<td align="right">8.9</td> +<td align="right">3.3</td> +</tr> +<tr class="even"> +<td align="right">26.1821936</td> +<td align="right">2.0</td> +<td align="right">4.9</td> +<td align="right">8.1</td> +<td align="right">2.1</td> +</tr> +<tr class="odd"> +<td align="right">26.1821936</td> +<td align="right">1.5</td> +<td align="right">4.3</td> +<td align="right">7.7</td> +<td align="right">1.7</td> +</tr> +<tr class="even"> +<td align="right">26.1821936</td> +<td align="right">1.9</td> +<td align="right">4.5</td> +<td align="right">7.4</td> +<td align="right">1.8</td> +</tr> +<tr class="odd"> +<td align="right">34.9095915</td> +<td align="right">1.3</td> +<td align="right">3.8</td> +<td align="right">5.9</td> +<td align="right">1.6</td> +</tr> +<tr class="even"> +<td align="right">34.9095915</td> +<td align="right">1.0</td> +<td align="right">3.1</td> +<td align="right">6.0</td> +<td align="right">1.6</td> +</tr> +<tr class="odd"> +<td align="right">34.9095915</td> +<td align="right">1.1</td> +<td align="right">3.1</td> +<td align="right">5.9</td> +<td align="right">1.4</td> +</tr> +<tr class="even"> +<td align="right">43.6369893</td> +<td align="right">0.9</td> +<td align="right">2.7</td> +<td align="right">5.6</td> +<td align="right">1.8</td> +</tr> +<tr class="odd"> +<td align="right">43.6369893</td> +<td align="right">0.7</td> +<td align="right">2.3</td> +<td align="right">5.2</td> +<td align="right">1.5</td> +</tr> +<tr class="even"> +<td align="right">43.6369893</td> +<td align="right">0.7</td> +<td align="right">2.1</td> +<td align="right">5.6</td> +<td align="right">1.3</td> +</tr> +<tr class="odd"> +<td align="right">52.3643872</td> +<td align="right">0.6</td> +<td align="right">1.6</td> +<td align="right">4.3</td> +<td align="right">1.2</td> +</tr> +<tr class="even"> +<td align="right">52.3643872</td> +<td align="right">0.4</td> +<td align="right">1.1</td> +<td align="right">3.7</td> +<td align="right">0.9</td> +</tr> +<tr class="odd"> +<td align="right">52.3643872</td> +<td align="right">0.5</td> +<td align="right">1.3</td> +<td align="right">3.9</td> +<td align="right">1.1</td> +</tr> +<tr class="even"> +<td align="right">74.8062674</td> +<td align="right">0.4</td> +<td align="right">0.4</td> +<td align="right">2.5</td> +<td align="right">0.5</td> +</tr> +<tr class="odd"> +<td align="right">74.8062674</td> +<td align="right">0.3</td> +<td align="right">0.4</td> +<td align="right">2.4</td> +<td align="right">0.5</td> +</tr> +<tr class="even"> +<td align="right">74.8062674</td> +<td align="right">0.3</td> +<td align="right">0.3</td> +<td align="right">2.2</td> +<td align="right">0.3</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset BBA 2.2</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +<th align="right">M23</th> +<th align="right">M27</th> +<th align="right">M31</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.0000000</td> +<td align="right">98.09</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">0.0000000</td> +<td align="right">98.77</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">0.7678922</td> +<td align="right">93.52</td> +<td align="right">0.36</td> +<td align="right">0.42</td> +<td align="right">0.36</td> +</tr> +<tr class="even"> +<td align="right">0.7678922</td> +<td align="right">92.03</td> +<td align="right">0.40</td> +<td align="right">0.47</td> +<td align="right">0.33</td> +</tr> +<tr class="odd"> +<td align="right">2.3036765</td> +<td align="right">88.39</td> +<td align="right">1.03</td> +<td align="right">0.71</td> +<td align="right">0.55</td> +</tr> +<tr class="even"> +<td align="right">2.3036765</td> +<td align="right">87.18</td> +<td align="right">1.07</td> +<td align="right">0.82</td> +<td align="right">0.64</td> +</tr> +<tr class="odd"> +<td align="right">5.3752452</td> +<td align="right">69.38</td> +<td align="right">3.60</td> +<td align="right">2.19</td> +<td align="right">1.94</td> +</tr> +<tr class="even"> +<td align="right">5.3752452</td> +<td align="right">71.06</td> +<td align="right">3.66</td> +<td align="right">2.28</td> +<td align="right">1.62</td> +</tr> +<tr class="odd"> +<td align="right">10.7504904</td> +<td align="right">45.21</td> +<td align="right">6.97</td> +<td align="right">5.45</td> +<td align="right">4.22</td> +</tr> +<tr class="even"> +<td align="right">10.7504904</td> +<td align="right">46.81</td> +<td align="right">7.22</td> +<td align="right">5.19</td> +<td align="right">4.37</td> +</tr> +<tr class="odd"> +<td align="right">16.1257355</td> +<td align="right">30.54</td> +<td align="right">8.65</td> +<td align="right">8.81</td> +<td align="right">6.31</td> +</tr> +<tr class="even"> +<td align="right">16.1257355</td> +<td align="right">30.07</td> +<td align="right">8.38</td> +<td align="right">7.93</td> +<td align="right">6.85</td> +</tr> +<tr class="odd"> +<td align="right">21.5009807</td> +<td align="right">21.60</td> +<td align="right">9.10</td> +<td align="right">10.25</td> +<td align="right">7.05</td> +</tr> +<tr class="even"> +<td align="right">21.5009807</td> +<td align="right">20.41</td> +<td align="right">8.63</td> +<td align="right">10.77</td> +<td align="right">6.84</td> +</tr> +<tr class="odd"> +<td align="right">32.2514711</td> +<td align="right">9.10</td> +<td align="right">7.63</td> +<td align="right">10.89</td> +<td align="right">6.53</td> +</tr> +<tr class="even"> +<td align="right">32.2514711</td> +<td align="right">9.70</td> +<td align="right">8.01</td> +<td align="right">10.85</td> +<td align="right">7.11</td> +</tr> +<tr class="odd"> +<td align="right">43.0019614</td> +<td align="right">6.58</td> +<td align="right">6.40</td> +<td align="right">10.41</td> +<td align="right">6.06</td> +</tr> +<tr class="even"> +<td align="right">43.0019614</td> +<td align="right">6.31</td> +<td align="right">6.35</td> +<td align="right">10.35</td> +<td align="right">6.05</td> +</tr> +<tr class="odd"> +<td align="right">53.7524518</td> +<td align="right">3.47</td> +<td align="right">5.35</td> +<td align="right">9.92</td> +<td align="right">5.50</td> +</tr> +<tr class="even"> +<td align="right">53.7524518</td> +<td align="right">3.52</td> +<td align="right">5.06</td> +<td align="right">9.42</td> +<td align="right">5.07</td> +</tr> +<tr class="odd"> +<td align="right">64.5029421</td> +<td align="right">3.40</td> +<td align="right">5.14</td> +<td align="right">9.15</td> +<td align="right">4.94</td> +</tr> +<tr class="even"> +<td align="right">64.5029421</td> +<td align="right">3.67</td> +<td align="right">5.91</td> +<td align="right">9.25</td> +<td align="right">4.39</td> +</tr> +<tr class="odd"> +<td align="right">91.3791680</td> +<td align="right">1.62</td> +<td align="right">3.35</td> +<td align="right">7.14</td> +<td align="right">3.64</td> +</tr> +<tr class="even"> +<td align="right">91.3791680</td> +<td align="right">1.62</td> +<td align="right">2.87</td> +<td align="right">7.13</td> +<td align="right">3.55</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset BBA 2.3</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +<th align="right">M23</th> +<th align="right">M27</th> +<th align="right">M31</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.0000000</td> +<td align="right">99.33</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">0.0000000</td> +<td align="right">97.44</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">0.6733938</td> +<td align="right">93.73</td> +<td align="right">0.18</td> +<td align="right">0.50</td> +<td align="right">0.47</td> +</tr> +<tr class="even"> +<td align="right">0.6733938</td> +<td align="right">93.77</td> +<td align="right">0.18</td> +<td align="right">0.83</td> +<td align="right">0.34</td> +</tr> +<tr class="odd"> +<td align="right">2.0201814</td> +<td align="right">87.84</td> +<td align="right">0.52</td> +<td align="right">1.25</td> +<td align="right">1.00</td> +</tr> +<tr class="even"> +<td align="right">2.0201814</td> +<td align="right">89.82</td> +<td align="right">0.43</td> +<td align="right">1.09</td> +<td align="right">0.89</td> +</tr> +<tr class="odd"> +<td align="right">4.7137565</td> +<td align="right">71.61</td> +<td align="right">1.19</td> +<td align="right">3.28</td> +<td align="right">3.58</td> +</tr> +<tr class="even"> +<td align="right">4.7137565</td> +<td align="right">71.42</td> +<td align="right">1.11</td> +<td align="right">3.24</td> +<td align="right">3.41</td> +</tr> +<tr class="odd"> +<td align="right">9.4275131</td> +<td align="right">45.60</td> +<td align="right">2.26</td> +<td align="right">7.17</td> +<td align="right">8.74</td> +</tr> +<tr class="even"> +<td align="right">9.4275131</td> +<td align="right">45.42</td> +<td align="right">1.99</td> +<td align="right">7.91</td> +<td align="right">8.28</td> +</tr> +<tr class="odd"> +<td align="right">14.1412696</td> +<td align="right">31.12</td> +<td align="right">2.81</td> +<td align="right">10.15</td> +<td align="right">9.67</td> +</tr> +<tr class="even"> +<td align="right">14.1412696</td> +<td align="right">31.68</td> +<td align="right">2.83</td> +<td align="right">9.55</td> +<td align="right">8.95</td> +</tr> +<tr class="odd"> +<td align="right">18.8550262</td> +<td align="right">23.20</td> +<td align="right">3.39</td> +<td align="right">12.09</td> +<td align="right">10.34</td> +</tr> +<tr class="even"> +<td align="right">18.8550262</td> +<td align="right">24.13</td> +<td align="right">3.56</td> +<td align="right">11.89</td> +<td align="right">10.00</td> +</tr> +<tr class="odd"> +<td align="right">28.2825393</td> +<td align="right">9.43</td> +<td align="right">3.49</td> +<td align="right">13.32</td> +<td align="right">7.89</td> +</tr> +<tr class="even"> +<td align="right">28.2825393</td> +<td align="right">9.82</td> +<td align="right">3.28</td> +<td align="right">12.05</td> +<td align="right">8.13</td> +</tr> +<tr class="odd"> +<td align="right">37.7100523</td> +<td align="right">7.08</td> +<td align="right">2.80</td> +<td align="right">10.04</td> +<td align="right">5.06</td> +</tr> +<tr class="even"> +<td align="right">37.7100523</td> +<td align="right">8.64</td> +<td align="right">2.97</td> +<td align="right">10.78</td> +<td align="right">5.54</td> +</tr> +<tr class="odd"> +<td align="right">47.1375654</td> +<td align="right">4.41</td> +<td align="right">2.42</td> +<td align="right">9.32</td> +<td align="right">3.79</td> +</tr> +<tr class="even"> +<td align="right">47.1375654</td> +<td align="right">4.78</td> +<td align="right">2.51</td> +<td align="right">9.62</td> +<td align="right">4.11</td> +</tr> +<tr class="odd"> +<td align="right">56.5650785</td> +<td align="right">4.92</td> +<td align="right">2.22</td> +<td align="right">8.00</td> +<td align="right">3.11</td> +</tr> +<tr class="even"> +<td align="right">56.5650785</td> +<td align="right">5.08</td> +<td align="right">1.95</td> +<td align="right">8.45</td> +<td align="right">2.98</td> +</tr> +<tr class="odd"> +<td align="right">80.1338612</td> +<td align="right">2.13</td> +<td align="right">1.28</td> +<td align="right">5.71</td> +<td align="right">1.78</td> +</tr> +<tr class="even"> +<td align="right">80.1338612</td> +<td align="right">2.23</td> +<td align="right">0.99</td> +<td align="right">3.33</td> +<td align="right">1.55</td> +</tr> +</tbody> +</table> +<table class="table"> +<caption>Dataset Elliot</caption> +<thead><tr class="header"> +<th align="right">time</th> +<th align="right">DMTA</th> +<th align="right">M23</th> +<th align="right">M27</th> +<th align="right">M31</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">97.5</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">0.000000</td> +<td align="right">100.7</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">1.228478</td> +<td align="right">86.4</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">1.228478</td> +<td align="right">88.5</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">1.5</td> +</tr> +<tr class="odd"> +<td align="right">3.685435</td> +<td align="right">69.8</td> +<td align="right">2.8</td> +<td align="right">2.3</td> +<td align="right">5.0</td> +</tr> +<tr class="even"> +<td align="right">3.685435</td> +<td align="right">77.1</td> +<td align="right">1.7</td> +<td align="right">2.1</td> +<td align="right">2.4</td> +</tr> +<tr class="odd"> +<td align="right">8.599349</td> +<td align="right">59.0</td> +<td align="right">4.3</td> +<td align="right">4.0</td> +<td align="right">4.3</td> +</tr> +<tr class="even"> +<td align="right">8.599349</td> +<td align="right">54.2</td> +<td align="right">5.8</td> +<td align="right">3.4</td> +<td align="right">5.0</td> +</tr> +<tr class="odd"> +<td align="right">17.198697</td> +<td align="right">31.3</td> +<td align="right">8.2</td> +<td align="right">6.6</td> +<td align="right">8.0</td> +</tr> +<tr class="even"> +<td align="right">17.198697</td> +<td align="right">33.5</td> +<td align="right">5.2</td> +<td align="right">6.9</td> +<td align="right">7.7</td> +</tr> +<tr class="odd"> +<td align="right">25.798046</td> +<td align="right">19.6</td> +<td align="right">5.1</td> +<td align="right">8.2</td> +<td align="right">7.8</td> +</tr> +<tr class="even"> +<td align="right">25.798046</td> +<td align="right">20.9</td> +<td align="right">6.1</td> +<td align="right">8.8</td> +<td align="right">6.5</td> +</tr> +<tr class="odd"> +<td align="right">34.397395</td> +<td align="right">13.3</td> +<td align="right">6.0</td> +<td align="right">9.7</td> +<td align="right">8.0</td> +</tr> +<tr class="even"> +<td align="right">34.397395</td> +<td align="right">15.8</td> +<td align="right">6.0</td> +<td align="right">8.8</td> +<td align="right">7.4</td> +</tr> +<tr class="odd"> +<td align="right">51.596092</td> +<td align="right">6.7</td> +<td align="right">5.0</td> +<td align="right">8.3</td> +<td align="right">6.9</td> +</tr> +<tr class="even"> +<td align="right">51.596092</td> +<td align="right">8.7</td> +<td align="right">4.2</td> +<td align="right">9.2</td> +<td align="right">9.0</td> +</tr> +<tr class="odd"> +<td align="right">68.794789</td> +<td align="right">8.8</td> +<td align="right">3.9</td> +<td align="right">9.3</td> +<td align="right">5.5</td> +</tr> +<tr class="even"> +<td align="right">68.794789</td> +<td align="right">8.7</td> +<td align="right">2.9</td> +<td align="right">8.5</td> +<td align="right">6.1</td> +</tr> +<tr class="odd"> +<td align="right">103.192184</td> +<td align="right">6.0</td> +<td align="right">1.9</td> +<td align="right">8.6</td> +<td align="right">6.1</td> +</tr> +<tr class="even"> +<td align="right">103.192184</td> +<td align="right">4.4</td> +<td align="right">1.5</td> +<td align="right">6.0</td> +<td align="right">4.0</td> +</tr> +<tr class="odd"> +<td align="right">146.188928</td> +<td align="right">3.3</td> +<td align="right">2.0</td> +<td align="right">5.6</td> +<td align="right">3.1</td> +</tr> +<tr class="even"> +<td align="right">146.188928</td> +<td align="right">2.8</td> +<td align="right">2.3</td> +<td align="right">4.5</td> +<td align="right">2.9</td> +</tr> +<tr class="odd"> +<td align="right">223.583066</td> +<td align="right">1.4</td> +<td align="right">1.2</td> +<td align="right">4.1</td> +<td align="right">1.8</td> +</tr> +<tr class="even"> +<td align="right">223.583066</td> +<td align="right">1.8</td> +<td align="right">1.9</td> +<td align="right">3.9</td> +<td align="right">2.6</td> +</tr> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">93.4</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="even"> +<td align="right">0.000000</td> +<td align="right">103.2</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">1.228478</td> +<td align="right">89.2</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">1.3</td> +</tr> +<tr class="even"> +<td align="right">1.228478</td> +<td align="right">86.6</td> +<td align="right">NA</td> +<td align="right">NA</td> +<td align="right">NA</td> +</tr> +<tr class="odd"> +<td align="right">3.685435</td> +<td align="right">78.2</td> +<td align="right">2.6</td> +<td align="right">1.0</td> +<td align="right">3.1</td> +</tr> +<tr class="even"> +<td align="right">3.685435</td> +<td align="right">78.1</td> +<td align="right">2.4</td> +<td align="right">2.6</td> +<td align="right">2.3</td> +</tr> +<tr class="odd"> +<td align="right">8.599349</td> +<td align="right">55.6</td> +<td align="right">5.5</td> +<td align="right">4.5</td> +<td align="right">3.4</td> +</tr> +<tr class="even"> +<td align="right">8.599349</td> +<td align="right">53.0</td> +<td align="right">5.6</td> +<td align="right">4.6</td> +<td align="right">4.3</td> +</tr> +<tr class="odd"> +<td align="right">17.198697</td> +<td align="right">33.7</td> +<td align="right">7.3</td> +<td align="right">7.6</td> +<td align="right">7.8</td> +</tr> +<tr class="even"> +<td align="right">17.198697</td> +<td align="right">33.2</td> +<td align="right">6.5</td> +<td align="right">6.7</td> +<td align="right">8.7</td> +</tr> +<tr class="odd"> +<td align="right">25.798046</td> +<td align="right">20.9</td> +<td align="right">5.8</td> +<td align="right">8.7</td> +<td align="right">7.7</td> +</tr> +<tr class="even"> +<td align="right">25.798046</td> +<td align="right">19.9</td> +<td align="right">7.7</td> +<td align="right">7.6</td> +<td align="right">6.5</td> +</tr> +<tr class="odd"> +<td align="right">34.397395</td> +<td align="right">18.2</td> +<td align="right">7.8</td> +<td align="right">8.0</td> +<td align="right">6.3</td> +</tr> +<tr class="even"> +<td align="right">34.397395</td> +<td align="right">12.7</td> +<td align="right">7.3</td> +<td align="right">8.6</td> +<td align="right">8.7</td> +</tr> +<tr class="odd"> +<td align="right">51.596092</td> +<td align="right">7.8</td> +<td align="right">7.0</td> +<td align="right">7.4</td> +<td align="right">5.7</td> +</tr> +<tr class="even"> +<td align="right">51.596092</td> +<td align="right">9.0</td> +<td align="right">6.3</td> +<td align="right">7.2</td> +<td align="right">4.2</td> +</tr> +<tr class="odd"> +<td align="right">68.794789</td> +<td align="right">11.4</td> +<td align="right">4.3</td> +<td align="right">10.3</td> +<td align="right">3.2</td> +</tr> +<tr class="even"> +<td align="right">68.794789</td> +<td align="right">9.0</td> +<td align="right">3.8</td> +<td align="right">9.4</td> +<td align="right">4.2</td> +</tr> +<tr class="odd"> +<td align="right">103.192184</td> +<td align="right">3.9</td> +<td align="right">2.6</td> +<td align="right">6.5</td> +<td align="right">3.8</td> +</tr> +<tr class="even"> +<td align="right">103.192184</td> +<td align="right">4.4</td> +<td align="right">2.8</td> +<td align="right">6.9</td> +<td align="right">4.0</td> +</tr> +<tr class="odd"> +<td align="right">146.188928</td> +<td align="right">2.6</td> +<td align="right">1.6</td> +<td align="right">4.6</td> +<td align="right">4.5</td> +</tr> +<tr class="even"> +<td align="right">146.188928</td> +<td align="right">3.4</td> +<td align="right">1.1</td> +<td align="right">4.5</td> +<td align="right">4.5</td> +</tr> +<tr class="odd"> +<td align="right">223.583066</td> +<td align="right">2.0</td> +<td align="right">1.4</td> +<td align="right">4.3</td> +<td align="right">3.8</td> +</tr> +<tr class="even"> +<td align="right">223.583066</td> +<td align="right">1.7</td> +<td align="right">1.3</td> +<td align="right">4.2</td> +<td align="right">2.3</td> +</tr> +</tbody> +</table> +</div> +<div class="section level2"> +<h2 id="separate-evaluations">Separate evaluations<a class="anchor" aria-label="anchor" href="#separate-evaluations"></a> +</h2> +<p>As a first step to obtain suitable starting parameters for the NLHM +fits, we do separate fits of several variants of the pathway model used +previously <span class="citation">(Ranke et al. 2021)</span>, varying +the kinetic model for the parent compound. Because the SFORB model often +provides faster convergence than the DFOP model, and can sometimes be +fitted where the DFOP model results in errors, it is included in the set +of parent models tested here.</p> +<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="kw">if</span> <span class="op">(</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/files2.html" class="external-link">dir.exists</a></span><span class="op">(</span><span class="st">"dmta_dlls"</span><span class="op">)</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/files2.html" class="external-link">dir.create</a></span><span class="op">(</span><span class="st">"dmta_dlls"</span><span class="op">)</span></span> +<span><span class="va">m_sfo_path_1</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> +<span> DMTA <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</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">"M23"</span>, <span class="st">"M27"</span>, <span class="st">"M31"</span><span class="op">)</span><span class="op">)</span>,</span> +<span> M23 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> M27 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> M31 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M27"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> name <span class="op">=</span> <span class="st">"m_sfo_path"</span>, dll_dir <span class="op">=</span> <span class="st">"dmta_dlls"</span>,</span> +<span> unload <span class="op">=</span> <span class="cn">TRUE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">TRUE</span></span> +<span><span class="op">)</span></span> +<span><span class="va">m_fomc_path_1</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> +<span> DMTA <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"FOMC"</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">"M23"</span>, <span class="st">"M27"</span>, <span class="st">"M31"</span><span class="op">)</span><span class="op">)</span>,</span> +<span> M23 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> M27 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> M31 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M27"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> name <span class="op">=</span> <span class="st">"m_fomc_path"</span>, dll_dir <span class="op">=</span> <span class="st">"dmta_dlls"</span>,</span> +<span> unload <span class="op">=</span> <span class="cn">TRUE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">TRUE</span></span> +<span><span class="op">)</span></span> +<span><span class="va">m_dfop_path_1</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> +<span> DMTA <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</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">"M23"</span>, <span class="st">"M27"</span>, <span class="st">"M31"</span><span class="op">)</span><span class="op">)</span>,</span> +<span> M23 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> M27 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> M31 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M27"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> name <span class="op">=</span> <span class="st">"m_dfop_path"</span>, dll_dir <span class="op">=</span> <span class="st">"dmta_dlls"</span>,</span> +<span> unload <span class="op">=</span> <span class="cn">TRUE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">TRUE</span></span> +<span><span class="op">)</span></span> +<span><span class="va">m_sforb_path_1</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> +<span> DMTA <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</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">"M23"</span>, <span class="st">"M27"</span>, <span class="st">"M31"</span><span class="op">)</span><span class="op">)</span>,</span> +<span> M23 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> M27 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> M31 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M27"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> name <span class="op">=</span> <span class="st">"m_sforb_path"</span>, dll_dir <span class="op">=</span> <span class="st">"dmta_dlls"</span>,</span> +<span> unload <span class="op">=</span> <span class="cn">TRUE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">TRUE</span></span> +<span><span class="op">)</span></span> +<span><span class="va">m_hs_path_1</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> +<span> DMTA <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"HS"</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">"M23"</span>, <span class="st">"M27"</span>, <span class="st">"M31"</span><span class="op">)</span><span class="op">)</span>,</span> +<span> M23 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> M27 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> +<span> M31 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"M27"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> +<span> name <span class="op">=</span> <span class="st">"m_hs_path"</span>, dll_dir <span class="op">=</span> <span class="st">"dmta_dlls"</span>,</span> +<span> unload <span class="op">=</span> <span class="cn">TRUE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">TRUE</span></span> +<span><span class="op">)</span></span> +<span><span class="va">cl</span> <span class="op"><-</span> <span class="fu">start_cluster</span><span class="op">(</span><span class="va">n_cores</span><span class="op">)</span></span> +<span></span> +<span><span class="va">deg_mods_1</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span> +<span> sfo_path_1 <span class="op">=</span> <span class="va">m_sfo_path_1</span>,</span> +<span> fomc_path_1 <span class="op">=</span> <span class="va">m_fomc_path_1</span>,</span> +<span> dfop_path_1 <span class="op">=</span> <span class="va">m_dfop_path_1</span>,</span> +<span> sforb_path_1 <span class="op">=</span> <span class="va">m_sforb_path_1</span>,</span> +<span> hs_path_1 <span class="op">=</span> <span class="va">m_hs_path_1</span><span class="op">)</span></span> +<span></span> +<span><span class="va">sep_1_const</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span></span> +<span> <span class="va">deg_mods_1</span>,</span> +<span> <span class="va">dmta_ds</span>,</span> +<span> error_model <span class="op">=</span> <span class="st">"const"</span>,</span> +<span> quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span></span> +<span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">sep_1_const</span><span class="op">)</span> <span class="op">|></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"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">Calke</th> +<th align="left">Borstel</th> +<th align="left">Flaach</th> +<th align="left">BBA 2.2</th> +<th align="left">BBA 2.3</th> +<th align="left">Elliot</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">sfo_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">fomc_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">dfop_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">sforb_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">hs_path_1</td> +<td align="left">C</td> +<td align="left">C</td> +<td align="left">C</td> +<td align="left">C</td> +<td align="left">C</td> +<td align="left">C</td> +</tr> +</tbody> +</table> +<p>All separate pathway fits with SFO or FOMC for the parent and +constant variance converged (status OK). Most fits with DFOP or SFORB +for the parent converged as well. The fits with HS for the parent did +not converge with default settings.</p> +<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">sep_1_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">sep_1_const</span>, error_model <span class="op">=</span> <span class="st">"tc"</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">sep_1_tc</span><span class="op">)</span> <span class="op">|></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"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">Calke</th> +<th align="left">Borstel</th> +<th align="left">Flaach</th> +<th align="left">BBA 2.2</th> +<th align="left">BBA 2.3</th> +<th align="left">Elliot</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">sfo_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">fomc_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +</tr> +<tr class="odd"> +<td align="left">dfop_path_1</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">sforb_path_1</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">hs_path_1</td> +<td align="left">C</td> +<td align="left">C</td> +<td align="left">C</td> +<td align="left">C</td> +<td align="left">C</td> +<td align="left">OK</td> +</tr> +</tbody> +</table> +<p>With the two-component error model, the set of fits with convergence +problems is slightly different, with convergence problems appearing for +different data sets when applying the DFOP and SFORB model and some +additional convergence problems when using the FOMC model for the +parent.</p> +</div> +<div class="section level2"> +<h2 id="hierarchichal-model-fits">Hierarchichal model fits<a class="anchor" aria-label="anchor" href="#hierarchichal-model-fits"></a> +</h2> +<p>The following code fits two sets of the corresponding hierarchical +models to the data, one assuming constant variance, and one assuming +two-component error.</p> +<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">saem_1</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mhmkin.html">mhmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">sep_1_const</span>, <span class="va">sep_1_tc</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> +<p>The run time for these fits was around two hours on five year old +hardware. After a recent hardware upgrade these fits complete in less +than twenty minutes.</p> +<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="../../reference/status.html">status</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">)</span> <span class="op">|></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"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">sfo_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">fomc_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">dfop_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">sforb_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">hs_path_1</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +</tbody> +</table> +<p>According to the <code>status</code> function, all fits terminated +successfully.</p> +<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">)</span> <span class="op">|></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">1</span><span class="op">)</span></span></code></pre></div> +<pre><code>Warning in FUN(X[[i]], ...): Could not obtain log likelihood with 'is' method +for sforb_path_1 const</code></pre> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="right">npar</th> +<th align="right">AIC</th> +<th align="right">BIC</th> +<th align="right">Lik</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">sfo_path_1 const</td> +<td align="right">17</td> +<td align="right">2291.8</td> +<td align="right">2288.3</td> +<td align="right">-1128.9</td> +</tr> +<tr class="even"> +<td align="left">sfo_path_1 tc</td> +<td align="right">18</td> +<td align="right">2276.3</td> +<td align="right">2272.5</td> +<td align="right">-1120.1</td> +</tr> +<tr class="odd"> +<td align="left">fomc_path_1 const</td> +<td align="right">19</td> +<td align="right">2099.0</td> +<td align="right">2095.0</td> +<td align="right">-1030.5</td> +</tr> +<tr class="even"> +<td align="left">fomc_path_1 tc</td> +<td align="right">20</td> +<td align="right">1939.6</td> +<td align="right">1935.5</td> +<td align="right">-949.8</td> +</tr> +<tr class="odd"> +<td align="left">dfop_path_1 const</td> +<td align="right">21</td> +<td align="right">2038.8</td> +<td align="right">2034.4</td> +<td align="right">-998.4</td> +</tr> +<tr class="even"> +<td align="left">hs_path_1 const</td> +<td align="right">21</td> +<td align="right">2024.2</td> +<td align="right">2019.8</td> +<td align="right">-991.1</td> +</tr> +<tr class="odd"> +<td align="left">dfop_path_1 tc</td> +<td align="right">22</td> +<td align="right">1879.8</td> +<td align="right">1875.2</td> +<td align="right">-917.9</td> +</tr> +<tr class="even"> +<td align="left">sforb_path_1 tc</td> +<td align="right">22</td> +<td align="right">1832.9</td> +<td align="right">1828.3</td> +<td align="right">-894.4</td> +</tr> +<tr class="odd"> +<td align="left">hs_path_1 tc</td> +<td align="right">22</td> +<td align="right">1831.4</td> +<td align="right">1826.8</td> +<td align="right">-893.7</td> +</tr> +</tbody> +</table> +<p>When the goodness-of-fit of the models is compared, a warning is +obtained, indicating that the likelihood of the pathway fit with SFORB +for the parent compound and constant variance could not be calculated +with importance sampling (method ‘is’). As this is the default method on +which all AIC and BIC comparisons are based, this variant is not +included in the model comparison table. Comparing the goodness-of-fit of +the remaining models, HS model model with two-component error provides +the best fit. However, for batch experiments performed with constant +conditions such as the experiments evaluated here, there is no reason to +assume a discontinuity, so the SFORB model is preferable from a +mechanistic viewpoint. In addition, the information criteria AIC and BIC +are very similar for HS and SFORB. Therefore, the SFORB model is +selected here for further refinements.</p> +<div class="section level3"> +<h3 id="parameter-identifiability-based-on-the-fisher-information-matrix">Parameter identifiability based on the Fisher Information +Matrix<a class="anchor" aria-label="anchor" href="#parameter-identifiability-based-on-the-fisher-information-matrix"></a> +</h3> +<p>Using the <code>illparms</code> function, ill-defined statistical +model parameters such as standard deviations of the degradation +parameters in the population and error model parameters can be +found.</p> +<div class="sourceCode" id="cb10"><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">saem_1</span><span class="op">)</span> <span class="op">|></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"> +<thead><tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">sfo_path_1</td> +<td align="left"></td> +<td align="left">sd(DMTA_0)</td> +</tr> +<tr class="even"> +<td align="left">fomc_path_1</td> +<td align="left"></td> +<td align="left">sd(DMTA_0)</td> +</tr> +<tr class="odd"> +<td align="left">dfop_path_1</td> +<td align="left"></td> +<td align="left"></td> +</tr> +<tr class="even"> +<td align="left">sforb_path_1</td> +<td align="left"></td> +<td align="left">sd(log_k_DMTA_bound_free)</td> +</tr> +<tr class="odd"> +<td align="left">hs_path_1</td> +<td align="left"></td> +<td align="left">sd(log_tb)</td> +</tr> +</tbody> +</table> +<p>When using constant variance, no ill-defined variance parameters are +identified with the <code>illparms</code> function in any of the +degradation models. When using the two-component error model, there is +one ill-defined variance parameter in all variants except for the +variant using DFOP for the parent compound.</p> +<p>For the selected combination of the SFORB pathway model with +two-component error, the random effect for the rate constant from +reversibly bound DMTA to the free DMTA (<code>k_DMTA_bound_free</code>) +is not well-defined. Therefore, the fit is updated without assuming a +random effect for this parameter.</p> +<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">saem_sforb_path_1_tc_reduced</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">[[</span><span class="st">"sforb_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>,</span> +<span> no_random_effect <span class="op">=</span> <span class="st">"log_k_DMTA_bound_free"</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">saem_sforb_path_1_tc_reduced</span><span class="op">)</span></span></code></pre></div> +<p>As expected, no ill-defined parameters remain. The model comparison +below shows that the reduced model is preferable.</p> +<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">[[</span><span class="st">"sforb_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span>, <span class="va">saem_sforb_path_1_tc_reduced</span><span class="op">)</span> <span class="op">|></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">1</span><span class="op">)</span></span></code></pre></div> +<table class="table"> +<thead><tr class="header"> +<th align="left"></th> +<th align="right">npar</th> +<th align="right">AIC</th> +<th align="right">BIC</th> +<th align="right">Lik</th> +</tr></thead> +<tbody> +<tr class="odd"> +<td align="left">saem_sforb_path_1_tc_reduced</td> +<td align="right">21</td> +<td align="right">1830.3</td> +<td align="right">1825.9</td> +<td align="right">-894.2</td> +</tr> +<tr class="even"> +<td align="left">saem_1[[“sforb_path_1”, “tc”]]</td> +<td align="right">22</td> +<td align="right">1832.9</td> +<td align="right">1828.3</td> +<td align="right">-894.4</td> +</tr> +</tbody> +</table> +<p>The convergence plot of the refined fit is shown below.</p> +<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">saem_sforb_path_1_tc_reduced</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></span></code></pre></div> +<p><img src="2022_dmta_pathway_files/figure-html/saem-sforb-path-1-tc-reduced-convergence-1.png" width="700" style="display: block; margin: auto;"></p> +<p>For some parameters, for example for <code>f_DMTA_ilr_1</code> and +<code>f_DMTA_ilr_2</code>, i.e. for two of the parameters determining +the formation fractions of the parallel formation of the three +metabolites, some movement of the parameters is still visible in the +second phase of the algorithm. However, the amplitude of this movement +is in the range of the amplitude towards the end of the first phase. +Therefore, it is likely that an increase in iterations would not improve +the parameter estimates very much, and it is proposed that the fit is +acceptable. No numeric convergence criterion is implemented in +saemix.</p> +</div> +<div class="section level3"> +<h3 id="alternative-check-of-parameter-identifiability">Alternative check of parameter identifiability<a class="anchor" aria-label="anchor" href="#alternative-check-of-parameter-identifiability"></a> +</h3> +<p>As an alternative check of parameter identifiability <span class="citation">(Duchesne et al. 2021)</span>, multistart runs were +performed on the basis of the refined fit shown above.</p> +<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="va">saem_sforb_path_1_tc_reduced_multi</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/multistart.html">multistart</a></span><span class="op">(</span><span class="va">saem_sforb_path_1_tc_reduced</span>,</span> +<span> n <span class="op">=</span> <span class="fl">32</span>, cores <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span></code></pre></div> +<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><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">saem_sforb_path_1_tc_reduced_multi</span><span class="op">)</span></span></code></pre></div> +<pre><code><multistart> object with 32 fits: + E OK +15 17 +OK: Fit terminated successfully +E: Error</code></pre> +<p>Out of the 32 fits that were initiated, only 17 terminated without an +error. The reason for this is that the wide variation of starting +parameters in combination with the parameter variation that is used in +the SAEM algorithm leads to parameter combinations for the degradation +model that the numerical integration routine cannot cope with. Because +of this variation of initial parameters, some of the model fits take up +to two times more time than the original fit.</p> +<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">12.1</span>, <span class="fl">4.1</span>, <span class="fl">2.1</span>, <span class="fl">2.1</span><span class="op">)</span><span class="op">)</span></span> +<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">saem_sforb_path_1_tc_reduced_multi</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">2</span><span class="op">)</span>, las <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_pathway_files/figure-html/unnamed-chunk-2-1.png" alt="Parameter boxplots for the multistart runs that succeeded" width="960"><p class="caption"> +Parameter boxplots for the multistart runs that succeeded +</p> +</div> +<p>However, visual analysis of the boxplot of the parameters obtained in +the successful fits confirms that the results are sufficiently +independent of the starting parameters, and there are no remaining +ill-defined parameters.</p> +</div> +</div> +<div class="section level2"> +<h2 id="plots-of-selected-fits">Plots of selected fits<a class="anchor" aria-label="anchor" href="#plots-of-selected-fits"></a> +</h2> +<p>The SFORB pathway fits with full and reduced parameter distribution +model are shown below.</p> +<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">[[</span><span class="st">"sforb_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_pathway_files/figure-html/unnamed-chunk-3-1.png" alt="SFORB pathway fit with two-component error" width="700"><p class="caption"> +SFORB pathway fit with two-component error +</p> +</div> +<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">saem_sforb_path_1_tc_reduced</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_pathway_files/figure-html/unnamed-chunk-4-1.png" alt="SFORB pathway fit with two-component error, reduced parameter model" width="700"><p class="caption"> +SFORB pathway fit with two-component error, reduced parameter model +</p> +</div> +<p>Plots of the remaining fits and listings for all successful fits are +shown in the Appendix.</p> +<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">stopCluster</a></span><span class="op">(</span><span class="va">cl</span><span class="op">)</span></span></code></pre></div> +</div> +<div class="section level2"> +<h2 id="conclusions">Conclusions<a class="anchor" aria-label="anchor" href="#conclusions"></a> +</h2> +<p>Pathway fits with SFO, FOMC, DFOP, SFORB and HS models for the parent +compound could be successfully performed.</p> +</div> +<div class="section level2"> +<h2 id="acknowledgements">Acknowledgements<a class="anchor" aria-label="anchor" href="#acknowledgements"></a> +</h2> +<p>The helpful comments by Janina Wöltjen of the German Environment +Agency on earlier versions of this document are gratefully +acknowledged.</p> +</div> +<div class="section level2"> +<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a> +</h2> +<div id="refs" class="references csl-bib-body hanging-indent"> +<div id="ref-duchesne_2021" class="csl-entry"> +Duchesne, Ronan, Anissa Guillemin, Olivier Gandrillon, and Fabien +Crauste. 2021. <span>“Practical Identifiability in the Frame of +Nonlinear Mixed Effects Models: The Example of the in Vitro +Erythropoiesis.”</span> <em>BMC Bioinformatics</em> 22 (478). <a href="https://doi.org/10.1186/s12859-021-04373-4" class="external-link">https://doi.org/10.1186/s12859-021-04373-4</a>. +</div> +<div id="ref-ranke2021" class="csl-entry"> +Ranke, Johannes, Janina Wöltjen, Jana Schmidt, and Emmanuelle Comets. +2021. <span>“Taking Kinetic Evaluations of Degradation Data to the Next +Level with Nonlinear Mixed-Effects Models.”</span> <em>Environments</em> +8 (8). <a href="https://doi.org/10.3390/environments8080071" class="external-link">https://doi.org/10.3390/environments8080071</a>. +</div> +</div> +</div> +<div class="section level2"> +<h2 id="appendix">Appendix<a class="anchor" aria-label="anchor" href="#appendix"></a> +</h2> +<div class="section level3"> +<h3 id="plots-of-hierarchical-fits-not-selected-for-refinement">Plots of hierarchical fits not selected for refinement<a class="anchor" aria-label="anchor" href="#plots-of-hierarchical-fits-not-selected-for-refinement"></a> +</h3> +<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">[[</span><span class="st">"sfo_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_pathway_files/figure-html/unnamed-chunk-6-1.png" alt="SFO pathway fit with two-component error" width="700"><p class="caption"> +SFO pathway fit with two-component error +</p> +</div> +<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">[[</span><span class="st">"fomc_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_pathway_files/figure-html/unnamed-chunk-7-1.png" alt="FOMC pathway fit with two-component error" width="700"><p class="caption"> +FOMC pathway fit with two-component error +</p> +</div> +<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r"> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">saem_1</span><span class="op">[[</span><span class="st">"sforb_path_1"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> +<div class="figure" style="text-align: center"> +<img src="2022_dmta_pathway_files/figure-html/unnamed-chunk-8-1.png" alt="HS pathway fit with two-component error" width="700"><p class="caption"> +HS pathway fit with two-component error +</p> +</div> +</div> +<div class="section level3"> +<h3 id="hierarchical-model-fit-listings">Hierarchical model fit listings<a class="anchor" aria-label="anchor" href="#hierarchical-model-fit-listings"></a> +</h3> +<div class="section level4"> +<h4 id="fits-with-random-effects-for-all-degradation-parameters">Fits with random effects for all degradation parameters<a class="anchor" aria-label="anchor" href="#fits-with-random-effects-for-all-degradation-parameters"></a> +</h4> + +</div> +<div class="section level4"> +<h4 id="improved-fit-of-the-sforb-pathway-model-with-two-component-error">Improved fit of the SFORB pathway model with two-component +error<a class="anchor" aria-label="anchor" href="#improved-fit-of-the-sforb-pathway-model-with-two-component-error"></a> +</h4> + +</div> +</div> +<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 4.2.3 (2023-03-15) +Platform: x86_64-pc-linux-gnu (64-bit) +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 + +locale: + [1] LC_CTYPE=de_DE.UTF-8 LC_NUMERIC=C + [3] LC_TIME=de_DE.UTF-8 LC_COLLATE=de_DE.UTF-8 + [5] LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=de_DE.UTF-8 + [7] LC_PAPER=de_DE.UTF-8 LC_NAME=C + [9] LC_ADDRESS=C LC_TELEPHONE=C +[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C + +attached base packages: +[1] parallel stats graphics grDevices utils datasets methods +[8] base + +other attached packages: +[1] saemix_3.2 npde_3.3 knitr_1.42 mkin_1.2.3 + +loaded via a namespace (and not attached): + [1] deSolve_1.35 zoo_1.8-12 tidyselect_1.2.0 xfun_0.38 + [5] bslib_0.4.2 purrr_1.0.1 lattice_0.21-8 colorspace_2.1-0 + [9] vctrs_0.6.1 generics_0.1.3 htmltools_0.5.5 yaml_2.3.7 +[13] utf8_1.2.3 rlang_1.1.0 pkgbuild_1.4.0 pkgdown_2.0.7 +[17] jquerylib_0.1.4 pillar_1.9.0 glue_1.6.2 DBI_1.1.3 +[21] lifecycle_1.0.3 stringr_1.5.0 munsell_0.5.0 gtable_0.3.3 +[25] ragg_1.2.5 codetools_0.2-19 memoise_2.0.1 evaluate_0.20 +[29] inline_0.3.19 callr_3.7.3 fastmap_1.1.1 ps_1.7.4 +[33] lmtest_0.9-40 fansi_1.0.4 highr_0.10 scales_1.2.1 +[37] cachem_1.0.7 desc_1.4.2 jsonlite_1.8.4 systemfonts_1.0.4 +[41] fs_1.6.1 textshaping_0.3.6 gridExtra_2.3 ggplot2_3.4.2 +[45] digest_0.6.31 stringi_1.7.12 processx_3.8.0 dplyr_1.1.1 +[49] grid_4.2.3 rprojroot_2.0.3 cli_3.6.1 tools_4.2.3 +[53] magrittr_2.0.3 sass_0.4.5 tibble_3.2.1 crayon_1.5.2 +[57] pkgconfig_2.0.3 prettyunits_1.1.1 rmarkdown_2.21 R6_2.5.1 +[61] mclust_6.0.0 nlme_3.1-162 compiler_4.2.3 </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: 64936316 kB</code></pre> +</div> +</div> + </div> + + <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> + + <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2> + </nav> +</div> + +</div> + + + + <footer><div class="copyright"> + <p></p> +<p>Developed by Johannes Ranke.</p> +</div> + +<div class="pkgdown"> + <p></p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> +</div> + + </footer> +</div> + + + + + + + </body> +</html> diff --git a/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/saem-sforb-path-1-tc-reduced-convergence-1.png b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/saem-sforb-path-1-tc-reduced-convergence-1.png Binary files differnew file mode 100644 index 00000000..206c424d --- /dev/null +++ b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/saem-sforb-path-1-tc-reduced-convergence-1.png diff --git a/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-2-1.png b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-2-1.png Binary files differnew file mode 100644 index 00000000..0fe084d3 --- /dev/null +++ b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-2-1.png diff --git a/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-3-1.png b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-3-1.png Binary files differnew file mode 100644 index 00000000..1c81601e --- /dev/null +++ b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-3-1.png diff --git a/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-4-1.png b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-4-1.png Binary files differnew file mode 100644 index 00000000..e0961dce --- /dev/null +++ b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-4-1.png diff --git a/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-6-1.png b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-6-1.png Binary files differnew file mode 100644 index 00000000..00db0c76 --- /dev/null +++ b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-6-1.png diff --git a/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-7-1.png b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-7-1.png Binary files differnew file mode 100644 index 00000000..ac5271ec --- /dev/null +++ b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-7-1.png diff --git a/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-8-1.png b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-8-1.png Binary files differnew file mode 100644 index 00000000..1c81601e --- /dev/null +++ b/docs/articles/prebuilt/2022_dmta_pathway_files/figure-html/unnamed-chunk-8-1.png diff --git a/docs/articles/twa.html b/docs/articles/twa.html index 41340e88..c8c91bcb 100644 --- a/docs/articles/twa.html +++ b/docs/articles/twa.html @@ -33,14 +33,14 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"> <li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -52,6 +52,9 @@ <li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -59,22 +62,31 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li class="divider"> </li> +<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + </li> +<li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -82,6 +94,15 @@ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul> </li> <li> @@ -105,13 +126,16 @@ - </header><script src="twa_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> - <h1 data-toc-skip>Calculation of time weighted average concentrations with mkin</h1> - <h4 data-toc-skip class="author">Johannes Ranke</h4> + <h1 data-toc-skip>Calculation of time weighted average +concentrations with mkin</h1> + <h4 data-toc-skip class="author">Johannes +Ranke</h4> - <h4 data-toc-skip class="date">Last change 18 September 2019 (rebuilt 2022-11-17)</h4> + <h4 data-toc-skip class="date">Last change 18 September 2019 +(rebuilt 2023-04-20)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/twa.rmd" class="external-link"><code>vignettes/twa.rmd</code></a></small> <div class="hidden name"><code>twa.rmd</code></div> @@ -120,13 +144,25 @@ -<p>Since version 0.9.45.1 of the ‘mkin’ package, a function for calculating time weighted average concentrations for decline kinetics (<em>i.e.</em> only for the compound applied in the experiment) is included. Strictly speaking, they are maximum moving window time weighted average concentrations, <em>i.e.</em> the maximum time weighted average concentration that can be found when moving a time window of a specified width over the decline curve.</p> -<p>Time weighted average concentrations for the SFO, FOMC and the DFOP model are calculated using the formulas given in the FOCUS kinetics guidance <span class="citation">(FOCUS Work Group on Degradation Kinetics 2014, 251)</span>:</p> +<p>Since version 0.9.45.1 of the ‘mkin’ package, a function for +calculating time weighted average concentrations for decline kinetics +(<em>i.e.</em> only for the compound applied in the experiment) is +included. Strictly speaking, they are maximum moving window time +weighted average concentrations, <em>i.e.</em> the maximum time weighted +average concentration that can be found when moving a time window of a +specified width over the decline curve.</p> +<p>Time weighted average concentrations for the SFO, FOMC and the DFOP +model are calculated using the formulas given in the FOCUS kinetics +guidance <span class="citation">(FOCUS Work Group on Degradation +Kinetics 2014, 251)</span>:</p> <p>SFO:</p> -<p><span class="math display">\[c_\textrm{twa} = c_0 \frac{\left( 1 - e^{- k t} \right)}{ k t} \]</span></p> +<p><span class="math display">\[c_\textrm{twa} = c_0 \frac{\left( 1 - +e^{- k t} \right)}{ k t} \]</span></p> <p>FOMC:</p> -<p><span class="math display">\[c_\textrm{twa} = c_0 \frac{\beta}{t (1 - \alpha)} - \left( \left(\frac{t}{\beta} + 1 \right)^{1 - \alpha} - 1 \right) \]</span></p> +<p><span class="math display">\[c_\textrm{twa} = c_0 \frac{\beta}{t (1 - +\alpha)} + \left( \left(\frac{t}{\beta} + 1 \right)^{1 - \alpha} +- 1 \right) \]</span></p> <p>DFOP:</p> <p><span class="math display">\[c_\textrm{twa} = \frac{c_0}{t} \left( \frac{g}{k_1} \left( 1 - e^{- k_1 t} \right) + @@ -134,15 +170,25 @@ <p>HS for <span class="math inline">\(t > t_b\)</span>:</p> <p><span class="math display">\[c_\textrm{twa} = \frac{c_0}{t} \left( \frac{1}{k_1} \left( 1 - e^{- k_1 t_b} \right) + - \frac{e^{- k_1 t_b}}{k_2} \left( 1 - e^{- k_2 (t - t_b)} \right) \right) \]</span></p> -<p>Often, the ratio between the time weighted average concentration <span class="math inline">\(c_\textrm{twa}\)</span> and the initial concentration <span class="math inline">\(c_0\)</span></p> -<p><span class="math display">\[f_\textrm{twa} = \frac{c_\textrm{twa}}{c_0}\]</span></p> -<p>is needed. This can be calculated from the fitted initial concentration <span class="math inline">\(c_0\)</span> and the time weighted average concentration <span class="math inline">\(c_\textrm{twa}\)</span>, or directly from the model parameters using the following formulas:</p> + \frac{e^{- k_1 t_b}}{k_2} \left( 1 - e^{- k_2 (t - t_b)} +\right) \right) \]</span></p> +<p>Often, the ratio between the time weighted average concentration +<span class="math inline">\(c_\textrm{twa}\)</span> and the initial +concentration <span class="math inline">\(c_0\)</span></p> +<p><span class="math display">\[f_\textrm{twa} = +\frac{c_\textrm{twa}}{c_0}\]</span></p> +<p>is needed. This can be calculated from the fitted initial +concentration <span class="math inline">\(c_0\)</span> and the time +weighted average concentration <span class="math inline">\(c_\textrm{twa}\)</span>, or directly from the +model parameters using the following formulas:</p> <p>SFO:</p> -<p><span class="math display">\[f_\textrm{twa} = \frac{\left( 1 - e^{- k t} \right)}{k t} \]</span></p> +<p><span class="math display">\[f_\textrm{twa} = \frac{\left( 1 - e^{- k +t} \right)}{k t} \]</span></p> <p>FOMC:</p> -<p><span class="math display">\[f_\textrm{twa} = \frac{\beta}{t (1 - \alpha)} - \left( \left(\frac{t}{\beta} + 1 \right)^{1 - \alpha} - 1 \right) \]</span></p> +<p><span class="math display">\[f_\textrm{twa} = \frac{\beta}{t (1 - +\alpha)} + \left( \left(\frac{t}{\beta} + 1 \right)^{1 - \alpha} +- 1 \right) \]</span></p> <p>DFOP:</p> <p><span class="math display">\[f_\textrm{twa} = \frac{1}{t} \left( \frac{g}{k_1} \left( 1 - e^{- k_1 t} \right) + @@ -150,11 +196,19 @@ <p>HS for <span class="math inline">\(t > t_b\)</span>:</p> <p><span class="math display">\[f_\textrm{twa} = \frac{1}{t} \left( \frac{1}{k_1} \left( 1 - e^{- k_1 t_b} \right) + - \frac{e^{- k_1 t_b}}{k_2} \left( 1 - e^{- k_2 (t - t_b)} \right) \right) \]</span></p> -<p>Note that a method for calculating maximum moving window time weighted average concentrations for a model fitted by ‘mkinfit’ or from parent decline model parameters is included in the <code><a href="../reference/max_twa_parent.html">max_twa_parent()</a></code> function. If the same is needed for metabolites, the function <code><a href="https://pkgdown.jrwb.de/pfm/reference/max_twa.html" class="external-link">pfm::max_twa()</a></code> from the ‘pfm’ package can be used.</p> -<div id="refs" class="references hanging-indent"> -<div id="ref-FOCUSkinetics2014"> -<p>FOCUS Work Group on Degradation Kinetics. 2014. <em>Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> + \frac{e^{- k_1 t_b}}{k_2} \left( 1 - e^{- k_2 (t - t_b)} +\right) \right) \]</span></p> +<p>Note that a method for calculating maximum moving window time +weighted average concentrations for a model fitted by ‘mkinfit’ or from +parent decline model parameters is included in the +<code><a href="../reference/max_twa_parent.html">max_twa_parent()</a></code> function. If the same is needed for +metabolites, the function <code><a href="https://pkgdown.jrwb.de/pfm/reference/max_twa.html" class="external-link">pfm::max_twa()</a></code> from the ‘pfm’ +package can be used.</p> +<div id="refs" class="references csl-bib-body hanging-indent"> +<div id="ref-FOCUSkinetics2014" class="csl-entry"> +FOCUS Work Group on Degradation Kinetics. 2014. <em>Generic Guidance for +Estimating Persistence and Degradation Kinetics from Environmental Fate +Studies on Pesticides in EU Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>. </div> </div> </div> @@ -174,7 +228,7 @@ <div class="pkgdown"> <p></p> -<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer> diff --git a/docs/articles/web_only/FOCUS_Z.html b/docs/articles/web_only/FOCUS_Z.html index ea20ecd9..9602adb5 100644 --- a/docs/articles/web_only/FOCUS_Z.html +++ b/docs/articles/web_only/FOCUS_Z.html @@ -33,14 +33,14 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"> <li> - <a href="../../reference/index.html">Functions and data</a> + <a href="../../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -52,6 +52,9 @@ <li> <a href="../../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -59,22 +62,31 @@ <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li class="divider"> </li> +<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + </li> +<li class="dropdown-header">Performance</li> + <li> + <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -82,6 +94,15 @@ <li> <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul> </li> <li> @@ -105,13 +126,15 @@ - </header><script src="FOCUS_Z_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> <h1 data-toc-skip>Example evaluation of FOCUS dataset Z</h1> - <h4 data-toc-skip class="author">Johannes Ranke</h4> + <h4 data-toc-skip class="author">Johannes +Ranke</h4> - <h4 data-toc-skip class="date">Last change 16 January 2018 (rebuilt 2022-11-17)</h4> + <h4 data-toc-skip class="date">Last change 16 January 2018 +(rebuilt 2023-04-20)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/FOCUS_Z.rmd" class="external-link"><code>vignettes/web_only/FOCUS_Z.rmd</code></a></small> <div class="hidden name"><code>FOCUS_Z.rmd</code></div> @@ -120,11 +143,15 @@ -<p><a href="http://www.jrwb.de" class="external-link">Wissenschaftlicher Berater, Kronacher Str. 12, 79639 Grenzach-Wyhlen, Germany</a><br><a href="http://chem.uft.uni-bremen.de/ranke" class="external-link">Privatdozent at the University of Bremen</a></p> +<p><a href="http://www.jrwb.de" class="external-link">Wissenschaftlicher Berater, Kronacher +Str. 12, 79639 Grenzach-Wyhlen, Germany</a><br><a href="http://chem.uft.uni-bremen.de/ranke" class="external-link">Privatdozent at the +University of Bremen</a></p> <div class="section level2"> <h2 id="the-data">The data<a class="anchor" aria-label="anchor" href="#the-data"></a> </h2> -<p>The following code defines the example dataset from Appendix 7 to the FOCUS kinetics report <span class="citation">(FOCUS Work Group on Degradation Kinetics 2014, 354)</span>.</p> +<p>The following code defines the example dataset from Appendix 7 to the +FOCUS kinetics report <span class="citation">(FOCUS Work Group on +Degradation Kinetics 2014, 354)</span>.</p> <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> <span><span class="va">LOD</span> <span class="op">=</span> <span class="fl">0.5</span></span> @@ -145,7 +172,11 @@ <div class="section level2"> <h2 id="parent-and-one-metabolite">Parent and one metabolite<a class="anchor" aria-label="anchor" href="#parent-and-one-metabolite"></a> </h2> -<p>The next step is to set up the models used for the kinetic analysis. As the simultaneous fit of parent and the first metabolite is usually straightforward, Step 1 (SFO for parent only) is skipped here. We start with the model 2a, with formation and decline of metabolite Z1 and the pathway from parent directly to sink included (default in mkin).</p> +<p>The next step is to set up the models used for the kinetic analysis. +As the simultaneous fit of parent and the first metabolite is usually +straightforward, Step 1 (SFO for parent only) is skipped here. We start +with the model 2a, with formation and decline of metabolite Z1 and the +pathway from parent directly to sink included (default in mkin).</p> <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">Z.2a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span><span class="op">)</span>,</span> <span> Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> @@ -158,15 +189,21 @@ <code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.2a</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png" width="700"></p> <div class="sourceCode" id="cb7"><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">m.Z.2a</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.Z.2a</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></code></pre></div> <pre><code><span><span class="co">## Estimate se_notrans t value Pr(>t) Lower Upper</span></span> <span><span class="co">## Z0_0 97.01488 3.301084 29.3888 3.2971e-21 91.66556 102.3642</span></span> <span><span class="co">## k_Z0 2.23601 0.207078 10.7979 3.3309e-11 1.95303 2.5600</span></span> <span><span class="co">## k_Z1 0.48212 0.063265 7.6207 2.8154e-08 0.40341 0.5762</span></span> <span><span class="co">## f_Z0_to_Z1 1.00000 0.094764 10.5525 5.3560e-11 0.00000 1.0000</span></span> <span><span class="co">## sigma 4.80411 0.635638 7.5579 3.2592e-08 3.52677 6.0815</span></span></code></pre> -<p>As obvious from the parameter summary (the component of the summary), the kinetic rate constant from parent compound Z to sink is very small and the t-test for this parameter suggests that it is not significantly different from zero. This suggests, in agreement with the analysis in the FOCUS kinetics report, to simplify the model by removing the pathway to sink.</p> -<p>A similar result can be obtained when formation fractions are used in the model formulation:</p> +<p>As obvious from the parameter summary (the component of the summary), +the kinetic rate constant from parent compound Z to sink is very small +and the t-test for this parameter suggests that it is not significantly +different from zero. This suggests, in agreement with the analysis in +the FOCUS kinetics report, to simplify the model by removing the pathway +to sink.</p> +<p>A similar result can be obtained when formation fractions are used in +the model formulation:</p> <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">Z.2a.ff</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span><span class="op">)</span>,</span> <span> Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>,</span> @@ -180,16 +217,24 @@ <code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.2a.ff</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_2-1.png" width="700"></p> <div class="sourceCode" id="cb14"><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">m.Z.2a.ff</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.Z.2a.ff</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></code></pre></div> <pre><code><span><span class="co">## Estimate se_notrans t value Pr(>t) Lower Upper</span></span> <span><span class="co">## Z0_0 97.01488 3.301084 29.3888 3.2971e-21 91.66556 102.3642</span></span> <span><span class="co">## k_Z0 2.23601 0.207078 10.7979 3.3309e-11 1.95303 2.5600</span></span> <span><span class="co">## k_Z1 0.48212 0.063265 7.6207 2.8154e-08 0.40341 0.5762</span></span> <span><span class="co">## f_Z0_to_Z1 1.00000 0.094764 10.5525 5.3560e-11 0.00000 1.0000</span></span> <span><span class="co">## sigma 4.80411 0.635638 7.5579 3.2592e-08 3.52677 6.0815</span></span></code></pre> -<p>Here, the ilr transformed formation fraction fitted in the model takes a very large value, and the backtransformed formation fraction from parent Z to Z1 is practically unity. Here, the covariance matrix used for the calculation of confidence intervals is not returned as the model is overparameterised.</p> -<p>A simplified model is obtained by removing the pathway to the sink. </p> -<p>In the following, we use the parameterisation with formation fractions in order to be able to compare with the results in the FOCUS guidance, and as it makes it easier to use parameters obtained in a previous fit when adding a further metabolite.</p> +<p>Here, the ilr transformed formation fraction fitted in the model +takes a very large value, and the backtransformed formation fraction +from parent Z to Z1 is practically unity. Here, the covariance matrix +used for the calculation of confidence intervals is not returned as the +model is overparameterised.</p> +<p>A simplified model is obtained by removing the pathway to the sink. +</p> +<p>In the following, we use the parameterisation with formation +fractions in order to be able to compare with the results in the FOCUS +guidance, and as it makes it easier to use parameters obtained in a +previous fit when adding a further metabolite.</p> <div class="sourceCode" id="cb16"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">Z.3</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> <span> Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></code></pre></div> @@ -202,18 +247,24 @@ <code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.3</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_3-1.png" width="700"></p> <div class="sourceCode" id="cb21"><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">m.Z.3</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.Z.3</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></code></pre></div> <pre><code><span><span class="co">## Estimate se_notrans t value Pr(>t) Lower Upper</span></span> <span><span class="co">## Z0_0 97.01488 2.597342 37.352 2.0106e-24 91.67597 102.3538</span></span> <span><span class="co">## k_Z0 2.23601 0.146904 15.221 9.1477e-15 1.95354 2.5593</span></span> <span><span class="co">## k_Z1 0.48212 0.041727 11.554 4.8268e-12 0.40355 0.5760</span></span> <span><span class="co">## sigma 4.80411 0.620208 7.746 1.6110e-08 3.52925 6.0790</span></span></code></pre> -<p>As there is only one transformation product for Z0 and no pathway to sink, the formation fraction is internally fixed to unity.</p> +<p>As there is only one transformation product for Z0 and no pathway to +sink, the formation fraction is internally fixed to unity.</p> </div> <div class="section level2"> <h2 id="metabolites-z2-and-z3">Metabolites Z2 and Z3<a class="anchor" aria-label="anchor" href="#metabolites-z2-and-z3"></a> </h2> -<p>As suggested in the FOCUS report, the pathway to sink was removed for metabolite Z1 as well in the next step. While this step appears questionable on the basis of the above results, it is followed here for the purpose of comparison. Also, in the FOCUS report, it is assumed that there is additional empirical evidence that Z1 quickly and exclusively hydrolyses to Z2.</p> +<p>As suggested in the FOCUS report, the pathway to sink was removed for +metabolite Z1 as well in the next step. While this step appears +questionable on the basis of the above results, it is followed here for +the purpose of comparison. Also, in the FOCUS report, it is assumed that +there is additional empirical evidence that Z1 quickly and exclusively +hydrolyses to Z2.</p> <div class="sourceCode" id="cb23"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">Z.5</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> <span> Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> @@ -226,7 +277,9 @@ <div class="sourceCode" id="cb27"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.5</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_5-1.png" width="700"></p> -<p>Finally, metabolite Z3 is added to the model. We use the optimised differential equation parameter values from the previous fit in order to accelerate the optimization.</p> +<p>Finally, metabolite Z3 is added to the model. We use the optimised +differential equation parameter values from the previous fit in order to +accelerate the optimization.</p> <div class="sourceCode" id="cb28"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">Z.FOCUS</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> <span> Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> @@ -246,7 +299,7 @@ <code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.FOCUS</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png" width="700"></p> <div class="sourceCode" id="cb34"><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">m.Z.FOCUS</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.Z.FOCUS</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">bpar</span></span></code></pre></div> <pre><code><span><span class="co">## Estimate se_notrans t value Pr(>t) Lower Upper</span></span> <span><span class="co">## Z0_0 96.838822 1.994274 48.5584 4.0280e-42 92.826981 100.850664</span></span> <span><span class="co">## k_Z0 2.215393 0.118458 18.7019 1.0413e-23 1.989456 2.466989</span></span> @@ -267,13 +320,22 @@ <span><span class="co">## Z1 1.44917 4.8141</span></span> <span><span class="co">## Z2 1.53478 5.0984</span></span> <span><span class="co">## Z3 11.80986 39.2315</span></span></code></pre> -<p>This fit corresponds to the final result chosen in Appendix 7 of the FOCUS report. Confidence intervals returned by mkin are based on internally transformed parameters, however.</p> +<p>This fit corresponds to the final result chosen in Appendix 7 of the +FOCUS report. Confidence intervals returned by mkin are based on +internally transformed parameters, however.</p> </div> <div class="section level2"> <h2 id="using-the-sforb-model">Using the SFORB model<a class="anchor" aria-label="anchor" href="#using-the-sforb-model"></a> </h2> -<p>As the FOCUS report states, there is a certain tailing of the time course of metabolite Z3. Also, the time course of the parent compound is not fitted very well using the SFO model, as residues at a certain low level remain.</p> -<p>Therefore, an additional model is offered here, using the single first-order reversible binding (SFORB) model for metabolite Z3. As expected, the <span class="math inline">\(\chi^2\)</span> error level is lower for metabolite Z3 using this model and the graphical fit for Z3 is improved. However, the covariance matrix is not returned.</p> +<p>As the FOCUS report states, there is a certain tailing of the time +course of metabolite Z3. Also, the time course of the parent compound is +not fitted very well using the SFO model, as residues at a certain low +level remain.</p> +<p>Therefore, an additional model is offered here, using the single +first-order reversible binding (SFORB) model for metabolite Z3. As +expected, the <span class="math inline">\(\chi^2\)</span> error level is +lower for metabolite Z3 using this model and the graphical fit for Z3 is +improved. However, the covariance matrix is not returned.</p> <div class="sourceCode" id="cb38"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">Z.mkin.1</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> <span> Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> @@ -282,15 +344,18 @@ <pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre> <div class="sourceCode" id="cb40"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">m.Z.mkin.1</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.mkin.1</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## Warning in mkinfit(Z.mkin.1, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with</span></span> -<span><span class="co">## value of zero were removed from the data</span></span></code></pre> +<pre><code><span><span class="co">## Warning in mkinfit(Z.mkin.1, FOCUS_2006_Z_mkin, quiet = TRUE): Observations</span></span> +<span><span class="co">## with value of zero were removed from the data</span></span></code></pre> <div class="sourceCode" id="cb42"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.1</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_7-1.png" width="700"></p> <div class="sourceCode" id="cb43"><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">m.Z.mkin.1</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">cov.unscaled</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">m.Z.mkin.1</span>, data <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">$</span><span class="va">cov.unscaled</span></span></code></pre></div> <pre><code><span><span class="co">## NULL</span></span></code></pre> -<p>Therefore, a further stepwise model building is performed starting from the stage of parent and two metabolites, starting from the assumption that the model fit for the parent compound can be improved by using the SFORB model.</p> +<p>Therefore, a further stepwise model building is performed starting +from the stage of parent and two metabolites, starting from the +assumption that the model fit for the parent compound can be improved by +using the SFORB model.</p> <div class="sourceCode" id="cb45"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">Z.mkin.3</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> <span> Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> @@ -298,13 +363,16 @@ <pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre> <div class="sourceCode" id="cb47"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">m.Z.mkin.3</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.mkin.3</span>, <span class="va">FOCUS_2006_Z_mkin</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> -<pre><code><span><span class="co">## Warning in mkinfit(Z.mkin.3, FOCUS_2006_Z_mkin, quiet = TRUE): Observations with</span></span> -<span><span class="co">## value of zero were removed from the data</span></span></code></pre> +<pre><code><span><span class="co">## Warning in mkinfit(Z.mkin.3, FOCUS_2006_Z_mkin, quiet = TRUE): Observations</span></span> +<span><span class="co">## with value of zero were removed from the data</span></span></code></pre> <div class="sourceCode" id="cb49"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.3</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png" width="700"></p> -<p>This results in a much better representation of the behaviour of the parent compound Z0.</p> -<p>Finally, Z3 is added as well. These models appear overparameterised (no covariance matrix returned) if the sink for Z1 is left in the models.</p> +<p>This results in a much better representation of the behaviour of the +parent compound Z0.</p> +<p>Finally, Z3 is added as well. These models appear overparameterised +(no covariance matrix returned) if the sink for Z1 is left in the +models.</p> <div class="sourceCode" id="cb50"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">Z.mkin.4</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> <span> Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> @@ -321,7 +389,10 @@ <div class="sourceCode" id="cb54"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.4</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png" width="700"></p> -<p>The error level of the fit, but especially of metabolite Z3, can be improved if the SFORB model is chosen for this metabolite, as this model is capable of representing the tailing of the metabolite decline phase.</p> +<p>The error level of the fit, but especially of metabolite Z3, can be +improved if the SFORB model is chosen for this metabolite, as this model +is capable of representing the tailing of the metabolite decline +phase.</p> <div class="sourceCode" id="cb55"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">Z.mkin.5</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span>Z0 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFORB"</span>, <span class="st">"Z1"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> <span> Z1 <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"Z2"</span>, sink <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span> @@ -338,7 +409,9 @@ <div class="sourceCode" id="cb59"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.5</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png" width="700"></p> -<p>The summary view of the backtransformed parameters shows that we get no confidence intervals due to overparameterisation. As the optimized is excessively small, it seems reasonable to fix it to zero.</p> +<p>The summary view of the backtransformed parameters shows that we get +no confidence intervals due to overparameterisation. As the optimized is +excessively small, it seems reasonable to fix it to zero.</p> <div class="sourceCode" id="cb60"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">m.Z.mkin.5a</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">Z.mkin.5</span>, <span class="va">FOCUS_2006_Z_mkin</span>,</span> <span> parms.ini <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">m.Z.mkin.5</span><span class="op">$</span><span class="va">bparms.ode</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span><span class="op">]</span>,</span> @@ -351,8 +424,12 @@ <div class="sourceCode" id="cb62"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">m.Z.mkin.5a</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png" width="700"></p> -<p>As expected, the residual plots for Z0 and Z3 are more random than in the case of the all SFO model for which they were shown above. In conclusion, the model is proposed as the best-fit model for the dataset from Appendix 7 of the FOCUS report.</p> -<p>A graphical representation of the confidence intervals can finally be obtained.</p> +<p>As expected, the residual plots for Z0 and Z3 are more random than in +the case of the all SFO model for which they were shown above. In +conclusion, the model is proposed as the best-fit model for the dataset +from Appendix 7 of the FOCUS report.</p> +<p>A graphical representation of the confidence intervals can finally be +obtained.</p> <div class="sourceCode" id="cb63"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="../../reference/mkinparplot.html">mkinparplot</a></span><span class="op">(</span><span class="va">m.Z.mkin.5a</span><span class="op">)</span></span></code></pre></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png" width="700"></p> @@ -373,15 +450,22 @@ <span><span class="co">## Z1 1.5148 5.0320 NA NA NA NA NA</span></span> <span><span class="co">## Z2 1.6414 5.4526 NA NA NA NA NA</span></span> <span><span class="co">## Z3 NA NA NA NA NA 8.6636 Inf</span></span></code></pre> -<p>It is clear the degradation rate of Z3 towards the end of the experiment is very low as DT50_Z3_b2 (the second Eigenvalue of the system of two differential equations representing the SFORB system for Z3, corresponding to the slower rate constant of the DFOP model) is reported to be infinity. However, this appears to be a feature of the data.</p> +<p>It is clear the degradation rate of Z3 towards the end of the +experiment is very low as DT50_Z3_b2 (the second Eigenvalue of the +system of two differential equations representing the SFORB system for +Z3, corresponding to the slower rate constant of the DFOP model) is +reported to be infinity. However, this appears to be a feature of the +data.</p> </div> <div class="section level2"> <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a> </h2> <!-- vim: set foldmethod=syntax: --> -<div id="refs" class="references hanging-indent"> -<div id="ref-FOCUSkinetics2014"> -<p>FOCUS Work Group on Degradation Kinetics. 2014. <em>Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> +<div id="refs" class="references csl-bib-body hanging-indent"> +<div id="ref-FOCUSkinetics2014" class="csl-entry"> +FOCUS Work Group on Degradation Kinetics. 2014. <em>Generic Guidance for +Estimating Persistence and Degradation Kinetics from Environmental Fate +Studies on Pesticides in EU Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="external-link">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>. </div> </div> </div> @@ -404,7 +488,7 @@ <div class="pkgdown"> <p></p> -<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer> diff --git a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png Binary files differindex be652d31..98bc135b 100644 --- a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png +++ b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_1-1.png diff --git a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png Binary files differindex bc6efaf7..33269a34 100644 --- a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png +++ b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png diff --git a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png Binary files differindex 55c1b645..6e1877f4 100644 --- a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png +++ b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png diff --git a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png Binary files differindex 8e63cd04..113c1b0b 100644 --- a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png +++ b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png diff --git a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png Binary files differindex 3902e059..6b0dbc34 100644 --- a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png +++ b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png diff --git a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_2-1.png b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_2-1.png Binary files differindex be652d31..98bc135b 100644 --- a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_2-1.png +++ b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_2-1.png diff --git a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_3-1.png b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_3-1.png Binary files differindex 59524035..0380ba43 100644 --- a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_3-1.png +++ b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_3-1.png diff --git a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_5-1.png b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_5-1.png Binary files differindex d95cac25..d080a57a 100644 --- a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_5-1.png +++ b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_5-1.png diff --git a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png Binary files differindex cb333a1c..3119be2d 100644 --- a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png +++ b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png diff --git a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_7-1.png b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_7-1.png Binary files differindex d87105fb..87af8874 100644 --- a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_7-1.png +++ b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_7-1.png diff --git a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png Binary files differindex db807f14..1938b499 100644 --- a/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png +++ b/docs/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png diff --git a/docs/articles/web_only/NAFTA_examples.html b/docs/articles/web_only/NAFTA_examples.html index b8ec5059..49d1db33 100644 --- a/docs/articles/web_only/NAFTA_examples.html +++ b/docs/articles/web_only/NAFTA_examples.html @@ -33,14 +33,14 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"> <li> - <a href="../../reference/index.html">Functions and data</a> + <a href="../../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -52,6 +52,9 @@ <li> <a href="../../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -59,22 +62,31 @@ <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li class="divider"> </li> +<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + </li> +<li class="dropdown-header">Performance</li> + <li> + <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -82,6 +94,15 @@ <li> <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul> </li> <li> @@ -105,13 +126,16 @@ - </header><script src="NAFTA_examples_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> - <h1 data-toc-skip>Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance</h1> - <h4 data-toc-skip class="author">Johannes Ranke</h4> + <h1 data-toc-skip>Evaluation of example datasets from Attachment 1 +to the US EPA SOP for the NAFTA guidance</h1> + <h4 data-toc-skip class="author">Johannes +Ranke</h4> - <h4 data-toc-skip class="date">26 February 2019 (rebuilt 2022-11-17)</h4> + <h4 data-toc-skip class="date">26 February 2019 (rebuilt +2023-04-20)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/NAFTA_examples.rmd" class="external-link"><code>vignettes/web_only/NAFTA_examples.rmd</code></a></small> <div class="hidden name"><code>NAFTA_examples.rmd</code></div> @@ -123,13 +147,22 @@ <div class="section level2"> <h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a> </h2> -<p>In this document, the example evaluations provided in Attachment 1 to the SOP of US EPA for using the NAFTA guidance <span class="citation">(US EPA 2015)</span> are repeated using mkin. The original evaluations reported in the attachment were performed using PestDF in version 0.8.4. Note that PestDF 0.8.13 is the version distributed at the US EPA website today (2019-02-26).</p> +<p>In this document, the example evaluations provided in Attachment 1 to +the SOP of US EPA for using the NAFTA guidance <span class="citation">(US EPA 2015)</span> are repeated using mkin. The +original evaluations reported in the attachment were performed using +PestDF in version 0.8.4. Note that PestDF 0.8.13 is the version +distributed at the US EPA website today (2019-02-26).</p> <p>The datasets are now distributed with the mkin package.</p> </div> <div class="section level2"> <h2 id="examples-where-dfop-did-not-converge-with-pestdf-0-8-4">Examples where DFOP did not converge with PestDF 0.8.4<a class="anchor" aria-label="anchor" href="#examples-where-dfop-did-not-converge-with-pestdf-0-8-4"></a> </h2> -<p>In attachment 1, it is reported that the DFOP model does not converge for these datasets when PestDF 0.8.4 was used. For all four datasets, the DFOP model can be fitted with mkin (see below). The negative half-life given by PestDF 0.8.4 for these fits appears to be the result of a bug. The results for the other two models (SFO and IORE) are the same.</p> +<p>In attachment 1, it is reported that the DFOP model does not converge +for these datasets when PestDF 0.8.4 was used. For all four datasets, +the DFOP model can be fitted with mkin (see below). The negative +half-life given by PestDF 0.8.4 for these fits appears to be the result +of a bug. The results for the other two models (SFO and IORE) are the +same.</p> <div class="section level3"> <h3 id="example-on-page-5-upper-panel">Example on page 5, upper panel<a class="anchor" aria-label="anchor" href="#example-on-page-5-upper-panel"></a> </h3> @@ -138,7 +171,7 @@ <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p5a</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p5a</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p5a-1.png" width="700"></p> <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r"> <code class="sourceCode R"><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">p5a</span><span class="op">)</span></span></code></pre></div> @@ -189,7 +222,7 @@ <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb10"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p5b</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p5b</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p5b-1.png" width="700"></p> <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r"> <code class="sourceCode R"><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">p5b</span><span class="op">)</span></span></code></pre></div> @@ -240,7 +273,7 @@ <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb16"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p6</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p6</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p6-1.png" width="700"></p> <div class="sourceCode" id="cb17"><pre class="downlit sourceCode r"> <code class="sourceCode R"><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">p6</span><span class="op">)</span></span></code></pre></div> @@ -291,7 +324,7 @@ <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb22"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p7</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p7</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p7-1.png" width="700"></p> <div class="sourceCode" id="cb23"><pre class="downlit sourceCode r"> <code class="sourceCode R"><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">p7</span><span class="op">)</span></span></code></pre></div> @@ -336,18 +369,21 @@ </div> </div> <div class="section level2"> -<h2 id="examples-where-the-representative-half-life-deviates-from-the-observed-dt50">Examples where the representative half-life deviates from the observed DT50<a class="anchor" aria-label="anchor" href="#examples-where-the-representative-half-life-deviates-from-the-observed-dt50"></a> +<h2 id="examples-where-the-representative-half-life-deviates-from-the-observed-dt50">Examples where the representative half-life deviates from the +observed DT50<a class="anchor" aria-label="anchor" href="#examples-where-the-representative-half-life-deviates-from-the-observed-dt50"></a> </h2> <div class="section level3"> <h3 id="example-on-page-8">Example on page 8<a class="anchor" aria-label="anchor" href="#example-on-page-8"></a> </h3> -<p>For this dataset, the IORE fit does not converge when the default starting values used by mkin for the IORE model are used. Therefore, a lower value for the rate constant is used here.</p> +<p>For this dataset, the IORE fit does not converge when the default +starting values used by mkin for the IORE model are used. Therefore, a +lower value for the rate constant is used here.</p> <div class="sourceCode" id="cb25"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">p8</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p8"</span><span class="op">]</span><span class="op">]</span>, parms.ini <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k__iore_parent <span class="op">=</span> <span class="fl">1e-3</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb28"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p8</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p8</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p8-1.png" width="700"></p> <div class="sourceCode" id="cb29"><pre class="downlit sourceCode r"> <code class="sourceCode R"><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">p8</span><span class="op">)</span></span></code></pre></div> @@ -402,7 +438,7 @@ <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb34"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p9a</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p9a</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p9a-1.png" width="700"></p> <div class="sourceCode" id="cb35"><pre class="downlit sourceCode r"> <code class="sourceCode R"><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">p9a</span><span class="op">)</span></span></code></pre></div> @@ -444,7 +480,9 @@ <span><span class="co">## </span></span> <span><span class="co">## Representative half-life:</span></span> <span><span class="co">## [1] 101.43</span></span></code></pre> -<p>In this example, the residuals of the SFO indicate a lack of fit of this model, so even if it was an abiotic experiment, the data do not suggest a simple exponential decline.</p> +<p>In this example, the residuals of the SFO indicate a lack of fit of +this model, so even if it was an abiotic experiment, the data do not +suggest a simple exponential decline.</p> </div> <div class="section level3"> <h3 id="example-on-page-9-lower-panel">Example on page 9, lower panel<a class="anchor" aria-label="anchor" href="#example-on-page-9-lower-panel"></a> @@ -454,12 +492,12 @@ <pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre> <pre><code><span><span class="co">## Warning in sqrt(diag(covar_notrans)): NaNs produced</span></span></code></pre> <pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre> -<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is</span></span> -<span><span class="co">## doubtful</span></span></code></pre> +<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result</span></span> +<span><span class="co">## is doubtful</span></span></code></pre> <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb44"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p9b</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p9b</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p9b-1.png" width="700"></p> <div class="sourceCode" id="cb45"><pre class="downlit sourceCode r"> <code class="sourceCode R"><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">p9b</span><span class="op">)</span></span></code></pre></div> @@ -501,7 +539,12 @@ <span><span class="co">## </span></span> <span><span class="co">## Representative half-life:</span></span> <span><span class="co">## [1] 14.8</span></span></code></pre> -<p>Here, mkin gives a longer slow DT50 for the DFOP model (17.8 days) than PestDF (13.5 days). Presumably, this is related to the fact that PestDF gives a negative value for the proportion of the fast degradation which should be between 0 and 1, inclusive. This parameter is called f in PestDF and g in mkin. In mkin, it is restricted to the interval from 0 to 1.</p> +<p>Here, mkin gives a longer slow DT50 for the DFOP model (17.8 days) +than PestDF (13.5 days). Presumably, this is related to the fact that +PestDF gives a negative value for the proportion of the fast degradation +which should be between 0 and 1, inclusive. This parameter is called f +in PestDF and g in mkin. In mkin, it is restricted to the interval from +0 to 1.</p> </div> <div class="section level3"> <h3 id="example-on-page-10">Example on page 10<a class="anchor" aria-label="anchor" href="#example-on-page-10"></a> @@ -510,12 +553,12 @@ <code class="sourceCode R"><span><span class="va">p10</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p10"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre> <pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre> -<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is</span></span> -<span><span class="co">## doubtful</span></span></code></pre> +<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result</span></span> +<span><span class="co">## is doubtful</span></span></code></pre> <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb53"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p10</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p10</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p10-1.png" width="700"></p> <div class="sourceCode" id="cb54"><pre class="downlit sourceCode r"> <code class="sourceCode R"><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">p10</span><span class="op">)</span></span></code></pre></div> @@ -557,7 +600,11 @@ <span><span class="co">## </span></span> <span><span class="co">## Representative half-life:</span></span> <span><span class="co">## [1] 8.86</span></span></code></pre> -<p>Here, a value below N is given for the IORE model, because the data suggests a faster decline towards the end of the experiment, which appears physically rather unlikely in the case of a photolysis study. It seems PestDF does not constrain N to values above zero, thus the slight difference in IORE model parameters between PestDF and mkin.</p> +<p>Here, a value below N is given for the IORE model, because the data +suggests a faster decline towards the end of the experiment, which +appears physically rather unlikely in the case of a photolysis study. It +seems PestDF does not constrain N to values above zero, thus the slight +difference in IORE model parameters between PestDF and mkin.</p> </div> </div> <div class="section level2"> @@ -571,7 +618,7 @@ <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb59"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p11</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p11</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p11-1.png" width="700"></p> <div class="sourceCode" id="cb60"><pre class="downlit sourceCode r"> <code class="sourceCode R"><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">p11</span><span class="op">)</span></span></code></pre></div> @@ -613,13 +660,19 @@ <span><span class="co">## </span></span> <span><span class="co">## Representative half-life:</span></span> <span><span class="co">## [1] 41148170</span></span></code></pre> -<p>In this case, the DFOP fit reported for PestDF resulted in a negative value for the slower rate constant, which is not possible in mkin. The other results are in agreement.</p> +<p>In this case, the DFOP fit reported for PestDF resulted in a negative +value for the slower rate constant, which is not possible in mkin. The +other results are in agreement.</p> </div> </div> <div class="section level2"> -<h2 id="n-is-less-than-1-and-the-dfop-rate-constants-are-like-the-sfo-rate-constant">N is less than 1 and the DFOP rate constants are like the SFO rate constant<a class="anchor" aria-label="anchor" href="#n-is-less-than-1-and-the-dfop-rate-constants-are-like-the-sfo-rate-constant"></a> +<h2 id="n-is-less-than-1-and-the-dfop-rate-constants-are-like-the-sfo-rate-constant">N is less than 1 and the DFOP rate constants are like the SFO rate +constant<a class="anchor" aria-label="anchor" href="#n-is-less-than-1-and-the-dfop-rate-constants-are-like-the-sfo-rate-constant"></a> </h2> -<p>In the following three examples, the same results are obtained with mkin as reported for PestDF. As in the case on page 10, the N values below 1 are deemed unrealistic and appear to be the result of an overparameterisation.</p> +<p>In the following three examples, the same results are obtained with +mkin as reported for PestDF. As in the case on page 10, the N values +below 1 are deemed unrealistic and appear to be the result of an +overparameterisation.</p> <div class="section level3"> <h3 id="example-on-page-12-upper-panel">Example on page 12, upper panel<a class="anchor" aria-label="anchor" href="#example-on-page-12-upper-panel"></a> </h3> @@ -630,12 +683,12 @@ <pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre> <pre><code><span><span class="co">## Warning in sqrt(diag(covar_notrans)): NaNs produced</span></span></code></pre> <pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre> -<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is</span></span> -<span><span class="co">## doubtful</span></span></code></pre> +<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result</span></span> +<span><span class="co">## is doubtful</span></span></code></pre> <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb70"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p12a</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p12a</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p12a-1.png" width="700"></p> <div class="sourceCode" id="cb71"><pre class="downlit sourceCode r"> <code class="sourceCode R"><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">p12a</span><span class="op">)</span></span></code></pre></div> @@ -690,7 +743,7 @@ <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb80"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p12b</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p12b</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p12b-1.png" width="700"></p> <div class="sourceCode" id="cb81"><pre class="downlit sourceCode r"> <code class="sourceCode R"><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">p12b</span><span class="op">)</span></span></code></pre></div> @@ -741,7 +794,7 @@ <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb86"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p13</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p13</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p13-1.png" width="700"></p> <div class="sourceCode" id="cb87"><pre class="downlit sourceCode r"> <code class="sourceCode R"><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">p13</span><span class="op">)</span></span></code></pre></div> @@ -792,12 +845,12 @@ <code class="sourceCode R"><span><span class="va">p14</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p14"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre> <pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre> -<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is</span></span> -<span><span class="co">## doubtful</span></span></code></pre> +<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result</span></span> +<span><span class="co">## is doubtful</span></span></code></pre> <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb95"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p14</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p14</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p14-1.png" width="700"></p> <div class="sourceCode" id="cb96"><pre class="downlit sourceCode r"> <code class="sourceCode R"><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">p14</span><span class="op">)</span></span></code></pre></div> @@ -839,7 +892,9 @@ <span><span class="co">## </span></span> <span><span class="co">## Representative half-life:</span></span> <span><span class="co">## [1] 6697.44</span></span></code></pre> -<p>The slower rate constant reported by PestDF is negative, which is not physically realistic, and not possible in mkin. The other fits give the same results in mkin and PestDF.</p> +<p>The slower rate constant reported by PestDF is negative, which is not +physically realistic, and not possible in mkin. The other fits give the +same results in mkin and PestDF.</p> </div> <div class="section level2"> <h2 id="n-is-less-than-1-and-dfop-fraction-parameter-is-below-zero">N is less than 1 and DFOP fraction parameter is below zero<a class="anchor" aria-label="anchor" href="#n-is-less-than-1-and-dfop-fraction-parameter-is-below-zero"></a> @@ -849,7 +904,7 @@ <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb101"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p15a</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p15a</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p15a-1.png" width="700"></p> <div class="sourceCode" id="cb102"><pre class="downlit sourceCode r"> <code class="sourceCode R"><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">p15a</span><span class="op">)</span></span></code></pre></div> @@ -895,12 +950,12 @@ <code class="sourceCode R"><span><span class="va">p15b</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span><span class="op">(</span><span class="va">NAFTA_SOP_Attachment</span><span class="op">[[</span><span class="st">"p15b"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## Warning in sqrt(diag(covar)): NaNs produced</span></span></code></pre> <pre><code><span><span class="co">## Warning in sqrt(1/diag(V)): NaNs produced</span></span></code></pre> -<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is</span></span> -<span><span class="co">## doubtful</span></span></code></pre> +<pre><code><span><span class="co">## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result</span></span> +<span><span class="co">## is doubtful</span></span></code></pre> <pre><code><span><span class="co">## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></span></code></pre> <pre><code><span><span class="co">## The half-life obtained from the IORE model may be used</span></span></code></pre> <div class="sourceCode" id="cb110"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p15b</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p15b</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p15b-1.png" width="700"></p> <div class="sourceCode" id="cb111"><pre class="downlit sourceCode r"> <code class="sourceCode R"><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">p15b</span><span class="op">)</span></span></code></pre></div> @@ -942,7 +997,10 @@ <span><span class="co">## </span></span> <span><span class="co">## Representative half-life:</span></span> <span><span class="co">## [1] 71.18</span></span></code></pre> -<p>In mkin, only the IORE fit is affected (deemed unrealistic), as the fraction parameter of the DFOP model is restricted to the interval between 0 and 1 in mkin. The SFO fits give the same results for both mkin and PestDF.</p> +<p>In mkin, only the IORE fit is affected (deemed unrealistic), as the +fraction parameter of the DFOP model is restricted to the interval +between 0 and 1 in mkin. The SFO fits give the same results for both +mkin and PestDF.</p> </div> <div class="section level2"> <h2 id="the-dfop-fraction-parameter-is-greater-than-1">The DFOP fraction parameter is greater than 1<a class="anchor" aria-label="anchor" href="#the-dfop-fraction-parameter-is-greater-than-1"></a> @@ -954,7 +1012,7 @@ <pre><code><span><span class="co">## to the terminal degradation rate found with the DFOP model.</span></span></code></pre> <pre><code><span><span class="co">## The representative half-life obtained from the DFOP model may be used</span></span></code></pre> <div class="sourceCode" id="cb118"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p16</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">p16</span><span class="op">)</span></span></code></pre></div> <p><img src="NAFTA_examples_files/figure-html/p16-1.png" width="700"></p> <div class="sourceCode" id="cb119"><pre class="downlit sourceCode r"> <code class="sourceCode R"><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">p16</span><span class="op">)</span></span></code></pre></div> @@ -996,20 +1054,32 @@ <span><span class="co">## </span></span> <span><span class="co">## Representative half-life:</span></span> <span><span class="co">## [1] 8.93</span></span></code></pre> -<p>In PestDF, the DFOP fit seems to have stuck in a local minimum, as mkin finds a solution with a much lower <span class="math inline">\(\chi^2\)</span> error level. As the half-life from the slower rate constant of the DFOP model is larger than the IORE derived half-life, the NAFTA recommendation obtained with mkin is to use the DFOP representative half-life of 8.9 days.</p> +<p>In PestDF, the DFOP fit seems to have stuck in a local minimum, as +mkin finds a solution with a much lower <span class="math inline">\(\chi^2\)</span> error level. As the half-life from +the slower rate constant of the DFOP model is larger than the IORE +derived half-life, the NAFTA recommendation obtained with mkin is to use +the DFOP representative half-life of 8.9 days.</p> </div> <div class="section level2"> <h2 id="conclusions">Conclusions<a class="anchor" aria-label="anchor" href="#conclusions"></a> </h2> -<p>The results obtained with mkin deviate from the results obtained with PestDF either in cases where one of the interpretive rules would apply, i.e. the IORE parameter N is less than one or the DFOP k values obtained with PestDF are equal to the SFO k values, or in cases where the DFOP model did not converge, which often lead to negative rate constants returned by PestDF.</p> -<p>Therefore, mkin appears to suitable for kinetic evaluations according to the NAFTA guidance.</p> +<p>The results obtained with mkin deviate from the results obtained with +PestDF either in cases where one of the interpretive rules would apply, +i.e. the IORE parameter N is less than one or the DFOP k values obtained +with PestDF are equal to the SFO k values, or in cases where the DFOP +model did not converge, which often lead to negative rate constants +returned by PestDF.</p> +<p>Therefore, mkin appears to suitable for kinetic evaluations according +to the NAFTA guidance.</p> </div> <div class="section level2"> <h2 class="unnumbered" id="references">References<a class="anchor" aria-label="anchor" href="#references"></a> </h2> -<div id="refs" class="references hanging-indent"> -<div id="ref-usepa2015"> -<p>US EPA. 2015. “Standard Operating Procedure for Using the NAFTA Guidance to Calculate Representative Half-Life Values and Characterizing Pesticide Degradation.”</p> +<div id="refs" class="references csl-bib-body hanging-indent"> +<div id="ref-usepa2015" class="csl-entry"> +US EPA. 2015. <span>“Standard Operating Procedure for Using the NAFTA +Guidance to Calculate Representative Half-Life Values and Characterizing +Pesticide Degradation.”</span> <a href="https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance" class="external-link">https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance</a>. </div> </div> </div> @@ -1032,7 +1102,7 @@ <div class="pkgdown"> <p></p> -<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer> diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p10-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p10-1.png Binary files differindex 75611a70..566625ea 100644 --- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p10-1.png +++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p10-1.png diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p11-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p11-1.png Binary files differindex 55466e47..71fc4699 100644 --- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p11-1.png +++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p11-1.png diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p12a-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p12a-1.png Binary files differindex d3143afa..a1d3a084 100644 --- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p12a-1.png +++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p12a-1.png diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p12b-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p12b-1.png Binary files differindex 3387ca69..1a6fdd03 100644 --- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p12b-1.png +++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p12b-1.png diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p13-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p13-1.png Binary files differindex 62a135f2..f9b9f637 100644 --- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p13-1.png +++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p13-1.png diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p14-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p14-1.png Binary files differindex ae4d83a4..9f7b0cc5 100644 --- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p14-1.png +++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p14-1.png diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p15a-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p15a-1.png Binary files differindex b6faeff9..b5fd7d91 100644 --- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p15a-1.png +++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p15a-1.png diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p15b-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p15b-1.png Binary files differindex 6b9ba98c..dfbc996f 100644 --- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p15b-1.png +++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p15b-1.png diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p16-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p16-1.png Binary files differindex 72df855b..75ac7e5b 100644 --- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p16-1.png +++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p16-1.png diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p5a-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p5a-1.png Binary files differindex 391dfb95..12a62954 100644 --- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p5a-1.png +++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p5a-1.png diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p5b-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p5b-1.png Binary files differindex db90244b..6fd175cb 100644 --- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p5b-1.png +++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p5b-1.png diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p6-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p6-1.png Binary files differindex a33372e8..856c6778 100644 --- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p6-1.png +++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p6-1.png diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p7-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p7-1.png Binary files differindex d64ea98d..b078fb88 100644 --- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p7-1.png +++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p7-1.png diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p8-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p8-1.png Binary files differindex 5cd6c806..a1e3bf25 100644 --- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p8-1.png +++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p8-1.png diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p9a-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p9a-1.png Binary files differindex 61359ea6..c247fd4e 100644 --- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p9a-1.png +++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p9a-1.png diff --git a/docs/articles/web_only/NAFTA_examples_files/figure-html/p9b-1.png b/docs/articles/web_only/NAFTA_examples_files/figure-html/p9b-1.png Binary files differindex 85790b1e..99d593fc 100644 --- a/docs/articles/web_only/NAFTA_examples_files/figure-html/p9b-1.png +++ b/docs/articles/web_only/NAFTA_examples_files/figure-html/p9b-1.png diff --git a/docs/articles/web_only/benchmarks.html b/docs/articles/web_only/benchmarks.html index 64c68ea0..3e73bd12 100644 --- a/docs/articles/web_only/benchmarks.html +++ b/docs/articles/web_only/benchmarks.html @@ -33,14 +33,14 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"> <li> - <a href="../../reference/index.html">Functions and data</a> + <a href="../../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -52,6 +52,9 @@ <li> <a href="../../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -59,22 +62,31 @@ <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li class="divider"> </li> +<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + </li> +<li class="dropdown-header">Performance</li> + <li> + <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -82,6 +94,15 @@ <li> <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul> </li> <li> @@ -105,13 +126,15 @@ - </header><script src="benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> <h1 data-toc-skip>Benchmark timings for mkin</h1> - <h4 data-toc-skip class="author">Johannes Ranke</h4> + <h4 data-toc-skip class="author">Johannes +Ranke</h4> - <h4 data-toc-skip class="date">Last change 14 July 2022 (rebuilt 2022-11-17)</h4> + <h4 data-toc-skip class="date">Last change 17 February 2023 +(rebuilt 2023-04-20)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/benchmarks.rmd" class="external-link"><code>vignettes/web_only/benchmarks.rmd</code></a></small> <div class="hidden name"><code>benchmarks.rmd</code></div> @@ -120,9 +143,15 @@ -<p>Each system is characterized by the operating system type, the CPU type, the mkin version, and, as in June 2022 the current R version lead to worse performance, the R version. A compiler was available, so if no analytical solution was available, compiled ODE models are used.</p> -<p>Every fit is only performed once, so the accuracy of the benchmarks is limited.</p> -<p>The following wrapper function for <code>mmkin</code> is used because the way the error model is specified was changed in mkin version 0.9.49.1.</p> +<p>Each system is characterized by the operating system type, the CPU +type, the mkin version, and, as in June 2022 the current R version lead +to worse performance, the R version. A compiler was available, so if no +analytical solution was available, compiled ODE models are used.</p> +<p>Every fit is only performed once, so the accuracy of the benchmarks +is limited.</p> +<p>The following wrapper function for <code>mmkin</code> is used because +the way the error model is specified was changed in mkin version +0.9.49.1.</p> <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/utils/packageDescription.html" class="external-link">packageVersion</a></span><span class="op">(</span><span class="st">"mkin"</span><span class="op">)</span> <span class="op">></span> <span class="st">"0.9.48.1"</span><span class="op">)</span> <span class="op">{</span></span> <span> <span class="va">mmkin_bench</span> <span class="op"><-</span> <span class="kw">function</span><span class="op">(</span><span class="va">models</span>, <span class="va">datasets</span>, <span class="va">error_model</span> <span class="op">=</span> <span class="st">"const"</span><span class="op">)</span> <span class="op">{</span></span> @@ -194,11 +223,14 @@ <div class="section level2"> <h2 id="results">Results<a class="anchor" aria-label="anchor" href="#results"></a> </h2> -<p>Benchmarks for all available error models are shown. They are intended for improving mkin, not for comparing CPUs or operating systems. All trademarks belong to their respective owners.</p> +<p>Benchmarks for all available error models are shown. They are +intended for improving mkin, not for comparing CPUs or operating +systems. All trademarks belong to their respective owners.</p> <div class="section level3"> <h3 id="parent-only">Parent only<a class="anchor" aria-label="anchor" href="#parent-only"></a> </h3> -<p>Constant variance (t1) and two-component error model (t2) for four models fitted to two datasets, i.e. eight fits for each test.</p> +<p>Constant variance (t1) and two-component error model (t2) for four +models fitted to two datasets, i.e. eight fits for each test.</p> <table class="table"> <thead><tr class="header"> <th align="left">OS</th> @@ -353,13 +385,55 @@ <td align="right">2.140</td> <td align="right">3.774</td> </tr> +<tr class="odd"> +<td align="left">Linux</td> +<td align="left">Ryzen 7 1700</td> +<td align="left">4.2.2</td> +<td align="left">1.2.2</td> +<td align="right">2.187</td> +<td align="right">3.851</td> +</tr> +<tr class="even"> +<td align="left">Linux</td> +<td align="left">Ryzen 9 7950X</td> +<td align="left">4.2.2</td> +<td align="left">1.2.0</td> +<td align="right">1.288</td> +<td align="right">1.794</td> +</tr> +<tr class="odd"> +<td align="left">Linux</td> +<td align="left">Ryzen 9 7950X</td> +<td align="left">4.2.2</td> +<td align="left">1.2.2</td> +<td align="right">1.276</td> +<td align="right">1.804</td> +</tr> +<tr class="even"> +<td align="left">Linux</td> +<td align="left">Ryzen 9 7950X</td> +<td align="left">4.2.2</td> +<td align="left">1.2.3</td> +<td align="right">1.370</td> +<td align="right">1.883</td> +</tr> +<tr class="odd"> +<td align="left">Linux</td> +<td align="left">Ryzen 9 7950X</td> +<td align="left">4.2.3</td> +<td align="left">1.2.3</td> +<td align="right">1.406</td> +<td align="right">1.948</td> +</tr> </tbody> </table> </div> <div class="section level3"> <h3 id="one-metabolite">One metabolite<a class="anchor" aria-label="anchor" href="#one-metabolite"></a> </h3> -<p>Constant variance (t3), two-component error model (t4), and variance by variable (t5) for three models fitted to one dataset, i.e. three fits for each test.</p> +<p>Constant variance (t3), two-component error model (t4), and variance +by variable (t5) for three models fitted to one dataset, i.e. three fits +for each test.</p> <table class="table"> <thead><tr class="header"> <th align="left">OS</th> @@ -533,14 +607,73 @@ <td align="right">6.193</td> <td align="right">2.843</td> </tr> +<tr class="odd"> +<td align="left">Linux</td> +<td align="left">Ryzen 7 1700</td> +<td align="left">4.2.2</td> +<td align="left">1.2.2</td> +<td align="right">1.585</td> +<td align="right">6.335</td> +<td align="right">3.003</td> +</tr> +<tr class="even"> +<td align="left">Linux</td> +<td align="left">Ryzen 9 7950X</td> +<td align="left">4.2.2</td> +<td align="left">1.2.0</td> +<td align="right">0.792</td> +<td align="right">2.378</td> +<td align="right">1.245</td> +</tr> +<tr class="odd"> +<td align="left">Linux</td> +<td align="left">Ryzen 9 7950X</td> +<td align="left">4.2.2</td> +<td align="left">1.2.2</td> +<td align="right">0.784</td> +<td align="right">2.355</td> +<td align="right">1.233</td> +</tr> +<tr class="even"> +<td align="left">Linux</td> +<td align="left">Ryzen 9 7950X</td> +<td align="left">4.2.2</td> +<td align="left">1.2.3</td> +<td align="right">0.770</td> +<td align="right">2.011</td> +<td align="right">1.123</td> +</tr> +<tr class="odd"> +<td align="left">Linux</td> +<td align="left">Ryzen 9 7950X</td> +<td align="left">4.2.3</td> +<td align="left">1.2.3</td> +<td align="right">0.793</td> +<td align="right">2.109</td> +<td align="right">1.178</td> +</tr> </tbody> </table> </div> <div class="section level3"> <h3 id="two-metabolites">Two metabolites<a class="anchor" aria-label="anchor" href="#two-metabolites"></a> </h3> -<p>Constant variance (t6 and t7), two-component error model (t8 and t9), and variance by variable (t10 and t11) for one model fitted to one dataset, i.e. one fit for each test.</p> +<p>Constant variance (t6 and t7), two-component error model (t8 and t9), +and variance by variable (t10 and t11) for one model fitted to one +dataset, i.e. one fit for each test.</p> <table class="table"> +<colgroup> +<col width="8%"> +<col width="19%"> +<col width="8%"> +<col width="12%"> +<col width="8%"> +<col width="8%"> +<col width="8%"> +<col width="9%"> +<col width="8%"> +<col width="9%"> +</colgroup> <thead><tr class="header"> <th align="left">OS</th> <th align="left">CPU</th> @@ -770,6 +903,66 @@ <td align="right">1.987</td> <td align="right">2.802</td> </tr> +<tr class="odd"> +<td align="left">Linux</td> +<td align="left">Ryzen 7 1700</td> +<td align="left">4.2.2</td> +<td align="left">1.2.2</td> +<td align="right">0.935</td> +<td align="right">1.381</td> +<td align="right">1.551</td> +<td align="right">3.209</td> +<td align="right">1.976</td> +<td align="right">3.013</td> +</tr> +<tr class="even"> +<td align="left">Linux</td> +<td align="left">Ryzen 9 7950X</td> +<td align="left">4.2.2</td> +<td align="left">1.2.0</td> +<td align="right">0.445</td> +<td align="right">0.591</td> +<td align="right">0.660</td> +<td align="right">1.190</td> +<td align="right">0.814</td> +<td align="right">1.100</td> +</tr> +<tr class="odd"> +<td align="left">Linux</td> +<td align="left">Ryzen 9 7950X</td> +<td align="left">4.2.2</td> +<td align="left">1.2.2</td> +<td align="right">0.443</td> +<td align="right">0.586</td> +<td align="right">0.661</td> +<td align="right">1.176</td> +<td align="right">0.803</td> +<td align="right">1.097</td> +</tr> +<tr class="even"> +<td align="left">Linux</td> +<td align="left">Ryzen 9 7950X</td> +<td align="left">4.2.2</td> +<td align="left">1.2.3</td> +<td align="right">0.418</td> +<td align="right">0.530</td> +<td align="right">0.591</td> +<td align="right">1.006</td> +<td align="right">0.716</td> +<td align="right">0.949</td> +</tr> +<tr class="odd"> +<td align="left">Linux</td> +<td align="left">Ryzen 9 7950X</td> +<td align="left">4.2.3</td> +<td align="left">1.2.3</td> +<td align="right">0.432</td> +<td align="right">0.549</td> +<td align="right">0.609</td> +<td align="right">1.052</td> +<td align="right">0.743</td> +<td align="right">0.989</td> +</tr> </tbody> </table> </div> @@ -793,7 +986,7 @@ <div class="pkgdown"> <p></p> -<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer> diff --git a/docs/articles/web_only/compiled_models.html b/docs/articles/web_only/compiled_models.html index d17d7aeb..a411dad1 100644 --- a/docs/articles/web_only/compiled_models.html +++ b/docs/articles/web_only/compiled_models.html @@ -33,14 +33,14 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"> <li> - <a href="../../reference/index.html">Functions and data</a> + <a href="../../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -52,6 +52,9 @@ <li> <a href="../../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -59,22 +62,31 @@ <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li class="divider"> </li> +<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + </li> +<li class="dropdown-header">Performance</li> + <li> + <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -82,6 +94,15 @@ <li> <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul> </li> <li> @@ -105,13 +126,15 @@ - </header><script src="compiled_models_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> - <h1 data-toc-skip>Performance benefit by using compiled model definitions in mkin</h1> - <h4 data-toc-skip class="author">Johannes Ranke</h4> + <h1 data-toc-skip>Performance benefit by using compiled model +definitions in mkin</h1> + <h4 data-toc-skip class="author">Johannes +Ranke</h4> - <h4 data-toc-skip class="date">2022-11-17</h4> + <h4 data-toc-skip class="date">2023-04-20</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/compiled_models.rmd" class="external-link"><code>vignettes/web_only/compiled_models.rmd</code></a></small> <div class="hidden name"><code>compiled_models.rmd</code></div> @@ -123,23 +146,39 @@ <div class="section level2"> <h2 id="how-to-benefit-from-compiled-models">How to benefit from compiled models<a class="anchor" aria-label="anchor" href="#how-to-benefit-from-compiled-models"></a> </h2> -<p>When using an mkin version equal to or greater than 0.9-36 and a C compiler is available, you will see a message that the model is being compiled from autogenerated C code when defining a model using mkinmod. Starting from version 0.9.49.9, the <code><a href="../../reference/mkinmod.html">mkinmod()</a></code> function checks for presence of a compiler using</p> +<p>When using an mkin version equal to or greater than 0.9-36 and a C +compiler is available, you will see a message that the model is being +compiled from autogenerated C code when defining a model using mkinmod. +Starting from version 0.9.49.9, the <code><a href="../../reference/mkinmod.html">mkinmod()</a></code> function +checks for presence of a compiler using</p> <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu">pkgbuild</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/pkgbuild/man/has_compiler.html" class="external-link">has_compiler</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> -<p>In previous versions, it used <code>Sys.which("gcc")</code> for this check.</p> -<p>On Linux, you need to have the essential build tools like make and gcc or clang installed. On Debian based linux distributions, these will be pulled in by installing the build-essential package.</p> -<p>On MacOS, which I do not use personally, I have had reports that a compiler is available by default.</p> -<p>On Windows, you need to install Rtools and have the path to its bin directory in your PATH variable. You do not need to modify the PATH variable when installing Rtools. Instead, I would recommend to put the line</p> +<code class="sourceCode R"><span><span class="fu">pkgbuild</span><span class="fu">::</span><span class="fu"><a href="https://r-lib.github.io/pkgbuild/reference/has_compiler.html" class="external-link">has_compiler</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> +<p>In previous versions, it used <code>Sys.which("gcc")</code> for this +check.</p> +<p>On Linux, you need to have the essential build tools like make and +gcc or clang installed. On Debian based linux distributions, these will +be pulled in by installing the build-essential package.</p> +<p>On MacOS, which I do not use personally, I have had reports that a +compiler is available by default.</p> +<p>On Windows, you need to install Rtools and have the path to its bin +directory in your PATH variable. You do not need to modify the PATH +variable when installing Rtools. Instead, I would recommend to put the +line</p> <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/Sys.setenv.html" class="external-link">Sys.setenv</a></span><span class="op">(</span>PATH <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"C:/Rtools/bin"</span>, <span class="fu"><a href="https://rdrr.io/r/base/Sys.getenv.html" class="external-link">Sys.getenv</a></span><span class="op">(</span><span class="st">"PATH"</span><span class="op">)</span>, sep<span class="op">=</span><span class="st">";"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> -<p>into your .Rprofile startup file. This is just a text file with some R code that is executed when your R session starts. It has to be named .Rprofile and has to be located in your home directory, which will generally be your Documents folder. You can check the location of the home directory used by R by issuing</p> +<p>into your .Rprofile startup file. This is just a text file with some +R code that is executed when your R session starts. It has to be named +.Rprofile and has to be located in your home directory, which will +generally be your Documents folder. You can check the location of the +home directory used by R by issuing</p> <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/Sys.getenv.html" class="external-link">Sys.getenv</a></span><span class="op">(</span><span class="st">"HOME"</span><span class="op">)</span></span></code></pre></div> </div> <div class="section level2"> <h2 id="comparison-with-other-solution-methods">Comparison with other solution methods<a class="anchor" aria-label="anchor" href="#comparison-with-other-solution-methods"></a> </h2> -<p>First, we build a simple degradation model for a parent compound with one metabolite, and we remove zero values from the dataset.</p> +<p>First, we build a simple degradation model for a parent compound with +one metabolite, and we remove zero values from the dataset.</p> <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="st"><a href="https://pkgdown.jrwb.de/mkin/">"mkin"</a></span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> <span><span class="va">SFO_SFO</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> @@ -148,7 +187,12 @@ <pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre> <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">FOCUS_D</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span></span></code></pre></div> -<p>We can compare the performance of the Eigenvalue based solution against the compiled version and the R implementation of the differential equations using the benchmark package. In the output of below code, the warnings about zero being removed from the FOCUS D dataset are suppressed. Since mkin version 0.9.49.11, an analytical solution is also implemented, which is included in the tests below.</p> +<p>We can compare the performance of the Eigenvalue based solution +against the compiled version and the R implementation of the +differential equations using the benchmark package. In the output of +below code, the warnings about zero being removed from the FOCUS D +dataset are suppressed. Since mkin version 0.9.49.11, an analytical +solution is also implemented, which is included in the tests below.</p> <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="kw">if</span> <span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="va"><a href="http://rbenchmark.googlecode.com" class="external-link">rbenchmark</a></span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span> <span> <span class="va">b.1</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/rbenchmark/man/benchmark.html" class="external-link">benchmark</a></span><span class="op">(</span></span> @@ -169,16 +213,20 @@ <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="st">"R package rbenchmark is not available"</span><span class="op">)</span></span> <span><span class="op">}</span></span></code></pre></div> <pre><code><span><span class="co">## test replications relative elapsed</span></span> -<span><span class="co">## 4 analytical 1 1.000 0.218</span></span> -<span><span class="co">## 3 deSolve, compiled 1 1.550 0.338</span></span> -<span><span class="co">## 2 Eigenvalue based 1 1.950 0.425</span></span> -<span><span class="co">## 1 deSolve, not compiled 1 33.041 7.203</span></span></code></pre> -<p>We see that using the compiled model is by more than a factor of 10 faster than using deSolve without compiled code.</p> +<span><span class="co">## 4 analytical 1 1.000 0.103</span></span> +<span><span class="co">## 3 deSolve, compiled 1 1.291 0.133</span></span> +<span><span class="co">## 2 Eigenvalue based 1 1.718 0.177</span></span> +<span><span class="co">## 1 deSolve, not compiled 1 22.136 2.280</span></span></code></pre> +<p>We see that using the compiled model is by more than a factor of 10 +faster than using deSolve without compiled code.</p> </div> <div class="section level2"> <h2 id="model-without-analytical-solution">Model without analytical solution<a class="anchor" aria-label="anchor" href="#model-without-analytical-solution"></a> </h2> -<p>This evaluation is also taken from the example section of mkinfit. No analytical solution is available for this system, and now Eigenvalue based solution is possible, so only deSolve using with or without compiled code is available.</p> +<p>This evaluation is also taken from the example section of mkinfit. No +analytical solution is available for this system, and now Eigenvalue +based solution is possible, so only deSolve using with or without +compiled code is available.</p> <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="kw">if</span> <span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="va"><a href="http://rbenchmark.googlecode.com" class="external-link">rbenchmark</a></span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span> <span> <span class="va">FOMC_SFO</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> @@ -199,14 +247,15 @@ <span><span class="op">}</span></span></code></pre></div> <pre><code><span><span class="co">## Temporary DLL for differentials generated and loaded</span></span></code></pre> <pre><code><span><span class="co">## test replications relative elapsed</span></span> -<span><span class="co">## 2 deSolve, compiled 1 1.000 0.510</span></span> -<span><span class="co">## 1 deSolve, not compiled 1 26.247 13.386</span></span></code></pre> -<p>Here we get a performance benefit of a factor of 26 using the version of the differential equation model compiled from C code!</p> -<p>This vignette was built with mkin 1.2.0 on</p> -<pre><code><span><span class="co">## R version 4.2.2 (2022-10-31)</span></span> +<span><span class="co">## 2 deSolve, compiled 1 1.000 0.171</span></span> +<span><span class="co">## 1 deSolve, not compiled 1 24.199 4.138</span></span></code></pre> +<p>Here we get a performance benefit of a factor of 24 using the version +of the differential equation model compiled from C code!</p> +<p>This vignette was built with mkin 1.2.3 on</p> +<pre><code><span><span class="co">## R version 4.2.3 (2023-03-15)</span></span> <span><span class="co">## Platform: x86_64-pc-linux-gnu (64-bit)</span></span> -<span><span class="co">## Running under: Debian GNU/Linux 11 (bullseye)</span></span></code></pre> -<pre><code><span><span class="co">## CPU model: AMD Ryzen 7 1700 Eight-Core Processor</span></span></code></pre> +<span><span class="co">## Running under: Debian GNU/Linux 12 (bookworm)</span></span></code></pre> +<pre><code><span><span class="co">## CPU model: AMD Ryzen 9 7950X 16-Core Processor</span></span></code></pre> </div> </div> @@ -227,7 +276,7 @@ <div class="pkgdown"> <p></p> -<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer> diff --git a/docs/articles/web_only/dimethenamid_2018.html b/docs/articles/web_only/dimethenamid_2018.html index 8c37edd6..4575067b 100644 --- a/docs/articles/web_only/dimethenamid_2018.html +++ b/docs/articles/web_only/dimethenamid_2018.html @@ -33,14 +33,14 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"> <li> - <a href="../../reference/index.html">Functions and data</a> + <a href="../../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -52,6 +52,9 @@ <li> <a href="../../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -59,22 +62,31 @@ <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li class="divider"> </li> +<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + </li> +<li class="dropdown-header">Performance</li> + <li> + <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -82,6 +94,15 @@ <li> <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul> </li> <li> @@ -105,13 +126,16 @@ - </header><script src="dimethenamid_2018_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> - <h1 data-toc-skip>Example evaluations of the dimethenamid data from 2018</h1> - <h4 data-toc-skip class="author">Johannes Ranke</h4> + <h1 data-toc-skip>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 17 Nov 2022</h4> + <h4 data-toc-skip class="date">Last change 1 July 2022, +built on 20 Apr 2023</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> @@ -120,19 +144,48 @@ -<p><a href="http://www.jrwb.de" class="external-link">Wissenschaftlicher Berater, Kronacher Str. 12, 79639 Grenzach-Wyhlen, Germany</a></p> +<p><a href="http://www.jrwb.de" class="external-link">Wissenschaftlicher Berater, Kronacher +Str. 12, 79639 Grenzach-Wyhlen, Germany</a></p> <div class="section level2"> <h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a> </h2> -<p>A first analysis of the data analysed here was presented in a recent journal article on nonlinear mixed-effects models in degradation kinetics <span class="citation">(Ranke et al. 2021)</span>. That analysis was based on the <code>nlme</code> package and a development version of the <code>saemix</code> package that was unpublished at the time. Meanwhile, version 3.0 of the <code>saemix</code> package is available from the CRAN repository. Also, it turned out that there was an error in the handling of the Borstel data in the mkin package at the time, leading to the duplication of a few data points from that soil. The dataset in the mkin package has been corrected, and the interface to <code>saemix</code> in the mkin package has been updated to use the released version.</p> -<p>This vignette is intended to present an up to date analysis of the data, using the corrected dataset and released versions of <code>mkin</code> and <code>saemix</code>.</p> +<p>A first analysis of the data analysed here was presented in a recent +journal article on nonlinear mixed-effects models in degradation +kinetics <span class="citation">(Ranke et al. 2021)</span>. That +analysis was based on the <code>nlme</code> package and a development +version of the <code>saemix</code> package that was unpublished at the +time. Meanwhile, version 3.0 of the <code>saemix</code> package is +available from the CRAN repository. Also, it turned out that there was +an error in the handling of the Borstel data in the mkin package at the +time, leading to the duplication of a few data points from that soil. +The dataset in the mkin package has been corrected, and the interface to +<code>saemix</code> in the mkin package has been updated to use the +released version.</p> +<p>This vignette is intended to present an up to date analysis of the +data, using the corrected dataset and released versions of +<code>mkin</code> and <code>saemix</code>.</p> </div> <div class="section level2"> <h2 id="data">Data<a class="anchor" aria-label="anchor" href="#data"></a> </h2> -<p>Residue data forming the basis for the endpoints derived in the conclusion on the peer review of the pesticide risk assessment of dimethenamid-P published by the European Food Safety Authority (EFSA) in 2018 <span class="citation">(EFSA 2018)</span> were transcribed from the risk assessment report <span class="citation">(Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria 2018)</span> which can be downloaded from the Open EFSA repository <a href="https://open.efsa.europa.eu" class="external-link">https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716</a>.</p> -<p>The data are <a href="https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html">available in the mkin package</a>. The following code (hidden by default, please use the button to the right to show it) treats the data available for the racemic mixture dimethenamid (DMTA) and its enantiomer dimethenamid-P (DMTAP) in the same way, as no difference between their degradation behaviour was identified in the EU risk assessment. The observation times of each dataset are multiplied with the corresponding normalisation factor also available in the dataset, in order to make it possible to describe all datasets with a single set of parameters.</p> -<p>Also, datasets observed in the same soil are merged, resulting in dimethenamid (DMTA) data from six soils.</p> +<p>Residue data forming the basis for the endpoints derived in the +conclusion on the peer review of the pesticide risk assessment of +dimethenamid-P published by the European Food Safety Authority (EFSA) in +2018 <span class="citation">(EFSA 2018)</span> were transcribed from the +risk assessment report <span class="citation">(Rapporteur Member State +Germany, Co-Rapporteur Member State Bulgaria 2018)</span> which can be +downloaded from the Open EFSA repository <a href="https://open.efsa.europa.eu" class="external-link">https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716</a>.</p> +<p>The data are <a href="https://pkgdown.jrwb.de/mkin/reference/dimethenamid_2018.html">available +in the mkin package</a>. The following code (hidden by default, please +use the button to the right to show it) treats the data available for +the racemic mixture dimethenamid (DMTA) and its enantiomer +dimethenamid-P (DMTAP) in the same way, as no difference between their +degradation behaviour was identified in the EU risk assessment. The +observation times of each dataset are multiplied with the corresponding +normalisation factor also available in the dataset, in order to make it +possible to describe all datasets with a single set of parameters.</p> +<p>Also, datasets observed in the same soil are merged, resulting in +dimethenamid (DMTA) data from six soils.</p> <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> <span><span class="va">dmta_ds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span> @@ -149,34 +202,60 @@ <div class="section level2"> <h2 id="parent-degradation">Parent degradation<a class="anchor" aria-label="anchor" href="#parent-degradation"></a> </h2> -<p>We evaluate the observed degradation of the parent compound using simple exponential decline (SFO) and biexponential decline (DFOP), using constant variance (const) and a two-component variance (tc) as error models.</p> +<p>We evaluate the observed degradation of the parent compound using +simple exponential decline (SFO) and biexponential decline (DFOP), using +constant variance (const) and a two-component variance (tc) as error +models.</p> <div class="section level3"> <h3 id="separate-evaluations">Separate evaluations<a class="anchor" aria-label="anchor" href="#separate-evaluations"></a> </h3> -<p>As a first step, to get a visual impression of the fit of the different models, we do separate evaluations for each soil using the mmkin function from the mkin package:</p> +<p>As a first step, to get a visual impression of the fit of the +different models, we do separate evaluations for each soil using the +mmkin function from the mkin package:</p> <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">f_parent_mkin_const</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, <span class="va">dmta_ds</span>,</span> <span> error_model <span class="op">=</span> <span class="st">"const"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> <span><span class="va">f_parent_mkin_tc</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, <span class="va">dmta_ds</span>,</span> <span> error_model <span class="op">=</span> <span class="st">"tc"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> -<p>The plot of the individual SFO fits shown below suggests that at least in some datasets the degradation slows down towards later time points, and that the scatter of the residuals error is smaller for smaller values (panel to the right):</p> +<p>The plot of the individual SFO fits shown below suggests that at +least in some datasets the degradation slows down towards later time +points, and that the scatter of the residuals error is smaller for +smaller values (panel to the right):</p> <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_mkin_sfo_const-1.png" width="700"></p> -<p>Using biexponential decline (DFOP) results in a slightly more random scatter of the residuals:</p> +<p>Using biexponential decline (DFOP) results in a slightly more random +scatter of the residuals:</p> <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png" width="700"></p> -<p>The population curve (bold line) in the above plot results from taking the mean of the individual transformed parameters, i.e. of log k1 and log k2, as well as of the logit of the g parameter of the DFOP model). Here, this procedure does not result in parameters that represent the degradation well, because in some datasets the fitted value for k2 is extremely close to zero, leading to a log k2 value that dominates the average. This is alleviated if only rate constants that pass the t-test for significant difference from zero (on the untransformed scale) are considered in the averaging:</p> +<p>The population curve (bold line) in the above plot results from +taking the mean of the individual transformed parameters, i.e. of log k1 +and log k2, as well as of the logit of the g parameter of the DFOP +model). Here, this procedure does not result in parameters that +represent the degradation well, because in some datasets the fitted +value for k2 is extremely close to zero, leading to a log k2 value that +dominates the average. This is alleviated if only rate constants that +pass the t-test for significant difference from zero (on the +untransformed scale) are considered in the averaging:</p> <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const_test-1.png" width="700"></p> -<p>While this is visually much more satisfactory, such an average procedure could introduce a bias, as not all results from the individual fits enter the population curve with the same weight. This is where nonlinear mixed-effects models can help out by treating all datasets with equally by fitting a parameter distribution model together with the degradation model and the error model (see below).</p> -<p>The remaining trend of the residuals to be higher for higher predicted residues is reduced by using the two-component error model:</p> +<p>While this is visually much more satisfactory, such an average +procedure could introduce a bias, as not all results from the individual +fits enter the population curve with the same weight. This is where +nonlinear mixed-effects models can help out by treating all datasets +with equally by fitting a parameter distribution model together with the +degradation model and the error model (see below).</p> +<p>The remaining trend of the residuals to be higher for higher +predicted residues is reduced by using the two-component error +model:</p> <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="../../reference/mixed.html">mixed</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_tc_test-1.png" width="700"></p> -<p>However, note that in the case of using this error model, the fits to the Flaach and BBA 2.3 datasets appear to be ill-defined, indicated by the fact that they did not converge:</p> +<p>However, note that in the case of using this error model, the fits to +the Flaach and BBA 2.3 datasets appear to be ill-defined, indicated by +the fact that they did not converge:</p> <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span></span></code></pre></div> <pre><code><mmkin> object @@ -186,26 +265,53 @@ Status of individual fits: model Calke Borstel Flaach BBA 2.2 BBA 2.3 Elliot DFOP OK OK C OK C OK -OK: No warnings C: Optimisation did not converge: -iteration limit reached without convergence (10)</code></pre> +iteration limit reached without convergence (10) +OK: No warnings</code></pre> </div> <div class="section level3"> <h3 id="nonlinear-mixed-effects-models">Nonlinear mixed-effects models<a class="anchor" aria-label="anchor" href="#nonlinear-mixed-effects-models"></a> </h3> -<p>Instead of taking a model selection decision for each of the individual fits, we fit nonlinear mixed-effects models (using different fitting algorithms as implemented in different packages) and do model selection using all available data at the same time. In order to make sure that these decisions are not unduly influenced by the type of algorithm used, by implementation details or by the use of wrong control parameters, we compare the model selection results obtained with different R packages, with different algorithms and checking control parameters.</p> +<p>Instead of taking a model selection decision for each of the +individual fits, we fit nonlinear mixed-effects models (using different +fitting algorithms as implemented in different packages) and do model +selection using all available data at the same time. In order to make +sure that these decisions are not unduly influenced by the type of +algorithm used, by implementation details or by the use of wrong control +parameters, we compare the model selection results obtained with +different R packages, with different algorithms and checking control +parameters.</p> <div class="section level4"> <h4 id="nlme">nlme<a class="anchor" aria-label="anchor" href="#nlme"></a> </h4> -<p>The nlme package was the first R extension providing facilities to fit nonlinear mixed-effects models. We would like to do model selection from all four combinations of degradation models and error models based on the AIC. However, fitting the DFOP model with constant variance and using default control parameters results in an error, signalling that the maximum number of 50 iterations was reached, potentially indicating overparameterisation. Nevertheless, the algorithm converges when the two-component error model is used in combination with the DFOP model. This can be explained by the fact that the smaller residues observed at later sampling times get more weight when using the two-component error model which will counteract the tendency of the algorithm to try parameter combinations unsuitable for fitting these data.</p> +<p>The nlme package was the first R extension providing facilities to +fit nonlinear mixed-effects models. We would like to do model selection +from all four combinations of degradation models and error models based +on the AIC. However, fitting the DFOP model with constant variance and +using default control parameters results in an error, signalling that +the maximum number of 50 iterations was reached, potentially indicating +overparameterisation. Nevertheless, the algorithm converges when the +two-component error model is used in combination with the DFOP model. +This can be explained by the fact that the smaller residues observed at +later sampling times get more weight when using the two-component error +model which will counteract the tendency of the algorithm to try +parameter combinations unsuitable for fitting these data.</p> <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://svn.r-project.org/R-packages/trunk/nlme/" class="external-link">nlme</a></span><span class="op">)</span></span> <span><span class="va">f_parent_nlme_sfo_const</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span></span> <span><span class="co"># f_parent_nlme_dfop_const <- nlme(f_parent_mkin_const["DFOP", ])</span></span> <span><span class="va">f_parent_nlme_sfo_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span></span> <span><span class="va">f_parent_nlme_dfop_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span></span></code></pre></div> -<p>Note that a certain degree of overparameterisation is also indicated by a warning obtained when fitting DFOP with the two-component error model (‘false convergence’ in the ‘LME step’ in iteration 3). However, as this warning does not occur in later iterations, and specifically not in the last of the 6 iterations, we can ignore this warning.</p> -<p>The model comparison function of the nlme package can directly be applied to these fits showing a much lower AIC for the DFOP model fitted with the two-component error model. Also, the likelihood ratio test indicates that this difference is significant as the p-value is below 0.0001.</p> +<p>Note that a certain degree of overparameterisation is also indicated +by a warning obtained when fitting DFOP with the two-component error +model (‘false convergence’ in the ‘LME step’ in iteration 3). However, +as this warning does not occur in later iterations, and specifically not +in the last of the 5 iterations, we can ignore this warning.</p> +<p>The model comparison function of the nlme package can directly be +applied to these fits showing a much lower AIC for the DFOP model fitted +with the two-component error model. Also, the likelihood ratio test +indicates that this difference is significant as the p-value is below +0.0001.</p> <div class="sourceCode" id="cb10"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span></span> <span> <span class="va">f_parent_nlme_sfo_const</span>, <span class="va">f_parent_nlme_sfo_tc</span>, <span class="va">f_parent_nlme_dfop_tc</span></span> @@ -214,7 +320,10 @@ iteration limit reached without convergence (10)</code></pre> 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 <.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 attempts can be made visible below.</p> +<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 +attempts can be made visible below.</p> <div class="sourceCode" id="cb12"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">f_parent_nlme_sfo_const_logchol</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span>,</span> <span> random <span class="op">=</span> <span class="fu">nlme</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/pdLogChol.html" class="external-link">pdLogChol</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">DMTA_0</span> <span class="op">~</span> <span class="fl">1</span>, <span class="va">log_k_DMTA</span> <span class="op">~</span> <span class="fl">1</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span> @@ -225,17 +334,29 @@ f_parent_nlme_dfop_tc 3 10 671.91 702.34 -325.96 2 vs 3 134.69 <.0001 <span><span class="va">f_parent_nlme_dfop_tc_logchol</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>,</span> <span> random <span class="op">=</span> <span class="fu">nlme</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/pdLogChol.html" class="external-link">pdLogChol</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">DMTA_0</span> <span class="op">~</span> <span class="fl">1</span>, <span class="va">log_k1</span> <span class="op">~</span> <span class="fl">1</span>, <span class="va">log_k2</span> <span class="op">~</span> <span class="fl">1</span>, <span class="va">g_qlogis</span> <span class="op">~</span> <span class="fl">1</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span> <span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_parent_nlme_dfop_tc</span>, <span class="va">f_parent_nlme_dfop_tc_logchol</span><span class="op">)</span></span></code></pre></div> -<p>While the SFO variants converge fast, the additional parameters introduced by this lead to convergence warnings for the DFOP model. The model comparison clearly show that adding correlations between random effects does not improve the fits.</p> -<p>The selected model (DFOP with two-component error) fitted to the data assuming no correlations between random effects is shown below.</p> +<p>While the SFO variants converge fast, the additional parameters +introduced by this lead to convergence warnings for the DFOP model. The +model comparison clearly show that adding correlations between random +effects does not improve the fits.</p> +<p>The selected model (DFOP with two-component error) fitted to the data +assuming no correlations between random effects is shown below.</p> <div class="sourceCode" id="cb13"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_parent_nlme_dfop_tc</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_parent_nlme_dfop_tc</span><span class="op">)</span></span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png" width="700"></p> </div> <div class="section level4"> <h4 id="saemix">saemix<a class="anchor" aria-label="anchor" href="#saemix"></a> </h4> -<p>The saemix package provided the first Open Source implementation of the Stochastic Approximation to the Expectation Maximisation (SAEM) algorithm. SAEM fits of degradation models can be conveniently performed using an interface to the saemix package available in current development versions of the mkin package.</p> -<p>The corresponding SAEM fits of the four combinations of degradation and error models are fitted below. As there is no convergence criterion implemented in the saemix package, the convergence plots need to be manually checked for every fit. We define control settings that work well for all the parent data fits shown in this vignette.</p> +<p>The saemix package provided the first Open Source implementation of +the Stochastic Approximation to the Expectation Maximisation (SAEM) +algorithm. SAEM fits of degradation models can be conveniently performed +using an interface to the saemix package available in current +development versions of the mkin package.</p> +<p>The corresponding SAEM fits of the four combinations of degradation +and error models are fitted below. As there is no convergence criterion +implemented in the saemix package, the convergence plots need to be +manually checked for every fit. We define control settings that work +well for all the parent data fits shown in this vignette.</p> <div class="sourceCode" id="cb14"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">saemix</span><span class="op">)</span></span> <span><span class="va">saemix_control</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/saemixControl.html" class="external-link">saemixControl</a></span><span class="op">(</span>nbiter.saemix <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">800</span>, <span class="fl">300</span><span class="op">)</span>, nb.chains <span class="op">=</span> <span class="fl">15</span>,</span> @@ -244,19 +365,23 @@ f_parent_nlme_dfop_tc 3 10 671.91 702.34 -325.96 2 vs 3 134.69 <.0001 <span> print <span class="op">=</span> <span class="cn">FALSE</span>, save <span class="op">=</span> <span class="cn">FALSE</span>, save.graphs <span class="op">=</span> <span class="cn">FALSE</span>, displayProgress <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span> <span><span class="va">saemix_control_10k</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/saemixControl.html" class="external-link">saemixControl</a></span><span class="op">(</span>nbiter.saemix <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10000</span>, <span class="fl">300</span><span class="op">)</span>, nb.chains <span class="op">=</span> <span class="fl">15</span>,</span> <span> print <span class="op">=</span> <span class="cn">FALSE</span>, save <span class="op">=</span> <span class="cn">FALSE</span>, save.graphs <span class="op">=</span> <span class="cn">FALSE</span>, displayProgress <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> -<p>The convergence plot for the SFO model using constant variance is shown below.</p> +<p>The convergence plot for the SFO model using constant variance is +shown below.</p> <div class="sourceCode" id="cb15"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">f_parent_saemix_sfo_const</span> <span class="op"><-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span> <span> control <span class="op">=</span> <span class="va">saemix_control</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span></span> <span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_parent_saemix_sfo_const</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_saemix_sfo_const-1.png" width="700"></p> -<p>Obviously the selected number of iterations is sufficient to reach convergence. This can also be said for the SFO fit using the two-component error model.</p> +<p>Obviously the selected number of iterations is sufficient to reach +convergence. This can also be said for the SFO fit using the +two-component error model.</p> <div class="sourceCode" id="cb16"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">f_parent_saemix_sfo_tc</span> <span class="op"><-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span> <span> control <span class="op">=</span> <span class="va">saemix_control</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span></span> <span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_parent_saemix_sfo_tc</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></span></code></pre></div> <p><img src="dimethenamid_2018_files/figure-html/f_parent_saemix_sfo_tc-1.png" width="700"></p> -<p>When fitting the DFOP model with constant variance (see below), parameter convergence is not as unambiguous.</p> +<p>When fitting the DFOP model with constant variance (see below), +parameter convergence is not as unambiguous.</p> <div class="sourceCode" id="cb17"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">f_parent_saemix_dfop_const</span> <span class="op"><-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_const</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span> <span> control <span class="op">=</span> <span class="va">saemix_control</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span></span> @@ -283,13 +408,21 @@ 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 1.65974 1.9831 -SD.DMTA_0 1.64787 0.45779 2.8379 +a.1 1.82141 1.65122 1.9916 +SD.DMTA_0 1.64787 0.45772 2.8380 SD.k1 0.57439 0.24731 0.9015 -SD.k2 0.03296 -2.50143 2.5673 -SD.g 1.10266 0.32371 1.8816</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 standard deviation of k2 across the population of soils (<code>SD.k2</code>) is ill-defined, indicating overparameterisation of this model.</p> -<p>When the DFOP model is fitted with the two-component error model, we also observe that the estimated variance of k2 becomes very small, while being ill-defined, as illustrated by the excessive confidence interval of <code>SD.k2</code>.</p> +SD.k2 0.03296 -2.50195 2.5679 +SD.g 1.10266 0.32369 1.8816</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 +standard deviation of k2 across the population of soils +(<code>SD.k2</code>) is ill-defined, indicating overparameterisation of +this model.</p> +<p>When the DFOP model is fitted with the two-component error model, we +also observe that the estimated variance of k2 becomes very small, while +being ill-defined, as illustrated by the excessive confidence interval +of <code>SD.k2</code>.</p> <div class="sourceCode" id="cb20"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">f_parent_saemix_dfop_tc</span> <span class="op"><-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>,</span> <span> control <span class="op">=</span> <span class="va">saemix_control</span>, transformations <span class="op">=</span> <span class="st">"saemix"</span><span class="op">)</span></span> @@ -324,9 +457,21 @@ 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> -<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 an error message. This is related to the variance of k2 approximating zero and has been submitted as a <a href="https://github.com/saemixdevelopment/saemixextension/issues/29" class="external-link">bug to the saemix package</a>, as the algorithm does not converge in this case.</p> -<p>An alternative way to fit DFOP in combination with the two-component error model is to use the model formulation with transformed parameters as used per default in mkin. When using this option, convergence is slower, but eventually the algorithm stops as well with the same error message.</p> -<p>The four combinations (SFO/const, SFO/tc, DFOP/const and DFOP/tc) and the version with increased iterations can be compared using the model comparison function of the saemix package:</p> +<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 +an error message. This is related to the variance of k2 approximating +zero and has been submitted as a <a href="https://github.com/saemixdevelopment/saemixextension/issues/29" class="external-link">bug +to the saemix package</a>, as the algorithm does not converge in this +case.</p> +<p>An alternative way to fit DFOP in combination with the two-component +error model is to use the model formulation with transformed parameters +as used per default in mkin. When using this option, convergence is +slower, but eventually the algorithm stops as well with the same error +message.</p> +<p>The four combinations (SFO/const, SFO/tc, DFOP/const and DFOP/tc) and +the version with increased iterations can be compared using the model +comparison function of the saemix package:</p> <div class="sourceCode" id="cb23"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">AIC_parent_saemix</span> <span class="op"><-</span> <span class="fu">saemix</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/compare.saemix.html" class="external-link">compare.saemix</a></span><span class="op">(</span></span> <span> <span class="va">f_parent_saemix_sfo_const</span><span class="op">$</span><span class="va">so</span>,</span> @@ -345,7 +490,10 @@ SFO tc 798.38 797.13 DFOP const 705.75 703.88 DFOP tc 665.65 663.57 DFOP tc more iterations 665.88 663.80</code></pre> -<p>In order to check the influence of the likelihood calculation algorithms implemented in saemix, the likelihood from Gaussian quadrature is added to the best fit, and the AIC values obtained from the three methods are compared.</p> +<p>In order to check the influence of the likelihood calculation +algorithms implemented in saemix, the likelihood from Gaussian +quadrature is added to the best fit, and the AIC values obtained from +the three methods are compared.</p> <div class="sourceCode" id="cb27"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span> <span class="op"><-</span></span> <span> <span class="fu">saemix</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/llgq.saemix.html" class="external-link">llgq.saemix</a></span><span class="op">(</span><span class="va">f_parent_saemix_dfop_tc</span><span class="op">$</span><span class="va">so</span><span class="op">)</span></span> @@ -357,9 +505,19 @@ DFOP tc more iterations 665.88 663.80</code></pre> <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> -<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> -<p>In order to illustrate that the comparison of the three method depends on the degree of convergence obtained in the fit, the same comparison is shown below for the fit using the defaults for the number of iterations and the number of MCMC chains.</p> -<p>When using OpenBlas for linear algebra, there is a large difference in the values obtained with Gaussian quadrature, so the larger number of iterations makes a lot of difference. When using the LAPACK version coming with Debian Bullseye, the AIC based on Gaussian quadrature is almost the same as the one obtained with the other methods, also when using defaults for the fit.</p> +<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> +<p>In order to illustrate that the comparison of the three method +depends on the degree of convergence obtained in the fit, the same +comparison is shown below for the fit using the defaults for the number +of iterations and the number of MCMC chains.</p> +<p>When using OpenBlas for linear algebra, there is a large difference +in the values obtained with Gaussian quadrature, so the larger number of +iterations makes a lot of difference. When using the LAPACK version +coming with Debian Bullseye, the AIC based on Gaussian quadrature is +almost the same as the one obtained with the other methods, also when +using defaults for the fit.</p> <div class="sourceCode" id="cb29"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">f_parent_saemix_dfop_tc_defaults</span> <span class="op"><-</span> <span class="fu">mkin</span><span class="fu">::</span><span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_parent_mkin_tc</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span></span> <span><span class="va">f_parent_saemix_dfop_tc_defaults</span><span class="op">$</span><span class="va">so</span> <span class="op"><-</span></span> @@ -377,7 +535,9 @@ DFOP tc more iterations 665.88 663.80</code></pre> <div class="section level3"> <h3 id="comparison">Comparison<a class="anchor" aria-label="anchor" href="#comparison"></a> </h3> -<p>The following table gives the AIC values obtained with both backend packages using the same control parameters (800 iterations burn-in, 300 iterations second phase, 15 chains).</p> +<p>The following table gives the AIC values obtained with both backend +packages using the same control parameters (800 iterations burn-in, 300 +iterations second phase, 15 chains).</p> <div class="sourceCode" id="cb31"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">AIC_all</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span> <span> check.names <span class="op">=</span> <span class="cn">FALSE</span>,</span> @@ -417,7 +577,7 @@ DFOP tc more iterations 665.88 663.80</code></pre> <td align="left">DFOP</td> <td align="left">const</td> <td align="right">NA</td> -<td align="right">671.98</td> +<td align="right">709.26</td> <td align="right">705.75</td> </tr> <tr class="even"> @@ -434,21 +594,33 @@ DFOP tc more iterations 665.88 663.80</code></pre> <div class="section level2"> <h2 id="conclusion">Conclusion<a class="anchor" aria-label="anchor" href="#conclusion"></a> </h2> -<p>A more detailed analysis of the dimethenamid dataset confirmed that the DFOP model provides the most appropriate description of the decline of the parent compound in these data. On the other hand, closer inspection of the results revealed that the variability of the k2 parameter across the population of soils is ill-defined. This coincides with the observation that this parameter cannot robustly be quantified for some of the soils.</p> -<p>Regarding the regulatory use of these data, it is claimed that an improved characterisation of the mean parameter values across the population is obtained using the nonlinear mixed-effects models presented here. However, attempts to quantify the variability of the slower rate constant of the biphasic decline of dimethenamid indicate that the data are not sufficient to characterise this variability to a satisfactory precision.</p> +<p>A more detailed analysis of the dimethenamid dataset confirmed that +the DFOP model provides the most appropriate description of the decline +of the parent compound in these data. On the other hand, closer +inspection of the results revealed that the variability of the k2 +parameter across the population of soils is ill-defined. This coincides +with the observation that this parameter cannot robustly be quantified +for some of the soils.</p> +<p>Regarding the regulatory use of these data, it is claimed that an +improved characterisation of the mean parameter values across the +population is obtained using the nonlinear mixed-effects models +presented here. However, attempts to quantify the variability of the +slower rate constant of the biphasic decline of dimethenamid indicate +that the data are not sufficient to characterise this variability to a +satisfactory precision.</p> </div> <div class="section level2"> <h2 id="session-info">Session Info<a class="anchor" aria-label="anchor" href="#session-info"></a> </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 4.2.2 (2022-10-31) +<pre><code>R version 4.2.3 (2023-03-15) Platform: x86_64-pc-linux-gnu (64-bit) -Running under: Debian GNU/Linux 11 (bullseye) +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-r0.3.13.so +LAPACK: /usr/lib/x86_64-linux-gnu/openblas-serial/libopenblas-r0.3.21.so locale: [1] LC_CTYPE=de_DE.UTF-8 LC_NUMERIC=C @@ -462,38 +634,44 @@ attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: -[1] nlme_3.1-160 mkin_1.2.0 knitr_1.40 +[1] saemix_3.2 npde_3.3 nlme_3.1-162 mkin_1.2.3 knitr_1.42 loaded via a namespace (and not attached): - [1] deSolve_1.34 zoo_1.8-11 tidyselect_1.2.0 xfun_0.33 - [5] bslib_0.4.0 purrr_0.3.5 lattice_0.20-45 colorspace_2.0-3 - [9] vctrs_0.5.0 generics_0.1.3 htmltools_0.5.3 yaml_2.3.6 -[13] utf8_1.2.2 rlang_1.0.6 pkgdown_2.0.6 saemix_3.2 -[17] jquerylib_0.1.4 pillar_1.8.1 glue_1.6.2 DBI_1.1.3 -[21] lifecycle_1.0.3 stringr_1.4.1 munsell_0.5.0 gtable_0.3.1 -[25] ragg_1.2.2 memoise_2.0.1 evaluate_0.18 npde_3.2 -[29] fastmap_1.1.0 lmtest_0.9-40 parallel_4.2.2 fansi_1.0.3 -[33] highr_0.9 scales_1.2.1 cachem_1.0.6 desc_1.4.2 -[37] jsonlite_1.8.3 systemfonts_1.0.4 fs_1.5.2 textshaping_0.3.6 -[41] gridExtra_2.3 ggplot2_3.4.0 digest_0.6.30 stringi_1.7.8 -[45] dplyr_1.0.10 grid_4.2.2 rprojroot_2.0.3 cli_3.4.1 -[49] tools_4.2.2 magrittr_2.0.3 sass_0.4.2 tibble_3.1.8 -[53] pkgconfig_2.0.3 assertthat_0.2.1 rmarkdown_2.16 R6_2.5.1 -[57] mclust_6.0.0 compiler_4.2.2 </code></pre> + [1] highr_0.10 pillar_1.9.0 bslib_0.4.2 compiler_4.2.3 + [5] jquerylib_0.1.4 tools_4.2.3 mclust_6.0.0 digest_0.6.31 + [9] tibble_3.2.1 jsonlite_1.8.4 evaluate_0.20 memoise_2.0.1 +[13] lifecycle_1.0.3 gtable_0.3.3 lattice_0.21-8 pkgconfig_2.0.3 +[17] rlang_1.1.0 DBI_1.1.3 cli_3.6.1 yaml_2.3.7 +[21] parallel_4.2.3 pkgdown_2.0.7 xfun_0.38 fastmap_1.1.1 +[25] gridExtra_2.3 dplyr_1.1.1 stringr_1.5.0 generics_0.1.3 +[29] desc_1.4.2 fs_1.6.1 vctrs_0.6.1 sass_0.4.5 +[33] systemfonts_1.0.4 tidyselect_1.2.0 rprojroot_2.0.3 lmtest_0.9-40 +[37] grid_4.2.3 glue_1.6.2 R6_2.5.1 textshaping_0.3.6 +[41] fansi_1.0.4 rmarkdown_2.21 purrr_1.0.1 ggplot2_3.4.2 +[45] magrittr_2.0.3 codetools_0.2-19 scales_1.2.1 htmltools_0.5.5 +[49] colorspace_2.1-0 ragg_1.2.5 utf8_1.2.3 stringi_1.7.12 +[53] munsell_0.5.0 cachem_1.0.7 zoo_1.8-12 </code></pre> </div> <div class="section level2"> <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a> </h2> <!-- vim: set foldmethod=syntax: --> -<div id="refs" class="references hanging-indent"> -<div id="ref-efsa_2018_dimethenamid"> -<p>EFSA. 2018. “Peer Review of the Pesticide Risk Assessment of the Active Substance Dimethenamid-P.” <em>EFSA Journal</em> 16 (4): 5211.</p> +<div id="refs" class="references csl-bib-body hanging-indent"> +<div id="ref-efsa_2018_dimethenamid" class="csl-entry"> +EFSA. 2018. <span>“Peer Review of the Pesticide Risk Assessment of the +Active Substance Dimethenamid-p.”</span> <em>EFSA Journal</em> 16: 5211. </div> -<div id="ref-ranke2021"> -<p>Ranke, Johannes, Janina Wöltjen, Jana Schmidt, and Emmanuelle Comets. 2021. “Taking Kinetic Evaluations of Degradation Data to the Next Level with Nonlinear Mixed-Effects Models.” <em>Environments</em> 8 (8). <a href="https://doi.org/10.3390/environments8080071" class="external-link">https://doi.org/10.3390/environments8080071</a>.</p> +<div id="ref-ranke2021" class="csl-entry"> +Ranke, Johannes, Janina Wöltjen, Jana Schmidt, and Emmanuelle Comets. +2021. <span>“Taking Kinetic Evaluations of Degradation Data to the Next +Level with Nonlinear Mixed-Effects Models.”</span> <em>Environments</em> +8 (8). <a href="https://doi.org/10.3390/environments8080071" class="external-link">https://doi.org/10.3390/environments8080071</a>. </div> -<div id="ref-dimethenamid_rar_2018_b8"> -<p>Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria. 2018. “Renewal Assessment Report Dimethenamid-P Volume 3 - B.8 Environmental fate and behaviour, Rev. 2 - November 2017.” <a href="https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716" class="external-link">https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716</a>.</p> +<div id="ref-dimethenamid_rar_2018_b8" class="csl-entry"> +Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria. +2018. <span>“<span class="nocase">Renewal Assessment Report +Dimethenamid-P Volume 3 - B.8 Environmental fate and behaviour, Rev. 2 - +November 2017</span>.”</span> <a href="https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716" class="external-link">https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716</a>. </div> </div> </div> @@ -516,7 +694,7 @@ loaded via a namespace (and not attached): <div class="pkgdown"> <p></p> -<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer> diff --git a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png b/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png Binary files differindex 4999e72c..505072ce 100644 --- a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png +++ b/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const-1.png diff --git a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const_test-1.png b/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const_test-1.png Binary files differindex b59764b1..505072ce 100644 --- a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const_test-1.png +++ b/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_const_test-1.png diff --git a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_tc_test-1.png b/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_tc_test-1.png Binary files differindex da7ceeb6..0dd4da39 100644 --- a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_tc_test-1.png +++ b/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_dfop_tc_test-1.png diff --git a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_sfo_const-1.png b/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_sfo_const-1.png Binary files differindex 467c3c1a..0ed7448d 100644 --- a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_sfo_const-1.png +++ b/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_mkin_sfo_const-1.png diff --git a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_sfo_tc-1.png b/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_sfo_tc-1.png Binary files differindex 800c320b..d941f3e6 100644 --- a/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_sfo_tc-1.png +++ b/docs/articles/web_only/dimethenamid_2018_files/figure-html/f_parent_saemix_sfo_tc-1.png diff --git a/docs/articles/web_only/dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png b/docs/articles/web_only/dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png Binary files differindex 4d2dc94e..a799b14c 100644 --- a/docs/articles/web_only/dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png +++ b/docs/articles/web_only/dimethenamid_2018_files/figure-html/plot_parent_nlme-1.png diff --git a/docs/articles/web_only/multistart.html b/docs/articles/web_only/multistart.html index 720c6742..04093e82 100644 --- a/docs/articles/web_only/multistart.html +++ b/docs/articles/web_only/multistart.html @@ -33,14 +33,14 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"> <li> - <a href="../../reference/index.html">Functions and data</a> + <a href="../../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -52,6 +52,9 @@ <li> <a href="../../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -59,22 +62,31 @@ <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li class="divider"> </li> +<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + </li> +<li class="dropdown-header">Performance</li> + <li> + <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -82,6 +94,15 @@ <li> <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul> </li> <li> @@ -105,13 +126,15 @@ - </header><script src="multistart_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> <h1 data-toc-skip>Short demo of the multistart method</h1> - <h4 data-toc-skip class="author">Johannes Ranke</h4> + <h4 data-toc-skip class="author">Johannes +Ranke</h4> - <h4 data-toc-skip class="date">Last change 26 September 2022 (rebuilt 2022-11-17)</h4> + <h4 data-toc-skip class="date">Last change 20 April 2023 +(rebuilt 2023-04-20)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/multistart.rmd" class="external-link"><code>vignettes/web_only/multistart.rmd</code></a></small> <div class="hidden name"><code>multistart.rmd</code></div> @@ -120,7 +143,8 @@ -<p>The dimethenamid data from 2018 from seven soils is used as example data in this vignette.</p> +<p>The dimethenamid data from 2018 from seven soils is used as example +data in this vignette.</p> <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://pkgdown.jrwb.de/mkin/">mkin</a></span><span class="op">)</span></span> <span><span class="va">dmta_ds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span> @@ -132,42 +156,52 @@ <span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span>, <span class="kw">function</span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="va">ds</span><span class="op">$</span><span class="va">title</span><span class="op">)</span></span> <span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span>, <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span> <span><span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 1"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="va">dmta_ds</span><span class="op">[[</span><span class="st">"Elliot 2"</span><span class="op">]</span><span class="op">]</span> <span class="op"><-</span> <span class="cn">NULL</span></span></code></pre></div> -<p>First, we check the DFOP model with the two-component error model and random effects for all degradation parameters.</p> +<p>First, we check the DFOP model with the two-component error model and +random effects for all degradation parameters.</p> <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">f_mmkin</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/mmkin.html">mmkin</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">dmta_ds</span>, error_model <span class="op">=</span> <span class="st">"tc"</span>, cores <span class="op">=</span> <span class="fl">7</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> <span><span class="va">f_saem_full</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/saem.html">saem</a></span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">)</span></span> <span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_full</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## [1] "sd(log_k2)"</span></span></code></pre> -<p>We see that not all variability parameters are identifiable. The <code>illparms</code> function tells us that the confidence interval for the standard deviation of ‘log_k2’ includes zero. We check this assessment using multiple runs with different starting values.</p> +<p>We see that not all variability parameters are identifiable. The +<code>illparms</code> function tells us that the confidence interval for +the standard deviation of ‘log_k2’ includes zero. We check this +assessment using multiple runs with different starting values.</p> <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">f_saem_full_multi</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/multistart.html">multistart</a></span><span class="op">(</span><span class="va">f_saem_full</span>, n <span class="op">=</span> <span class="fl">16</span>, cores <span class="op">=</span> <span class="fl">16</span><span class="op">)</span></span> -<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_full_multi</span><span class="op">)</span></span></code></pre></div> +<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_full_multi</span>, lpos <span class="op">=</span> <span class="st">"topleft"</span><span class="op">)</span></span></code></pre></div> <p><img src="multistart_files/figure-html/unnamed-chunk-3-1.png" width="700"></p> -<p>This confirms that the variance of k2 is the most problematic parameter, so we reduce the parameter distribution model by removing the intersoil variability for k2.</p> +<p>This confirms that the variance of k2 is the most problematic +parameter, so we reduce the parameter distribution model by removing the +intersoil variability for k2.</p> <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">f_saem_reduced</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_full</span>, no_random_effect <span class="op">=</span> <span class="st">"log_k2"</span><span class="op">)</span></span> <span><span class="fu"><a href="../../reference/illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_reduced</span><span class="op">)</span></span> <span><span class="va">f_saem_reduced_multi</span> <span class="op"><-</span> <span class="fu"><a href="../../reference/multistart.html">multistart</a></span><span class="op">(</span><span class="va">f_saem_reduced</span>, n <span class="op">=</span> <span class="fl">16</span>, cores <span class="op">=</span> <span class="fl">16</span><span class="op">)</span></span> -<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_reduced_multi</span>, lpos <span class="op">=</span> <span class="st">"topright"</span><span class="op">)</span></span></code></pre></div> +<span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_reduced_multi</span>, lpos <span class="op">=</span> <span class="st">"topright"</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> <p><img src="multistart_files/figure-html/unnamed-chunk-4-1.png" width="700"></p> -<p>The results confirm that all remaining parameters can be determined with sufficient certainty.</p> -<p>We can also analyse the log-likelihoods obtained in the multiple runs:</p> +<p>The results confirm that all remaining parameters can be determined +with sufficient certainty.</p> +<p>We can also analyse the log-likelihoods obtained in the multiple +runs:</p> <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="../../reference/llhist.html">llhist</a></span><span class="op">(</span><span class="va">f_saem_reduced_multi</span><span class="op">)</span></span></code></pre></div> <p><img src="multistart_files/figure-html/unnamed-chunk-5-1.png" width="700"></p> -<p>The parameter histograms can be further improved by excluding the result with the low likelihood.</p> +<p>We can use the <code>anova</code> method to compare the models.</p> <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="../../reference/parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_reduced_multi</span>, lpos <span class="op">=</span> <span class="st">"topright"</span>, llmin <span class="op">=</span> <span class="op">-</span><span class="fl">326</span>, ylim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> -<p><img src="multistart_files/figure-html/unnamed-chunk-6-1.png" width="700"></p> -<p>We can use the <code>anova</code> method to compare the models, including a likelihood ratio test if the models are nested.</p> -<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r"> -<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_full</span>, <span class="fu"><a href="../../reference/multistart.html">best</a></span><span class="op">(</span><span class="va">f_saem_reduced_multi</span><span class="op">)</span>, test <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div> +<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_full</span>, <span class="fu"><a href="../../reference/multistart.html">best</a></span><span class="op">(</span><span class="va">f_saem_full_multi</span><span class="op">)</span>,</span> +<span> <span class="va">f_saem_reduced</span>, <span class="fu"><a href="../../reference/multistart.html">best</a></span><span class="op">(</span><span class="va">f_saem_reduced_multi</span><span class="op">)</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## Data: 155 observations of 1 variable(s) grouped in 6 datasets</span></span> <span><span class="co">## </span></span> -<span><span class="co">## npar AIC BIC Lik Chisq Df Pr(>Chisq)</span></span> -<span><span class="co">## best(f_saem_reduced_multi) 9 663.69 661.82 -322.85 </span></span> -<span><span class="co">## f_saem_full 10 669.77 667.69 -324.89 0 1 1</span></span></code></pre> -<p>While AIC and BIC are lower for the reduced model, the likelihood ratio test does not indicate a significant difference between the fits.</p> +<span><span class="co">## npar AIC BIC Lik</span></span> +<span><span class="co">## f_saem_reduced 9 663.73 661.86 -322.86</span></span> +<span><span class="co">## best(f_saem_reduced_multi) 9 663.69 661.82 -322.85</span></span> +<span><span class="co">## f_saem_full 10 669.77 667.69 -324.89</span></span> +<span><span class="co">## best(f_saem_full_multi) 10 665.56 663.48 -322.78</span></span></code></pre> +<p>The reduced model gives the lowest information criteria and similar +likelihoods as the best variant of the full model. The multistart method +leads to a much lower improvement of the likelihood for the reduced +model, indicating that it converges faster.</p> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> @@ -185,7 +219,7 @@ <div class="pkgdown"> <p></p> -<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer> diff --git a/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-3-1.png b/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-3-1.png Binary files differindex 28991ae8..1ef2ba24 100644 --- a/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-3-1.png +++ b/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-3-1.png diff --git a/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-4-1.png b/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-4-1.png Binary files differindex 56147ae2..b1582557 100644 --- a/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-4-1.png +++ b/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-4-1.png diff --git a/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-5-1.png b/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-5-1.png Binary files differindex 7ce108a2..f0270537 100644 --- a/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-5-1.png +++ b/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-5-1.png diff --git a/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-6-1.png b/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-6-1.png Binary files differindex 00ccbaa8..b1582557 100644 --- a/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-6-1.png +++ b/docs/articles/web_only/multistart_files/figure-html/unnamed-chunk-6-1.png diff --git a/docs/articles/web_only/saem_benchmarks.html b/docs/articles/web_only/saem_benchmarks.html index 523d028c..587ee4a2 100644 --- a/docs/articles/web_only/saem_benchmarks.html +++ b/docs/articles/web_only/saem_benchmarks.html @@ -33,14 +33,14 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"> <li> - <a href="../../reference/index.html">Functions and data</a> + <a href="../../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -52,6 +52,9 @@ <li> <a href="../../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -59,22 +62,31 @@ <a href="../../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + </li> +<li class="dropdown-header">Performance</li> + <li> + <a href="../../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -82,6 +94,15 @@ <li> <a href="../../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul> </li> <li> @@ -105,13 +126,15 @@ - </header><script src="saem_benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> <h1 data-toc-skip>Benchmark timings for saem.mmkin</h1> - <h4 data-toc-skip class="author">Johannes Ranke</h4> + <h4 data-toc-skip class="author">Johannes +Ranke</h4> - <h4 data-toc-skip class="date">Last change 14 November 2022 (rebuilt 2022-11-17)</h4> + <h4 data-toc-skip class="date">Last change 17 February 2023 +(rebuilt 2023-04-20)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/vignettes/web_only/saem_benchmarks.rmd" class="external-link"><code>vignettes/web_only/saem_benchmarks.rmd</code></a></small> <div class="hidden name"><code>saem_benchmarks.rmd</code></div> @@ -120,15 +143,19 @@ -<p>Each system is characterized by operating system type, CPU type, mkin version, saemix version and R version. A compiler was available, so if no analytical solution was available, compiled ODE models are used.</p> -<p>Every fit is only performed once, so the accuracy of the benchmarks is limited.</p> +<p>Each system is characterized by operating system type, CPU type, mkin +version, saemix version and R version. A compiler was available, so if +no analytical solution was available, compiled ODE models are used.</p> +<p>Every fit is only performed once, so the accuracy of the benchmarks +is limited.</p> <p>For the initial mmkin fits, we use all available cores.</p> <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">n_cores</span> <span class="op"><-</span> <span class="fu">parallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/r/parallel/detectCores.html" class="external-link">detectCores</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div> <div class="section level2"> <h2 id="test-data">Test data<a class="anchor" aria-label="anchor" href="#test-data"></a> </h2> -<p>Please refer to the vignette <code>dimethenamid_2018</code> for an explanation of the following preprocessing.</p> +<p>Please refer to the vignette <code>dimethenamid_2018</code> for an +explanation of the following preprocessing.</p> <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">dmta_ds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">7</span>, <span class="kw">function</span><span class="op">(</span><span class="va">i</span><span class="op">)</span> <span class="op">{</span></span> <span> <span class="va">ds_i</span> <span class="op"><-</span> <span class="va">dimethenamid_2018</span><span class="op">$</span><span class="va">ds</span><span class="op">[[</span><span class="va">i</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span></span> @@ -163,7 +190,7 @@ <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span></span> <span> <span class="va">sfo_const</span>, <span class="va">dfop_const</span>, <span class="va">sforb_const</span>, <span class="va">hs_const</span>,</span> -<span> <span class="va">sfo_tc</span>, <span class="va">dfop_tc</span>, <span class="va">sforb_tc</span>, <span class="va">hs_tc</span><span class="op">)</span> <span class="op">|></span> <span class="fu">kable</span><span class="op">(</span>, digits <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div> +<span> <span class="va">sfo_tc</span>, <span class="va">dfop_tc</span>, <span class="va">sforb_tc</span>, <span class="va">hs_tc</span><span class="op">)</span> <span class="op">|></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">1</span><span class="op">)</span></span></code></pre></div> <table class="table"> <thead><tr class="header"> <th align="left"></th> @@ -231,19 +258,24 @@ </tr> </tbody> </table> -<p>The above model comparison suggests to use the SFORB model with two-component error. For comparison, we keep the DFOP model with two-component error, as it competes with SFORB for biphasic curves.</p> +<p>The above model comparison suggests to use the SFORB model with +two-component error. For comparison, we keep the DFOP model with +two-component error, as it competes with SFORB for biphasic curves.</p> <div class="sourceCode" id="cb5"><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">dfop_tc</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## [1] "sd(log_k2)"</span></span></code></pre> <div class="sourceCode" id="cb7"><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">sforb_tc</span><span class="op">)</span></span></code></pre></div> <pre><code><span><span class="co">## [1] "sd(log_k_DMTA_bound_free)"</span></span></code></pre> -<p>For these two models, random effects for the transformed parameters <code>k2</code> and <code>k_DMTA_bound_free</code> could not be quantified.</p> +<p>For these two models, random effects for the transformed parameters +<code>k2</code> and <code>k_DMTA_bound_free</code> could not be +quantified.</p> </div> <div class="section level3"> <h3 id="one-metabolite">One metabolite<a class="anchor" aria-label="anchor" href="#one-metabolite"></a> </h3> -<p>We remove parameters that were found to be ill-defined in the parent only fits.</p> +<p>We remove parameters that were found to be ill-defined in the parent +only fits.</p> <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span><span class="va">one_met_mods</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span> <span> DFOP_SFO <span class="op">=</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span><span class="op">(</span></span> @@ -266,7 +298,11 @@ <div class="section level3"> <h3 id="three-metabolites">Three metabolites<a class="anchor" aria-label="anchor" href="#three-metabolites"></a> </h3> -<p>For the case of three metabolites, we only keep the SFORB model in order to limit the time for compiling this vignette, and as fitting in parallel may disturb the benchmark. Again, we do not include random effects that were ill-defined in previous fits of subsets of the degradation model.</p> +<p>For the case of three metabolites, we only keep the SFORB model in +order to limit the time for compiling this vignette, and as fitting in +parallel may disturb the benchmark. Again, we do not include random +effects that were ill-defined in previous fits of subsets of the +degradation model.</p> <div class="sourceCode" id="cb10"><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">sforb_sfo_tc</span><span class="op">)</span></span></code></pre></div> <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r"> @@ -287,7 +323,9 @@ <div class="section level2"> <h2 id="results">Results<a class="anchor" aria-label="anchor" href="#results"></a> </h2> -<p>Benchmarks for all available error models are shown. They are intended for improving mkin, not for comparing CPUs or operating systems. All trademarks belong to their respective owners.</p> +<p>Benchmarks for all available error models are shown. They are +intended for improving mkin, not for comparing CPUs or operating +systems. All trademarks belong to their respective owners.</p> <div class="section level3"> <h3 id="parent-only-1">Parent only<a class="anchor" aria-label="anchor" href="#parent-only-1"></a> </h3> @@ -303,16 +341,68 @@ <th align="right">t3</th> <th align="right">t4</th> </tr></thead> -<tbody><tr class="odd"> +<tbody> +<tr class="odd"> <td align="left">Ryzen 7 1700</td> <td align="left">Linux</td> <td align="left">1.2.0</td> <td align="left">3.2</td> -<td align="right">2.14</td> +<td align="right">2.140</td> <td align="right">4.626</td> <td align="right">4.328</td> <td align="right">4.998</td> -</tr></tbody> +</tr> +<tr class="even"> +<td align="left">Ryzen 7 1700</td> +<td align="left">Linux</td> +<td align="left">1.2.2</td> +<td align="left">3.2</td> +<td align="right">2.427</td> +<td align="right">4.550</td> +<td align="right">4.217</td> +<td align="right">4.851</td> +</tr> +<tr class="odd"> +<td align="left">Ryzen 9 7950X</td> +<td align="left">Linux</td> +<td align="left">1.2.1</td> +<td align="left">3.2</td> +<td align="right">1.352</td> +<td align="right">2.813</td> +<td align="right">2.401</td> +<td align="right">2.074</td> +</tr> +<tr class="even"> +<td align="left">Ryzen 9 7950X</td> +<td align="left">Linux</td> +<td align="left">1.2.2</td> +<td align="left">3.2</td> +<td align="right">1.328</td> +<td align="right">2.738</td> +<td align="right">2.336</td> +<td align="right">2.023</td> +</tr> +<tr class="odd"> +<td align="left">Ryzen 9 7950X</td> +<td align="left">Linux</td> +<td align="left">1.2.3</td> +<td align="left">3.2</td> +<td align="right">1.118</td> +<td align="right">2.036</td> +<td align="right">2.010</td> +<td align="right">2.088</td> +</tr> +<tr class="even"> +<td align="left">Ryzen 9 7950X</td> +<td align="left">Linux</td> +<td align="left">1.2.3</td> +<td align="left">3.2</td> +<td align="right">1.419</td> +<td align="right">2.374</td> +<td align="right">1.926</td> +<td align="right">2.398</td> +</tr> +</tbody> </table> <p>Two-component error fits for SFO, DFOP, SFORB and HS.</p> <table class="table"> @@ -326,16 +416,68 @@ <th align="right">t7</th> <th align="right">t8</th> </tr></thead> -<tbody><tr class="odd"> +<tbody> +<tr class="odd"> <td align="left">Ryzen 7 1700</td> <td align="left">Linux</td> <td align="left">1.2.0</td> <td align="left">3.2</td> <td align="right">5.678</td> <td align="right">7.441</td> -<td align="right">8</td> -<td align="right">7.98</td> -</tr></tbody> +<td align="right">8.000</td> +<td align="right">7.980</td> +</tr> +<tr class="even"> +<td align="left">Ryzen 7 1700</td> +<td align="left">Linux</td> +<td align="left">1.2.2</td> +<td align="left">3.2</td> +<td align="right">5.352</td> +<td align="right">7.201</td> +<td align="right">8.174</td> +<td align="right">8.401</td> +</tr> +<tr class="odd"> +<td align="left">Ryzen 9 7950X</td> +<td align="left">Linux</td> +<td align="left">1.2.1</td> +<td align="left">3.2</td> +<td align="right">2.388</td> +<td align="right">3.033</td> +<td align="right">3.532</td> +<td align="right">3.310</td> +</tr> +<tr class="even"> +<td align="left">Ryzen 9 7950X</td> +<td align="left">Linux</td> +<td align="left">1.2.2</td> +<td align="left">3.2</td> +<td align="right">2.341</td> +<td align="right">2.968</td> +<td align="right">3.465</td> +<td align="right">3.341</td> +</tr> +<tr class="odd"> +<td align="left">Ryzen 9 7950X</td> +<td align="left">Linux</td> +<td align="left">1.2.3</td> +<td align="left">3.2</td> +<td align="right">2.159</td> +<td align="right">3.584</td> +<td align="right">3.307</td> +<td align="right">3.460</td> +</tr> +<tr class="even"> +<td align="left">Ryzen 9 7950X</td> +<td align="left">Linux</td> +<td align="left">1.2.3</td> +<td align="left">3.2</td> +<td align="right">2.348</td> +<td align="right">3.134</td> +<td align="right">3.253</td> +<td align="right">3.530</td> +</tr> +</tbody> </table> </div> <div class="section level3"> @@ -351,14 +493,56 @@ <th align="right">t9</th> <th align="right">t10</th> </tr></thead> -<tbody><tr class="odd"> +<tbody> +<tr class="odd"> <td align="left">Ryzen 7 1700</td> <td align="left">Linux</td> <td align="left">1.2.0</td> <td align="left">3.2</td> <td align="right">24.465</td> <td align="right">800.266</td> -</tr></tbody> +</tr> +<tr class="even"> +<td align="left">Ryzen 7 1700</td> +<td align="left">Linux</td> +<td align="left">1.2.2</td> +<td align="left">3.2</td> +<td align="right">25.193</td> +<td align="right">798.580</td> +</tr> +<tr class="odd"> +<td align="left">Ryzen 9 7950X</td> +<td align="left">Linux</td> +<td align="left">1.2.1</td> +<td align="left">3.2</td> +<td align="right">11.247</td> +<td align="right">285.216</td> +</tr> +<tr class="even"> +<td align="left">Ryzen 9 7950X</td> +<td align="left">Linux</td> +<td align="left">1.2.2</td> +<td align="left">3.2</td> +<td align="right">11.242</td> +<td align="right">284.258</td> +</tr> +<tr class="odd"> +<td align="left">Ryzen 9 7950X</td> +<td align="left">Linux</td> +<td align="left">1.2.3</td> +<td align="left">3.2</td> +<td align="right">11.796</td> +<td align="right">216.012</td> +</tr> +<tr class="even"> +<td align="left">Ryzen 9 7950X</td> +<td align="left">Linux</td> +<td align="left">1.2.3</td> +<td align="left">3.2</td> +<td align="right">12.841</td> +<td align="right">292.688</td> +</tr> +</tbody> </table> </div> <div class="section level3"> @@ -373,13 +557,50 @@ <th align="left">saemix</th> <th align="right">t11</th> </tr></thead> -<tbody><tr class="odd"> +<tbody> +<tr class="odd"> <td align="left">Ryzen 7 1700</td> <td align="left">Linux</td> <td align="left">1.2.0</td> <td align="left">3.2</td> <td align="right">1289.198</td> -</tr></tbody> +</tr> +<tr class="even"> +<td align="left">Ryzen 7 1700</td> +<td align="left">Linux</td> +<td align="left">1.2.2</td> +<td align="left">3.2</td> +<td align="right">1312.445</td> +</tr> +<tr class="odd"> +<td align="left">Ryzen 9 7950X</td> +<td align="left">Linux</td> +<td align="left">1.2.1</td> +<td align="left">3.2</td> +<td align="right">489.939</td> +</tr> +<tr class="even"> +<td align="left">Ryzen 9 7950X</td> +<td align="left">Linux</td> +<td align="left">1.2.2</td> +<td align="left">3.2</td> +<td align="right">482.970</td> +</tr> +<tr class="odd"> +<td align="left">Ryzen 9 7950X</td> +<td align="left">Linux</td> +<td align="left">1.2.3</td> +<td align="left">3.2</td> +<td align="right">392.364</td> +</tr> +<tr class="even"> +<td align="left">Ryzen 9 7950X</td> +<td align="left">Linux</td> +<td align="left">1.2.3</td> +<td align="left">3.2</td> +<td align="right">483.027</td> +</tr> +</tbody> </table> </div> </div> @@ -402,7 +623,7 @@ <div class="pkgdown"> <p></p> -<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer> diff --git a/docs/authors.html b/docs/authors.html index 7afe3000..775f84d8 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -17,13 +17,13 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.1</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3.1</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="reference/index.html">Functions and data</a> + <a href="reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -34,6 +34,8 @@ <ul class="dropdown-menu" role="menu"><li> <a href="articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -41,22 +43,29 @@ <a href="articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -64,6 +73,14 @@ <li> <a href="articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="news/index.html">News</a> @@ -113,15 +130,15 @@ </div> - <p>Ranke J (2022). + <p>Ranke J (2023). <em>mkin: Kinetic Evaluation of Chemical Degradation Data</em>. -R package version 1.2.1, <a href="https://pkgdown.jrwb.de/mkin/">https://pkgdown.jrwb.de/mkin/</a>. +R package version 1.2.3.1, <a href="https://pkgdown.jrwb.de/mkin/">https://pkgdown.jrwb.de/mkin/</a>. </p> <pre>@Manual{, title = {mkin: Kinetic Evaluation of Chemical Degradation Data}, author = {Johannes Ranke}, - year = {2022}, - note = {R package version 1.2.1}, + year = {2023}, + note = {R package version 1.2.3.1}, url = {https://pkgdown.jrwb.de/mkin/}, }</pre> @@ -136,7 +153,7 @@ R package version 1.2.1, <a href="https://pkgdown.jrwb.de/mkin/">https://pkgdown </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/index.html b/docs/index.html index bb14906d..01300749 100644 --- a/docs/index.html +++ b/docs/index.html @@ -19,11 +19,11 @@ equation models are solved using automatically generated C functions. Heteroscedasticity can be taken into account using variance by variable or two-component error models as described by Ranke and Meinecke (2018) - <doi:10.3390/environments6120124>. Interfaces to several nonlinear - mixed-effects model packages are available, some of which are described by - Ranke et al. (2021) <doi:10.3390/environments8080071>. Please note that no - warranty is implied for correctness of results or fitness for a particular - purpose."> + <doi:10.3390/environments6120124>. Hierarchical degradation models can + be fitted using nonlinear mixed-effects model packages as a back end as + described by Ranke et al. (2021) <doi:10.3390/environments8080071>. Please + note that no warranty is implied for correctness of results or fitness for a + particular purpose."> <!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> @@ -44,14 +44,14 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.1</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3.1</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"> <li> - <a href="reference/index.html">Functions and data</a> + <a href="reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -63,6 +63,9 @@ <li> <a href="articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -70,22 +73,31 @@ <a href="articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li class="divider"> </li> +<li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + </li> +<li class="dropdown-header">Performance</li> + <li> + <a href="articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -93,6 +105,15 @@ <li> <a href="articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + </li> +<li class="dropdown-header">Miscellaneous</li> + <li> + <a href="articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul> </li> <li> @@ -121,8 +142,8 @@ <div class="section level1"> <div class="page-header"><h1 id="mkin">mkin<a class="anchor" aria-label="anchor" href="#mkin"></a> </h1></div> - -<p>The R package <strong>mkin</strong> provides calculation routines for the analysis of chemical degradation data, including <b>m</b>ulticompartment <b>kin</b>etics as needed for modelling the formation and decline of transformation products, or if several degradation compartments are involved.</p> +<p><a href="https://cran.r-project.org/package=mkin" class="external-link"><img src="https://www.r-pkg.org/badges/version/mkin"></a> <a href="https://jranke.r-universe.dev/ui/#package:mkin" class="external-link"><img src="https://jranke.r-universe.dev/badges/mkin" alt="mkin status badge"></a> <a href="https://app.travis-ci.com/github/jranke/mkin" class="external-link"><img src="https://travis-ci.com/jranke/mkin.svg?branch=main" alt="Build Status"></a> <a href="https://app.codecov.io/gh/jranke/mkin" class="external-link"><img src="https://codecov.io/github/jranke/mkin/branch/main/graphs/badge.svg" alt="codecov"></a></p> +<p>The <a href="https://www.r-project.org" class="external-link">R</a> package <strong>mkin</strong> provides calculation routines for the analysis of chemical degradation data, including <b>m</b>ulticompartment <b>kin</b>etics as needed for modelling the formation and decline of transformation products, or if several degradation compartments are involved. It provides stable functionality for kinetic evaluations according to the FOCUS guidance (see below for details). In addition, it provides functionality to do hierarchical kinetics based on nonlinear mixed-effects models.</p> <div class="section level2"> <h2 id="installation">Installation<a class="anchor" aria-label="anchor" href="#installation"></a> </h2> @@ -133,17 +154,18 @@ <div class="section level2"> <h2 id="background">Background<a class="anchor" aria-label="anchor" href="#background"></a> </h2> -<p>In the regulatory evaluation of chemical substances like plant protection products (pesticides), biocides and other chemicals, degradation data play an important role. For the evaluation of pesticide degradation experiments, detailed guidance and helpful tools have been developed as detailed in ‘Credits and historical remarks’ below.</p> +<p>In the regulatory evaluation of chemical substances like plant protection products (pesticides), biocides and other chemicals, degradation data play an important role. For the evaluation of pesticide degradation experiments, detailed guidance and various helpful tools have been developed as detailed in ‘Credits and historical remarks’ below. This package aims to provide a one stop solution for degradation kinetics, addressing modellers that are willing to, or even prefer to work with R.</p> </div> <div class="section level2"> -<h2 id="usage">Usage<a class="anchor" aria-label="anchor" href="#usage"></a> +<h2 id="basic-usage">Basic usage<a class="anchor" aria-label="anchor" href="#basic-usage"></a> </h2> <p>For a start, have a look at the code examples provided for <a href="https://pkgdown.jrwb.de/mkin/reference/plot.mkinfit.html"><code>plot.mkinfit</code></a> and <a href="https://pkgdown.jrwb.de/mkin/reference/plot.mmkin.html"><code>plot.mmkin</code></a>, and at the package vignettes <a href="https://pkgdown.jrwb.de/mkin/articles/FOCUS_L.html"><code>FOCUS L</code></a> and <a href="https://pkgdown.jrwb.de/mkin/articles/FOCUS_D.html"><code>FOCUS D</code></a>.</p> </div> <div class="section level2"> <h2 id="documentation">Documentation<a class="anchor" aria-label="anchor" href="#documentation"></a> </h2> -<p>The HTML documentation of the latest version released to CRAN is available at <a href="https://pkgdown.jrwb.de/mkin/">jrwb.de</a> and <a href="https://jranke.github.io/mkin/" class="external-link">github</a>. Documentation of the development version is found in the <a href="https://pkgdown.jrwb.de/mkin/dev/">‘dev’ subdirectory</a>.</p> +<p>The HTML documentation of the latest version released to CRAN is available at <a href="https://pkgdown.jrwb.de/mkin/">jrwb.de</a> and <a href="https://jranke.github.io/mkin/" class="external-link">github</a>.</p> +<p>Documentation of the development version is found in the <a href="https://pkgdown.jrwb.de/mkin/dev/">‘dev’ subdirectory</a>. In the articles section of this documentation, you can also find demonstrations of the application of nonlinear hierarchical models, also known as nonlinear mixed-effects models, to more complex data, including transformation products and covariates.</p> </div> <div class="section level2"> <h2 id="features">Features<a class="anchor" aria-label="anchor" href="#features"></a> @@ -152,9 +174,9 @@ <h3 id="general">General<a class="anchor" aria-label="anchor" href="#general"></a> </h3> <ul> -<li>Highly flexible model specification using <a href="https://pkgdown.jrwb.de/mkin/reference/mkinmod.html"><code>mkinmod</code></a>, including equilibrium reactions and using the single first-order reversible binding (SFORB) model, which will automatically create two latent state variables for the observed variable.</li> -<li>Model solution (forward modelling) in the function <a href="https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html"><code>mkinpredict</code></a> is performed either using the analytical solution for the case of parent only degradation, an eigenvalue based solution if only simple first-order (SFO) or SFORB kinetics are used in the model, or using a numeric solver from the <code>deSolve</code> package (default is <code>lsoda</code>).</li> -<li>The usual one-sided t-test for significant difference from zero is nevertheless shown based on estimators for the untransformed parameters.</li> +<li>Highly flexible model specification using <a href="https://pkgdown.jrwb.de/mkin/reference/mkinmod.html"><code>mkinmod</code></a>, including equilibrium reactions and using the single first-order reversible binding (SFORB) model, which will automatically create two state variables for the observed variable.</li> +<li>Model solution (forward modelling) in the function <a href="https://pkgdown.jrwb.de/mkin/reference/mkinpredict.html"><code>mkinpredict</code></a> is performed either using the analytical solution for the case of parent only degradation or some simple models involving a single transformation product, , an eigenvalue based solution if only simple first-order (SFO) or SFORB kinetics are used in the model, or using a numeric solver from the <code>deSolve</code> package (default is <code>lsoda</code>).</li> +<li>The usual one-sided t-test for significant difference from zero is shown based on estimators for the untransformed parameters.</li> <li>Summary and plotting functions. The <code>summary</code> of an <code>mkinfit</code> object is in fact a full report that should give enough information to be able to approximately reproduce the fit with other tools.</li> <li>The chi-squared error level as defined in the FOCUS kinetics guidance (see below) is calculated for each observed variable.</li> <li>The ‘variance by variable’ error model which is often fitted using Iteratively Reweighted Least Squares (IRLS) can be specified as <code>error_model = "obs"</code>.</li> @@ -168,8 +190,8 @@ <li>Model comparisons using the Akaike Information Criterion (AIC) are supported which can also be used for non-constant variance. In such cases the FOCUS chi-squared error level is not meaningful.</li> <li>By default, kinetic rate constants and kinetic formation fractions are transformed internally using <a href="https://pkgdown.jrwb.de/mkin/reference/transform_odeparms.html"><code>transform_odeparms</code></a> so their estimators can more reasonably be expected to follow a normal distribution.</li> <li>When parameter estimates are backtransformed to match the model definition, confidence intervals calculated from standard errors are also backtransformed to the correct scale, and will not include meaningless values like negative rate constants or formation fractions adding up to more than 1, which cannot occur in a single experiment with a single defined radiolabel position.</li> -<li>When a metabolite decline phase is not described well by SFO kinetics, SFORB kinetics can be used for the metabolite. Mathematically, the SFORB model is equivalent to the DFOP model used by other tools for biphasic metabolite curves. However, the SFORB model has the advantage that there is a mechanistic interpretation of the model parameters.</li> -<li>Nonlinear mixed-effects models can be created from fits of the same degradation model to different datasets for the same compound by using the <a href="https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html">nlme.mmkin</a> and <a href="https://pkgdown.jrwb.de/mkin/reference/saem.html">saem.mmkin</a> and methods. Note that the convergence of the nlme fits depends on the quality of the data. Convergence is better for simple models and data for many groups (e.g. soils). The saem method uses the <code>saemix</code> package as a backend. Analytical solutions suitable for use with this package have been implemented for parent only models and the most important models including one metabolite (SFO-SFO and DFOP-SFO). Fitting other models with <code>saem.mmkin</code>, while it makes use of the compiled ODE models that mkin provides, has longer run times (at least six minutes on my system).</li> +<li>When a metabolite decline phase is not described well by SFO kinetics, SFORB kinetics can be used for the metabolite. Mathematically, the SFORB model is equivalent to the DFOP model. However, the SFORB model has the advantage that there is a mechanistic interpretation of the model parameters.</li> +<li>Nonlinear mixed-effects models (hierarchical models) can be created from fits of the same degradation model to different datasets for the same compound by using the <a href="https://pkgdown.jrwb.de/mkin/reference/nlme.mmkin.html">nlme.mmkin</a> and <a href="https://pkgdown.jrwb.de/mkin/reference/saem.html">saem.mmkin</a> methods. Note that the convergence of the nlme fits depends on the quality of the data. Convergence is better for simple models and data for many groups (e.g. soils). The saem method uses the <code>saemix</code> package as a backend. Analytical solutions suitable for use with this package have been implemented for parent only models and the most important models including one metabolite (SFO-SFO and DFOP-SFO). Fitting other models with <code>saem.mmkin</code>, while it makes use of the compiled ODE models that mkin provides, has longer run times (from a couple of minutes to more than an hour).</li> </ul> </div> <div class="section level3"> @@ -185,7 +207,7 @@ <div class="section level2"> <h2 id="gui">GUI<a class="anchor" aria-label="anchor" href="#gui"></a> </h2> -<p>There is a graphical user interface that may be useful. Please refer to its <a href="https://pkgdown.jrwb.de/gmkin/" class="external-link">documentation page</a> for installation instructions and a manual.</p> +<p>There is a graphical user interface that may be useful. Please refer to its <a href="https://pkgdown.jrwb.de/gmkin/" class="external-link">documentation page</a> for installation instructions and a manual. It only supports evaluations using (generalised) nonlinear regression, but not simultaneous fits using nonlinear mixed-effects models.</p> </div> <div class="section level2"> <h2 id="news">News<a class="anchor" aria-label="anchor" href="#news"></a> @@ -202,8 +224,8 @@ <p>The first <code>mkin</code> code was <a href="https://r-forge.r-project.org/scm/viewvc.php?view=rev&root=kinfit&revision=8" class="external-link">published on 11 May 2010</a> and the <a href="https://cran.r-project.org/src/contrib/Archive/mkin/" class="external-link">first CRAN version</a> on 18 May 2010.</p> <p>In 2011, Bayer Crop Science started to distribute an R based successor to KinGUI named KinGUII whose R code is based on <code>mkin</code>, but which added, among other refinements, a closed source graphical user interface (GUI), iteratively reweighted least squares (IRLS) optimisation of the variance for each of the observed variables, and Markov Chain Monte Carlo (MCMC) simulation functionality, similar to what is available e.g. in the <code>FME</code> package.</p> <p>Somewhat in parallel, Syngenta has sponsored the development of an <code>mkin</code> and KinGUII based GUI application called CAKE, which also adds IRLS and MCMC, is more limited in the model formulation, but puts more weight on usability. CAKE is available for download from the <a href="https://cake-kinetics.org" class="external-link">CAKE website</a>, where you can also find a zip archive of the R scripts derived from <code>mkin</code>, published under the GPL license.</p> -<p>Finally, there is <a href="https://github.com/zhenglei-gao/KineticEval" class="external-link">KineticEval</a>, which contains a further development of the scripts used for KinGUII, so the different tools will hopefully be able to learn from each other in the future as well.</p> -<p>Thanks to René Lehmann, formerly working at the Umweltbundesamt, for the nice cooperation cooperation on parameter transformations, especially the isometric log-ratio transformation that is now used for formation fractions in case there are more than two transformation targets.</p> +<p>Finally, there is <a href="https://github.com/zhenglei-gao/KineticEval" class="external-link">KineticEval</a>, which contains some further development of the scripts used for KinGUII.</p> +<p>Thanks to René Lehmann, formerly working at the Umweltbundesamt, for the nice cooperation on parameter transformations, especially the isometric log-ratio transformation that is now used for formation fractions in case there are more than two transformation targets.</p> <p>Many inspirations for improvements of mkin resulted from doing kinetic evaluations of degradation data for my clients while working at Harlan Laboratories and at Eurofins Regulatory AG, and now as an independent consultant.</p> <p>Funding was received from the Umweltbundesamt in the course of the projects</p> <ul> @@ -213,19 +235,30 @@ <li>Project Number 112407 (Testing the feasibility of using an error model according to Rocke and Lorenzato for more realistic parameter estimates in the kinetic evaluation of degradation data, 2018-2019)</li> <li>Project Number 120667 (Development of objective criteria for the evaluation of the visual fit in the kinetic evaluation of degradation data, 2019-2020)</li> <li>Project Number 146839 (Checking the feasibility of using mixed-effects models for the derivation of kinetic modelling parameters from degradation studies, 2020-2021)</li> +<li>Project Number 173340 (Application of nonlinear hierarchical models to the kinetic evaluation of chemical degradation data)</li> </ul> -<p>Thanks are due also to Emmanuelle Comets, maintainer of the saemix package, for the nice collaboration on using the SAEM algorithm and its implementation in saemix for the evaluation of chemical degradation data.</p> +<p>Thanks to everyone involved for collaboration and support!</p> +<p>Thanks are due also to Emmanuelle Comets, maintainer of the saemix package, for her interest and support for using the SAEM algorithm and its implementation in saemix for the evaluation of chemical degradation data.</p> </div> <div class="section level2"> <h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a> </h2> <table class="table"> -<tr><td>Ranke J, Wöltjen J, Schmidt J, and Comets E (2021) Taking kinetic evaluations of degradation data to the next level with nonlinear mixed-effects models. <i>Environments</i> <b>8</b> (8) 71 <a href="https://doi.org/10.3390/environments8080071" class="external-link">doi:10.3390/environments8080071</a> -</td></tr> -<tr><td>Ranke J, Meinecke S (2019) Error Models for the Kinetic Evaluation of Chemical Degradation Data <i>Environments</i> <b>6</b> (12) 124 <a href="https://doi.org/10.3390/environments6120124" class="external-link">doi:10.3390/environments6120124</a> -</td></tr> -<tr><td>Ranke J, Wöltjen J, Meinecke S (2018) Comparison of software tools for kinetic evaluation of chemical degradation data <i>Environmental Sciences Europe</i> <b>30</b> 17 <a href="https://doi.org/10.1186/s12302-018-0145-1" class="external-link">doi:10.1186/s12302-018-0145-1</a> -</td></tr> +<tr> +<td> +Ranke J, Wöltjen J, Schmidt J, and Comets E (2021) Taking kinetic evaluations of degradation data to the next level with nonlinear mixed-effects models. <i>Environments</i> <b>8</b> (8) 71 <a href="https://doi.org/10.3390/environments8080071" class="external-link">doi:10.3390/environments8080071</a> +</td> +</tr> +<tr> +<td> +Ranke J, Meinecke S (2019) Error Models for the Kinetic Evaluation of Chemical Degradation Data <i>Environments</i> <b>6</b> (12) 124 <a href="https://doi.org/10.3390/environments6120124" class="external-link">doi:10.3390/environments6120124</a> +</td> +</tr> +<tr> +<td> +Ranke J, Wöltjen J, Meinecke S (2018) Comparison of software tools for kinetic evaluation of chemical degradation data <i>Environmental Sciences Europe</i> <b>30</b> 17 <a href="https://doi.org/10.1186/s12302-018-0145-1" class="external-link">doi:10.1186/s12302-018-0145-1</a> +</td> +</tr> </table> </div> <div class="section level2"> @@ -282,7 +315,7 @@ <div class="pkgdown"> <p></p> -<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> +<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer> diff --git a/docs/news/index.html b/docs/news/index.html index f6883766..7dcccffb 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -17,13 +17,13 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.1</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3.1</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -34,6 +34,8 @@ <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -41,22 +43,29 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -64,6 +73,14 @@ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -88,7 +105,30 @@ </div> <div class="section level2"> -<h2 class="page-header" data-toc-text="1.2.1" id="mkin-121-unreleased">mkin 1.2.1 (unreleased)<a class="anchor" aria-label="anchor" href="#mkin-121-unreleased"></a></h2> +<h2 class="page-header" data-toc-text="1.2.3.1" id="mkin-1231">mkin 1.2.3.1<a class="anchor" aria-label="anchor" href="#mkin-1231"></a></h2> +<ul><li>Small fixes to get the online docs right, rebuild online docs</li> +</ul></div> + <div class="section level2"> +<h2 class="page-header" data-toc-text="1.2.3" id="mkin-123">mkin 1.2.3<a class="anchor" aria-label="anchor" href="#mkin-123"></a></h2> +<ul><li><p>‘R/{endpoints,parms,plot.mixed.mmkin,summary.saem.mmkin}.R’: Calculate parameters and endpoints and plot population curves for specific covariate values, or specific percentiles of covariate values used in saem fits.</p></li> +<li><p>Depend on current deSolve version with the possibility to avoid resolving symbols in a shared library (compiled models) over and over, thanks to Thomas Petzoldt.</p></li> +<li><p>‘inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton.Rmd’: Start a new cluster after creating a model stored in the user specified location, because otherwise symbols are not found by the worker processes.</p></li> +<li><p>‘tests/testthat/test_compiled_symbols.R’: Some new tests to control problems that may have been introduced by the possibility to use pre-resolved symbols.</p></li> +<li><p>‘R/mkinerrmin.R’: Fix typo in subset (use of = instead of ==), thanks to Sebastian Meyer for spotting this during his work on R 4.3.0.</p></li> +</ul></div> + <div class="section level2"> +<h2 class="page-header" data-toc-text="1.2.2" id="mkin-122-unreleased">mkin 1.2.2 (unreleased)<a class="anchor" aria-label="anchor" href="#mkin-122-unreleased"></a></h2> +<ul><li><p>‘inst/rmarkdown/templates/hierarchical_kinetics’: R markdown template to facilitate the application of hierarchical kinetic models.</p></li> +<li><p>‘inst/testdata/{cyantraniliprole_soil_efsa_2014,lambda-cyhalothrin_soil_efsa_2014}.xlsx’: Example spreadsheets for use with ‘read_spreadsheet()’.</p></li> +<li><p>‘R/mhmkin.R’: Allow an ‘illparms.mhmkin’ object or a list with suitable dimensions as value of the argument ‘no_random_effects’, making it possible to exclude random effects that were ill-defined in simpler variants of the set of degradation models. Remove the possibility to exclude random effects based on separate fits, as it did not work well.</p></li> +<li><p>‘R/summary.saem.mmkin.R’: List all initial parameter values in the summary, including random effects and error model parameters. Avoid redundant warnings that occurred in the calculation of correlations of the fixed effects in the case that the Fisher information matrix could not be inverted. List correlations of random effects if specified by the user in the covariance model.</p></li> +<li><p>‘R/parplot.R’: Possibility to select the top ‘llquant’ fraction of the fits for the parameter plots, and improved legend text.</p></li> +<li><p>‘R/illparms.R’: Also check if confidence intervals for slope parameters in covariate models include zero. Only implemented for fits obtained with the saemix backend.</p></li> +<li><p>‘R/parplot.R’: Make the function work also in the case that some of the multistart runs failed.</p></li> +<li><p>‘R/intervals.R’: Include correlations of random effects in the model in case there are any.</p></li> +</ul></div> + <div class="section level2"> +<h2 class="page-header" data-toc-text="1.2.1" id="mkin-121-2022-11-19">mkin 1.2.1 (2022-11-19)<a class="anchor" aria-label="anchor" href="#mkin-121-2022-11-19"></a></h2> <ul><li><p>‘{data,R}/ds_mixed.rda’: Include the test data in the package instead of generating it in ‘tests/testthat/setup_script.R’. Refactor the generating code to make it consistent and update tests.</p></li> <li><p>‘tests/testthat/setup_script.R’: Excluded another ill-defined random effect for the DFOP fit with ‘saem’, in an attempt to avoid a platform dependence that surfaced on Fedora systems on the CRAN check farm</p></li> <li><p>‘tests/testthat/test_mixed.R’: Round parameters found by saemix to two significant digits before printing, to also help to avoid platform dependence of tests</p></li> @@ -135,7 +175,8 @@ </div> <div class="section level2"> <h2 class="page-header" data-toc-text="1.0.5" id="mkin-105-2021-09-15">mkin 1.0.5 (2021-09-15)<a class="anchor" aria-label="anchor" href="#mkin-105-2021-09-15"></a></h2> -<ul><li>‘dimethenamid_2018’: Correct the data for the Borstel soil. The five observations from Staudenmaier (2013) that were previously stored as “Borstel 2” are actually just a subset of the 16 observations in “Borstel 1” which is now simply “Borstel”</li></ul></div> +<ul><li>‘dimethenamid_2018’: Correct the data for the Borstel soil. The five observations from Staudenmaier (2013) that were previously stored as “Borstel 2” are actually just a subset of the 16 observations in “Borstel 1” which is now simply “Borstel”</li> +</ul></div> <div class="section level2"> <h2 class="page-header" data-toc-text="1.0.4" id="mkin-104-2021-04-20">mkin 1.0.4 (2021-04-20)<a class="anchor" aria-label="anchor" href="#mkin-104-2021-04-20"></a></h2> <ul><li><p>All plotting functions setting graphical parameters: Use on.exit() for resetting graphical parameters</p></li> @@ -144,10 +185,12 @@ </ul></div> <div class="section level2"> <h2 class="page-header" data-toc-text="1.0.3" id="mkin-103-2021-02-15">mkin 1.0.3 (2021-02-15)<a class="anchor" aria-label="anchor" href="#mkin-103-2021-02-15"></a></h2> -<ul><li>Review and update README, the ‘Introduction to mkin’ vignette and some of the help pages</li></ul></div> +<ul><li>Review and update README, the ‘Introduction to mkin’ vignette and some of the help pages</li> +</ul></div> <div class="section level2"> <h2 class="page-header" data-toc-text="1.0.2" id="mkin-102-unreleased">mkin 1.0.2 (Unreleased)<a class="anchor" aria-label="anchor" href="#mkin-102-unreleased"></a></h2> -<ul><li>‘mkinfit’: Keep model names stored in ‘mkinmod’ objects, avoiding their loss in ‘gmkin’</li></ul></div> +<ul><li>‘mkinfit’: Keep model names stored in ‘mkinmod’ objects, avoiding their loss in ‘gmkin’</li> +</ul></div> <div class="section level2"> <h2 class="page-header" data-toc-text="1.0.1" id="mkin-101-2021-02-10">mkin 1.0.1 (2021-02-10)<a class="anchor" aria-label="anchor" href="#mkin-101-2021-02-10"></a></h2> <ul><li><p>‘confint.mmkin’, ‘nlme.mmkin’, ‘transform_odeparms’: Fix example code in dontrun sections that failed with current defaults</p></li> @@ -202,7 +245,8 @@ </ul></div> <div class="section level2"> <h2 class="page-header" data-toc-text="0.9.49.11" id="mkin-094911-2020-04-20">mkin 0.9.49.11 (2020-04-20)<a class="anchor" aria-label="anchor" href="#mkin-094911-2020-04-20"></a></h2> -<ul><li>Increase a test tolerance to make it pass on all CRAN check machines</li></ul></div> +<ul><li>Increase a test tolerance to make it pass on all CRAN check machines</li> +</ul></div> <div class="section level2"> <h2 class="page-header" data-toc-text="0.9.49.10" id="mkin-094910-2020-04-18">mkin 0.9.49.10 (2020-04-18)<a class="anchor" aria-label="anchor" href="#mkin-094910-2020-04-18"></a></h2> <ul><li><p>‘nlme.mmkin’: An nlme method for mmkin row objects and an associated S3 class with print, plot, anova and endpoint methods</p></li> @@ -317,7 +361,8 @@ </ul></div> <div class="section level2"> <h2 class="page-header" data-toc-text="0.9.46" id="mkin-0946-2017-07-24">mkin 0.9.46 (2017-07-24)<a class="anchor" aria-label="anchor" href="#mkin-0946-2017-07-24"></a></h2> -<ul><li>Remove <code>test_FOMC_ill-defined.R</code> as it is too platform dependent</li></ul></div> +<ul><li>Remove <code>test_FOMC_ill-defined.R</code> as it is too platform dependent</li> +</ul></div> <div class="section level2"> <h2 class="page-header" data-toc-text="0.9.45.2" id="mkin-09452-2017-07-24">mkin 0.9.45.2 (2017-07-24)<a class="anchor" aria-label="anchor" href="#mkin-09452-2017-07-24"></a></h2> <ul><li><p>Rename <code>twa</code> to <code>max_twa_parent</code> to avoid conflict with <code>twa</code> from my <code>pfm</code> package</p></li> @@ -329,7 +374,8 @@ <h2 class="page-header" data-toc-text="0.9.45.1" id="mkin-09451-2016-12-20">mkin 0.9.45.1 (2016-12-20)<a class="anchor" aria-label="anchor" href="#mkin-09451-2016-12-20"></a></h2> <div class="section level3"> <h3 id="new-features-0-9-45-1">New features<a class="anchor" aria-label="anchor" href="#new-features-0-9-45-1"></a></h3> -<ul><li>A <code>twa</code> function, calculating maximum time weighted average concentrations for the parent (SFO, FOMC and DFOP).</li></ul></div> +<ul><li>A <code>twa</code> function, calculating maximum time weighted average concentrations for the parent (SFO, FOMC and DFOP).</li> +</ul></div> </div> <div class="section level2"> <h2 class="page-header" data-toc-text="0.9.45" id="mkin-0945-2016-12-08">mkin 0.9.45 (2016-12-08)<a class="anchor" aria-label="anchor" href="#mkin-0945-2016-12-08"></a></h2> @@ -344,7 +390,8 @@ <h2 class="page-header" data-toc-text="0.9.44" id="mkin-0944-2016-06-29">mkin 0.9.44 (2016-06-29)<a class="anchor" aria-label="anchor" href="#mkin-0944-2016-06-29"></a></h2> <div class="section level3"> <h3 id="bug-fixes-0-9-44">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-44"></a></h3> -<ul><li>The test <code>test_FOMC_ill-defined</code> failed on several architectures, so the test is now skipped</li></ul></div> +<ul><li>The test <code>test_FOMC_ill-defined</code> failed on several architectures, so the test is now skipped</li> +</ul></div> </div> <div class="section level2"> <h2 class="page-header" data-toc-text="0.9.43" id="mkin-0943-2016-06-28">mkin 0.9.43 (2016-06-28)<a class="anchor" aria-label="anchor" href="#mkin-0943-2016-06-28"></a></h2> @@ -378,7 +425,8 @@ <h2 class="page-header" data-toc-text="0.9.42" id="mkin-0942-2016-03-25">mkin 0.9.42 (2016-03-25)<a class="anchor" aria-label="anchor" href="#mkin-0942-2016-03-25"></a></h2> <div class="section level3"> <h3 id="major-changes-0-9-42">Major changes<a class="anchor" aria-label="anchor" href="#major-changes-0-9-42"></a></h3> -<ul><li>Add the argument <code>from_max_mean</code> to <code>mkinfit</code>, for fitting only the decline from the maximum observed value for models with a single observed variable</li></ul></div> +<ul><li>Add the argument <code>from_max_mean</code> to <code>mkinfit</code>, for fitting only the decline from the maximum observed value for models with a single observed variable</li> +</ul></div> <div class="section level3"> <h3 id="minor-changes-0-9-42">Minor changes<a class="anchor" aria-label="anchor" href="#minor-changes-0-9-42"></a></h3> <ul><li><p>Add plots to <code>compiled_models</code> vignette</p></li> @@ -398,18 +446,21 @@ <div class="section level3"> <h3 id="bug-fixes-0-9-41">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-41"></a></h3> <ul><li> -<code><a href="../reference/summary.mkinfit.html">print.summary.mkinfit()</a></code>: Avoid an error that occurred when printing summaries generated with mkin versions before 0.9-36</li></ul></div> +<code><a href="../reference/summary.mkinfit.html">print.summary.mkinfit()</a></code>: Avoid an error that occurred when printing summaries generated with mkin versions before 0.9-36</li> +</ul></div> </div> <div class="section level2"> <h2 class="page-header" data-toc-text="0.9-40" id="mkin-09-40-2015-07-21">mkin 0.9-40 (2015-07-21)<a class="anchor" aria-label="anchor" href="#mkin-09-40-2015-07-21"></a></h2> <div class="section level3"> <h3 id="bug-fixes-0-9-40">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-40"></a></h3> <ul><li> -<code><a href="../reference/endpoints.html">endpoints()</a></code>: For DFOP and SFORB models, where <code><a href="https://rdrr.io/r/stats/optimize.html" class="external-link">optimize()</a></code> is used, make use of the fact that the DT50 must be between DT50_k1 and DT50_k2 (DFOP) or DT50_b1 and DT50_b2 (SFORB), as <code><a href="https://rdrr.io/r/stats/optimize.html" class="external-link">optimize()</a></code> sometimes did not find the minimum. Likewise for finding DT90 values. Also fit on the log scale to make the function more efficient.</li></ul></div> +<code><a href="../reference/endpoints.html">endpoints()</a></code>: For DFOP and SFORB models, where <code><a href="https://rdrr.io/r/stats/optimize.html" class="external-link">optimize()</a></code> is used, make use of the fact that the DT50 must be between DT50_k1 and DT50_k2 (DFOP) or DT50_b1 and DT50_b2 (SFORB), as <code><a href="https://rdrr.io/r/stats/optimize.html" class="external-link">optimize()</a></code> sometimes did not find the minimum. Likewise for finding DT90 values. Also fit on the log scale to make the function more efficient.</li> +</ul></div> <div class="section level3"> <h3 id="internal-changes-0-9-40">Internal changes<a class="anchor" aria-label="anchor" href="#internal-changes-0-9-40"></a></h3> <ul><li> -<code>DESCRIPTION</code>, <code>NAMESPACE</code>, <code>R/*.R</code>: Import (from) stats, graphics and methods packages, and qualify some function calls for non-base packages installed with R to avoid NOTES made by R CMD check –as-cran with upcoming R versions.</li></ul></div> +<code>DESCRIPTION</code>, <code>NAMESPACE</code>, <code>R/*.R</code>: Import (from) stats, graphics and methods packages, and qualify some function calls for non-base packages installed with R to avoid NOTES made by R CMD check –as-cran with upcoming R versions.</li> +</ul></div> </div> <div class="section level2"> <h2 class="page-header" data-toc-text="0.9-39" id="mkin-09-39-2015-06-26">mkin 0.9-39 (2015-06-26)<a class="anchor" aria-label="anchor" href="#mkin-09-39-2015-06-26"></a></h2> @@ -421,7 +472,8 @@ <div class="section level3"> <h3 id="bug-fixes-0-9-39">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-39"></a></h3> <ul><li> -<code><a href="../reference/mkinparplot.html">mkinparplot()</a></code>: Fix the x axis scaling for rate constants and formation fractions that got confused by the introduction of the t-values of transformed parameters.</li></ul></div> +<code><a href="../reference/mkinparplot.html">mkinparplot()</a></code>: Fix the x axis scaling for rate constants and formation fractions that got confused by the introduction of the t-values of transformed parameters.</li> +</ul></div> </div> <div class="section level2"> <h2 class="page-header" data-toc-text="0.9-38" id="mkin-09-38-2015-06-24">mkin 0.9-38 (2015-06-24)<a class="anchor" aria-label="anchor" href="#mkin-09-38-2015-06-24"></a></h2> @@ -433,7 +485,8 @@ <div class="section level3"> <h3 id="bug-fixes-0-9-38">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-38"></a></h3> <ul><li> -<code><a href="../reference/mkinmod.html">mkinmod()</a></code>: When generating the C code for the derivatives, only declare the time variable when it is needed and remove the ‘-W-no-unused-variable’ compiler flag as the C compiler used in the CRAN checks on Solaris does not know it.</li></ul></div> +<code><a href="../reference/mkinmod.html">mkinmod()</a></code>: When generating the C code for the derivatives, only declare the time variable when it is needed and remove the ‘-W-no-unused-variable’ compiler flag as the C compiler used in the CRAN checks on Solaris does not know it.</li> +</ul></div> </div> <div class="section level2"> <h2 class="page-header" data-toc-text="0.9-36" id="mkin-09-36-2015-06-21">mkin 0.9-36 (2015-06-21)<a class="anchor" aria-label="anchor" href="#mkin-09-36-2015-06-21"></a></h2> @@ -446,13 +499,15 @@ </ul></div> <div class="section level3"> <h3 id="minor-changes-0-9-36">Minor changes<a class="anchor" aria-label="anchor" href="#minor-changes-0-9-36"></a></h3> -<ul><li>Added a simple showcase vignette with an evaluation of FOCUS example dataset D</li></ul></div> +<ul><li>Added a simple showcase vignette with an evaluation of FOCUS example dataset D</li> +</ul></div> </div> <div class="section level2"> <h2 class="page-header" data-toc-text="0.9-35" id="mkin-09-35-2015-05-15">mkin 0.9-35 (2015-05-15)<a class="anchor" aria-label="anchor" href="#mkin-09-35-2015-05-15"></a></h2> <div class="section level3"> <h3 id="major-changes-0-9-35">Major changes<a class="anchor" aria-label="anchor" href="#major-changes-0-9-35"></a></h3> -<ul><li>Switch from RUnit to testthat for testing</li></ul></div> +<ul><li>Switch from RUnit to testthat for testing</li> +</ul></div> <div class="section level3"> <h3 id="bug-fixes-0-9-35">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-35"></a></h3> <ul><li><p><code><a href="../reference/mkinparplot.html">mkinparplot()</a></code>: Avoid warnings that occurred when not all confidence intervals were available in the summary of the fit</p></li> @@ -534,13 +589,15 @@ <h2 class="page-header" data-toc-text="0.9-31" id="mkin-09-31-2014-07-14">mkin 0.9-31 (2014-07-14)<a class="anchor" aria-label="anchor" href="#mkin-09-31-2014-07-14"></a></h2> <div class="section level3"> <h3 id="bug-fixes-0-9-31">Bug fixes<a class="anchor" aria-label="anchor" href="#bug-fixes-0-9-31"></a></h3> -<ul><li>The internal renaming of optimised parameters in Version 0.9-30 led to errors in the determination of the degrees of freedom for the chi2 error level calulations in <code><a href="../reference/mkinerrmin.html">mkinerrmin()</a></code> used by the summary function.</li></ul></div> +<ul><li>The internal renaming of optimised parameters in Version 0.9-30 led to errors in the determination of the degrees of freedom for the chi2 error level calulations in <code><a href="../reference/mkinerrmin.html">mkinerrmin()</a></code> used by the summary function.</li> +</ul></div> </div> <div class="section level2"> <h2 class="page-header" data-toc-text="0.9-30" id="mkin-09-30-2014-07-11">mkin 0.9-30 (2014-07-11)<a class="anchor" aria-label="anchor" href="#mkin-09-30-2014-07-11"></a></h2> <div class="section level3"> <h3 id="new-features-0-9-30">New features<a class="anchor" aria-label="anchor" href="#new-features-0-9-30"></a></h3> -<ul><li>It is now possible to use formation fractions in combination with turning off the sink in <code><a href="../reference/mkinmod.html">mkinmod()</a></code>.</li></ul></div> +<ul><li>It is now possible to use formation fractions in combination with turning off the sink in <code><a href="../reference/mkinmod.html">mkinmod()</a></code>.</li> +</ul></div> <div class="section level3"> <h3 id="major-changes-0-9-30">Major changes<a class="anchor" aria-label="anchor" href="#major-changes-0-9-30"></a></h3> <ul><li><p>The original and the transformed parameters now have different names (e.g. <code>k_parent</code> and <code>log_k_parent</code>. They also differ in how many they are when we have formation fractions but no pathway to sink.</p></li> @@ -623,7 +680,7 @@ </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 7cf87069..655e436a 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -1,10 +1,13 @@ -pandoc: 2.9.2.1 -pkgdown: 2.0.6 +pandoc: 2.17.1.1 +pkgdown: 2.0.7 pkgdown_sha: ~ articles: FOCUS_D: FOCUS_D.html FOCUS_L: FOCUS_L.html mkin: mkin.html + 2022_cyan_pathway: prebuilt/2022_cyan_pathway.html + 2022_dmta_parent: prebuilt/2022_dmta_parent.html + 2022_dmta_pathway: prebuilt/2022_dmta_pathway.html twa: twa.html FOCUS_Z: web_only/FOCUS_Z.html NAFTA_examples: web_only/NAFTA_examples.html @@ -13,7 +16,7 @@ articles: dimethenamid_2018: web_only/dimethenamid_2018.html multistart: web_only/multistart.html saem_benchmarks: web_only/saem_benchmarks.html -last_built: 2022-11-18T18:28Z +last_built: 2023-04-20T17:56Z urls: reference: https://pkgdown.jrwb.de/mkin/reference article: https://pkgdown.jrwb.de/mkin/articles diff --git a/docs/reference/AIC.mmkin.html b/docs/reference/AIC.mmkin.html index 48e3b7e2..a53a2735 100644 --- a/docs/reference/AIC.mmkin.html +++ b/docs/reference/AIC.mmkin.html @@ -18,13 +18,13 @@ same dataset."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/li </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -35,6 +35,8 @@ same dataset."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/li <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -42,22 +44,29 @@ same dataset."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/li <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -65,6 +74,14 @@ same dataset."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/li <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -189,7 +206,7 @@ dataframe if there are several fits in the column).</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/CAKE_export.html b/docs/reference/CAKE_export.html index f1edaab2..b6b26286 100644 --- a/docs/reference/CAKE_export.html +++ b/docs/reference/CAKE_export.html @@ -18,13 +18,13 @@ specified as well."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/aj </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -35,6 +35,8 @@ specified as well."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/aj <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -42,22 +44,29 @@ specified as well."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/aj <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -65,6 +74,14 @@ specified as well."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/aj <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -194,7 +211,7 @@ compatible with CAKE.</p></dd> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/D24_2014.html b/docs/reference/D24_2014.html index a22c2f73..128d3e73 100644 --- a/docs/reference/D24_2014.html +++ b/docs/reference/D24_2014.html @@ -22,13 +22,13 @@ constrained by data protection regulations."><!-- mathjax --><script src="https: </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -39,6 +39,8 @@ constrained by data protection regulations."><!-- mathjax --><script src="https: <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -46,22 +48,29 @@ constrained by data protection regulations."><!-- mathjax --><script src="https: <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -69,6 +78,14 @@ constrained by data protection regulations."><!-- mathjax --><script src="https: <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -230,7 +247,7 @@ specific pieces of information in the comments.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/DFOP.solution-1.png b/docs/reference/DFOP.solution-1.png Binary files differindex 5ebba336..6b78836f 100644 --- a/docs/reference/DFOP.solution-1.png +++ b/docs/reference/DFOP.solution-1.png diff --git a/docs/reference/DFOP.solution.html b/docs/reference/DFOP.solution.html index 4a8cb640..261db8e8 100644 --- a/docs/reference/DFOP.solution.html +++ b/docs/reference/DFOP.solution.html @@ -18,13 +18,13 @@ two exponential decline functions."><!-- mathjax --><script src="https://cdnjs.c </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -35,6 +35,8 @@ two exponential decline functions."><!-- mathjax --><script src="https://cdnjs.c <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -42,22 +44,29 @@ two exponential decline functions."><!-- mathjax --><script src="https://cdnjs.c <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -65,6 +74,14 @@ two exponential decline functions."><!-- mathjax --><script src="https://cdnjs.c <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -171,7 +188,7 @@ Version 1.1, 18 December 2014 </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/Extract.mmkin.html b/docs/reference/Extract.mmkin.html index 1f528615..cfee02f5 100644 --- a/docs/reference/Extract.mmkin.html +++ b/docs/reference/Extract.mmkin.html @@ -17,13 +17,13 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -34,6 +34,8 @@ <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -41,22 +43,29 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -64,6 +73,14 @@ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -204,7 +221,7 @@ either a list of mkinfit objects or a single mkinfit object.</p></dd> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html b/docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html index e6052063..55f7dafe 100644 --- a/docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html +++ b/docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html @@ -21,13 +21,13 @@ in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/lib </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -38,6 +38,8 @@ in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/lib <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -45,22 +47,29 @@ in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/lib <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -68,6 +77,14 @@ in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/lib <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -158,7 +175,7 @@ in this fit.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html b/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html index 76c72fd5..d3f727d0 100644 --- a/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html +++ b/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html @@ -21,13 +21,13 @@ in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/lib </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -38,6 +38,8 @@ in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/lib <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -45,22 +47,29 @@ in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/lib <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -68,6 +77,14 @@ in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/lib <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -155,7 +172,7 @@ in this fit.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/FOCUS_2006_HS_ref_A_to_F.html b/docs/reference/FOCUS_2006_HS_ref_A_to_F.html index de4908e6..bd03647d 100644 --- a/docs/reference/FOCUS_2006_HS_ref_A_to_F.html +++ b/docs/reference/FOCUS_2006_HS_ref_A_to_F.html @@ -21,13 +21,13 @@ in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/lib </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -38,6 +38,8 @@ in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/lib <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -45,22 +47,29 @@ in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/lib <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -68,6 +77,14 @@ in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/lib <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -158,7 +175,7 @@ in this fit.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html b/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html index 1ba63264..a489ecc8 100644 --- a/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html +++ b/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html @@ -21,13 +21,13 @@ in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/lib </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -38,6 +38,8 @@ in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/lib <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -45,22 +47,29 @@ in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/lib <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -68,6 +77,14 @@ in this fit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/lib <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -152,7 +169,7 @@ in this fit.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/FOCUS_2006_datasets.html b/docs/reference/FOCUS_2006_datasets.html index 385c0f2b..3a0cd6bd 100644 --- a/docs/reference/FOCUS_2006_datasets.html +++ b/docs/reference/FOCUS_2006_datasets.html @@ -17,13 +17,13 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -34,6 +34,8 @@ <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -41,22 +43,29 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -64,6 +73,14 @@ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -150,7 +167,7 @@ </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/FOMC.solution-1.png b/docs/reference/FOMC.solution-1.png Binary files differindex 9d222d42..18a4b586 100644 --- a/docs/reference/FOMC.solution-1.png +++ b/docs/reference/FOMC.solution-1.png diff --git a/docs/reference/FOMC.solution.html b/docs/reference/FOMC.solution.html index e288c955..076b5860 100644 --- a/docs/reference/FOMC.solution.html +++ b/docs/reference/FOMC.solution.html @@ -18,13 +18,13 @@ a decreasing rate constant."><!-- mathjax --><script src="https://cdnjs.cloudfla </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -35,6 +35,8 @@ a decreasing rate constant."><!-- mathjax --><script src="https://cdnjs.cloudfla <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -42,22 +44,29 @@ a decreasing rate constant."><!-- mathjax --><script src="https://cdnjs.cloudfla <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -65,6 +74,14 @@ a decreasing rate constant."><!-- mathjax --><script src="https://cdnjs.cloudfla <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -182,7 +199,7 @@ Technology</em> <b>24</b>, 1032-1038</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/HS.solution-1.png b/docs/reference/HS.solution-1.png Binary files differindex dd7a76c8..61d89dbc 100644 --- a/docs/reference/HS.solution-1.png +++ b/docs/reference/HS.solution-1.png diff --git a/docs/reference/HS.solution.html b/docs/reference/HS.solution.html index 21bd919a..41e722d6 100644 --- a/docs/reference/HS.solution.html +++ b/docs/reference/HS.solution.html @@ -18,13 +18,13 @@ between them."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/li </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -35,6 +35,8 @@ between them."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/li <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -42,22 +44,29 @@ between them."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/li <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -65,6 +74,14 @@ between them."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/li <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -172,7 +189,7 @@ Version 1.1, 18 December 2014 </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/IORE.solution-1.png b/docs/reference/IORE.solution-1.png Binary files differindex 9b6ab58f..54c9dcae 100644 --- a/docs/reference/IORE.solution-1.png +++ b/docs/reference/IORE.solution-1.png diff --git a/docs/reference/IORE.solution.html b/docs/reference/IORE.solution.html index 57e2e1f4..5d416409 100644 --- a/docs/reference/IORE.solution.html +++ b/docs/reference/IORE.solution.html @@ -18,13 +18,13 @@ a concentration dependent rate constant."><!-- mathjax --><script src="https://c </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -35,6 +35,8 @@ a concentration dependent rate constant."><!-- mathjax --><script src="https://c <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -42,22 +44,29 @@ a concentration dependent rate constant."><!-- mathjax --><script src="https://c <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -65,6 +74,14 @@ a concentration dependent rate constant."><!-- mathjax --><script src="https://c <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -155,6 +172,8 @@ for Evaluating and Calculating Degradation Kinetics in Environmental Media</p> <span class="r-in"><span> <span class="va">fit.fomc</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-in"><span> <span class="va">fit.iore</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"IORE"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-in"><span> <span class="va">fit.iore.deS</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"IORE"</span>, <span class="va">FOCUS_2006_C</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Error in is.loaded(initfunc, PACKAGE = dllname, type = "") : </span> +<span class="r-out co"><span class="r-pr">#></span> invalid 'PACKAGE' argument</span> <span class="r-in"><span></span></span> <span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span><span class="va">fit.fomc</span><span class="op">$</span><span class="va">par</span>, <span class="va">fit.iore</span><span class="op">$</span><span class="va">par</span>, <span class="va">fit.iore.deS</span><span class="op">$</span><span class="va">par</span>,</span></span> <span class="r-in"><span> row.names <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"model par"</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">4</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span> @@ -185,7 +204,7 @@ for Evaluating and Calculating Degradation Kinetics in Environmental Media</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/NAFTA_SOP_2015-1.png b/docs/reference/NAFTA_SOP_2015-1.png Binary files differindex 5d2d434b..98d4246c 100644 --- a/docs/reference/NAFTA_SOP_2015-1.png +++ b/docs/reference/NAFTA_SOP_2015-1.png diff --git a/docs/reference/NAFTA_SOP_2015.html b/docs/reference/NAFTA_SOP_2015.html index 3d00e9f6..41629ed8 100644 --- a/docs/reference/NAFTA_SOP_2015.html +++ b/docs/reference/NAFTA_SOP_2015.html @@ -17,13 +17,13 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -34,6 +34,8 @@ <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -41,22 +43,29 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -64,6 +73,14 @@ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -184,7 +201,7 @@ </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/NAFTA_SOP_Attachment-1.png b/docs/reference/NAFTA_SOP_Attachment-1.png Binary files differindex d8951fc3..a6066441 100644 --- a/docs/reference/NAFTA_SOP_Attachment-1.png +++ b/docs/reference/NAFTA_SOP_Attachment-1.png diff --git a/docs/reference/NAFTA_SOP_Attachment.html b/docs/reference/NAFTA_SOP_Attachment.html index 04f38b78..0ab49cb7 100644 --- a/docs/reference/NAFTA_SOP_Attachment.html +++ b/docs/reference/NAFTA_SOP_Attachment.html @@ -17,13 +17,13 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -34,6 +34,8 @@ <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -41,22 +43,29 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -64,6 +73,14 @@ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -173,7 +190,7 @@ </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/Rplot001.png b/docs/reference/Rplot001.png Binary files differindex b3448db0..5de2bdc7 100644 --- a/docs/reference/Rplot001.png +++ b/docs/reference/Rplot001.png diff --git a/docs/reference/Rplot002.png b/docs/reference/Rplot002.png Binary files differindex 27feab09..556ca0a7 100644 --- a/docs/reference/Rplot002.png +++ b/docs/reference/Rplot002.png diff --git a/docs/reference/Rplot003.png b/docs/reference/Rplot003.png Binary files differindex 774715e0..30cf38f3 100644 --- a/docs/reference/Rplot003.png +++ b/docs/reference/Rplot003.png diff --git a/docs/reference/Rplot004.png b/docs/reference/Rplot004.png Binary files differindex 37e0e95e..377229db 100644 --- a/docs/reference/Rplot004.png +++ b/docs/reference/Rplot004.png diff --git a/docs/reference/Rplot005.png b/docs/reference/Rplot005.png Binary files differindex 76f25647..c1324477 100644 --- a/docs/reference/Rplot005.png +++ b/docs/reference/Rplot005.png diff --git a/docs/reference/Rplot006.png b/docs/reference/Rplot006.png Binary files differindex 48f5bbd8..f646fa66 100644 --- a/docs/reference/Rplot006.png +++ b/docs/reference/Rplot006.png diff --git a/docs/reference/Rplot007.png b/docs/reference/Rplot007.png Binary files differindex 21a6ea76..d3b6ddd4 100644 --- a/docs/reference/Rplot007.png +++ b/docs/reference/Rplot007.png diff --git a/docs/reference/SFO.solution-1.png b/docs/reference/SFO.solution-1.png Binary files differindex a00499cb..34fdd460 100644 --- a/docs/reference/SFO.solution-1.png +++ b/docs/reference/SFO.solution-1.png diff --git a/docs/reference/SFO.solution.html b/docs/reference/SFO.solution.html index 3aabc1d6..17555d63 100644 --- a/docs/reference/SFO.solution.html +++ b/docs/reference/SFO.solution.html @@ -17,13 +17,13 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -34,6 +34,8 @@ <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -41,22 +43,29 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -64,6 +73,14 @@ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -160,7 +177,7 @@ Version 1.1, 18 December 2014 </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/SFORB.solution-1.png b/docs/reference/SFORB.solution-1.png Binary files differindex 7bea3b78..08d25616 100644 --- a/docs/reference/SFORB.solution-1.png +++ b/docs/reference/SFORB.solution-1.png diff --git a/docs/reference/SFORB.solution.html b/docs/reference/SFORB.solution.html index 89c932b6..0ae76e25 100644 --- a/docs/reference/SFORB.solution.html +++ b/docs/reference/SFORB.solution.html @@ -21,13 +21,13 @@ and no substance in the bound fraction."><!-- mathjax --><script src="https://cd </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -38,6 +38,8 @@ and no substance in the bound fraction."><!-- mathjax --><script src="https://cd <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -45,22 +47,29 @@ and no substance in the bound fraction."><!-- mathjax --><script src="https://cd <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -68,6 +77,14 @@ and no substance in the bound fraction."><!-- mathjax --><script src="https://cd <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -178,7 +195,7 @@ Version 1.1, 18 December 2014 </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/add_err-1.png b/docs/reference/add_err-1.png Binary files differindex 4a3b4062..68cfb344 100644 --- a/docs/reference/add_err-1.png +++ b/docs/reference/add_err-1.png diff --git a/docs/reference/add_err-2.png b/docs/reference/add_err-2.png Binary files differindex 5aec1744..d2f0cf08 100644 --- a/docs/reference/add_err-2.png +++ b/docs/reference/add_err-2.png diff --git a/docs/reference/add_err-3.png b/docs/reference/add_err-3.png Binary files differindex 2e71f02f..17b5416a 100644 --- a/docs/reference/add_err-3.png +++ b/docs/reference/add_err-3.png diff --git a/docs/reference/add_err.html b/docs/reference/add_err.html index 4332ba05..225f62d9 100644 --- a/docs/reference/add_err.html +++ b/docs/reference/add_err.html @@ -19,13 +19,13 @@ may depend on the predicted value and is specified as a standard deviation."><!- </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -36,6 +36,8 @@ may depend on the predicted value and is specified as a standard deviation."><!- <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -43,22 +45,29 @@ may depend on the predicted value and is specified as a standard deviation."><!- <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -66,6 +75,14 @@ may depend on the predicted value and is specified as a standard deviation."><!- <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -231,7 +248,7 @@ https://jrwb.de/posters/piacenza_2015.pdf</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/anova.saem.mmkin.html b/docs/reference/anova.saem.mmkin.html index 02be017b..273122e1 100644 --- a/docs/reference/anova.saem.mmkin.html +++ b/docs/reference/anova.saem.mmkin.html @@ -20,13 +20,13 @@ the model on the previous line."><!-- mathjax --><script src="https://cdnjs.clou </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -37,6 +37,8 @@ the model on the previous line."><!-- mathjax --><script src="https://cdnjs.clou <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -44,22 +46,29 @@ the model on the previous line."><!-- mathjax --><script src="https://cdnjs.clou <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -67,6 +76,14 @@ the model on the previous line."><!-- mathjax --><script src="https://cdnjs.clou <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -154,7 +171,7 @@ only be done for nested models.</p></dd> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/aw.html b/docs/reference/aw.html index c1e1b4ed..7d4b28ef 100644 --- a/docs/reference/aw.html +++ b/docs/reference/aw.html @@ -19,13 +19,13 @@ by Burnham and Anderson (2004)."><!-- mathjax --><script src="https://cdnjs.clou </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -36,6 +36,8 @@ by Burnham and Anderson (2004)."><!-- mathjax --><script src="https://cdnjs.clou <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -43,22 +45,29 @@ by Burnham and Anderson (2004)."><!-- mathjax --><script src="https://cdnjs.clou <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -66,6 +75,14 @@ by Burnham and Anderson (2004)."><!-- mathjax --><script src="https://cdnjs.clou <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -166,7 +183,7 @@ Inference: Understanding AIC and BIC in Model Selection. </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/confint.mkinfit.html b/docs/reference/confint.mkinfit.html index 8d7c272f..dd6409b6 100644 --- a/docs/reference/confint.mkinfit.html +++ b/docs/reference/confint.mkinfit.html @@ -24,13 +24,13 @@ method of Venzon and Moolgavkar (1988)."><!-- mathjax --><script src="https://cd </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -41,6 +41,8 @@ method of Venzon and Moolgavkar (1988)."><!-- mathjax --><script src="https://cd <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -48,22 +50,29 @@ method of Venzon and Moolgavkar (1988)."><!-- mathjax --><script src="https://cd <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -71,6 +80,14 @@ method of Venzon and Moolgavkar (1988)."><!-- mathjax --><script src="https://cd <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -237,7 +254,7 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, <span class="r-in"><span><span class="va">f_d_1</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">FOCUS_2006_D</span>, <span class="va">value</span> <span class="op">!=</span> <span class="fl">0</span><span class="op">)</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span><span class="va">ci_profile</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/confint.html" class="external-link">confint</a></span><span class="op">(</span><span class="va">f_d_1</span>, method <span class="op">=</span> <span class="st">"profile"</span>, cores <span class="op">=</span> <span class="fl">1</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> user system elapsed </span> -<span class="r-out co"><span class="r-pr">#></span> 3.931 0.000 3.932 </span> +<span class="r-out co"><span class="r-pr">#></span> 1.26 0.00 1.26 </span> <span class="r-in"><span><span class="co"># Using more cores does not save much time here, as parent_0 takes up most of the time</span></span></span> <span class="r-in"><span><span class="co"># If we additionally exclude parent_0 (the confidence of which is often of</span></span></span> <span class="r-in"><span><span class="co"># minor interest), we get a nice performance improvement if we use at least 4 cores</span></span></span> @@ -245,7 +262,7 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, <span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"k_parent_sink"</span>, <span class="st">"k_parent_m1"</span>, <span class="st">"k_m1_sink"</span>, <span class="st">"sigma"</span><span class="op">)</span>, cores <span class="op">=</span> <span class="va">n_cores</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Profiling the likelihood</span> <span class="r-out co"><span class="r-pr">#></span> user system elapsed </span> -<span class="r-out co"><span class="r-pr">#></span> 2.219 0.000 2.219 </span> +<span class="r-out co"><span class="r-pr">#></span> 0.417 0.103 0.291 </span> <span class="r-in"><span><span class="va">ci_profile</span></span></span> <span class="r-out co"><span class="r-pr">#></span> 2.5% 97.5%</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 96.456003640 1.027703e+02</span> @@ -392,7 +409,7 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/create_deg_func.html b/docs/reference/create_deg_func.html index b93f1c4a..6e31b269 100644 --- a/docs/reference/create_deg_func.html +++ b/docs/reference/create_deg_func.html @@ -17,13 +17,13 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -34,6 +34,8 @@ <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -41,22 +43,29 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -64,6 +73,14 @@ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -131,8 +148,8 @@ <span class="r-in"><span> replications <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></span> <span class="r-msg co"><span class="r-pr">#></span> Loading required package: rbenchmark</span> <span class="r-out co"><span class="r-pr">#></span> test replications elapsed relative user.self sys.self user.child</span> -<span class="r-out co"><span class="r-pr">#></span> 1 analytical 2 0.445 1.000 0.444 0 0</span> -<span class="r-out co"><span class="r-pr">#></span> 2 deSolve 2 0.693 1.557 0.692 0 0</span> +<span class="r-out co"><span class="r-pr">#></span> 1 analytical 2 0.238 1.000 0.239 0 0</span> +<span class="r-out co"><span class="r-pr">#></span> 2 deSolve 2 0.293 1.231 0.294 0 0</span> <span class="r-out co"><span class="r-pr">#></span> sys.child</span> <span class="r-out co"><span class="r-pr">#></span> 1 0</span> <span class="r-out co"><span class="r-pr">#></span> 2 0</span> @@ -145,8 +162,8 @@ <span class="r-in"><span> deSolve <span class="op">=</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">DFOP_SFO</span>, <span class="va">FOCUS_D</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span></span> <span class="r-in"><span> replications <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> test replications elapsed relative user.self sys.self user.child</span> -<span class="r-out co"><span class="r-pr">#></span> 1 analytical 2 0.871 1.000 0.871 0 0</span> -<span class="r-out co"><span class="r-pr">#></span> 2 deSolve 2 1.519 1.744 1.519 0 0</span> +<span class="r-out co"><span class="r-pr">#></span> 1 analytical 2 0.369 1.000 0.37 0 0</span> +<span class="r-out co"><span class="r-pr">#></span> 2 deSolve 2 0.541 1.466 0.54 0 0</span> <span class="r-out co"><span class="r-pr">#></span> sys.child</span> <span class="r-out co"><span class="r-pr">#></span> 1 0</span> <span class="r-out co"><span class="r-pr">#></span> 2 0</span> @@ -165,7 +182,7 @@ </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/dimethenamid_2018-1.png b/docs/reference/dimethenamid_2018-1.png Binary files differindex c8b05bf5..27ed5329 100644 --- a/docs/reference/dimethenamid_2018-1.png +++ b/docs/reference/dimethenamid_2018-1.png diff --git a/docs/reference/dimethenamid_2018.html b/docs/reference/dimethenamid_2018.html index 6d8c0157..0581830c 100644 --- a/docs/reference/dimethenamid_2018.html +++ b/docs/reference/dimethenamid_2018.html @@ -22,13 +22,13 @@ constrained by data protection regulations."><!-- mathjax --><script src="https: </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -39,6 +39,8 @@ constrained by data protection regulations."><!-- mathjax --><script src="https: <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -46,22 +48,29 @@ constrained by data protection regulations."><!-- mathjax --><script src="https: <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -69,6 +78,14 @@ constrained by data protection regulations."><!-- mathjax --><script src="https: <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -205,10 +222,10 @@ specific pieces of information in the comments.</p> <span class="r-in"><span><span class="co">#saemix::plot(f_dmta_saem_tc$so, plot.type = "convergence")</span></span></span> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_dmta_saem_tc</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> saemix version used for fitting: 3.2 </span> -<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.2.0 </span> -<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.2 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Nov 17 13:57:51 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Nov 17 13:57:51 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.2.3 </span> +<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.3 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Apr 20 07:32:09 2023 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Apr 20 07:32:09 2023 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_DMTA/dt = - k_DMTA * DMTA</span> @@ -221,12 +238,12 @@ specific pieces of information in the comments.</p> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type deSolve </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted in 802.957 s</span> +<span class="r-out co"><span class="r-pr">#></span> Fitted in 301.026 s</span> <span class="r-out co"><span class="r-pr">#></span> Using 300, 100 iterations and 9 chains</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Variance model: Two-component variance function </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Mean of starting values for individual parameters:</span> +<span class="r-out co"><span class="r-pr">#></span> Starting values for degradation parameters:</span> <span class="r-out co"><span class="r-pr">#></span> DMTA_0 log_k_DMTA log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1 </span> <span class="r-out co"><span class="r-pr">#></span> 95.5662 -2.9048 -3.8130 -4.1600 -4.1486 0.1341 </span> <span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_2 f_DMTA_ilr_3 </span> @@ -235,6 +252,30 @@ specific pieces of information in the comments.</p> <span class="r-out co"><span class="r-pr">#></span> Fixed degradation parameter values:</span> <span class="r-out co"><span class="r-pr">#></span> None</span> <span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Starting values for random effects (square root of initial entries in omega):</span> +<span class="r-out co"><span class="r-pr">#></span> DMTA_0 log_k_DMTA log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1</span> +<span class="r-out co"><span class="r-pr">#></span> DMTA_0 4.802 0.0000 0.0000 0.000 0.0000 0.0000</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_DMTA 0.000 0.9834 0.0000 0.000 0.0000 0.0000</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_M23 0.000 0.0000 0.6983 0.000 0.0000 0.0000</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_M27 0.000 0.0000 0.0000 1.028 0.0000 0.0000</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_M31 0.000 0.0000 0.0000 0.000 0.9841 0.0000</span> +<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_1 0.000 0.0000 0.0000 0.000 0.0000 0.7185</span> +<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_2 0.000 0.0000 0.0000 0.000 0.0000 0.0000</span> +<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_3 0.000 0.0000 0.0000 0.000 0.0000 0.0000</span> +<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_2 f_DMTA_ilr_3</span> +<span class="r-out co"><span class="r-pr">#></span> DMTA_0 0.0000 0.0000</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_DMTA 0.0000 0.0000</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_M23 0.0000 0.0000</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_M27 0.0000 0.0000</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_M31 0.0000 0.0000</span> +<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_1 0.0000 0.0000</span> +<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_2 0.7378 0.0000</span> +<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_3 0.0000 0.4451</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Starting values for error model parameters:</span> +<span class="r-out co"><span class="r-pr">#></span> a.1 b.1 </span> +<span class="r-out co"><span class="r-pr">#></span> 1 1 </span> +<span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Results:</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Likelihood computed by importance sampling</span> @@ -251,9 +292,9 @@ specific pieces of information in the comments.</p> <span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_1 0.1346 -0.2150 0.4841</span> <span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_2 0.1449 -0.2593 0.5491</span> <span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_3 -1.3882 -1.7011 -1.0753</span> -<span class="r-out co"><span class="r-pr">#></span> a.1 0.9156 0.8229 1.0084</span> -<span class="r-out co"><span class="r-pr">#></span> b.1 0.1383 0.1215 0.1551</span> -<span class="r-out co"><span class="r-pr">#></span> SD.DMTA_0 3.7280 -0.6951 8.1511</span> +<span class="r-out co"><span class="r-pr">#></span> a.1 0.9156 0.8217 1.0095</span> +<span class="r-out co"><span class="r-pr">#></span> b.1 0.1383 0.1216 0.1550</span> +<span class="r-out co"><span class="r-pr">#></span> SD.DMTA_0 3.7280 -0.6949 8.1508</span> <span class="r-out co"><span class="r-pr">#></span> SD.log_k_DMTA 0.6431 0.2781 1.0080</span> <span class="r-out co"><span class="r-pr">#></span> SD.log_k_M23 1.0096 0.3782 1.6409</span> <span class="r-out co"><span class="r-pr">#></span> SD.log_k_M27 0.4583 0.1541 0.7625</span> @@ -274,7 +315,7 @@ specific pieces of information in the comments.</p> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Random effects:</span> <span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> SD.DMTA_0 3.7280 -0.6951 8.1511</span> +<span class="r-out co"><span class="r-pr">#></span> SD.DMTA_0 3.7280 -0.6949 8.1508</span> <span class="r-out co"><span class="r-pr">#></span> SD.log_k_DMTA 0.6431 0.2781 1.0080</span> <span class="r-out co"><span class="r-pr">#></span> SD.log_k_M23 1.0096 0.3782 1.6409</span> <span class="r-out co"><span class="r-pr">#></span> SD.log_k_M27 0.4583 0.1541 0.7625</span> @@ -284,9 +325,9 @@ specific pieces of information in the comments.</p> <span class="r-out co"><span class="r-pr">#></span> SD.f_DMTA_ilr_3 0.3657 0.1383 0.5931</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Variance model:</span> -<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> a.1 0.9156 0.8229 1.0084</span> -<span class="r-out co"><span class="r-pr">#></span> b.1 0.1383 0.1215 0.1551</span> +<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> +<span class="r-out co"><span class="r-pr">#></span> a.1 0.9156 0.8217 1.009</span> +<span class="r-out co"><span class="r-pr">#></span> b.1 0.1383 0.1216 0.155</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Backtransformed parameters:</span> <span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> @@ -335,7 +376,7 @@ specific pieces of information in the comments.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/ds_mixed-1.png b/docs/reference/ds_mixed-1.png Binary files differindex a7f5c395..d8505ffd 100644 --- a/docs/reference/ds_mixed-1.png +++ b/docs/reference/ds_mixed-1.png diff --git a/docs/reference/ds_mixed.html b/docs/reference/ds_mixed.html index 64b02749..b32d5d4b 100644 --- a/docs/reference/ds_mixed.html +++ b/docs/reference/ds_mixed.html @@ -18,13 +18,13 @@ the 'dataset_generation' directory."><!-- mathjax --><script src="https://cdnjs. </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.1</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -35,6 +35,8 @@ the 'dataset_generation' directory."><!-- mathjax --><script src="https://cdnjs. <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -42,22 +44,29 @@ the 'dataset_generation' directory."><!-- mathjax --><script src="https://cdnjs. <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -65,6 +74,14 @@ the 'dataset_generation' directory."><!-- mathjax --><script src="https://cdnjs. <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -226,7 +243,7 @@ the 'dataset_generation' directory.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/endpoints.html b/docs/reference/endpoints.html index 1f49092e..6029a656 100644 --- a/docs/reference/endpoints.html +++ b/docs/reference/endpoints.html @@ -23,13 +23,13 @@ advantage that the SFORB model can also be used for metabolites."><!-- mathjax - </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -40,6 +40,8 @@ advantage that the SFORB model can also be used for metabolites."><!-- mathjax - <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -47,22 +49,29 @@ advantage that the SFORB model can also be used for metabolites."><!-- mathjax - <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -70,6 +79,14 @@ advantage that the SFORB model can also be used for metabolites."><!-- mathjax - <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -104,7 +121,7 @@ advantage that the SFORB model can also be used for metabolites.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">endpoints</span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></code></pre></div> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">endpoints</span><span class="op">(</span><span class="va">fit</span>, covariates <span class="op">=</span> <span class="cn">NULL</span>, covariate_quantile <span class="op">=</span> <span class="fl">0.5</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> @@ -115,6 +132,17 @@ another object that has list components mkinmod containing an <a href="mkinmod.h degradation model, and two numeric vectors, bparms.optim and bparms.fixed, that contain parameter values for that model.</p></dd> + +<dt>covariates</dt> +<dd><p>Numeric vector with covariate values for all variables in +any covariate models in the object. If given, it overrides 'covariate_quantile'.</p></dd> + + +<dt>covariate_quantile</dt> +<dd><p>This argument only has an effect if the fitted +object has covariate models. If so, the default is to show endpoints +for the median of the covariate values (50th percentile).</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> @@ -186,7 +214,7 @@ HS and DFOP, as well as from Eigenvalues b1 and b2 of any SFORB models</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/experimental_data_for_UBA-1.png b/docs/reference/experimental_data_for_UBA-1.png Binary files differindex b7b4d63b..49e1c6c9 100644 --- a/docs/reference/experimental_data_for_UBA-1.png +++ b/docs/reference/experimental_data_for_UBA-1.png diff --git a/docs/reference/experimental_data_for_UBA.html b/docs/reference/experimental_data_for_UBA.html index 08d2de00..2c6b7c8d 100644 --- a/docs/reference/experimental_data_for_UBA.html +++ b/docs/reference/experimental_data_for_UBA.html @@ -45,13 +45,13 @@ Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -62,6 +62,8 @@ Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -69,22 +71,29 @@ Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -92,6 +101,14 @@ Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -244,7 +261,7 @@ Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/f_time_norm_focus.html b/docs/reference/f_time_norm_focus.html index caeb25a1..8d0446d5 100644 --- a/docs/reference/f_time_norm_focus.html +++ b/docs/reference/f_time_norm_focus.html @@ -18,13 +18,13 @@ in Appendix 8 to the FOCUS kinetics guidance (FOCUS 2014, p. 369)."><!-- mathjax </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -35,6 +35,8 @@ in Appendix 8 to the FOCUS kinetics guidance (FOCUS 2014, p. 369)."><!-- mathjax <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -42,22 +44,29 @@ in Appendix 8 to the FOCUS kinetics guidance (FOCUS 2014, p. 369)."><!-- mathjax <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -65,6 +74,14 @@ in Appendix 8 to the FOCUS kinetics guidance (FOCUS 2014, p. 369)."><!-- mathjax <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -217,7 +234,7 @@ Version 1.1, 18 December 2014 </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/focus_soil_moisture.html b/docs/reference/focus_soil_moisture.html index 088c7bc3..2204c64f 100644 --- a/docs/reference/focus_soil_moisture.html +++ b/docs/reference/focus_soil_moisture.html @@ -18,13 +18,13 @@ corresponds to pF2, MWHC to pF 1 and 1/3 bar to pF 2.5."><!-- mathjax --><script </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -35,6 +35,8 @@ corresponds to pF2, MWHC to pF 1 and 1/3 bar to pF 2.5."><!-- mathjax --><script <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -42,22 +44,29 @@ corresponds to pF2, MWHC to pF 1 and 1/3 bar to pF 2.5."><!-- mathjax --><script <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -65,6 +74,14 @@ corresponds to pF2, MWHC to pF 1 and 1/3 bar to pF 2.5."><!-- mathjax --><script <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -139,7 +156,7 @@ Version 2.2, May 2014 <a href="https://esdac.jrc.ec.europa.eu/projects/ground-wa </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/get_deg_func.html b/docs/reference/get_deg_func.html index dc6fee5e..1d4ae683 100644 --- a/docs/reference/get_deg_func.html +++ b/docs/reference/get_deg_func.html @@ -17,13 +17,13 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -34,6 +34,8 @@ <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -41,22 +43,29 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -64,6 +73,14 @@ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -116,7 +133,7 @@ nlme.mmkin</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/hierarchical_kinetics.html b/docs/reference/hierarchical_kinetics.html new file mode 100644 index 00000000..98764bf0 --- /dev/null +++ b/docs/reference/hierarchical_kinetics.html @@ -0,0 +1,197 @@ +<!DOCTYPE html> +<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Hierarchical kinetics template — hierarchical_kinetics • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Hierarchical kinetics template — hierarchical_kinetics"><meta property="og:description" content='R markdown format for setting up hierarchical kinetics based on a template +provided with the mkin package. This format is based on rmarkdown::pdf_document. +Chunk options are adapted. Echoing R code from code chunks and caching are +turned on per default. character for prepending output from code chunks is +set to the empty string, code tidying is off, figure alignment defaults to +centering, and positioning of figures is set to "H", which means that +figures will not move around in the document, but stay where the user +includes them.'><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> +<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> +<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + + + <div class="container template-reference-topic"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <div class="container"> + <div class="navbar-header"> + <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> + <span class="sr-only">Toggle navigation</span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + </button> + <span class="navbar-brand"> + <a class="navbar-link" href="../index.html">mkin</a> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> + </span> + </div> + + <div id="navbar" class="navbar-collapse collapse"> + <ul class="nav navbar-nav"><li> + <a href="../reference/index.html">Reference</a> +</li> +<li class="dropdown"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> + Articles + + <span class="caret"></span> + </a> + <ul class="dropdown-menu" role="menu"><li> + <a href="../articles/mkin.html">Introduction to mkin</a> + </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> + <li> + <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> + </li> + <li> + <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> + </li> + <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> + <li> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> + </li> + <li> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> + </li> + <li> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> + </li> + <li> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + </li> + <li> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> + </ul></li> +<li> + <a href="../news/index.html">News</a> +</li> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> + <span class="fab fa-github fa-lg"></span> + + </a> +</li> + </ul></div><!--/.nav-collapse --> + </div><!--/.container --> +</div><!--/.navbar --> + + + + </header><div class="row"> + <div class="col-md-9 contents"> + <div class="page-header"> + <h1>Hierarchical kinetics template</h1> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/hierarchical_kinetics.R" class="external-link"><code>R/hierarchical_kinetics.R</code></a></small> + <div class="hidden name"><code>hierarchical_kinetics.Rd</code></div> + </div> + + <div class="ref-description"> + <p>R markdown format for setting up hierarchical kinetics based on a template +provided with the mkin package. This format is based on <a href="https://pkgs.rstudio.com/rmarkdown/reference/pdf_document.html" class="external-link">rmarkdown::pdf_document</a>. +Chunk options are adapted. Echoing R code from code chunks and caching are +turned on per default. character for prepending output from code chunks is +set to the empty string, code tidying is off, figure alignment defaults to +centering, and positioning of figures is set to "H", which means that +figures will not move around in the document, but stay where the user +includes them.</p> + </div> + + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">hierarchical_kinetics</span><span class="op">(</span><span class="va">...</span>, keep_tex <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>...</dt> +<dd><p>Arguments to <code><a href="https://pkgs.rstudio.com/rmarkdown/reference/pdf_document.html" class="external-link">rmarkdown::pdf_document</a></code></p></dd> + + +<dt>keep_tex</dt> +<dd><p>Keep the intermediate tex file used in the conversion to PDF</p></dd> + +</dl></div> + <div id="value"> + <h2>Value</h2> + + +<p>R Markdown output format to pass to +<code><a href="https://pkgs.rstudio.com/rmarkdown/reference/render.html" class="external-link">render</a></code></p> + + + </div> + <div id="details"> + <h2>Details</h2> + <p>The latter feature (positioning the figures with "H") depends on the LaTeX +package 'float'. In addition, the LaTeX package 'listing' is used in the +template for showing model fit summaries in the Appendix. This means that +the LaTeX packages 'float' and 'listing' need to be installed in the TeX +distribution used.</p> +<p>On Windows, the easiest way to achieve this (if no TeX distribution +is present before) is to install the 'tinytex' R package, to run +'tinytex::install_tinytex()' to get the basic tiny Tex distribution, +and then to run 'tinytex::tlmgr_install(c("float", "listing"))'.</p> + </div> + + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> +<span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/rstudio/rmarkdown" class="external-link">rmarkdown</a></span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="co"># The following is now commented out after the relase of v1.2.3 for the generation</span></span></span> +<span class="r-in"><span><span class="co"># of online docs, as the command creates a directory and opens an editor</span></span></span> +<span class="r-in"><span><span class="co">#draft("example_analysis.rmd", template = "hierarchical_kinetics", package = "mkin")</span></span></span> +<span class="r-in"><span><span class="co"># }</span></span></span> +<span class="r-in"><span></span></span> +</code></pre></div> + </div> + </div> + <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> +</div> + + + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> +</div> + +<div class="pkgdown"> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> +</div> + + </footer></div> + + + + + + + </body></html> + diff --git a/docs/reference/illparms.html b/docs/reference/illparms.html index 52f130e6..f0829482 100644 --- a/docs/reference/illparms.html +++ b/docs/reference/illparms.html @@ -21,13 +21,13 @@ without parameter transformations is used."><!-- mathjax --><script src="https:/ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -38,6 +38,8 @@ without parameter transformations is used."><!-- mathjax --><script src="https:/ <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -45,22 +47,29 @@ without parameter transformations is used."><!-- mathjax --><script src="https:/ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -68,6 +77,14 @@ without parameter transformations is used."><!-- mathjax --><script src="https:/ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -116,7 +133,14 @@ without parameter transformations is used.</p> <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span> <span></span> <span><span class="co"># S3 method for saem.mmkin</span></span> -<span><span class="fu">illparms</span><span class="op">(</span><span class="va">object</span>, conf.level <span class="op">=</span> <span class="fl">0.95</span>, random <span class="op">=</span> <span class="cn">TRUE</span>, errmod <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span> +<span><span class="fu">illparms</span><span class="op">(</span></span> +<span> <span class="va">object</span>,</span> +<span> conf.level <span class="op">=</span> <span class="fl">0.95</span>,</span> +<span> random <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> errmod <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> slopes <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> <span class="va">...</span></span> +<span><span class="op">)</span></span> <span></span> <span><span class="co"># S3 method for illparms.saem.mmkin</span></span> <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span></span> @@ -154,6 +178,12 @@ without parameter transformations is used.</p> <dd><p>For hierarchical fits, should error model parameters be tested?</p></dd> + +<dt>slopes</dt> +<dd><p>For hierarchical <a href="saem.html">saem</a> fits using saemix as backend, +should slope parameters in the covariate model(starting with 'beta_') be +tested?</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> @@ -209,7 +239,7 @@ does not output anything in the case no ill-defined parameters are found.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/ilr.html b/docs/reference/ilr.html index 7c3a2e33..48f80fdd 100644 --- a/docs/reference/ilr.html +++ b/docs/reference/ilr.html @@ -18,13 +18,13 @@ transformations."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -35,6 +35,8 @@ transformations."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -42,22 +44,29 @@ transformations."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -65,6 +74,14 @@ transformations."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -179,7 +196,7 @@ Compositional Data Using Robust Methods. Math Geosci 40 233-248</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/index.html b/docs/reference/index.html index 9fddf541..ebfb3673 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -17,13 +17,13 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.1</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3.1</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -34,6 +34,8 @@ <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -41,22 +43,29 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -64,6 +73,14 @@ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -195,6 +212,10 @@ of an mmkin object</p></td> <p class="section-desc"></p><p>Create and work with nonlinear hierarchical models</p> </th> </tr></tbody><tbody><tr><td> + <p><code><a href="hierarchical_kinetics.html">hierarchical_kinetics()</a></code> </p> + </td> + <td><p>Hierarchical kinetics template</p></td> + </tr><tr><td> <p><code><a href="read_spreadsheet.html">read_spreadsheet()</a></code> </p> </td> <td><p>Read datasets and relevant meta information from a spreadsheet file</p></td> @@ -203,7 +224,7 @@ of an mmkin object</p></td> </td> <td><p>Create an nlme model for an mmkin row object</p></td> </tr><tr><td> - <p><code><a href="saem.html">saem()</a></code> <code><a href="saem.html">print(<i><saem.mmkin></i>)</a></code> <code><a href="saem.html">saemix_model()</a></code> <code><a href="saem.html">saemix_data()</a></code> <code><a href="saem.html">parms(<i><saem.mmkin></i>)</a></code> </p> + <p><code><a href="saem.html">saem()</a></code> <code><a href="saem.html">print(<i><saem.mmkin></i>)</a></code> <code><a href="saem.html">saemix_model()</a></code> <code><a href="saem.html">saemix_data()</a></code> </p> </td> <td><p>Fit nonlinear mixed models with SAEM</p></td> </tr><tr><td> @@ -356,9 +377,9 @@ degradation models and one or more error models</p></td> <p class="section-desc"></p> </th> </tr></tbody><tbody><tr><td> - <p><code><a href="tex_listing.html">tex_listing()</a></code> </p> + <p><code><a href="summary_listing.html">summary_listing()</a></code> <code><a href="summary_listing.html">tex_listing()</a></code> <code><a href="summary_listing.html">html_listing()</a></code> </p> </td> - <td><p>Wrap the output of a summary function in tex listing environment</p></td> + <td><p>Display the output of a summary function according to the output format</p></td> </tr><tr><td> <p><code><a href="f_time_norm_focus.html">f_time_norm_focus()</a></code> </p> </td> @@ -493,7 +514,7 @@ kinetic models fitted with mkinfit</p></td> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/intervals.saem.mmkin.html b/docs/reference/intervals.saem.mmkin.html index 1547f3af..2a714fdb 100644 --- a/docs/reference/intervals.saem.mmkin.html +++ b/docs/reference/intervals.saem.mmkin.html @@ -17,13 +17,13 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -34,6 +34,8 @@ <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -41,22 +43,29 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -64,6 +73,14 @@ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -138,7 +155,7 @@ class attribute</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/llhist.html b/docs/reference/llhist.html index cc58e481..7b106009 100644 --- a/docs/reference/llhist.html +++ b/docs/reference/llhist.html @@ -18,13 +18,13 @@ original fit is shown as a red vertical line."><!-- mathjax --><script src="http </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -35,6 +35,8 @@ original fit is shown as a red vertical line."><!-- mathjax --><script src="http <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -42,22 +44,29 @@ original fit is shown as a red vertical line."><!-- mathjax --><script src="http <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -65,6 +74,14 @@ original fit is shown as a red vertical line."><!-- mathjax --><script src="http <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -137,7 +154,7 @@ original fit is shown as a red vertical line.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/loftest-1.png b/docs/reference/loftest-1.png Binary files differindex f1dc5fa7..750a2acf 100644 --- a/docs/reference/loftest-1.png +++ b/docs/reference/loftest-1.png diff --git a/docs/reference/loftest-2.png b/docs/reference/loftest-2.png Binary files differindex 3f1015a9..d12c26e7 100644 --- a/docs/reference/loftest-2.png +++ b/docs/reference/loftest-2.png diff --git a/docs/reference/loftest-3.png b/docs/reference/loftest-3.png Binary files differindex d897c363..9f45c74d 100644 --- a/docs/reference/loftest-3.png +++ b/docs/reference/loftest-3.png diff --git a/docs/reference/loftest-4.png b/docs/reference/loftest-4.png Binary files differindex ac44c162..3beb9d1a 100644 --- a/docs/reference/loftest-4.png +++ b/docs/reference/loftest-4.png diff --git a/docs/reference/loftest-5.png b/docs/reference/loftest-5.png Binary files differindex 0847bbec..1a3aaeea 100644 --- a/docs/reference/loftest-5.png +++ b/docs/reference/loftest-5.png diff --git a/docs/reference/loftest.html b/docs/reference/loftest.html index 254b568f..cbb7e766 100644 --- a/docs/reference/loftest.html +++ b/docs/reference/loftest.html @@ -20,13 +20,13 @@ lrtest.default from the lmtest package."><!-- mathjax --><script src="https://cd </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -37,6 +37,8 @@ lrtest.default from the lmtest package."><!-- mathjax --><script src="https://cd <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -44,22 +46,29 @@ lrtest.default from the lmtest package."><!-- mathjax --><script src="https://cd <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -67,6 +76,14 @@ lrtest.default from the lmtest package."><!-- mathjax --><script src="https://cd <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -311,7 +328,7 @@ of replicate samples.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/logLik.mkinfit.html b/docs/reference/logLik.mkinfit.html index 76fa4645..2c1c9df6 100644 --- a/docs/reference/logLik.mkinfit.html +++ b/docs/reference/logLik.mkinfit.html @@ -21,13 +21,13 @@ the error model."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -38,6 +38,8 @@ the error model."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -45,22 +47,29 @@ the error model."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -68,6 +77,14 @@ the error model."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -173,7 +190,7 @@ and the fitted error model parameters.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/logLik.saem.mmkin.html b/docs/reference/logLik.saem.mmkin.html index 36ba6957..9624e67c 100644 --- a/docs/reference/logLik.saem.mmkin.html +++ b/docs/reference/logLik.saem.mmkin.html @@ -17,13 +17,13 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -34,6 +34,8 @@ <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -41,22 +43,29 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -64,6 +73,14 @@ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -124,7 +141,7 @@ </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/logistic.solution-1.png b/docs/reference/logistic.solution-1.png Binary files differindex 73dad0a4..23c031de 100644 --- a/docs/reference/logistic.solution-1.png +++ b/docs/reference/logistic.solution-1.png diff --git a/docs/reference/logistic.solution-2.png b/docs/reference/logistic.solution-2.png Binary files differindex 8d2514a3..b56db0cb 100644 --- a/docs/reference/logistic.solution-2.png +++ b/docs/reference/logistic.solution-2.png diff --git a/docs/reference/logistic.solution.html b/docs/reference/logistic.solution.html index a63b1b1b..f4d4c952 100644 --- a/docs/reference/logistic.solution.html +++ b/docs/reference/logistic.solution.html @@ -18,13 +18,13 @@ an increasing rate constant, supposedly caused by microbial growth"><!-- mathjax </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -35,6 +35,8 @@ an increasing rate constant, supposedly caused by microbial growth"><!-- mathjax <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -42,22 +44,29 @@ an increasing rate constant, supposedly caused by microbial growth"><!-- mathjax <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -65,6 +74,14 @@ an increasing rate constant, supposedly caused by microbial growth"><!-- mathjax <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -222,7 +239,7 @@ Version 1.1, 18 December 2014 </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/lrtest.mkinfit.html b/docs/reference/lrtest.mkinfit.html index a053a032..66237be1 100644 --- a/docs/reference/lrtest.mkinfit.html +++ b/docs/reference/lrtest.mkinfit.html @@ -21,13 +21,13 @@ and can be expressed by fixing the parameters of the other."><!-- mathjax --><sc </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -38,6 +38,8 @@ and can be expressed by fixing the parameters of the other."><!-- mathjax --><sc <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -45,22 +47,29 @@ and can be expressed by fixing the parameters of the other."><!-- mathjax --><sc <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -68,6 +77,14 @@ and can be expressed by fixing the parameters of the other."><!-- mathjax --><sc <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -202,7 +219,7 @@ lower number of fitted parameters (null hypothesis).</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/max_twa_parent.html b/docs/reference/max_twa_parent.html index 3c8e1662..85428bf7 100644 --- a/docs/reference/max_twa_parent.html +++ b/docs/reference/max_twa_parent.html @@ -23,13 +23,13 @@ soil section of the FOCUS guidance."><!-- mathjax --><script src="https://cdnjs. </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -40,6 +40,8 @@ soil section of the FOCUS guidance."><!-- mathjax --><script src="https://cdnjs. <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -47,22 +49,29 @@ soil section of the FOCUS guidance."><!-- mathjax --><script src="https://cdnjs. <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -70,6 +79,14 @@ soil section of the FOCUS guidance."><!-- mathjax --><script src="https://cdnjs. <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -208,7 +225,7 @@ EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/mccall81_245T-1.png b/docs/reference/mccall81_245T-1.png Binary files differindex 79c45fe6..5e4ea6ef 100644 --- a/docs/reference/mccall81_245T-1.png +++ b/docs/reference/mccall81_245T-1.png diff --git a/docs/reference/mccall81_245T.html b/docs/reference/mccall81_245T.html index 0460e376..7470008d 100644 --- a/docs/reference/mccall81_245T.html +++ b/docs/reference/mccall81_245T.html @@ -19,13 +19,13 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -36,6 +36,8 @@ <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -43,22 +45,29 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -66,6 +75,14 @@ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -216,7 +233,7 @@ </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/mean_degparms.html b/docs/reference/mean_degparms.html index dedb8660..964e8fd4 100644 --- a/docs/reference/mean_degparms.html +++ b/docs/reference/mean_degparms.html @@ -17,13 +17,13 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -34,6 +34,8 @@ <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -41,22 +43,29 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -64,6 +73,14 @@ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -151,7 +168,7 @@ nlme for the case of a single grouping variable ds.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/mhmkin-1.png b/docs/reference/mhmkin-1.png Binary files differnew file mode 100644 index 00000000..1c99aead --- /dev/null +++ b/docs/reference/mhmkin-1.png diff --git a/docs/reference/mhmkin-2.png b/docs/reference/mhmkin-2.png Binary files differnew file mode 100644 index 00000000..ea04ebfd --- /dev/null +++ b/docs/reference/mhmkin-2.png diff --git a/docs/reference/mhmkin.html b/docs/reference/mhmkin.html index e4b3e9d0..0ca948c8 100644 --- a/docs/reference/mhmkin.html +++ b/docs/reference/mhmkin.html @@ -22,13 +22,13 @@ mixed-effects model fitting functions."><!-- mathjax --><script src="https://cdn </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -39,6 +39,8 @@ mixed-effects model fitting functions."><!-- mathjax --><script src="https://cdn <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -46,22 +48,29 @@ mixed-effects model fitting functions."><!-- mathjax --><script src="https://cdn <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -69,6 +78,14 @@ mixed-effects model fitting functions."><!-- mathjax --><script src="https://cdn <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -113,7 +130,6 @@ mhmkin( backend = "saemix", algorithm = "saem", no_random_effect = NULL, - auto_ranef_threshold = 3, ..., cores = if (Sys.info()["sysname"] == "Windows") 1 else parallel::detectCores(), cluster = NULL @@ -150,16 +166,14 @@ supported</p></dd> <dt>no_random_effect</dt> -<dd><p>Default is NULL and will be passed to <a href="saem.html">saem</a>. If -you specify "auto", random effects are only included if the number -of datasets in which the parameter passed the t-test is at least 'auto_ranef_threshold'. -Beware that while this may make for convenient model reduction or even -numerical stability of the algorithm, it will likely lead to -underparameterised models.</p></dd> - - -<dt>auto_ranef_threshold</dt> -<dd><p>See 'no_random_effect.</p></dd> +<dd><p>Default is NULL and will be passed to <a href="saem.html">saem</a>. If a +character vector is supplied, it will be passed to all calls to <a href="saem.html">saem</a>, +which will exclude random effects for all matching parameters. Alternatively, +a list of character vectors or an object of class <a href="illparms.html">illparms.mhmkin</a> can be +specified. They have to have the same dimensions that the return object of +the current call will have, i.e. the number of rows must match the number +of degradation models in the mmkin object(s), and the number of columns must +match the number of error models used in the mmkin object(s).</p></dd> <dt>cores</dt> @@ -203,7 +217,7 @@ and the error model names for the second index (column index), with class attribute 'mhmkin'.</p> -<p>An object of class <code>mhmkin</code>.</p> +<p>An object inheriting from <code>mhmkin</code>.</p> </div> <div id="see-also"> <h2>See also</h2> @@ -214,6 +228,88 @@ attribute 'mhmkin'.</p> <p>Johannes Ranke</p> </div> + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span> +<span class="r-in"><span><span class="co"># We start with separate evaluations of all the first six datasets with two</span></span></span> +<span class="r-in"><span><span class="co"># degradation models and two error models</span></span></span> +<span class="r-in"><span><span class="va">f_sep_const</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span><span class="op">)</span>, <span class="va">ds_fomc</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">6</span><span class="op">]</span>, cores <span class="op">=</span> <span class="fl">2</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">f_sep_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_sep_const</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="co"># The mhmkin function sets up hierarchical degradation models aka</span></span></span> +<span class="r-in"><span><span class="co"># nonlinear mixed-effects models for all four combinations, specifying</span></span></span> +<span class="r-in"><span><span class="co"># uncorrelated random effects for all degradation parameters</span></span></span> +<span class="r-in"><span><span class="va">f_saem_1</span> <span class="op"><-</span> <span class="fu">mhmkin</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="va">f_sep_const</span>, <span class="va">f_sep_tc</span><span class="op">)</span>, cores <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="status.html">status</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> error</span> +<span class="r-out co"><span class="r-pr">#></span> degradation const tc</span> +<span class="r-out co"><span class="r-pr">#></span> SFO OK OK</span> +<span class="r-out co"><span class="r-pr">#></span> FOMC OK OK</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> OK: Fit terminated successfully</span> +<span class="r-in"><span><span class="co"># The 'illparms' function shows that in all hierarchical fits, at least</span></span></span> +<span class="r-in"><span><span class="co"># one random effect is ill-defined (the confidence interval for the</span></span></span> +<span class="r-in"><span><span class="co"># random effect expressed as standard deviation includes zero)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> error</span> +<span class="r-out co"><span class="r-pr">#></span> degradation const tc </span> +<span class="r-out co"><span class="r-pr">#></span> SFO sd(parent_0) sd(parent_0) </span> +<span class="r-out co"><span class="r-pr">#></span> FOMC sd(log_beta) sd(parent_0), sd(log_beta)</span> +<span class="r-in"><span><span class="co"># Therefore we repeat the fits, excluding the ill-defined random effects</span></span></span> +<span class="r-in"><span><span class="va">f_saem_2</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_1</span>, no_random_effect <span class="op">=</span> <span class="fu"><a href="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="r-in"><span><span class="fu"><a href="status.html">status</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> error</span> +<span class="r-out co"><span class="r-pr">#></span> degradation const tc</span> +<span class="r-out co"><span class="r-pr">#></span> SFO OK OK</span> +<span class="r-out co"><span class="r-pr">#></span> FOMC OK OK</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> OK: Fit terminated successfully</span> +<span class="r-in"><span><span class="fu"><a href="illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> error</span> +<span class="r-out co"><span class="r-pr">#></span> degradation const tc</span> +<span class="r-out co"><span class="r-pr">#></span> SFO </span> +<span class="r-out co"><span class="r-pr">#></span> FOMC </span> +<span class="r-in"><span><span class="co"># Model comparisons show that FOMC with two-component error is preferable,</span></span></span> +<span class="r-in"><span><span class="co"># and confirms our reduction of the default parameter model</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_1</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Data: 95 observations of 1 variable(s) grouped in 6 datasets</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> npar AIC BIC Lik</span> +<span class="r-out co"><span class="r-pr">#></span> SFO const 5 574.40 573.35 -282.20</span> +<span class="r-out co"><span class="r-pr">#></span> SFO tc 6 543.72 542.47 -265.86</span> +<span class="r-out co"><span class="r-pr">#></span> FOMC const 7 489.67 488.22 -237.84</span> +<span class="r-out co"><span class="r-pr">#></span> FOMC tc 8 406.11 404.44 -195.05</span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Data: 95 observations of 1 variable(s) grouped in 6 datasets</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> npar AIC BIC Lik</span> +<span class="r-out co"><span class="r-pr">#></span> SFO const 4 572.22 571.39 -282.11</span> +<span class="r-out co"><span class="r-pr">#></span> SFO tc 5 541.63 540.59 -265.81</span> +<span class="r-out co"><span class="r-pr">#></span> FOMC const 6 487.38 486.13 -237.69</span> +<span class="r-out co"><span class="r-pr">#></span> FOMC tc 6 402.12 400.88 -195.06</span> +<span class="r-in"><span><span class="co"># The convergence plot for the selected model looks fine</span></span></span> +<span class="r-in"><span><span class="fu">saemix</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">[[</span><span class="st">"FOMC"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="mhmkin-1.png" alt="" width="700" height="433"></span> +<span class="r-in"><span><span class="co"># The plot of predictions versus data shows that we have a pretty data-rich</span></span></span> +<span class="r-in"><span><span class="co"># situation with homogeneous distribution of residuals, because we used the</span></span></span> +<span class="r-in"><span><span class="co"># same degradation model, error model and parameter distribution model that</span></span></span> +<span class="r-in"><span><span class="co"># was used in the data generation.</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_2</span><span class="op">[[</span><span class="st">"FOMC"</span>, <span class="st">"tc"</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></span> +<span class="r-plt img"><img src="mhmkin-2.png" alt="" width="700" height="433"></span> +<span class="r-in"><span><span class="co"># We can specify the same parameter model reductions manually</span></span></span> +<span class="r-in"><span><span class="va">no_ranef</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"parent_0"</span>, <span class="st">"log_beta"</span>, <span class="st">"parent_0"</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">"parent_0"</span>, <span class="st">"log_beta"</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/dim.html" class="external-link">dim</a></span><span class="op">(</span><span class="va">no_ranef</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">f_saem_2m</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_1</span>, no_random_effect <span class="op">=</span> <span class="va">no_ranef</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_2m</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Data: 95 observations of 1 variable(s) grouped in 6 datasets</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> npar AIC BIC Lik</span> +<span class="r-out co"><span class="r-pr">#></span> SFO const 4 572.22 571.39 -282.11</span> +<span class="r-out co"><span class="r-pr">#></span> SFO tc 5 541.63 540.59 -265.81</span> +<span class="r-out co"><span class="r-pr">#></span> FOMC const 6 487.38 486.13 -237.69</span> +<span class="r-out co"><span class="r-pr">#></span> FOMC tc 6 402.12 400.88 -195.06</span> +<span class="r-in"><span><span class="co"># }</span></span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> @@ -226,7 +322,7 @@ attribute 'mhmkin'.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/mixed-1.png b/docs/reference/mixed-1.png Binary files differindex dbba1b03..b053d9c9 100644 --- a/docs/reference/mixed-1.png +++ b/docs/reference/mixed-1.png diff --git a/docs/reference/mixed.html b/docs/reference/mixed.html index 5b250072..b35794d9 100644 --- a/docs/reference/mixed.html +++ b/docs/reference/mixed.html @@ -17,13 +17,13 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -34,6 +34,8 @@ <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -41,22 +43,29 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -64,6 +73,14 @@ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -214,7 +231,7 @@ single dataframe which is convenient for plotting</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/mkin_long_to_wide.html b/docs/reference/mkin_long_to_wide.html index 1008876d..cef18893 100644 --- a/docs/reference/mkin_long_to_wide.html +++ b/docs/reference/mkin_long_to_wide.html @@ -19,13 +19,13 @@ variable and several dependent variables as columns."><!-- mathjax --><script sr </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -36,6 +36,8 @@ variable and several dependent variables as columns."><!-- mathjax --><script sr <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -43,22 +45,29 @@ variable and several dependent variables as columns."><!-- mathjax --><script sr <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -66,6 +75,14 @@ variable and several dependent variables as columns."><!-- mathjax --><script sr <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -170,7 +187,7 @@ observed values called "value".</p></dd> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/mkin_wide_to_long.html b/docs/reference/mkin_wide_to_long.html index 15ead67f..02524d3a 100644 --- a/docs/reference/mkin_wide_to_long.html +++ b/docs/reference/mkin_wide_to_long.html @@ -19,13 +19,13 @@ mkinfit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/ma </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -36,6 +36,8 @@ mkinfit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/ma <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -43,22 +45,29 @@ mkinfit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/ma <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -66,6 +75,14 @@ mkinfit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/ma <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -150,7 +167,7 @@ column of observed values.</p></dd> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/mkinds.html b/docs/reference/mkinds.html index 801d2364..453bf7f2 100644 --- a/docs/reference/mkinds.html +++ b/docs/reference/mkinds.html @@ -20,13 +20,13 @@ provided by this package come as mkinds objects nevertheless."><!-- mathjax -->< </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -37,6 +37,8 @@ provided by this package come as mkinds objects nevertheless."><!-- mathjax -->< <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -44,22 +46,29 @@ provided by this package come as mkinds objects nevertheless."><!-- mathjax -->< <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -67,6 +76,14 @@ provided by this package come as mkinds objects nevertheless."><!-- mathjax -->< <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -231,7 +248,7 @@ and value in order to be compatible with mkinfit</p></dd> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/mkindsg.html b/docs/reference/mkindsg.html index c72fe585..dfd3cbb4 100644 --- a/docs/reference/mkindsg.html +++ b/docs/reference/mkindsg.html @@ -20,13 +20,13 @@ dataset if no data are supplied."><!-- mathjax --><script src="https://cdnjs.clo </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -37,6 +37,8 @@ dataset if no data are supplied."><!-- mathjax --><script src="https://cdnjs.clo <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -44,22 +46,29 @@ dataset if no data are supplied."><!-- mathjax --><script src="https://cdnjs.clo <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -67,6 +76,14 @@ dataset if no data are supplied."><!-- mathjax --><script src="https://cdnjs.clo <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -419,7 +436,7 @@ or covariates like soil pH).</p></dd> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/mkinerrmin.html b/docs/reference/mkinerrmin.html index eaed3aa4..5270f6c4 100644 --- a/docs/reference/mkinerrmin.html +++ b/docs/reference/mkinerrmin.html @@ -18,13 +18,13 @@ the chi-squared test as defined in the FOCUS kinetics report from 2006."><!-- ma </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -35,6 +35,8 @@ the chi-squared test as defined in the FOCUS kinetics report from 2006."><!-- ma <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -42,22 +44,29 @@ the chi-squared test as defined in the FOCUS kinetics report from 2006."><!-- ma <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -65,6 +74,14 @@ the chi-squared test as defined in the FOCUS kinetics report from 2006."><!-- ma <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -179,7 +196,7 @@ Document Reference Sanco/10058/2005 version 2.0, 434 pp, </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/mkinerrplot-1.png b/docs/reference/mkinerrplot-1.png Binary files differindex 49bb1c0e..078614fc 100644 --- a/docs/reference/mkinerrplot-1.png +++ b/docs/reference/mkinerrplot-1.png diff --git a/docs/reference/mkinerrplot.html b/docs/reference/mkinerrplot.html index 6c640652..9fcef920 100644 --- a/docs/reference/mkinerrplot.html +++ b/docs/reference/mkinerrplot.html @@ -21,13 +21,13 @@ using the argument show_errplot = TRUE."><!-- mathjax --><script src="https://cd </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -38,6 +38,8 @@ using the argument show_errplot = TRUE."><!-- mathjax --><script src="https://cd <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -45,22 +47,29 @@ using the argument show_errplot = TRUE."><!-- mathjax --><script src="https://cd <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -68,6 +77,14 @@ using the argument show_errplot = TRUE."><!-- mathjax --><script src="https://cd <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -214,7 +231,7 @@ lines of the mkinfit object.</p></div> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/mkinfit-1.png b/docs/reference/mkinfit-1.png Binary files differindex 7c51deb6..578f64a5 100644 --- a/docs/reference/mkinfit-1.png +++ b/docs/reference/mkinfit-1.png diff --git a/docs/reference/mkinfit.html b/docs/reference/mkinfit.html index 62e4bd8d..cb5039d1 100644 --- a/docs/reference/mkinfit.html +++ b/docs/reference/mkinfit.html @@ -25,13 +25,13 @@ likelihood function."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -42,6 +42,8 @@ likelihood function."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -49,22 +51,29 @@ likelihood function."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -72,6 +81,14 @@ likelihood function."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -384,17 +401,17 @@ Degradation Data. <em>Environments</em> 6(12) 124 <span class="r-in"><span><span class="co"># Use shorthand notation for parent only degradation</span></span></span> <span class="r-in"><span><span class="va">fit</span> <span class="op"><-</span> <span class="fu">mkinfit</span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> mkin version used for fitting: 1.2.0 </span> -<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.2 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Nov 17 13:58:19 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Nov 17 13:58:19 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> mkin version used for fitting: 1.2.3 </span> +<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.3 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Apr 20 07:32:45 2023 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Apr 20 07:32:45 2023 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type analytical </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted using 222 model solutions performed in 0.044 s</span> +<span class="r-out co"><span class="r-pr">#></span> Fitted using 222 model solutions performed in 0.013 s</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Error model: Constant variance </span> <span class="r-out co"><span class="r-pr">#></span> </span> @@ -535,9 +552,9 @@ Degradation Data. <em>Environments</em> 6(12) 124 <span class="r-in"><span> solution_type <span class="op">=</span> <span class="st">"analytical"</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-in"><span><span class="op">}</span></span></span> <span class="r-out co"><span class="r-pr">#></span> test relative elapsed</span> -<span class="r-out co"><span class="r-pr">#></span> 3 analytical 1.000 0.570</span> -<span class="r-out co"><span class="r-pr">#></span> 1 deSolve_compiled 1.682 0.959</span> -<span class="r-out co"><span class="r-pr">#></span> 2 eigen 2.609 1.487</span> +<span class="r-out co"><span class="r-pr">#></span> 3 analytical 1.000 0.264</span> +<span class="r-out co"><span class="r-pr">#></span> 1 deSolve_compiled 1.197 0.316</span> +<span class="r-out co"><span class="r-pr">#></span> 2 eigen 2.227 0.588</span> <span class="r-in"><span><span class="co"># }</span></span></span> <span class="r-in"><span></span></span> <span class="r-in"><span><span class="co"># Use stepwise fitting, using optimised parameters from parent only fit, FOMC-SFO</span></span></span> @@ -564,10 +581,10 @@ Degradation Data. <em>Environments</em> 6(12) 124 <span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>NaNs produced</span> <span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>NaNs produced</span> <span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>diag(.) had 0 or NA entries; non-finite result is doubtful</span> -<span class="r-out co"><span class="r-pr">#></span> mkin version used for fitting: 1.2.0 </span> -<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.2 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Nov 17 13:58:30 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Nov 17 13:58:30 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> mkin version used for fitting: 1.2.3 </span> +<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.3 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Apr 20 07:32:49 2023 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Apr 20 07:32:49 2023 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent</span> @@ -576,7 +593,7 @@ Degradation Data. <em>Environments</em> 6(12) 124 <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type deSolve </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted using 3729 model solutions performed in 2.464 s</span> +<span class="r-out co"><span class="r-pr">#></span> Fitted using 3729 model solutions performed in 0.694 s</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Error model: Two-component variance function </span> <span class="r-out co"><span class="r-pr">#></span> </span> @@ -688,7 +705,7 @@ Degradation Data. <em>Environments</em> 6(12) 124 </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/mkinmod.html b/docs/reference/mkinmod.html index 7dfa740a..15be073c 100644 --- a/docs/reference/mkinmod.html +++ b/docs/reference/mkinmod.html @@ -21,13 +21,13 @@ components."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -38,6 +38,8 @@ components."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -45,22 +47,29 @@ components."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -68,6 +77,14 @@ components."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -132,7 +149,7 @@ the source compartment. Additionally, <code>mkinsub()</code> has an argument <code>to</code>, specifying names of variables to which a transfer is to be assumed in the model. If the argument <code>use_of_ff</code> is set to "min" -(default) and the model for the compartment is "SFO" or "SFORB", an +and the model for the compartment is "SFO" or "SFORB", an additional <code>mkinsub()</code> argument can be <code>sink = FALSE</code>, effectively fixing the flux to sink to zero. In print.mkinmod, this argument is currently not used.</p></dd> @@ -169,7 +186,9 @@ applicable to give detailed information about the C function being built.</p></d <dd><p>Directory where an DLL object, if generated internally by <code><a href="https://rdrr.io/pkg/inline/man/cfunction.html" class="external-link">inline::cfunction()</a></code>, should be saved. The DLL will only be stored in a permanent location for use in future sessions, if 'dll_dir' and 'name' -are specified.</p></dd> +are specified. This is helpful if fit objects are cached e.g. by knitr, +as the cache remains functional across sessions if the DLL is stored in +a user defined location.</p></dd> <dt>unload</dt> @@ -247,7 +266,7 @@ in the FOCUS and NAFTA guidance documents are used.</p> <p>For kinetic models with more than one observed variable, a symbolic solution of the system of differential equations is included in the resulting mkinmod object in some cases, speeding up the solution.</p> -<p>If a C compiler is found by <code><a href="https://rdrr.io/pkg/pkgbuild/man/has_compiler.html" class="external-link">pkgbuild::has_compiler()</a></code> and there +<p>If a C compiler is found by <code><a href="https://r-lib.github.io/pkgbuild/reference/has_compiler.html" class="external-link">pkgbuild::has_compiler()</a></code> and there is more than one observed variable in the specification, C code is generated for evaluating the differential equations, compiled using <code><a href="https://rdrr.io/pkg/inline/man/cfunction.html" class="external-link">inline::cfunction()</a></code> and added to the resulting mkinmod object.</p> @@ -310,7 +329,8 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p> <span class="r-in"><span> parent <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"m1"</span>, full_name <span class="op">=</span> <span class="st">"Test compound"</span><span class="op">)</span>,</span></span> <span class="r-in"><span> m1 <span class="op">=</span> <span class="fu">mkinsub</span><span class="op">(</span><span class="st">"SFO"</span>, full_name <span class="op">=</span> <span class="st">"Metabolite M1"</span><span class="op">)</span>,</span></span> <span class="r-in"><span> name <span class="op">=</span> <span class="st">"SFO_SFO"</span>, dll_dir <span class="op">=</span> <span class="va">DLL_dir</span>, unload <span class="op">=</span> <span class="cn">TRUE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> -<span class="r-msg co"><span class="r-pr">#></span> Copied DLL from /tmp/Rtmp6DB2Bl/file3f5b93b53e71f.so to /home/jranke/.local/share/mkin/SFO_SFO.so</span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-msg co"><span class="r-pr">#></span> Copied DLL from /tmp/Rtmp887oxB/file67a5068775fd4.so to /home/jranke/.local/share/mkin/SFO_SFO.so</span> <span class="r-in"><span><span class="co"># Now we can save the model and restore it in a new session</span></span></span> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/readRDS.html" class="external-link">saveRDS</a></span><span class="op">(</span><span class="va">SFO_SFO.2</span>, file <span class="op">=</span> <span class="st">"~/SFO_SFO.rds"</span><span class="op">)</span></span></span> <span class="r-in"><span><span class="co"># Terminate the R session here if you would like to check, and then do</span></span></span> @@ -363,7 +383,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p> <span class="r-out co"><span class="r-pr">#></span> })</span> <span class="r-out co"><span class="r-pr">#></span> return(predicted)</span> <span class="r-out co"><span class="r-pr">#></span> }</span> -<span class="r-out co"><span class="r-pr">#></span> <environment: 0x55556401ac50></span> +<span class="r-out co"><span class="r-pr">#></span> <environment: 0x55558a8511d0></span> <span class="r-in"><span></span></span> <span class="r-in"><span><span class="co"># If we have several parallel metabolites</span></span></span> <span class="r-in"><span><span class="co"># (compare tests/testthat/test_synthetic_data_for_UBA_2014.R)</span></span></span> @@ -392,7 +412,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/mkinparplot-1.png b/docs/reference/mkinparplot-1.png Binary files differindex fff98391..6e7bf34f 100644 --- a/docs/reference/mkinparplot-1.png +++ b/docs/reference/mkinparplot-1.png diff --git a/docs/reference/mkinparplot.html b/docs/reference/mkinparplot.html index a41456f2..79e20139 100644 --- a/docs/reference/mkinparplot.html +++ b/docs/reference/mkinparplot.html @@ -18,13 +18,13 @@ mkinfit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/ma </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -35,6 +35,8 @@ mkinfit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/ma <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -42,22 +44,29 @@ mkinfit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/ma <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -65,6 +74,14 @@ mkinfit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/ma <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -146,7 +163,7 @@ effect, namely to produce a plot.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/mkinplot.html b/docs/reference/mkinplot.html index 46ffe33e..6b88c87c 100644 --- a/docs/reference/mkinplot.html +++ b/docs/reference/mkinplot.html @@ -18,13 +18,13 @@ plot.mkinfit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/li </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -35,6 +35,8 @@ plot.mkinfit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/li <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -42,22 +44,29 @@ plot.mkinfit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/li <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -65,6 +74,14 @@ plot.mkinfit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/li <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -131,7 +148,7 @@ plot.mkinfit."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/li </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/mkinpredict.html b/docs/reference/mkinpredict.html index 2aab0b50..20ace74a 100644 --- a/docs/reference/mkinpredict.html +++ b/docs/reference/mkinpredict.html @@ -19,13 +19,13 @@ kinetic parameters and initial values for the state variables."><!-- mathjax --> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -36,6 +36,8 @@ kinetic parameters and initial values for the state variables."><!-- mathjax --> <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -43,22 +45,29 @@ kinetic parameters and initial values for the state variables."><!-- mathjax --> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -66,6 +75,14 @@ kinetic parameters and initial values for the state variables."><!-- mathjax --> <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -107,10 +124,11 @@ kinetic parameters and initial values for the state variables.</p> <span> outtimes <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">120</span>, by <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>,</span> <span> solution_type <span class="op">=</span> <span class="st">"deSolve"</span>,</span> <span> use_compiled <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> use_symbols <span class="op">=</span> <span class="cn">FALSE</span>,</span> <span> method.ode <span class="op">=</span> <span class="st">"lsoda"</span>,</span> <span> atol <span class="op">=</span> <span class="fl">1e-08</span>,</span> <span> rtol <span class="op">=</span> <span class="fl">1e-10</span>,</span> -<span> maxsteps <span class="op">=</span> <span class="fl">20000</span>,</span> +<span> maxsteps <span class="op">=</span> <span class="fl">20000L</span>,</span> <span> map_output <span class="op">=</span> <span class="cn">TRUE</span>,</span> <span> na_stop <span class="op">=</span> <span class="cn">TRUE</span>,</span> <span> <span class="va">...</span></span> @@ -166,8 +184,9 @@ solver is used.</p></dd> <dd><p>The method that should be used for producing the predictions. This should generally be "analytical" if there is only one observed variable, and usually "deSolve" in the case of several observed -variables. The third possibility "eigen" is faster but not applicable to -some models e.g. using FOMC for the parent compound.</p></dd> +variables. The third possibility "eigen" is fast in comparison to uncompiled +ODE models, but not applicable to some models, e.g. using FOMC for the +parent compound.</p></dd> <dt>use_compiled</dt> @@ -175,24 +194,27 @@ some models e.g. using FOMC for the parent compound.</p></dd> <a href="mkinmod.html">mkinmod</a> model is used, even if is present.</p></dd> +<dt>use_symbols</dt> +<dd><p>If set to <code>TRUE</code> (default), symbol info present in +the <a href="mkinmod.html">mkinmod</a> object is used if available for accessing compiled code</p></dd> + + <dt>method.ode</dt> <dd><p>The solution method passed via mkinpredict to ode] in -case the solution type is "deSolve". The default "lsoda" is performant, but -sometimes fails to converge.</p></dd> +case the solution type is "deSolve" and we are not using compiled code. +When using compiled code, only lsoda is supported.</p></dd> <dt>atol</dt> -<dd><p>Absolute error tolerance, passed to ode. Default is 1e-8, -lower than in lsoda.</p></dd> +<dd><p>Absolute error tolerance, passed to the ode solver.</p></dd> <dt>rtol</dt> -<dd><p>Absolute error tolerance, passed to ode. Default is 1e-10, -much lower than in lsoda.</p></dd> +<dd><p>Absolute error tolerance, passed to the ode solver.</p></dd> <dt>maxsteps</dt> -<dd><p>Maximum number of steps, passed to ode.</p></dd> +<dd><p>Maximum number of steps, passed to the ode solver.</p></dd> <dt>map_output</dt> @@ -325,15 +347,15 @@ as these always return mapped output.</p></dd> <span class="r-out co"><span class="r-pr">#></span> time degradinol </span> <span class="r-out co"><span class="r-pr">#></span> 20.0000000 0.2478752 </span> <span class="r-in"><span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, <span class="fl">0</span><span class="op">:</span><span class="fl">20</span>,</span></span> -<span class="r-in"><span> method <span class="op">=</span> <span class="st">"lsoda"</span><span class="op">)</span><span class="op">[</span><span class="fl">21</span>,<span class="op">]</span></span></span> +<span class="r-in"><span> method <span class="op">=</span> <span class="st">"lsoda"</span>, use_compiled <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">[</span><span class="fl">21</span>,<span class="op">]</span></span></span> <span class="r-out co"><span class="r-pr">#></span> time degradinol </span> <span class="r-out co"><span class="r-pr">#></span> 20.0000000 0.2478752 </span> <span class="r-in"><span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, <span class="fl">0</span><span class="op">:</span><span class="fl">20</span>,</span></span> -<span class="r-in"><span> method <span class="op">=</span> <span class="st">"ode45"</span><span class="op">)</span><span class="op">[</span><span class="fl">21</span>,<span class="op">]</span></span></span> +<span class="r-in"><span> method <span class="op">=</span> <span class="st">"ode45"</span>, use_compiled <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">[</span><span class="fl">21</span>,<span class="op">]</span></span></span> <span class="r-out co"><span class="r-pr">#></span> time degradinol </span> <span class="r-out co"><span class="r-pr">#></span> 20.0000000 0.2478752 </span> <span class="r-in"><span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">SFO</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>k_degradinol <span class="op">=</span> <span class="fl">0.3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>degradinol <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, <span class="fl">0</span><span class="op">:</span><span class="fl">20</span>,</span></span> -<span class="r-in"><span> method <span class="op">=</span> <span class="st">"rk4"</span><span class="op">)</span><span class="op">[</span><span class="fl">21</span>,<span class="op">]</span></span></span> +<span class="r-in"><span> method <span class="op">=</span> <span class="st">"rk4"</span>, use_compiled <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">[</span><span class="fl">21</span>,<span class="op">]</span></span></span> <span class="r-out co"><span class="r-pr">#></span> time degradinol </span> <span class="r-out co"><span class="r-pr">#></span> 20.0000000 0.2480043 </span> <span class="r-in"><span><span class="co"># rk4 is not as precise here</span></span></span> @@ -373,25 +395,17 @@ as these always return mapped output.</p></dd> <span class="r-in"><span> solution_type <span class="op">=</span> <span class="st">"analytical"</span>, use_compiled <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">[</span><span class="fl">201</span>,<span class="op">]</span><span class="op">)</span></span></span> <span class="r-in"><span><span class="op">}</span></span></span> <span class="r-out co"><span class="r-pr">#></span> test relative elapsed</span> -<span class="r-out co"><span class="r-pr">#></span> 2 deSolve_compiled 1.0 0.004</span> -<span class="r-out co"><span class="r-pr">#></span> 4 analytical 1.0 0.004</span> -<span class="r-out co"><span class="r-pr">#></span> 1 eigen 5.5 0.022</span> -<span class="r-out co"><span class="r-pr">#></span> 3 deSolve 51.0 0.204</span> +<span class="r-out co"><span class="r-pr">#></span> 2 deSolve_compiled 1 0.002</span> +<span class="r-out co"><span class="r-pr">#></span> 4 analytical 1 0.002</span> +<span class="r-out co"><span class="r-pr">#></span> 1 eigen 4 0.008</span> +<span class="r-out co"><span class="r-pr">#></span> 3 deSolve 30 0.060</span> <span class="r-in"><span></span></span> <span class="r-in"><span><span class="co"># \dontrun{</span></span></span> <span class="r-in"><span> <span class="co"># Predict from a fitted model</span></span></span> <span class="r-in"><span> <span class="va">f</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-in"><span> <span class="va">f</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">SFO_SFO</span>, <span class="va">FOCUS_2006_C</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span><span class="op">)</span></span></span> <span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="fu">mkinpredict</span><span class="op">(</span><span class="va">f</span><span class="op">)</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> DLSODA- At current T (=R1), MXSTEP (=I1) steps </span> -<span class="r-out co"><span class="r-pr">#></span> taken on this call before reaching TOUT </span> -<span class="r-out co"><span class="r-pr">#></span> In above message, I1 = 1</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> In above message, R1 = 9.99904e-07</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>an excessive amount of work (> maxsteps ) was done, but integration was not successful - increase maxsteps</span> -<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Returning early. Results are accurate, as far as they go</span> -<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in out[available, var]:</span> (subscript) logical subscript too long</span> +<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in !is.null(x$symbols) & use_symbols:</span> operations are possible only for numeric, logical or complex types</span> <span class="r-in"><span><span class="co"># }</span></span></span> <span class="r-in"><span></span></span> </code></pre></div> @@ -408,7 +422,7 @@ as these always return mapped output.</p></dd> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/mkinresplot-1.png b/docs/reference/mkinresplot-1.png Binary files differindex 97ccd762..7c64d0f0 100644 --- a/docs/reference/mkinresplot-1.png +++ b/docs/reference/mkinresplot-1.png diff --git a/docs/reference/mkinresplot.html b/docs/reference/mkinresplot.html index 6966cd90..cf21b7a1 100644 --- a/docs/reference/mkinresplot.html +++ b/docs/reference/mkinresplot.html @@ -20,13 +20,13 @@ argument show_residuals = TRUE."><!-- mathjax --><script src="https://cdnjs.clou </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -37,6 +37,8 @@ argument show_residuals = TRUE."><!-- mathjax --><script src="https://cdnjs.clou <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -44,22 +46,29 @@ argument show_residuals = TRUE."><!-- mathjax --><script src="https://cdnjs.clou <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -67,6 +76,14 @@ argument show_residuals = TRUE."><!-- mathjax --><script src="https://cdnjs.clou <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -217,7 +234,7 @@ combining the plot of the fit and the residual plot.</p></div> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/mmkin-1.png b/docs/reference/mmkin-1.png Binary files differindex 8ad9c11d..dae64316 100644 --- a/docs/reference/mmkin-1.png +++ b/docs/reference/mmkin-1.png diff --git a/docs/reference/mmkin-2.png b/docs/reference/mmkin-2.png Binary files differindex da2a48a8..b281cd7e 100644 --- a/docs/reference/mmkin-2.png +++ b/docs/reference/mmkin-2.png diff --git a/docs/reference/mmkin-3.png b/docs/reference/mmkin-3.png Binary files differindex 10d3f35b..23b0725c 100644 --- a/docs/reference/mmkin-3.png +++ b/docs/reference/mmkin-3.png diff --git a/docs/reference/mmkin-4.png b/docs/reference/mmkin-4.png Binary files differindex 132380a8..11eae1f9 100644 --- a/docs/reference/mmkin-4.png +++ b/docs/reference/mmkin-4.png diff --git a/docs/reference/mmkin-5.png b/docs/reference/mmkin-5.png Binary files differindex 4bfcc55e..e88bd59f 100644 --- a/docs/reference/mmkin-5.png +++ b/docs/reference/mmkin-5.png diff --git a/docs/reference/mmkin.html b/docs/reference/mmkin.html index 686c9310..6c09f0de 100644 --- a/docs/reference/mmkin.html +++ b/docs/reference/mmkin.html @@ -20,13 +20,13 @@ datasets specified in its first two arguments."><!-- mathjax --><script src="htt </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -37,6 +37,8 @@ datasets specified in its first two arguments."><!-- mathjax --><script src="htt <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -44,22 +46,29 @@ datasets specified in its first two arguments."><!-- mathjax --><script src="htt <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -67,6 +76,14 @@ datasets specified in its first two arguments."><!-- mathjax --><script src="htt <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -189,10 +206,10 @@ plotting.</p></div> <span class="r-in"><span></span></span> <span class="r-in"><span><span class="va">time_default</span></span></span> <span class="r-out co"><span class="r-pr">#></span> user system elapsed </span> -<span class="r-out co"><span class="r-pr">#></span> 5.526 0.809 2.006 </span> +<span class="r-out co"><span class="r-pr">#></span> 1.596 0.611 0.715 </span> <span class="r-in"><span><span class="va">time_1</span></span></span> <span class="r-out co"><span class="r-pr">#></span> user system elapsed </span> -<span class="r-out co"><span class="r-pr">#></span> 5.403 0.008 5.412 </span> +<span class="r-out co"><span class="r-pr">#></span> 2.060 0.016 2.076 </span> <span class="r-in"><span></span></span> <span class="r-in"><span><span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[[</span><span class="st">"SFO_lin"</span>, <span class="fl">2</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> $ff</span> @@ -223,21 +240,25 @@ plotting.</p></div> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">fits.0</span><span class="op">[</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">]</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="mmkin-5.png" alt="" width="700" height="433"></span> <span class="r-in"><span></span></span> -<span class="r-in"><span><span class="co"># On Windows, we can use multiple cores by making a cluster using the parallel</span></span></span> -<span class="r-in"><span><span class="co"># package, which gets loaded with mkin, and passing it to mmkin, e.g.</span></span></span> -<span class="r-in"><span><span class="va">cl</span> <span class="op"><-</span> <span class="fu">makePSOCKcluster</span><span class="op">(</span><span class="fl">12</span><span class="op">)</span></span></span> -<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in makePSOCKcluster(12):</span> could not find function "makePSOCKcluster"</span> +<span class="r-in"><span><span class="co"># On Windows, we can use multiple cores by making a cluster first</span></span></span> +<span class="r-in"><span><span class="va">cl</span> <span class="op"><-</span> <span class="fu">parallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makePSOCKcluster</a></span><span class="op">(</span><span class="fl">12</span><span class="op">)</span></span></span> <span class="r-in"><span><span class="va">f</span> <span class="op"><-</span> <span class="fu">mmkin</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>,</span></span> <span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>A <span class="op">=</span> <span class="va">FOCUS_2006_A</span>, B <span class="op">=</span> <span class="va">FOCUS_2006_B</span>, C <span class="op">=</span> <span class="va">FOCUS_2006_C</span>, D <span class="op">=</span> <span class="va">FOCUS_2006_D</span><span class="op">)</span>,</span></span> <span class="r-in"><span> cluster <span class="op">=</span> <span class="va">cl</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> -<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in system.time({ if (is.null(cluster)) { results <- parallel::mclapply(as.list(1:n.fits), fit_function, mc.cores = cores, mc.preschedule = FALSE) } else { results <- parallel::parLapply(cluster, as.list(1:n.fits), fit_function) }}):</span> object 'cl' not found</span> -<span class="r-msg co"><span class="r-pr">#></span> Timing stopped at: 0 0 0</span> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span></span> -<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in print(f):</span> object 'f' not found</span> +<span class="r-out co"><span class="r-pr">#></span> <mmkin> object</span> +<span class="r-out co"><span class="r-pr">#></span> Status of individual fits:</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> dataset</span> +<span class="r-out co"><span class="r-pr">#></span> model A B C D </span> +<span class="r-out co"><span class="r-pr">#></span> SFO OK OK OK OK</span> +<span class="r-out co"><span class="r-pr">#></span> FOMC OK OK OK OK</span> +<span class="r-out co"><span class="r-pr">#></span> DFOP OK OK OK OK</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> OK: No warnings</span> <span class="r-in"><span><span class="co"># We get false convergence for the FOMC fit to FOCUS_2006_A because this</span></span></span> <span class="r-in"><span><span class="co"># dataset is really SFO, and the FOMC fit is overparameterised</span></span></span> -<span class="r-in"><span><span class="fu">stopCluster</span><span class="op">(</span><span class="va">cl</span><span class="op">)</span></span></span> -<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in stopCluster(cl):</span> could not find function "stopCluster"</span> +<span class="r-in"><span><span class="fu">parallel</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">stopCluster</a></span><span class="op">(</span><span class="va">cl</span><span class="op">)</span></span></span> <span class="r-in"><span><span class="co"># }</span></span></span> <span class="r-in"><span></span></span> </code></pre></div> @@ -254,7 +275,7 @@ plotting.</p></div> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/multistart-1.png b/docs/reference/multistart-1.png Binary files differindex cd12bac1..c7937d67 100644 --- a/docs/reference/multistart-1.png +++ b/docs/reference/multistart-1.png diff --git a/docs/reference/multistart.html b/docs/reference/multistart.html index 8bdce122..a18a79b5 100644 --- a/docs/reference/multistart.html +++ b/docs/reference/multistart.html @@ -22,13 +22,13 @@ mixed-effects models by Duchesne et al (2021)."><!-- mathjax --><script src="htt </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -39,6 +39,8 @@ mixed-effects models by Duchesne et al (2021)."><!-- mathjax --><script src="htt <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -46,22 +48,29 @@ mixed-effects models by Duchesne et al (2021)."><!-- mathjax --><script src="htt <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -69,6 +78,14 @@ mixed-effects models by Duchesne et al (2021)."><!-- mathjax --><script src="htt <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -211,8 +228,9 @@ doi: 10.1186/s12859-021-04373-4.</p> <span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">parallel</span><span class="op">)</span></span></span> <span class="r-in"><span><span class="va">cl</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">makePSOCKcluster</a></span><span class="op">(</span><span class="fl">12</span><span class="op">)</span></span></span> <span class="r-in"><span><span class="va">f_saem_reduced_multi</span> <span class="op"><-</span> <span class="fu">multistart</span><span class="op">(</span><span class="va">f_saem_reduced</span>, n <span class="op">=</span> <span class="fl">16</span>, cluster <span class="op">=</span> <span class="va">cl</span><span class="op">)</span></span></span> +<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in checkForRemoteErrors(val):</span> 16 nodes produced errors; first error: unused argument (mc.preschedule = FALSE)</span> <span class="r-in"><span><span class="fu"><a href="parplot.html">parplot</a></span><span class="op">(</span><span class="va">f_saem_reduced_multi</span>, lpos <span class="op">=</span> <span class="st">"topright"</span><span class="op">)</span></span></span> -<span class="r-plt img"><img src="multistart-2.png" alt="" width="700" height="433"></span> +<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in parplot(f_saem_reduced_multi, lpos = "topright"):</span> object 'f_saem_reduced_multi' not found</span> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/parallel/makeCluster.html" class="external-link">stopCluster</a></span><span class="op">(</span><span class="va">cl</span><span class="op">)</span></span></span> <span class="r-in"><span><span class="co"># }</span></span></span> </code></pre></div> @@ -229,7 +247,7 @@ doi: 10.1186/s12859-021-04373-4.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/nafta-1.png b/docs/reference/nafta-1.png Binary files differindex 5d2d434b..98d4246c 100644 --- a/docs/reference/nafta-1.png +++ b/docs/reference/nafta-1.png diff --git a/docs/reference/nafta.html b/docs/reference/nafta.html index 5906db4c..35b67aa5 100644 --- a/docs/reference/nafta.html +++ b/docs/reference/nafta.html @@ -21,13 +21,13 @@ order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP."><! </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -38,6 +38,8 @@ order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP."><! <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -45,22 +47,29 @@ order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP."><! <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -68,6 +77,14 @@ order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP."><! <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -228,7 +245,7 @@ list element "data" contains the dataset used in the fits.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/nlme-1.png b/docs/reference/nlme-1.png Binary files differindex f4d04e1d..9583da2a 100644 --- a/docs/reference/nlme-1.png +++ b/docs/reference/nlme-1.png diff --git a/docs/reference/nlme-2.png b/docs/reference/nlme-2.png Binary files differindex d9512f41..e941687c 100644 --- a/docs/reference/nlme-2.png +++ b/docs/reference/nlme-2.png diff --git a/docs/reference/nlme.html b/docs/reference/nlme.html index 83576e56..f09fe66f 100644 --- a/docs/reference/nlme.html +++ b/docs/reference/nlme.html @@ -20,13 +20,13 @@ datasets. They are used internally by the nlme.mmkin() method."><!-- mathjax --> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -37,6 +37,8 @@ datasets. They are used internally by the nlme.mmkin() method."><!-- mathjax --> <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -44,22 +46,29 @@ datasets. They are used internally by the nlme.mmkin() method."><!-- mathjax --> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -67,6 +76,14 @@ datasets. They are used internally by the nlme.mmkin() method."><!-- mathjax --> <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -173,12 +190,12 @@ datasets. They are used internally by the <code><a href="nlme.mmkin.html">nlme.m <span class="r-out co"><span class="r-pr">#></span> Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1)</span> <span class="r-out co"><span class="r-pr">#></span> Level: ds</span> <span class="r-out co"><span class="r-pr">#></span> Structure: Diagonal</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_parent_sink Residual</span> -<span class="r-out co"><span class="r-pr">#></span> StdDev: 0.000368491 0.7058039 3.065183</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_parent_sink Residual</span> +<span class="r-out co"><span class="r-pr">#></span> StdDev: 0.0004253489 0.7058039 3.065183</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Fixed effects: parent_0 + log_k_parent_sink ~ 1 </span> <span class="r-out co"><span class="r-pr">#></span> Value Std.Error DF t-value p-value</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 101.18323 0.7900461 43 128.07257 0</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 101.18323 0.7900461 43 128.07256 0</span> <span class="r-out co"><span class="r-pr">#></span> log_k_parent_sink -3.08708 0.4171755 43 -7.39995 0</span> <span class="r-out co"><span class="r-pr">#></span> Correlation: </span> <span class="r-out co"><span class="r-pr">#></span> prnt_0</span> @@ -186,7 +203,7 @@ datasets. They are used internally by the <code><a href="nlme.mmkin.html">nlme.m <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Standardized Within-Group Residuals:</span> <span class="r-out co"><span class="r-pr">#></span> Min Q1 Med Q3 Max </span> -<span class="r-out co"><span class="r-pr">#></span> -2.38427070 -0.52059848 0.03593021 0.39987268 2.73188969 </span> +<span class="r-out co"><span class="r-pr">#></span> -2.38427071 -0.52059848 0.03593021 0.39987268 2.73188969 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Number of Observations: 47</span> <span class="r-out co"><span class="r-pr">#></span> Number of Groups: 3 </span> @@ -213,7 +230,7 @@ datasets. They are used internally by the <code><a href="nlme.mmkin.html">nlme.m </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/nlme.mmkin-1.png b/docs/reference/nlme.mmkin-1.png Binary files differindex 45e4eebe..818c23a2 100644 --- a/docs/reference/nlme.mmkin-1.png +++ b/docs/reference/nlme.mmkin-1.png diff --git a/docs/reference/nlme.mmkin-2.png b/docs/reference/nlme.mmkin-2.png Binary files differindex b9a68e92..779adbdb 100644 --- a/docs/reference/nlme.mmkin-2.png +++ b/docs/reference/nlme.mmkin-2.png diff --git a/docs/reference/nlme.mmkin-3.png b/docs/reference/nlme.mmkin-3.png Binary files differindex 2a724bed..b3785a78 100644 --- a/docs/reference/nlme.mmkin-3.png +++ b/docs/reference/nlme.mmkin-3.png diff --git a/docs/reference/nlme.mmkin.html b/docs/reference/nlme.mmkin.html index 1e294eaf..d446a2a2 100644 --- a/docs/reference/nlme.mmkin.html +++ b/docs/reference/nlme.mmkin.html @@ -19,13 +19,13 @@ have been obtained by fitting the same model to a list of datasets."><!-- mathja </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -36,6 +36,8 @@ have been obtained by fitting the same model to a list of datasets."><!-- mathja <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -43,22 +45,29 @@ have been obtained by fitting the same model to a list of datasets."><!-- mathja <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -66,6 +75,14 @@ have been obtained by fitting the same model to a list of datasets."><!-- mathja <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -239,7 +256,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as <span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_nlme_sfo</span>, <span class="va">f_nlme_dfop</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Model df AIC BIC logLik Test L.Ratio p-value</span> <span class="r-out co"><span class="r-pr">#></span> f_nlme_sfo 1 5 625.0539 637.5529 -307.5269 </span> -<span class="r-out co"><span class="r-pr">#></span> f_nlme_dfop 2 9 495.1270 517.6253 -238.5635 1 vs 2 137.9269 <.0001</span> +<span class="r-out co"><span class="r-pr">#></span> f_nlme_dfop 2 9 495.1270 517.6253 -238.5635 1 vs 2 137.9268 <.0001</span> <span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_nlme_dfop</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Kinetic nonlinear mixed-effects model fit by maximum likelihood</span> <span class="r-out co"><span class="r-pr">#></span> </span> @@ -270,7 +287,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as <span class="r-in"><span> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">f_nlme_dfop</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> $distimes</span> <span class="r-out co"><span class="r-pr">#></span> DT50 DT90 DT50back DT50_k1 DT50_k2</span> -<span class="r-out co"><span class="r-pr">#></span> parent 10.79857 100.7937 30.34193 4.193938 43.85443</span> +<span class="r-out co"><span class="r-pr">#></span> parent 10.79857 100.7937 30.34192 4.193936 43.85441</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-in"><span></span></span> <span class="r-in"><span> <span class="va">ds_2</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">experimental_data_for_UBA_2019</span><span class="op">[</span><span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span>,</span></span> @@ -321,12 +338,12 @@ methods that will automatically work on 'nlme.mmkin' objects, such as <span class="r-in"><span> <span class="fu"><a href="endpoints.html">endpoints</a></span><span class="op">(</span><span class="va">f_nlme_dfop_sfo</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> $ff</span> <span class="r-out co"><span class="r-pr">#></span> parent_A1 parent_sink </span> -<span class="r-out co"><span class="r-pr">#></span> 0.2768574 0.7231426 </span> +<span class="r-out co"><span class="r-pr">#></span> 0.2768575 0.7231425 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> $distimes</span> <span class="r-out co"><span class="r-pr">#></span> DT50 DT90 DT50back DT50_k1 DT50_k2</span> -<span class="r-out co"><span class="r-pr">#></span> parent 11.07091 104.6320 31.49737 4.462383 46.20825</span> -<span class="r-out co"><span class="r-pr">#></span> A1 162.30519 539.1662 NA NA NA</span> +<span class="r-out co"><span class="r-pr">#></span> parent 11.07091 104.6320 31.49737 4.462384 46.20825</span> +<span class="r-out co"><span class="r-pr">#></span> A1 162.30507 539.1658 NA NA NA</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-in"><span></span></span> <span class="r-in"><span> <span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="fu">findFunction</span><span class="op">(</span><span class="st">"varConstProp"</span><span class="op">)</span><span class="op">)</span> <span class="op">></span> <span class="fl">0</span><span class="op">)</span> <span class="op">{</span> <span class="co"># tc error model for nlme available</span></span></span> @@ -354,7 +371,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as <span class="r-out co"><span class="r-pr">#></span> Fixed effects:</span> <span class="r-out co"><span class="r-pr">#></span> list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) </span> <span class="r-out co"><span class="r-pr">#></span> parent_0 log_k1 log_k2 g_qlogis </span> -<span class="r-out co"><span class="r-pr">#></span> 94.04774 -1.82340 -4.16716 0.05686 </span> +<span class="r-out co"><span class="r-pr">#></span> 94.04773 -1.82340 -4.16716 0.05686 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Random effects:</span> <span class="r-out co"><span class="r-pr">#></span> Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1)</span> @@ -368,7 +385,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as <span class="r-out co"><span class="r-pr">#></span> Formula: ~fitted(.) </span> <span class="r-out co"><span class="r-pr">#></span> Parameter estimates:</span> <span class="r-out co"><span class="r-pr">#></span> const prop </span> -<span class="r-out co"><span class="r-pr">#></span> 2.23223147 0.01262395 </span> +<span class="r-out co"><span class="r-pr">#></span> 2.23223593 0.01262367 </span> <span class="r-in"><span></span></span> <span class="r-in"><span> <span class="va">f_2_obs</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_2</span>, error_model <span class="op">=</span> <span class="st">"obs"</span><span class="op">)</span></span></span> <span class="r-in"><span> <span class="va">f_nlme_sfo_sfo_obs</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_2_obs</span><span class="op">[</span><span class="st">"SFO-SFO"</span>, <span class="op">]</span><span class="op">)</span></span></span> @@ -401,7 +418,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as <span class="r-out co"><span class="r-pr">#></span> Formula: ~1 | name </span> <span class="r-out co"><span class="r-pr">#></span> Parameter estimates:</span> <span class="r-out co"><span class="r-pr">#></span> parent A1 </span> -<span class="r-out co"><span class="r-pr">#></span> 1.0000000 0.2049995 </span> +<span class="r-out co"><span class="r-pr">#></span> 1.0000000 0.2049994 </span> <span class="r-in"><span> <span class="va">f_nlme_dfop_sfo_obs</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_2_obs</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span>,</span></span> <span class="r-in"><span> control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>pnlsMaxIter <span class="op">=</span> <span class="fl">120</span>, tolerance <span class="op">=</span> <span class="fl">5e-4</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-in"><span></span></span> @@ -413,7 +430,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as <span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_nlme_dfop_sfo</span>, <span class="va">f_nlme_dfop_sfo_obs</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Model df AIC BIC logLik Test L.Ratio</span> <span class="r-out co"><span class="r-pr">#></span> f_nlme_dfop_sfo 1 13 843.8547 884.6201 -408.9274 </span> -<span class="r-out co"><span class="r-pr">#></span> f_nlme_dfop_sfo_obs 2 14 817.5338 861.4350 -394.7669 1 vs 2 28.32091</span> +<span class="r-out co"><span class="r-pr">#></span> f_nlme_dfop_sfo_obs 2 14 817.5338 861.4350 -394.7669 1 vs 2 28.32093</span> <span class="r-out co"><span class="r-pr">#></span> p-value</span> <span class="r-out co"><span class="r-pr">#></span> f_nlme_dfop_sfo </span> <span class="r-out co"><span class="r-pr">#></span> f_nlme_dfop_sfo_obs <.0001</span> @@ -433,7 +450,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/nobs.mkinfit.html b/docs/reference/nobs.mkinfit.html index edf97142..186cda01 100644 --- a/docs/reference/nobs.mkinfit.html +++ b/docs/reference/nobs.mkinfit.html @@ -17,13 +17,13 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -34,6 +34,8 @@ <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -41,22 +43,29 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -64,6 +73,14 @@ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -126,7 +143,7 @@ </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/parms.html b/docs/reference/parms.html index a3d62d1e..50034ac4 100644 --- a/docs/reference/parms.html +++ b/docs/reference/parms.html @@ -19,13 +19,13 @@ without considering the error structure that was assumed for the fit."><!-- math </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -36,6 +36,8 @@ without considering the error structure that was assumed for the fit."><!-- math <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -43,22 +45,29 @@ without considering the error structure that was assumed for the fit."><!-- math <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -66,6 +75,14 @@ without considering the error structure that was assumed for the fit."><!-- math <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -86,7 +103,7 @@ without considering the error structure that was assumed for the fit."><!-- math <div class="col-md-9 contents"> <div class="page-header"> <h1>Extract model parameters</h1> - <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parms.R" class="external-link"><code>R/parms.R</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/parms.R" class="external-link"><code>R/parms.R</code></a>, <a href="https://github.com/jranke/mkin/blob/HEAD/R/saem.R" class="external-link"><code>R/saem.R</code></a></small> <div class="hidden name"><code>parms.Rd</code></div> </div> @@ -106,7 +123,10 @@ without considering the error structure that was assumed for the fit.</p> <span><span class="fu">parms</span><span class="op">(</span><span class="va">object</span>, transformed <span class="op">=</span> <span class="cn">FALSE</span>, errparms <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span> <span></span> <span><span class="co"># S3 method for multistart</span></span> -<span><span class="fu">parms</span><span class="op">(</span><span class="va">object</span>, exclude_failed <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> +<span><span class="fu">parms</span><span class="op">(</span><span class="va">object</span>, exclude_failed <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span> +<span></span> +<span><span class="co"># S3 method for saem.mmkin</span></span> +<span><span class="fu">parms</span><span class="op">(</span><span class="va">object</span>, ci <span class="op">=</span> <span class="cn">FALSE</span>, covariates <span class="op">=</span> <span class="cn">NULL</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> @@ -133,6 +153,18 @@ in addition to the degradation parameters?</p></dd> <dd><p>For <a href="multistart.html">multistart</a> objects, should rows for failed fits be removed from the returned parameter matrix?</p></dd> + +<dt>ci</dt> +<dd><p>Should a matrix with estimates and confidence interval boundaries +be returned? If FALSE (default), a vector of estimates is returned if no +covariates are given, otherwise a matrix of estimates is returned, with +each column corresponding to a row of the data frame holding the covariates</p></dd> + + +<dt>covariates</dt> +<dd><p>A data frame holding covariate values for which to +return parameter values. Only has an effect if 'ci' is FALSE.</p></dd> + </dl></div> <div id="value"> <h2>Value</h2> @@ -250,7 +282,7 @@ mmkin objects with more than one row).</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/parplot.html b/docs/reference/parplot.html index ab02cbb3..b4d6c077 100644 --- a/docs/reference/parplot.html +++ b/docs/reference/parplot.html @@ -19,13 +19,13 @@ or by their medians as proposed in the paper by Duchesne et al. (2021)."><!-- ma </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.1</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -36,6 +36,8 @@ or by their medians as proposed in the paper by Duchesne et al. (2021)."><!-- ma <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -43,22 +45,29 @@ or by their medians as proposed in the paper by Duchesne et al. (2021)."><!-- ma <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -66,6 +75,14 @@ or by their medians as proposed in the paper by Duchesne et al. (2021)."><!-- ma <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -103,6 +120,7 @@ or by their medians as proposed in the paper by Duchesne et al. (2021).</p> <span><span class="fu">parplot</span><span class="op">(</span></span> <span> <span class="va">object</span>,</span> <span> llmin <span class="op">=</span> <span class="op">-</span><span class="cn">Inf</span>,</span> +<span> llquant <span class="op">=</span> <span class="cn">NA</span>,</span> <span> scale <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">"best"</span>, <span class="st">"median"</span><span class="op">)</span>,</span> <span> lpos <span class="op">=</span> <span class="st">"bottomleft"</span>,</span> <span> main <span class="op">=</span> <span class="st">""</span>,</span> @@ -124,8 +142,14 @@ or by their medians as proposed in the paper by Duchesne et al. (2021).</p> <dd><p>The minimum likelihood of objects to be shown</p></dd> +<dt>llquant</dt> +<dd><p>Fractional value for selecting only the fits with higher +likelihoods. Overrides 'llmin'.</p></dd> + + <dt>scale</dt> -<dd><p>By default, scale parameters using the best available fit. +<dd><p>By default, scale parameters using the best +available fit. If 'median', parameters are scaled using the median parameters from all fits.</p></dd> @@ -167,7 +191,7 @@ doi: 10.1186/s12859-021-04373-4.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/plot.mixed.mmkin-1.png b/docs/reference/plot.mixed.mmkin-1.png Binary files differindex 8a09a167..2e145bb7 100644 --- a/docs/reference/plot.mixed.mmkin-1.png +++ b/docs/reference/plot.mixed.mmkin-1.png diff --git a/docs/reference/plot.mixed.mmkin-2.png b/docs/reference/plot.mixed.mmkin-2.png Binary files differindex 8678c166..b22c1dbb 100644 --- a/docs/reference/plot.mixed.mmkin-2.png +++ b/docs/reference/plot.mixed.mmkin-2.png diff --git a/docs/reference/plot.mixed.mmkin-3.png b/docs/reference/plot.mixed.mmkin-3.png Binary files differindex 9bd01852..cd424bf2 100644 --- a/docs/reference/plot.mixed.mmkin-3.png +++ b/docs/reference/plot.mixed.mmkin-3.png diff --git a/docs/reference/plot.mixed.mmkin-4.png b/docs/reference/plot.mixed.mmkin-4.png Binary files differindex a849aaee..f6ffe4d5 100644 --- a/docs/reference/plot.mixed.mmkin-4.png +++ b/docs/reference/plot.mixed.mmkin-4.png diff --git a/docs/reference/plot.mixed.mmkin.html b/docs/reference/plot.mixed.mmkin.html index b1083204..eb0e60b3 100644 --- a/docs/reference/plot.mixed.mmkin.html +++ b/docs/reference/plot.mixed.mmkin.html @@ -17,13 +17,13 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -34,6 +34,8 @@ <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -41,22 +43,29 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -64,6 +73,14 @@ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -99,10 +116,12 @@ <span> i <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">ncol</a></span><span class="op">(</span><span class="va">x</span><span class="op">$</span><span class="va">mmkin</span><span class="op">)</span>,</span> <span> obs_vars <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">x</span><span class="op">$</span><span class="va">mkinmod</span><span class="op">$</span><span class="va">map</span><span class="op">)</span>,</span> <span> standardized <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> covariates <span class="op">=</span> <span class="cn">NULL</span>,</span> +<span> covariate_quantiles <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.5</span>, <span class="fl">0.05</span>, <span class="fl">0.95</span><span class="op">)</span>,</span> <span> xlab <span class="op">=</span> <span class="st">"Time"</span>,</span> <span> xlim <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/range.html" class="external-link">range</a></span><span class="op">(</span><span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">$</span><span class="va">time</span><span class="op">)</span>,</span> <span> resplot <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">"predicted"</span>, <span class="st">"time"</span><span class="op">)</span>,</span> -<span> pop_curve <span class="op">=</span> <span class="st">"auto"</span>,</span> +<span> pop_curves <span class="op">=</span> <span class="st">"auto"</span>,</span> <span> pred_over <span class="op">=</span> <span class="cn">NULL</span>,</span> <span> test_log_parms <span class="op">=</span> <span class="cn">FALSE</span>,</span> <span> conf.level <span class="op">=</span> <span class="fl">0.6</span>,</span> @@ -143,6 +162,20 @@ variables in the model.</p></dd> <code>resplot = "time"</code>.</p></dd> +<dt>covariates</dt> +<dd><p>Data frame with covariate values for all variables in +any covariate models in the object. If given, it overrides 'covariate_quantiles'. +Each line in the data frame will result in a line drawn for the population. +Rownames are used in the legend to label the lines.</p></dd> + + +<dt>covariate_quantiles</dt> +<dd><p>This argument only has an effect if the fitted +object has covariate models. If so, the default is to show three population +curves, for the 5th percentile, the 50th percentile and the 95th percentile +of the covariate values used for fitting the model.</p></dd> + + <dt>xlab</dt> <dd><p>Label for the x axis.</p></dd> @@ -156,10 +189,11 @@ variables in the model.</p></dd> predicted values?</p></dd> -<dt>pop_curve</dt> -<dd><p>Per default, a population curve is drawn in case +<dt>pop_curves</dt> +<dd><p>Per default, one population curve is drawn in case population parameters are fitted by the model, e.g. for saem objects. -In case there is a covariate model, no population curve is currently shown.</p></dd> +In case there is a covariate model, the behaviour depends on the value +of 'covariates'</p></dd> <dt>pred_over</dt> @@ -234,6 +268,10 @@ corresponding model prediction lines for the different datasets.</p></dd> <p>The function is called for its side effect.</p> </div> + <div id="note"> + <h2>Note</h2> + <p>Covariate models are currently only supported for saem.mmkin objects.</p> + </div> <div id="author"> <h2>Author</h2> <p>Johannes Ranke</p> @@ -290,7 +328,7 @@ corresponding model prediction lines for the different datasets.</p></dd> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/plot.mkinfit-1.png b/docs/reference/plot.mkinfit-1.png Binary files differindex ea1032fb..a48ea72b 100644 --- a/docs/reference/plot.mkinfit-1.png +++ b/docs/reference/plot.mkinfit-1.png diff --git a/docs/reference/plot.mkinfit-2.png b/docs/reference/plot.mkinfit-2.png Binary files differindex cef94cb8..4bc1815c 100644 --- a/docs/reference/plot.mkinfit-2.png +++ b/docs/reference/plot.mkinfit-2.png diff --git a/docs/reference/plot.mkinfit-3.png b/docs/reference/plot.mkinfit-3.png Binary files differindex 8a9dbd13..8de1babc 100644 --- a/docs/reference/plot.mkinfit-3.png +++ b/docs/reference/plot.mkinfit-3.png diff --git a/docs/reference/plot.mkinfit-4.png b/docs/reference/plot.mkinfit-4.png Binary files differindex a7164caa..4b7a5f27 100644 --- a/docs/reference/plot.mkinfit-4.png +++ b/docs/reference/plot.mkinfit-4.png diff --git a/docs/reference/plot.mkinfit-5.png b/docs/reference/plot.mkinfit-5.png Binary files differindex f90b3f54..a8525aaa 100644 --- a/docs/reference/plot.mkinfit-5.png +++ b/docs/reference/plot.mkinfit-5.png diff --git a/docs/reference/plot.mkinfit-6.png b/docs/reference/plot.mkinfit-6.png Binary files differindex 3d0fb25e..878e3dd6 100644 --- a/docs/reference/plot.mkinfit-6.png +++ b/docs/reference/plot.mkinfit-6.png diff --git a/docs/reference/plot.mkinfit-7.png b/docs/reference/plot.mkinfit-7.png Binary files differindex 3e5d828e..c2537ea7 100644 --- a/docs/reference/plot.mkinfit-7.png +++ b/docs/reference/plot.mkinfit-7.png diff --git a/docs/reference/plot.mkinfit.html b/docs/reference/plot.mkinfit.html index cf96990e..21bc9513 100644 --- a/docs/reference/plot.mkinfit.html +++ b/docs/reference/plot.mkinfit.html @@ -19,13 +19,13 @@ observed data together with the solution of the fitted model."><!-- mathjax -->< </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -36,6 +36,8 @@ observed data together with the solution of the fitted model."><!-- mathjax -->< <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -43,22 +45,29 @@ observed data together with the solution of the fitted model."><!-- mathjax -->< <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -66,6 +75,14 @@ observed data together with the solution of the fitted model."><!-- mathjax -->< <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -336,7 +353,7 @@ latex is being used for the formatting of the chi2 error level, if </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/plot.mmkin-1.png b/docs/reference/plot.mmkin-1.png Binary files differindex 235e33a7..c4fcb9ac 100644 --- a/docs/reference/plot.mmkin-1.png +++ b/docs/reference/plot.mmkin-1.png diff --git a/docs/reference/plot.mmkin-2.png b/docs/reference/plot.mmkin-2.png Binary files differindex 7af84edf..8cc727c3 100644 --- a/docs/reference/plot.mmkin-2.png +++ b/docs/reference/plot.mmkin-2.png diff --git a/docs/reference/plot.mmkin-3.png b/docs/reference/plot.mmkin-3.png Binary files differindex 56bfac50..066958c9 100644 --- a/docs/reference/plot.mmkin-3.png +++ b/docs/reference/plot.mmkin-3.png diff --git a/docs/reference/plot.mmkin-4.png b/docs/reference/plot.mmkin-4.png Binary files differindex 5da05f40..c91410fa 100644 --- a/docs/reference/plot.mmkin-4.png +++ b/docs/reference/plot.mmkin-4.png diff --git a/docs/reference/plot.mmkin-5.png b/docs/reference/plot.mmkin-5.png Binary files differindex 3ec224f4..f0a03694 100644 --- a/docs/reference/plot.mmkin-5.png +++ b/docs/reference/plot.mmkin-5.png diff --git a/docs/reference/plot.mmkin.html b/docs/reference/plot.mmkin.html index 30d0406e..18f25bd4 100644 --- a/docs/reference/plot.mmkin.html +++ b/docs/reference/plot.mmkin.html @@ -21,13 +21,13 @@ the fit of at least one model to the same dataset is shown."><!-- mathjax --><sc </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -38,6 +38,8 @@ the fit of at least one model to the same dataset is shown."><!-- mathjax --><sc <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -45,22 +47,29 @@ the fit of at least one model to the same dataset is shown."><!-- mathjax --><sc <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -68,6 +77,14 @@ the fit of at least one model to the same dataset is shown."><!-- mathjax --><sc <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -240,7 +257,7 @@ latex is being used for the formatting of the chi2 error level.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/plot.nafta.html b/docs/reference/plot.nafta.html index a7939f90..600a828e 100644 --- a/docs/reference/plot.nafta.html +++ b/docs/reference/plot.nafta.html @@ -18,13 +18,13 @@ function (SFO, then IORE, then DFOP)."><!-- mathjax --><script src="https://cdnj </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -35,6 +35,8 @@ function (SFO, then IORE, then DFOP)."><!-- mathjax --><script src="https://cdnj <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -42,22 +44,29 @@ function (SFO, then IORE, then DFOP)."><!-- mathjax --><script src="https://cdnj <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -65,6 +74,14 @@ function (SFO, then IORE, then DFOP)."><!-- mathjax --><script src="https://cdnj <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -144,7 +161,7 @@ function (SFO, then IORE, then DFOP).</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/read_spreadsheet.html b/docs/reference/read_spreadsheet.html index d2873cee..84e839a6 100644 --- a/docs/reference/read_spreadsheet.html +++ b/docs/reference/read_spreadsheet.html @@ -22,13 +22,13 @@ factors can be given in columns named 'Temperature' and 'Moisture'."><!-- mathja </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -39,6 +39,8 @@ factors can be given in columns named 'Temperature' and 'Moisture'."><!-- mathja <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -46,22 +48,29 @@ factors can be given in columns named 'Temperature' and 'Moisture'."><!-- mathja <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -69,6 +78,14 @@ factors can be given in columns named 'Temperature' and 'Moisture'."><!-- mathja <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -165,7 +182,7 @@ is probably more complicated to use.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/reexports.html b/docs/reference/reexports.html index 24b9771e..55315e1c 100644 --- a/docs/reference/reexports.html +++ b/docs/reference/reexports.html @@ -28,13 +28,13 @@ intervals, nlme </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -45,6 +45,8 @@ intervals, nlme <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -52,22 +54,29 @@ intervals, nlme <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -75,6 +84,14 @@ intervals, nlme <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -126,7 +143,7 @@ below to see their documentation.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/residuals.mkinfit.html b/docs/reference/residuals.mkinfit.html index 07395436..acefa606 100644 --- a/docs/reference/residuals.mkinfit.html +++ b/docs/reference/residuals.mkinfit.html @@ -17,13 +17,13 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -34,6 +34,8 @@ <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -41,22 +43,29 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -64,6 +73,14 @@ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -136,7 +153,7 @@ standard deviation obtained from the fitted error model?</p></dd> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/saem-1.png b/docs/reference/saem-1.png Binary files differindex 9e310252..1fa206c4 100644 --- a/docs/reference/saem-1.png +++ b/docs/reference/saem-1.png diff --git a/docs/reference/saem-2.png b/docs/reference/saem-2.png Binary files differindex de1bcf57..e5c62c35 100644 --- a/docs/reference/saem-2.png +++ b/docs/reference/saem-2.png diff --git a/docs/reference/saem-3.png b/docs/reference/saem-3.png Binary files differindex de569ce0..f191ad3a 100644 --- a/docs/reference/saem-3.png +++ b/docs/reference/saem-3.png diff --git a/docs/reference/saem-4.png b/docs/reference/saem-4.png Binary files differindex 0f2ee3e7..a74e21f8 100644 --- a/docs/reference/saem-4.png +++ b/docs/reference/saem-4.png diff --git a/docs/reference/saem.html b/docs/reference/saem.html index 957c098e..e308af61 100644 --- a/docs/reference/saem.html +++ b/docs/reference/saem.html @@ -19,13 +19,13 @@ Expectation Maximisation algorithm (SAEM)."><!-- mathjax --><script src="https:/ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.1</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -36,6 +36,8 @@ Expectation Maximisation algorithm (SAEM)."><!-- mathjax --><script src="https:/ <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -43,22 +45,29 @@ Expectation Maximisation algorithm (SAEM)."><!-- mathjax --><script src="https:/ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -66,6 +75,14 @@ Expectation Maximisation algorithm (SAEM)."><!-- mathjax --><script src="https:/ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -143,10 +160,7 @@ Expectation Maximisation algorithm (SAEM).</p> <span> <span class="va">...</span></span> <span><span class="op">)</span></span> <span></span> -<span><span class="fu">saemix_data</span><span class="op">(</span><span class="va">object</span>, covariates <span class="op">=</span> <span class="cn">NULL</span>, verbose <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span> -<span></span> -<span><span class="co"># S3 method for saem.mmkin</span></span> -<span><span class="fu"><a href="parms.html">parms</a></span><span class="op">(</span><span class="va">object</span>, ci <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> +<span><span class="fu">saemix_data</span><span class="op">(</span><span class="va">object</span>, covariates <span class="op">=</span> <span class="cn">NULL</span>, verbose <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> </div> <div id="arguments"> @@ -257,11 +271,6 @@ and the end of the optimisation process?</p></dd> <dt>digits</dt> <dd><p>Number of digits to use for printing</p></dd> - -<dt>ci</dt> -<dd><p>Should a matrix with estimates and confidence interval boundaries -be returned? If FALSE (default), a vector of estimates is returned.</p></dd> - </dl></div> <div id="value"> <h2>Value</h2> @@ -430,10 +439,10 @@ using <a href="mmkin.html">mmkin</a>.</p> <span class="r-plt img"><img src="saem-4.png" alt="" width="700" height="433"></span> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span>, data <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> saemix version used for fitting: 3.2 </span> -<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.2.1 </span> -<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.2 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Fri Nov 18 19:19:25 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Fri Nov 18 19:19:25 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.2.3 </span> +<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.3 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Apr 20 07:34:38 2023 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Apr 20 07:34:38 2023 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span> @@ -448,12 +457,12 @@ using <a href="mmkin.html">mmkin</a>.</p> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type analytical </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted in 9.068 s</span> +<span class="r-out co"><span class="r-pr">#></span> Fitted in 3.757 s</span> <span class="r-out co"><span class="r-pr">#></span> Using 300, 100 iterations and 10 chains</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Variance model: Constant variance </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Mean of starting values for individual parameters:</span> +<span class="r-out co"><span class="r-pr">#></span> Starting values for degradation parameters:</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_A1 f_parent_qlogis log_k1 log_k2 </span> <span class="r-out co"><span class="r-pr">#></span> 93.8102 -5.3734 -0.9711 -1.8799 -4.2708 </span> <span class="r-out co"><span class="r-pr">#></span> g_qlogis </span> @@ -462,6 +471,19 @@ using <a href="mmkin.html">mmkin</a>.</p> <span class="r-out co"><span class="r-pr">#></span> Fixed degradation parameter values:</span> <span class="r-out co"><span class="r-pr">#></span> None</span> <span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Starting values for random effects (square root of initial entries in omega):</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_A1 f_parent_qlogis log_k1 log_k2 g_qlogis</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 4.941 0.000 0.0000 0.000 0.000 0.0000</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_A1 0.000 2.551 0.0000 0.000 0.000 0.0000</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis 0.000 0.000 0.7251 0.000 0.000 0.0000</span> +<span class="r-out co"><span class="r-pr">#></span> log_k1 0.000 0.000 0.0000 1.449 0.000 0.0000</span> +<span class="r-out co"><span class="r-pr">#></span> log_k2 0.000 0.000 0.0000 0.000 2.228 0.0000</span> +<span class="r-out co"><span class="r-pr">#></span> g_qlogis 0.000 0.000 0.0000 0.000 0.000 0.7814</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Starting values for error model parameters:</span> +<span class="r-out co"><span class="r-pr">#></span> a.1 </span> +<span class="r-out co"><span class="r-pr">#></span> 1 </span> +<span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Results:</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Likelihood computed by importance sampling</span> @@ -698,12 +720,31 @@ using <a href="mmkin.html">mmkin</a>.</p> <span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 120 12.1 12.79238 -0.69238 1.882 -0.36791</span> <span class="r-in"><span></span></span> <span class="r-in"><span><span class="co"># The following takes about 6 minutes</span></span></span> -<span class="r-in"><span><span class="co">#f_saem_dfop_sfo_deSolve <- saem(f_mmkin["DFOP-SFO", ], solution_type = "deSolve",</span></span></span> -<span class="r-in"><span><span class="co"># control = list(nbiter.saemix = c(200, 80), nbdisplay = 10))</span></span></span> +<span class="r-in"><span><span class="va">f_saem_dfop_sfo_deSolve</span> <span class="op"><-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span>,</span></span> +<span class="r-in"><span> nbiter.saemix <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">200</span>, <span class="fl">80</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> DINTDY- T (=R1) illegal </span> +<span class="r-out co"><span class="r-pr">#></span> In above message, R1 = 70</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> T not in interval TCUR - HU (= R1) to TCUR (=R2) </span> +<span class="r-out co"><span class="r-pr">#></span> In above message, R1 = 53.1122, R2 = 56.6407</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> DINTDY- T (=R1) illegal </span> +<span class="r-out co"><span class="r-pr">#></span> In above message, R1 = 91</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> T not in interval TCUR - HU (= R1) to TCUR (=R2) </span> +<span class="r-out co"><span class="r-pr">#></span> In above message, R1 = 53.1122, R2 = 56.6407</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> DLSODA- Trouble in DINTDY. ITASK = I1, TOUT = R1</span> +<span class="r-out co"><span class="r-pr">#></span> In above message, I1 = 1</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> In above message, R1 = 91</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Error in deSolve::lsoda(y = odeini, times = outtimes, func = lsoda_func, : </span> +<span class="r-out co"><span class="r-pr">#></span> illegal input detected before taking any integration steps - see written message</span> <span class="r-in"><span></span></span> -<span class="r-in"><span><span class="co">#saemix::compare.saemix(list(</span></span></span> -<span class="r-in"><span><span class="co"># f_saem_dfop_sfo$so,</span></span></span> -<span class="r-in"><span><span class="co"># f_saem_dfop_sfo_deSolve$so))</span></span></span> +<span class="r-in"><span><span class="co">#anova(</span></span></span> +<span class="r-in"><span><span class="co"># f_saem_dfop_sfo,</span></span></span> +<span class="r-in"><span><span class="co"># f_saem_dfop_sfo_deSolve))</span></span></span> <span class="r-in"><span></span></span> <span class="r-in"><span><span class="co"># If the model supports it, we can also use eigenvalue based solutions, which</span></span></span> <span class="r-in"><span><span class="co"># take a similar amount of time</span></span></span> @@ -724,7 +765,7 @@ using <a href="mmkin.html">mmkin</a>.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/schaefer07_complex_case-1.png b/docs/reference/schaefer07_complex_case-1.png Binary files differindex eee9e0cc..7622ae7f 100644 --- a/docs/reference/schaefer07_complex_case-1.png +++ b/docs/reference/schaefer07_complex_case-1.png diff --git a/docs/reference/schaefer07_complex_case.html b/docs/reference/schaefer07_complex_case.html index 365f2e4c..1f3d7860 100644 --- a/docs/reference/schaefer07_complex_case.html +++ b/docs/reference/schaefer07_complex_case.html @@ -19,13 +19,13 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -36,6 +36,8 @@ <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -43,22 +45,29 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -66,6 +75,14 @@ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -190,7 +207,7 @@ </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/set_nd_nq.html b/docs/reference/set_nd_nq.html index a16c02d7..5a487649 100644 --- a/docs/reference/set_nd_nq.html +++ b/docs/reference/set_nd_nq.html @@ -21,13 +21,13 @@ it automates the proposal of Boesten et al (2015)."><!-- mathjax --><script src= </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -38,6 +38,8 @@ it automates the proposal of Boesten et al (2015)."><!-- mathjax --><script src= <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -45,22 +47,29 @@ it automates the proposal of Boesten et al (2015)."><!-- mathjax --><script src= <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -68,6 +77,14 @@ it automates the proposal of Boesten et al (2015)."><!-- mathjax --><script src= <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -247,7 +264,7 @@ Kinetics from Environmental Fate Studies on Pesticides in EU Registration, Versi </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/sigma_twocomp-1.png b/docs/reference/sigma_twocomp-1.png Binary files differindex 0353b72c..3671c658 100644 --- a/docs/reference/sigma_twocomp-1.png +++ b/docs/reference/sigma_twocomp-1.png diff --git a/docs/reference/sigma_twocomp.html b/docs/reference/sigma_twocomp.html index c1db0d30..3018fb28 100644 --- a/docs/reference/sigma_twocomp.html +++ b/docs/reference/sigma_twocomp.html @@ -18,13 +18,13 @@ dependence of the measured value \(y\):"><!-- mathjax --><script src="https://cd </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -35,6 +35,8 @@ dependence of the measured value \(y\):"><!-- mathjax --><script src="https://cd <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -42,22 +44,29 @@ dependence of the measured value \(y\):"><!-- mathjax --><script src="https://cd <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -65,6 +74,14 @@ dependence of the measured value \(y\):"><!-- mathjax --><script src="https://cd <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -185,7 +202,7 @@ Degradation Data. <em>Environments</em> 6(12) 124 </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/status.html b/docs/reference/status.html index 8adf0113..6b9c8f3b 100644 --- a/docs/reference/status.html +++ b/docs/reference/status.html @@ -17,13 +17,13 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -34,6 +34,8 @@ <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -41,22 +43,29 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -64,6 +73,14 @@ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -160,7 +177,7 @@ suitable printing method.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/summary.mkinfit.html b/docs/reference/summary.mkinfit.html index 0bb7f424..9a419ed5 100644 --- a/docs/reference/summary.mkinfit.html +++ b/docs/reference/summary.mkinfit.html @@ -21,13 +21,13 @@ values."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mat </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -38,6 +38,8 @@ values."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mat <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -45,22 +47,29 @@ values."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mat <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -68,6 +77,14 @@ values."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mat <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -214,17 +231,17 @@ EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, <h2>Examples</h2> <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span> <span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">FOCUS_2006_A</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> mkin version used for fitting: 1.2.0 </span> -<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.2 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Nov 17 14:03:23 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Nov 17 14:03:23 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> mkin version used for fitting: 1.2.3 </span> +<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.3 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Apr 20 07:36:41 2023 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Apr 20 07:36:41 2023 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - k_parent * parent</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type analytical </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted using 131 model solutions performed in 0.028 s</span> +<span class="r-out co"><span class="r-pr">#></span> Fitted using 131 model solutions performed in 0.009 s</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Error model: Constant variance </span> <span class="r-out co"><span class="r-pr">#></span> </span> @@ -303,7 +320,7 @@ EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/summary.mmkin.html b/docs/reference/summary.mmkin.html index 9edf10c0..32ee9940 100644 --- a/docs/reference/summary.mmkin.html +++ b/docs/reference/summary.mmkin.html @@ -18,13 +18,13 @@ and gives an overview of ill-defined parameters calculated by illparms."><!-- ma </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -35,6 +35,8 @@ and gives an overview of ill-defined parameters calculated by illparms."><!-- ma <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -42,22 +44,29 @@ and gives an overview of ill-defined parameters calculated by illparms."><!-- ma <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -65,6 +74,14 @@ and gives an overview of ill-defined parameters calculated by illparms."><!-- ma <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -135,7 +152,7 @@ and gives an overview of ill-defined parameters calculated by <a href="illparms. <span class="r-in"><span> quiet <span class="op">=</span> <span class="cn">TRUE</span>, cores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></span> <span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fits</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Error model: Constant variance </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted in 0.835 s</span> +<span class="r-out co"><span class="r-pr">#></span> Fitted in 0.503 s</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Status:</span> <span class="r-out co"><span class="r-pr">#></span> dataset</span> @@ -165,7 +182,7 @@ and gives an overview of ill-defined parameters calculated by <a href="illparms. </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/summary.nlme.mmkin.html b/docs/reference/summary.nlme.mmkin.html index eb01ef7a..64ae46c4 100644 --- a/docs/reference/summary.nlme.mmkin.html +++ b/docs/reference/summary.nlme.mmkin.html @@ -21,13 +21,13 @@ endpoints such as formation fractions and DT50 values. Optionally </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -38,6 +38,8 @@ endpoints such as formation fractions and DT50 values. Optionally <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -45,22 +47,29 @@ endpoints such as formation fractions and DT50 values. Optionally <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -68,6 +77,14 @@ endpoints such as formation fractions and DT50 values. Optionally <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -233,13 +250,12 @@ José Pinheiro and Douglas Bates for the components inherited from nlme</p> <span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Optimisation did not converge:</span> <span class="r-wrn co"><span class="r-pr">#></span> iteration limit reached without convergence (10)</span> <span class="r-in"><span><span class="va">f_nlme</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlme/man/nlme.html" class="external-link">nlme</a></span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">)</span></span></span> -<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>Iteration 4, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_nlme</span>, data <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> nlme version used for fitting: 3.1.160 </span> -<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.2.0 </span> -<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.2 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Nov 17 14:03:27 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Nov 17 14:03:27 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> nlme version used for fitting: 3.1.162 </span> +<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.2.3 </span> +<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.3 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Apr 20 07:36:43 2023 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Apr 20 07:36:43 2023 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - k_parent * parent</span> @@ -249,7 +265,7 @@ José Pinheiro and Douglas Bates for the components inherited from nlme</p> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type analytical </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted in 0.538 s using 4 iterations</span> +<span class="r-out co"><span class="r-pr">#></span> Fitted in 0.187 s using 4 iterations</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Variance model: Two-component variance function </span> <span class="r-out co"><span class="r-pr">#></span> </span> @@ -278,15 +294,15 @@ José Pinheiro and Douglas Bates for the components inherited from nlme</p> <span class="r-out co"><span class="r-pr">#></span> Formula: list(parent_0 ~ 1, log_k_parent ~ 1)</span> <span class="r-out co"><span class="r-pr">#></span> Level: ds</span> <span class="r-out co"><span class="r-pr">#></span> Structure: Diagonal</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_parent Residual</span> -<span class="r-out co"><span class="r-pr">#></span> StdDev: 6.924e-05 0.5863 1</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_parent Residual</span> +<span class="r-out co"><span class="r-pr">#></span> StdDev: 6.92e-05 0.5863 1</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Variance function:</span> <span class="r-out co"><span class="r-pr">#></span> Structure: Constant plus proportion of variance covariate</span> <span class="r-out co"><span class="r-pr">#></span> Formula: ~fitted(.) </span> <span class="r-out co"><span class="r-pr">#></span> Parameter estimates:</span> <span class="r-out co"><span class="r-pr">#></span> const prop </span> -<span class="r-out co"><span class="r-pr">#></span> 0.0001208853 0.0789968036 </span> +<span class="r-out co"><span class="r-pr">#></span> 0.0001208154 0.0789968021 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Backtransformed parameters with asymmetric confidence intervals:</span> <span class="r-out co"><span class="r-pr">#></span> lower est. upper</span> @@ -365,8 +381,8 @@ José Pinheiro and Douglas Bates for the components inherited from nlme</p> <span class="r-out co"><span class="r-pr">#></span> ds 4 parent 14 104.8 95.234 9.56590 7.5232 1.271521</span> <span class="r-out co"><span class="r-pr">#></span> ds 4 parent 28 85.0 89.274 -4.27372 7.0523 -0.606001</span> <span class="r-out co"><span class="r-pr">#></span> ds 4 parent 28 77.2 89.274 -12.07372 7.0523 -1.712017</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 60 82.2 77.013 5.18661 6.0838 0.852526</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 60 86.1 77.013 9.08661 6.0838 1.493571</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 60 82.2 77.013 5.18660 6.0838 0.852526</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 60 86.1 77.013 9.08660 6.0838 1.493571</span> <span class="r-out co"><span class="r-pr">#></span> ds 4 parent 90 70.5 67.053 3.44692 5.2970 0.650733</span> <span class="r-out co"><span class="r-pr">#></span> ds 4 parent 90 61.7 67.053 -5.35308 5.2970 -1.010591</span> <span class="r-out co"><span class="r-pr">#></span> ds 4 parent 120 60.0 58.381 1.61905 4.6119 0.351058</span> @@ -376,7 +392,7 @@ José Pinheiro and Douglas Bates for the components inherited from nlme</p> <span class="r-out co"><span class="r-pr">#></span> ds 5 parent 1 108.0 99.914 8.08560 7.8929 1.024413</span> <span class="r-out co"><span class="r-pr">#></span> ds 5 parent 1 104.9 99.914 4.98560 7.8929 0.631655</span> <span class="r-out co"><span class="r-pr">#></span> ds 5 parent 3 100.5 96.641 3.85898 7.6343 0.505477</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 3 89.5 96.641 -7.14102 7.6343 -0.935382</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 3 89.5 96.641 -7.14102 7.6343 -0.935383</span> <span class="r-out co"><span class="r-pr">#></span> ds 5 parent 7 91.7 90.412 1.28752 7.1423 0.180267</span> <span class="r-out co"><span class="r-pr">#></span> ds 5 parent 7 95.1 90.412 4.68752 7.1423 0.656304</span> <span class="r-out co"><span class="r-pr">#></span> ds 5 parent 14 82.2 80.463 1.73715 6.3563 0.273295</span> @@ -405,7 +421,7 @@ José Pinheiro and Douglas Bates for the components inherited from nlme</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/summary.saem.mmkin.html b/docs/reference/summary.saem.mmkin.html index dfa0b776..6f7b1ac3 100644 --- a/docs/reference/summary.saem.mmkin.html +++ b/docs/reference/summary.saem.mmkin.html @@ -21,13 +21,13 @@ endpoints such as formation fractions and DT50 values. Optionally </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -38,6 +38,8 @@ endpoints such as formation fractions and DT50 values. Optionally <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -45,22 +47,29 @@ endpoints such as formation fractions and DT50 values. Optionally <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -68,6 +77,14 @@ endpoints such as formation fractions and DT50 values. Optionally <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -102,7 +119,15 @@ endpoints such as formation fractions and DT50 values. Optionally <div id="ref-usage"> <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for saem.mmkin</span></span> -<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">object</span>, data <span class="op">=</span> <span class="cn">FALSE</span>, verbose <span class="op">=</span> <span class="cn">FALSE</span>, distimes <span class="op">=</span> <span class="cn">TRUE</span>, <span class="va">...</span><span class="op">)</span></span> +<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> +<span> <span class="va">object</span>,</span> +<span> data <span class="op">=</span> <span class="cn">FALSE</span>,</span> +<span> verbose <span class="op">=</span> <span class="cn">FALSE</span>,</span> +<span> covariates <span class="op">=</span> <span class="cn">NULL</span>,</span> +<span> covariate_quantile <span class="op">=</span> <span class="fl">0.5</span>,</span> +<span> distimes <span class="op">=</span> <span class="cn">TRUE</span>,</span> +<span> <span class="va">...</span></span> +<span><span class="op">)</span></span> <span></span> <span><span class="co"># S3 method for summary.saem.mmkin</span></span> <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, verbose <span class="op">=</span> <span class="va">x</span><span class="op">$</span><span class="va">verbose</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div> @@ -123,6 +148,17 @@ the summary.</p></dd> <dd><p>Should the summary be verbose?</p></dd> +<dt>covariates</dt> +<dd><p>Numeric vector with covariate values for all variables in +any covariate models in the object. If given, it overrides 'covariate_quantile'.</p></dd> + + +<dt>covariate_quantile</dt> +<dd><p>This argument only has an effect if the fitted +object has covariate models. If so, the default is to show endpoints +for the median of the covariate values (50th percentile).</p></dd> + + <dt>distimes</dt> <dd><p>logical, indicating whether DT50 and DT90 values should be included.</p></dd> @@ -266,36 +302,38 @@ saemix authors for the parts inherited from saemix.</p> <span class="r-out co"><span class="r-pr">#></span> SD.g_qlogis 0.37478 0.04490 0.70467</span> <span class="r-in"><span><span class="fu"><a href="illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> [1] "sd(parent_0)" "sd(log_k_m1)"</span> -<span class="r-in"><span><span class="va">f_saem_dfop_sfo_2</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span>, covariance.model <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/diag.html" class="external-link">diag</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">0</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="va">f_saem_dfop_sfo_2</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span>,</span></span> +<span class="r-in"><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">"parent_0"</span>, <span class="st">"log_k_m1"</span><span class="op">)</span><span class="op">)</span></span></span> <span class="r-in"><span><span class="fu"><a href="illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo_2</span><span class="op">)</span></span></span> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/intervals.html" class="external-link">intervals</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo_2</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> Approximate 95% confidence intervals</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Fixed effects:</span> <span class="r-out co"><span class="r-pr">#></span> lower est. upper</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 97.57609542 100.73343868 103.89078195</span> -<span class="r-out co"><span class="r-pr">#></span> k_m1 0.01549292 0.01714893 0.01898194</span> -<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 0.20720315 0.28358738 0.37481744</span> -<span class="r-out co"><span class="r-pr">#></span> k1 0.06149334 0.08733164 0.12402670</span> -<span class="r-out co"><span class="r-pr">#></span> k2 0.01448390 0.01699942 0.01995184</span> -<span class="r-out co"><span class="r-pr">#></span> g 0.45084762 0.51075839 0.57036168</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 98.36731429 101.42508066 104.48284703</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1 0.01513234 0.01670094 0.01843214</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 0.20221431 0.27608850 0.36461630</span> +<span class="r-out co"><span class="r-pr">#></span> k1 0.06915073 0.09759718 0.13774560</span> +<span class="r-out co"><span class="r-pr">#></span> k2 0.01487068 0.01740389 0.02036863</span> +<span class="r-out co"><span class="r-pr">#></span> g 0.37365671 0.48384821 0.59563299</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Random effects:</span> <span class="r-out co"><span class="r-pr">#></span> lower est. upper</span> -<span class="r-out co"><span class="r-pr">#></span> sd(f_parent_qlogis) 0.16606767 0.4479731 0.7298784</span> -<span class="r-out co"><span class="r-pr">#></span> sd(log_k1) 0.12284609 0.3588446 0.5948430</span> -<span class="r-out co"><span class="r-pr">#></span> sd(log_k2) 0.05379723 0.1548780 0.2559588</span> +<span class="r-out co"><span class="r-pr">#></span> sd(f_parent_qlogis) 0.16439770 0.4427585 0.7211193</span> +<span class="r-out co"><span class="r-pr">#></span> sd(log_k1) 0.08304243 0.3345213 0.5860002</span> +<span class="r-out co"><span class="r-pr">#></span> sd(log_k2) 0.03146410 0.1490210 0.2665779</span> +<span class="r-out co"><span class="r-pr">#></span> sd(g_qlogis) 0.06216385 0.4023430 0.7425221</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> lower est. upper</span> -<span class="r-out co"><span class="r-pr">#></span> a.1 0.6811490 0.88503409 1.08891921</span> -<span class="r-out co"><span class="r-pr">#></span> b.1 0.0676515 0.08336272 0.09907394</span> +<span class="r-out co"><span class="r-pr">#></span> lower est. upper</span> +<span class="r-out co"><span class="r-pr">#></span> a.1 0.67696663 0.87777355 1.07858048</span> +<span class="r-out co"><span class="r-pr">#></span> b.1 0.06363957 0.07878001 0.09392044</span> <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo_2</span>, data <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span> <span class="r-out co"><span class="r-pr">#></span> saemix version used for fitting: 3.2 </span> -<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.2.0 </span> -<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.2 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Nov 17 14:04:08 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Nov 17 14:04:08 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.2.3 </span> +<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.3 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Apr 20 07:36:59 2023 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Apr 20 07:36:59 2023 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span> @@ -310,12 +348,12 @@ saemix authors for the parts inherited from saemix.</p> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type analytical </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted in 26.014 s</span> +<span class="r-out co"><span class="r-pr">#></span> Fitted in 9.185 s</span> <span class="r-out co"><span class="r-pr">#></span> Using 300, 100 iterations and 10 chains</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Variance model: Two-component variance function </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Mean of starting values for individual parameters:</span> +<span class="r-out co"><span class="r-pr">#></span> Starting values for degradation parameters:</span> <span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 </span> <span class="r-out co"><span class="r-pr">#></span> 101.65645 -4.05368 -0.94311 -2.35943 -4.07006 </span> <span class="r-out co"><span class="r-pr">#></span> g_qlogis </span> @@ -324,237 +362,291 @@ saemix authors for the parts inherited from saemix.</p> <span class="r-out co"><span class="r-pr">#></span> Fixed degradation parameter values:</span> <span class="r-out co"><span class="r-pr">#></span> None</span> <span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Starting values for random effects (square root of initial entries in omega):</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 g_qlogis</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 6.742 0.0000 0.0000 0.0000 0.0000 0.000</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_m1 0.000 0.2236 0.0000 0.0000 0.0000 0.000</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis 0.000 0.0000 0.5572 0.0000 0.0000 0.000</span> +<span class="r-out co"><span class="r-pr">#></span> log_k1 0.000 0.0000 0.0000 0.8031 0.0000 0.000</span> +<span class="r-out co"><span class="r-pr">#></span> log_k2 0.000 0.0000 0.0000 0.0000 0.2931 0.000</span> +<span class="r-out co"><span class="r-pr">#></span> g_qlogis 0.000 0.0000 0.0000 0.0000 0.0000 0.807</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Starting values for error model parameters:</span> +<span class="r-out co"><span class="r-pr">#></span> a.1 b.1 </span> +<span class="r-out co"><span class="r-pr">#></span> 1 1 </span> +<span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Results:</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Likelihood computed by importance sampling</span> -<span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span> -<span class="r-out co"><span class="r-pr">#></span> 809.5 805.2 -393.7</span> +<span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span> +<span class="r-out co"><span class="r-pr">#></span> 807 802.3 -391.5</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Optimised parameters:</span> <span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 100.73344 97.57610 103.89078</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_m1 -4.06582 -4.16737 -3.96427</span> -<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.92674 -1.34187 -0.51160</span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 -2.43804 -2.78883 -2.08726</span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 -4.07458 -4.23472 -3.91443</span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis 0.04304 -0.19725 0.28333</span> -<span class="r-out co"><span class="r-pr">#></span> a.1 0.88503 0.68115 1.08892</span> -<span class="r-out co"><span class="r-pr">#></span> b.1 0.08336 0.06765 0.09907</span> -<span class="r-out co"><span class="r-pr">#></span> SD.f_parent_qlogis 0.44797 0.16607 0.72988</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 0.35884 0.12285 0.59484</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 0.15488 0.05380 0.25596</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 101.42508 98.36731 104.48285</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_m1 -4.09229 -4.19092 -3.99366</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.96395 -1.37251 -0.55538</span> +<span class="r-out co"><span class="r-pr">#></span> log_k1 -2.32691 -2.67147 -1.98235</span> +<span class="r-out co"><span class="r-pr">#></span> log_k2 -4.05106 -4.20836 -3.89376</span> +<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.06463 -0.51656 0.38730</span> +<span class="r-out co"><span class="r-pr">#></span> a.1 0.87777 0.67697 1.07858</span> +<span class="r-out co"><span class="r-pr">#></span> b.1 0.07878 0.06364 0.09392</span> +<span class="r-out co"><span class="r-pr">#></span> SD.f_parent_qlogis 0.44276 0.16440 0.72112</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 0.33452 0.08304 0.58600</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 0.14902 0.03146 0.26658</span> +<span class="r-out co"><span class="r-pr">#></span> SD.g_qlogis 0.40234 0.06216 0.74252</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Correlation: </span> <span class="r-out co"><span class="r-pr">#></span> parnt_0 lg_k_m1 f_prnt_ log_k1 log_k2 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k_m1 -0.4698 </span> -<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.2461 0.2709 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 0.1572 -0.1517 -0.0648 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 -0.0023 0.0835 0.0125 0.1420 </span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis 0.2314 -0.2337 -0.0755 -0.2762 -0.4797</span> +<span class="r-out co"><span class="r-pr">#></span> log_k_m1 -0.4693 </span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.2378 0.2595 </span> +<span class="r-out co"><span class="r-pr">#></span> log_k1 0.1720 -0.1593 -0.0669 </span> +<span class="r-out co"><span class="r-pr">#></span> log_k2 0.0179 0.0594 0.0035 0.1995 </span> +<span class="r-out co"><span class="r-pr">#></span> g_qlogis 0.1073 -0.1060 -0.0322 -0.2299 -0.3168</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Random effects:</span> -<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> SD.f_parent_qlogis 0.4480 0.1661 0.7299</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 0.3588 0.1228 0.5948</span> -<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 0.1549 0.0538 0.2560</span> +<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> +<span class="r-out co"><span class="r-pr">#></span> SD.f_parent_qlogis 0.4428 0.16440 0.7211</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 0.3345 0.08304 0.5860</span> +<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 0.1490 0.03146 0.2666</span> +<span class="r-out co"><span class="r-pr">#></span> SD.g_qlogis 0.4023 0.06216 0.7425</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Variance model:</span> <span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> a.1 0.88503 0.68115 1.08892</span> -<span class="r-out co"><span class="r-pr">#></span> b.1 0.08336 0.06765 0.09907</span> +<span class="r-out co"><span class="r-pr">#></span> a.1 0.87777 0.67697 1.07858</span> +<span class="r-out co"><span class="r-pr">#></span> b.1 0.07878 0.06364 0.09392</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Backtransformed parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> parent_0 100.73344 97.57610 103.89078</span> -<span class="r-out co"><span class="r-pr">#></span> k_m1 0.01715 0.01549 0.01898</span> -<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 0.28359 0.20720 0.37482</span> -<span class="r-out co"><span class="r-pr">#></span> k1 0.08733 0.06149 0.12403</span> -<span class="r-out co"><span class="r-pr">#></span> k2 0.01700 0.01448 0.01995</span> -<span class="r-out co"><span class="r-pr">#></span> g 0.51076 0.45085 0.57036</span> +<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 101.4251 98.36731 104.48285</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1 0.0167 0.01513 0.01843</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 0.2761 0.20221 0.36462</span> +<span class="r-out co"><span class="r-pr">#></span> k1 0.0976 0.06915 0.13775</span> +<span class="r-out co"><span class="r-pr">#></span> k2 0.0174 0.01487 0.02037</span> +<span class="r-out co"><span class="r-pr">#></span> g 0.4838 0.37366 0.59563</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Resulting formation fractions:</span> <span class="r-out co"><span class="r-pr">#></span> ff</span> -<span class="r-out co"><span class="r-pr">#></span> parent_m1 0.2836</span> -<span class="r-out co"><span class="r-pr">#></span> parent_sink 0.7164</span> +<span class="r-out co"><span class="r-pr">#></span> parent_m1 0.2761</span> +<span class="r-out co"><span class="r-pr">#></span> parent_sink 0.7239</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Estimated disappearance times:</span> <span class="r-out co"><span class="r-pr">#></span> DT50 DT90 DT50back DT50_k1 DT50_k2</span> -<span class="r-out co"><span class="r-pr">#></span> parent 15.94 93.48 28.14 7.937 40.77</span> -<span class="r-out co"><span class="r-pr">#></span> m1 40.42 134.27 NA NA NA</span> +<span class="r-out co"><span class="r-pr">#></span> parent 15.54 94.33 28.4 7.102 39.83</span> +<span class="r-out co"><span class="r-pr">#></span> m1 41.50 137.87 NA NA NA</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Data:</span> -<span class="r-out co"><span class="r-pr">#></span> ds name time observed predicted residual std standardized</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 0 89.8 1.007e+02 -10.93344 8.4439 -1.29483</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 0 104.1 1.007e+02 3.36656 8.4439 0.39870</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 1 88.7 9.591e+01 -7.20789 8.0440 -0.89606</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 1 95.5 9.591e+01 -0.40789 8.0440 -0.05071</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 3 81.8 8.712e+01 -5.31561 7.3159 -0.72658</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 3 94.5 8.712e+01 7.38439 7.3159 1.00936</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 7 71.5 7.246e+01 -0.95675 6.1047 -0.15672</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 7 70.3 7.246e+01 -2.15675 6.1047 -0.35329</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 14 54.2 5.382e+01 0.38143 4.5729 0.08341</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 14 49.6 5.382e+01 -4.21857 4.5729 -0.92251</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 28 31.5 3.230e+01 -0.80120 2.8344 -0.28267</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 28 28.8 3.230e+01 -3.50120 2.8344 -1.23524</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 60 12.1 1.307e+01 -0.97165 1.4038 -0.69215</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 60 13.6 1.307e+01 0.52835 1.4038 0.37637</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 90 6.2 6.353e+00 -0.15285 1.0314 -0.14820</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 90 8.3 6.353e+00 1.94715 1.0314 1.88790</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 120 2.2 3.175e+00 -0.97462 0.9238 -1.05506</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 120 2.4 3.175e+00 -0.77462 0.9238 -0.83855</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 1 0.3 1.183e+00 -0.88350 0.8905 -0.99212</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 1 0.2 1.183e+00 -0.98350 0.8905 -1.10441</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 3 2.2 3.281e+00 -1.08106 0.9263 -1.16703</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 3 3.0 3.281e+00 -0.28106 0.9263 -0.30341</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 7 6.5 6.564e+00 -0.06353 1.0405 -0.06106</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 7 5.0 6.564e+00 -1.56353 1.0405 -1.50266</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 14 10.2 1.015e+01 0.05147 1.2243 0.04204</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 14 9.5 1.015e+01 -0.64853 1.2243 -0.52970</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 28 12.2 1.265e+01 -0.44824 1.3766 -0.32561</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 28 13.4 1.265e+01 0.75176 1.3766 0.54610</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 60 11.8 1.078e+01 1.02355 1.2611 0.81165</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 60 13.2 1.078e+01 2.42355 1.2611 1.92181</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 90 6.6 7.698e+00 -1.09840 1.0932 -1.00474</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 90 9.3 7.698e+00 1.60160 1.0932 1.46502</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 120 3.5 5.199e+00 -1.69853 0.9854 -1.72363</span> -<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 120 5.4 5.199e+00 0.20147 0.9854 0.20445</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 0 118.0 1.007e+02 17.26656 8.4439 2.04485</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 0 99.8 1.007e+02 -0.93344 8.4439 -0.11055</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 1 90.2 9.584e+01 -5.63852 8.0382 -0.70146</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 1 94.6 9.584e+01 -1.23852 8.0382 -0.15408</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 3 96.1 8.706e+01 9.04068 7.3113 1.23654</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 3 78.4 8.706e+01 -8.65932 7.3113 -1.18438</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 7 77.9 7.286e+01 5.04438 6.1376 0.82188</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 7 77.7 7.286e+01 4.84438 6.1376 0.78930</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 14 56.0 5.567e+01 0.33336 4.7242 0.07057</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 14 54.7 5.567e+01 -0.96664 4.7242 -0.20462</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 28 36.6 3.705e+01 -0.44800 3.2127 -0.13944</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 28 36.8 3.705e+01 -0.24800 3.2127 -0.07719</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 60 22.1 2.008e+01 2.01984 1.8935 1.06672</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 60 24.7 2.008e+01 4.61984 1.8935 2.43984</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 90 12.4 1.253e+01 -0.12814 1.3689 -0.09360</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 90 10.8 1.253e+01 -1.72814 1.3689 -1.26238</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 120 6.8 7.916e+00 -1.11595 1.1040 -1.01085</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 120 7.9 7.916e+00 -0.01595 1.1040 -0.01445</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 1 1.3 1.317e+00 -0.01669 0.8918 -0.01871</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 3 3.7 3.613e+00 0.08699 0.9349 0.09305</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 3 4.7 3.613e+00 1.08699 0.9349 1.16270</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 7 8.1 7.092e+00 1.00781 1.0643 0.94688</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 7 7.9 7.092e+00 0.80781 1.0643 0.75897</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 14 10.1 1.066e+01 -0.56458 1.2545 -0.45006</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 14 10.3 1.066e+01 -0.36458 1.2545 -0.29063</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 28 10.7 1.281e+01 -2.11106 1.3870 -1.52201</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 28 12.2 1.281e+01 -0.61106 1.3870 -0.44055</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 60 10.7 1.078e+01 -0.08464 1.2616 -0.06709</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 60 12.5 1.078e+01 1.71536 1.2616 1.35970</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 90 9.1 8.013e+00 1.08684 1.1088 0.98016</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 90 7.4 8.013e+00 -0.61316 1.1088 -0.55298</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 120 6.1 5.749e+00 0.35063 1.0065 0.34838</span> -<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 120 4.5 5.749e+00 -1.24937 1.0065 -1.24133</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 0 106.2 1.007e+02 5.46656 8.4439 0.64740</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 0 106.9 1.007e+02 6.16656 8.4439 0.73030</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 1 107.4 9.369e+01 13.70530 7.8606 1.74354</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 1 96.1 9.369e+01 2.40530 7.8606 0.30599</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 3 79.4 8.185e+01 -2.45363 6.8807 -0.35660</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 3 82.6 8.185e+01 0.74637 6.8807 0.10847</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 7 63.9 6.487e+01 -0.97153 5.4798 -0.17729</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 7 62.4 6.487e+01 -2.47153 5.4798 -0.45103</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 14 51.0 4.791e+01 3.09024 4.0908 0.75542</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 14 47.1 4.791e+01 -0.80976 4.0908 -0.19795</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 28 36.1 3.313e+01 2.97112 2.9001 1.02450</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 28 36.6 3.313e+01 3.47112 2.9001 1.19691</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 60 20.1 1.927e+01 0.83265 1.8339 0.45404</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 60 19.8 1.927e+01 0.53265 1.8339 0.29045</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 90 11.3 1.203e+01 -0.72783 1.3374 -0.54421</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 90 10.7 1.203e+01 -1.32783 1.3374 -0.99284</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 120 8.2 7.516e+00 0.68382 1.0844 0.63061</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 120 7.3 7.516e+00 -0.21618 1.0844 -0.19936</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 0 0.8 -9.948e-14 0.80000 0.8850 0.90392</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 1 1.8 1.682e+00 0.11759 0.8961 0.13123</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 1 2.3 1.682e+00 0.61759 0.8961 0.68921</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 3 4.2 4.431e+00 -0.23052 0.9590 -0.24037</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 3 4.1 4.431e+00 -0.33052 0.9590 -0.34465</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 7 6.8 8.084e+00 -1.28422 1.1124 -1.15445</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 7 10.1 8.084e+00 2.01578 1.1124 1.81208</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 14 11.4 1.100e+01 0.40274 1.2743 0.31606</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 14 12.8 1.100e+01 1.80274 1.2743 1.41474</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 28 11.5 1.176e+01 -0.25977 1.3207 -0.19669</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 28 10.6 1.176e+01 -1.15977 1.3207 -0.87813</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 60 7.5 9.277e+00 -1.77696 1.1753 -1.51190</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 60 8.6 9.277e+00 -0.67696 1.1753 -0.57598</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 90 7.3 6.883e+00 0.41708 1.0548 0.39542</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 90 8.1 6.883e+00 1.21708 1.0548 1.15389</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 120 5.3 4.948e+00 0.35179 0.9764 0.36028</span> -<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 120 3.8 4.948e+00 -1.14821 0.9764 -1.17591</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 0 104.7 1.007e+02 3.96656 8.4439 0.46975</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 0 88.3 1.007e+02 -12.43344 8.4439 -1.47247</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 1 94.2 9.738e+01 -3.18358 8.1663 -0.38985</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 1 94.6 9.738e+01 -2.78358 8.1663 -0.34086</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 3 78.1 9.110e+01 -12.99595 7.6454 -1.69984</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 3 96.5 9.110e+01 5.40405 7.6454 0.70684</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 7 76.2 8.000e+01 -3.79797 6.7273 -0.56456</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 7 77.8 8.000e+01 -2.19797 6.7273 -0.32672</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 14 70.8 6.446e+01 6.34396 5.4456 1.16496</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 14 67.3 6.446e+01 2.84396 5.4456 0.52225</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 28 43.1 4.359e+01 -0.48960 3.7400 -0.13091</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 28 45.1 4.359e+01 1.51040 3.7400 0.40385</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 60 21.3 2.095e+01 0.35282 1.9577 0.18022</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 60 23.5 2.095e+01 2.55282 1.9577 1.30400</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 90 11.8 1.188e+01 -0.07874 1.3281 -0.05929</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 90 12.1 1.188e+01 0.22126 1.3281 0.16660</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 120 7.0 7.072e+00 -0.07245 1.0634 -0.06813</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 120 6.2 7.072e+00 -0.87245 1.0634 -0.82041</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 0 1.6 5.684e-14 1.60000 0.8850 1.80784</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 1 0.9 6.960e-01 0.20399 0.8869 0.23000</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 3 3.7 1.968e+00 1.73240 0.9001 1.92466</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 3 2.0 1.968e+00 0.03240 0.9001 0.03599</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 7 3.6 4.083e+00 -0.48287 0.9482 -0.50924</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 7 3.8 4.083e+00 -0.28287 0.9482 -0.29832</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 14 7.1 6.682e+00 0.41836 1.0457 0.40007</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 14 6.6 6.682e+00 -0.08164 1.0457 -0.07807</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 28 9.5 9.103e+00 0.39733 1.1658 0.34082</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 28 9.3 9.103e+00 0.19733 1.1658 0.16926</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 60 8.3 8.750e+00 -0.44979 1.1469 -0.39218</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 60 9.0 8.750e+00 0.25021 1.1469 0.21817</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 90 6.6 6.673e+00 -0.07285 1.0453 -0.06969</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 90 7.7 6.673e+00 1.02715 1.0453 0.98261</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 120 3.7 4.757e+00 -1.05747 0.9698 -1.09036</span> -<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 120 3.5 4.757e+00 -1.25747 0.9698 -1.29658</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 0 110.4 1.007e+02 9.66656 8.4439 1.14480</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 0 112.1 1.007e+02 11.36656 8.4439 1.34612</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 1 93.5 9.395e+01 -0.45394 7.8821 -0.05759</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 1 91.0 9.395e+01 -2.95394 7.8821 -0.37477</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 3 71.0 8.245e+01 -11.44783 6.9298 -1.65197</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 3 89.7 8.245e+01 7.25217 6.9298 1.04652</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 7 60.4 6.567e+01 -5.27002 5.5455 -0.95032</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 7 59.1 6.567e+01 -6.57002 5.5455 -1.18475</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 14 56.5 4.847e+01 8.03029 4.1364 1.94139</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 14 47.0 4.847e+01 -1.46971 4.1364 -0.35532</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 28 30.2 3.309e+01 -2.89206 2.8971 -0.99825</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 28 23.9 3.309e+01 -9.19206 2.8971 -3.17281</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 60 17.0 1.891e+01 -1.90623 1.8076 -1.05458</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 60 18.7 1.891e+01 -0.20623 1.8076 -0.11409</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 90 11.3 1.168e+01 -0.38263 1.3160 -0.29076</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 90 11.9 1.168e+01 0.21737 1.3160 0.16518</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 120 9.0 7.230e+00 1.77031 1.0708 1.65333</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 120 8.1 7.230e+00 0.87031 1.0708 0.81280</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 0 0.7 -5.116e-13 0.70000 0.8850 0.79093</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 1 3.0 3.244e+00 -0.24430 0.9254 -0.26398</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 1 2.6 3.244e+00 -0.64430 0.9254 -0.69621</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 3 5.1 8.592e+00 -3.49175 1.1385 -3.06686</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 3 7.5 8.592e+00 -1.09175 1.1385 -0.95890</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 7 16.5 1.583e+01 0.66887 1.5890 0.42093</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 7 19.0 1.583e+01 3.16887 1.5890 1.99424</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 14 22.9 2.181e+01 1.08658 2.0224 0.53728</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 14 23.2 2.181e+01 1.38658 2.0224 0.68562</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 28 22.2 2.364e+01 -1.43659 2.1600 -0.66508</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 28 24.4 2.364e+01 0.76341 2.1600 0.35342</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 60 15.5 1.873e+01 -3.23377 1.7950 -1.80150</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 60 19.8 1.873e+01 1.06623 1.7950 0.59398</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 90 14.9 1.387e+01 1.03117 1.4560 0.70822</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 90 14.2 1.387e+01 0.33117 1.4560 0.22745</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 120 10.9 9.937e+00 0.96270 1.2122 0.79415</span> -<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 120 10.4 9.937e+00 0.46270 1.2122 0.38169</span> +<span class="r-out co"><span class="r-pr">#></span> ds name time observed predicted residual std standardized</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 0 89.8 1.014e+02 -11.62508 8.0383 -1.44620</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 0 104.1 1.014e+02 2.67492 8.0383 0.33277</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 1 88.7 9.650e+01 -7.80311 7.6530 -1.01961</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 1 95.5 9.650e+01 -1.00311 7.6530 -0.13107</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 3 81.8 8.753e+01 -5.72638 6.9510 -0.82382</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 3 94.5 8.753e+01 6.97362 6.9510 1.00326</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 7 71.5 7.254e+01 -1.04133 5.7818 -0.18010</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 7 70.3 7.254e+01 -2.24133 5.7818 -0.38765</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 14 54.2 5.349e+01 0.71029 4.3044 0.16502</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 14 49.6 5.349e+01 -3.88971 4.3044 -0.90366</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 28 31.5 3.167e+01 -0.16616 2.6446 -0.06283</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 28 28.8 3.167e+01 -2.86616 2.6446 -1.08379</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 60 12.1 1.279e+01 -0.69287 1.3365 -0.51843</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 60 13.6 1.279e+01 0.80713 1.3365 0.60392</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 90 6.2 6.397e+00 -0.19718 1.0122 -0.19481</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 90 8.3 6.397e+00 1.90282 1.0122 1.87996</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 120 2.2 3.323e+00 -1.12320 0.9160 -1.22623</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 parent 120 2.4 3.323e+00 -0.92320 0.9160 -1.00788</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 1 0.3 1.179e+00 -0.87919 0.8827 -0.99605</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 1 0.2 1.179e+00 -0.97919 0.8827 -1.10935</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 3 2.2 3.273e+00 -1.07272 0.9149 -1.17256</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 3 3.0 3.273e+00 -0.27272 0.9149 -0.29811</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 7 6.5 6.559e+00 -0.05872 1.0186 -0.05765</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 7 5.0 6.559e+00 -1.55872 1.0186 -1.53032</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 14 10.2 1.016e+01 0.03787 1.1880 0.03188</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 14 9.5 1.016e+01 -0.66213 1.1880 -0.55734</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 28 12.2 1.268e+01 -0.47913 1.3297 -0.36032</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 28 13.4 1.268e+01 0.72087 1.3297 0.54211</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 60 11.8 1.078e+01 1.02493 1.2211 0.83936</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 60 13.2 1.078e+01 2.42493 1.2211 1.98588</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 90 6.6 7.705e+00 -1.10464 1.0672 -1.03509</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 90 9.3 7.705e+00 1.59536 1.0672 1.49491</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 120 3.5 5.236e+00 -1.73617 0.9699 -1.79010</span> +<span class="r-out co"><span class="r-pr">#></span> ds 1 m1 120 5.4 5.236e+00 0.16383 0.9699 0.16892</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 0 118.0 1.014e+02 16.57492 8.0383 2.06198</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 0 99.8 1.014e+02 -1.62508 8.0383 -0.20217</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 1 90.2 9.599e+01 -5.79045 7.6129 -0.76061</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 1 94.6 9.599e+01 -1.39045 7.6129 -0.18264</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 3 96.1 8.652e+01 9.57931 6.8724 1.39388</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 3 78.4 8.652e+01 -8.12069 6.8724 -1.18164</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 7 77.9 7.197e+01 5.93429 5.7370 1.03439</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 7 77.7 7.197e+01 5.73429 5.7370 0.99953</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 14 56.0 5.555e+01 0.44657 4.4637 0.10005</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 14 54.7 5.555e+01 -0.85343 4.4637 -0.19120</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 28 36.6 3.853e+01 -1.93170 3.1599 -0.61132</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 28 36.8 3.853e+01 -1.73170 3.1599 -0.54803</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 60 22.1 2.110e+01 1.00360 1.8795 0.53396</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 60 24.7 2.110e+01 3.60360 1.8795 1.91728</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 90 12.4 1.250e+01 -0.09712 1.3190 -0.07363</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 90 10.8 1.250e+01 -1.69712 1.3190 -1.28667</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 120 6.8 7.419e+00 -0.61913 1.0546 -0.58709</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 parent 120 7.9 7.419e+00 0.48087 1.0546 0.45599</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 1 1.3 1.422e+00 -0.12194 0.8849 -0.13781</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 3 3.7 3.831e+00 -0.13149 0.9282 -0.14166</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 3 4.7 3.831e+00 0.86851 0.9282 0.93567</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 7 8.1 7.292e+00 0.80812 1.0490 0.77034</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 7 7.9 7.292e+00 0.60812 1.0490 0.57969</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 14 10.1 1.055e+01 -0.45332 1.2090 -0.37495</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 14 10.3 1.055e+01 -0.25332 1.2090 -0.20953</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 28 10.7 1.230e+01 -1.59960 1.3074 -1.22347</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 28 12.2 1.230e+01 -0.09960 1.3074 -0.07618</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 60 10.7 1.065e+01 0.05342 1.2141 0.04400</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 60 12.5 1.065e+01 1.85342 1.2141 1.52661</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 90 9.1 8.196e+00 0.90368 1.0897 0.82930</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 90 7.4 8.196e+00 -0.79632 1.0897 -0.73078</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 120 6.1 5.997e+00 0.10252 0.9969 0.10284</span> +<span class="r-out co"><span class="r-pr">#></span> ds 2 m1 120 4.5 5.997e+00 -1.49748 0.9969 -1.50220</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 0 106.2 1.014e+02 4.77492 8.0383 0.59402</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 0 106.9 1.014e+02 5.47492 8.0383 0.68110</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 1 107.4 9.390e+01 13.49935 7.4494 1.81214</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 1 96.1 9.390e+01 2.19935 7.4494 0.29524</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 3 79.4 8.152e+01 -2.12307 6.4821 -0.32753</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 3 82.6 8.152e+01 1.07693 6.4821 0.16614</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 7 63.9 6.446e+01 -0.55834 5.1533 -0.10834</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 7 62.4 6.446e+01 -2.05834 5.1533 -0.39942</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 14 51.0 4.826e+01 2.74073 3.9019 0.70241</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 14 47.1 4.826e+01 -1.15927 3.9019 -0.29711</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 28 36.1 3.424e+01 1.86399 2.8364 0.65718</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 28 36.6 3.424e+01 2.36399 2.8364 0.83346</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 60 20.1 1.968e+01 0.42172 1.7815 0.23672</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 60 19.8 1.968e+01 0.12172 1.7815 0.06833</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 90 11.3 1.195e+01 -0.64633 1.2869 -0.50222</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 90 10.7 1.195e+01 -1.24633 1.2869 -0.96844</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 120 8.2 7.255e+00 0.94532 1.0474 0.90251</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 parent 120 7.3 7.255e+00 0.04532 1.0474 0.04327</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 0 0.8 2.956e-11 0.80000 0.8778 0.91140</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 1 1.8 1.758e+00 0.04187 0.8886 0.04712</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 1 2.3 1.758e+00 0.54187 0.8886 0.60978</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 3 4.2 4.567e+00 -0.36697 0.9486 -0.38683</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 3 4.1 4.567e+00 -0.46697 0.9486 -0.49224</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 7 6.8 8.151e+00 -1.35124 1.0876 -1.24242</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 7 10.1 8.151e+00 1.94876 1.0876 1.79182</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 14 11.4 1.083e+01 0.57098 1.2240 0.46647</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 14 12.8 1.083e+01 1.97098 1.2240 1.61022</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 28 11.5 1.147e+01 0.03175 1.2597 0.02520</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 28 10.6 1.147e+01 -0.86825 1.2597 -0.68928</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 60 7.5 9.298e+00 -1.79834 1.1433 -1.57298</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 60 8.6 9.298e+00 -0.69834 1.1433 -0.61083</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 90 7.3 7.038e+00 0.26249 1.0382 0.25283</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 90 8.1 7.038e+00 1.06249 1.0382 1.02340</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 120 5.3 5.116e+00 0.18417 0.9659 0.19068</span> +<span class="r-out co"><span class="r-pr">#></span> ds 3 m1 120 3.8 5.116e+00 -1.31583 0.9659 -1.36232</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 0 104.7 1.014e+02 3.27492 8.0383 0.40741</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 0 88.3 1.014e+02 -13.12508 8.0383 -1.63281</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 1 94.2 9.781e+01 -3.61183 7.7555 -0.46572</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 1 94.6 9.781e+01 -3.21183 7.7555 -0.41414</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 3 78.1 9.110e+01 -13.00467 7.2307 -1.79853</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 3 96.5 9.110e+01 5.39533 7.2307 0.74617</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 7 76.2 7.951e+01 -3.30511 6.3246 -0.52258</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 7 77.8 7.951e+01 -1.70511 6.3246 -0.26960</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 14 70.8 6.376e+01 7.03783 5.0993 1.38016</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 14 67.3 6.376e+01 3.53783 5.0993 0.69379</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 28 43.1 4.340e+01 -0.30456 3.5303 -0.08627</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 28 45.1 4.340e+01 1.69544 3.5303 0.48026</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 60 21.3 2.142e+01 -0.12077 1.9022 -0.06349</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 60 23.5 2.142e+01 2.07923 1.9022 1.09308</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 90 11.8 1.207e+01 -0.26813 1.2940 -0.20721</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 90 12.1 1.207e+01 0.03187 1.2940 0.02463</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 120 7.0 6.954e+00 0.04554 1.0347 0.04402</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 parent 120 6.2 6.954e+00 -0.75446 1.0347 -0.72914</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 0 1.6 1.990e-13 1.60000 0.8778 1.82279</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 1 0.9 7.305e-01 0.16949 0.8797 0.19267</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 3 3.7 2.051e+00 1.64896 0.8925 1.84753</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 3 2.0 2.051e+00 -0.05104 0.8925 -0.05719</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 7 3.6 4.204e+00 -0.60375 0.9382 -0.64354</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 7 3.8 4.204e+00 -0.40375 0.9382 -0.43036</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 14 7.1 6.760e+00 0.34021 1.0267 0.33137</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 14 6.6 6.760e+00 -0.15979 1.0267 -0.15563</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 28 9.5 9.011e+00 0.48856 1.1289 0.43277</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 28 9.3 9.011e+00 0.28856 1.1289 0.25561</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 60 8.3 8.611e+00 -0.31077 1.1093 -0.28014</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 60 9.0 8.611e+00 0.38923 1.1093 0.35086</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 90 6.6 6.678e+00 -0.07753 1.0233 -0.07576</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 90 7.7 6.678e+00 1.02247 1.0233 0.99915</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 120 3.7 4.847e+00 -1.14679 0.9572 -1.19804</span> +<span class="r-out co"><span class="r-pr">#></span> ds 4 m1 120 3.5 4.847e+00 -1.34679 0.9572 -1.40698</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 0 110.4 1.014e+02 8.97492 8.0383 1.11651</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 0 112.1 1.014e+02 10.67492 8.0383 1.32800</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 1 93.5 9.466e+01 -1.16118 7.5089 -0.15464</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 1 91.0 9.466e+01 -3.66118 7.5089 -0.48758</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 3 71.0 8.302e+01 -12.01844 6.5988 -1.82130</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 3 89.7 8.302e+01 6.68156 6.5988 1.01254</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 7 60.4 6.563e+01 -5.22574 5.2440 -0.99652</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 7 59.1 6.563e+01 -6.52574 5.2440 -1.24442</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 14 56.5 4.727e+01 9.22621 3.8263 2.41128</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 14 47.0 4.727e+01 -0.27379 3.8263 -0.07156</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 28 30.2 3.103e+01 -0.83405 2.5977 -0.32108</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 28 23.9 3.103e+01 -7.13405 2.5977 -2.74634</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 60 17.0 1.800e+01 -0.99696 1.6675 -0.59787</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 60 18.7 1.800e+01 0.70304 1.6675 0.42161</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 90 11.3 1.167e+01 -0.36809 1.2710 -0.28961</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 90 11.9 1.167e+01 0.23191 1.2710 0.18246</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 120 9.0 7.595e+00 1.40496 1.0623 1.32256</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 parent 120 8.1 7.595e+00 0.50496 1.0623 0.47535</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 0 0.7 0.000e+00 0.70000 0.8778 0.79747</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 1 3.0 3.158e+00 -0.15799 0.9123 -0.17317</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 1 2.6 3.158e+00 -0.55799 0.9123 -0.61160</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 3 5.1 8.443e+00 -3.34286 1.1013 -3.03535</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 3 7.5 8.443e+00 -0.94286 1.1013 -0.85613</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 7 16.5 1.580e+01 0.69781 1.5232 0.45811</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 7 19.0 1.580e+01 3.19781 1.5232 2.09935</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 14 22.9 2.216e+01 0.73604 1.9543 0.37663</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 14 23.2 2.216e+01 1.03604 1.9543 0.53014</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 28 22.2 2.423e+01 -2.03128 2.1011 -0.96678</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 28 24.4 2.423e+01 0.16872 2.1011 0.08030</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 60 15.5 1.876e+01 -3.25610 1.7187 -1.89455</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 60 19.8 1.876e+01 1.04390 1.7187 0.60739</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 90 14.9 1.366e+01 1.23585 1.3890 0.88976</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 90 14.2 1.366e+01 0.53585 1.3890 0.38579</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 120 10.9 9.761e+00 1.13911 1.1670 0.97613</span> +<span class="r-out co"><span class="r-pr">#></span> ds 5 m1 120 10.4 9.761e+00 0.63911 1.1670 0.54767</span> +<span class="r-in"><span><span class="co"># Add a correlation between random effects of g and k2</span></span></span> +<span class="r-in"><span><span class="va">cov_model_3</span> <span class="op"><-</span> <span class="va">f_saem_dfop_sfo_2</span><span class="op">$</span><span class="va">so</span><span class="op">@</span><span class="va">model</span><span class="op">@</span><span class="va">covariance.model</span></span></span> +<span class="r-in"><span><span class="va">cov_model_3</span><span class="op">[</span><span class="st">"log_k2"</span>, <span class="st">"g_qlogis"</span><span class="op">]</span> <span class="op"><-</span> <span class="fl">1</span></span></span> +<span class="r-in"><span><span class="va">cov_model_3</span><span class="op">[</span><span class="st">"g_qlogis"</span>, <span class="st">"log_k2"</span><span class="op">]</span> <span class="op"><-</span> <span class="fl">1</span></span></span> +<span class="r-in"><span><span class="va">f_saem_dfop_sfo_3</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span>,</span></span> +<span class="r-in"><span> covariance.model <span class="op">=</span> <span class="va">cov_model_3</span><span class="op">)</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/nlme/man/intervals.html" class="external-link">intervals</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo_3</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Approximate 95% confidence intervals</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Fixed effects:</span> +<span class="r-out co"><span class="r-pr">#></span> lower est. upper</span> +<span class="r-out co"><span class="r-pr">#></span> parent_0 98.39888363 101.48951337 104.58014311</span> +<span class="r-out co"><span class="r-pr">#></span> k_m1 0.01508704 0.01665986 0.01839665</span> +<span class="r-out co"><span class="r-pr">#></span> f_parent_to_m1 0.20141557 0.27540583 0.36418131</span> +<span class="r-out co"><span class="r-pr">#></span> k1 0.07708759 0.10430866 0.14114200</span> +<span class="r-out co"><span class="r-pr">#></span> k2 0.01476621 0.01786384 0.02161129</span> +<span class="r-out co"><span class="r-pr">#></span> g 0.33679867 0.45083525 0.57028162</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Random effects:</span> +<span class="r-out co"><span class="r-pr">#></span> lower est. upper</span> +<span class="r-out co"><span class="r-pr">#></span> sd(f_parent_qlogis) 0.38085375 0.4441841 0.5075145</span> +<span class="r-out co"><span class="r-pr">#></span> sd(log_k1) 0.04774819 0.2660384 0.4843286</span> +<span class="r-out co"><span class="r-pr">#></span> sd(log_k2) -0.63842736 0.1977024 1.0338321</span> +<span class="r-out co"><span class="r-pr">#></span> sd(g_qlogis) 0.22711289 0.4502227 0.6733326</span> +<span class="r-out co"><span class="r-pr">#></span> corr(log_k2,g_qlogis) -0.83271473 -0.6176939 -0.4026730</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> lower est. upper</span> +<span class="r-out co"><span class="r-pr">#></span> a.1 0.67347568 0.87437392 1.07527216</span> +<span class="r-out co"><span class="r-pr">#></span> b.1 0.06393032 0.07912417 0.09431802</span> +<span class="r-in"><span><span class="co"># The correlation does not improve the fit judged by AIC and BIC, although</span></span></span> +<span class="r-in"><span><span class="co"># the likelihood is higher with the additional parameter</span></span></span> +<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span>, <span class="va">f_saem_dfop_sfo_2</span>, <span class="va">f_saem_dfop_sfo_3</span><span class="op">)</span></span></span> +<span class="r-out co"><span class="r-pr">#></span> Data: 171 observations of 2 variable(s) grouped in 5 datasets</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> npar AIC BIC Lik</span> +<span class="r-out co"><span class="r-pr">#></span> f_saem_dfop_sfo_2 12 806.96 802.27 -391.48</span> +<span class="r-out co"><span class="r-pr">#></span> f_saem_dfop_sfo_3 13 807.99 802.91 -391.00</span> +<span class="r-out co"><span class="r-pr">#></span> f_saem_dfop_sfo 14 810.83 805.36 -391.42</span> <span class="r-in"><span><span class="co"># }</span></span></span> <span class="r-in"><span></span></span> </code></pre></div> @@ -571,7 +663,7 @@ saemix authors for the parts inherited from saemix.</p> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/summary_listing.html b/docs/reference/summary_listing.html new file mode 100644 index 00000000..0dcc5456 --- /dev/null +++ b/docs/reference/summary_listing.html @@ -0,0 +1,164 @@ +<!DOCTYPE html> +<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Display the output of a summary function according to the output format — summary_listing • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Display the output of a summary function according to the output format — summary_listing"><meta property="og:description" content='This function is intended for use in a R markdown code chunk with the chunk +option results = "asis".'><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> +<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> +<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + + + <div class="container template-reference-topic"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <div class="container"> + <div class="navbar-header"> + <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> + <span class="sr-only">Toggle navigation</span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + <span class="icon-bar"></span> + </button> + <span class="navbar-brand"> + <a class="navbar-link" href="../index.html">mkin</a> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> + </span> + </div> + + <div id="navbar" class="navbar-collapse collapse"> + <ul class="nav navbar-nav"><li> + <a href="../reference/index.html">Reference</a> +</li> +<li class="dropdown"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> + Articles + + <span class="caret"></span> + </a> + <ul class="dropdown-menu" role="menu"><li> + <a href="../articles/mkin.html">Introduction to mkin</a> + </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> + <li> + <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> + </li> + <li> + <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> + </li> + <li> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> + <li> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> + </li> + <li> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> + </li> + <li> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> + </li> + <li> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> + </li> + <li> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + </li> + <li> + <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> + </li> + <li> + <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> + </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> + </ul></li> +<li> + <a href="../news/index.html">News</a> +</li> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> + <span class="fab fa-github fa-lg"></span> + + </a> +</li> + </ul></div><!--/.nav-collapse --> + </div><!--/.container --> +</div><!--/.navbar --> + + + + </header><div class="row"> + <div class="col-md-9 contents"> + <div class="page-header"> + <h1>Display the output of a summary function according to the output format</h1> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/summary_listing.R" class="external-link"><code>R/summary_listing.R</code></a></small> + <div class="hidden name"><code>summary_listing.Rd</code></div> + </div> + + <div class="ref-description"> + <p>This function is intended for use in a R markdown code chunk with the chunk +option <code>results = "asis"</code>.</p> + </div> + + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">summary_listing</span><span class="op">(</span><span class="va">object</span>, caption <span class="op">=</span> <span class="cn">NULL</span>, label <span class="op">=</span> <span class="cn">NULL</span>, clearpage <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span></span> +<span><span class="fu">tex_listing</span><span class="op">(</span><span class="va">object</span>, caption <span class="op">=</span> <span class="cn">NULL</span>, label <span class="op">=</span> <span class="cn">NULL</span>, clearpage <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span></span> +<span><span class="fu">html_listing</span><span class="op">(</span><span class="va">object</span>, caption <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span></code></pre></div> + </div> + + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>object</dt> +<dd><p>The object for which the summary is to be listed</p></dd> + + +<dt>caption</dt> +<dd><p>An optional caption</p></dd> + + +<dt>label</dt> +<dd><p>An optional label, ignored in html output</p></dd> + + +<dt>clearpage</dt> +<dd><p>Should a new page be started after the listing? Ignored in html output</p></dd> + +</dl></div> + + </div> + <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> +</div> + + + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> +</div> + +<div class="pkgdown"> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> +</div> + + </footer></div> + + + + + + + </body></html> + diff --git a/docs/reference/synthetic_data_for_UBA_2014-1.png b/docs/reference/synthetic_data_for_UBA_2014-1.png Binary files differindex 132380a8..11eae1f9 100644 --- a/docs/reference/synthetic_data_for_UBA_2014-1.png +++ b/docs/reference/synthetic_data_for_UBA_2014-1.png diff --git a/docs/reference/synthetic_data_for_UBA_2014.html b/docs/reference/synthetic_data_for_UBA_2014.html index c00d1b55..cd2338eb 100644 --- a/docs/reference/synthetic_data_for_UBA_2014.html +++ b/docs/reference/synthetic_data_for_UBA_2014.html @@ -32,13 +32,13 @@ Compare also the code in the example section to see the degradation models."><!- </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -49,6 +49,8 @@ Compare also the code in the example section to see the degradation models."><!- <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -56,22 +58,29 @@ Compare also the code in the example section to see the degradation models."><!- <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -79,6 +88,14 @@ Compare also the code in the example section to see the degradation models."><!- <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -252,10 +269,10 @@ Compare also the code in the example section to see the degradation models."><!- <span class="r-in"><span> <span class="fu"><a href="plot.mkinfit.html">plot_sep</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span> <span class="r-plt img"><img src="synthetic_data_for_UBA_2014-1.png" alt="" width="700" height="433"></span> <span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></span></span> -<span class="r-out co"><span class="r-pr">#></span> mkin version used for fitting: 1.2.0 </span> -<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.2 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Nov 17 14:04:10 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Nov 17 14:04:11 2022 </span> +<span class="r-out co"><span class="r-pr">#></span> mkin version used for fitting: 1.2.3 </span> +<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.2.3 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Apr 20 07:37:10 2023 </span> +<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Apr 20 07:37:10 2023 </span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Equations:</span> <span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - k_parent * parent</span> @@ -264,7 +281,7 @@ Compare also the code in the example section to see the degradation models."><!- <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type deSolve </span> <span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted using 833 model solutions performed in 0.574 s</span> +<span class="r-out co"><span class="r-pr">#></span> Fitted using 833 model solutions performed in 0.161 s</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-out co"><span class="r-pr">#></span> Error model: Constant variance </span> <span class="r-out co"><span class="r-pr">#></span> </span> @@ -415,7 +432,7 @@ Compare also the code in the example section to see the degradation models."><!- </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/test_data_from_UBA_2014-1.png b/docs/reference/test_data_from_UBA_2014-1.png Binary files differindex e4fc2a4c..a007a102 100644 --- a/docs/reference/test_data_from_UBA_2014-1.png +++ b/docs/reference/test_data_from_UBA_2014-1.png diff --git a/docs/reference/test_data_from_UBA_2014-2.png b/docs/reference/test_data_from_UBA_2014-2.png Binary files differindex 4ce36561..f460ac83 100644 --- a/docs/reference/test_data_from_UBA_2014-2.png +++ b/docs/reference/test_data_from_UBA_2014-2.png diff --git a/docs/reference/test_data_from_UBA_2014.html b/docs/reference/test_data_from_UBA_2014.html index a76f23ab..e57141ec 100644 --- a/docs/reference/test_data_from_UBA_2014.html +++ b/docs/reference/test_data_from_UBA_2014.html @@ -18,13 +18,13 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -35,6 +35,8 @@ <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -42,22 +44,29 @@ <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -65,6 +74,14 @@ <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -205,7 +222,7 @@ </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/transform_odeparms.html b/docs/reference/transform_odeparms.html index 66e94941..2845c6c1 100644 --- a/docs/reference/transform_odeparms.html +++ b/docs/reference/transform_odeparms.html @@ -22,13 +22,13 @@ the ilr transformation is used."><!-- mathjax --><script src="https://cdnjs.clou </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -39,6 +39,8 @@ the ilr transformation is used."><!-- mathjax --><script src="https://cdnjs.clou <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -46,22 +48,29 @@ the ilr transformation is used."><!-- mathjax --><script src="https://cdnjs.clou <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -69,6 +78,14 @@ the ilr transformation is used."><!-- mathjax --><script src="https://cdnjs.clou <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -305,7 +322,7 @@ This is no problem for the internal use in <a href="mkinfit.html">mkinfit</a>.</ </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/reference/update.mkinfit-1.png b/docs/reference/update.mkinfit-1.png Binary files differindex 12fe1f5b..9278aefd 100644 --- a/docs/reference/update.mkinfit-1.png +++ b/docs/reference/update.mkinfit-1.png diff --git a/docs/reference/update.mkinfit-2.png b/docs/reference/update.mkinfit-2.png Binary files differindex 21817f94..f73a6180 100644 --- a/docs/reference/update.mkinfit-2.png +++ b/docs/reference/update.mkinfit-2.png diff --git a/docs/reference/update.mkinfit.html b/docs/reference/update.mkinfit.html index d5b4f6f3..f941af5f 100644 --- a/docs/reference/update.mkinfit.html +++ b/docs/reference/update.mkinfit.html @@ -20,13 +20,13 @@ override these starting values."><!-- mathjax --><script src="https://cdnjs.clou </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.0</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.2.3</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> <ul class="nav navbar-nav"><li> - <a href="../reference/index.html">Functions and data</a> + <a href="../reference/index.html">Reference</a> </li> <li class="dropdown"> <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> @@ -37,6 +37,8 @@ override these starting values."><!-- mathjax --><script src="https://cdnjs.clou <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with (generalised) nonlinear least squares</li> <li> <a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a> </li> @@ -44,22 +46,29 @@ override these starting values."><!-- mathjax --><script src="https://cdnjs.clou <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> - <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> + <li class="divider"> + <li class="dropdown-header">Example evaluations with hierarchical models (nonlinear mixed-effects models)</li> <li> - <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + <a href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> + <a href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a> </li> <li> - <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> + <a href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a> </li> <li> - <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + <a href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a> </li> <li> - <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + <a href="../articles/web_only/multistart.html">Short demo of the multistart method</a> + </li> + <li class="divider"> + <li class="dropdown-header">Performance</li> + <li> + <a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a> </li> <li> <a href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a> @@ -67,6 +76,14 @@ override these starting values."><!-- mathjax --><script src="https://cdnjs.clou <li> <a href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a> </li> + <li class="divider"> + <li class="dropdown-header">Miscellaneous</li> + <li> + <a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a> + </li> + <li> + <a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a> + </li> </ul></li> <li> <a href="../news/index.html">News</a> @@ -150,7 +167,7 @@ remove arguments given in the original call</p></dd> </div> <div class="pkgdown"> - <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.6.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p> </div> </footer></div> diff --git a/docs/sitemap.xml b/docs/sitemap.xml index 2571bb4b..f1b20197 100644 --- a/docs/sitemap.xml +++ b/docs/sitemap.xml @@ -16,6 +16,15 @@ <loc>https://pkgdown.jrwb.de/mkin/articles/mkin.html</loc> </url> <url> + <loc>https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_cyan_pathway.html</loc> + </url> + <url> + <loc>https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_parent.html</loc> + </url> + <url> + <loc>https://pkgdown.jrwb.de/mkin/articles/prebuilt/2022_dmta_pathway.html</loc> + </url> + <url> <loc>https://pkgdown.jrwb.de/mkin/articles/twa.html</loc> </url> <url> @@ -139,6 +148,9 @@ <loc>https://pkgdown.jrwb.de/mkin/reference/get_deg_func.html</loc> </url> <url> + <loc>https://pkgdown.jrwb.de/mkin/reference/hierarchical_kinetics.html</loc> + </url> + <url> <loc>https://pkgdown.jrwb.de/mkin/reference/illparms.html</loc> </url> <url> @@ -316,6 +328,9 @@ <loc>https://pkgdown.jrwb.de/mkin/reference/summary.saem.mmkin.html</loc> </url> <url> + <loc>https://pkgdown.jrwb.de/mkin/reference/summary_listing.html</loc> + </url> + <url> <loc>https://pkgdown.jrwb.de/mkin/reference/synthetic_data_for_UBA_2014.html</loc> </url> <url> diff --git a/man/hierarchical_kinetics.Rd b/man/hierarchical_kinetics.Rd index f965df1a..bcbe1e06 100644 --- a/man/hierarchical_kinetics.Rd +++ b/man/hierarchical_kinetics.Rd @@ -41,7 +41,9 @@ and then to run 'tinytex::tlmgr_install(c("float", "listing"))'. \dontrun{ library(rmarkdown) -draft("example_analysis.rmd", template = "hierarchical_kinetics", package = "mkin") +# The following is now commented out after the relase of v1.2.3 for the generation +# of online docs, as the command creates a directory and opens an editor +#draft("example_analysis.rmd", template = "hierarchical_kinetics", package = "mkin") } } diff --git a/vignettes/prebuilt/2022_cyan_pathway.pdf b/vignettes/prebuilt/2022_cyan_pathway.pdf Binary files differindex 9d2d22ea..ec37706f 100644 --- a/vignettes/prebuilt/2022_cyan_pathway.pdf +++ b/vignettes/prebuilt/2022_cyan_pathway.pdf diff --git a/vignettes/prebuilt/2022_cyan_pathway.rmd b/vignettes/prebuilt/2022_cyan_pathway.rmd index d0b2c889..8463c854 100644 --- a/vignettes/prebuilt/2022_cyan_pathway.rmd +++ b/vignettes/prebuilt/2022_cyan_pathway.rmd @@ -1,7 +1,7 @@ --- title: "Testing hierarchical pathway kinetics with residue data on cyantraniliprole" author: Johannes Ranke -date: Last change on 6 January 2023, last compiled on `r format(Sys.time(), "%e +date: Last change on 20 April 2023, last compiled on `r format(Sys.time(), "%e %B %Y")` output: pdf_document: @@ -542,7 +542,6 @@ for (deg_mod in rownames(f_saem_3)) { ## Session info ```{r, echo = FALSE, cache = FALSE} -parallel::stopCluster(cl = cl) sessionInfo() ``` diff --git a/vignettes/prebuilt/2022_dmta_pathway.pdf b/vignettes/prebuilt/2022_dmta_pathway.pdf Binary files differindex 56534ebf..95d0964d 100644 --- a/vignettes/prebuilt/2022_dmta_pathway.pdf +++ b/vignettes/prebuilt/2022_dmta_pathway.pdf diff --git a/vignettes/prebuilt/2022_dmta_pathway.rmd b/vignettes/prebuilt/2022_dmta_pathway.rmd index f787daf2..1e1a0719 100644 --- a/vignettes/prebuilt/2022_dmta_pathway.rmd +++ b/vignettes/prebuilt/2022_dmta_pathway.rmd @@ -1,7 +1,7 @@ --- title: "Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P" author: Johannes Ranke -date: Last change on 8 January 2023, last compiled on `r format(Sys.time(), "%e %B %Y")` +date: Last change on 20 April 2023, last compiled on `r format(Sys.time(), "%e %B %Y")` geometry: margin=2cm bibliography: references.bib toc: true @@ -423,7 +423,6 @@ tex_listing(saem_sforb_path_1_tc_reduced, caption) ## Session info ```{r, echo = FALSE} -parallel::stopCluster(cl) sessionInfo() ``` diff --git a/vignettes/web_only/mkin_benchmarks.rda b/vignettes/web_only/mkin_benchmarks.rda Binary files differindex 827c3437..fda76929 100644 --- a/vignettes/web_only/mkin_benchmarks.rda +++ b/vignettes/web_only/mkin_benchmarks.rda diff --git a/vignettes/web_only/multistart.html b/vignettes/web_only/multistart.html index 5568ad2c..88c64a28 100644 --- a/vignettes/web_only/multistart.html +++ b/vignettes/web_only/multistart.html @@ -31,7 +31,7 @@ document.addEventListener('DOMContentLoaded', function(e) { !function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType&&"function"!=typeof e.item},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof e?n[o.call(e)]||"object":typeof e}var f="3.6.0",S=function(e,t){return new S.fn.init(e,t)};function p(e){var t=!!e&&"length"in e&&e.length,n=w(e);return!m(e)&&!x(e)&&("array"===n||0===t||"number"==typeof t&&0<t&&t-1 in e)}S.fn=S.prototype={jquery:f,constructor:S,length:0,toArray:function(){return s.call(this)},get:function(e){return null==e?s.call(this):e<0?this[e+this.length]:this[e]},pushStack:function(e){var t=S.merge(this.constructor(),e);return t.prevObject=this,t},each:function(e){return S.each(this,e)},map:function(n){return this.pushStack(S.map(this,function(e,t){return n.call(e,t,e)}))},slice:function(){return this.pushStack(s.apply(this,arguments))},first:function(){return this.eq(0)},last:function(){return this.eq(-1)},even:function(){return this.pushStack(S.grep(this,function(e,t){return(t+1)%2}))},odd:function(){return this.pushStack(S.grep(this,function(e,t){return t%2}))},eq:function(e){var t=this.length,n=+e+(e<0?t:0);return this.pushStack(0<=n&&n<t?[this[n]]:[])},end:function(){return this.prevObject||this.constructor()},push:u,sort:t.sort,splice:t.splice},S.extend=S.fn.extend=function(){var e,t,n,r,i,o,a=arguments[0]||{},s=1,u=arguments.length,l=!1;for("boolean"==typeof a&&(l=a,a=arguments[s]||{},s++),"object"==typeof a||m(a)||(a={}),s===u&&(a=this,s--);s<u;s++)if(null!=(e=arguments[s]))for(t in e)r=e[t],"__proto__"!==t&&a!==r&&(l&&r&&(S.isPlainObject(r)||(i=Array.isArray(r)))?(n=a[t],o=i&&!Array.isArray(n)?[]:i||S.isPlainObject(n)?n:{},i=!1,a[t]=S.extend(l,o,r)):void 0!==r&&(a[t]=r));return a},S.extend({expando:"jQuery"+(f+Math.random()).replace(/\D/g,""),isReady:!0,error:function(e){throw new Error(e)},noop:function(){},isPlainObject:function(e){var t,n;return!(!e||"[object Object]"!==o.call(e))&&(!(t=r(e))||"function"==typeof(n=v.call(t,"constructor")&&t.constructor)&&a.call(n)===l)},isEmptyObject:function(e){var t;for(t in e)return!1;return!0},globalEval:function(e,t,n){b(e,{nonce:t&&t.nonce},n)},each:function(e,t){var n,r=0;if(p(e)){for(n=e.length;r<n;r++)if(!1===t.call(e[r],r,e[r]))break}else for(r in e)if(!1===t.call(e[r],r,e[r]))break;return e},makeArray:function(e,t){var n=t||[];return null!=e&&(p(Object(e))?S.merge(n,"string"==typeof e?[e]:e):u.call(n,e)),n},inArray:function(e,t,n){return null==t?-1:i.call(t,e,n)},merge:function(e,t){for(var n=+t.length,r=0,i=e.length;r<n;r++)e[i++]=t[r];return e.length=i,e},grep:function(e,t,n){for(var r=[],i=0,o=e.length,a=!n;i<o;i++)!t(e[i],i)!==a&&r.push(e[i]);return r},map:function(e,t,n){var r,i,o=0,a=[];if(p(e))for(r=e.length;o<r;o++)null!=(i=t(e[o],o,n))&&a.push(i);else for(o in e)null!=(i=t(e[o],o,n))&&a.push(i);return g(a)},guid:1,support:y}),"function"==typeof Symbol&&(S.fn[Symbol.iterator]=t[Symbol.iterator]),S.each("Boolean Number String Function Array Date RegExp Object Error Symbol".split(" "),function(e,t){n["[object "+t+"]"]=t.toLowerCase()});var d=function(n){var e,d,b,o,i,h,f,g,w,u,l,T,C,a,E,v,s,c,y,S="sizzle"+1*new Date,p=n.document,k=0,r=0,m=ue(),x=ue(),A=ue(),N=ue(),j=function(e,t){return e===t&&(l=!0),0},D={}.hasOwnProperty,t=[],q=t.pop,L=t.push,H=t.push,O=t.slice,P=function(e,t){for(var n=0,r=e.length;n<r;n++)if(e[n]===t)return n;return-1},R="checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped",M="[\\x20\\t\\r\\n\\f]",I="(?:\\\\[\\da-fA-F]{1,6}"+M+"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",W="\\["+M+"*("+I+")(?:"+M+"*([*^$|!~]?=)"+M+"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|("+I+"))|)"+M+"*\\]",F=":("+I+")(?:\\((('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|((?:\\\\.|[^\\\\()[\\]]|"+W+")*)|.*)\\)|)",B=new RegExp(M+"+","g"),$=new RegExp("^"+M+"+|((?:^|[^\\\\])(?:\\\\.)*)"+M+"+$","g"),_=new RegExp("^"+M+"*,"+M+"*"),z=new RegExp("^"+M+"*([>+~]|"+M+")"+M+"*"),U=new RegExp(M+"|>"),X=new RegExp(F),V=new RegExp("^"+I+"$"),G={ID:new RegExp("^#("+I+")"),CLASS:new RegExp("^\\.("+I+")"),TAG:new RegExp("^("+I+"|[*])"),ATTR:new RegExp("^"+W),PSEUDO:new RegExp("^"+F),CHILD:new RegExp("^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\("+M+"*(even|odd|(([+-]|)(\\d*)n|)"+M+"*(?:([+-]|)"+M+"*(\\d+)|))"+M+"*\\)|)","i"),bool:new RegExp("^(?:"+R+")$","i"),needsContext:new RegExp("^"+M+"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\("+M+"*((?:-\\d)?\\d*)"+M+"*\\)|)(?=[^-]|$)","i")},Y=/HTML$/i,Q=/^(?:input|select|textarea|button)$/i,J=/^h\d$/i,K=/^[^{]+\{\s*\[native \w/,Z=/^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,ee=/[+~]/,te=new RegExp("\\\\[\\da-fA-F]{1,6}"+M+"?|\\\\([^\\r\\n\\f])","g"),ne=function(e,t){var n="0x"+e.slice(1)-65536;return t||(n<0?String.fromCharCode(n+65536):String.fromCharCode(n>>10|55296,1023&n|56320))},re=/([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,ie=function(e,t){return t?"\0"===e?"\ufffd":e.slice(0,-1)+"\\"+e.charCodeAt(e.length-1).toString(16)+" ":"\\"+e},oe=function(){T()},ae=be(function(e){return!0===e.disabled&&"fieldset"===e.nodeName.toLowerCase()},{dir:"parentNode",next:"legend"});try{H.apply(t=O.call(p.childNodes),p.childNodes),t[p.childNodes.length].nodeType}catch(e){H={apply:t.length?function(e,t){L.apply(e,O.call(t))}:function(e,t){var n=e.length,r=0;while(e[n++]=t[r++]);e.length=n-1}}}function se(t,e,n,r){var i,o,a,s,u,l,c,f=e&&e.ownerDocument,p=e?e.nodeType:9;if(n=n||[],"string"!=typeof t||!t||1!==p&&9!==p&&11!==p)return n;if(!r&&(T(e),e=e||C,E)){if(11!==p&&(u=Z.exec(t)))if(i=u[1]){if(9===p){if(!(a=e.getElementById(i)))return n;if(a.id===i)return n.push(a),n}else if(f&&(a=f.getElementById(i))&&y(e,a)&&a.id===i)return n.push(a),n}else{if(u[2])return H.apply(n,e.getElementsByTagName(t)),n;if((i=u[3])&&d.getElementsByClassName&&e.getElementsByClassName)return H.apply(n,e.getElementsByClassName(i)),n}if(d.qsa&&!N[t+" "]&&(!v||!v.test(t))&&(1!==p||"object"!==e.nodeName.toLowerCase())){if(c=t,f=e,1===p&&(U.test(t)||z.test(t))){(f=ee.test(t)&&ye(e.parentNode)||e)===e&&d.scope||((s=e.getAttribute("id"))?s=s.replace(re,ie):e.setAttribute("id",s=S)),o=(l=h(t)).length;while(o--)l[o]=(s?"#"+s:":scope")+" "+xe(l[o]);c=l.join(",")}try{return H.apply(n,f.querySelectorAll(c)),n}catch(e){N(t,!0)}finally{s===S&&e.removeAttribute("id")}}}return g(t.replace($,"$1"),e,n,r)}function ue(){var r=[];return function e(t,n){return r.push(t+" ")>b.cacheLength&&delete e[r.shift()],e[t+" "]=n}}function le(e){return e[S]=!0,e}function ce(e){var t=C.createElement("fieldset");try{return!!e(t)}catch(e){return!1}finally{t.parentNode&&t.parentNode.removeChild(t),t=null}}function fe(e,t){var n=e.split("|"),r=n.length;while(r--)b.attrHandle[n[r]]=t}function pe(e,t){var n=t&&e,r=n&&1===e.nodeType&&1===t.nodeType&&e.sourceIndex-t.sourceIndex;if(r)return r;if(n)while(n=n.nextSibling)if(n===t)return-1;return e?1:-1}function de(t){return function(e){return"input"===e.nodeName.toLowerCase()&&e.type===t}}function he(n){return function(e){var t=e.nodeName.toLowerCase();return("input"===t||"button"===t)&&e.type===n}}function ge(t){return function(e){return"form"in e?e.parentNode&&!1===e.disabled?"label"in e?"label"in e.parentNode?e.parentNode.disabled===t:e.disabled===t:e.isDisabled===t||e.isDisabled!==!t&&ae(e)===t:e.disabled===t:"label"in e&&e.disabled===t}}function ve(a){return le(function(o){return o=+o,le(function(e,t){var n,r=a([],e.length,o),i=r.length;while(i--)e[n=r[i]]&&(e[n]=!(t[n]=e[n]))})})}function ye(e){return e&&"undefined"!=typeof e.getElementsByTagName&&e}for(e in d=se.support={},i=se.isXML=function(e){var t=e&&e.namespaceURI,n=e&&(e.ownerDocument||e).documentElement;return!Y.test(t||n&&n.nodeName||"HTML")},T=se.setDocument=function(e){var t,n,r=e?e.ownerDocument||e:p;return r!=C&&9===r.nodeType&&r.documentElement&&(a=(C=r).documentElement,E=!i(C),p!=C&&(n=C.defaultView)&&n.top!==n&&(n.addEventListener?n.addEventListener("unload",oe,!1):n.attachEvent&&n.attachEvent("onunload",oe)),d.scope=ce(function(e){return a.appendChild(e).appendChild(C.createElement("div")),"undefined"!=typeof e.querySelectorAll&&!e.querySelectorAll(":scope fieldset div").length}),d.attributes=ce(function(e){return e.className="i",!e.getAttribute("className")}),d.getElementsByTagName=ce(function(e){return e.appendChild(C.createComment("")),!e.getElementsByTagName("*").length}),d.getElementsByClassName=K.test(C.getElementsByClassName),d.getById=ce(function(e){return a.appendChild(e).id=S,!C.getElementsByName||!C.getElementsByName(S).length}),d.getById?(b.filter.ID=function(e){var t=e.replace(te,ne);return function(e){return e.getAttribute("id")===t}},b.find.ID=function(e,t){if("undefined"!=typeof t.getElementById&&E){var n=t.getElementById(e);return n?[n]:[]}}):(b.filter.ID=function(e){var n=e.replace(te,ne);return function(e){var t="undefined"!=typeof e.getAttributeNode&&e.getAttributeNode("id");return t&&t.value===n}},b.find.ID=function(e,t){if("undefined"!=typeof t.getElementById&&E){var n,r,i,o=t.getElementById(e);if(o){if((n=o.getAttributeNode("id"))&&n.value===e)return[o];i=t.getElementsByName(e),r=0;while(o=i[r++])if((n=o.getAttributeNode("id"))&&n.value===e)return[o]}return[]}}),b.find.TAG=d.getElementsByTagName?function(e,t){return"undefined"!=typeof t.getElementsByTagName?t.getElementsByTagName(e):d.qsa?t.querySelectorAll(e):void 0}:function(e,t){var n,r=[],i=0,o=t.getElementsByTagName(e);if("*"===e){while(n=o[i++])1===n.nodeType&&r.push(n);return r}return o},b.find.CLASS=d.getElementsByClassName&&function(e,t){if("undefined"!=typeof t.getElementsByClassName&&E)return t.getElementsByClassName(e)},s=[],v=[],(d.qsa=K.test(C.querySelectorAll))&&(ce(function(e){var t;a.appendChild(e).innerHTML="<a id='"+S+"'></a><select id='"+S+"-\r\\' msallowcapture=''><option selected=''></option></select>",e.querySelectorAll("[msallowcapture^='']").length&&v.push("[*^$]="+M+"*(?:''|\"\")"),e.querySelectorAll("[selected]").length||v.push("\\["+M+"*(?:value|"+R+")"),e.querySelectorAll("[id~="+S+"-]").length||v.push("~="),(t=C.createElement("input")).setAttribute("name",""),e.appendChild(t),e.querySelectorAll("[name='']").length||v.push("\\["+M+"*name"+M+"*="+M+"*(?:''|\"\")"),e.querySelectorAll(":checked").length||v.push(":checked"),e.querySelectorAll("a#"+S+"+*").length||v.push(".#.+[+~]"),e.querySelectorAll("\\\f"),v.push("[\\r\\n\\f]")}),ce(function(e){e.innerHTML="<a href='' disabled='disabled'></a><select disabled='disabled'><option/></select>";var t=C.createElement("input");t.setAttribute("type","hidden"),e.appendChild(t).setAttribute("name","D"),e.querySelectorAll("[name=d]").length&&v.push("name"+M+"*[*^$|!~]?="),2!==e.querySelectorAll(":enabled").length&&v.push(":enabled",":disabled"),a.appendChild(e).disabled=!0,2!==e.querySelectorAll(":disabled").length&&v.push(":enabled",":disabled"),e.querySelectorAll("*,:x"),v.push(",.*:")})),(d.matchesSelector=K.test(c=a.matches||a.webkitMatchesSelector||a.mozMatchesSelector||a.oMatchesSelector||a.msMatchesSelector))&&ce(function(e){d.disconnectedMatch=c.call(e,"*"),c.call(e,"[s!='']:x"),s.push("!=",F)}),v=v.length&&new RegExp(v.join("|")),s=s.length&&new RegExp(s.join("|")),t=K.test(a.compareDocumentPosition),y=t||K.test(a.contains)?function(e,t){var n=9===e.nodeType?e.documentElement:e,r=t&&t.parentNode;return e===r||!(!r||1!==r.nodeType||!(n.contains?n.contains(r):e.compareDocumentPosition&&16&e.compareDocumentPosition(r)))}:function(e,t){if(t)while(t=t.parentNode)if(t===e)return!0;return!1},j=t?function(e,t){if(e===t)return l=!0,0;var n=!e.compareDocumentPosition-!t.compareDocumentPosition;return n||(1&(n=(e.ownerDocument||e)==(t.ownerDocument||t)?e.compareDocumentPosition(t):1)||!d.sortDetached&&t.compareDocumentPosition(e)===n?e==C||e.ownerDocument==p&&y(p,e)?-1:t==C||t.ownerDocument==p&&y(p,t)?1:u?P(u,e)-P(u,t):0:4&n?-1:1)}:function(e,t){if(e===t)return l=!0,0;var n,r=0,i=e.parentNode,o=t.parentNode,a=[e],s=[t];if(!i||!o)return e==C?-1:t==C?1:i?-1:o?1:u?P(u,e)-P(u,t):0;if(i===o)return pe(e,t);n=e;while(n=n.parentNode)a.unshift(n);n=t;while(n=n.parentNode)s.unshift(n);while(a[r]===s[r])r++;return r?pe(a[r],s[r]):a[r]==p?-1:s[r]==p?1:0}),C},se.matches=function(e,t){return se(e,null,null,t)},se.matchesSelector=function(e,t){if(T(e),d.matchesSelector&&E&&!N[t+" "]&&(!s||!s.test(t))&&(!v||!v.test(t)))try{var n=c.call(e,t);if(n||d.disconnectedMatch||e.document&&11!==e.document.nodeType)return n}catch(e){N(t,!0)}return 0<se(t,C,null,[e]).length},se.contains=function(e,t){return(e.ownerDocument||e)!=C&&T(e),y(e,t)},se.attr=function(e,t){(e.ownerDocument||e)!=C&&T(e);var n=b.attrHandle[t.toLowerCase()],r=n&&D.call(b.attrHandle,t.toLowerCase())?n(e,t,!E):void 0;return void 0!==r?r:d.attributes||!E?e.getAttribute(t):(r=e.getAttributeNode(t))&&r.specified?r.value:null},se.escape=function(e){return(e+"").replace(re,ie)},se.error=function(e){throw new Error("Syntax error, unrecognized expression: "+e)},se.uniqueSort=function(e){var t,n=[],r=0,i=0;if(l=!d.detectDuplicates,u=!d.sortStable&&e.slice(0),e.sort(j),l){while(t=e[i++])t===e[i]&&(r=n.push(i));while(r--)e.splice(n[r],1)}return u=null,e},o=se.getText=function(e){var t,n="",r=0,i=e.nodeType;if(i){if(1===i||9===i||11===i){if("string"==typeof e.textContent)return e.textContent;for(e=e.firstChild;e;e=e.nextSibling)n+=o(e)}else if(3===i||4===i)return e.nodeValue}else while(t=e[r++])n+=o(t);return n},(b=se.selectors={cacheLength:50,createPseudo:le,match:G,attrHandle:{},find:{},relative:{">":{dir:"parentNode",first:!0}," ":{dir:"parentNode"},"+":{dir:"previousSibling",first:!0},"~":{dir:"previousSibling"}},preFilter:{ATTR:function(e){return e[1]=e[1].replace(te,ne),e[3]=(e[3]||e[4]||e[5]||"").replace(te,ne),"~="===e[2]&&(e[3]=" "+e[3]+" "),e.slice(0,4)},CHILD:function(e){return e[1]=e[1].toLowerCase(),"nth"===e[1].slice(0,3)?(e[3]||se.error(e[0]),e[4]=+(e[4]?e[5]+(e[6]||1):2*("even"===e[3]||"odd"===e[3])),e[5]=+(e[7]+e[8]||"odd"===e[3])):e[3]&&se.error(e[0]),e},PSEUDO:function(e){var t,n=!e[6]&&e[2];return G.CHILD.test(e[0])?null:(e[3]?e[2]=e[4]||e[5]||"":n&&X.test(n)&&(t=h(n,!0))&&(t=n.indexOf(")",n.length-t)-n.length)&&(e[0]=e[0].slice(0,t),e[2]=n.slice(0,t)),e.slice(0,3))}},filter:{TAG:function(e){var t=e.replace(te,ne).toLowerCase();return"*"===e?function(){return!0}:function(e){return e.nodeName&&e.nodeName.toLowerCase()===t}},CLASS:function(e){var t=m[e+" "];return t||(t=new RegExp("(^|"+M+")"+e+"("+M+"|$)"))&&m(e,function(e){return t.test("string"==typeof e.className&&e.className||"undefined"!=typeof e.getAttribute&&e.getAttribute("class")||"")})},ATTR:function(n,r,i){return function(e){var t=se.attr(e,n);return null==t?"!="===r:!r||(t+="","="===r?t===i:"!="===r?t!==i:"^="===r?i&&0===t.indexOf(i):"*="===r?i&&-1<t.indexOf(i):"$="===r?i&&t.slice(-i.length)===i:"~="===r?-1<(" "+t.replace(B," ")+" ").indexOf(i):"|="===r&&(t===i||t.slice(0,i.length+1)===i+"-"))}},CHILD:function(h,e,t,g,v){var y="nth"!==h.slice(0,3),m="last"!==h.slice(-4),x="of-type"===e;return 1===g&&0===v?function(e){return!!e.parentNode}:function(e,t,n){var r,i,o,a,s,u,l=y!==m?"nextSibling":"previousSibling",c=e.parentNode,f=x&&e.nodeName.toLowerCase(),p=!n&&!x,d=!1;if(c){if(y){while(l){a=e;while(a=a[l])if(x?a.nodeName.toLowerCase()===f:1===a.nodeType)return!1;u=l="only"===h&&!u&&"nextSibling"}return!0}if(u=[m?c.firstChild:c.lastChild],m&&p){d=(s=(r=(i=(o=(a=c)[S]||(a[S]={}))[a.uniqueID]||(o[a.uniqueID]={}))[h]||[])[0]===k&&r[1])&&r[2],a=s&&c.childNodes[s];while(a=++s&&a&&a[l]||(d=s=0)||u.pop())if(1===a.nodeType&&++d&&a===e){i[h]=[k,s,d];break}}else if(p&&(d=s=(r=(i=(o=(a=e)[S]||(a[S]={}))[a.uniqueID]||(o[a.uniqueID]={}))[h]||[])[0]===k&&r[1]),!1===d)while(a=++s&&a&&a[l]||(d=s=0)||u.pop())if((x?a.nodeName.toLowerCase()===f:1===a.nodeType)&&++d&&(p&&((i=(o=a[S]||(a[S]={}))[a.uniqueID]||(o[a.uniqueID]={}))[h]=[k,d]),a===e))break;return(d-=v)===g||d%g==0&&0<=d/g}}},PSEUDO:function(e,o){var t,a=b.pseudos[e]||b.setFilters[e.toLowerCase()]||se.error("unsupported pseudo: "+e);return a[S]?a(o):1<a.length?(t=[e,e,"",o],b.setFilters.hasOwnProperty(e.toLowerCase())?le(function(e,t){var n,r=a(e,o),i=r.length;while(i--)e[n=P(e,r[i])]=!(t[n]=r[i])}):function(e){return a(e,0,t)}):a}},pseudos:{not:le(function(e){var r=[],i=[],s=f(e.replace($,"$1"));return s[S]?le(function(e,t,n,r){var i,o=s(e,null,r,[]),a=e.length;while(a--)(i=o[a])&&(e[a]=!(t[a]=i))}):function(e,t,n){return r[0]=e,s(r,null,n,i),r[0]=null,!i.pop()}}),has:le(function(t){return function(e){return 0<se(t,e).length}}),contains:le(function(t){return t=t.replace(te,ne),function(e){return-1<(e.textContent||o(e)).indexOf(t)}}),lang:le(function(n){return V.test(n||"")||se.error("unsupported lang: "+n),n=n.replace(te,ne).toLowerCase(),function(e){var t;do{if(t=E?e.lang:e.getAttribute("xml:lang")||e.getAttribute("lang"))return(t=t.toLowerCase())===n||0===t.indexOf(n+"-")}while((e=e.parentNode)&&1===e.nodeType);return!1}}),target:function(e){var t=n.location&&n.location.hash;return t&&t.slice(1)===e.id},root:function(e){return e===a},focus:function(e){return e===C.activeElement&&(!C.hasFocus||C.hasFocus())&&!!(e.type||e.href||~e.tabIndex)},enabled:ge(!1),disabled:ge(!0),checked:function(e){var t=e.nodeName.toLowerCase();return"input"===t&&!!e.checked||"option"===t&&!!e.selected},selected:function(e){return e.parentNode&&e.parentNode.selectedIndex,!0===e.selected},empty:function(e){for(e=e.firstChild;e;e=e.nextSibling)if(e.nodeType<6)return!1;return!0},parent:function(e){return!b.pseudos.empty(e)},header:function(e){return J.test(e.nodeName)},input:function(e){return Q.test(e.nodeName)},button:function(e){var t=e.nodeName.toLowerCase();return"input"===t&&"button"===e.type||"button"===t},text:function(e){var t;return"input"===e.nodeName.toLowerCase()&&"text"===e.type&&(null==(t=e.getAttribute("type"))||"text"===t.toLowerCase())},first:ve(function(){return[0]}),last:ve(function(e,t){return[t-1]}),eq:ve(function(e,t,n){return[n<0?n+t:n]}),even:ve(function(e,t){for(var n=0;n<t;n+=2)e.push(n);return e}),odd:ve(function(e,t){for(var n=1;n<t;n+=2)e.push(n);return e}),lt:ve(function(e,t,n){for(var r=n<0?n+t:t<n?t:n;0<=--r;)e.push(r);return e}),gt:ve(function(e,t,n){for(var r=n<0?n+t:n;++r<t;)e.push(r);return e})}}).pseudos.nth=b.pseudos.eq,{radio:!0,checkbox:!0,file:!0,password:!0,image:!0})b.pseudos[e]=de(e);for(e in{submit:!0,reset:!0})b.pseudos[e]=he(e);function me(){}function xe(e){for(var t=0,n=e.length,r="";t<n;t++)r+=e[t].value;return r}function be(s,e,t){var u=e.dir,l=e.next,c=l||u,f=t&&"parentNode"===c,p=r++;return e.first?function(e,t,n){while(e=e[u])if(1===e.nodeType||f)return s(e,t,n);return!1}:function(e,t,n){var r,i,o,a=[k,p];if(n){while(e=e[u])if((1===e.nodeType||f)&&s(e,t,n))return!0}else while(e=e[u])if(1===e.nodeType||f)if(i=(o=e[S]||(e[S]={}))[e.uniqueID]||(o[e.uniqueID]={}),l&&l===e.nodeName.toLowerCase())e=e[u]||e;else{if((r=i[c])&&r[0]===k&&r[1]===p)return a[2]=r[2];if((i[c]=a)[2]=s(e,t,n))return!0}return!1}}function we(i){return 1<i.length?function(e,t,n){var r=i.length;while(r--)if(!i[r](e,t,n))return!1;return!0}:i[0]}function Te(e,t,n,r,i){for(var o,a=[],s=0,u=e.length,l=null!=t;s<u;s++)(o=e[s])&&(n&&!n(o,r,i)||(a.push(o),l&&t.push(s)));return a}function Ce(d,h,g,v,y,e){return v&&!v[S]&&(v=Ce(v)),y&&!y[S]&&(y=Ce(y,e)),le(function(e,t,n,r){var i,o,a,s=[],u=[],l=t.length,c=e||function(e,t,n){for(var r=0,i=t.length;r<i;r++)se(e,t[r],n);return n}(h||"*",n.nodeType?[n]:n,[]),f=!d||!e&&h?c:Te(c,s,d,n,r),p=g?y||(e?d:l||v)?[]:t:f;if(g&&g(f,p,n,r),v){i=Te(p,u),v(i,[],n,r),o=i.length;while(o--)(a=i[o])&&(p[u[o]]=!(f[u[o]]=a))}if(e){if(y||d){if(y){i=[],o=p.length;while(o--)(a=p[o])&&i.push(f[o]=a);y(null,p=[],i,r)}o=p.length;while(o--)(a=p[o])&&-1<(i=y?P(e,a):s[o])&&(e[i]=!(t[i]=a))}}else p=Te(p===t?p.splice(l,p.length):p),y?y(null,t,p,r):H.apply(t,p)})}function Ee(e){for(var i,t,n,r=e.length,o=b.relative[e[0].type],a=o||b.relative[" "],s=o?1:0,u=be(function(e){return e===i},a,!0),l=be(function(e){return-1<P(i,e)},a,!0),c=[function(e,t,n){var r=!o&&(n||t!==w)||((i=t).nodeType?u(e,t,n):l(e,t,n));return i=null,r}];s<r;s++)if(t=b.relative[e[s].type])c=[be(we(c),t)];else{if((t=b.filter[e[s].type].apply(null,e[s].matches))[S]){for(n=++s;n<r;n++)if(b.relative[e[n].type])break;return Ce(1<s&&we(c),1<s&&xe(e.slice(0,s-1).concat({value:" "===e[s-2].type?"*":""})).replace($,"$1"),t,s<n&&Ee(e.slice(s,n)),n<r&&Ee(e=e.slice(n)),n<r&&xe(e))}c.push(t)}return we(c)}return me.prototype=b.filters=b.pseudos,b.setFilters=new me,h=se.tokenize=function(e,t){var n,r,i,o,a,s,u,l=x[e+" "];if(l)return t?0:l.slice(0);a=e,s=[],u=b.preFilter;while(a){for(o in n&&!(r=_.exec(a))||(r&&(a=a.slice(r[0].length)||a),s.push(i=[])),n=!1,(r=z.exec(a))&&(n=r.shift(),i.push({value:n,type:r[0].replace($," ")}),a=a.slice(n.length)),b.filter)!(r=G[o].exec(a))||u[o]&&!(r=u[o](r))||(n=r.shift(),i.push({value:n,type:o,matches:r}),a=a.slice(n.length));if(!n)break}return t?a.length:a?se.error(e):x(e,s).slice(0)},f=se.compile=function(e,t){var n,v,y,m,x,r,i=[],o=[],a=A[e+" "];if(!a){t||(t=h(e)),n=t.length;while(n--)(a=Ee(t[n]))[S]?i.push(a):o.push(a);(a=A(e,(v=o,m=0<(y=i).length,x=0<v.length,r=function(e,t,n,r,i){var o,a,s,u=0,l="0",c=e&&[],f=[],p=w,d=e||x&&b.find.TAG("*",i),h=k+=null==p?1:Math.random()||.1,g=d.length;for(i&&(w=t==C||t||i);l!==g&&null!=(o=d[l]);l++){if(x&&o){a=0,t||o.ownerDocument==C||(T(o),n=!E);while(s=v[a++])if(s(o,t||C,n)){r.push(o);break}i&&(k=h)}m&&((o=!s&&o)&&u--,e&&c.push(o))}if(u+=l,m&&l!==u){a=0;while(s=y[a++])s(c,f,t,n);if(e){if(0<u)while(l--)c[l]||f[l]||(f[l]=q.call(r));f=Te(f)}H.apply(r,f),i&&!e&&0<f.length&&1<u+y.length&&se.uniqueSort(r)}return i&&(k=h,w=p),c},m?le(r):r))).selector=e}return a},g=se.select=function(e,t,n,r){var i,o,a,s,u,l="function"==typeof e&&e,c=!r&&h(e=l.selector||e);if(n=n||[],1===c.length){if(2<(o=c[0]=c[0].slice(0)).length&&"ID"===(a=o[0]).type&&9===t.nodeType&&E&&b.relative[o[1].type]){if(!(t=(b.find.ID(a.matches[0].replace(te,ne),t)||[])[0]))return n;l&&(t=t.parentNode),e=e.slice(o.shift().value.length)}i=G.needsContext.test(e)?0:o.length;while(i--){if(a=o[i],b.relative[s=a.type])break;if((u=b.find[s])&&(r=u(a.matches[0].replace(te,ne),ee.test(o[0].type)&&ye(t.parentNode)||t))){if(o.splice(i,1),!(e=r.length&&xe(o)))return H.apply(n,r),n;break}}}return(l||f(e,c))(r,t,!E,n,!t||ee.test(e)&&ye(t.parentNode)||t),n},d.sortStable=S.split("").sort(j).join("")===S,d.detectDuplicates=!!l,T(),d.sortDetached=ce(function(e){return 1&e.compareDocumentPosition(C.createElement("fieldset"))}),ce(function(e){return e.innerHTML="<a href='#'></a>","#"===e.firstChild.getAttribute("href")})||fe("type|href|height|width",function(e,t,n){if(!n)return e.getAttribute(t,"type"===t.toLowerCase()?1:2)}),d.attributes&&ce(function(e){return e.innerHTML="<input/>",e.firstChild.setAttribute("value",""),""===e.firstChild.getAttribute("value")})||fe("value",function(e,t,n){if(!n&&"input"===e.nodeName.toLowerCase())return e.defaultValue}),ce(function(e){return null==e.getAttribute("disabled")})||fe(R,function(e,t,n){var r;if(!n)return!0===e[t]?t.toLowerCase():(r=e.getAttributeNode(t))&&r.specified?r.value:null}),se}(C);S.find=d,S.expr=d.selectors,S.expr[":"]=S.expr.pseudos,S.uniqueSort=S.unique=d.uniqueSort,S.text=d.getText,S.isXMLDoc=d.isXML,S.contains=d.contains,S.escapeSelector=d.escape;var h=function(e,t,n){var r=[],i=void 0!==n;while((e=e[t])&&9!==e.nodeType)if(1===e.nodeType){if(i&&S(e).is(n))break;r.push(e)}return r},T=function(e,t){for(var n=[];e;e=e.nextSibling)1===e.nodeType&&e!==t&&n.push(e);return n},k=S.expr.match.needsContext;function A(e,t){return e.nodeName&&e.nodeName.toLowerCase()===t.toLowerCase()}var N=/^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i;function j(e,n,r){return m(n)?S.grep(e,function(e,t){return!!n.call(e,t,e)!==r}):n.nodeType?S.grep(e,function(e){return e===n!==r}):"string"!=typeof n?S.grep(e,function(e){return-1<i.call(n,e)!==r}):S.filter(n,e,r)}S.filter=function(e,t,n){var r=t[0];return n&&(e=":not("+e+")"),1===t.length&&1===r.nodeType?S.find.matchesSelector(r,e)?[r]:[]:S.find.matches(e,S.grep(t,function(e){return 1===e.nodeType}))},S.fn.extend({find:function(e){var t,n,r=this.length,i=this;if("string"!=typeof e)return this.pushStack(S(e).filter(function(){for(t=0;t<r;t++)if(S.contains(i[t],this))return!0}));for(n=this.pushStack([]),t=0;t<r;t++)S.find(e,i[t],n);return 1<r?S.uniqueSort(n):n},filter:function(e){return this.pushStack(j(this,e||[],!1))},not:function(e){return this.pushStack(j(this,e||[],!0))},is:function(e){return!!j(this,"string"==typeof e&&k.test(e)?S(e):e||[],!1).length}});var D,q=/^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/;(S.fn.init=function(e,t,n){var r,i;if(!e)return this;if(n=n||D,"string"==typeof e){if(!(r="<"===e[0]&&">"===e[e.length-1]&&3<=e.length?[null,e,null]:q.exec(e))||!r[1]&&t)return!t||t.jquery?(t||n).find(e):this.constructor(t).find(e);if(r[1]){if(t=t instanceof S?t[0]:t,S.merge(this,S.parseHTML(r[1],t&&t.nodeType?t.ownerDocument||t:E,!0)),N.test(r[1])&&S.isPlainObject(t))for(r in t)m(this[r])?this[r](t[r]):this.attr(r,t[r]);return this}return(i=E.getElementById(r[2]))&&(this[0]=i,this.length=1),this}return e.nodeType?(this[0]=e,this.length=1,this):m(e)?void 0!==n.ready?n.ready(e):e(S):S.makeArray(e,this)}).prototype=S.fn,D=S(E);var L=/^(?:parents|prev(?:Until|All))/,H={children:!0,contents:!0,next:!0,prev:!0};function O(e,t){while((e=e[t])&&1!==e.nodeType);return e}S.fn.extend({has:function(e){var t=S(e,this),n=t.length;return this.filter(function(){for(var e=0;e<n;e++)if(S.contains(this,t[e]))return!0})},closest:function(e,t){var n,r=0,i=this.length,o=[],a="string"!=typeof e&&S(e);if(!k.test(e))for(;r<i;r++)for(n=this[r];n&&n!==t;n=n.parentNode)if(n.nodeType<11&&(a?-1<a.index(n):1===n.nodeType&&S.find.matchesSelector(n,e))){o.push(n);break}return this.pushStack(1<o.length?S.uniqueSort(o):o)},index:function(e){return e?"string"==typeof e?i.call(S(e),this[0]):i.call(this,e.jquery?e[0]:e):this[0]&&this[0].parentNode?this.first().prevAll().length:-1},add:function(e,t){return this.pushStack(S.uniqueSort(S.merge(this.get(),S(e,t))))},addBack:function(e){return this.add(null==e?this.prevObject:this.prevObject.filter(e))}}),S.each({parent:function(e){var t=e.parentNode;return t&&11!==t.nodeType?t:null},parents:function(e){return h(e,"parentNode")},parentsUntil:function(e,t,n){return h(e,"parentNode",n)},next:function(e){return O(e,"nextSibling")},prev:function(e){return O(e,"previousSibling")},nextAll:function(e){return h(e,"nextSibling")},prevAll:function(e){return h(e,"previousSibling")},nextUntil:function(e,t,n){return h(e,"nextSibling",n)},prevUntil:function(e,t,n){return h(e,"previousSibling",n)},siblings:function(e){return T((e.parentNode||{}).firstChild,e)},children:function(e){return T(e.firstChild)},contents:function(e){return null!=e.contentDocument&&r(e.contentDocument)?e.contentDocument:(A(e,"template")&&(e=e.content||e),S.merge([],e.childNodes))}},function(r,i){S.fn[r]=function(e,t){var n=S.map(this,i,e);return"Until"!==r.slice(-5)&&(t=e),t&&"string"==typeof t&&(n=S.filter(t,n)),1<this.length&&(H[r]||S.uniqueSort(n),L.test(r)&&n.reverse()),this.pushStack(n)}});var P=/[^\x20\t\r\n\f]+/g;function R(e){return e}function M(e){throw e}function I(e,t,n,r){var i;try{e&&m(i=e.promise)?i.call(e).done(t).fail(n):e&&m(i=e.then)?i.call(e,t,n):t.apply(void 0,[e].slice(r))}catch(e){n.apply(void 0,[e])}}S.Callbacks=function(r){var e,n;r="string"==typeof r?(e=r,n={},S.each(e.match(P)||[],function(e,t){n[t]=!0}),n):S.extend({},r);var i,t,o,a,s=[],u=[],l=-1,c=function(){for(a=a||r.once,o=i=!0;u.length;l=-1){t=u.shift();while(++l<s.length)!1===s[l].apply(t[0],t[1])&&r.stopOnFalse&&(l=s.length,t=!1)}r.memory||(t=!1),i=!1,a&&(s=t?[]:"")},f={add:function(){return s&&(t&&!i&&(l=s.length-1,u.push(t)),function n(e){S.each(e,function(e,t){m(t)?r.unique&&f.has(t)||s.push(t):t&&t.length&&"string"!==w(t)&&n(t)})}(arguments),t&&!i&&c()),this},remove:function(){return S.each(arguments,function(e,t){var n;while(-1<(n=S.inArray(t,s,n)))s.splice(n,1),n<=l&&l--}),this},has:function(e){return e?-1<S.inArray(e,s):0<s.length},empty:function(){return s&&(s=[]),this},disable:function(){return a=u=[],s=t="",this},disabled:function(){return!s},lock:function(){return a=u=[],t||i||(s=t=""),this},locked:function(){return!!a},fireWith:function(e,t){return a||(t=[e,(t=t||[]).slice?t.slice():t],u.push(t),i||c()),this},fire:function(){return f.fireWith(this,arguments),this},fired:function(){return!!o}};return f},S.extend({Deferred:function(e){var o=[["notify","progress",S.Callbacks("memory"),S.Callbacks("memory"),2],["resolve","done",S.Callbacks("once memory"),S.Callbacks("once memory"),0,"resolved"],["reject","fail",S.Callbacks("once memory"),S.Callbacks("once memory"),1,"rejected"]],i="pending",a={state:function(){return i},always:function(){return s.done(arguments).fail(arguments),this},"catch":function(e){return a.then(null,e)},pipe:function(){var i=arguments;return S.Deferred(function(r){S.each(o,function(e,t){var n=m(i[t[4]])&&i[t[4]];s[t[1]](function(){var e=n&&n.apply(this,arguments);e&&m(e.promise)?e.promise().progress(r.notify).done(r.resolve).fail(r.reject):r[t[0]+"With"](this,n?[e]:arguments)})}),i=null}).promise()},then:function(t,n,r){var u=0;function l(i,o,a,s){return function(){var n=this,r=arguments,e=function(){var e,t;if(!(i<u)){if((e=a.apply(n,r))===o.promise())throw new TypeError("Thenable self-resolution");t=e&&("object"==typeof e||"function"==typeof e)&&e.then,m(t)?s?t.call(e,l(u,o,R,s),l(u,o,M,s)):(u++,t.call(e,l(u,o,R,s),l(u,o,M,s),l(u,o,R,o.notifyWith))):(a!==R&&(n=void 0,r=[e]),(s||o.resolveWith)(n,r))}},t=s?e:function(){try{e()}catch(e){S.Deferred.exceptionHook&&S.Deferred.exceptionHook(e,t.stackTrace),u<=i+1&&(a!==M&&(n=void 0,r=[e]),o.rejectWith(n,r))}};i?t():(S.Deferred.getStackHook&&(t.stackTrace=S.Deferred.getStackHook()),C.setTimeout(t))}}return S.Deferred(function(e){o[0][3].add(l(0,e,m(r)?r:R,e.notifyWith)),o[1][3].add(l(0,e,m(t)?t:R)),o[2][3].add(l(0,e,m(n)?n:M))}).promise()},promise:function(e){return null!=e?S.extend(e,a):a}},s={};return S.each(o,function(e,t){var n=t[2],r=t[5];a[t[1]]=n.add,r&&n.add(function(){i=r},o[3-e][2].disable,o[3-e][3].disable,o[0][2].lock,o[0][3].lock),n.add(t[3].fire),s[t[0]]=function(){return s[t[0]+"With"](this===s?void 0:this,arguments),this},s[t[0]+"With"]=n.fireWith}),a.promise(s),e&&e.call(s,s),s},when:function(e){var n=arguments.length,t=n,r=Array(t),i=s.call(arguments),o=S.Deferred(),a=function(t){return function(e){r[t]=this,i[t]=1<arguments.length?s.call(arguments):e,--n||o.resolveWith(r,i)}};if(n<=1&&(I(e,o.done(a(t)).resolve,o.reject,!n),"pending"===o.state()||m(i[t]&&i[t].then)))return o.then();while(t--)I(i[t],a(t),o.reject);return o.promise()}});var W=/^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;S.Deferred.exceptionHook=function(e,t){C.console&&C.console.warn&&e&&W.test(e.name)&&C.console.warn("jQuery.Deferred exception: "+e.message,e.stack,t)},S.readyException=function(e){C.setTimeout(function(){throw e})};var F=S.Deferred();function B(){E.removeEventListener("DOMContentLoaded",B),C.removeEventListener("load",B),S.ready()}S.fn.ready=function(e){return F.then(e)["catch"](function(e){S.readyException(e)}),this},S.extend({isReady:!1,readyWait:1,ready:function(e){(!0===e?--S.readyWait:S.isReady)||(S.isReady=!0)!==e&&0<--S.readyWait||F.resolveWith(E,[S])}}),S.ready.then=F.then,"complete"===E.readyState||"loading"!==E.readyState&&!E.documentElement.doScroll?C.setTimeout(S.ready):(E.addEventListener("DOMContentLoaded",B),C.addEventListener("load",B));var $=function(e,t,n,r,i,o,a){var s=0,u=e.length,l=null==n;if("object"===w(n))for(s in i=!0,n)$(e,t,s,n[s],!0,o,a);else if(void 0!==r&&(i=!0,m(r)||(a=!0),l&&(a?(t.call(e,r),t=null):(l=t,t=function(e,t,n){return l.call(S(e),n)})),t))for(;s<u;s++)t(e[s],n,a?r:r.call(e[s],s,t(e[s],n)));return i?e:l?t.call(e):u?t(e[0],n):o},_=/^-ms-/,z=/-([a-z])/g;function U(e,t){return t.toUpperCase()}function X(e){return e.replace(_,"ms-").replace(z,U)}var V=function(e){return 1===e.nodeType||9===e.nodeType||!+e.nodeType};function G(){this.expando=S.expando+G.uid++}G.uid=1,G.prototype={cache:function(e){var t=e[this.expando];return t||(t={},V(e)&&(e.nodeType?e[this.expando]=t:Object.defineProperty(e,this.expando,{value:t,configurable:!0}))),t},set:function(e,t,n){var r,i=this.cache(e);if("string"==typeof t)i[X(t)]=n;else for(r in t)i[X(r)]=t[r];return i},get:function(e,t){return void 0===t?this.cache(e):e[this.expando]&&e[this.expando][X(t)]},access:function(e,t,n){return void 0===t||t&&"string"==typeof t&&void 0===n?this.get(e,t):(this.set(e,t,n),void 0!==n?n:t)},remove:function(e,t){var n,r=e[this.expando];if(void 0!==r){if(void 0!==t){n=(t=Array.isArray(t)?t.map(X):(t=X(t))in r?[t]:t.match(P)||[]).length;while(n--)delete r[t[n]]}(void 0===t||S.isEmptyObject(r))&&(e.nodeType?e[this.expando]=void 0:delete e[this.expando])}},hasData:function(e){var t=e[this.expando];return void 0!==t&&!S.isEmptyObject(t)}};var Y=new G,Q=new G,J=/^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,K=/[A-Z]/g;function Z(e,t,n){var r,i;if(void 0===n&&1===e.nodeType)if(r="data-"+t.replace(K,"-$&").toLowerCase(),"string"==typeof(n=e.getAttribute(r))){try{n="true"===(i=n)||"false"!==i&&("null"===i?null:i===+i+""?+i:J.test(i)?JSON.parse(i):i)}catch(e){}Q.set(e,t,n)}else n=void 0;return n}S.extend({hasData:function(e){return Q.hasData(e)||Y.hasData(e)},data:function(e,t,n){return Q.access(e,t,n)},removeData:function(e,t){Q.remove(e,t)},_data:function(e,t,n){return Y.access(e,t,n)},_removeData:function(e,t){Y.remove(e,t)}}),S.fn.extend({data:function(n,e){var t,r,i,o=this[0],a=o&&o.attributes;if(void 0===n){if(this.length&&(i=Q.get(o),1===o.nodeType&&!Y.get(o,"hasDataAttrs"))){t=a.length;while(t--)a[t]&&0===(r=a[t].name).indexOf("data-")&&(r=X(r.slice(5)),Z(o,r,i[r]));Y.set(o,"hasDataAttrs",!0)}return i}return"object"==typeof n?this.each(function(){Q.set(this,n)}):$(this,function(e){var t;if(o&&void 0===e)return void 0!==(t=Q.get(o,n))?t:void 0!==(t=Z(o,n))?t:void 0;this.each(function(){Q.set(this,n,e)})},null,e,1<arguments.length,null,!0)},removeData:function(e){return this.each(function(){Q.remove(this,e)})}}),S.extend({queue:function(e,t,n){var r;if(e)return t=(t||"fx")+"queue",r=Y.get(e,t),n&&(!r||Array.isArray(n)?r=Y.access(e,t,S.makeArray(n)):r.push(n)),r||[]},dequeue:function(e,t){t=t||"fx";var n=S.queue(e,t),r=n.length,i=n.shift(),o=S._queueHooks(e,t);"inprogress"===i&&(i=n.shift(),r--),i&&("fx"===t&&n.unshift("inprogress"),delete o.stop,i.call(e,function(){S.dequeue(e,t)},o)),!r&&o&&o.empty.fire()},_queueHooks:function(e,t){var n=t+"queueHooks";return Y.get(e,n)||Y.access(e,n,{empty:S.Callbacks("once memory").add(function(){Y.remove(e,[t+"queue",n])})})}}),S.fn.extend({queue:function(t,n){var e=2;return"string"!=typeof t&&(n=t,t="fx",e--),arguments.length<e?S.queue(this[0],t):void 0===n?this:this.each(function(){var e=S.queue(this,t,n);S._queueHooks(this,t),"fx"===t&&"inprogress"!==e[0]&&S.dequeue(this,t)})},dequeue:function(e){return this.each(function(){S.dequeue(this,e)})},clearQueue:function(e){return this.queue(e||"fx",[])},promise:function(e,t){var n,r=1,i=S.Deferred(),o=this,a=this.length,s=function(){--r||i.resolveWith(o,[o])};"string"!=typeof e&&(t=e,e=void 0),e=e||"fx";while(a--)(n=Y.get(o[a],e+"queueHooks"))&&n.empty&&(r++,n.empty.add(s));return s(),i.promise(t)}});var ee=/[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/.source,te=new RegExp("^(?:([+-])=|)("+ee+")([a-z%]*)$","i"),ne=["Top","Right","Bottom","Left"],re=E.documentElement,ie=function(e){return S.contains(e.ownerDocument,e)},oe={composed:!0};re.getRootNode&&(ie=function(e){return S.contains(e.ownerDocument,e)||e.getRootNode(oe)===e.ownerDocument});var ae=function(e,t){return"none"===(e=t||e).style.display||""===e.style.display&&ie(e)&&"none"===S.css(e,"display")};function se(e,t,n,r){var i,o,a=20,s=r?function(){return r.cur()}:function(){return S.css(e,t,"")},u=s(),l=n&&n[3]||(S.cssNumber[t]?"":"px"),c=e.nodeType&&(S.cssNumber[t]||"px"!==l&&+u)&&te.exec(S.css(e,t));if(c&&c[3]!==l){u/=2,l=l||c[3],c=+u||1;while(a--)S.style(e,t,c+l),(1-o)*(1-(o=s()/u||.5))<=0&&(a=0),c/=o;c*=2,S.style(e,t,c+l),n=n||[]}return n&&(c=+c||+u||0,i=n[1]?c+(n[1]+1)*n[2]:+n[2],r&&(r.unit=l,r.start=c,r.end=i)),i}var ue={};function le(e,t){for(var n,r,i,o,a,s,u,l=[],c=0,f=e.length;c<f;c++)(r=e[c]).style&&(n=r.style.display,t?("none"===n&&(l[c]=Y.get(r,"display")||null,l[c]||(r.style.display="")),""===r.style.display&&ae(r)&&(l[c]=(u=a=o=void 0,a=(i=r).ownerDocument,s=i.nodeName,(u=ue[s])||(o=a.body.appendChild(a.createElement(s)),u=S.css(o,"display"),o.parentNode.removeChild(o),"none"===u&&(u="block"),ue[s]=u)))):"none"!==n&&(l[c]="none",Y.set(r,"display",n)));for(c=0;c<f;c++)null!=l[c]&&(e[c].style.display=l[c]);return e}S.fn.extend({show:function(){return le(this,!0)},hide:function(){return le(this)},toggle:function(e){return"boolean"==typeof e?e?this.show():this.hide():this.each(function(){ae(this)?S(this).show():S(this).hide()})}});var ce,fe,pe=/^(?:checkbox|radio)$/i,de=/<([a-z][^\/\0>\x20\t\r\n\f]*)/i,he=/^$|^module$|\/(?:java|ecma)script/i;ce=E.createDocumentFragment().appendChild(E.createElement("div")),(fe=E.createElement("input")).setAttribute("type","radio"),fe.setAttribute("checked","checked"),fe.setAttribute("name","t"),ce.appendChild(fe),y.checkClone=ce.cloneNode(!0).cloneNode(!0).lastChild.checked,ce.innerHTML="<textarea>x</textarea>",y.noCloneChecked=!!ce.cloneNode(!0).lastChild.defaultValue,ce.innerHTML="<option></option>",y.option=!!ce.lastChild;var ge={thead:[1,"<table>","</table>"],col:[2,"<table><colgroup>","</colgroup></table>"],tr:[2,"<table><tbody>","</tbody></table>"],td:[3,"<table><tbody><tr>","</tr></tbody></table>"],_default:[0,"",""]};function ve(e,t){var n;return n="undefined"!=typeof e.getElementsByTagName?e.getElementsByTagName(t||"*"):"undefined"!=typeof e.querySelectorAll?e.querySelectorAll(t||"*"):[],void 0===t||t&&A(e,t)?S.merge([e],n):n}function ye(e,t){for(var n=0,r=e.length;n<r;n++)Y.set(e[n],"globalEval",!t||Y.get(t[n],"globalEval"))}ge.tbody=ge.tfoot=ge.colgroup=ge.caption=ge.thead,ge.th=ge.td,y.option||(ge.optgroup=ge.option=[1,"<select multiple='multiple'>","</select>"]);var me=/<|&#?\w+;/;function xe(e,t,n,r,i){for(var o,a,s,u,l,c,f=t.createDocumentFragment(),p=[],d=0,h=e.length;d<h;d++)if((o=e[d])||0===o)if("object"===w(o))S.merge(p,o.nodeType?[o]:o);else if(me.test(o)){a=a||f.appendChild(t.createElement("div")),s=(de.exec(o)||["",""])[1].toLowerCase(),u=ge[s]||ge._default,a.innerHTML=u[1]+S.htmlPrefilter(o)+u[2],c=u[0];while(c--)a=a.lastChild;S.merge(p,a.childNodes),(a=f.firstChild).textContent=""}else p.push(t.createTextNode(o));f.textContent="",d=0;while(o=p[d++])if(r&&-1<S.inArray(o,r))i&&i.push(o);else if(l=ie(o),a=ve(f.appendChild(o),"script"),l&&ye(a),n){c=0;while(o=a[c++])he.test(o.type||"")&&n.push(o)}return f}var be=/^([^.]*)(?:\.(.+)|)/;function we(){return!0}function Te(){return!1}function Ce(e,t){return e===function(){try{return E.activeElement}catch(e){}}()==("focus"===t)}function Ee(e,t,n,r,i,o){var a,s;if("object"==typeof t){for(s in"string"!=typeof n&&(r=r||n,n=void 0),t)Ee(e,s,n,r,t[s],o);return e}if(null==r&&null==i?(i=n,r=n=void 0):null==i&&("string"==typeof n?(i=r,r=void 0):(i=r,r=n,n=void 0)),!1===i)i=Te;else if(!i)return e;return 1===o&&(a=i,(i=function(e){return S().off(e),a.apply(this,arguments)}).guid=a.guid||(a.guid=S.guid++)),e.each(function(){S.event.add(this,t,i,r,n)})}function Se(e,i,o){o?(Y.set(e,i,!1),S.event.add(e,i,{namespace:!1,handler:function(e){var t,n,r=Y.get(this,i);if(1&e.isTrigger&&this[i]){if(r.length)(S.event.special[i]||{}).delegateType&&e.stopPropagation();else if(r=s.call(arguments),Y.set(this,i,r),t=o(this,i),this[i](),r!==(n=Y.get(this,i))||t?Y.set(this,i,!1):n={},r!==n)return e.stopImmediatePropagation(),e.preventDefault(),n&&n.value}else r.length&&(Y.set(this,i,{value:S.event.trigger(S.extend(r[0],S.Event.prototype),r.slice(1),this)}),e.stopImmediatePropagation())}})):void 0===Y.get(e,i)&&S.event.add(e,i,we)}S.event={global:{},add:function(t,e,n,r,i){var o,a,s,u,l,c,f,p,d,h,g,v=Y.get(t);if(V(t)){n.handler&&(n=(o=n).handler,i=o.selector),i&&S.find.matchesSelector(re,i),n.guid||(n.guid=S.guid++),(u=v.events)||(u=v.events=Object.create(null)),(a=v.handle)||(a=v.handle=function(e){return"undefined"!=typeof S&&S.event.triggered!==e.type?S.event.dispatch.apply(t,arguments):void 0}),l=(e=(e||"").match(P)||[""]).length;while(l--)d=g=(s=be.exec(e[l])||[])[1],h=(s[2]||"").split(".").sort(),d&&(f=S.event.special[d]||{},d=(i?f.delegateType:f.bindType)||d,f=S.event.special[d]||{},c=S.extend({type:d,origType:g,data:r,handler:n,guid:n.guid,selector:i,needsContext:i&&S.expr.match.needsContext.test(i),namespace:h.join(".")},o),(p=u[d])||((p=u[d]=[]).delegateCount=0,f.setup&&!1!==f.setup.call(t,r,h,a)||t.addEventListener&&t.addEventListener(d,a)),f.add&&(f.add.call(t,c),c.handler.guid||(c.handler.guid=n.guid)),i?p.splice(p.delegateCount++,0,c):p.push(c),S.event.global[d]=!0)}},remove:function(e,t,n,r,i){var o,a,s,u,l,c,f,p,d,h,g,v=Y.hasData(e)&&Y.get(e);if(v&&(u=v.events)){l=(t=(t||"").match(P)||[""]).length;while(l--)if(d=g=(s=be.exec(t[l])||[])[1],h=(s[2]||"").split(".").sort(),d){f=S.event.special[d]||{},p=u[d=(r?f.delegateType:f.bindType)||d]||[],s=s[2]&&new RegExp("(^|\\.)"+h.join("\\.(?:.*\\.|)")+"(\\.|$)"),a=o=p.length;while(o--)c=p[o],!i&&g!==c.origType||n&&n.guid!==c.guid||s&&!s.test(c.namespace)||r&&r!==c.selector&&("**"!==r||!c.selector)||(p.splice(o,1),c.selector&&p.delegateCount--,f.remove&&f.remove.call(e,c));a&&!p.length&&(f.teardown&&!1!==f.teardown.call(e,h,v.handle)||S.removeEvent(e,d,v.handle),delete u[d])}else for(d in u)S.event.remove(e,d+t[l],n,r,!0);S.isEmptyObject(u)&&Y.remove(e,"handle events")}},dispatch:function(e){var t,n,r,i,o,a,s=new Array(arguments.length),u=S.event.fix(e),l=(Y.get(this,"events")||Object.create(null))[u.type]||[],c=S.event.special[u.type]||{};for(s[0]=u,t=1;t<arguments.length;t++)s[t]=arguments[t];if(u.delegateTarget=this,!c.preDispatch||!1!==c.preDispatch.call(this,u)){a=S.event.handlers.call(this,u,l),t=0;while((i=a[t++])&&!u.isPropagationStopped()){u.currentTarget=i.elem,n=0;while((o=i.handlers[n++])&&!u.isImmediatePropagationStopped())u.rnamespace&&!1!==o.namespace&&!u.rnamespace.test(o.namespace)||(u.handleObj=o,u.data=o.data,void 0!==(r=((S.event.special[o.origType]||{}).handle||o.handler).apply(i.elem,s))&&!1===(u.result=r)&&(u.preventDefault(),u.stopPropagation()))}return c.postDispatch&&c.postDispatch.call(this,u),u.result}},handlers:function(e,t){var n,r,i,o,a,s=[],u=t.delegateCount,l=e.target;if(u&&l.nodeType&&!("click"===e.type&&1<=e.button))for(;l!==this;l=l.parentNode||this)if(1===l.nodeType&&("click"!==e.type||!0!==l.disabled)){for(o=[],a={},n=0;n<u;n++)void 0===a[i=(r=t[n]).selector+" "]&&(a[i]=r.needsContext?-1<S(i,this).index(l):S.find(i,this,null,[l]).length),a[i]&&o.push(r);o.length&&s.push({elem:l,handlers:o})}return l=this,u<t.length&&s.push({elem:l,handlers:t.slice(u)}),s},addProp:function(t,e){Object.defineProperty(S.Event.prototype,t,{enumerable:!0,configurable:!0,get:m(e)?function(){if(this.originalEvent)return e(this.originalEvent)}:function(){if(this.originalEvent)return this.originalEvent[t]},set:function(e){Object.defineProperty(this,t,{enumerable:!0,configurable:!0,writable:!0,value:e})}})},fix:function(e){return e[S.expando]?e:new S.Event(e)},special:{load:{noBubble:!0},click:{setup:function(e){var t=this||e;return pe.test(t.type)&&t.click&&A(t,"input")&&Se(t,"click",we),!1},trigger:function(e){var t=this||e;return pe.test(t.type)&&t.click&&A(t,"input")&&Se(t,"click"),!0},_default:function(e){var t=e.target;return pe.test(t.type)&&t.click&&A(t,"input")&&Y.get(t,"click")||A(t,"a")}},beforeunload:{postDispatch:function(e){void 0!==e.result&&e.originalEvent&&(e.originalEvent.returnValue=e.result)}}}},S.removeEvent=function(e,t,n){e.removeEventListener&&e.removeEventListener(t,n)},S.Event=function(e,t){if(!(this instanceof S.Event))return new S.Event(e,t);e&&e.type?(this.originalEvent=e,this.type=e.type,this.isDefaultPrevented=e.defaultPrevented||void 0===e.defaultPrevented&&!1===e.returnValue?we:Te,this.target=e.target&&3===e.target.nodeType?e.target.parentNode:e.target,this.currentTarget=e.currentTarget,this.relatedTarget=e.relatedTarget):this.type=e,t&&S.extend(this,t),this.timeStamp=e&&e.timeStamp||Date.now(),this[S.expando]=!0},S.Event.prototype={constructor:S.Event,isDefaultPrevented:Te,isPropagationStopped:Te,isImmediatePropagationStopped:Te,isSimulated:!1,preventDefault:function(){var e=this.originalEvent;this.isDefaultPrevented=we,e&&!this.isSimulated&&e.preventDefault()},stopPropagation:function(){var e=this.originalEvent;this.isPropagationStopped=we,e&&!this.isSimulated&&e.stopPropagation()},stopImmediatePropagation:function(){var e=this.originalEvent;this.isImmediatePropagationStopped=we,e&&!this.isSimulated&&e.stopImmediatePropagation(),this.stopPropagation()}},S.each({altKey:!0,bubbles:!0,cancelable:!0,changedTouches:!0,ctrlKey:!0,detail:!0,eventPhase:!0,metaKey:!0,pageX:!0,pageY:!0,shiftKey:!0,view:!0,"char":!0,code:!0,charCode:!0,key:!0,keyCode:!0,button:!0,buttons:!0,clientX:!0,clientY:!0,offsetX:!0,offsetY:!0,pointerId:!0,pointerType:!0,screenX:!0,screenY:!0,targetTouches:!0,toElement:!0,touches:!0,which:!0},S.event.addProp),S.each({focus:"focusin",blur:"focusout"},function(e,t){S.event.special[e]={setup:function(){return Se(this,e,Ce),!1},trigger:function(){return Se(this,e),!0},_default:function(){return!0},delegateType:t}}),S.each({mouseenter:"mouseover",mouseleave:"mouseout",pointerenter:"pointerover",pointerleave:"pointerout"},function(e,i){S.event.special[e]={delegateType:i,bindType:i,handle:function(e){var t,n=e.relatedTarget,r=e.handleObj;return n&&(n===this||S.contains(this,n))||(e.type=r.origType,t=r.handler.apply(this,arguments),e.type=i),t}}}),S.fn.extend({on:function(e,t,n,r){return Ee(this,e,t,n,r)},one:function(e,t,n,r){return Ee(this,e,t,n,r,1)},off:function(e,t,n){var r,i;if(e&&e.preventDefault&&e.handleObj)return r=e.handleObj,S(e.delegateTarget).off(r.namespace?r.origType+"."+r.namespace:r.origType,r.selector,r.handler),this;if("object"==typeof e){for(i in e)this.off(i,t,e[i]);return this}return!1!==t&&"function"!=typeof t||(n=t,t=void 0),!1===n&&(n=Te),this.each(function(){S.event.remove(this,e,n,t)})}});var ke=/<script|<style|<link/i,Ae=/checked\s*(?:[^=]|=\s*.checked.)/i,Ne=/^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;function je(e,t){return A(e,"table")&&A(11!==t.nodeType?t:t.firstChild,"tr")&&S(e).children("tbody")[0]||e}function De(e){return e.type=(null!==e.getAttribute("type"))+"/"+e.type,e}function qe(e){return"true/"===(e.type||"").slice(0,5)?e.type=e.type.slice(5):e.removeAttribute("type"),e}function Le(e,t){var n,r,i,o,a,s;if(1===t.nodeType){if(Y.hasData(e)&&(s=Y.get(e).events))for(i in Y.remove(t,"handle events"),s)for(n=0,r=s[i].length;n<r;n++)S.event.add(t,i,s[i][n]);Q.hasData(e)&&(o=Q.access(e),a=S.extend({},o),Q.set(t,a))}}function He(n,r,i,o){r=g(r);var e,t,a,s,u,l,c=0,f=n.length,p=f-1,d=r[0],h=m(d);if(h||1<f&&"string"==typeof d&&!y.checkClone&&Ae.test(d))return n.each(function(e){var t=n.eq(e);h&&(r[0]=d.call(this,e,t.html())),He(t,r,i,o)});if(f&&(t=(e=xe(r,n[0].ownerDocument,!1,n,o)).firstChild,1===e.childNodes.length&&(e=t),t||o)){for(s=(a=S.map(ve(e,"script"),De)).length;c<f;c++)u=e,c!==p&&(u=S.clone(u,!0,!0),s&&S.merge(a,ve(u,"script"))),i.call(n[c],u,c);if(s)for(l=a[a.length-1].ownerDocument,S.map(a,qe),c=0;c<s;c++)u=a[c],he.test(u.type||"")&&!Y.access(u,"globalEval")&&S.contains(l,u)&&(u.src&&"module"!==(u.type||"").toLowerCase()?S._evalUrl&&!u.noModule&&S._evalUrl(u.src,{nonce:u.nonce||u.getAttribute("nonce")},l):b(u.textContent.replace(Ne,""),u,l))}return n}function Oe(e,t,n){for(var r,i=t?S.filter(t,e):e,o=0;null!=(r=i[o]);o++)n||1!==r.nodeType||S.cleanData(ve(r)),r.parentNode&&(n&&ie(r)&&ye(ve(r,"script")),r.parentNode.removeChild(r));return e}S.extend({htmlPrefilter:function(e){return e},clone:function(e,t,n){var r,i,o,a,s,u,l,c=e.cloneNode(!0),f=ie(e);if(!(y.noCloneChecked||1!==e.nodeType&&11!==e.nodeType||S.isXMLDoc(e)))for(a=ve(c),r=0,i=(o=ve(e)).length;r<i;r++)s=o[r],u=a[r],void 0,"input"===(l=u.nodeName.toLowerCase())&&pe.test(s.type)?u.checked=s.checked:"input"!==l&&"textarea"!==l||(u.defaultValue=s.defaultValue);if(t)if(n)for(o=o||ve(e),a=a||ve(c),r=0,i=o.length;r<i;r++)Le(o[r],a[r]);else Le(e,c);return 0<(a=ve(c,"script")).length&&ye(a,!f&&ve(e,"script")),c},cleanData:function(e){for(var t,n,r,i=S.event.special,o=0;void 0!==(n=e[o]);o++)if(V(n)){if(t=n[Y.expando]){if(t.events)for(r in t.events)i[r]?S.event.remove(n,r):S.removeEvent(n,r,t.handle);n[Y.expando]=void 0}n[Q.expando]&&(n[Q.expando]=void 0)}}}),S.fn.extend({detach:function(e){return Oe(this,e,!0)},remove:function(e){return Oe(this,e)},text:function(e){return $(this,function(e){return void 0===e?S.text(this):this.empty().each(function(){1!==this.nodeType&&11!==this.nodeType&&9!==this.nodeType||(this.textContent=e)})},null,e,arguments.length)},append:function(){return He(this,arguments,function(e){1!==this.nodeType&&11!==this.nodeType&&9!==this.nodeType||je(this,e).appendChild(e)})},prepend:function(){return He(this,arguments,function(e){if(1===this.nodeType||11===this.nodeType||9===this.nodeType){var t=je(this,e);t.insertBefore(e,t.firstChild)}})},before:function(){return He(this,arguments,function(e){this.parentNode&&this.parentNode.insertBefore(e,this)})},after:function(){return He(this,arguments,function(e){this.parentNode&&this.parentNode.insertBefore(e,this.nextSibling)})},empty:function(){for(var e,t=0;null!=(e=this[t]);t++)1===e.nodeType&&(S.cleanData(ve(e,!1)),e.textContent="");return this},clone:function(e,t){return e=null!=e&&e,t=null==t?e:t,this.map(function(){return S.clone(this,e,t)})},html:function(e){return $(this,function(e){var t=this[0]||{},n=0,r=this.length;if(void 0===e&&1===t.nodeType)return t.innerHTML;if("string"==typeof e&&!ke.test(e)&&!ge[(de.exec(e)||["",""])[1].toLowerCase()]){e=S.htmlPrefilter(e);try{for(;n<r;n++)1===(t=this[n]||{}).nodeType&&(S.cleanData(ve(t,!1)),t.innerHTML=e);t=0}catch(e){}}t&&this.empty().append(e)},null,e,arguments.length)},replaceWith:function(){var n=[];return He(this,arguments,function(e){var t=this.parentNode;S.inArray(this,n)<0&&(S.cleanData(ve(this)),t&&t.replaceChild(e,this))},n)}}),S.each({appendTo:"append",prependTo:"prepend",insertBefore:"before",insertAfter:"after",replaceAll:"replaceWith"},function(e,a){S.fn[e]=function(e){for(var t,n=[],r=S(e),i=r.length-1,o=0;o<=i;o++)t=o===i?this:this.clone(!0),S(r[o])[a](t),u.apply(n,t.get());return this.pushStack(n)}});var Pe=new RegExp("^("+ee+")(?!px)[a-z%]+$","i"),Re=function(e){var t=e.ownerDocument.defaultView;return t&&t.opener||(t=C),t.getComputedStyle(e)},Me=function(e,t,n){var r,i,o={};for(i in t)o[i]=e.style[i],e.style[i]=t[i];for(i in r=n.call(e),t)e.style[i]=o[i];return r},Ie=new RegExp(ne.join("|"),"i");function We(e,t,n){var r,i,o,a,s=e.style;return(n=n||Re(e))&&(""!==(a=n.getPropertyValue(t)||n[t])||ie(e)||(a=S.style(e,t)),!y.pixelBoxStyles()&&Pe.test(a)&&Ie.test(t)&&(r=s.width,i=s.minWidth,o=s.maxWidth,s.minWidth=s.maxWidth=s.width=a,a=n.width,s.width=r,s.minWidth=i,s.maxWidth=o)),void 0!==a?a+"":a}function Fe(e,t){return{get:function(){if(!e())return(this.get=t).apply(this,arguments);delete this.get}}}!function(){function e(){if(l){u.style.cssText="position:absolute;left:-11111px;width:60px;margin-top:1px;padding:0;border:0",l.style.cssText="position:relative;display:block;box-sizing:border-box;overflow:scroll;margin:auto;border:1px;padding:1px;width:60%;top:1%",re.appendChild(u).appendChild(l);var e=C.getComputedStyle(l);n="1%"!==e.top,s=12===t(e.marginLeft),l.style.right="60%",o=36===t(e.right),r=36===t(e.width),l.style.position="absolute",i=12===t(l.offsetWidth/3),re.removeChild(u),l=null}}function t(e){return Math.round(parseFloat(e))}var n,r,i,o,a,s,u=E.createElement("div"),l=E.createElement("div");l.style&&(l.style.backgroundClip="content-box",l.cloneNode(!0).style.backgroundClip="",y.clearCloneStyle="content-box"===l.style.backgroundClip,S.extend(y,{boxSizingReliable:function(){return e(),r},pixelBoxStyles:function(){return e(),o},pixelPosition:function(){return e(),n},reliableMarginLeft:function(){return e(),s},scrollboxSize:function(){return e(),i},reliableTrDimensions:function(){var e,t,n,r;return null==a&&(e=E.createElement("table"),t=E.createElement("tr"),n=E.createElement("div"),e.style.cssText="position:absolute;left:-11111px;border-collapse:separate",t.style.cssText="border:1px solid",t.style.height="1px",n.style.height="9px",n.style.display="block",re.appendChild(e).appendChild(t).appendChild(n),r=C.getComputedStyle(t),a=parseInt(r.height,10)+parseInt(r.borderTopWidth,10)+parseInt(r.borderBottomWidth,10)===t.offsetHeight,re.removeChild(e)),a}}))}();var Be=["Webkit","Moz","ms"],$e=E.createElement("div").style,_e={};function ze(e){var t=S.cssProps[e]||_e[e];return t||(e in $e?e:_e[e]=function(e){var t=e[0].toUpperCase()+e.slice(1),n=Be.length;while(n--)if((e=Be[n]+t)in $e)return e}(e)||e)}var Ue=/^(none|table(?!-c[ea]).+)/,Xe=/^--/,Ve={position:"absolute",visibility:"hidden",display:"block"},Ge={letterSpacing:"0",fontWeight:"400"};function Ye(e,t,n){var r=te.exec(t);return r?Math.max(0,r[2]-(n||0))+(r[3]||"px"):t}function Qe(e,t,n,r,i,o){var a="width"===t?1:0,s=0,u=0;if(n===(r?"border":"content"))return 0;for(;a<4;a+=2)"margin"===n&&(u+=S.css(e,n+ne[a],!0,i)),r?("content"===n&&(u-=S.css(e,"padding"+ne[a],!0,i)),"margin"!==n&&(u-=S.css(e,"border"+ne[a]+"Width",!0,i))):(u+=S.css(e,"padding"+ne[a],!0,i),"padding"!==n?u+=S.css(e,"border"+ne[a]+"Width",!0,i):s+=S.css(e,"border"+ne[a]+"Width",!0,i));return!r&&0<=o&&(u+=Math.max(0,Math.ceil(e["offset"+t[0].toUpperCase()+t.slice(1)]-o-u-s-.5))||0),u}function Je(e,t,n){var r=Re(e),i=(!y.boxSizingReliable()||n)&&"border-box"===S.css(e,"boxSizing",!1,r),o=i,a=We(e,t,r),s="offset"+t[0].toUpperCase()+t.slice(1);if(Pe.test(a)){if(!n)return a;a="auto"}return(!y.boxSizingReliable()&&i||!y.reliableTrDimensions()&&A(e,"tr")||"auto"===a||!parseFloat(a)&&"inline"===S.css(e,"display",!1,r))&&e.getClientRects().length&&(i="border-box"===S.css(e,"boxSizing",!1,r),(o=s in e)&&(a=e[s])),(a=parseFloat(a)||0)+Qe(e,t,n||(i?"border":"content"),o,r,a)+"px"}function Ke(e,t,n,r,i){return new Ke.prototype.init(e,t,n,r,i)}S.extend({cssHooks:{opacity:{get:function(e,t){if(t){var n=We(e,"opacity");return""===n?"1":n}}}},cssNumber:{animationIterationCount:!0,columnCount:!0,fillOpacity:!0,flexGrow:!0,flexShrink:!0,fontWeight:!0,gridArea:!0,gridColumn:!0,gridColumnEnd:!0,gridColumnStart:!0,gridRow:!0,gridRowEnd:!0,gridRowStart:!0,lineHeight:!0,opacity:!0,order:!0,orphans:!0,widows:!0,zIndex:!0,zoom:!0},cssProps:{},style:function(e,t,n,r){if(e&&3!==e.nodeType&&8!==e.nodeType&&e.style){var i,o,a,s=X(t),u=Xe.test(t),l=e.style;if(u||(t=ze(s)),a=S.cssHooks[t]||S.cssHooks[s],void 0===n)return a&&"get"in a&&void 0!==(i=a.get(e,!1,r))?i:l[t];"string"===(o=typeof n)&&(i=te.exec(n))&&i[1]&&(n=se(e,t,i),o="number"),null!=n&&n==n&&("number"!==o||u||(n+=i&&i[3]||(S.cssNumber[s]?"":"px")),y.clearCloneStyle||""!==n||0!==t.indexOf("background")||(l[t]="inherit"),a&&"set"in a&&void 0===(n=a.set(e,n,r))||(u?l.setProperty(t,n):l[t]=n))}},css:function(e,t,n,r){var i,o,a,s=X(t);return Xe.test(t)||(t=ze(s)),(a=S.cssHooks[t]||S.cssHooks[s])&&"get"in a&&(i=a.get(e,!0,n)),void 0===i&&(i=We(e,t,r)),"normal"===i&&t in Ge&&(i=Ge[t]),""===n||n?(o=parseFloat(i),!0===n||isFinite(o)?o||0:i):i}}),S.each(["height","width"],function(e,u){S.cssHooks[u]={get:function(e,t,n){if(t)return!Ue.test(S.css(e,"display"))||e.getClientRects().length&&e.getBoundingClientRect().width?Je(e,u,n):Me(e,Ve,function(){return Je(e,u,n)})},set:function(e,t,n){var r,i=Re(e),o=!y.scrollboxSize()&&"absolute"===i.position,a=(o||n)&&"border-box"===S.css(e,"boxSizing",!1,i),s=n?Qe(e,u,n,a,i):0;return a&&o&&(s-=Math.ceil(e["offset"+u[0].toUpperCase()+u.slice(1)]-parseFloat(i[u])-Qe(e,u,"border",!1,i)-.5)),s&&(r=te.exec(t))&&"px"!==(r[3]||"px")&&(e.style[u]=t,t=S.css(e,u)),Ye(0,t,s)}}}),S.cssHooks.marginLeft=Fe(y.reliableMarginLeft,function(e,t){if(t)return(parseFloat(We(e,"marginLeft"))||e.getBoundingClientRect().left-Me(e,{marginLeft:0},function(){return e.getBoundingClientRect().left}))+"px"}),S.each({margin:"",padding:"",border:"Width"},function(i,o){S.cssHooks[i+o]={expand:function(e){for(var t=0,n={},r="string"==typeof e?e.split(" "):[e];t<4;t++)n[i+ne[t]+o]=r[t]||r[t-2]||r[0];return n}},"margin"!==i&&(S.cssHooks[i+o].set=Ye)}),S.fn.extend({css:function(e,t){return $(this,function(e,t,n){var r,i,o={},a=0;if(Array.isArray(t)){for(r=Re(e),i=t.length;a<i;a++)o[t[a]]=S.css(e,t[a],!1,r);return o}return void 0!==n?S.style(e,t,n):S.css(e,t)},e,t,1<arguments.length)}}),((S.Tween=Ke).prototype={constructor:Ke,init:function(e,t,n,r,i,o){this.elem=e,this.prop=n,this.easing=i||S.easing._default,this.options=t,this.start=this.now=this.cur(),this.end=r,this.unit=o||(S.cssNumber[n]?"":"px")},cur:function(){var e=Ke.propHooks[this.prop];return e&&e.get?e.get(this):Ke.propHooks._default.get(this)},run:function(e){var t,n=Ke.propHooks[this.prop];return this.options.duration?this.pos=t=S.easing[this.easing](e,this.options.duration*e,0,1,this.options.duration):this.pos=t=e,this.now=(this.end-this.start)*t+this.start,this.options.step&&this.options.step.call(this.elem,this.now,this),n&&n.set?n.set(this):Ke.propHooks._default.set(this),this}}).init.prototype=Ke.prototype,(Ke.propHooks={_default:{get:function(e){var t;return 1!==e.elem.nodeType||null!=e.elem[e.prop]&&null==e.elem.style[e.prop]?e.elem[e.prop]:(t=S.css(e.elem,e.prop,""))&&"auto"!==t?t:0},set:function(e){S.fx.step[e.prop]?S.fx.step[e.prop](e):1!==e.elem.nodeType||!S.cssHooks[e.prop]&&null==e.elem.style[ze(e.prop)]?e.elem[e.prop]=e.now:S.style(e.elem,e.prop,e.now+e.unit)}}}).scrollTop=Ke.propHooks.scrollLeft={set:function(e){e.elem.nodeType&&e.elem.parentNode&&(e.elem[e.prop]=e.now)}},S.easing={linear:function(e){return e},swing:function(e){return.5-Math.cos(e*Math.PI)/2},_default:"swing"},S.fx=Ke.prototype.init,S.fx.step={};var Ze,et,tt,nt,rt=/^(?:toggle|show|hide)$/,it=/queueHooks$/;function ot(){et&&(!1===E.hidden&&C.requestAnimationFrame?C.requestAnimationFrame(ot):C.setTimeout(ot,S.fx.interval),S.fx.tick())}function at(){return C.setTimeout(function(){Ze=void 0}),Ze=Date.now()}function st(e,t){var n,r=0,i={height:e};for(t=t?1:0;r<4;r+=2-t)i["margin"+(n=ne[r])]=i["padding"+n]=e;return t&&(i.opacity=i.width=e),i}function ut(e,t,n){for(var r,i=(lt.tweeners[t]||[]).concat(lt.tweeners["*"]),o=0,a=i.length;o<a;o++)if(r=i[o].call(n,t,e))return r}function lt(o,e,t){var n,a,r=0,i=lt.prefilters.length,s=S.Deferred().always(function(){delete u.elem}),u=function(){if(a)return!1;for(var e=Ze||at(),t=Math.max(0,l.startTime+l.duration-e),n=1-(t/l.duration||0),r=0,i=l.tweens.length;r<i;r++)l.tweens[r].run(n);return s.notifyWith(o,[l,n,t]),n<1&&i?t:(i||s.notifyWith(o,[l,1,0]),s.resolveWith(o,[l]),!1)},l=s.promise({elem:o,props:S.extend({},e),opts:S.extend(!0,{specialEasing:{},easing:S.easing._default},t),originalProperties:e,originalOptions:t,startTime:Ze||at(),duration:t.duration,tweens:[],createTween:function(e,t){var n=S.Tween(o,l.opts,e,t,l.opts.specialEasing[e]||l.opts.easing);return l.tweens.push(n),n},stop:function(e){var t=0,n=e?l.tweens.length:0;if(a)return this;for(a=!0;t<n;t++)l.tweens[t].run(1);return e?(s.notifyWith(o,[l,1,0]),s.resolveWith(o,[l,e])):s.rejectWith(o,[l,e]),this}}),c=l.props;for(!function(e,t){var n,r,i,o,a;for(n in e)if(i=t[r=X(n)],o=e[n],Array.isArray(o)&&(i=o[1],o=e[n]=o[0]),n!==r&&(e[r]=o,delete e[n]),(a=S.cssHooks[r])&&"expand"in a)for(n in o=a.expand(o),delete e[r],o)n in e||(e[n]=o[n],t[n]=i);else t[r]=i}(c,l.opts.specialEasing);r<i;r++)if(n=lt.prefilters[r].call(l,o,c,l.opts))return m(n.stop)&&(S._queueHooks(l.elem,l.opts.queue).stop=n.stop.bind(n)),n;return S.map(c,ut,l),m(l.opts.start)&&l.opts.start.call(o,l),l.progress(l.opts.progress).done(l.opts.done,l.opts.complete).fail(l.opts.fail).always(l.opts.always),S.fx.timer(S.extend(u,{elem:o,anim:l,queue:l.opts.queue})),l}S.Animation=S.extend(lt,{tweeners:{"*":[function(e,t){var n=this.createTween(e,t);return se(n.elem,e,te.exec(t),n),n}]},tweener:function(e,t){m(e)?(t=e,e=["*"]):e=e.match(P);for(var n,r=0,i=e.length;r<i;r++)n=e[r],lt.tweeners[n]=lt.tweeners[n]||[],lt.tweeners[n].unshift(t)},prefilters:[function(e,t,n){var r,i,o,a,s,u,l,c,f="width"in t||"height"in t,p=this,d={},h=e.style,g=e.nodeType&&ae(e),v=Y.get(e,"fxshow");for(r in n.queue||(null==(a=S._queueHooks(e,"fx")).unqueued&&(a.unqueued=0,s=a.empty.fire,a.empty.fire=function(){a.unqueued||s()}),a.unqueued++,p.always(function(){p.always(function(){a.unqueued--,S.queue(e,"fx").length||a.empty.fire()})})),t)if(i=t[r],rt.test(i)){if(delete t[r],o=o||"toggle"===i,i===(g?"hide":"show")){if("show"!==i||!v||void 0===v[r])continue;g=!0}d[r]=v&&v[r]||S.style(e,r)}if((u=!S.isEmptyObject(t))||!S.isEmptyObject(d))for(r in f&&1===e.nodeType&&(n.overflow=[h.overflow,h.overflowX,h.overflowY],null==(l=v&&v.display)&&(l=Y.get(e,"display")),"none"===(c=S.css(e,"display"))&&(l?c=l:(le([e],!0),l=e.style.display||l,c=S.css(e,"display"),le([e]))),("inline"===c||"inline-block"===c&&null!=l)&&"none"===S.css(e,"float")&&(u||(p.done(function(){h.display=l}),null==l&&(c=h.display,l="none"===c?"":c)),h.display="inline-block")),n.overflow&&(h.overflow="hidden",p.always(function(){h.overflow=n.overflow[0],h.overflowX=n.overflow[1],h.overflowY=n.overflow[2]})),u=!1,d)u||(v?"hidden"in v&&(g=v.hidden):v=Y.access(e,"fxshow",{display:l}),o&&(v.hidden=!g),g&&le([e],!0),p.done(function(){for(r in g||le([e]),Y.remove(e,"fxshow"),d)S.style(e,r,d[r])})),u=ut(g?v[r]:0,r,p),r in v||(v[r]=u.start,g&&(u.end=u.start,u.start=0))}],prefilter:function(e,t){t?lt.prefilters.unshift(e):lt.prefilters.push(e)}}),S.speed=function(e,t,n){var r=e&&"object"==typeof e?S.extend({},e):{complete:n||!n&&t||m(e)&&e,duration:e,easing:n&&t||t&&!m(t)&&t};return S.fx.off?r.duration=0:"number"!=typeof r.duration&&(r.duration in S.fx.speeds?r.duration=S.fx.speeds[r.duration]:r.duration=S.fx.speeds._default),null!=r.queue&&!0!==r.queue||(r.queue="fx"),r.old=r.complete,r.complete=function(){m(r.old)&&r.old.call(this),r.queue&&S.dequeue(this,r.queue)},r},S.fn.extend({fadeTo:function(e,t,n,r){return this.filter(ae).css("opacity",0).show().end().animate({opacity:t},e,n,r)},animate:function(t,e,n,r){var i=S.isEmptyObject(t),o=S.speed(e,n,r),a=function(){var e=lt(this,S.extend({},t),o);(i||Y.get(this,"finish"))&&e.stop(!0)};return a.finish=a,i||!1===o.queue?this.each(a):this.queue(o.queue,a)},stop:function(i,e,o){var a=function(e){var t=e.stop;delete e.stop,t(o)};return"string"!=typeof i&&(o=e,e=i,i=void 0),e&&this.queue(i||"fx",[]),this.each(function(){var e=!0,t=null!=i&&i+"queueHooks",n=S.timers,r=Y.get(this);if(t)r[t]&&r[t].stop&&a(r[t]);else for(t in r)r[t]&&r[t].stop&&it.test(t)&&a(r[t]);for(t=n.length;t--;)n[t].elem!==this||null!=i&&n[t].queue!==i||(n[t].anim.stop(o),e=!1,n.splice(t,1));!e&&o||S.dequeue(this,i)})},finish:function(a){return!1!==a&&(a=a||"fx"),this.each(function(){var e,t=Y.get(this),n=t[a+"queue"],r=t[a+"queueHooks"],i=S.timers,o=n?n.length:0;for(t.finish=!0,S.queue(this,a,[]),r&&r.stop&&r.stop.call(this,!0),e=i.length;e--;)i[e].elem===this&&i[e].queue===a&&(i[e].anim.stop(!0),i.splice(e,1));for(e=0;e<o;e++)n[e]&&n[e].finish&&n[e].finish.call(this);delete t.finish})}}),S.each(["toggle","show","hide"],function(e,r){var i=S.fn[r];S.fn[r]=function(e,t,n){return null==e||"boolean"==typeof e?i.apply(this,arguments):this.animate(st(r,!0),e,t,n)}}),S.each({slideDown:st("show"),slideUp:st("hide"),slideToggle:st("toggle"),fadeIn:{opacity:"show"},fadeOut:{opacity:"hide"},fadeToggle:{opacity:"toggle"}},function(e,r){S.fn[e]=function(e,t,n){return this.animate(r,e,t,n)}}),S.timers=[],S.fx.tick=function(){var e,t=0,n=S.timers;for(Ze=Date.now();t<n.length;t++)(e=n[t])()||n[t]!==e||n.splice(t--,1);n.length||S.fx.stop(),Ze=void 0},S.fx.timer=function(e){S.timers.push(e),S.fx.start()},S.fx.interval=13,S.fx.start=function(){et||(et=!0,ot())},S.fx.stop=function(){et=null},S.fx.speeds={slow:600,fast:200,_default:400},S.fn.delay=function(r,e){return r=S.fx&&S.fx.speeds[r]||r,e=e||"fx",this.queue(e,function(e,t){var n=C.setTimeout(e,r);t.stop=function(){C.clearTimeout(n)}})},tt=E.createElement("input"),nt=E.createElement("select").appendChild(E.createElement("option")),tt.type="checkbox",y.checkOn=""!==tt.value,y.optSelected=nt.selected,(tt=E.createElement("input")).value="t",tt.type="radio",y.radioValue="t"===tt.value;var ct,ft=S.expr.attrHandle;S.fn.extend({attr:function(e,t){return $(this,S.attr,e,t,1<arguments.length)},removeAttr:function(e){return this.each(function(){S.removeAttr(this,e)})}}),S.extend({attr:function(e,t,n){var r,i,o=e.nodeType;if(3!==o&&8!==o&&2!==o)return"undefined"==typeof e.getAttribute?S.prop(e,t,n):(1===o&&S.isXMLDoc(e)||(i=S.attrHooks[t.toLowerCase()]||(S.expr.match.bool.test(t)?ct:void 0)),void 0!==n?null===n?void S.removeAttr(e,t):i&&"set"in i&&void 0!==(r=i.set(e,n,t))?r:(e.setAttribute(t,n+""),n):i&&"get"in i&&null!==(r=i.get(e,t))?r:null==(r=S.find.attr(e,t))?void 0:r)},attrHooks:{type:{set:function(e,t){if(!y.radioValue&&"radio"===t&&A(e,"input")){var n=e.value;return e.setAttribute("type",t),n&&(e.value=n),t}}}},removeAttr:function(e,t){var n,r=0,i=t&&t.match(P);if(i&&1===e.nodeType)while(n=i[r++])e.removeAttribute(n)}}),ct={set:function(e,t,n){return!1===t?S.removeAttr(e,n):e.setAttribute(n,n),n}},S.each(S.expr.match.bool.source.match(/\w+/g),function(e,t){var a=ft[t]||S.find.attr;ft[t]=function(e,t,n){var r,i,o=t.toLowerCase();return n||(i=ft[o],ft[o]=r,r=null!=a(e,t,n)?o:null,ft[o]=i),r}});var pt=/^(?:input|select|textarea|button)$/i,dt=/^(?:a|area)$/i;function ht(e){return(e.match(P)||[]).join(" ")}function gt(e){return e.getAttribute&&e.getAttribute("class")||""}function vt(e){return Array.isArray(e)?e:"string"==typeof e&&e.match(P)||[]}S.fn.extend({prop:function(e,t){return $(this,S.prop,e,t,1<arguments.length)},removeProp:function(e){return this.each(function(){delete this[S.propFix[e]||e]})}}),S.extend({prop:function(e,t,n){var r,i,o=e.nodeType;if(3!==o&&8!==o&&2!==o)return 1===o&&S.isXMLDoc(e)||(t=S.propFix[t]||t,i=S.propHooks[t]),void 0!==n?i&&"set"in i&&void 0!==(r=i.set(e,n,t))?r:e[t]=n:i&&"get"in i&&null!==(r=i.get(e,t))?r:e[t]},propHooks:{tabIndex:{get:function(e){var t=S.find.attr(e,"tabindex");return t?parseInt(t,10):pt.test(e.nodeName)||dt.test(e.nodeName)&&e.href?0:-1}}},propFix:{"for":"htmlFor","class":"className"}}),y.optSelected||(S.propHooks.selected={get:function(e){var t=e.parentNode;return t&&t.parentNode&&t.parentNode.selectedIndex,null},set:function(e){var t=e.parentNode;t&&(t.selectedIndex,t.parentNode&&t.parentNode.selectedIndex)}}),S.each(["tabIndex","readOnly","maxLength","cellSpacing","cellPadding","rowSpan","colSpan","useMap","frameBorder","contentEditable"],function(){S.propFix[this.toLowerCase()]=this}),S.fn.extend({addClass:function(t){var e,n,r,i,o,a,s,u=0;if(m(t))return this.each(function(e){S(this).addClass(t.call(this,e,gt(this)))});if((e=vt(t)).length)while(n=this[u++])if(i=gt(n),r=1===n.nodeType&&" "+ht(i)+" "){a=0;while(o=e[a++])r.indexOf(" "+o+" ")<0&&(r+=o+" ");i!==(s=ht(r))&&n.setAttribute("class",s)}return this},removeClass:function(t){var e,n,r,i,o,a,s,u=0;if(m(t))return this.each(function(e){S(this).removeClass(t.call(this,e,gt(this)))});if(!arguments.length)return this.attr("class","");if((e=vt(t)).length)while(n=this[u++])if(i=gt(n),r=1===n.nodeType&&" "+ht(i)+" "){a=0;while(o=e[a++])while(-1<r.indexOf(" "+o+" "))r=r.replace(" "+o+" "," ");i!==(s=ht(r))&&n.setAttribute("class",s)}return this},toggleClass:function(i,t){var o=typeof i,a="string"===o||Array.isArray(i);return"boolean"==typeof t&&a?t?this.addClass(i):this.removeClass(i):m(i)?this.each(function(e){S(this).toggleClass(i.call(this,e,gt(this),t),t)}):this.each(function(){var e,t,n,r;if(a){t=0,n=S(this),r=vt(i);while(e=r[t++])n.hasClass(e)?n.removeClass(e):n.addClass(e)}else void 0!==i&&"boolean"!==o||((e=gt(this))&&Y.set(this,"__className__",e),this.setAttribute&&this.setAttribute("class",e||!1===i?"":Y.get(this,"__className__")||""))})},hasClass:function(e){var t,n,r=0;t=" "+e+" ";while(n=this[r++])if(1===n.nodeType&&-1<(" "+ht(gt(n))+" ").indexOf(t))return!0;return!1}});var yt=/\r/g;S.fn.extend({val:function(n){var r,e,i,t=this[0];return arguments.length?(i=m(n),this.each(function(e){var t;1===this.nodeType&&(null==(t=i?n.call(this,e,S(this).val()):n)?t="":"number"==typeof t?t+="":Array.isArray(t)&&(t=S.map(t,function(e){return null==e?"":e+""})),(r=S.valHooks[this.type]||S.valHooks[this.nodeName.toLowerCase()])&&"set"in r&&void 0!==r.set(this,t,"value")||(this.value=t))})):t?(r=S.valHooks[t.type]||S.valHooks[t.nodeName.toLowerCase()])&&"get"in r&&void 0!==(e=r.get(t,"value"))?e:"string"==typeof(e=t.value)?e.replace(yt,""):null==e?"":e:void 0}}),S.extend({valHooks:{option:{get:function(e){var t=S.find.attr(e,"value");return null!=t?t:ht(S.text(e))}},select:{get:function(e){var t,n,r,i=e.options,o=e.selectedIndex,a="select-one"===e.type,s=a?null:[],u=a?o+1:i.length;for(r=o<0?u:a?o:0;r<u;r++)if(((n=i[r]).selected||r===o)&&!n.disabled&&(!n.parentNode.disabled||!A(n.parentNode,"optgroup"))){if(t=S(n).val(),a)return t;s.push(t)}return s},set:function(e,t){var n,r,i=e.options,o=S.makeArray(t),a=i.length;while(a--)((r=i[a]).selected=-1<S.inArray(S.valHooks.option.get(r),o))&&(n=!0);return n||(e.selectedIndex=-1),o}}}}),S.each(["radio","checkbox"],function(){S.valHooks[this]={set:function(e,t){if(Array.isArray(t))return e.checked=-1<S.inArray(S(e).val(),t)}},y.checkOn||(S.valHooks[this].get=function(e){return null===e.getAttribute("value")?"on":e.value})}),y.focusin="onfocusin"in C;var mt=/^(?:focusinfocus|focusoutblur)$/,xt=function(e){e.stopPropagation()};S.extend(S.event,{trigger:function(e,t,n,r){var i,o,a,s,u,l,c,f,p=[n||E],d=v.call(e,"type")?e.type:e,h=v.call(e,"namespace")?e.namespace.split("."):[];if(o=f=a=n=n||E,3!==n.nodeType&&8!==n.nodeType&&!mt.test(d+S.event.triggered)&&(-1<d.indexOf(".")&&(d=(h=d.split(".")).shift(),h.sort()),u=d.indexOf(":")<0&&"on"+d,(e=e[S.expando]?e:new S.Event(d,"object"==typeof e&&e)).isTrigger=r?2:3,e.namespace=h.join("."),e.rnamespace=e.namespace?new RegExp("(^|\\.)"+h.join("\\.(?:.*\\.|)")+"(\\.|$)"):null,e.result=void 0,e.target||(e.target=n),t=null==t?[e]:S.makeArray(t,[e]),c=S.event.special[d]||{},r||!c.trigger||!1!==c.trigger.apply(n,t))){if(!r&&!c.noBubble&&!x(n)){for(s=c.delegateType||d,mt.test(s+d)||(o=o.parentNode);o;o=o.parentNode)p.push(o),a=o;a===(n.ownerDocument||E)&&p.push(a.defaultView||a.parentWindow||C)}i=0;while((o=p[i++])&&!e.isPropagationStopped())f=o,e.type=1<i?s:c.bindType||d,(l=(Y.get(o,"events")||Object.create(null))[e.type]&&Y.get(o,"handle"))&&l.apply(o,t),(l=u&&o[u])&&l.apply&&V(o)&&(e.result=l.apply(o,t),!1===e.result&&e.preventDefault());return e.type=d,r||e.isDefaultPrevented()||c._default&&!1!==c._default.apply(p.pop(),t)||!V(n)||u&&m(n[d])&&!x(n)&&((a=n[u])&&(n[u]=null),S.event.triggered=d,e.isPropagationStopped()&&f.addEventListener(d,xt),n[d](),e.isPropagationStopped()&&f.removeEventListener(d,xt),S.event.triggered=void 0,a&&(n[u]=a)),e.result}},simulate:function(e,t,n){var r=S.extend(new S.Event,n,{type:e,isSimulated:!0});S.event.trigger(r,null,t)}}),S.fn.extend({trigger:function(e,t){return this.each(function(){S.event.trigger(e,t,this)})},triggerHandler:function(e,t){var n=this[0];if(n)return S.event.trigger(e,t,n,!0)}}),y.focusin||S.each({focus:"focusin",blur:"focusout"},function(n,r){var i=function(e){S.event.simulate(r,e.target,S.event.fix(e))};S.event.special[r]={setup:function(){var e=this.ownerDocument||this.document||this,t=Y.access(e,r);t||e.addEventListener(n,i,!0),Y.access(e,r,(t||0)+1)},teardown:function(){var e=this.ownerDocument||this.document||this,t=Y.access(e,r)-1;t?Y.access(e,r,t):(e.removeEventListener(n,i,!0),Y.remove(e,r))}}});var bt=C.location,wt={guid:Date.now()},Tt=/\?/;S.parseXML=function(e){var t,n;if(!e||"string"!=typeof e)return null;try{t=(new C.DOMParser).parseFromString(e,"text/xml")}catch(e){}return n=t&&t.getElementsByTagName("parsererror")[0],t&&!n||S.error("Invalid XML: "+(n?S.map(n.childNodes,function(e){return e.textContent}).join("\n"):e)),t};var Ct=/\[\]$/,Et=/\r?\n/g,St=/^(?:submit|button|image|reset|file)$/i,kt=/^(?:input|select|textarea|keygen)/i;function At(n,e,r,i){var t;if(Array.isArray(e))S.each(e,function(e,t){r||Ct.test(n)?i(n,t):At(n+"["+("object"==typeof t&&null!=t?e:"")+"]",t,r,i)});else if(r||"object"!==w(e))i(n,e);else for(t in e)At(n+"["+t+"]",e[t],r,i)}S.param=function(e,t){var n,r=[],i=function(e,t){var n=m(t)?t():t;r[r.length]=encodeURIComponent(e)+"="+encodeURIComponent(null==n?"":n)};if(null==e)return"";if(Array.isArray(e)||e.jquery&&!S.isPlainObject(e))S.each(e,function(){i(this.name,this.value)});else for(n in e)At(n,e[n],t,i);return r.join("&")},S.fn.extend({serialize:function(){return S.param(this.serializeArray())},serializeArray:function(){return this.map(function(){var e=S.prop(this,"elements");return e?S.makeArray(e):this}).filter(function(){var e=this.type;return this.name&&!S(this).is(":disabled")&&kt.test(this.nodeName)&&!St.test(e)&&(this.checked||!pe.test(e))}).map(function(e,t){var n=S(this).val();return null==n?null:Array.isArray(n)?S.map(n,function(e){return{name:t.name,value:e.replace(Et,"\r\n")}}):{name:t.name,value:n.replace(Et,"\r\n")}}).get()}});var Nt=/%20/g,jt=/#.*$/,Dt=/([?&])_=[^&]*/,qt=/^(.*?):[ \t]*([^\r\n]*)$/gm,Lt=/^(?:GET|HEAD)$/,Ht=/^\/\//,Ot={},Pt={},Rt="*/".concat("*"),Mt=E.createElement("a");function It(o){return function(e,t){"string"!=typeof e&&(t=e,e="*");var n,r=0,i=e.toLowerCase().match(P)||[];if(m(t))while(n=i[r++])"+"===n[0]?(n=n.slice(1)||"*",(o[n]=o[n]||[]).unshift(t)):(o[n]=o[n]||[]).push(t)}}function Wt(t,i,o,a){var s={},u=t===Pt;function l(e){var r;return s[e]=!0,S.each(t[e]||[],function(e,t){var n=t(i,o,a);return"string"!=typeof n||u||s[n]?u?!(r=n):void 0:(i.dataTypes.unshift(n),l(n),!1)}),r}return l(i.dataTypes[0])||!s["*"]&&l("*")}function Ft(e,t){var n,r,i=S.ajaxSettings.flatOptions||{};for(n in t)void 0!==t[n]&&((i[n]?e:r||(r={}))[n]=t[n]);return r&&S.extend(!0,e,r),e}Mt.href=bt.href,S.extend({active:0,lastModified:{},etag:{},ajaxSettings:{url:bt.href,type:"GET",isLocal:/^(?:about|app|app-storage|.+-extension|file|res|widget):$/.test(bt.protocol),global:!0,processData:!0,async:!0,contentType:"application/x-www-form-urlencoded; charset=UTF-8",accepts:{"*":Rt,text:"text/plain",html:"text/html",xml:"application/xml, text/xml",json:"application/json, text/javascript"},contents:{xml:/\bxml\b/,html:/\bhtml/,json:/\bjson\b/},responseFields:{xml:"responseXML",text:"responseText",json:"responseJSON"},converters:{"* text":String,"text html":!0,"text json":JSON.parse,"text xml":S.parseXML},flatOptions:{url:!0,context:!0}},ajaxSetup:function(e,t){return t?Ft(Ft(e,S.ajaxSettings),t):Ft(S.ajaxSettings,e)},ajaxPrefilter:It(Ot),ajaxTransport:It(Pt),ajax:function(e,t){"object"==typeof e&&(t=e,e=void 0),t=t||{};var c,f,p,n,d,r,h,g,i,o,v=S.ajaxSetup({},t),y=v.context||v,m=v.context&&(y.nodeType||y.jquery)?S(y):S.event,x=S.Deferred(),b=S.Callbacks("once memory"),w=v.statusCode||{},a={},s={},u="canceled",T={readyState:0,getResponseHeader:function(e){var t;if(h){if(!n){n={};while(t=qt.exec(p))n[t[1].toLowerCase()+" "]=(n[t[1].toLowerCase()+" "]||[]).concat(t[2])}t=n[e.toLowerCase()+" "]}return null==t?null:t.join(", ")},getAllResponseHeaders:function(){return h?p:null},setRequestHeader:function(e,t){return null==h&&(e=s[e.toLowerCase()]=s[e.toLowerCase()]||e,a[e]=t),this},overrideMimeType:function(e){return null==h&&(v.mimeType=e),this},statusCode:function(e){var t;if(e)if(h)T.always(e[T.status]);else for(t in e)w[t]=[w[t],e[t]];return this},abort:function(e){var t=e||u;return c&&c.abort(t),l(0,t),this}};if(x.promise(T),v.url=((e||v.url||bt.href)+"").replace(Ht,bt.protocol+"//"),v.type=t.method||t.type||v.method||v.type,v.dataTypes=(v.dataType||"*").toLowerCase().match(P)||[""],null==v.crossDomain){r=E.createElement("a");try{r.href=v.url,r.href=r.href,v.crossDomain=Mt.protocol+"//"+Mt.host!=r.protocol+"//"+r.host}catch(e){v.crossDomain=!0}}if(v.data&&v.processData&&"string"!=typeof v.data&&(v.data=S.param(v.data,v.traditional)),Wt(Ot,v,t,T),h)return T;for(i in(g=S.event&&v.global)&&0==S.active++&&S.event.trigger("ajaxStart"),v.type=v.type.toUpperCase(),v.hasContent=!Lt.test(v.type),f=v.url.replace(jt,""),v.hasContent?v.data&&v.processData&&0===(v.contentType||"").indexOf("application/x-www-form-urlencoded")&&(v.data=v.data.replace(Nt,"+")):(o=v.url.slice(f.length),v.data&&(v.processData||"string"==typeof v.data)&&(f+=(Tt.test(f)?"&":"?")+v.data,delete v.data),!1===v.cache&&(f=f.replace(Dt,"$1"),o=(Tt.test(f)?"&":"?")+"_="+wt.guid+++o),v.url=f+o),v.ifModified&&(S.lastModified[f]&&T.setRequestHeader("If-Modified-Since",S.lastModified[f]),S.etag[f]&&T.setRequestHeader("If-None-Match",S.etag[f])),(v.data&&v.hasContent&&!1!==v.contentType||t.contentType)&&T.setRequestHeader("Content-Type",v.contentType),T.setRequestHeader("Accept",v.dataTypes[0]&&v.accepts[v.dataTypes[0]]?v.accepts[v.dataTypes[0]]+("*"!==v.dataTypes[0]?", "+Rt+"; q=0.01":""):v.accepts["*"]),v.headers)T.setRequestHeader(i,v.headers[i]);if(v.beforeSend&&(!1===v.beforeSend.call(y,T,v)||h))return T.abort();if(u="abort",b.add(v.complete),T.done(v.success),T.fail(v.error),c=Wt(Pt,v,t,T)){if(T.readyState=1,g&&m.trigger("ajaxSend",[T,v]),h)return T;v.async&&0<v.timeout&&(d=C.setTimeout(function(){T.abort("timeout")},v.timeout));try{h=!1,c.send(a,l)}catch(e){if(h)throw e;l(-1,e)}}else l(-1,"No Transport");function l(e,t,n,r){var i,o,a,s,u,l=t;h||(h=!0,d&&C.clearTimeout(d),c=void 0,p=r||"",T.readyState=0<e?4:0,i=200<=e&&e<300||304===e,n&&(s=function(e,t,n){var r,i,o,a,s=e.contents,u=e.dataTypes;while("*"===u[0])u.shift(),void 0===r&&(r=e.mimeType||t.getResponseHeader("Content-Type"));if(r)for(i in s)if(s[i]&&s[i].test(r)){u.unshift(i);break}if(u[0]in n)o=u[0];else{for(i in n){if(!u[0]||e.converters[i+" "+u[0]]){o=i;break}a||(a=i)}o=o||a}if(o)return o!==u[0]&&u.unshift(o),n[o]}(v,T,n)),!i&&-1<S.inArray("script",v.dataTypes)&&S.inArray("json",v.dataTypes)<0&&(v.converters["text script"]=function(){}),s=function(e,t,n,r){var i,o,a,s,u,l={},c=e.dataTypes.slice();if(c[1])for(a in e.converters)l[a.toLowerCase()]=e.converters[a];o=c.shift();while(o)if(e.responseFields[o]&&(n[e.responseFields[o]]=t),!u&&r&&e.dataFilter&&(t=e.dataFilter(t,e.dataType)),u=o,o=c.shift())if("*"===o)o=u;else if("*"!==u&&u!==o){if(!(a=l[u+" "+o]||l["* "+o]))for(i in l)if((s=i.split(" "))[1]===o&&(a=l[u+" "+s[0]]||l["* "+s[0]])){!0===a?a=l[i]:!0!==l[i]&&(o=s[0],c.unshift(s[1]));break}if(!0!==a)if(a&&e["throws"])t=a(t);else try{t=a(t)}catch(e){return{state:"parsererror",error:a?e:"No conversion from "+u+" to "+o}}}return{state:"success",data:t}}(v,s,T,i),i?(v.ifModified&&((u=T.getResponseHeader("Last-Modified"))&&(S.lastModified[f]=u),(u=T.getResponseHeader("etag"))&&(S.etag[f]=u)),204===e||"HEAD"===v.type?l="nocontent":304===e?l="notmodified":(l=s.state,o=s.data,i=!(a=s.error))):(a=l,!e&&l||(l="error",e<0&&(e=0))),T.status=e,T.statusText=(t||l)+"",i?x.resolveWith(y,[o,l,T]):x.rejectWith(y,[T,l,a]),T.statusCode(w),w=void 0,g&&m.trigger(i?"ajaxSuccess":"ajaxError",[T,v,i?o:a]),b.fireWith(y,[T,l]),g&&(m.trigger("ajaxComplete",[T,v]),--S.active||S.event.trigger("ajaxStop")))}return T},getJSON:function(e,t,n){return S.get(e,t,n,"json")},getScript:function(e,t){return S.get(e,void 0,t,"script")}}),S.each(["get","post"],function(e,i){S[i]=function(e,t,n,r){return m(t)&&(r=r||n,n=t,t=void 0),S.ajax(S.extend({url:e,type:i,dataType:r,data:t,success:n},S.isPlainObject(e)&&e))}}),S.ajaxPrefilter(function(e){var t;for(t in e.headers)"content-type"===t.toLowerCase()&&(e.contentType=e.headers[t]||"")}),S._evalUrl=function(e,t,n){return S.ajax({url:e,type:"GET",dataType:"script",cache:!0,async:!1,global:!1,converters:{"text script":function(){}},dataFilter:function(e){S.globalEval(e,t,n)}})},S.fn.extend({wrapAll:function(e){var t;return this[0]&&(m(e)&&(e=e.call(this[0])),t=S(e,this[0].ownerDocument).eq(0).clone(!0),this[0].parentNode&&t.insertBefore(this[0]),t.map(function(){var e=this;while(e.firstElementChild)e=e.firstElementChild;return e}).append(this)),this},wrapInner:function(n){return m(n)?this.each(function(e){S(this).wrapInner(n.call(this,e))}):this.each(function(){var e=S(this),t=e.contents();t.length?t.wrapAll(n):e.append(n)})},wrap:function(t){var n=m(t);return this.each(function(e){S(this).wrapAll(n?t.call(this,e):t)})},unwrap:function(e){return this.parent(e).not("body").each(function(){S(this).replaceWith(this.childNodes)}),this}}),S.expr.pseudos.hidden=function(e){return!S.expr.pseudos.visible(e)},S.expr.pseudos.visible=function(e){return!!(e.offsetWidth||e.offsetHeight||e.getClientRects().length)},S.ajaxSettings.xhr=function(){try{return new C.XMLHttpRequest}catch(e){}};var Bt={0:200,1223:204},$t=S.ajaxSettings.xhr();y.cors=!!$t&&"withCredentials"in $t,y.ajax=$t=!!$t,S.ajaxTransport(function(i){var o,a;if(y.cors||$t&&!i.crossDomain)return{send:function(e,t){var n,r=i.xhr();if(r.open(i.type,i.url,i.async,i.username,i.password),i.xhrFields)for(n in i.xhrFields)r[n]=i.xhrFields[n];for(n in i.mimeType&&r.overrideMimeType&&r.overrideMimeType(i.mimeType),i.crossDomain||e["X-Requested-With"]||(e["X-Requested-With"]="XMLHttpRequest"),e)r.setRequestHeader(n,e[n]);o=function(e){return function(){o&&(o=a=r.onload=r.onerror=r.onabort=r.ontimeout=r.onreadystatechange=null,"abort"===e?r.abort():"error"===e?"number"!=typeof r.status?t(0,"error"):t(r.status,r.statusText):t(Bt[r.status]||r.status,r.statusText,"text"!==(r.responseType||"text")||"string"!=typeof r.responseText?{binary:r.response}:{text:r.responseText},r.getAllResponseHeaders()))}},r.onload=o(),a=r.onerror=r.ontimeout=o("error"),void 0!==r.onabort?r.onabort=a:r.onreadystatechange=function(){4===r.readyState&&C.setTimeout(function(){o&&a()})},o=o("abort");try{r.send(i.hasContent&&i.data||null)}catch(e){if(o)throw e}},abort:function(){o&&o()}}}),S.ajaxPrefilter(function(e){e.crossDomain&&(e.contents.script=!1)}),S.ajaxSetup({accepts:{script:"text/javascript, application/javascript, application/ecmascript, application/x-ecmascript"},contents:{script:/\b(?:java|ecma)script\b/},converters:{"text script":function(e){return S.globalEval(e),e}}}),S.ajaxPrefilter("script",function(e){void 0===e.cache&&(e.cache=!1),e.crossDomain&&(e.type="GET")}),S.ajaxTransport("script",function(n){var r,i;if(n.crossDomain||n.scriptAttrs)return{send:function(e,t){r=S("<script>").attr(n.scriptAttrs||{}).prop({charset:n.scriptCharset,src:n.url}).on("load error",i=function(e){r.remove(),i=null,e&&t("error"===e.type?404:200,e.type)}),E.head.appendChild(r[0])},abort:function(){i&&i()}}});var _t,zt=[],Ut=/(=)\?(?=&|$)|\?\?/;S.ajaxSetup({jsonp:"callback",jsonpCallback:function(){var e=zt.pop()||S.expando+"_"+wt.guid++;return this[e]=!0,e}}),S.ajaxPrefilter("json jsonp",function(e,t,n){var r,i,o,a=!1!==e.jsonp&&(Ut.test(e.url)?"url":"string"==typeof e.data&&0===(e.contentType||"").indexOf("application/x-www-form-urlencoded")&&Ut.test(e.data)&&"data");if(a||"jsonp"===e.dataTypes[0])return r=e.jsonpCallback=m(e.jsonpCallback)?e.jsonpCallback():e.jsonpCallback,a?e[a]=e[a].replace(Ut,"$1"+r):!1!==e.jsonp&&(e.url+=(Tt.test(e.url)?"&":"?")+e.jsonp+"="+r),e.converters["script json"]=function(){return o||S.error(r+" was not called"),o[0]},e.dataTypes[0]="json",i=C[r],C[r]=function(){o=arguments},n.always(function(){void 0===i?S(C).removeProp(r):C[r]=i,e[r]&&(e.jsonpCallback=t.jsonpCallback,zt.push(r)),o&&m(i)&&i(o[0]),o=i=void 0}),"script"}),y.createHTMLDocument=((_t=E.implementation.createHTMLDocument("").body).innerHTML="<form></form><form></form>",2===_t.childNodes.length),S.parseHTML=function(e,t,n){return"string"!=typeof e?[]:("boolean"==typeof t&&(n=t,t=!1),t||(y.createHTMLDocument?((r=(t=E.implementation.createHTMLDocument("")).createElement("base")).href=E.location.href,t.head.appendChild(r)):t=E),o=!n&&[],(i=N.exec(e))?[t.createElement(i[1])]:(i=xe([e],t,o),o&&o.length&&S(o).remove(),S.merge([],i.childNodes)));var r,i,o},S.fn.load=function(e,t,n){var r,i,o,a=this,s=e.indexOf(" ");return-1<s&&(r=ht(e.slice(s)),e=e.slice(0,s)),m(t)?(n=t,t=void 0):t&&"object"==typeof t&&(i="POST"),0<a.length&&S.ajax({url:e,type:i||"GET",dataType:"html",data:t}).done(function(e){o=arguments,a.html(r?S("<div>").append(S.parseHTML(e)).find(r):e)}).always(n&&function(e,t){a.each(function(){n.apply(this,o||[e.responseText,t,e])})}),this},S.expr.pseudos.animated=function(t){return S.grep(S.timers,function(e){return t===e.elem}).length},S.offset={setOffset:function(e,t,n){var r,i,o,a,s,u,l=S.css(e,"position"),c=S(e),f={};"static"===l&&(e.style.position="relative"),s=c.offset(),o=S.css(e,"top"),u=S.css(e,"left"),("absolute"===l||"fixed"===l)&&-1<(o+u).indexOf("auto")?(a=(r=c.position()).top,i=r.left):(a=parseFloat(o)||0,i=parseFloat(u)||0),m(t)&&(t=t.call(e,n,S.extend({},s))),null!=t.top&&(f.top=t.top-s.top+a),null!=t.left&&(f.left=t.left-s.left+i),"using"in t?t.using.call(e,f):c.css(f)}},S.fn.extend({offset:function(t){if(arguments.length)return void 0===t?this:this.each(function(e){S.offset.setOffset(this,t,e)});var e,n,r=this[0];return r?r.getClientRects().length?(e=r.getBoundingClientRect(),n=r.ownerDocument.defaultView,{top:e.top+n.pageYOffset,left:e.left+n.pageXOffset}):{top:0,left:0}:void 0},position:function(){if(this[0]){var e,t,n,r=this[0],i={top:0,left:0};if("fixed"===S.css(r,"position"))t=r.getBoundingClientRect();else{t=this.offset(),n=r.ownerDocument,e=r.offsetParent||n.documentElement;while(e&&(e===n.body||e===n.documentElement)&&"static"===S.css(e,"position"))e=e.parentNode;e&&e!==r&&1===e.nodeType&&((i=S(e).offset()).top+=S.css(e,"borderTopWidth",!0),i.left+=S.css(e,"borderLeftWidth",!0))}return{top:t.top-i.top-S.css(r,"marginTop",!0),left:t.left-i.left-S.css(r,"marginLeft",!0)}}},offsetParent:function(){return this.map(function(){var e=this.offsetParent;while(e&&"static"===S.css(e,"position"))e=e.offsetParent;return e||re})}}),S.each({scrollLeft:"pageXOffset",scrollTop:"pageYOffset"},function(t,i){var o="pageYOffset"===i;S.fn[t]=function(e){return $(this,function(e,t,n){var r;if(x(e)?r=e:9===e.nodeType&&(r=e.defaultView),void 0===n)return r?r[i]:e[t];r?r.scrollTo(o?r.pageXOffset:n,o?n:r.pageYOffset):e[t]=n},t,e,arguments.length)}}),S.each(["top","left"],function(e,n){S.cssHooks[n]=Fe(y.pixelPosition,function(e,t){if(t)return t=We(e,n),Pe.test(t)?S(e).position()[n]+"px":t})}),S.each({Height:"height",Width:"width"},function(a,s){S.each({padding:"inner"+a,content:s,"":"outer"+a},function(r,o){S.fn[o]=function(e,t){var n=arguments.length&&(r||"boolean"!=typeof e),i=r||(!0===e||!0===t?"margin":"border");return $(this,function(e,t,n){var r;return x(e)?0===o.indexOf("outer")?e["inner"+a]:e.document.documentElement["client"+a]:9===e.nodeType?(r=e.documentElement,Math.max(e.body["scroll"+a],r["scroll"+a],e.body["offset"+a],r["offset"+a],r["client"+a])):void 0===n?S.css(e,t,i):S.style(e,t,n,i)},s,n?e:void 0,n)}})}),S.each(["ajaxStart","ajaxStop","ajaxComplete","ajaxError","ajaxSuccess","ajaxSend"],function(e,t){S.fn[t]=function(e){return this.on(t,e)}}),S.fn.extend({bind:function(e,t,n){return this.on(e,null,t,n)},unbind:function(e,t){return this.off(e,null,t)},delegate:function(e,t,n,r){return this.on(t,e,n,r)},undelegate:function(e,t,n){return 1===arguments.length?this.off(e,"**"):this.off(t,e||"**",n)},hover:function(e,t){return this.mouseenter(e).mouseleave(t||e)}}),S.each("blur focus focusin focusout resize scroll click dblclick mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave change select submit keydown keypress keyup contextmenu".split(" "),function(e,n){S.fn[n]=function(e,t){return 0<arguments.length?this.on(n,null,e,t):this.trigger(n)}});var Xt=/^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;S.proxy=function(e,t){var n,r,i;if("string"==typeof t&&(n=e[t],t=e,e=n),m(e))return r=s.call(arguments,2),(i=function(){return e.apply(t||this,r.concat(s.call(arguments)))}).guid=e.guid=e.guid||S.guid++,i},S.holdReady=function(e){e?S.readyWait++:S.ready(!0)},S.isArray=Array.isArray,S.parseJSON=JSON.parse,S.nodeName=A,S.isFunction=m,S.isWindow=x,S.camelCase=X,S.type=w,S.now=Date.now,S.isNumeric=function(e){var t=S.type(e);return("number"===t||"string"===t)&&!isNaN(e-parseFloat(e))},S.trim=function(e){return null==e?"":(e+"").replace(Xt,"")},"function"==typeof define&&define.amd&&define("jquery",[],function(){return S});var Vt=C.jQuery,Gt=C.$;return S.noConflict=function(e){return C.$===S&&(C.$=Gt),e&&C.jQuery===S&&(C.jQuery=Vt),S},"undefined"==typeof e&&(C.jQuery=C.$=S),S}); </script> <meta name="viewport" content="width=device-width, initial-scale=1" /> -<style type="text/css">html{font-family:sans-serif;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}body{margin:0}article,aside,details,figcaption,figure,footer,header,hgroup,main,menu,nav,section,summary{display:block}audio,canvas,progress,video{display:inline-block;vertical-align:baseline}audio:not([controls]){display:none;height:0}[hidden],template{display:none}a{background-color:transparent}a:active,a:hover{outline:0}abbr[title]{border-bottom:1px dotted}b,strong{font-weight:700}dfn{font-style:italic}h1{margin:.67em 0;font-size:2em}mark{color:#000;background:#ff0}small{font-size:80%}sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}sup{top:-.5em}sub{bottom:-.25em}img{border:0}svg:not(:root){overflow:hidden}figure{margin:1em 40px}hr{height:0;-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box}pre{overflow:auto}code,kbd,pre,samp{font-family:monospace,monospace;font-size:1em}button,input,optgroup,select,textarea{margin:0;font:inherit;color:inherit}button{overflow:visible}button,select{text-transform:none}button,html input[type=button],input[type=reset],input[type=submit]{-webkit-appearance:button;cursor:pointer}button[disabled],html input[disabled]{cursor:default}button::-moz-focus-inner,input::-moz-focus-inner{padding:0;border:0}input{line-height:normal}input[type=checkbox],input[type=radio]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;padding:0}input[type=number]::-webkit-inner-spin-button,input[type=number]::-webkit-outer-spin-button{height:auto}input[type=search]{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;-webkit-appearance:textfield}input[type=search]::-webkit-search-cancel-button,input[type=search]::-webkit-search-decoration{-webkit-appearance:none}fieldset{padding:.35em .625em .75em;margin:0 2px;border:1px solid silver}legend{padding:0;border:0}textarea{overflow:auto}optgroup{font-weight:700}table{border-spacing:0;border-collapse:collapse}td,th{padding:0}@media print{*,:after,:before{color:#000!important;text-shadow:none!important;background:0 0!important;-webkit-box-shadow:none!important;box-shadow:none!important}a,a:visited{text-decoration:underline}a[href]:after{content:" (" attr(href) ")"}abbr[title]:after{content:" (" attr(title) ")"}a[href^="javascript:"]:after,a[href^="#"]:after{content:""}blockquote,pre{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}img,tr{page-break-inside:avoid}img{max-width:100%!important}h2,h3,p{orphans:3;widows:3}h2,h3{page-break-after:avoid}.navbar{display:none}.btn>.caret,.dropup>.btn>.caret{border-top-color:#000!important}.label{border:1px solid #000}.table{border-collapse:collapse!important}.table td,.table th{background-color:#fff!important}.table-bordered td,.table-bordered th{border:1px solid #ddd!important}}@font-face{font-family:'Glyphicons Halflings';src:url(data:application/vnd.ms-fontobject;base64,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);src:url(data:application/vnd.ms-fontobject;base64,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) format('embedded-opentype'),url(data:application/font-woff;base64,d09GRgABAAAAAFuAAA8AAAAAsVwAAQAAAAAAAAAAAAAAAAAAAAAAAAAAAABGRlRNAAABWAAAABwAAAAcbSqX3EdERUYAAAF0AAAAHwAAACABRAAET1MvMgAAAZQAAABFAAAAYGe5a4ljbWFwAAAB3AAAAsAAAAZy2q3jgWN2dCAAAAScAAAABAAAAAQAKAL4Z2FzcAAABKAAAAAIAAAACP//AANnbHlmAAAEqAAATRcAAJSkfV3Cb2hlYWQAAFHAAAAANAAAADYFTS/YaGhlYQAAUfQAAAAcAAAAJApEBBFobXR4AABSEAAAAU8AAAN00scgYGxvY2EAAFNgAAACJwAAAjBv+5XObWF4cAAAVYgAAAAgAAAAIAFqANhuYW1lAABVqAAAAZ4AAAOisyygm3Bvc3QAAFdIAAAELQAACtG6o+U1d2ViZgAAW3gAAAAGAAAABsMYVFAAAAABAAAAAMw9os8AAAAA0HaBdQAAAADQdnOXeNpjYGRgYOADYgkGEGBiYGRgZBQDkixgHgMABUgASgB42mNgZulmnMDAysDCzMN0gYGBIQpCMy5hMGLaAeQDpRCACYkd6h3ux+DAoPD/P/OB/wJAdSIM1UBhRiQlCgyMADGWCwwAAAB42u2UP2hTQRzHf5ekaVPExv6JjW3fvTQ0sa3QLA5xylBLgyBx0gzSWEUaXbIoBBQyCQGHLqXUqYNdtIIgIg5FHJxEtwqtpbnfaV1E1KFaSvX5vVwGEbW6OPngk8/vvXfv7pt3v4SImojIDw6BViKxRgIVBaZwVdSv+xvXA+Iuzqcog2cOkkvDNE8Lbqs74k64i+5Sf3u8Z2AnIRLbyVCyTflVSEXVoEqrrMqrgiqqsqqqWQ5xlAc5zWOc5TwXucxVnuE5HdQhHdFRHdNJndZZndeFLc/zsKJLQ/WV6BcrCdWkwspVKZVROaw0qUqqoqZZcJhdTnGGxznHBS5xhad5VhNWCuturBTXKZ3RObuS98pb9c57k6ql9rp2v1as5deb1r6s9q1GV2IrHSt73T631424YXzjgPwqt+Rn+VG+lRvyirwsS/KCPCfPytPypDwhj8mjctRZd9acF86y89x55jxxHjkPnXstXfbt/pNjj/nwXW+cHa6/SYvZ7yEwbDYazDcIgoUGzY3h2HtqgUcs1AFPWKgTXrRQF7xkoQhRf7uF9hPFeyzUTTSwY6EoUUJY6AC8bSGMS4Ys1Au3WaiPSGGsMtkdGH2rzJgYHAaYjxIwQqtB1CnYkEZ9BM6ALOpROAfyqI/DBQudgidBETXuqRIooz4DV0AV9UV4GsyivkTEyMMmw1UYGdhkuAYjA5sMGMvIwCbDDRgZeAz1TXgcmDy3YeRhk+cOjCxsMjyAkYFNhscwMrDJ8BQ2886gXoaRhedQvyTSkDZ7uA6HLLQBI5vGntAbGHugTc53cMxC7+E4SKL+ACOzNpk3YWTWJid+iRo5NXIKM3fBItAPW55FdJLY3FeHBDr90606JCIU9Jk+Ms3/Y/8L8jUq3y79bJ/0/+ROoP4v9v/4/mj+i7HBXUd0/elU6IHfHt8Aj9EPGAAoAvgAAAAB//8AAnjaxb0JfBvVtTA+dxaN1hltI1m2ZVuSJVneLVlSHCdy9oTEWchqtrBEJRAgCYEsQNhC2EsbWmpI2dqkQBoSYgKlpaQthVL0yusrpW77aEubfq/ly+ujvJampSTW5Dvnzmi1E+jr//3+Xmbu3Llz77nnbuece865DMu0MAy5jGtiOEZkOp8lTNeUwyLP/DH+rEH41ZTDHAtB5lkOowWMPiwayNiUwwTjE46AI5xwhFrINPXYn/7ENY0dbWHfZAiTZbL8ID/InAd5xz2NpIH4STpDGonHIJNE3OP1KG4ISaSNeBuITAyRLgIxoiEUhFAnmUpEiXSRSGqAQEw0kuyFUIb0k2gnGSApyBFi0il2SI5YLGb5MdFjXCey4mNHzQ7WwLGEdZiPPgYR64we8THZHAt+wnT84D/x8YTpGPgheKH4CMEDVF9xBOIeP3EbQgGH29BGgpGkIxCMTCW9qUTA0Zsir+QUP1mt+P2KusevwIO6Bx/Iaj8/OD5O0VNrZW2EsqZBWbO1skRiEKE0DdlKKaSVO5VAuRpqk8VQJAqY7ydxaK44YJvrO2EWjOoDBoFYzQbDNkON+UbiKoRkywMWWf1j4bEY2iIY1AeMgvmEz/kVo9v4FSc/aMZMrFbjl4zWLL0+Y5FlyzNlEVYDudJohg8gPUP7kcB/mn+G6cd+5PV4Q72dXCgocWJADBgUuDTwiXiGSyZo14HOEQ2lE6k0XDIEusexDzZOMXwt1Dutz+tqmxTvlskNWXXUQIbhaurum9GrePqm9Yaeabjkiqf+bUvzDOvb2Y1E+EX2DnemcTP/zLcuu7xjQXdAtjR0Lo5n4/Hs/GtntMlysHt+29NXbH6se//WbFcyu+r28H0MwzI30DYeYTLMXIA2EG8QlHpAsyS0EfEToR0a3utIxFPJ3kiIHCCrZ66b0e2xEmL1dM9YN/MwS5p01N5jMX/BLKt/1R83l0LyC29M6+iYxo/UNg/EF7c2WyyW5tYl8WnhWg2/hyySbD5UhnDyS7OcU0dnrFw+DfGdI7v4QfYIIzOMq9hFtY55gmvC7jZ2FK7sEdrn6IXBuucYhjsGdQ8z0yEbWkkczjjsE5hNAIZrPx2zOLZDmKNXcXtg7EMqidAEEWg+SJCBBNwxvxJfc/bZa+KKf+xoKZybnq5vaqpPTye7CiF+ZFjxZ8/7Qij0hfOG/cowPA1rT1l4ymWnrKmxxqfErTVrpgwPlz1kC+Oy8NMDz6c+IO38K/x0xkPnLW8Kx6qGAoQdL+TD9V9rb+/ctn//trxz8dUrZrD/zk/ferF0cNt1BzctmX2FZPXt/jnFCQNz4Ah/iKllGiCMs1w5Lkg0kiEwj6VTXCDKsX9rMpnvIj9pcDecXAIXMnqn2dTUbN6w0XQ9ue6FV/nnXCH7S3lPWGltVcLsH75ub3ab7A8M28caNrIeOr3o5Q0yFsYL80xaa0EY/UEczV7icUMY5pnelAkmUAXmHYjvFWFGxuqlSaow3OM+/iYY7/l/hVELF4EjRqNR/bvRbOY+DUGzGR/Oh3EqmE/ugIQQguGt/eMYz/+L0cimjeZfQDI3phXMbMQsqH+CjwVz/hf4idHovgVmB8gLvjbicDcC/NypP536E/9N/puMibExdohBmNwyiaZdJGoigos7GpF222xrfnZhML/7Z+ylaqP63Hr+m7bdUkQ6/2cXqdfmvwixY+s2ksXFeXcE+iX0Z+Iow76DBNgjJ7TOdUK18iPsPflfQD+DPsZG2Aj9VmKMMJ4fYRrhIaxhTDR0Elh2vA6h/AE6xUb29mj3sjmL72petXjejPy+oel60M99tFduCI59N3221xe7apOvxs6aHs7vab1IqY2tv7q2xsHeHGml/cV06u/8S/xTjJ+JYc0bWEX0ukW6YmIbGkJRMdjJ9mYIH5QIdJF4hvRGyK7cC7ctImQRcUET99fGXOoft35GYLMQu+g2smnkgZUrH8AL/9Si217IssJ916nv14ZrJrvdxLkQvrvtBcjgPC0NXOicO8Qf4mcxPqh3hgUw3DDfdvLJXngg7N3dN2zbPJSaed3OfZnMU7dvmznp3C3bruO+Nmue0LFsy7S+6265+fCKFYdvvuW6vmlblnUI8xCXp37CrOZv4B9gauDBlYp7adcUXB5DNCwYImlXOJJKkAdvExXxVvKEYnCo+3eIskP9qrrfIYs71CccBjfXRC52udTHHdaP1A1ui/VvH1otbrLrpNXBsGX5B89QghDyimlvNB2KfkxZ5C9/em3+d1+d//IfFp2+2Oxn/s+9n/79p39S3s8idN6g0yZObwJOgKUpNB3GyU0Ls0PbRzIRq4lcarLKOJBkLRzJQD4j2090XrbA7DW8K3jNF5hlGS5e4V2D17zgss4T20egOJte5iD0bReM9yjTxnQxCRj3c5kFzGJmGbNKmwGw39IJDJcXJZGMkaAB4jyJAKw0jt5IAuIE+A+U3cVAZZrq9zhDyBrU8oosuxcGNTzCKJfla7JjNVmuSb/+tuzN2H+X4vlB+PpdfMXXmuVsNiub1T34SFbjYw5itEvVi0K0Nt9pNJUMI7SLGRhf2xipfCYf8z5OdlGKayOucFeVPeS/dbo3lBrbSMmwUiQN5/ed7g0Ds1s17IuZC5kNzM3MZ6EWCa0DtekdJfAxz+R/OX28sND7yRMTBcf++s8mQCQWHya4qBv/ufeMoWyslPA9DtMxUknxkH/yfTnm2CMYzs+Cq3r7PxY/MXomrvTEsRpfEGHa+WN8E1AHjElb7d06ddA7oK/+5Mdsv9EtPms0jv0Z5kf1FqPxWdFtfFr0kHfgDX0Y+5PRSG7RUj0tQr7rmfX8DH4G5W28kKeJLtmQsQkuwMP1pk16EV4sl7vrMJATfyUWo/GwEco4rh4XFQgaiUX9qxZHrMQqKnz/c2d8b9TysYrAuXpP/Rf/Gr8b1qwwc5a+euLa6S6sneNXToG2XrEJi4R5SGs8Sq2S3d97bsfCRaTdaLwKClRHt37mkudvXbjwVrLhuYeGhh56bvfQkHpk2CwvwClqgWwuBfndC3c8dwmstj81KkagcUgbfPY8Zje0W/82VPWJHmSq6pP8hPWpotc/EexDOK3qU+wngPhOCiO9MJRm8TJefjelrzoKnG2Bn+1NCUmPE4gHFmBN9jrTigRIpsACrc9Gstg58ULkp9467+Gf/eFnD5/31lNrt2967dhrm7bzI+VT5m+fzKhvf2MzpICEm79Bopkn07lt1762adNr127LwVqQLdJ5+lpQDcvHPQtVY5knhYrK6q8/JsiP6EuhGZdFdaNszjvpqvc+PI0CdjN0AXsFOC3ZfALDJwr4q2Xq+GF+GNbsxUg5NLLIEXi8otcDQcUts0D8eQ1iVDRAMBTsYiNdRIxE09EIBJO9A2xqgERTaW86BUFn0OD2xFO97FAgFhF6OoQ7prYt4XwSeUgQHiJyDbeke9IdQntciLQ1FlJMaYcUNvZBg+FB1ubjlnRNvl3o6IEU2w7fdNPhm/hh+FLysUu6++DLHkOkrSHYEjH0tEPe7WdD3uyDgvAgK/m4szFFR7ch0toUgBTdWHr7EpaWru6+6dmbbnqWEbV2EtxAsXiZAPTtGPSbHsotI2leoM8TePEqgSQprs7AGFf8kuOkPdZPXGb55POAW1d/jLST9v5YflasP6v/CO7+GNAPC2BMZWmsOjp2NNbfHwMCJD+LPVL+D/OYlWEEI/9jpPddOFkB5d1GSuKZYggmCCd7JUxD7EXAzxyirYnNDLdDZoFdx14kivkvGc3579Jm36reTTvDgBnaO6vzyQ6chQmlsMoIkIQ2+bBDWBud1Va4pcCn8CPqxlh/fgtG8IPaPH8C5wk6/nZDv69jurV5QhtwE0x2iqOsj9Mx8B9/0EaUdiPfOYYDCi/q9jhWRuupMDEU0+CtX0sDFxv07T/K5niBPqN9+tQjgEc31NGCXFeMcCEuQBIc/BK4CO78u7EPYvl3yaEfK3vcb6qP1R2tI7vUjVDDUdKubsSrNjYKY1qBEa2P50SJoaXiksIoLiCwnxS6EBuBde87botNfdEWwYvF/R0/u5yCqhGeEOR2ynSeyXjt6ka7neyye8kryBSWE52y+RBgogrXPZ8E1yIHoHIFUM+AbJhE7lbMtt8ApL+xmZW7PwbjAO0fAVoXQOuiSP/ksIVdFZ0aulsamKUzwPZ/NYDMJRBPCxsBqLzqHyneXF6Ej9HlIFo7+pg+jUb3unRmGpstGkm6etOuDBGA5wCMefp1gTHcdZlvPBXlOslvYTp1cd8UjYLVd/J5awNrIOKLnIt9MD9qdrKrWCvA6ALm3QV9VrsPm60Q7+RHJHP+2hqfugo/MvI2H/mqr4b9tFnKSRY1Y5Ek80Nm/WIhr1ikKnxGz9TWXrokf9xwujfvcOTtNTWnxd0F37Y2W79tteBqZ4G5qLCuomw+nSr28QESCRVLTyYKILGJOPfcnaIFOsewhRdvv+rWa/Wih0vlbX6Zb75T5C0qNKVFvH1QL/vazSWgC2s6oWXXIuUxQelKiJbowuJDQViatLmLijg9CQBMg8WiPgiw3LEeYRmm5f+XdnvkDnxLLjMLxtvX74C3OlwPQqx4xwIdpPx38LrlDphiyWUWHWKAzzxurS/xTo+P5wGFak62ap1PVFFN4v/y+xuR39WnIO7lsWfwgVsK17wxrs9K8ltIKuhkw7f/6dhK6gQokFKhWX3urrjk/rnI0pgfpGMeuQIUaEM7+GF5q2iMkCaMQwxxOzcvU0eXbsnS9XknXvP7Gtw5dwPXlFu2ecvSHEZgNDsU6x/GdXBYXyOQjzZReSedeEPY6nEv9gJR4oBQJtFO6Kd0fwC6BO4LNHDeBujB6dSNcUQC9zIv2LnAzGk99bUDrdFY+9yGFQtEo0GQPNv6vS2drj4+1jHbv3aJSMUWP+QTZrmbNTjU8wyG/iXNNpskybLcJ3CiTF5Ir+JYzmJwE0mSVhlxbtbmvweB3ulB6Til5UuUZydpgiFVeobhU0WaBqpJ198d+/XeNRTZ9/1OPfG7+2hwzd5W3D+hmyjsRcUg/+Cavb++Vh2ls3L7zT/etOnHNxeerv313vzLVqPai4nJv+K1FC6040/4udw7sAb3laSg0XCkAAs0npBO6VJabS4Elk/U+D4gTXW+j0wnrMlqNamq4tMIYB87tE10i0FR3LZNhJsb7/R561btmes8YBCRkhYNByRtKd55mqTas9FYhJnbRGHuOh3M4QTdgQSqmgRxuzGdSvZGcbMxNQGk5C3ebLjoXIOFM4l+WKHmLTJwRv9E8GWJ6dYvf/FmEyEGr+gyrr1p5zrgkz0Cw2j94Hv8Jdx7dIVegBSNtgsqGsRQEYiIBoXwD0LNvQ5d7s5Z00QzwNhqZA0b+tMG1tQq5nd84uq8R0zPvX35G8uRaze4jcOHzz0w1+Q2BIRvf6J6Kgatnrbiem+CFvAxfkrndzD9MFPP1GWTUHclpASUkCNAQkpCCcCgDSUDAhDZ+CuEkgn8J7i9nMA7pA4lISappxILKfAeSAbIcSDuN2bJcfZILqeO5rLs0MnngSHYRdrHjmaz7JEsEPw51ZqDJDmUIOZIe34WaQeegNsJn1qz8AIpT3yCjyEih/xELkuJ0lEMYTLVCiWpo5oYMleMH6USyYJcD+uOe+kWKpn1Qns34iyYDjkSLvgnZXcgVQNeqINXr48m3iS7cjm8tedyY0f1QvTnHHdsrKby/+SSbPY8/NH6vpl/Esq3Ae4ZU1HC44KFiI9o7CEgab/RqHbj7s5KAg06s39ZP/zxI/mVuF/TbTSy+3Fb8If9/cv7+wt91yy8RfP1QXtW5RzQn7qIiZyuFM5QfJ5E9uVnqT85TanFx0lkP3ukBAMprvsRyi/C8NAJL1xbIIirSvnSj4O5netb4JxmNANHPssHAcHMHsFRgEug816gDBeMbdfiuRcghqYcm0+Xxx/5IAEtN3fqFF3LzAXqwoT0PN0OVTNqxo8sxMkd5Ig6k79Zk7VxxX6gMLOZFQgvpW2RrMW1D0BDihaXQ9wVRoBxPLfpknmkeMtoB/qM9cRc9IqmMD2XUmdZ7GSRKPUZvChf8BoykriM2MnKYbOHX8R7cLdNCxSFFVQqoYswnlWtlFS2mNkhswVpZiQW1J/UKFfipHGlUkM6UKBhMz1istELIHJLMSctu3ugzfaVSOjKvUgc/THK4Sdg2Wscz69leKIkkrwuuWiOe9yGYKQXRumkC3qbRcMwrvhjNXgdZk3RxAUEhuSPvn3nnd++U/3vlVOmrJzCD8JLxV1OHRjrZifbcFDOuRNTGqdgQm1tSNJ2OcQ04YiEXuxtII1ECSQRoQGYioEsgCfchB4ghAtw7FfJre4WZ9hkVi9MtjuWqtdNDlpMrfEG9fOT6q21okg+e4As38MfGquNt7oUws6Ysarj1/efE+yst86YUVNvDdts3Pv5c8m/aP0C+f8/Qb+IMnGq09BgwN01oIOAnAdagI8mBSrqk1gxTDUBOtk2ousEtBH2z4Ir2d3f6k8PXXVlt2qN9RODxRuoJT/v27wm09jRYVc/e++iyx2tyzJb/n3J0htXP87eSsQaf2Ly0s6Zmxela88REy1cf4273mI3iXNJ7KxrZibOm9xm6rl4fqy/t27smU8tOfdW2ucBzg2UfmOIVyLIl3kpYlwphDISTXJXsctmiDtN7fNV6zelgxwnWxsVr83Aj/S5ki1jL/a0GC6+2L6Um+aoddlNFuj+bJ8mH/iaLh8I0/U51NspIEfq0dohwyFXKgm4NggwQ4rRhCOUFtxxo8XnitT4cnGfT93IS8FaT85XE3H5LMY4zIEPL1hw443wz+1UmhTJyJGxZzw+wsKkKZgUiVtKOKMEb2AKHTv61FNc01PQFwKnvsZ/9pPA4RKTASWahmh+8MxwzHxKy74IRn5LGRjsPUUwTu64UYNY38caqd7HKucZ/tHnODtENw/2UfHRMaq1UUPDJQ0OKkWCeet5fYOhII1VRz8+/Elg5j4Gxur3J8o2PJ4rg+2d08T/fwEzSVbyZ9XPro95T477lRKqUSRXQnauHNsISAl27oWi6Fv9z48JMv8r/aMMj8onCP/DuDZOuN+GPPr/+p7bx+7JlbYdppcNhzKU/1Px5aiaGDn/s1iGMaBcleKUo/v9rcxkZj7DBEKOfrayytXNLYiUdBY+pleQXdnscKlQcpzuWluxsieeyuXIK6SdxozitWyGOV3vOHHjguyCQ6fpIYy2JwvrQEF/Qa9Pdf/QqOSqCiE/EE1/XIVKTc2tzWbHnimrEd+Vyz311Ml3P0GVTj7PD5aDnsvCvH36alEaPMePcMegXs7x8igTu4B9v7G9vTHvhCu/kzIdx+BxC0ay9zRSvoS0F2lIxI+X7klU63I40gLQ3w5ep5na+SFnba3z5D64zv+QtM4n4ffG3tq4aNHGRfxgrXPMim+5487abL7xhdseIRn1KDl+7aINixdv0OD+JSPwKf5+xoP6aiTeQIDVlIhMcL1H5R9PYXvprs3fv2bO7MOplCmweuiq2JRZ1zz+9a/v2PH1Hfz9236w+ZrPXvWfAxlj4NLLHpq3c/PQ3uvmvbrjG7fe+o2y/cLdtE6VUlXi0ASb1VLUBVSUWSU4HdvAraTyS8xzM8NxvxFkXV6pUVRiJwcgC5zEeht4rwcp7ki0k41G0qlQhG1Vzlq8alEmnFi58caB5Q9vn988MLhqyVlHvLEWjtQFeupdiocF/tkkOGPW2ibWaBTkeZ/dvPWazXfOnnvL6jkRXpi85sFzZt+55ZptW3bl1cCCHZPD06MhySha7UFzjcjbp8fOecFCirzAG/yVjBX6OFIaadSjQq1nNhyIe8tVbaaSdHlXIWKacMeuZA1uxS95zILhyrxAdsXTL6m7kNQlx2P9uZf2qhufePFFbpI6/OU0WcP99RrCsrwseVot5mtytpf6Y0gm9sdeyKnPQ7onyK4nXlR/rg7H95M1upzu89DH6pgUcikoiihJ6NJKmRxV1x+MJiOA3YwhDRQrWU0u/0rvq0VYXnyCwsLeTJYBq3dAtJDavuzyoVpzZ99Z0+a0uoiFH/xcqgDR7rUFeOrUn6Cywb8ZeNMbhLV5ugP9l0zv9UN5b5mFkjzxUcpPJCn3V402pRxtJd2GrnLdhtVk9ZSZh9W91fCSH5B7ofxPiWL+j3D/uwhBRdyAyozeZwvQzs79soi+BKSnafLviZCcfrpBpLyimfLfTyJtbyruIQKD01tUwJyKEo/ybaxkSNFUMdMkhQoJyRBQFhnUkDQSXhTM+3NmY0EDM7ffLIjqWEGt8lCO6mLia3PukFnghosJD5p5SIho/VDkzQfLE+IrYoJXkD19pdP7OwG/voIUtagiWiZ4PAFTHHlTVhRZ7dYmPar+NJ+8JhmR6DFK5DV1foHoLNO/pHrvZfmWZ15RQlwvoVDKhCWNK3CCch9lfFBuAqUgpFSShmNaPj+i5++WZfKeViJfW5HnUakVL4UCNVkA4+ETfIqx4B5xSaP2L1yn0zn2ltPn4+OqZGmwwEVCaCSqG53ldtL1oLGAhdMLd09MpCCF6tD6ZnAZBY9hDaYsP0jzZ0j5ZjKsF4i1UmLuhbJMCnYJPt5VwFNvmZawXjEvLJqIH8STonZjq7BZ8gKgR20C9MDFqJAX1H64QW2NEup6qgzLP8cvppL/NNTOBTCJABOHeWoXzLhw4Wuy7gaBtjKr9kgKq8ZlRYBS32Lpxc8vIhpNDTfyNXWybMJbn2RyQ5EmWc2QF9wmSZ0KYCE+cPuYO6b15Uotj2Kd4MItLS7gtFbkTdrFND6pvEZqv5Yv7jXAus7Pg7avo7KDot50NX3CPkP+Kps8J9/3mGQIteY/LGPC+L7872SPR2br5fy8MtKBMHedGuM28/MZmPJMrGgi3Gb1S+Si1/L/zrZwO9XH1ce/z7ZQ1WSoY/+pMb5FT4ua0Wm+Jf/298nFmChEQ+Ti71est4mq9VYI6RsymoRJKYidElT2FGnDTZvqtfhGAFTbeqEw68GqtfmbVa/1IFO1/jdWr/8BDRRtQh9XNjubEm4aWVpVonpTGR7PVGc+KJNoBIWF7kYi4gUV3r1U6723i6TxUl3n3/tM27aZfKb7THiHW9VzFSwHJ05VfK6Ar7kaB0XgPPE0BSkSFKsBUpaLihEWoA9wBt8qirh2VSOkZwXEwyrxZ5jyt2rJmSo9gX7cg6jsEUGJU9z9xJPOEM3uQQxKgkh35DNATnVyrmJ3mbCNyIB/yox4wH1bg2DwN7q9kov4pFqny8oSm3RQbGgJ1QQTs6ZMLilOVYJ9v6Wha3HcJ9jddsXp9YhGUXLXt/qMDnvLpPNTXfNa60z5/yjXQOMq+lNmwh5egpYrdfZQZV9rI47xlRkuyTjpzsmCBSWNkAXVoK8sgYWqQJWbo1RLo6QH0YW6pxqfCnRgkd+RiFjUQUQ7poIaYoakgXxwFd9BuuI38H1xBxXSFb/pBDIKQFn7YB3dB36l7sG1FLaKiBdp1KxLvfswap/30lnVESgNnvjbUoT6w9N+Xoio0qcYOIM+heg940YimsucQVvli9NEcft2UZwGQwLuilj1fFr1i3NP94X+PE7Hpvtj6lBJfJ4R6NvWiaL6MgzWHxiN66DExa+dAdAbMYX6HVF8A+7rjEZIXAVbDe7PVI9rmN69JOLV1DOSvRPxWNPZBZf/Nf+Ny65BhYxxxV+77XJ2wfQ389/IQPgajXbwMsuAz/0IaQcXJavKbRqR2IqyZruXjVC2+hdee/5vdnYOedpmVtR3NGXldxSzDSIiBVpkGb9by89UpEPKrSLZmyFDzMab/wXl2CNe7s/qCtTvWgG5kpBmCBlSzDS/r8N4uwBwohRW63JTS1y32f0TQsPfXVGEHQrV8/NCfiOUVirYcBbIeA2+iF68rQIo3B/S628vYESr79ehzS7Q9LEL9UXmik9XVHb1yBO3Ngvt5935+k1efkV51mzzrM0LL3/20avnwMeKuWyOUZg2TasSqZ+KcZQiOn1Iu2Vh497ALUVZiCKt/gh6IvTIj1ZLRjWAkpHKOKovNwp00eqPROiAbiNEKieXwMLcXhVJ1/uzmLP4tfxaHR59cBdJVG1kTAgl9ze9QKUEQ946Hkb+okJ5JRDyf54Axur1D+WS49cLr0tTPEu7UmXrxcSr3XNvumv4yXzInXKH4F7Tc7p17Zt+t/qW2+93k063X7VW6lALxTY7i1nBXMxcxmzQbabxz+tJo+wijYaIGMNS8AoSMgAPt84DdHOoMPfjXhF+kuH1tZvuFQrRCN07xGcXRX9MYxYchDe5BcHj+Z4i+42WyPc8Xofi7bbZJN5nJLJ5qr6IqRtzqNlM17SpFsnkEyTWoABEjz4JXOQvzWYuwdnV5LNGOwTM5v9r4RpQ8ZXsYodks3o31JBlzbYtNotisnm22MxiwGFXam5oN1n0TA/hRvshvTSDwHff4nNzRo9Dum6PaJbMXzDz+x+Fkj4L4bFNBb1asqsgH7Dyh4DvbkPtf5yMDKzEwyoaESMSNS9P9gJVA3/RTlwoMwZvxECFWxIPNw9gi01nOHjP32esZTtmXHnxvZd8ZtakqQ7ekajbXetpNa6ocTVxJtY+uSe69OLz77zh5bDR3xjZMzUz6fxrz1nqrZGcHQHfPVefN+fiK86LeXj+Sc5lPKy+k/vCUI/DaLFYCWHr6nbXuILTIsb5imNKY/rCm28fSMxPhkN1XbNMNZGuqwOBhtTSxWuTk6bw0ZaG86b1hKddePOKuBvmiguYBn4T/yOqOyGRBt7bKUI1GjioBC8aUKwF7Q319UgcmtFGIzCJGBqwQij0ynDsfdFGc3TS3BlNfJ25xmzniMkpXXTPvCaD3ZaZvyzjmZdudBostmhb0ORZNN2sJBeed1HXkrUsywueQH+L0eCPxmsa5ZpgRJSDZ11yDv+jmbd86vxZfc1WcZJ3UkMq1BOOOVtvu/+pB+en186d3GTwWAw2jheaJs09/+LNfZft37DALyrNj1wABMuUKbODyTVnT/KYbJ3Tpq8IrNh92dkxOj5P/YpZx4/ycyiVcDYdn4JbEoKdQi9054iBKsygLW46FRGxAb0NPNCm8BSNCPjoKcj6EAus4SuP3rB+cV99/eTF6294dA8+TK6v74MHVpYNRt/I30e8QGTOOdfGWzzxcy+87a7bLjw37rHw1nPzp0KyyRSeZO+QQhInt3dYgvycjrPOv+T8s1rptaP84VeywdWX2T4ysr0/7TLIs6+x9zib56ye1dM9e/XsZmePY3NDs9zlnNVt4+WgHJbbz3Livg4P9WWgviOMm4kCRT6I8vw0NbUUEnFvOuFKoxQW1gTsvFirsF5pb7qTUCx4i7VmtToveaDxvK9uOaedVvPRpVOnNz0Q6bry7uiSdQ8t7Vy4JQKVS+XPplV2ts4bvCwZu+KzgITtxepaPRzWdpv74muvv6RO0SorX6cu/dqKn/XWnrtp/Zragz13DUCl5myiFW2Ycvb0PtsXnU+tx8pvLFbUspLX68mdegwmOif/NPDONajTGoUh6tU56HBJCTBASVvNUB5VIiKpc9kd7kludodSFz7xQbiOmMk5dOYk56gzL6uaf7N8a6MQOHm0ae6snZpFDfuT3/jdYzjzwkXXIVHoXNuCfQslQZqBZjTsoHMqrkE4jaYdgkGz2ATOgB3cPkSukD01DnV3ttb1wx+6arPqbkcNAHoFPzKUUQ+qL0k97pjbZv1I/egC9zTFbrrlFpNdmea+gIgfWW3wqkcis8ky5FAcRd1If5nNZrl2FFpungc8wpoCl1BpQV/ScS+zjlASyUTVv/AJ46gkJI4bHX4lTnloctxPZE1ckS3+jG2fKIjkQFyzuo8jvYQG1OrGvJPSTu/nSp9PHNTl4z5hK/8gtXVKF6gEKiglgcKiRlCESsQCV5QIlKWKpr34lt/wkSx/JCmP5/cBKQfl/5gd+rOS/+p91/+YCg5CXK2W4M9fu+/6xxX+vnelVuldIDCG0VQTpU9Dw4pRfei+6zWx0MLie0gPbyrkmRU7OwT16JGeyXLHqOLqAfVN1GPlBzWtFNzj0TRTCjogtP1NjIvu5habN5Aoa1k66wGpqriVetJgiGdwDZtKhnN0y4n9sXYnsqGmZfDSR15+5NLBlhoDaedEm7sxmpqRija6ZEEg2EAnTiAC8IrmFbGz1q08P9PSkjl/5bqzYqT9hMmptEXDgTqP3Wiye+sD4Wir4jCeoHbbp5hRfpB7BakUIppIlPCD30dR1GtslDz8OsqbXmejFC/v8wu5X2myq7SJ8Avzv9DFUJySf5uNvq4+Ti7W9D/OZrLChdwxmPNiBRqVjnpK/aGxRCDspVYKAW9AN1JANoo8wP4BJUlGqdgw6m1qPQ2QW3+OfU5/ieLS/NuKpDU3uf8bcAXyBal5jMR2NEAbPAZt0K3hvxHBEDlUxfIGcD+N2gNSNx36nfqlAYow0puatNpRz0e4W2oahKzQHsjf2c16ad/3t2KTtPobnX6D8C8pd0MDP+Kx7wnXqGGlLQcvikMErm6TmfsuxJXbSAxqNjOogJLQBLiKEHAE+JGTS3JoEhTrz8/CB+5YlupJ58aOat8Kv4JvregxwcU5Cp8GFAFm1FyOfto6GS2m1NGTS6CPNKkbsTdCBlnN9onMho55BX8IJZtEQ35lk+htwN5A0V3RCPoD/yXAcv6pAtbZczRUA64JmcUf4q7Q89ZHLeJVZ5D1Ps/t+0iCT3AHVtZC7JDCXfR7OSb/Xja5H3zQbZL1B+ULX1BMTEk3AseSpmnKEK4T9ekMIidUCRQFfcbj7z8gNLvzF7mbhQN8h6ZbRset+nQWdS/ZX3k7WpS8P9sfo0iGS64wV516pOhjI6TZ2dApgI5+LhxywYoWxKUrykKJsIoDsR4mSrCTg0egMPnLW/3Q5Nn8BZEuzqEI7HK3n0+zFmuO3TtWQ5WJoG9YqCD6Gc32SxnbnVPfsxvrFXK2dILl7bLthDp6glhcsfp4bYvbSmj/mQ94uBTw0E73x2jbNRCvC6VL6GCFDwU7eWQDcC5FY5s0slieRDwtAbRsbLXbaXAuu14e2OJw1dc6jQ3ZdY8v7rv2/BWZLqvFWVvvcmwZkK9f5jS4muO9yR5res4kfkRxhV03L1RfPOiPtYi8pd7jNEsOpyTwxpaY/yCZu/Amd5Or9uS3DYaeqVOhH7gZN/8I/wi1fEuLXvyNivibjuKvN+1Nc01HF/3h+ef/sOhox8MPd5SFucPjorQwXT+ytA8EmA5mamHNFDVhBI5pjZbQpugBNkO8MvRub8KVDKST1Wag7D3xlin1ZF7LFP/79nbvCXFOY+PUjrT7/otsPXXZ4exdPzuhZuL5LUXVAn7k7PbhG89uz3b41X01gbjP1xwlu5rrvvf9+pbs6E/Vu7Nk642/PYRaAiUBdrmO6CDTBLPQFA1ur0uXoBR1INDMkypKpoTqnSMx5GiEdTEaSHLs0Alvu/19/5QW9Rv1U1ridT22i+53pzumbs+XFFXYC++CGsTj5JUT/GCgRt3n78i2n71FHG4/u6X++9+raya7os3ZbDmgWfXun44e+u2NZKuGZ0HiF8M4TlMPR+EU6rPKRJ8wOU2RFUFLex3egEsz3YqEAq0cqhAAW19dBZIlVzR61tuIdTnpXH7l+uXrbjPUyep+8cl6aXKWhPHpDcXl9KiTWDNr4mBQc8Tq+NzK/OKSbsfl79o9G20R+brBXYvUg0rLHhtrc4TN81TTOWSZ0gL1ZVlOYH2ery/7XVUjFMbzYpg7UswcqJPQwBd0LKLabJ8IaCr2otcjSkIrGwootKECaUd4XH1+SdazRrfddkBU98t1htvWrbjqSqjaCguxrffM/5zDCpBALUycmajhd+R6ww4SWafuZ5eU+tPid4lgd3gt+b/Y9rQoZNmiXYPXyRHbRs8zX/f4WIFjWZJtUdSD55AP3xtXH+ZipC0EqdBGDA4CoYEU6gRLGPU11QhkLTBiEYPiqOeQgwTCl9aok1Qr5pFf71qEeNxjy/8F0GoqYPv75Yh9j3x4DuJ+uEzHRpAq2lMqb+qfTdiq6kGtzfOWsv0c7lSeMXDHBDe1MT+LUgx0Pg/p87u2UicdIvqQi8DkxhcUwUXCedMpb4NQjwY3npTmgsURJavLwCRyEcN2HfWsDVGfv/u9ZUWUx+PYFueUKwaNvbtu+Xps3eVWbN1GcgVrdMnWJ7WmJz9SD66EBidag0NF1Ukep0t5A7sFCWdhzvYwHv6L/BehXuHqfaBwBEU7hfVLcXvS4VQv+T/vaSIl7cbeMc7ekv9i8S3e1L5xxpvMGcu1EYPbKyCiijjGXcDKckm43PqU2qNWlXusZMiqF82cuVzolUHN9NNR0HZPxFPV9V0wLtvq+k4DqOwVWDlzuQLVdqFiP08cRX7aRlBVfR8cb55bWe5LExnlcsDp1vAP8Q9BucPMk1Ulh4GnN0SAdxcNHv3q9ohx1Ati4S/tkWjIDe3hQdkUGrGRaFBiUdiTSkI41UkMuuQHP+EaSQYlPQTFWJF03BNPpTu5KFAdkWgDukzsZKMG0Q1TAQQglScOaP/dsZ8+fP75D/9Uu5Gs3FY/2SxPld0DHOciXI9gqjcEidXjE+3BLosy0OcX3T7O5g65ROGyzQ2BZs7WbZVnO5ydLe32hMwTQ4wnnKXW6XW5LAa7oaXOIHoUl0FgLQLH2by8wSTWeAx2Y5PDazK3BqZbeJZwXGPaYhX87ZNszoDdaRxotXO1nNlpdvAPFWHDm8PqEE0sZxDEqGzxisFNnuCWetPcGrObN0p23tTZwMuRVodSV8+LTrOV3eRvzjQZiSjaLYS1WEJe0kNsJlZu9LFun7++wW4gRDRbaxw2nrOGm+xOj9cmtbp9ZqeTM1m8UXfQQCSTVSQox6pvtjot/FpHvIUjJovFEoYvHYV9C5Y/xN9OfcalvII37UEhTbTg/AQIaPb4Vz6j5u8/aViycMod/fkDcpu8QZbZoeBi/vbzP3XPsZvOubMtaPHkD9jt6+U2O7vqU/9C9SMvgrXpQNG/E0oJxun+CiElUa0IKQSUwERxOntKSV7ekcuh9VBZBBo3VUcB58ofKBHCwLyf9qFosz9Ibf8dGqwaBMjRig4SGOZ2UkWI7UiO9OfUPdxOYFApUZyfpY7mgEc5rtNGGk2H1lPhAk1Hp/VAMqQEHEUfEYkkUQq1JMdzsX7kklRrTrUi1wMcDjmu1YYfATj7Y+pGpPEBXuoQIj8rR9mgCl4C9yqmF7xnVWxGVniNqtpVmXBvQ6iwni5YQ8a1jYrXtc2J13HvgkvqWxuva1sbr+P2S5ceKGyBwDv2DbrToe1u6BkAJV7xnVLUaq0sJB8pFqcUIPi3yuwxi4JuLr+P30f3OkPQ72aO0xYo3/EsmO3QO5qEF8S0qQH0UsKXv0brnl9+8M7jF174+DsfvPOl1au/RL5/9DsbNnwHL2pHR1NTRxMZhJtHktOOxLxErPF6YlLvpC9YP73x+4ofw+3xVdrHcDE0dQQCmCRgvt9b35xINDf1CDcRSfJ+pYl+Sf8YcurfmXP5F/kj6J82jNsrkWiEuhVlgFfyNkB3S5MUzLhoNiwSCYcxQ7Ui4J0Xh7fmqRbaPa1tzujxkBRlsEHy0/OM4pYLPb7g9O6BQJN6l9zQ0OGyCaZz0vMTbHOzXfQ7a2tsterTcqxeInODoemdktw+1SbVhKwtW9ffe8VKadK0OVuC3bWzyKm5LeddsWTeorWyY9IMtUFutdu5g+Rn533qkocdvLs2HmhU75br/MmWtD8zA3OP2t1ea636jEzqYxJZGAwFiDEd61oTsrRuW3/3pYNi3bS+Rd+GjOfVpAPNd6y64Gsz1GaZleWIPoYL/v9mTeQBENVEguiF1aC4YeXxFETw6QyPfn0m9g8IrMFAvKM1EI11DARnbqibHk/Iojy5rSdgCyZi06y8sS024PeuO4MfwQ5Y9yKRZCqyYaF30vzeHlmUprR21tR0t0yz8KZY66zWuGvxVQB/36kP+K38t2Hu6NQ9SFJfw0AdpqPEK2qTMpf2VCqJwqPoJezTL824b8akoL+x03nhh+oNo5e77psxg9Q5LzebIKD+fsY34f2MtB9fk9v5b8PT6tYrgv4kRPwd0q9z3gdJSJ0653KjCYPwCaR5aUY63eW48O/kdo33yxX9wCiMv2QTrk8eGSI6Ag6moG9t2P/F7GRNlDjl0gw7pJ5aOXXqyqn8SENnXBmbSwUYLyqJjv3UmY1nKr4t80no0faXsaIEiF/BRaIBnItSce4OUif7W6Vm9T9H1X9Vj71BEm+RdmIJQST/ZfVdudUvh9S/qqNvqT98g9SQ3lHibZY0mRVHooyDN/FHmTgzjdozKw28NwQ0hwN6BCoPKaEk3YtKwNhwRLXuk076CGoZNXDQcRwZvreTZY9EZi+d0s4+ztv8iei04JQl6ZbDD2eHV7X4uHuFVfPrOmcs6m6Kr7hssr+1VZFcEZ/PdJkn1hOs8SXS/NFFgqt94PIZzZ3tdaL6Q5vo6piSzdy737pwsX1VyxUrF15iJ4uNkq+rbyg1Z+O8VsNC1UmcvORPRfxtPrfRwL2p/oA1eZp6Z/aGffoewaXcA/xBlKlQLfhQL/oPgBGP3qsA7IQS8qDVNswHKRSheDUvA3Q7MZoRcJMxlEygujn1QdyzfPfq3dEp/bXh5e5YXW2Ngfvza0ZF6UgFL/E0fTq4LBlvTE2qb/KuuzYSXVnjTfM1osvqMHVbm9950quIZlbqaL6YP7jk3kUtA0GnX2nvq53f3WoSsvEdDRnULgo2fN7lNZJgI8/VWi33c3bBZnGY05+dm+3qc7fNmj4YGKLj2nfqFP+g7jdDlxEV5XsJQZP6hYrS1l0VQr4c69Xueixp90gnZPmE5OF22j+SYEWHlZ0K/Hgsh/Ztsbh6h2DNRlvv6jJh9XaJaHCZDiUDKNTMkvb8vsqCyf3ZNdSmO0fa0Y4baJTtpbKzuVzeeSI7fCKr2Z0WypapnXJ4gnoWy3PoUIlIQ1TXdqhQJIXp9Wx5fYdpeWh2TY5D+YVyKd0jw3iumwi/BC3cEy4o83QlZnW79MrCgCjbhWXBlRZVVZZv4rIKpXC01HFlHdHLoeWVl6UVc/J5uGm6CViW5mulYMk+HqNYr0AyUPivLg2oMs2MPqtuhHyRyiwvNJej1Br+fcLyoAyu8D9B7bgmzUqfFobF5nKnK4+t8MPJkI/xHUNWk117jugWF+xazTAALQn6+UE9lhoI5ApGA/iuJOsrlNP28SVVuBVajXmircLel46w2bJS1Q0Ft0KDuikDFL/3pYrid1Q4FvofwRIo4R9h2ftSwc6jHAMqLcCql8YPHtlzGoByNXYN6v8hXnRaOhUvx0sVLCexwupGDR4NOYC7PePa5keIPACnuAdD7dEadRuTIiS6Lb7uskb381My5yjzF8lGCjBRqdwrWJCagfB3yCy7XT1i92hbcZ5Ci1FJkgYMDf6n+jspIsHFjJrTOdzSMuOa9DbDcj/nH9N9bIoGVgzHPWIQuFuYtaMRaq8eCKI0gEF6lPOZjBz3EEvaaxwSUT9U/8JbJZPJJLBLolH1La/RbF9AbC8JJjv/mMnssKjLRBJyqj9QXxNko0Ux/X79epfiXkm6fmKwF/en1HLc6LxloXWKvGa5rVCVL83VuiPcDEX/K5pTXOxHfx6HHB0t2FI0qI2rCZFTrvPWU67zVuS/kTsLnc7IKhFg30e4FOkqNSfH5PtkmUy6Cpiv/36k2sbqCeCFNa+URpoY0sZoYmCgCr3qgZz6s8I0gP1bYiR+D79H56NOz0EVWCTy2/fffvSCCx59W7uRV9995eqrX8GLesOXNm360iZ+T/El3uZqL+FyzSZ8XxpTiI/G0nkT4zznFZ0t4ipMz5v4q9ssqbdKUZt6u82knPCrt6PZwsnn0XySVnyPR1ZXAn72yx48bWJsu7apnI3Hy8bygUK5Js32qcytapqgmn95uexccj205vGgJ+euOeG2SORmKZr/qKzcx9SFctMJdwMUFZDJITs7dnOp1EKZCxg304Cevyfya+vlKqv6aXK1qIj3imL+L6hL+yvUlFfE0VKZ7E8gBY3M/8VoJCFgizH1W6VyC76nH6b7jiibYVxUmVIEspry/LgZIlCeP11Z4zs/AwvVwtGFEut5S1JY4lfyT0N/evOLo+rUEgjcqc9IkGpQbv3iW7Co5b+KgjvpzYdH85PLcc4X21ouwEGl/S4qnUAvoSlXUUhR1eKr2VWFTB+GMl6FsiQsVD1R3urlAAIoSn7JQkmiVVCHSpCwDH/qPepXQ0Db77CJOAImohB+RPWr31ev5g/kE+zTa4lbvZo8xdWPffQu9yJTPCNB66s+zXoJt/0L6hSoCuBIoK8fnBGG87OoRckJpLqyWe4YbpGi50g0+3I3UD85Oa0fzubfoXxPLbW3FDWzigmyJeM0tQkax7PqTy80+UxfUHPlBZIRVNQ+v0xRm8REKPoLmNr0+Uo48v9GFbXPKylqQ2IKm00QddgyWGMROCTxdLB9nCY8P7j2DjlsV/+mfr0C0r/NkeXbbpPlOTBBwT0mVz1zx9S/wJecBF9Wgv3p032iP2v4VSgfgW2G+HUEdEXU6iq4CtpLJfIN9XQG8dwa1VoO8XC2SrPDDyCOQptXgbcPvlAgBfxBoGwftQKeKFrNTASPt3pGGqDt/QRasn2kri+H6L80MJRsmVYJrAKyDItpJUy3/15WYIJqcJ9Q5N/LFJ4c3dc1URpWl9hW6mu50MUIelg4ucTPf15zs5DFo1c0VSp1tKB9jkwIyuM45kb+IP8gHed+6jO3v0KbIknzLy636E8KPTdCuUpB0wLo9JKnAO6pv0vS31EtBha/fJemkgLVVnd8KCk4qBTpQ5m7FbifBKrPJcq0pZAFVG/XbOFz+Tcq2MLrcmV28Nmi/OHskh82bau0k8eWCaPijQPWQ5lUvslwVCfHkXBMIehqUgtDNLeauH1huvZTbYmw+luPjyWoNGEuxRLR7LK5fSyXFUyK7PURQv2v8D3XOt2NJ6liBbmPGOsakw1kbeOs+31Wm5qpH+iJWSzqdPr2O7zc2TmtnrzCig6bBd/vgQmzOlz0STWIlmZEQfupogOZFHUZ7EkUnMn0RrpIMqAgHRJAOjIJ3yGw1I/MAp9q9S3Q/clADNm1wEeO+xbwg5OIYHZLY3ehG5lJk2xhco+6JWybpEVz2wrR6hZyD0QXZbeDVB+onmlimpkWprdAs4WEZDSQppsDlcdCBJJESIYFuAtUnC4GIF2C3Uu2Kv7L1bdz6FxtqxpG4TqQOqOUNAJ2HLvPWA2GgDy4O4vaDrtyl6P+1fAll+SyFcQ28GHqh7fvvf37udylf0fNwhzgz87Y+cf5x9GnF6ygHu18sAbipWeF0YPBgp2GaKeQduxxdEr3SgbH1kvH7tvqSLhedomOvZyts2dw8acu3dY/f+ucuMtCuP/e4zC4XnH3OLZ8ZuxTWxy8dJfU5dhDeKPSlJy5pn/+7u3XrJhmr9C5CuleGflGQocKnlAUaRKp0BAHV0ZwUt9VCqk6zYOgRIuMfePJzdmBdpPJ7/6B23+f+sp9NMDZevovvfYHG5dGPISQq1DojqNckchVrCcCYz/Q0hI0m3NKDRfkgsrnamo+p0CAq1FyvC3a3Nak/s5VX282x9Ufy3E39VAx6o7LpCvO2wK+ch9jNqpJCutcIOooKnYWtDK8gTRVYygRQfwgzKM5+jP2jOZdx3r32Py7rQUPOzAnoRs95NvRAR0qLGU11Taqu1bUYSzMcWjMEir067JQQHfIrLBHsrgv00/Wavd8HRLMEEYFSW3HCSNQehnrHztKqHcDyo4VfZ6gPKCR+gufwA8GegxUEo4A+gd0BASHiH6jYMLIsUdQJTs/C641KN4oCHWolCMLlMfIdtWKScjx7SM5LD9HnfmhrGI0S139UWfUnxgOXdJFW+AMcGjKr6eHAttHF5sUoeArYKDcxMSYcKA/xUDhPiEOEAPafSIUFArN0r24ynI91EPARDXvIDYyvqZaWeroBOUABQA/E+DXC7PWafDLQY2oiwpUEyj4RQtVlUp1GrM7In2p2A7VuiOW6otMiGOo5Mrp05ejVuTy6dNX/k/7mybZQ0nUmfrbx3U4KueDnlHm5wdh8FFeKnoaKKh/TK18StOPhwG9Xo5mqXAxvw/79YQwwDR+nAKQQ4izVXioB84qcppWB7IqjU45z4CE17OvF1Dw+oTFqxtz8dxwtogBnF9MjIl/in+K8s3hM9laIn0TiCbTAXL0T798bPXqx36p3chrv0O+GC9Xaj48Ecv8U8UEeBvUEsDlTepiU5OvlpeNGvpnKF0RvUooWhIjnx6GeBapXCQYTw9DNg6/OC3gZjp76oNTj9Kz6Jqobxb9NDqc08vcKReOpcsQV2K8InXFaXW3aI6Ofr1k48rp7CX7rx+v1UKPsfvzQU0Kc83i2VdILmd2/yX55zT9luN2+Cu4nKfwPcK/CvDVU+pHh8+LaldIf1fA5h3ndT6Fln9/W/9Ce1vndfvJtnPVO2xhm3qbafHVCN1X363UXHq9xuVD8OSD29Z8pZ5cZrern9cAdGW/uib/ud+VK0L9a42r6C90kL8KzxwLQw9NkIQJL0ASU8M+VG0KsUdgdvpgP/6NqqP0/gHZFUfGEijZLHpiIgvV5/Bltrj8Qd7XQd5p4P+7tJo30NMO6VGBwahSPMYiaaBYoLY6uEnciyhhh1Z/vvacG/rjpsvnpzs0B1Id6fmX8119l88XnOxe/uGrzzHcdu7UtY3+2vmXN5zUyj3ZcPl8p1sZSs6/nGXtwrV7Ka0XZdz83fwjjINpZWYw85lL8BRK4nGyIir2RiOsEyipuEcIakpGjWgBjLiHWOgj0Yi34gW1kKPxHt2Na5q+lwg1RdRSpFDNzosb44YJXnAfoEOpZW//6u1lhYA6leevezbI26zNHO811M2dc5HFxpk4i1jPC0s21/BWW5DnPQbn2X1WK43/aM2n18DfSoybbNHijFpamzXI31eRibGUOxSu/lT96YZlq1Yt20DaSBuG6knw2eusHs5EPBfNmVvHKdaQzcDfz9ZsXmLDWGXy2U5OsYSsIn8CS12jQIyD12KKqZrLPy7mSPdICmd6WGHG8NDZkkHuE4h9TU8FpmUO/VjC/EinToFyoNDz2p9XD6g78WgQdPG7Z3R0T/Z5dTM9lsL8Ktek7szl2L+gQwGgwkZHc2g5Su7NvVqwGy2Ua4KSXUwt1X4PaM5paaEu6jQ5zVFyNabxvUksVt2T/4VeamYPlLtffdQsk+2sUTY/zDXl/05W53/Bz9UK3p7LjapZ2ZxOm+UlZXrL3HHGqO8+wVroDaCTTnTxitMxmiAAYQzVJQH+nj3oIHnPaN6Zq6sNSLjBl8tKgVr2mj/9CWi9dnKca8rBQBsd5R1tzVlgrl5pbnPw6kZclCr2CHxMnHohLz+3KRQokzALyeIKFU1TNCiayJdoHvDYe7K6mZLm8S3uJ9dojuaJ62/qN/tjQxnSnhnKPw+LNrLi8ZKyJ3x1YhiI1aNAtP6NzCGzYv3DmaGh/LvQZnt0evgIhTFV0kE/PYxAnOHhCQUZdCWY5JWJwMzlAGl1mpNbDU7yyGnhRMILsYhH3VRAijrPcBU8/Cj1Y9NY6cnGVW0CjTLaz7E3epvaT/LtTV72Rs+0WVVmd0dz/MGTI5F0OsIviaqDlbbO5X6xT3PeXbXHRtf/z+fdka+eKPr8KF7IF4vBsT9MFPuPJMBTBMq9hQxXelQ+bewnf18ap4Ib+mSMrtDU5zqlD8QANa5MBGh/OwOvSDfcV2d66mfEWsbGWmIz6nsyZDWQSmqmxDneYyvjHPmRXHZxeueyRGLZzvRioKnGto9nIPkibAJA16adcOZRQr1iAP3bUyBR7T4RgAWTKxhkCYFwshq+7iV9r0whk50cmRcTg4fy5x4OmmNkHndIA2+YuMbmE9dwGYB4KFTsvnDE6Ah47r/fE3AYI+oXADpkdlENcZ8OZEEf8FFGZNxMs6ZLpG3SUFLL7Q2kcFU/A/Jsw+vWDa/7emewLaoeibaF1B9qUNnuqWK3+UfXYVL1v/omD15xxeDkPnXTOKSVcCbDGtOu0YQNpGAP7U1HU58UrqGu8xIbHtkQ3LVhb7Dx46ET3Ffcm1q0YcOizNmf3bC3VjWfAcpSv3MyTlgJ23FHQgmgvk+gk8pL0mcCDOn08MDAQlf+/SlTZ1z12fnqntOhbOTL9/ZdevbAPN+yby1f/uUtC/ixm8ZBo59LTXEW060hGrTDplNprWd58fwB/b/E27BdS/s7U+rGVCeQ46nzaw9QccnmZerGZZs3Yw9aVHt+Kh6HN4ti6lxIhT/wahnZtWwzlY9QHQ2c79C+dxzvVDKy8GqKWQERO9YAKbpsDUTLdWV5dE8PVPjvj9pqw7ah/PFVtkit7aj6G5xY9mfJrCz1j1e0BcnPol4UjtrCdbahIVtd2HaURujnFJR8CuOuUUfhrGhgKKgjCYNSvCc1WKlEp8wHUaAYynFNyzZn+2MnYv36dbMDBTonl/T/ma5IKAyEGz+4eRnVtaX6tss2o34u8mWorFtuFgm4A6qK/yp/gLEBVat5WnPDdKA574ubuFJ/IUfZ/Y2Nt6mN+ZNNTSTaeI56gKwkXerTe9DDHUw8/H35FY3nNN7GGuBKWhrV9ep+0k1WjNWVaHkW1yA+QHWNu8rtBw2a5YXuE40rs7/GA+j09V3hA98yRnFPOGr8ltGlsFdD/7tRce3LH6Trcneuiy7K7J3khKu+3qUaXPWaX7T6/Kfj9BX2eZq2XAcZT79u1ClJzUtHUqfqSMWBcZS43Ena0cUGLgpkKxB1QM+0Fxz10wgg6r5rltnFpH05pepUq3Y2HfYqeKRntmUFNz+XmcOs1H31U6cC6RTVLfCg7RNBF1UF2/wBgu0fFQtPEU1sSg3VcNsR7dWq3af87tUFn1l3ltXpaJxpNvtcZkH2WmMst3JqRpxUH+WC0E1qOGtP66s1MYv+VLu8/XFXvV/ZbunYYBeVN64ls0ur6NzpV9xzlmQwB5qC4Tq70WC0tk8dWJXeHvkD0h9zJOM0vD86/1NJMaIAolctvlByferCsqOKDKceOfUu1PsmoFCamV5mCrMUOCi6V6FJosMF22AcrKJgQDVhfYh6tepp/lYgvnCEAbJQ1L0rOpajEmRcasMiPfxhgGoVo4rwreQpV6fUJHH2e8fa1s2c13Apl1b89a58ozdoap2sjgLN9uISl7P1DrulyeIkt0zr6JjWocoPOZsaXPb6jtqBblsgsaRre2xHi4nELm0MhG1+x1SXwLpFi53b+aHRYo/IrbZtuWAKu5cSEXfybnnmUCaXGTpQr0xK2O2WWY76f+nAjNVf7nCZHU5XqIkTnpt6VtvsFlPXg1031g/VRdpkkyVpD7jnmax88QwDvg/66NnMRdRXTcGTmQc3cuINwN5IQqi0yzb+YFVHuVqI5s4ADfg5oE4ybDLd28mFSFmYvRoomsWXEdLU2Wl3GJy93ZNb/d5gqmNaqJZSO1l6PVRy0nZIj/45EetjLguh1rLqR+SK0hO6NrsqcNX8zoUdjQYDJ7tb4os6+i+Y0qpY2AWlnLRDWdGFTfGY1gV0zNAtJ7pdo24se0D88AwLY/gZmE9iuP4V5v7CSR/RThaHLh+UeBkXwU6BC7lGOevK65udTv+tS/PfW7qj3ljTcj3b9OkbV85t8xsMj7Ddj7DGpthZKwKPvso/c/1K9aLE12fMWLV1y1D9ua8lyJdWXr/bG+noCFutf/mLILe39ITUV4igr3876fpX5g2zeB52sWnIL4fXHlgeUzOx5QfIvJQyrKQE9wHUqVq+PEaOrz0wVvNbJZVSfsuMzxN4l9PkedFzw9V5Dj+nzpgoT4ZxCxJfC5RWLc74YVHxKlExCYt0JAOMatREhHBSCAtSfod6x6Ls8HCWECLwXZ9nd5Dz1T24JUdWs6fU3++fcnT49Qe+kBs+wdsMZgPXMp3U5S958snPP/EE7bvkOPCuTUDTUQ/UzirLhML9yPahoe1D5Fj5jWsaoveyP00PehdUAHk/seDVWsvDWXXXsyn/4wfpXc2V3/Qxli3jl/5hj/83avSCfpTNxOEKLmTjxOEKuxgNlsQn0xgct724mhynupNW1Ph6o3RYS3/+2TJrzLlkFz+ip3qCHKf6eqW02QJLjBYuuj4sobhCWqa/YHGEHpcnumuWSOhxeaL7sOakNR6vvmo+YcfFA8UFXEPZf9UjyudIOyNwx/i90DdsujS/FX2UAwvWSVK4NxaMhAGw3oowp/uc8CTi7D2rBgZWwb/60faR7SPsEbjkXy4G0XaqhXPwe2cePjxjxuHD6ssQuR1fq6PF0E+o2t1nePTn8TUmxz/A3crMoCc7egESuoTHYc7mYdg6etORoOhR7BBGD+qJopELrl4S6cJNRtEAsLP/OdvnJq0Wo0GolY2Et9VFB2Kf+4bZvVyxfOMz3WdFfSIryj6DwWghre7aQbdiDrkTL3A3vNDuDpk93HqXwam+bWmUJZfNn5ozKV5Pmmq8PF/jVY+2Tlk2M2RzSXKjmbQ4RZcQavEYrN/9rlXwtIQqzxQNMzPPfHYLvuPoO9TbT8bpGw5CQPGd+SyX/Cyf0Vxjd2R9NmsunnXYa8xGHzn+sSfM5J0y0DZEXWWxkXjcR75KBLNLHi7XvX2G8VOrf4Ykg0AMdBESIpo7MgAfyakA6rkqpI6UjNs0px7cMV+D5BF49Tez1VGnYmq0WIijp985m4Sn2gJR9b07riPPFo97OYbUZbxJCpot7H/lpZBicglCPN7WOfJkcHqc3ElWqvvz/1E6bIQrG+tz6WkM1SM9FBTR7FSs8KyBBytSmNEoquJNFN5EQyTiCrnKDx1h58yxCepPHU5nxGoxEQeeOZi2m80DxNxncVhr6BmEfUarxejw+WSiHhWk19bSY7aKR5MsteblJpfTLtjimBouXsm3d3djjYM+wEW0El9dM/ueVRWIsXwe43R7SgbVZqrnqoJ1X/kuF7pcgf8duv4q6vayV5U9zMV91GxO59UUjW8rHV6u799WzKMT7umRCXbYUKM+foaCcwgaoqZUtmodV3p+X7akb4dnU9B9La38RPFUG2SCC90tVA4XwEFhyOpZZrUCsgWYHsczLFBBVGNtstoN1bw0Z+O4fYIbvZVt4EUcJEKOhHeincWqONw+q6w5Go+WGOSR7LhKV+KBqbBPpfUvOf9QqkpDyVhBeyyZQGMsdA5FBUqvFMtUyGq9vjnsAJU4UcrxldP1CCaofyDkSAifoP5QwWx+SyUGxp75BzGAvtG7uQ38LehlyEQMeh0TeE6Bm7tYdXqdkt0uOb3kfYlNwmOdDyacOq/qlFo1v+PTmTi3E/glC9W11b34A22zmLzvb231Q0L2Bgg60OTW4YdstO+YOJnO38TtpH7zy9ymokWyA79qlVSn38HtpFlImFnhu3b4boNWXklOXV0Iwo7lQ1hrZyPFcwtjwFP7iEKSHSSJw509kh8kj6pr+H1jR7km9vcvqN9657vffefkv+fKxge1X+7RdjYUPIESN7gTvRkB/RMYtEkaVkdHApmdBPpnKmz0n1xSWFOyVIuLrinZwpoCRe6kyiVZoHX088F+UX4+WKS4iBTP0IWxGtZgOdMaV4KTayqHQF/VihBwTbgDXTCmKoOBJeNhwJMzEVjtjIFLuU38fPR7hqNG1JS7g/qRCuy3vmQ3W9Vu8qbVbP+SzazGRJH83MzP90Ck2m31mMjP8TiLn5uwD2Ugr2PFvPQjB5BnSJvQxGQZZEB+LopqzGzDbMmbkAPkZVJjeO5FzOSBKCgJze2ZS4Gemc9twrwY6u9H61iUQTcRvtdT9RW3tRxAWwFs2tcuJRnI6xjmBdWjbgFNRHMHiF1uHYBfUR/ut5Ug2jXAaT96+9RH/FToRwIzGbKmVJ1AZQnoabSB1yyIg7ByAridHApPMjyw0OiV6RjSbCuzwLAvFizBliWJua1tsuAgvNPbmljYbpt8lkWam7b3XZiOiKJskMOtmfScnsbPW208knwjuXrXK4Q1iKIgNyYXXDVT9C2Ye/78GQ5BEEXfFdde2RwauOysdJNL5AzCy84ard/nGAVN8alecnFdgu5Gbd5DJTL+hHZK0vApVy3OfU8XTSJg1TlssivsPYUlIqvn66PzrVTymCc4wgF6SDNR0pDf+9Gp+VnsUH5WtpHYsuhOaey8zdwLN47V8MTbm78g687+P3cx6tcAeNpjYGRgYGBk8s0/zBIfz2/zlUGeZQNQhOFCWfF0GP0/8P8c1jusIkAuBwMTSBQAYwQM6HjaY2BkYGAV+d8KJgP/XWG9wwAUQQGLAYqPBl942n1TvUoDQRCe1VM8kWARjNrZGIurBAsRBIuA2vkAFsJiKTYW4guIjT5ARMgTxCLoA1hcb5OgDyGHrY7f7M65e8fpLF++2W/nZ2eTmGfaIJi5I0qGDlZZcD51QzTTJirZPAI9JIwVA+wT8L5nOdMaV0AuMJ+icRHq8of6LSD18fzq8ds7xjpwBnQiSI9V5QVl6NwPvgM15NXn/AtWZyj3W0HjEXitOc/dIdbetPdFTZ+P6t+X7xU0/k6GJtOe1/B3arN0/pmz1J4UZc+D6ExwjD7vioeGd5HvhvU+R+DZcGZ6YBPNfAi0G97iBPwFXqph2cW8+D7kjMfwtinHb6kLb6Wygk3cZytSEoptGrlScdHtLPeri1JKueACMZfU1ViJG1Sq5E43dIt7SZZFl1zuRhb/GOs44xFVDbrJzB5tYs35OmaXTrEmkv0DajnMWQB42mNgYNCCwk0MLxheMPrhgUuY2JiUmOqY2pjWMD1hdmPOY+5hPsLCwWLEksSyiOUOawzrLrYiti/sCuxJ7Kc45DiSOPZxmnG2cG7jvMelweXDNYXrEbcBdxf3KR4OngheLd443g18fHwZfFv4NfiX8T8TEBIIEZggsEpQS7BMcJsQl5CFUI3QAWEp4RLhCyJaIldEbURXiJ4RYxEzE0sQ2yD2TzxIfJkEk4SeRJbENIkNEg8k/klqSGZITpE8InlL8p2UmVSG1A6pb9Jx0ltkjGSmyDySlZF1kc2RnSK7R/aZnJ5cmdwB+ST5SwpuCvsUjRTLFHcoOShNU9qhzKespGyhXKV8SPmBCpOKgUqcyjSVR6omqgmqe9RE1OrUnqkHqO9R/6FholGgsUZzgeYZLTUtL60WbS7tKh0OnQydXTpvdGV0O3S/6Gnopekt0ruhz6fvpl+nv0n/h4GdQYvBJUMhwwTDdYYvjFSM4oxmGd0zVjK2M84w3mYiYZJgssLkkqmO6TzTF2Z2ZjVmd8ylzP3MJ5lfsRCwcLJoszhhyWXpZdlhecZKxirHapbVPesF1ndsJGwCbBbZ/LA1sn1jZ2XXY3fFXsM+z36V/S8HD4cGh2OOTI51ThJOK5zeOUs4OzmXOS9wPuUi4JLgss7lm2uU6zY3NrcSty1u39zN3Mvct7l/8xDzMPLw88jyaPM44ynkaeEZ59niucqLyUvPKwgAn3OqOQAAAQAAARcApwARAAAAAAACAAAAAQABAAAAQAAuAAAAAHjarZK9TgJBEMf/d6CRaAyRhMLqCgsbL4ciglTGRPEjSiSKlnLycXJ86CEniU/hM9jYWPgIFkYfwd6nsDD+d1mBIIUx3mZnfzs3MzszuwDCeIYG8UUwQxmAFgxxPeeuyxrmcaNYxzTuFAewi0fFQSTxqXgM11pC8TgS2oPiCUS1d8Uh8ofiSczpYcVT5LjiCPlY8Qui+ncOr7D02y6/BTCrP/m+b5bdTrPi2I26Z9qNGtbRQBMdXMJBGRW0YOCecxEWYoiTCvxrYBunqHPdoX2bLOyrMKlZg8thDETw5K7Itci1TXlGy0124QRZZLDFU/exhxztMozlosTpMH6ZPge0L+OKGnFKjJ4WRwppHPL0PP3SI2P9jLQwFOu3GRhDfkeyDo//G7IHgzllZQxLdquvrdCyBVvat3seJlYo06gxapUxhU2JWnFygR03sSxnEkvcpf5Y5eibGq315TDp7fKWm8zbUVl71Aqq/ZtNnlkWmLnQtno9ycvXYbA6W2pF3aKfCayyC0Ja7Fr/PW70/HO4YM0OKxFvzf0C1MyPjwAAeNpt1VWUU2cYRuHsgxenQt1d8/3JOUnqAyR1d/cCLQVKO22pu7tQd3d3d3d3d3cXmGzumrWy3pWLs/NdPDMpZaWu1783l1Lpf14MnfzO6FbqVupfGkD30iR60JNe9KYP09CXfvRnAAMZxGCGMG3pW6ZjemZgKDMyEzMzC7MyG7MzB3MyF3MzD/MyH/OzAAuyEAuzCIuyGIuzBGWCRIUqOQU16jRYkqVYmmVYluVYng6GMZwRNGmxAiuyEiuzCquyGquzBmuyFmuzDuuyHuuzARuyERuzCZuyGZuzBVuyFVuzDduyHdszklGMZgd2ZAw7MZZxjGdnJrALu9LJbuzOHkxkT/Zib/ZhX/Zjfw7gQA7iYA7hUA7jcI7gSI7iaI7hWI7jeE7gRE7iZE5hEqdyGqdzBmdyFmdzDudyHudzARdyERdzCZdyGZdzBVdyFVdzDddyHddzAzdyEzdzC7dyG7dzB3dyF3dzD/dyH/fzAA/yEA/zCI/yGI/zBE/yFE/zDM/yHM/zAi/yEi/zCq/yGq/zBm/yFm/zDu/yHu/zAR/yER/zCZ/yGZ/zBV/yFV/zDd/yHd/zAz/yEz/zC7/yG7/zB3/yF3/zD/9mpYwsy7pl3bMeWc+sV9Y765NNk/XN+mX9swHZwGxQNjgb0nPkmInjR0V7Uq/OsaPL5Y7ylE3l8tQNN7kVt+rmbuHW3LrbcDvam1rtzVvdm50TxrU/DBvRtZUY1rV5a3jXFn550Wo/XDNWK3dFmh7X9LimxzU9qulRTY9qelTTo5rlKLt2wk7YiaprL+yFvbAX9pK9ZC/ZS/aSvWQv2Uv2kr1kr2KvYq9ir2KvYq9ir2KvYq9ir2Kvaq9qr2qvaq9qr2qvaq9qr2qvai+3l9vL7eX2cnu5vdxebi+3l9sr7BV2CjuFncJOYaewU9gp7NTs1LyrZq9mr2avZq9mr2avZq9mr26vbq9ur26vbq9ur26vbq9ur26vYa9hr2GvYa9hr2GvYa/R7oXuQ/eh+2j/UU7e3C3cqc/V3fYdof/Qf+g/9B/6D/2H/kP/of/Qf+g/9B/6D/2H/kP/of/Qf+g/9B/6D/2H/kP/of/Qf+g/9B/6D/2H/kP/of/Qf+g/9B/6D92H7kP3ofvQfeg+dB+6D92H7kP3ofvQfRT29B/6D/2H/kP/of/Qf+g/9B/6D/2H/kP/of/Qf+g/9B/6D/2H/kP/of/Qf+g/9B/6D/2H/kP/of/Qf+g/9B/6j6nuG3Ya7U5q/0hN3nCTW3Grbu4Wrs/rP+k/6T/pP+k/6T/pP+k+6T7pPek86TzpPOk86TzpOuk66TrpOuk66TrpOlWmPu/36zrpOuk66TrpOuk66TrpOvl/Pek76TvpO+k76TvpO+k76TvpO+k76TvpO7V9t+qtVs/OaOURU6bo6PgPt6rZbwAAAAABVFDDFwAA) format('woff'),url(data:application/x-font-truetype;base64,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) format('truetype'),url(data:image/svg+xml;base64,<?xml version="1.0" standalone="no"?>
<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd" >
<svg xmlns="http://www.w3.org/2000/svg">
<metadata></metadata>
<defs>
<font id="glyphicons_halflingsregular" horiz-adv-x="1200" >
<font-face units-per-em="1200" ascent="960" descent="-240" />
<missing-glyph horiz-adv-x="500" />
<glyph horiz-adv-x="0" />
<glyph horiz-adv-x="400" />
<glyph unicode=" " />
<glyph unicode="*" d="M600 1100q15 0 34 -1.5t30 -3.5l11 -1q10 -2 17.5 -10.5t7.5 -18.5v-224l158 158q7 7 18 8t19 -6l106 -106q7 -8 6 -19t-8 -18l-158 -158h224q10 0 18.5 -7.5t10.5 -17.5q6 -41 6 -75q0 -15 -1.5 -34t-3.5 -30l-1 -11q-2 -10 -10.5 -17.5t-18.5 -7.5h-224l158 -158 q7 -7 8 -18t-6 -19l-106 -106q-8 -7 -19 -6t-18 8l-158 158v-224q0 -10 -7.5 -18.5t-17.5 -10.5q-41 -6 -75 -6q-15 0 -34 1.5t-30 3.5l-11 1q-10 2 -17.5 10.5t-7.5 18.5v224l-158 -158q-7 -7 -18 -8t-19 6l-106 106q-7 8 -6 19t8 18l158 158h-224q-10 0 -18.5 7.5 t-10.5 17.5q-6 41 -6 75q0 15 1.5 34t3.5 30l1 11q2 10 10.5 17.5t18.5 7.5h224l-158 158q-7 7 -8 18t6 19l106 106q8 7 19 6t18 -8l158 -158v224q0 10 7.5 18.5t17.5 10.5q41 6 75 6z" />
<glyph unicode="+" d="M450 1100h200q21 0 35.5 -14.5t14.5 -35.5v-350h350q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-350v-350q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v350h-350q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5 h350v350q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xa0;" />
<glyph unicode="&#xa5;" d="M825 1100h250q10 0 12.5 -5t-5.5 -13l-364 -364q-6 -6 -11 -18h268q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-125v-100h275q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-125v-174q0 -11 -7.5 -18.5t-18.5 -7.5h-148q-11 0 -18.5 7.5t-7.5 18.5v174 h-275q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h125v100h-275q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h118q-5 12 -11 18l-364 364q-8 8 -5.5 13t12.5 5h250q25 0 43 -18l164 -164q8 -8 18 -8t18 8l164 164q18 18 43 18z" />
<glyph unicode="&#x2000;" horiz-adv-x="650" />
<glyph unicode="&#x2001;" horiz-adv-x="1300" />
<glyph unicode="&#x2002;" horiz-adv-x="650" />
<glyph unicode="&#x2003;" horiz-adv-x="1300" />
<glyph unicode="&#x2004;" horiz-adv-x="433" />
<glyph unicode="&#x2005;" horiz-adv-x="325" />
<glyph unicode="&#x2006;" horiz-adv-x="216" />
<glyph unicode="&#x2007;" horiz-adv-x="216" />
<glyph unicode="&#x2008;" horiz-adv-x="162" />
<glyph unicode="&#x2009;" horiz-adv-x="260" />
<glyph unicode="&#x200a;" horiz-adv-x="72" />
<glyph unicode="&#x202f;" horiz-adv-x="260" />
<glyph unicode="&#x205f;" horiz-adv-x="325" />
<glyph unicode="&#x20ac;" d="M744 1198q242 0 354 -189q60 -104 66 -209h-181q0 45 -17.5 82.5t-43.5 61.5t-58 40.5t-60.5 24t-51.5 7.5q-19 0 -40.5 -5.5t-49.5 -20.5t-53 -38t-49 -62.5t-39 -89.5h379l-100 -100h-300q-6 -50 -6 -100h406l-100 -100h-300q9 -74 33 -132t52.5 -91t61.5 -54.5t59 -29 t47 -7.5q22 0 50.5 7.5t60.5 24.5t58 41t43.5 61t17.5 80h174q-30 -171 -128 -278q-107 -117 -274 -117q-206 0 -324 158q-36 48 -69 133t-45 204h-217l100 100h112q1 47 6 100h-218l100 100h134q20 87 51 153.5t62 103.5q117 141 297 141z" />
<glyph unicode="&#x20bd;" d="M428 1200h350q67 0 120 -13t86 -31t57 -49.5t35 -56.5t17 -64.5t6.5 -60.5t0.5 -57v-16.5v-16.5q0 -36 -0.5 -57t-6.5 -61t-17 -65t-35 -57t-57 -50.5t-86 -31.5t-120 -13h-178l-2 -100h288q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-138v-175q0 -11 -5.5 -18 t-15.5 -7h-149q-10 0 -17.5 7.5t-7.5 17.5v175h-267q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h117v100h-267q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h117v475q0 10 7.5 17.5t17.5 7.5zM600 1000v-300h203q64 0 86.5 33t22.5 119q0 84 -22.5 116t-86.5 32h-203z" />
<glyph unicode="&#x2212;" d="M250 700h800q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#x231b;" d="M1000 1200v-150q0 -21 -14.5 -35.5t-35.5 -14.5h-50v-100q0 -91 -49.5 -165.5t-130.5 -109.5q81 -35 130.5 -109.5t49.5 -165.5v-150h50q21 0 35.5 -14.5t14.5 -35.5v-150h-800v150q0 21 14.5 35.5t35.5 14.5h50v150q0 91 49.5 165.5t130.5 109.5q-81 35 -130.5 109.5 t-49.5 165.5v100h-50q-21 0 -35.5 14.5t-14.5 35.5v150h800zM400 1000v-100q0 -60 32.5 -109.5t87.5 -73.5q28 -12 44 -37t16 -55t-16 -55t-44 -37q-55 -24 -87.5 -73.5t-32.5 -109.5v-150h400v150q0 60 -32.5 109.5t-87.5 73.5q-28 12 -44 37t-16 55t16 55t44 37 q55 24 87.5 73.5t32.5 109.5v100h-400z" />
<glyph unicode="&#x25fc;" horiz-adv-x="500" d="M0 0z" />
<glyph unicode="&#x2601;" d="M503 1089q110 0 200.5 -59.5t134.5 -156.5q44 14 90 14q120 0 205 -86.5t85 -206.5q0 -121 -85 -207.5t-205 -86.5h-750q-79 0 -135.5 57t-56.5 137q0 69 42.5 122.5t108.5 67.5q-2 12 -2 37q0 153 108 260.5t260 107.5z" />
<glyph unicode="&#x26fa;" d="M774 1193.5q16 -9.5 20.5 -27t-5.5 -33.5l-136 -187l467 -746h30q20 0 35 -18.5t15 -39.5v-42h-1200v42q0 21 15 39.5t35 18.5h30l468 746l-135 183q-10 16 -5.5 34t20.5 28t34 5.5t28 -20.5l111 -148l112 150q9 16 27 20.5t34 -5zM600 200h377l-182 112l-195 534v-646z " />
<glyph unicode="&#x2709;" d="M25 1100h1150q10 0 12.5 -5t-5.5 -13l-564 -567q-8 -8 -18 -8t-18 8l-564 567q-8 8 -5.5 13t12.5 5zM18 882l264 -264q8 -8 8 -18t-8 -18l-264 -264q-8 -8 -13 -5.5t-5 12.5v550q0 10 5 12.5t13 -5.5zM918 618l264 264q8 8 13 5.5t5 -12.5v-550q0 -10 -5 -12.5t-13 5.5 l-264 264q-8 8 -8 18t8 18zM818 482l364 -364q8 -8 5.5 -13t-12.5 -5h-1150q-10 0 -12.5 5t5.5 13l364 364q8 8 18 8t18 -8l164 -164q8 -8 18 -8t18 8l164 164q8 8 18 8t18 -8z" />
<glyph unicode="&#x270f;" d="M1011 1210q19 0 33 -13l153 -153q13 -14 13 -33t-13 -33l-99 -92l-214 214l95 96q13 14 32 14zM1013 800l-615 -614l-214 214l614 614zM317 96l-333 -112l110 335z" />
<glyph unicode="&#xe001;" d="M700 650v-550h250q21 0 35.5 -14.5t14.5 -35.5v-50h-800v50q0 21 14.5 35.5t35.5 14.5h250v550l-500 550h1200z" />
<glyph unicode="&#xe002;" d="M368 1017l645 163q39 15 63 0t24 -49v-831q0 -55 -41.5 -95.5t-111.5 -63.5q-79 -25 -147 -4.5t-86 75t25.5 111.5t122.5 82q72 24 138 8v521l-600 -155v-606q0 -42 -44 -90t-109 -69q-79 -26 -147 -5.5t-86 75.5t25.5 111.5t122.5 82.5q72 24 138 7v639q0 38 14.5 59 t53.5 34z" />
<glyph unicode="&#xe003;" d="M500 1191q100 0 191 -39t156.5 -104.5t104.5 -156.5t39 -191l-1 -2l1 -5q0 -141 -78 -262l275 -274q23 -26 22.5 -44.5t-22.5 -42.5l-59 -58q-26 -20 -46.5 -20t-39.5 20l-275 274q-119 -77 -261 -77l-5 1l-2 -1q-100 0 -191 39t-156.5 104.5t-104.5 156.5t-39 191 t39 191t104.5 156.5t156.5 104.5t191 39zM500 1022q-88 0 -162 -43t-117 -117t-43 -162t43 -162t117 -117t162 -43t162 43t117 117t43 162t-43 162t-117 117t-162 43z" />
<glyph unicode="&#xe005;" d="M649 949q48 68 109.5 104t121.5 38.5t118.5 -20t102.5 -64t71 -100.5t27 -123q0 -57 -33.5 -117.5t-94 -124.5t-126.5 -127.5t-150 -152.5t-146 -174q-62 85 -145.5 174t-150 152.5t-126.5 127.5t-93.5 124.5t-33.5 117.5q0 64 28 123t73 100.5t104 64t119 20 t120.5 -38.5t104.5 -104z" />
<glyph unicode="&#xe006;" d="M407 800l131 353q7 19 17.5 19t17.5 -19l129 -353h421q21 0 24 -8.5t-14 -20.5l-342 -249l130 -401q7 -20 -0.5 -25.5t-24.5 6.5l-343 246l-342 -247q-17 -12 -24.5 -6.5t-0.5 25.5l130 400l-347 251q-17 12 -14 20.5t23 8.5h429z" />
<glyph unicode="&#xe007;" d="M407 800l131 353q7 19 17.5 19t17.5 -19l129 -353h421q21 0 24 -8.5t-14 -20.5l-342 -249l130 -401q7 -20 -0.5 -25.5t-24.5 6.5l-343 246l-342 -247q-17 -12 -24.5 -6.5t-0.5 25.5l130 400l-347 251q-17 12 -14 20.5t23 8.5h429zM477 700h-240l197 -142l-74 -226 l193 139l195 -140l-74 229l192 140h-234l-78 211z" />
<glyph unicode="&#xe008;" d="M600 1200q124 0 212 -88t88 -212v-250q0 -46 -31 -98t-69 -52v-75q0 -10 6 -21.5t15 -17.5l358 -230q9 -5 15 -16.5t6 -21.5v-93q0 -10 -7.5 -17.5t-17.5 -7.5h-1150q-10 0 -17.5 7.5t-7.5 17.5v93q0 10 6 21.5t15 16.5l358 230q9 6 15 17.5t6 21.5v75q-38 0 -69 52 t-31 98v250q0 124 88 212t212 88z" />
<glyph unicode="&#xe009;" d="M25 1100h1150q10 0 17.5 -7.5t7.5 -17.5v-1050q0 -10 -7.5 -17.5t-17.5 -7.5h-1150q-10 0 -17.5 7.5t-7.5 17.5v1050q0 10 7.5 17.5t17.5 7.5zM100 1000v-100h100v100h-100zM875 1000h-550q-10 0 -17.5 -7.5t-7.5 -17.5v-350q0 -10 7.5 -17.5t17.5 -7.5h550 q10 0 17.5 7.5t7.5 17.5v350q0 10 -7.5 17.5t-17.5 7.5zM1000 1000v-100h100v100h-100zM100 800v-100h100v100h-100zM1000 800v-100h100v100h-100zM100 600v-100h100v100h-100zM1000 600v-100h100v100h-100zM875 500h-550q-10 0 -17.5 -7.5t-7.5 -17.5v-350q0 -10 7.5 -17.5 t17.5 -7.5h550q10 0 17.5 7.5t7.5 17.5v350q0 10 -7.5 17.5t-17.5 7.5zM100 400v-100h100v100h-100zM1000 400v-100h100v100h-100zM100 200v-100h100v100h-100zM1000 200v-100h100v100h-100z" />
<glyph unicode="&#xe010;" d="M50 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM650 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400 q0 21 14.5 35.5t35.5 14.5zM50 500h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM650 500h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe011;" d="M50 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200 q0 21 14.5 35.5t35.5 14.5zM850 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200 q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM850 700h200q21 0 35.5 -14.5t14.5 -35.5v-200 q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 300h200 q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM850 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5 t35.5 14.5z" />
<glyph unicode="&#xe012;" d="M50 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 1100h700q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v200 q0 21 14.5 35.5t35.5 14.5zM50 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 700h700q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-700 q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 300h700q21 0 35.5 -14.5t14.5 -35.5v-200 q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe013;" d="M465 477l571 571q8 8 18 8t17 -8l177 -177q8 -7 8 -17t-8 -18l-783 -784q-7 -8 -17.5 -8t-17.5 8l-384 384q-8 8 -8 18t8 17l177 177q7 8 17 8t18 -8l171 -171q7 -7 18 -7t18 7z" />
<glyph unicode="&#xe014;" d="M904 1083l178 -179q8 -8 8 -18.5t-8 -17.5l-267 -268l267 -268q8 -7 8 -17.5t-8 -18.5l-178 -178q-8 -8 -18.5 -8t-17.5 8l-268 267l-268 -267q-7 -8 -17.5 -8t-18.5 8l-178 178q-8 8 -8 18.5t8 17.5l267 268l-267 268q-8 7 -8 17.5t8 18.5l178 178q8 8 18.5 8t17.5 -8 l268 -267l268 268q7 7 17.5 7t18.5 -7z" />
<glyph unicode="&#xe015;" d="M507 1177q98 0 187.5 -38.5t154.5 -103.5t103.5 -154.5t38.5 -187.5q0 -141 -78 -262l300 -299q8 -8 8 -18.5t-8 -18.5l-109 -108q-7 -8 -17.5 -8t-18.5 8l-300 299q-119 -77 -261 -77q-98 0 -188 38.5t-154.5 103t-103 154.5t-38.5 188t38.5 187.5t103 154.5 t154.5 103.5t188 38.5zM506.5 1023q-89.5 0 -165.5 -44t-120 -120.5t-44 -166t44 -165.5t120 -120t165.5 -44t166 44t120.5 120t44 165.5t-44 166t-120.5 120.5t-166 44zM425 900h150q10 0 17.5 -7.5t7.5 -17.5v-75h75q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5 t-17.5 -7.5h-75v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-75q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h75v75q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe016;" d="M507 1177q98 0 187.5 -38.5t154.5 -103.5t103.5 -154.5t38.5 -187.5q0 -141 -78 -262l300 -299q8 -8 8 -18.5t-8 -18.5l-109 -108q-7 -8 -17.5 -8t-18.5 8l-300 299q-119 -77 -261 -77q-98 0 -188 38.5t-154.5 103t-103 154.5t-38.5 188t38.5 187.5t103 154.5 t154.5 103.5t188 38.5zM506.5 1023q-89.5 0 -165.5 -44t-120 -120.5t-44 -166t44 -165.5t120 -120t165.5 -44t166 44t120.5 120t44 165.5t-44 166t-120.5 120.5t-166 44zM325 800h350q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-350q-10 0 -17.5 7.5 t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe017;" d="M550 1200h100q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM800 975v166q167 -62 272 -209.5t105 -331.5q0 -117 -45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5 t-184.5 123t-123 184.5t-45.5 224q0 184 105 331.5t272 209.5v-166q-103 -55 -165 -155t-62 -220q0 -116 57 -214.5t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5q0 120 -62 220t-165 155z" />
<glyph unicode="&#xe018;" d="M1025 1200h150q10 0 17.5 -7.5t7.5 -17.5v-1150q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v1150q0 10 7.5 17.5t17.5 7.5zM725 800h150q10 0 17.5 -7.5t7.5 -17.5v-750q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v750 q0 10 7.5 17.5t17.5 7.5zM425 500h150q10 0 17.5 -7.5t7.5 -17.5v-450q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v450q0 10 7.5 17.5t17.5 7.5zM125 300h150q10 0 17.5 -7.5t7.5 -17.5v-250q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5 v250q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe019;" d="M600 1174q33 0 74 -5l38 -152l5 -1q49 -14 94 -39l5 -2l134 80q61 -48 104 -105l-80 -134l3 -5q25 -44 39 -93l1 -6l152 -38q5 -43 5 -73q0 -34 -5 -74l-152 -38l-1 -6q-15 -49 -39 -93l-3 -5l80 -134q-48 -61 -104 -105l-134 81l-5 -3q-44 -25 -94 -39l-5 -2l-38 -151 q-43 -5 -74 -5q-33 0 -74 5l-38 151l-5 2q-49 14 -94 39l-5 3l-134 -81q-60 48 -104 105l80 134l-3 5q-25 45 -38 93l-2 6l-151 38q-6 42 -6 74q0 33 6 73l151 38l2 6q13 48 38 93l3 5l-80 134q47 61 105 105l133 -80l5 2q45 25 94 39l5 1l38 152q43 5 74 5zM600 815 q-89 0 -152 -63t-63 -151.5t63 -151.5t152 -63t152 63t63 151.5t-63 151.5t-152 63z" />
<glyph unicode="&#xe020;" d="M500 1300h300q41 0 70.5 -29.5t29.5 -70.5v-100h275q10 0 17.5 -7.5t7.5 -17.5v-75h-1100v75q0 10 7.5 17.5t17.5 7.5h275v100q0 41 29.5 70.5t70.5 29.5zM500 1200v-100h300v100h-300zM1100 900v-800q0 -41 -29.5 -70.5t-70.5 -29.5h-700q-41 0 -70.5 29.5t-29.5 70.5 v800h900zM300 800v-700h100v700h-100zM500 800v-700h100v700h-100zM700 800v-700h100v700h-100zM900 800v-700h100v700h-100z" />
<glyph unicode="&#xe021;" d="M18 618l620 608q8 7 18.5 7t17.5 -7l608 -608q8 -8 5.5 -13t-12.5 -5h-175v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v375h-300v-375q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v575h-175q-10 0 -12.5 5t5.5 13z" />
<glyph unicode="&#xe022;" d="M600 1200v-400q0 -41 29.5 -70.5t70.5 -29.5h300v-650q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v1100q0 21 14.5 35.5t35.5 14.5h450zM1000 800h-250q-21 0 -35.5 14.5t-14.5 35.5v250z" />
<glyph unicode="&#xe023;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM525 900h50q10 0 17.5 -7.5t7.5 -17.5v-275h175q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe024;" d="M1300 0h-538l-41 400h-242l-41 -400h-538l431 1200h209l-21 -300h162l-20 300h208zM515 800l-27 -300h224l-27 300h-170z" />
<glyph unicode="&#xe025;" d="M550 1200h200q21 0 35.5 -14.5t14.5 -35.5v-450h191q20 0 25.5 -11.5t-7.5 -27.5l-327 -400q-13 -16 -32 -16t-32 16l-327 400q-13 16 -7.5 27.5t25.5 11.5h191v450q0 21 14.5 35.5t35.5 14.5zM1125 400h50q10 0 17.5 -7.5t7.5 -17.5v-350q0 -10 -7.5 -17.5t-17.5 -7.5 h-1050q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h50q10 0 17.5 -7.5t7.5 -17.5v-175h900v175q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe026;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM525 900h150q10 0 17.5 -7.5t7.5 -17.5v-275h137q21 0 26 -11.5t-8 -27.5l-223 -275q-13 -16 -32 -16t-32 16l-223 275q-13 16 -8 27.5t26 11.5h137v275q0 10 7.5 17.5t17.5 7.5z " />
<glyph unicode="&#xe027;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM632 914l223 -275q13 -16 8 -27.5t-26 -11.5h-137v-275q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v275h-137q-21 0 -26 11.5t8 27.5l223 275q13 16 32 16 t32 -16z" />
<glyph unicode="&#xe028;" d="M225 1200h750q10 0 19.5 -7t12.5 -17l186 -652q7 -24 7 -49v-425q0 -12 -4 -27t-9 -17q-12 -6 -37 -6h-1100q-12 0 -27 4t-17 8q-6 13 -6 38l1 425q0 25 7 49l185 652q3 10 12.5 17t19.5 7zM878 1000h-556q-10 0 -19 -7t-11 -18l-87 -450q-2 -11 4 -18t16 -7h150 q10 0 19.5 -7t11.5 -17l38 -152q2 -10 11.5 -17t19.5 -7h250q10 0 19.5 7t11.5 17l38 152q2 10 11.5 17t19.5 7h150q10 0 16 7t4 18l-87 450q-2 11 -11 18t-19 7z" />
<glyph unicode="&#xe029;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM540 820l253 -190q17 -12 17 -30t-17 -30l-253 -190q-16 -12 -28 -6.5t-12 26.5v400q0 21 12 26.5t28 -6.5z" />
<glyph unicode="&#xe030;" d="M947 1060l135 135q7 7 12.5 5t5.5 -13v-362q0 -10 -7.5 -17.5t-17.5 -7.5h-362q-11 0 -13 5.5t5 12.5l133 133q-109 76 -238 76q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5h150q0 -117 -45.5 -224 t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5q192 0 347 -117z" />
<glyph unicode="&#xe031;" d="M947 1060l135 135q7 7 12.5 5t5.5 -13v-361q0 -11 -7.5 -18.5t-18.5 -7.5h-361q-11 0 -13 5.5t5 12.5l134 134q-110 75 -239 75q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5h-150q0 117 45.5 224t123 184.5t184.5 123t224 45.5q192 0 347 -117zM1027 600h150 q0 -117 -45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5q-192 0 -348 118l-134 -134q-7 -8 -12.5 -5.5t-5.5 12.5v360q0 11 7.5 18.5t18.5 7.5h360q10 0 12.5 -5.5t-5.5 -12.5l-133 -133q110 -76 240 -76q116 0 214.5 57t155.5 155.5t57 214.5z" />
<glyph unicode="&#xe032;" d="M125 1200h1050q10 0 17.5 -7.5t7.5 -17.5v-1150q0 -10 -7.5 -17.5t-17.5 -7.5h-1050q-10 0 -17.5 7.5t-7.5 17.5v1150q0 10 7.5 17.5t17.5 7.5zM1075 1000h-850q-10 0 -17.5 -7.5t-7.5 -17.5v-850q0 -10 7.5 -17.5t17.5 -7.5h850q10 0 17.5 7.5t7.5 17.5v850 q0 10 -7.5 17.5t-17.5 7.5zM325 900h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 900h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 700h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 700h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 500h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 500h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 300h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 300h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe033;" d="M900 800v200q0 83 -58.5 141.5t-141.5 58.5h-300q-82 0 -141 -59t-59 -141v-200h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-600q0 -41 29.5 -70.5t70.5 -29.5h900q41 0 70.5 29.5t29.5 70.5v600q0 41 -29.5 70.5t-70.5 29.5h-100zM400 800v150q0 21 15 35.5t35 14.5h200 q20 0 35 -14.5t15 -35.5v-150h-300z" />
<glyph unicode="&#xe034;" d="M125 1100h50q10 0 17.5 -7.5t7.5 -17.5v-1075h-100v1075q0 10 7.5 17.5t17.5 7.5zM1075 1052q4 0 9 -2q16 -6 16 -23v-421q0 -6 -3 -12q-33 -59 -66.5 -99t-65.5 -58t-56.5 -24.5t-52.5 -6.5q-26 0 -57.5 6.5t-52.5 13.5t-60 21q-41 15 -63 22.5t-57.5 15t-65.5 7.5 q-85 0 -160 -57q-7 -5 -15 -5q-6 0 -11 3q-14 7 -14 22v438q22 55 82 98.5t119 46.5q23 2 43 0.5t43 -7t32.5 -8.5t38 -13t32.5 -11q41 -14 63.5 -21t57 -14t63.5 -7q103 0 183 87q7 8 18 8z" />
<glyph unicode="&#xe035;" d="M600 1175q116 0 227 -49.5t192.5 -131t131 -192.5t49.5 -227v-300q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v300q0 127 -70.5 231.5t-184.5 161.5t-245 57t-245 -57t-184.5 -161.5t-70.5 -231.5v-300q0 -10 -7.5 -17.5t-17.5 -7.5h-50 q-10 0 -17.5 7.5t-7.5 17.5v300q0 116 49.5 227t131 192.5t192.5 131t227 49.5zM220 500h160q8 0 14 -6t6 -14v-460q0 -8 -6 -14t-14 -6h-160q-8 0 -14 6t-6 14v460q0 8 6 14t14 6zM820 500h160q8 0 14 -6t6 -14v-460q0 -8 -6 -14t-14 -6h-160q-8 0 -14 6t-6 14v460 q0 8 6 14t14 6z" />
<glyph unicode="&#xe036;" d="M321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM900 668l120 120q7 7 17 7t17 -7l34 -34q7 -7 7 -17t-7 -17l-120 -120l120 -120q7 -7 7 -17 t-7 -17l-34 -34q-7 -7 -17 -7t-17 7l-120 119l-120 -119q-7 -7 -17 -7t-17 7l-34 34q-7 7 -7 17t7 17l119 120l-119 120q-7 7 -7 17t7 17l34 34q7 8 17 8t17 -8z" />
<glyph unicode="&#xe037;" d="M321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM766 900h4q10 -1 16 -10q96 -129 96 -290q0 -154 -90 -281q-6 -9 -17 -10l-3 -1q-9 0 -16 6 l-29 23q-7 7 -8.5 16.5t4.5 17.5q72 103 72 229q0 132 -78 238q-6 8 -4.5 18t9.5 17l29 22q7 5 15 5z" />
<glyph unicode="&#xe038;" d="M967 1004h3q11 -1 17 -10q135 -179 135 -396q0 -105 -34 -206.5t-98 -185.5q-7 -9 -17 -10h-3q-9 0 -16 6l-42 34q-8 6 -9 16t5 18q111 150 111 328q0 90 -29.5 176t-84.5 157q-6 9 -5 19t10 16l42 33q7 5 15 5zM321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5 t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM766 900h4q10 -1 16 -10q96 -129 96 -290q0 -154 -90 -281q-6 -9 -17 -10l-3 -1q-9 0 -16 6l-29 23q-7 7 -8.5 16.5t4.5 17.5q72 103 72 229q0 132 -78 238 q-6 8 -4.5 18.5t9.5 16.5l29 22q7 5 15 5z" />
<glyph unicode="&#xe039;" d="M500 900h100v-100h-100v-100h-400v-100h-100v600h500v-300zM1200 700h-200v-100h200v-200h-300v300h-200v300h-100v200h600v-500zM100 1100v-300h300v300h-300zM800 1100v-300h300v300h-300zM300 900h-100v100h100v-100zM1000 900h-100v100h100v-100zM300 500h200v-500 h-500v500h200v100h100v-100zM800 300h200v-100h-100v-100h-200v100h-100v100h100v200h-200v100h300v-300zM100 400v-300h300v300h-300zM300 200h-100v100h100v-100zM1200 200h-100v100h100v-100zM700 0h-100v100h100v-100zM1200 0h-300v100h300v-100z" />
<glyph unicode="&#xe040;" d="M100 200h-100v1000h100v-1000zM300 200h-100v1000h100v-1000zM700 200h-200v1000h200v-1000zM900 200h-100v1000h100v-1000zM1200 200h-200v1000h200v-1000zM400 0h-300v100h300v-100zM600 0h-100v91h100v-91zM800 0h-100v91h100v-91zM1100 0h-200v91h200v-91z" />
<glyph unicode="&#xe041;" d="M500 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-682 682l1 475q0 10 7.5 17.5t17.5 7.5h474zM319.5 1024.5q-29.5 29.5 -71 29.5t-71 -29.5t-29.5 -71.5t29.5 -71.5t71 -29.5t71 29.5t29.5 71.5t-29.5 71.5z" />
<glyph unicode="&#xe042;" d="M500 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-682 682l1 475q0 10 7.5 17.5t17.5 7.5h474zM800 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-56 56l424 426l-700 700h150zM319.5 1024.5q-29.5 29.5 -71 29.5t-71 -29.5 t-29.5 -71.5t29.5 -71.5t71 -29.5t71 29.5t29.5 71.5t-29.5 71.5z" />
<glyph unicode="&#xe043;" d="M300 1200h825q75 0 75 -75v-900q0 -25 -18 -43l-64 -64q-8 -8 -13 -5.5t-5 12.5v950q0 10 -7.5 17.5t-17.5 7.5h-700q-25 0 -43 -18l-64 -64q-8 -8 -5.5 -13t12.5 -5h700q10 0 17.5 -7.5t7.5 -17.5v-950q0 -10 -7.5 -17.5t-17.5 -7.5h-850q-10 0 -17.5 7.5t-7.5 17.5v975 q0 25 18 43l139 139q18 18 43 18z" />
<glyph unicode="&#xe044;" d="M250 1200h800q21 0 35.5 -14.5t14.5 -35.5v-1150l-450 444l-450 -445v1151q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe045;" d="M822 1200h-444q-11 0 -19 -7.5t-9 -17.5l-78 -301q-7 -24 7 -45l57 -108q6 -9 17.5 -15t21.5 -6h450q10 0 21.5 6t17.5 15l62 108q14 21 7 45l-83 301q-1 10 -9 17.5t-19 7.5zM1175 800h-150q-10 0 -21 -6.5t-15 -15.5l-78 -156q-4 -9 -15 -15.5t-21 -6.5h-550 q-10 0 -21 6.5t-15 15.5l-78 156q-4 9 -15 15.5t-21 6.5h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-650q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h750q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5 t7.5 17.5v650q0 10 -7.5 17.5t-17.5 7.5zM850 200h-500q-10 0 -19.5 -7t-11.5 -17l-38 -152q-2 -10 3.5 -17t15.5 -7h600q10 0 15.5 7t3.5 17l-38 152q-2 10 -11.5 17t-19.5 7z" />
<glyph unicode="&#xe046;" d="M500 1100h200q56 0 102.5 -20.5t72.5 -50t44 -59t25 -50.5l6 -20h150q41 0 70.5 -29.5t29.5 -70.5v-600q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v600q0 41 29.5 70.5t70.5 29.5h150q2 8 6.5 21.5t24 48t45 61t72 48t102.5 21.5zM900 800v-100 h100v100h-100zM600 730q-95 0 -162.5 -67.5t-67.5 -162.5t67.5 -162.5t162.5 -67.5t162.5 67.5t67.5 162.5t-67.5 162.5t-162.5 67.5zM600 603q43 0 73 -30t30 -73t-30 -73t-73 -30t-73 30t-30 73t30 73t73 30z" />
<glyph unicode="&#xe047;" d="M681 1199l385 -998q20 -50 60 -92q18 -19 36.5 -29.5t27.5 -11.5l10 -2v-66h-417v66q53 0 75 43.5t5 88.5l-82 222h-391q-58 -145 -92 -234q-11 -34 -6.5 -57t25.5 -37t46 -20t55 -6v-66h-365v66q56 24 84 52q12 12 25 30.5t20 31.5l7 13l399 1006h93zM416 521h340 l-162 457z" />
<glyph unicode="&#xe048;" d="M753 641q5 -1 14.5 -4.5t36 -15.5t50.5 -26.5t53.5 -40t50.5 -54.5t35.5 -70t14.5 -87q0 -67 -27.5 -125.5t-71.5 -97.5t-98.5 -66.5t-108.5 -40.5t-102 -13h-500v89q41 7 70.5 32.5t29.5 65.5v827q0 24 -0.5 34t-3.5 24t-8.5 19.5t-17 13.5t-28 12.5t-42.5 11.5v71 l471 -1q57 0 115.5 -20.5t108 -57t80.5 -94t31 -124.5q0 -51 -15.5 -96.5t-38 -74.5t-45 -50.5t-38.5 -30.5zM400 700h139q78 0 130.5 48.5t52.5 122.5q0 41 -8.5 70.5t-29.5 55.5t-62.5 39.5t-103.5 13.5h-118v-350zM400 200h216q80 0 121 50.5t41 130.5q0 90 -62.5 154.5 t-156.5 64.5h-159v-400z" />
<glyph unicode="&#xe049;" d="M877 1200l2 -57q-83 -19 -116 -45.5t-40 -66.5l-132 -839q-9 -49 13 -69t96 -26v-97h-500v97q186 16 200 98l173 832q3 17 3 30t-1.5 22.5t-9 17.5t-13.5 12.5t-21.5 10t-26 8.5t-33.5 10q-13 3 -19 5v57h425z" />
<glyph unicode="&#xe050;" d="M1300 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-850q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v850h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM175 1000h-75v-800h75l-125 -167l-125 167h75v800h-75l125 167z" />
<glyph unicode="&#xe051;" d="M1100 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-650q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v650h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM1167 50l-167 -125v75h-800v-75l-167 125l167 125v-75h800v75z" />
<glyph unicode="&#xe052;" d="M50 1100h600q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 500h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe053;" d="M250 1100h700q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM250 500h700q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe054;" d="M500 950v100q0 21 14.5 35.5t35.5 14.5h600q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5zM100 650v100q0 21 14.5 35.5t35.5 14.5h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000 q-21 0 -35.5 14.5t-14.5 35.5zM300 350v100q0 21 14.5 35.5t35.5 14.5h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5zM0 50v100q0 21 14.5 35.5t35.5 14.5h1100q21 0 35.5 -14.5t14.5 -35.5v-100 q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5z" />
<glyph unicode="&#xe055;" d="M50 1100h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 500h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe056;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 1100h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 800h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 500h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 500h800q21 0 35.5 -14.5t14.5 -35.5v-100 q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 200h800 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe057;" d="M400 0h-100v1100h100v-1100zM550 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM550 800h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM267 550l-167 -125v75h-200v100h200v75zM550 500h300q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM550 200h600 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe058;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM900 0h-100v1100h100v-1100zM50 800h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM1100 600h200v-100h-200v-75l-167 125l167 125v-75zM50 500h300q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h600 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe059;" d="M75 1000h750q31 0 53 -22t22 -53v-650q0 -31 -22 -53t-53 -22h-750q-31 0 -53 22t-22 53v650q0 31 22 53t53 22zM1200 300l-300 300l300 300v-600z" />
<glyph unicode="&#xe060;" d="M44 1100h1112q18 0 31 -13t13 -31v-1012q0 -18 -13 -31t-31 -13h-1112q-18 0 -31 13t-13 31v1012q0 18 13 31t31 13zM100 1000v-737l247 182l298 -131l-74 156l293 318l236 -288v500h-1000zM342 884q56 0 95 -39t39 -94.5t-39 -95t-95 -39.5t-95 39.5t-39 95t39 94.5 t95 39z" />
<glyph unicode="&#xe062;" d="M648 1169q117 0 216 -60t156.5 -161t57.5 -218q0 -115 -70 -258q-69 -109 -158 -225.5t-143 -179.5l-54 -62q-9 8 -25.5 24.5t-63.5 67.5t-91 103t-98.5 128t-95.5 148q-60 132 -60 249q0 88 34 169.5t91.5 142t137 96.5t166.5 36zM652.5 974q-91.5 0 -156.5 -65 t-65 -157t65 -156.5t156.5 -64.5t156.5 64.5t65 156.5t-65 157t-156.5 65z" />
<glyph unicode="&#xe063;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 173v854q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57z" />
<glyph unicode="&#xe064;" d="M554 1295q21 -72 57.5 -143.5t76 -130t83 -118t82.5 -117t70 -116t49.5 -126t18.5 -136.5q0 -71 -25.5 -135t-68.5 -111t-99 -82t-118.5 -54t-125.5 -23q-84 5 -161.5 34t-139.5 78.5t-99 125t-37 164.5q0 69 18 136.5t49.5 126.5t69.5 116.5t81.5 117.5t83.5 119 t76.5 131t58.5 143zM344 710q-23 -33 -43.5 -70.5t-40.5 -102.5t-17 -123q1 -37 14.5 -69.5t30 -52t41 -37t38.5 -24.5t33 -15q21 -7 32 -1t13 22l6 34q2 10 -2.5 22t-13.5 19q-5 4 -14 12t-29.5 40.5t-32.5 73.5q-26 89 6 271q2 11 -6 11q-8 1 -15 -10z" />
<glyph unicode="&#xe065;" d="M1000 1013l108 115q2 1 5 2t13 2t20.5 -1t25 -9.5t28.5 -21.5q22 -22 27 -43t0 -32l-6 -10l-108 -115zM350 1100h400q50 0 105 -13l-187 -187h-368q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v182l200 200v-332 q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5zM1009 803l-362 -362l-161 -50l55 170l355 355z" />
<glyph unicode="&#xe066;" d="M350 1100h361q-164 -146 -216 -200h-195q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5l200 153v-103q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5z M824 1073l339 -301q8 -7 8 -17.5t-8 -17.5l-340 -306q-7 -6 -12.5 -4t-6.5 11v203q-26 1 -54.5 0t-78.5 -7.5t-92 -17.5t-86 -35t-70 -57q10 59 33 108t51.5 81.5t65 58.5t68.5 40.5t67 24.5t56 13.5t40 4.5v210q1 10 6.5 12.5t13.5 -4.5z" />
<glyph unicode="&#xe067;" d="M350 1100h350q60 0 127 -23l-178 -177h-349q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v69l200 200v-219q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5z M643 639l395 395q7 7 17.5 7t17.5 -7l101 -101q7 -7 7 -17.5t-7 -17.5l-531 -532q-7 -7 -17.5 -7t-17.5 7l-248 248q-7 7 -7 17.5t7 17.5l101 101q7 7 17.5 7t17.5 -7l111 -111q8 -7 18 -7t18 7z" />
<glyph unicode="&#xe068;" d="M318 918l264 264q8 8 18 8t18 -8l260 -264q7 -8 4.5 -13t-12.5 -5h-170v-200h200v173q0 10 5 12t13 -5l264 -260q8 -7 8 -17.5t-8 -17.5l-264 -265q-8 -7 -13 -5t-5 12v173h-200v-200h170q10 0 12.5 -5t-4.5 -13l-260 -264q-8 -8 -18 -8t-18 8l-264 264q-8 8 -5.5 13 t12.5 5h175v200h-200v-173q0 -10 -5 -12t-13 5l-264 265q-8 7 -8 17.5t8 17.5l264 260q8 7 13 5t5 -12v-173h200v200h-175q-10 0 -12.5 5t5.5 13z" />
<glyph unicode="&#xe069;" d="M250 1100h100q21 0 35.5 -14.5t14.5 -35.5v-438l464 453q15 14 25.5 10t10.5 -25v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v1000q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe070;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-438l464 453q15 14 25.5 10t10.5 -25v-438l464 453q15 14 25.5 10t10.5 -25v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5 t-14.5 35.5v1000q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe071;" d="M1200 1050v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -10.5 -25t-25.5 10l-492 480q-15 14 -15 35t15 35l492 480q15 14 25.5 10t10.5 -25v-438l464 453q15 14 25.5 10t10.5 -25z" />
<glyph unicode="&#xe072;" d="M243 1074l814 -498q18 -11 18 -26t-18 -26l-814 -498q-18 -11 -30.5 -4t-12.5 28v1000q0 21 12.5 28t30.5 -4z" />
<glyph unicode="&#xe073;" d="M250 1000h200q21 0 35.5 -14.5t14.5 -35.5v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5zM650 1000h200q21 0 35.5 -14.5t14.5 -35.5v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v800 q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe074;" d="M1100 950v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5h800q21 0 35.5 -14.5t14.5 -35.5z" />
<glyph unicode="&#xe075;" d="M500 612v438q0 21 10.5 25t25.5 -10l492 -480q15 -14 15 -35t-15 -35l-492 -480q-15 -14 -25.5 -10t-10.5 25v438l-464 -453q-15 -14 -25.5 -10t-10.5 25v1000q0 21 10.5 25t25.5 -10z" />
<glyph unicode="&#xe076;" d="M1048 1102l100 1q20 0 35 -14.5t15 -35.5l5 -1000q0 -21 -14.5 -35.5t-35.5 -14.5l-100 -1q-21 0 -35.5 14.5t-14.5 35.5l-2 437l-463 -454q-14 -15 -24.5 -10.5t-10.5 25.5l-2 437l-462 -455q-15 -14 -25.5 -9.5t-10.5 24.5l-5 1000q0 21 10.5 25.5t25.5 -10.5l466 -450 l-2 438q0 20 10.5 24.5t25.5 -9.5l466 -451l-2 438q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe077;" d="M850 1100h100q21 0 35.5 -14.5t14.5 -35.5v-1000q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v438l-464 -453q-15 -14 -25.5 -10t-10.5 25v1000q0 21 10.5 25t25.5 -10l464 -453v438q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe078;" d="M686 1081l501 -540q15 -15 10.5 -26t-26.5 -11h-1042q-22 0 -26.5 11t10.5 26l501 540q15 15 36 15t36 -15zM150 400h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe079;" d="M885 900l-352 -353l352 -353l-197 -198l-552 552l552 550z" />
<glyph unicode="&#xe080;" d="M1064 547l-551 -551l-198 198l353 353l-353 353l198 198z" />
<glyph unicode="&#xe081;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM650 900h-100q-21 0 -35.5 -14.5t-14.5 -35.5v-150h-150 q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5t35.5 -14.5h150v-150q0 -21 14.5 -35.5t35.5 -14.5h100q21 0 35.5 14.5t14.5 35.5v150h150q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5h-150v150q0 21 -14.5 35.5t-35.5 14.5z" />
<glyph unicode="&#xe082;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM850 700h-500q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5 t35.5 -14.5h500q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5z" />
<glyph unicode="&#xe083;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM741.5 913q-12.5 0 -21.5 -9l-120 -120l-120 120q-9 9 -21.5 9 t-21.5 -9l-141 -141q-9 -9 -9 -21.5t9 -21.5l120 -120l-120 -120q-9 -9 -9 -21.5t9 -21.5l141 -141q9 -9 21.5 -9t21.5 9l120 120l120 -120q9 -9 21.5 -9t21.5 9l141 141q9 9 9 21.5t-9 21.5l-120 120l120 120q9 9 9 21.5t-9 21.5l-141 141q-9 9 -21.5 9z" />
<glyph unicode="&#xe084;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM546 623l-84 85q-7 7 -17.5 7t-18.5 -7l-139 -139q-7 -8 -7 -18t7 -18 l242 -241q7 -8 17.5 -8t17.5 8l375 375q7 7 7 17.5t-7 18.5l-139 139q-7 7 -17.5 7t-17.5 -7z" />
<glyph unicode="&#xe085;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM588 941q-29 0 -59 -5.5t-63 -20.5t-58 -38.5t-41.5 -63t-16.5 -89.5 q0 -25 20 -25h131q30 -5 35 11q6 20 20.5 28t45.5 8q20 0 31.5 -10.5t11.5 -28.5q0 -23 -7 -34t-26 -18q-1 0 -13.5 -4t-19.5 -7.5t-20 -10.5t-22 -17t-18.5 -24t-15.5 -35t-8 -46q-1 -8 5.5 -16.5t20.5 -8.5h173q7 0 22 8t35 28t37.5 48t29.5 74t12 100q0 47 -17 83 t-42.5 57t-59.5 34.5t-64 18t-59 4.5zM675 400h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe086;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM675 1000h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5 t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5zM675 700h-250q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h75v-200h-75q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h350q10 0 17.5 7.5t7.5 17.5v50q0 10 -7.5 17.5 t-17.5 7.5h-75v275q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe087;" d="M525 1200h150q10 0 17.5 -7.5t7.5 -17.5v-194q103 -27 178.5 -102.5t102.5 -178.5h194q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-194q-27 -103 -102.5 -178.5t-178.5 -102.5v-194q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v194 q-103 27 -178.5 102.5t-102.5 178.5h-194q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h194q27 103 102.5 178.5t178.5 102.5v194q0 10 7.5 17.5t17.5 7.5zM700 893v-168q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v168q-68 -23 -119 -74 t-74 -119h168q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-168q23 -68 74 -119t119 -74v168q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-168q68 23 119 74t74 119h-168q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h168 q-23 68 -74 119t-119 74z" />
<glyph unicode="&#xe088;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM759 823l64 -64q7 -7 7 -17.5t-7 -17.5l-124 -124l124 -124q7 -7 7 -17.5t-7 -17.5l-64 -64q-7 -7 -17.5 -7t-17.5 7l-124 124l-124 -124q-7 -7 -17.5 -7t-17.5 7l-64 64 q-7 7 -7 17.5t7 17.5l124 124l-124 124q-7 7 -7 17.5t7 17.5l64 64q7 7 17.5 7t17.5 -7l124 -124l124 124q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe089;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM782 788l106 -106q7 -7 7 -17.5t-7 -17.5l-320 -321q-8 -7 -18 -7t-18 7l-202 203q-8 7 -8 17.5t8 17.5l106 106q7 8 17.5 8t17.5 -8l79 -79l197 197q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe090;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5q0 -120 65 -225 l587 587q-105 65 -225 65zM965 819l-584 -584q104 -62 219 -62q116 0 214.5 57t155.5 155.5t57 214.5q0 115 -62 219z" />
<glyph unicode="&#xe091;" d="M39 582l522 427q16 13 27.5 8t11.5 -26v-291h550q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-550v-291q0 -21 -11.5 -26t-27.5 8l-522 427q-16 13 -16 32t16 32z" />
<glyph unicode="&#xe092;" d="M639 1009l522 -427q16 -13 16 -32t-16 -32l-522 -427q-16 -13 -27.5 -8t-11.5 26v291h-550q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h550v291q0 21 11.5 26t27.5 -8z" />
<glyph unicode="&#xe093;" d="M682 1161l427 -522q13 -16 8 -27.5t-26 -11.5h-291v-550q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v550h-291q-21 0 -26 11.5t8 27.5l427 522q13 16 32 16t32 -16z" />
<glyph unicode="&#xe094;" d="M550 1200h200q21 0 35.5 -14.5t14.5 -35.5v-550h291q21 0 26 -11.5t-8 -27.5l-427 -522q-13 -16 -32 -16t-32 16l-427 522q-13 16 -8 27.5t26 11.5h291v550q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe095;" d="M639 1109l522 -427q16 -13 16 -32t-16 -32l-522 -427q-16 -13 -27.5 -8t-11.5 26v291q-94 -2 -182 -20t-170.5 -52t-147 -92.5t-100.5 -135.5q5 105 27 193.5t67.5 167t113 135t167 91.5t225.5 42v262q0 21 11.5 26t27.5 -8z" />
<glyph unicode="&#xe096;" d="M850 1200h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94l-249 -249q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l249 249l-94 94q-14 14 -10 24.5t25 10.5zM350 0h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l249 249 q8 7 18 7t18 -7l106 -106q7 -8 7 -18t-7 -18l-249 -249l94 -94q14 -14 10 -24.5t-25 -10.5z" />
<glyph unicode="&#xe097;" d="M1014 1120l106 -106q7 -8 7 -18t-7 -18l-249 -249l94 -94q14 -14 10 -24.5t-25 -10.5h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l249 249q8 7 18 7t18 -7zM250 600h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94 l-249 -249q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l249 249l-94 94q-14 14 -10 24.5t25 10.5z" />
<glyph unicode="&#xe101;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM704 900h-208q-20 0 -32 -14.5t-8 -34.5l58 -302q4 -20 21.5 -34.5 t37.5 -14.5h54q20 0 37.5 14.5t21.5 34.5l58 302q4 20 -8 34.5t-32 14.5zM675 400h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe102;" d="M260 1200q9 0 19 -2t15 -4l5 -2q22 -10 44 -23l196 -118q21 -13 36 -24q29 -21 37 -12q11 13 49 35l196 118q22 13 45 23q17 7 38 7q23 0 47 -16.5t37 -33.5l13 -16q14 -21 18 -45l25 -123l8 -44q1 -9 8.5 -14.5t17.5 -5.5h61q10 0 17.5 -7.5t7.5 -17.5v-50 q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 -7.5t-7.5 -17.5v-175h-400v300h-200v-300h-400v175q0 10 -7.5 17.5t-17.5 7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5h61q11 0 18 3t7 8q0 4 9 52l25 128q5 25 19 45q2 3 5 7t13.5 15t21.5 19.5t26.5 15.5 t29.5 7zM915 1079l-166 -162q-7 -7 -5 -12t12 -5h219q10 0 15 7t2 17l-51 149q-3 10 -11 12t-15 -6zM463 917l-177 157q-8 7 -16 5t-11 -12l-51 -143q-3 -10 2 -17t15 -7h231q11 0 12.5 5t-5.5 12zM500 0h-375q-10 0 -17.5 7.5t-7.5 17.5v375h400v-400zM1100 400v-375 q0 -10 -7.5 -17.5t-17.5 -7.5h-375v400h400z" />
<glyph unicode="&#xe103;" d="M1165 1190q8 3 21 -6.5t13 -17.5q-2 -178 -24.5 -323.5t-55.5 -245.5t-87 -174.5t-102.5 -118.5t-118 -68.5t-118.5 -33t-120 -4.5t-105 9.5t-90 16.5q-61 12 -78 11q-4 1 -12.5 0t-34 -14.5t-52.5 -40.5l-153 -153q-26 -24 -37 -14.5t-11 43.5q0 64 42 102q8 8 50.5 45 t66.5 58q19 17 35 47t13 61q-9 55 -10 102.5t7 111t37 130t78 129.5q39 51 80 88t89.5 63.5t94.5 45t113.5 36t129 31t157.5 37t182 47.5zM1116 1098q-8 9 -22.5 -3t-45.5 -50q-38 -47 -119 -103.5t-142 -89.5l-62 -33q-56 -30 -102 -57t-104 -68t-102.5 -80.5t-85.5 -91 t-64 -104.5q-24 -56 -31 -86t2 -32t31.5 17.5t55.5 59.5q25 30 94 75.5t125.5 77.5t147.5 81q70 37 118.5 69t102 79.5t99 111t86.5 148.5q22 50 24 60t-6 19z" />
<glyph unicode="&#xe104;" d="M653 1231q-39 -67 -54.5 -131t-10.5 -114.5t24.5 -96.5t47.5 -80t63.5 -62.5t68.5 -46.5t65 -30q-4 7 -17.5 35t-18.5 39.5t-17 39.5t-17 43t-13 42t-9.5 44.5t-2 42t4 43t13.5 39t23 38.5q96 -42 165 -107.5t105 -138t52 -156t13 -159t-19 -149.5q-13 -55 -44 -106.5 t-68 -87t-78.5 -64.5t-72.5 -45t-53 -22q-72 -22 -127 -11q-31 6 -13 19q6 3 17 7q13 5 32.5 21t41 44t38.5 63.5t21.5 81.5t-6.5 94.5t-50 107t-104 115.5q10 -104 -0.5 -189t-37 -140.5t-65 -93t-84 -52t-93.5 -11t-95 24.5q-80 36 -131.5 114t-53.5 171q-2 23 0 49.5 t4.5 52.5t13.5 56t27.5 60t46 64.5t69.5 68.5q-8 -53 -5 -102.5t17.5 -90t34 -68.5t44.5 -39t49 -2q31 13 38.5 36t-4.5 55t-29 64.5t-36 75t-26 75.5q-15 85 2 161.5t53.5 128.5t85.5 92.5t93.5 61t81.5 25.5z" />
<glyph unicode="&#xe105;" d="M600 1094q82 0 160.5 -22.5t140 -59t116.5 -82.5t94.5 -95t68 -95t42.5 -82.5t14 -57.5t-14 -57.5t-43 -82.5t-68.5 -95t-94.5 -95t-116.5 -82.5t-140 -59t-159.5 -22.5t-159.5 22.5t-140 59t-116.5 82.5t-94.5 95t-68.5 95t-43 82.5t-14 57.5t14 57.5t42.5 82.5t68 95 t94.5 95t116.5 82.5t140 59t160.5 22.5zM888 829q-15 15 -18 12t5 -22q25 -57 25 -119q0 -124 -88 -212t-212 -88t-212 88t-88 212q0 59 23 114q8 19 4.5 22t-17.5 -12q-70 -69 -160 -184q-13 -16 -15 -40.5t9 -42.5q22 -36 47 -71t70 -82t92.5 -81t113 -58.5t133.5 -24.5 t133.5 24t113 58.5t92.5 81.5t70 81.5t47 70.5q11 18 9 42.5t-14 41.5q-90 117 -163 189zM448 727l-35 -36q-15 -15 -19.5 -38.5t4.5 -41.5q37 -68 93 -116q16 -13 38.5 -11t36.5 17l35 34q14 15 12.5 33.5t-16.5 33.5q-44 44 -89 117q-11 18 -28 20t-32 -12z" />
<glyph unicode="&#xe106;" d="M592 0h-148l31 120q-91 20 -175.5 68.5t-143.5 106.5t-103.5 119t-66.5 110t-22 76q0 21 14 57.5t42.5 82.5t68 95t94.5 95t116.5 82.5t140 59t160.5 22.5q61 0 126 -15l32 121h148zM944 770l47 181q108 -85 176.5 -192t68.5 -159q0 -26 -19.5 -71t-59.5 -102t-93 -112 t-129 -104.5t-158 -75.5l46 173q77 49 136 117t97 131q11 18 9 42.5t-14 41.5q-54 70 -107 130zM310 824q-70 -69 -160 -184q-13 -16 -15 -40.5t9 -42.5q18 -30 39 -60t57 -70.5t74 -73t90 -61t105 -41.5l41 154q-107 18 -178.5 101.5t-71.5 193.5q0 59 23 114q8 19 4.5 22 t-17.5 -12zM448 727l-35 -36q-15 -15 -19.5 -38.5t4.5 -41.5q37 -68 93 -116q16 -13 38.5 -11t36.5 17l12 11l22 86l-3 4q-44 44 -89 117q-11 18 -28 20t-32 -12z" />
<glyph unicode="&#xe107;" d="M-90 100l642 1066q20 31 48 28.5t48 -35.5l642 -1056q21 -32 7.5 -67.5t-50.5 -35.5h-1294q-37 0 -50.5 34t7.5 66zM155 200h345v75q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-75h345l-445 723zM496 700h208q20 0 32 -14.5t8 -34.5l-58 -252 q-4 -20 -21.5 -34.5t-37.5 -14.5h-54q-20 0 -37.5 14.5t-21.5 34.5l-58 252q-4 20 8 34.5t32 14.5z" />
<glyph unicode="&#xe108;" d="M650 1200q62 0 106 -44t44 -106v-339l363 -325q15 -14 26 -38.5t11 -44.5v-41q0 -20 -12 -26.5t-29 5.5l-359 249v-263q100 -93 100 -113v-64q0 -21 -13 -29t-32 1l-205 128l-205 -128q-19 -9 -32 -1t-13 29v64q0 20 100 113v263l-359 -249q-17 -12 -29 -5.5t-12 26.5v41 q0 20 11 44.5t26 38.5l363 325v339q0 62 44 106t106 44z" />
<glyph unicode="&#xe109;" d="M850 1200h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-150h-1100v150q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5h100q21 0 35.5 -14.5t14.5 -35.5v-50h500v50q0 21 14.5 35.5t35.5 14.5zM1100 800v-750q0 -21 -14.5 -35.5 t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v750h1100zM100 600v-100h100v100h-100zM300 600v-100h100v100h-100zM500 600v-100h100v100h-100zM700 600v-100h100v100h-100zM900 600v-100h100v100h-100zM100 400v-100h100v100h-100zM300 400v-100h100v100h-100zM500 400 v-100h100v100h-100zM700 400v-100h100v100h-100zM900 400v-100h100v100h-100zM100 200v-100h100v100h-100zM300 200v-100h100v100h-100zM500 200v-100h100v100h-100zM700 200v-100h100v100h-100zM900 200v-100h100v100h-100z" />
<glyph unicode="&#xe110;" d="M1135 1165l249 -230q15 -14 15 -35t-15 -35l-249 -230q-14 -14 -24.5 -10t-10.5 25v150h-159l-600 -600h-291q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h209l600 600h241v150q0 21 10.5 25t24.5 -10zM522 819l-141 -141l-122 122h-209q-21 0 -35.5 14.5 t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h291zM1135 565l249 -230q15 -14 15 -35t-15 -35l-249 -230q-14 -14 -24.5 -10t-10.5 25v150h-241l-181 181l141 141l122 -122h159v150q0 21 10.5 25t24.5 -10z" />
<glyph unicode="&#xe111;" d="M100 1100h1000q41 0 70.5 -29.5t29.5 -70.5v-600q0 -41 -29.5 -70.5t-70.5 -29.5h-596l-304 -300v300h-100q-41 0 -70.5 29.5t-29.5 70.5v600q0 41 29.5 70.5t70.5 29.5z" />
<glyph unicode="&#xe112;" d="M150 1200h200q21 0 35.5 -14.5t14.5 -35.5v-250h-300v250q0 21 14.5 35.5t35.5 14.5zM850 1200h200q21 0 35.5 -14.5t14.5 -35.5v-250h-300v250q0 21 14.5 35.5t35.5 14.5zM1100 800v-300q0 -41 -3 -77.5t-15 -89.5t-32 -96t-58 -89t-89 -77t-129 -51t-174 -20t-174 20 t-129 51t-89 77t-58 89t-32 96t-15 89.5t-3 77.5v300h300v-250v-27v-42.5t1.5 -41t5 -38t10 -35t16.5 -30t25.5 -24.5t35 -19t46.5 -12t60 -4t60 4.5t46.5 12.5t35 19.5t25 25.5t17 30.5t10 35t5 38t2 40.5t-0.5 42v25v250h300z" />
<glyph unicode="&#xe113;" d="M1100 411l-198 -199l-353 353l-353 -353l-197 199l551 551z" />
<glyph unicode="&#xe114;" d="M1101 789l-550 -551l-551 551l198 199l353 -353l353 353z" />
<glyph unicode="&#xe115;" d="M404 1000h746q21 0 35.5 -14.5t14.5 -35.5v-551h150q21 0 25 -10.5t-10 -24.5l-230 -249q-14 -15 -35 -15t-35 15l-230 249q-14 14 -10 24.5t25 10.5h150v401h-381zM135 984l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-400h385l215 -200h-750q-21 0 -35.5 14.5 t-14.5 35.5v550h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe116;" d="M56 1200h94q17 0 31 -11t18 -27l38 -162h896q24 0 39 -18.5t10 -42.5l-100 -475q-5 -21 -27 -42.5t-55 -21.5h-633l48 -200h535q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-50q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v50h-300v-50 q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v50h-31q-18 0 -32.5 10t-20.5 19l-5 10l-201 961h-54q-20 0 -35 14.5t-15 35.5t15 35.5t35 14.5z" />
<glyph unicode="&#xe117;" d="M1200 1000v-100h-1200v100h200q0 41 29.5 70.5t70.5 29.5h300q41 0 70.5 -29.5t29.5 -70.5h500zM0 800h1200v-800h-1200v800z" />
<glyph unicode="&#xe118;" d="M200 800l-200 -400v600h200q0 41 29.5 70.5t70.5 29.5h300q42 0 71 -29.5t29 -70.5h500v-200h-1000zM1500 700l-300 -700h-1200l300 700h1200z" />
<glyph unicode="&#xe119;" d="M635 1184l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-601h150q21 0 25 -10.5t-10 -24.5l-230 -249q-14 -15 -35 -15t-35 15l-230 249q-14 14 -10 24.5t25 10.5h150v601h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe120;" d="M936 864l249 -229q14 -15 14 -35.5t-14 -35.5l-249 -229q-15 -15 -25.5 -10.5t-10.5 24.5v151h-600v-151q0 -20 -10.5 -24.5t-25.5 10.5l-249 229q-14 15 -14 35.5t14 35.5l249 229q15 15 25.5 10.5t10.5 -25.5v-149h600v149q0 21 10.5 25.5t25.5 -10.5z" />
<glyph unicode="&#xe121;" d="M1169 400l-172 732q-5 23 -23 45.5t-38 22.5h-672q-20 0 -38 -20t-23 -41l-172 -739h1138zM1100 300h-1000q-41 0 -70.5 -29.5t-29.5 -70.5v-100q0 -41 29.5 -70.5t70.5 -29.5h1000q41 0 70.5 29.5t29.5 70.5v100q0 41 -29.5 70.5t-70.5 29.5zM800 100v100h100v-100h-100 zM1000 100v100h100v-100h-100z" />
<glyph unicode="&#xe122;" d="M1150 1100q21 0 35.5 -14.5t14.5 -35.5v-850q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v850q0 21 14.5 35.5t35.5 14.5zM1000 200l-675 200h-38l47 -276q3 -16 -5.5 -20t-29.5 -4h-7h-84q-20 0 -34.5 14t-18.5 35q-55 337 -55 351v250v6q0 16 1 23.5t6.5 14 t17.5 6.5h200l675 250v-850zM0 750v-250q-4 0 -11 0.5t-24 6t-30 15t-24 30t-11 48.5v50q0 26 10.5 46t25 30t29 16t25.5 7z" />
<glyph unicode="&#xe123;" d="M553 1200h94q20 0 29 -10.5t3 -29.5l-18 -37q83 -19 144 -82.5t76 -140.5l63 -327l118 -173h17q19 0 33 -14.5t14 -35t-13 -40.5t-31 -27q-8 -4 -23 -9.5t-65 -19.5t-103 -25t-132.5 -20t-158.5 -9q-57 0 -115 5t-104 12t-88.5 15.5t-73.5 17.5t-54.5 16t-35.5 12l-11 4 q-18 8 -31 28t-13 40.5t14 35t33 14.5h17l118 173l63 327q15 77 76 140t144 83l-18 32q-6 19 3.5 32t28.5 13zM498 110q50 -6 102 -6q53 0 102 6q-12 -49 -39.5 -79.5t-62.5 -30.5t-63 30.5t-39 79.5z" />
<glyph unicode="&#xe124;" d="M800 946l224 78l-78 -224l234 -45l-180 -155l180 -155l-234 -45l78 -224l-224 78l-45 -234l-155 180l-155 -180l-45 234l-224 -78l78 224l-234 45l180 155l-180 155l234 45l-78 224l224 -78l45 234l155 -180l155 180z" />
<glyph unicode="&#xe125;" d="M650 1200h50q40 0 70 -40.5t30 -84.5v-150l-28 -125h328q40 0 70 -40.5t30 -84.5v-100q0 -45 -29 -74l-238 -344q-16 -24 -38 -40.5t-45 -16.5h-250q-7 0 -42 25t-66 50l-31 25h-61q-45 0 -72.5 18t-27.5 57v400q0 36 20 63l145 196l96 198q13 28 37.5 48t51.5 20z M650 1100l-100 -212l-150 -213v-375h100l136 -100h214l250 375v125h-450l50 225v175h-50zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe126;" d="M600 1100h250q23 0 45 -16.5t38 -40.5l238 -344q29 -29 29 -74v-100q0 -44 -30 -84.5t-70 -40.5h-328q28 -118 28 -125v-150q0 -44 -30 -84.5t-70 -40.5h-50q-27 0 -51.5 20t-37.5 48l-96 198l-145 196q-20 27 -20 63v400q0 39 27.5 57t72.5 18h61q124 100 139 100z M50 1000h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5zM636 1000l-136 -100h-100v-375l150 -213l100 -212h50v175l-50 225h450v125l-250 375h-214z" />
<glyph unicode="&#xe127;" d="M356 873l363 230q31 16 53 -6l110 -112q13 -13 13.5 -32t-11.5 -34l-84 -121h302q84 0 138 -38t54 -110t-55 -111t-139 -39h-106l-131 -339q-6 -21 -19.5 -41t-28.5 -20h-342q-7 0 -90 81t-83 94v525q0 17 14 35.5t28 28.5zM400 792v-503l100 -89h293l131 339 q6 21 19.5 41t28.5 20h203q21 0 30.5 25t0.5 50t-31 25h-456h-7h-6h-5.5t-6 0.5t-5 1.5t-5 2t-4 2.5t-4 4t-2.5 4.5q-12 25 5 47l146 183l-86 83zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500 q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe128;" d="M475 1103l366 -230q2 -1 6 -3.5t14 -10.5t18 -16.5t14.5 -20t6.5 -22.5v-525q0 -13 -86 -94t-93 -81h-342q-15 0 -28.5 20t-19.5 41l-131 339h-106q-85 0 -139.5 39t-54.5 111t54 110t138 38h302l-85 121q-11 15 -10.5 34t13.5 32l110 112q22 22 53 6zM370 945l146 -183 q17 -22 5 -47q-2 -2 -3.5 -4.5t-4 -4t-4 -2.5t-5 -2t-5 -1.5t-6 -0.5h-6h-6.5h-6h-475v-100h221q15 0 29 -20t20 -41l130 -339h294l106 89v503l-342 236zM1050 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5 v500q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe129;" d="M550 1294q72 0 111 -55t39 -139v-106l339 -131q21 -6 41 -19.5t20 -28.5v-342q0 -7 -81 -90t-94 -83h-525q-17 0 -35.5 14t-28.5 28l-9 14l-230 363q-16 31 6 53l112 110q13 13 32 13.5t34 -11.5l121 -84v302q0 84 38 138t110 54zM600 972v203q0 21 -25 30.5t-50 0.5 t-25 -31v-456v-7v-6v-5.5t-0.5 -6t-1.5 -5t-2 -5t-2.5 -4t-4 -4t-4.5 -2.5q-25 -12 -47 5l-183 146l-83 -86l236 -339h503l89 100v293l-339 131q-21 6 -41 19.5t-20 28.5zM450 200h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe130;" d="M350 1100h500q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5h-500q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5t35.5 -14.5zM600 306v-106q0 -84 -39 -139t-111 -55t-110 54t-38 138v302l-121 -84q-15 -12 -34 -11.5t-32 13.5l-112 110 q-22 22 -6 53l230 363q1 2 3.5 6t10.5 13.5t16.5 17t20 13.5t22.5 6h525q13 0 94 -83t81 -90v-342q0 -15 -20 -28.5t-41 -19.5zM308 900l-236 -339l83 -86l183 146q22 17 47 5q2 -1 4.5 -2.5t4 -4t2.5 -4t2 -5t1.5 -5t0.5 -6v-5.5v-6v-7v-456q0 -22 25 -31t50 0.5t25 30.5 v203q0 15 20 28.5t41 19.5l339 131v293l-89 100h-503z" />
<glyph unicode="&#xe131;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM914 632l-275 223q-16 13 -27.5 8t-11.5 -26v-137h-275 q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h275v-137q0 -21 11.5 -26t27.5 8l275 223q16 13 16 32t-16 32z" />
<glyph unicode="&#xe132;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM561 855l-275 -223q-16 -13 -16 -32t16 -32l275 -223q16 -13 27.5 -8 t11.5 26v137h275q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5h-275v137q0 21 -11.5 26t-27.5 -8z" />
<glyph unicode="&#xe133;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM855 639l-223 275q-13 16 -32 16t-32 -16l-223 -275q-13 -16 -8 -27.5 t26 -11.5h137v-275q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v275h137q21 0 26 11.5t-8 27.5z" />
<glyph unicode="&#xe134;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM675 900h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-275h-137q-21 0 -26 -11.5 t8 -27.5l223 -275q13 -16 32 -16t32 16l223 275q13 16 8 27.5t-26 11.5h-137v275q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe135;" d="M600 1176q116 0 222.5 -46t184 -123.5t123.5 -184t46 -222.5t-46 -222.5t-123.5 -184t-184 -123.5t-222.5 -46t-222.5 46t-184 123.5t-123.5 184t-46 222.5t46 222.5t123.5 184t184 123.5t222.5 46zM627 1101q-15 -12 -36.5 -20.5t-35.5 -12t-43 -8t-39 -6.5 q-15 -3 -45.5 0t-45.5 -2q-20 -7 -51.5 -26.5t-34.5 -34.5q-3 -11 6.5 -22.5t8.5 -18.5q-3 -34 -27.5 -91t-29.5 -79q-9 -34 5 -93t8 -87q0 -9 17 -44.5t16 -59.5q12 0 23 -5t23.5 -15t19.5 -14q16 -8 33 -15t40.5 -15t34.5 -12q21 -9 52.5 -32t60 -38t57.5 -11 q7 -15 -3 -34t-22.5 -40t-9.5 -38q13 -21 23 -34.5t27.5 -27.5t36.5 -18q0 -7 -3.5 -16t-3.5 -14t5 -17q104 -2 221 112q30 29 46.5 47t34.5 49t21 63q-13 8 -37 8.5t-36 7.5q-15 7 -49.5 15t-51.5 19q-18 0 -41 -0.5t-43 -1.5t-42 -6.5t-38 -16.5q-51 -35 -66 -12 q-4 1 -3.5 25.5t0.5 25.5q-6 13 -26.5 17.5t-24.5 6.5q1 15 -0.5 30.5t-7 28t-18.5 11.5t-31 -21q-23 -25 -42 4q-19 28 -8 58q6 16 22 22q6 -1 26 -1.5t33.5 -4t19.5 -13.5q7 -12 18 -24t21.5 -20.5t20 -15t15.5 -10.5l5 -3q2 12 7.5 30.5t8 34.5t-0.5 32q-3 18 3.5 29 t18 22.5t15.5 24.5q6 14 10.5 35t8 31t15.5 22.5t34 22.5q-6 18 10 36q8 0 24 -1.5t24.5 -1.5t20 4.5t20.5 15.5q-10 23 -31 42.5t-37.5 29.5t-49 27t-43.5 23q0 1 2 8t3 11.5t1.5 10.5t-1 9.5t-4.5 4.5q31 -13 58.5 -14.5t38.5 2.5l12 5q5 28 -9.5 46t-36.5 24t-50 15 t-41 20q-18 -4 -37 0zM613 994q0 -17 8 -42t17 -45t9 -23q-8 1 -39.5 5.5t-52.5 10t-37 16.5q3 11 16 29.5t16 25.5q10 -10 19 -10t14 6t13.5 14.5t16.5 12.5z" />
<glyph unicode="&#xe136;" d="M756 1157q164 92 306 -9l-259 -138l145 -232l251 126q6 -89 -34 -156.5t-117 -110.5q-60 -34 -127 -39.5t-126 16.5l-596 -596q-15 -16 -36.5 -16t-36.5 16l-111 110q-15 15 -15 36.5t15 37.5l600 599q-34 101 5.5 201.5t135.5 154.5z" />
<glyph unicode="&#xe137;" horiz-adv-x="1220" d="M100 1196h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 1096h-200v-100h200v100zM100 796h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000 q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 696h-500v-100h500v100zM100 396h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 296h-300v-100h300v100z " />
<glyph unicode="&#xe138;" d="M150 1200h900q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM700 500v-300l-200 -200v500l-350 500h900z" />
<glyph unicode="&#xe139;" d="M500 1200h200q41 0 70.5 -29.5t29.5 -70.5v-100h300q41 0 70.5 -29.5t29.5 -70.5v-400h-500v100h-200v-100h-500v400q0 41 29.5 70.5t70.5 29.5h300v100q0 41 29.5 70.5t70.5 29.5zM500 1100v-100h200v100h-200zM1200 400v-200q0 -41 -29.5 -70.5t-70.5 -29.5h-1000 q-41 0 -70.5 29.5t-29.5 70.5v200h1200z" />
<glyph unicode="&#xe140;" d="M50 1200h300q21 0 25 -10.5t-10 -24.5l-94 -94l199 -199q7 -8 7 -18t-7 -18l-106 -106q-8 -7 -18 -7t-18 7l-199 199l-94 -94q-14 -14 -24.5 -10t-10.5 25v300q0 21 14.5 35.5t35.5 14.5zM850 1200h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94 l-199 -199q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l199 199l-94 94q-14 14 -10 24.5t25 10.5zM364 470l106 -106q7 -8 7 -18t-7 -18l-199 -199l94 -94q14 -14 10 -24.5t-25 -10.5h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l199 199 q8 7 18 7t18 -7zM1071 271l94 94q14 14 24.5 10t10.5 -25v-300q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -25 10.5t10 24.5l94 94l-199 199q-7 8 -7 18t7 18l106 106q8 7 18 7t18 -7z" />
<glyph unicode="&#xe141;" d="M596 1192q121 0 231.5 -47.5t190 -127t127 -190t47.5 -231.5t-47.5 -231.5t-127 -190.5t-190 -127t-231.5 -47t-231.5 47t-190.5 127t-127 190.5t-47 231.5t47 231.5t127 190t190.5 127t231.5 47.5zM596 1010q-112 0 -207.5 -55.5t-151 -151t-55.5 -207.5t55.5 -207.5 t151 -151t207.5 -55.5t207.5 55.5t151 151t55.5 207.5t-55.5 207.5t-151 151t-207.5 55.5zM454.5 905q22.5 0 38.5 -16t16 -38.5t-16 -39t-38.5 -16.5t-38.5 16.5t-16 39t16 38.5t38.5 16zM754.5 905q22.5 0 38.5 -16t16 -38.5t-16 -39t-38 -16.5q-14 0 -29 10l-55 -145 q17 -23 17 -51q0 -36 -25.5 -61.5t-61.5 -25.5t-61.5 25.5t-25.5 61.5q0 32 20.5 56.5t51.5 29.5l122 126l1 1q-9 14 -9 28q0 23 16 39t38.5 16zM345.5 709q22.5 0 38.5 -16t16 -38.5t-16 -38.5t-38.5 -16t-38.5 16t-16 38.5t16 38.5t38.5 16zM854.5 709q22.5 0 38.5 -16 t16 -38.5t-16 -38.5t-38.5 -16t-38.5 16t-16 38.5t16 38.5t38.5 16z" />
<glyph unicode="&#xe142;" d="M546 173l469 470q91 91 99 192q7 98 -52 175.5t-154 94.5q-22 4 -47 4q-34 0 -66.5 -10t-56.5 -23t-55.5 -38t-48 -41.5t-48.5 -47.5q-376 -375 -391 -390q-30 -27 -45 -41.5t-37.5 -41t-32 -46.5t-16 -47.5t-1.5 -56.5q9 -62 53.5 -95t99.5 -33q74 0 125 51l548 548 q36 36 20 75q-7 16 -21.5 26t-32.5 10q-26 0 -50 -23q-13 -12 -39 -38l-341 -338q-15 -15 -35.5 -15.5t-34.5 13.5t-14 34.5t14 34.5q327 333 361 367q35 35 67.5 51.5t78.5 16.5q14 0 29 -1q44 -8 74.5 -35.5t43.5 -68.5q14 -47 2 -96.5t-47 -84.5q-12 -11 -32 -32 t-79.5 -81t-114.5 -115t-124.5 -123.5t-123 -119.5t-96.5 -89t-57 -45q-56 -27 -120 -27q-70 0 -129 32t-93 89q-48 78 -35 173t81 163l511 511q71 72 111 96q91 55 198 55q80 0 152 -33q78 -36 129.5 -103t66.5 -154q17 -93 -11 -183.5t-94 -156.5l-482 -476 q-15 -15 -36 -16t-37 14t-17.5 34t14.5 35z" />
<glyph unicode="&#xe143;" d="M649 949q48 68 109.5 104t121.5 38.5t118.5 -20t102.5 -64t71 -100.5t27 -123q0 -57 -33.5 -117.5t-94 -124.5t-126.5 -127.5t-150 -152.5t-146 -174q-62 85 -145.5 174t-150 152.5t-126.5 127.5t-93.5 124.5t-33.5 117.5q0 64 28 123t73 100.5t104 64t119 20 t120.5 -38.5t104.5 -104zM896 972q-33 0 -64.5 -19t-56.5 -46t-47.5 -53.5t-43.5 -45.5t-37.5 -19t-36 19t-40 45.5t-43 53.5t-54 46t-65.5 19q-67 0 -122.5 -55.5t-55.5 -132.5q0 -23 13.5 -51t46 -65t57.5 -63t76 -75l22 -22q15 -14 44 -44t50.5 -51t46 -44t41 -35t23 -12 t23.5 12t42.5 36t46 44t52.5 52t44 43q4 4 12 13q43 41 63.5 62t52 55t46 55t26 46t11.5 44q0 79 -53 133.5t-120 54.5z" />
<glyph unicode="&#xe144;" d="M776.5 1214q93.5 0 159.5 -66l141 -141q66 -66 66 -160q0 -42 -28 -95.5t-62 -87.5l-29 -29q-31 53 -77 99l-18 18l95 95l-247 248l-389 -389l212 -212l-105 -106l-19 18l-141 141q-66 66 -66 159t66 159l283 283q65 66 158.5 66zM600 706l105 105q10 -8 19 -17l141 -141 q66 -66 66 -159t-66 -159l-283 -283q-66 -66 -159 -66t-159 66l-141 141q-66 66 -66 159.5t66 159.5l55 55q29 -55 75 -102l18 -17l-95 -95l247 -248l389 389z" />
<glyph unicode="&#xe145;" d="M603 1200q85 0 162 -15t127 -38t79 -48t29 -46v-953q0 -41 -29.5 -70.5t-70.5 -29.5h-600q-41 0 -70.5 29.5t-29.5 70.5v953q0 21 30 46.5t81 48t129 37.5t163 15zM300 1000v-700h600v700h-600zM600 254q-43 0 -73.5 -30.5t-30.5 -73.5t30.5 -73.5t73.5 -30.5t73.5 30.5 t30.5 73.5t-30.5 73.5t-73.5 30.5z" />
<glyph unicode="&#xe146;" d="M902 1185l283 -282q15 -15 15 -36t-14.5 -35.5t-35.5 -14.5t-35 15l-36 35l-279 -267v-300l-212 210l-308 -307l-280 -203l203 280l307 308l-210 212h300l267 279l-35 36q-15 14 -15 35t14.5 35.5t35.5 14.5t35 -15z" />
<glyph unicode="&#xe148;" d="M700 1248v-78q38 -5 72.5 -14.5t75.5 -31.5t71 -53.5t52 -84t24 -118.5h-159q-4 36 -10.5 59t-21 45t-40 35.5t-64.5 20.5v-307l64 -13q34 -7 64 -16.5t70 -32t67.5 -52.5t47.5 -80t20 -112q0 -139 -89 -224t-244 -97v-77h-100v79q-150 16 -237 103q-40 40 -52.5 93.5 t-15.5 139.5h139q5 -77 48.5 -126t117.5 -65v335l-27 8q-46 14 -79 26.5t-72 36t-63 52t-40 72.5t-16 98q0 70 25 126t67.5 92t94.5 57t110 27v77h100zM600 754v274q-29 -4 -50 -11t-42 -21.5t-31.5 -41.5t-10.5 -65q0 -29 7 -50.5t16.5 -34t28.5 -22.5t31.5 -14t37.5 -10 q9 -3 13 -4zM700 547v-310q22 2 42.5 6.5t45 15.5t41.5 27t29 42t12 59.5t-12.5 59.5t-38 44.5t-53 31t-66.5 24.5z" />
<glyph unicode="&#xe149;" d="M561 1197q84 0 160.5 -40t123.5 -109.5t47 -147.5h-153q0 40 -19.5 71.5t-49.5 48.5t-59.5 26t-55.5 9q-37 0 -79 -14.5t-62 -35.5q-41 -44 -41 -101q0 -26 13.5 -63t26.5 -61t37 -66q6 -9 9 -14h241v-100h-197q8 -50 -2.5 -115t-31.5 -95q-45 -62 -99 -112 q34 10 83 17.5t71 7.5q32 1 102 -16t104 -17q83 0 136 30l50 -147q-31 -19 -58 -30.5t-55 -15.5t-42 -4.5t-46 -0.5q-23 0 -76 17t-111 32.5t-96 11.5q-39 -3 -82 -16t-67 -25l-23 -11l-55 145q4 3 16 11t15.5 10.5t13 9t15.5 12t14.5 14t17.5 18.5q48 55 54 126.5 t-30 142.5h-221v100h166q-23 47 -44 104q-7 20 -12 41.5t-6 55.5t6 66.5t29.5 70.5t58.5 71q97 88 263 88z" />
<glyph unicode="&#xe150;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM935 1184l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-900h-200v900h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe151;" d="M1000 700h-100v100h-100v-100h-100v500h300v-500zM400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM801 1100v-200h100v200h-100zM1000 350l-200 -250h200v-100h-300v150l200 250h-200v100h300v-150z " />
<glyph unicode="&#xe152;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1000 1050l-200 -250h200v-100h-300v150l200 250h-200v100h300v-150zM1000 0h-100v100h-100v-100h-100v500h300v-500zM801 400v-200h100v200h-100z " />
<glyph unicode="&#xe153;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1000 700h-100v400h-100v100h200v-500zM1100 0h-100v100h-200v400h300v-500zM901 400v-200h100v200h-100z" />
<glyph unicode="&#xe154;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1100 700h-100v100h-200v400h300v-500zM901 1100v-200h100v200h-100zM1000 0h-100v400h-100v100h200v-500z" />
<glyph unicode="&#xe155;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM900 1000h-200v200h200v-200zM1000 700h-300v200h300v-200zM1100 400h-400v200h400v-200zM1200 100h-500v200h500v-200z" />
<glyph unicode="&#xe156;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1200 1000h-500v200h500v-200zM1100 700h-400v200h400v-200zM1000 400h-300v200h300v-200zM900 100h-200v200h200v-200z" />
<glyph unicode="&#xe157;" d="M350 1100h400q162 0 256 -93.5t94 -256.5v-400q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5z" />
<glyph unicode="&#xe158;" d="M350 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-163 0 -256.5 92.5t-93.5 257.5v400q0 163 94 256.5t256 93.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM440 770l253 -190q17 -12 17 -30t-17 -30l-253 -190q-16 -12 -28 -6.5t-12 26.5v400q0 21 12 26.5t28 -6.5z" />
<glyph unicode="&#xe159;" d="M350 1100h400q163 0 256.5 -94t93.5 -256v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 163 92.5 256.5t257.5 93.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM350 700h400q21 0 26.5 -12t-6.5 -28l-190 -253q-12 -17 -30 -17t-30 17l-190 253q-12 16 -6.5 28t26.5 12z" />
<glyph unicode="&#xe160;" d="M350 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -163 -92.5 -256.5t-257.5 -93.5h-400q-163 0 -256.5 94t-93.5 256v400q0 165 92.5 257.5t257.5 92.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM580 693l190 -253q12 -16 6.5 -28t-26.5 -12h-400q-21 0 -26.5 12t6.5 28l190 253q12 17 30 17t30 -17z" />
<glyph unicode="&#xe161;" d="M550 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h450q41 0 70.5 29.5t29.5 70.5v500q0 41 -29.5 70.5t-70.5 29.5h-450q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM338 867l324 -284q16 -14 16 -33t-16 -33l-324 -284q-16 -14 -27 -9t-11 26v150h-250q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h250v150q0 21 11 26t27 -9z" />
<glyph unicode="&#xe162;" d="M793 1182l9 -9q8 -10 5 -27q-3 -11 -79 -225.5t-78 -221.5l300 1q24 0 32.5 -17.5t-5.5 -35.5q-1 0 -133.5 -155t-267 -312.5t-138.5 -162.5q-12 -15 -26 -15h-9l-9 8q-9 11 -4 32q2 9 42 123.5t79 224.5l39 110h-302q-23 0 -31 19q-10 21 6 41q75 86 209.5 237.5 t228 257t98.5 111.5q9 16 25 16h9z" />
<glyph unicode="&#xe163;" d="M350 1100h400q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-450q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h450q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400 q0 165 92.5 257.5t257.5 92.5zM938 867l324 -284q16 -14 16 -33t-16 -33l-324 -284q-16 -14 -27 -9t-11 26v150h-250q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h250v150q0 21 11 26t27 -9z" />
<glyph unicode="&#xe164;" d="M750 1200h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -10.5 -25t-24.5 10l-109 109l-312 -312q-15 -15 -35.5 -15t-35.5 15l-141 141q-15 15 -15 35.5t15 35.5l312 312l-109 109q-14 14 -10 24.5t25 10.5zM456 900h-156q-41 0 -70.5 -29.5t-29.5 -70.5v-500 q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v148l200 200v-298q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5h300z" />
<glyph unicode="&#xe165;" d="M600 1186q119 0 227.5 -46.5t187 -125t125 -187t46.5 -227.5t-46.5 -227.5t-125 -187t-187 -125t-227.5 -46.5t-227.5 46.5t-187 125t-125 187t-46.5 227.5t46.5 227.5t125 187t187 125t227.5 46.5zM600 1022q-115 0 -212 -56.5t-153.5 -153.5t-56.5 -212t56.5 -212 t153.5 -153.5t212 -56.5t212 56.5t153.5 153.5t56.5 212t-56.5 212t-153.5 153.5t-212 56.5zM600 794q80 0 137 -57t57 -137t-57 -137t-137 -57t-137 57t-57 137t57 137t137 57z" />
<glyph unicode="&#xe166;" d="M450 1200h200q21 0 35.5 -14.5t14.5 -35.5v-350h245q20 0 25 -11t-9 -26l-383 -426q-14 -15 -33.5 -15t-32.5 15l-379 426q-13 15 -8.5 26t25.5 11h250v350q0 21 14.5 35.5t35.5 14.5zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5z M900 200v-50h100v50h-100z" />
<glyph unicode="&#xe167;" d="M583 1182l378 -435q14 -15 9 -31t-26 -16h-244v-250q0 -20 -17 -35t-39 -15h-200q-20 0 -32 14.5t-12 35.5v250h-250q-20 0 -25.5 16.5t8.5 31.5l383 431q14 16 33.5 17t33.5 -14zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5z M900 200v-50h100v50h-100z" />
<glyph unicode="&#xe168;" d="M396 723l369 369q7 7 17.5 7t17.5 -7l139 -139q7 -8 7 -18.5t-7 -17.5l-525 -525q-7 -8 -17.5 -8t-17.5 8l-292 291q-7 8 -7 18t7 18l139 139q8 7 18.5 7t17.5 -7zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50 h-100z" />
<glyph unicode="&#xe169;" d="M135 1023l142 142q14 14 35 14t35 -14l77 -77l-212 -212l-77 76q-14 15 -14 36t14 35zM655 855l210 210q14 14 24.5 10t10.5 -25l-2 -599q-1 -20 -15.5 -35t-35.5 -15l-597 -1q-21 0 -25 10.5t10 24.5l208 208l-154 155l212 212zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5 v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50h-100z" />
<glyph unicode="&#xe170;" d="M350 1200l599 -2q20 -1 35 -15.5t15 -35.5l1 -597q0 -21 -10.5 -25t-24.5 10l-208 208l-155 -154l-212 212l155 154l-210 210q-14 14 -10 24.5t25 10.5zM524 512l-76 -77q-15 -14 -36 -14t-35 14l-142 142q-14 14 -14 35t14 35l77 77zM50 300h1000q21 0 35.5 -14.5 t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50h-100z" />
<glyph unicode="&#xe171;" d="M1200 103l-483 276l-314 -399v423h-399l1196 796v-1096zM483 424v-230l683 953z" />
<glyph unicode="&#xe172;" d="M1100 1000v-850q0 -21 -14.5 -35.5t-35.5 -14.5h-150v400h-700v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200z" />
<glyph unicode="&#xe173;" d="M1100 1000l-2 -149l-299 -299l-95 95q-9 9 -21.5 9t-21.5 -9l-149 -147h-312v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM1132 638l106 -106q7 -7 7 -17.5t-7 -17.5l-420 -421q-8 -7 -18 -7 t-18 7l-202 203q-8 7 -8 17.5t8 17.5l106 106q7 8 17.5 8t17.5 -8l79 -79l297 297q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe174;" d="M1100 1000v-269l-103 -103l-134 134q-15 15 -33.5 16.5t-34.5 -12.5l-266 -266h-329v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM1202 572l70 -70q15 -15 15 -35.5t-15 -35.5l-131 -131 l131 -131q15 -15 15 -35.5t-15 -35.5l-70 -70q-15 -15 -35.5 -15t-35.5 15l-131 131l-131 -131q-15 -15 -35.5 -15t-35.5 15l-70 70q-15 15 -15 35.5t15 35.5l131 131l-131 131q-15 15 -15 35.5t15 35.5l70 70q15 15 35.5 15t35.5 -15l131 -131l131 131q15 15 35.5 15 t35.5 -15z" />
<glyph unicode="&#xe175;" d="M1100 1000v-300h-350q-21 0 -35.5 -14.5t-14.5 -35.5v-150h-500v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM850 600h100q21 0 35.5 -14.5t14.5 -35.5v-250h150q21 0 25 -10.5t-10 -24.5 l-230 -230q-14 -14 -35 -14t-35 14l-230 230q-14 14 -10 24.5t25 10.5h150v250q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe176;" d="M1100 1000v-400l-165 165q-14 15 -35 15t-35 -15l-263 -265h-402v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM935 565l230 -229q14 -15 10 -25.5t-25 -10.5h-150v-250q0 -20 -14.5 -35 t-35.5 -15h-100q-21 0 -35.5 15t-14.5 35v250h-150q-21 0 -25 10.5t10 25.5l230 229q14 15 35 15t35 -15z" />
<glyph unicode="&#xe177;" d="M50 1100h1100q21 0 35.5 -14.5t14.5 -35.5v-150h-1200v150q0 21 14.5 35.5t35.5 14.5zM1200 800v-550q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v550h1200zM100 500v-200h400v200h-400z" />
<glyph unicode="&#xe178;" d="M935 1165l248 -230q14 -14 14 -35t-14 -35l-248 -230q-14 -14 -24.5 -10t-10.5 25v150h-400v200h400v150q0 21 10.5 25t24.5 -10zM200 800h-50q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v-200zM400 800h-100v200h100v-200zM18 435l247 230 q14 14 24.5 10t10.5 -25v-150h400v-200h-400v-150q0 -21 -10.5 -25t-24.5 10l-247 230q-15 14 -15 35t15 35zM900 300h-100v200h100v-200zM1000 500h51q20 0 34.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-34.5 -14.5h-51v200z" />
<glyph unicode="&#xe179;" d="M862 1073l276 116q25 18 43.5 8t18.5 -41v-1106q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v397q-4 1 -11 5t-24 17.5t-30 29t-24 42t-11 56.5v359q0 31 18.5 65t43.5 52zM550 1200q22 0 34.5 -12.5t14.5 -24.5l1 -13v-450q0 -28 -10.5 -59.5 t-25 -56t-29 -45t-25.5 -31.5l-10 -11v-447q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v447q-4 4 -11 11.5t-24 30.5t-30 46t-24 55t-11 60v450q0 2 0.5 5.5t4 12t8.5 15t14.5 12t22.5 5.5q20 0 32.5 -12.5t14.5 -24.5l3 -13v-350h100v350v5.5t2.5 12 t7 15t15 12t25.5 5.5q23 0 35.5 -12.5t13.5 -24.5l1 -13v-350h100v350q0 2 0.5 5.5t3 12t7 15t15 12t24.5 5.5z" />
<glyph unicode="&#xe180;" d="M1200 1100v-56q-4 0 -11 -0.5t-24 -3t-30 -7.5t-24 -15t-11 -24v-888q0 -22 25 -34.5t50 -13.5l25 -2v-56h-400v56q75 0 87.5 6.5t12.5 43.5v394h-500v-394q0 -37 12.5 -43.5t87.5 -6.5v-56h-400v56q4 0 11 0.5t24 3t30 7.5t24 15t11 24v888q0 22 -25 34.5t-50 13.5 l-25 2v56h400v-56q-75 0 -87.5 -6.5t-12.5 -43.5v-394h500v394q0 37 -12.5 43.5t-87.5 6.5v56h400z" />
<glyph unicode="&#xe181;" d="M675 1000h375q21 0 35.5 -14.5t14.5 -35.5v-150h-105l-295 -98v98l-200 200h-400l100 100h375zM100 900h300q41 0 70.5 -29.5t29.5 -70.5v-500q0 -41 -29.5 -70.5t-70.5 -29.5h-300q-41 0 -70.5 29.5t-29.5 70.5v500q0 41 29.5 70.5t70.5 29.5zM100 800v-200h300v200 h-300zM1100 535l-400 -133v163l400 133v-163zM100 500v-200h300v200h-300zM1100 398v-248q0 -21 -14.5 -35.5t-35.5 -14.5h-375l-100 -100h-375l-100 100h400l200 200h105z" />
<glyph unicode="&#xe182;" d="M17 1007l162 162q17 17 40 14t37 -22l139 -194q14 -20 11 -44.5t-20 -41.5l-119 -118q102 -142 228 -268t267 -227l119 118q17 17 42.5 19t44.5 -12l192 -136q19 -14 22.5 -37.5t-13.5 -40.5l-163 -162q-3 -1 -9.5 -1t-29.5 2t-47.5 6t-62.5 14.5t-77.5 26.5t-90 42.5 t-101.5 60t-111 83t-119 108.5q-74 74 -133.5 150.5t-94.5 138.5t-60 119.5t-34.5 100t-15 74.5t-4.5 48z" />
<glyph unicode="&#xe183;" d="M600 1100q92 0 175 -10.5t141.5 -27t108.5 -36.5t81.5 -40t53.5 -37t31 -27l9 -10v-200q0 -21 -14.5 -33t-34.5 -9l-202 34q-20 3 -34.5 20t-14.5 38v146q-141 24 -300 24t-300 -24v-146q0 -21 -14.5 -38t-34.5 -20l-202 -34q-20 -3 -34.5 9t-14.5 33v200q3 4 9.5 10.5 t31 26t54 37.5t80.5 39.5t109 37.5t141 26.5t175 10.5zM600 795q56 0 97 -9.5t60 -23.5t30 -28t12 -24l1 -10v-50l365 -303q14 -15 24.5 -40t10.5 -45v-212q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v212q0 20 10.5 45t24.5 40l365 303v50 q0 4 1 10.5t12 23t30 29t60 22.5t97 10z" />
<glyph unicode="&#xe184;" d="M1100 700l-200 -200h-600l-200 200v500h200v-200h200v200h200v-200h200v200h200v-500zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-12l137 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5 t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe185;" d="M700 1100h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-1000h300v1000q0 41 -29.5 70.5t-70.5 29.5zM1100 800h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-700h300v700q0 41 -29.5 70.5t-70.5 29.5zM400 0h-300v400q0 41 29.5 70.5t70.5 29.5h100q41 0 70.5 -29.5t29.5 -70.5v-400z " />
<glyph unicode="&#xe186;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-100h200v-300h-300v100h200v100h-200v300h300v-100zM900 700v-300l-100 -100h-200v500h200z M700 700v-300h100v300h-100z" />
<glyph unicode="&#xe187;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 300h-100v200h-100v-200h-100v500h100v-200h100v200h100v-500zM900 700v-300l-100 -100h-200v500h200z M700 700v-300h100v300h-100z" />
<glyph unicode="&#xe188;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-300h200v-100h-300v500h300v-100zM900 700h-200v-300h200v-100h-300v500h300v-100z" />
<glyph unicode="&#xe189;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 400l-300 150l300 150v-300zM900 550l-300 -150v300z" />
<glyph unicode="&#xe190;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM900 300h-700v500h700v-500zM800 700h-130q-38 0 -66.5 -43t-28.5 -108t27 -107t68 -42h130v300zM300 700v-300 h130q41 0 68 42t27 107t-28.5 108t-66.5 43h-130z" />
<glyph unicode="&#xe191;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-100h200v-300h-300v100h200v100h-200v300h300v-100zM900 300h-100v400h-100v100h200v-500z M700 300h-100v100h100v-100z" />
<glyph unicode="&#xe192;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM300 700h200v-400h-300v500h100v-100zM900 300h-100v400h-100v100h200v-500zM300 600v-200h100v200h-100z M700 300h-100v100h100v-100z" />
<glyph unicode="&#xe193;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 500l-199 -200h-100v50l199 200v150h-200v100h300v-300zM900 300h-100v400h-100v100h200v-500zM701 300h-100 v100h100v-100z" />
<glyph unicode="&#xe194;" d="M600 1191q120 0 229.5 -47t188.5 -126t126 -188.5t47 -229.5t-47 -229.5t-126 -188.5t-188.5 -126t-229.5 -47t-229.5 47t-188.5 126t-126 188.5t-47 229.5t47 229.5t126 188.5t188.5 126t229.5 47zM600 1021q-114 0 -211 -56.5t-153.5 -153.5t-56.5 -211t56.5 -211 t153.5 -153.5t211 -56.5t211 56.5t153.5 153.5t56.5 211t-56.5 211t-153.5 153.5t-211 56.5zM800 700h-300v-200h300v-100h-300l-100 100v200l100 100h300v-100z" />
<glyph unicode="&#xe195;" d="M600 1191q120 0 229.5 -47t188.5 -126t126 -188.5t47 -229.5t-47 -229.5t-126 -188.5t-188.5 -126t-229.5 -47t-229.5 47t-188.5 126t-126 188.5t-47 229.5t47 229.5t126 188.5t188.5 126t229.5 47zM600 1021q-114 0 -211 -56.5t-153.5 -153.5t-56.5 -211t56.5 -211 t153.5 -153.5t211 -56.5t211 56.5t153.5 153.5t56.5 211t-56.5 211t-153.5 153.5t-211 56.5zM800 700v-100l-50 -50l100 -100v-50h-100l-100 100h-150v-100h-100v400h300zM500 700v-100h200v100h-200z" />
<glyph unicode="&#xe197;" d="M503 1089q110 0 200.5 -59.5t134.5 -156.5q44 14 90 14q120 0 205 -86.5t85 -207t-85 -207t-205 -86.5h-128v250q0 21 -14.5 35.5t-35.5 14.5h-300q-21 0 -35.5 -14.5t-14.5 -35.5v-250h-222q-80 0 -136 57.5t-56 136.5q0 69 43 122.5t108 67.5q-2 19 -2 37q0 100 49 185 t134 134t185 49zM525 500h150q10 0 17.5 -7.5t7.5 -17.5v-275h137q21 0 26 -11.5t-8 -27.5l-223 -244q-13 -16 -32 -16t-32 16l-223 244q-13 16 -8 27.5t26 11.5h137v275q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe198;" d="M502 1089q110 0 201 -59.5t135 -156.5q43 15 89 15q121 0 206 -86.5t86 -206.5q0 -99 -60 -181t-150 -110l-378 360q-13 16 -31.5 16t-31.5 -16l-381 -365h-9q-79 0 -135.5 57.5t-56.5 136.5q0 69 43 122.5t108 67.5q-2 19 -2 38q0 100 49 184.5t133.5 134t184.5 49.5z M632 467l223 -228q13 -16 8 -27.5t-26 -11.5h-137v-275q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v275h-137q-21 0 -26 11.5t8 27.5q199 204 223 228q19 19 31.5 19t32.5 -19z" />
<glyph unicode="&#xe199;" d="M700 100v100h400l-270 300h170l-270 300h170l-300 333l-300 -333h170l-270 -300h170l-270 -300h400v-100h-50q-21 0 -35.5 -14.5t-14.5 -35.5v-50h400v50q0 21 -14.5 35.5t-35.5 14.5h-50z" />
<glyph unicode="&#xe200;" d="M600 1179q94 0 167.5 -56.5t99.5 -145.5q89 -6 150.5 -71.5t61.5 -155.5q0 -61 -29.5 -112.5t-79.5 -82.5q9 -29 9 -55q0 -74 -52.5 -126.5t-126.5 -52.5q-55 0 -100 30v-251q21 0 35.5 -14.5t14.5 -35.5v-50h-300v50q0 21 14.5 35.5t35.5 14.5v251q-45 -30 -100 -30 q-74 0 -126.5 52.5t-52.5 126.5q0 18 4 38q-47 21 -75.5 65t-28.5 97q0 74 52.5 126.5t126.5 52.5q5 0 23 -2q0 2 -1 10t-1 13q0 116 81.5 197.5t197.5 81.5z" />
<glyph unicode="&#xe201;" d="M1010 1010q111 -111 150.5 -260.5t0 -299t-150.5 -260.5q-83 -83 -191.5 -126.5t-218.5 -43.5t-218.5 43.5t-191.5 126.5q-111 111 -150.5 260.5t0 299t150.5 260.5q83 83 191.5 126.5t218.5 43.5t218.5 -43.5t191.5 -126.5zM476 1065q-4 0 -8 -1q-121 -34 -209.5 -122.5 t-122.5 -209.5q-4 -12 2.5 -23t18.5 -14l36 -9q3 -1 7 -1q23 0 29 22q27 96 98 166q70 71 166 98q11 3 17.5 13.5t3.5 22.5l-9 35q-3 13 -14 19q-7 4 -15 4zM512 920q-4 0 -9 -2q-80 -24 -138.5 -82.5t-82.5 -138.5q-4 -13 2 -24t19 -14l34 -9q4 -1 8 -1q22 0 28 21 q18 58 58.5 98.5t97.5 58.5q12 3 18 13.5t3 21.5l-9 35q-3 12 -14 19q-7 4 -15 4zM719.5 719.5q-49.5 49.5 -119.5 49.5t-119.5 -49.5t-49.5 -119.5t49.5 -119.5t119.5 -49.5t119.5 49.5t49.5 119.5t-49.5 119.5zM855 551q-22 0 -28 -21q-18 -58 -58.5 -98.5t-98.5 -57.5 q-11 -4 -17 -14.5t-3 -21.5l9 -35q3 -12 14 -19q7 -4 15 -4q4 0 9 2q80 24 138.5 82.5t82.5 138.5q4 13 -2.5 24t-18.5 14l-34 9q-4 1 -8 1zM1000 515q-23 0 -29 -22q-27 -96 -98 -166q-70 -71 -166 -98q-11 -3 -17.5 -13.5t-3.5 -22.5l9 -35q3 -13 14 -19q7 -4 15 -4 q4 0 8 1q121 34 209.5 122.5t122.5 209.5q4 12 -2.5 23t-18.5 14l-36 9q-3 1 -7 1z" />
<glyph unicode="&#xe202;" d="M700 800h300v-380h-180v200h-340v-200h-380v755q0 10 7.5 17.5t17.5 7.5h575v-400zM1000 900h-200v200zM700 300h162l-212 -212l-212 212h162v200h100v-200zM520 0h-395q-10 0 -17.5 7.5t-7.5 17.5v395zM1000 220v-195q0 -10 -7.5 -17.5t-17.5 -7.5h-195z" />
<glyph unicode="&#xe203;" d="M700 800h300v-520l-350 350l-550 -550v1095q0 10 7.5 17.5t17.5 7.5h575v-400zM1000 900h-200v200zM862 200h-162v-200h-100v200h-162l212 212zM480 0h-355q-10 0 -17.5 7.5t-7.5 17.5v55h380v-80zM1000 80v-55q0 -10 -7.5 -17.5t-17.5 -7.5h-155v80h180z" />
<glyph unicode="&#xe204;" d="M1162 800h-162v-200h100l100 -100h-300v300h-162l212 212zM200 800h200q27 0 40 -2t29.5 -10.5t23.5 -30t7 -57.5h300v-100h-600l-200 -350v450h100q0 36 7 57.5t23.5 30t29.5 10.5t40 2zM800 400h240l-240 -400h-800l300 500h500v-100z" />
<glyph unicode="&#xe205;" d="M650 1100h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5zM1000 850v150q41 0 70.5 -29.5t29.5 -70.5v-800 q0 -41 -29.5 -70.5t-70.5 -29.5h-600q-1 0 -20 4l246 246l-326 326v324q0 41 29.5 70.5t70.5 29.5v-150q0 -62 44 -106t106 -44h300q62 0 106 44t44 106zM412 250l-212 -212v162h-200v100h200v162z" />
<glyph unicode="&#xe206;" d="M450 1100h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5zM800 850v150q41 0 70.5 -29.5t29.5 -70.5v-500 h-200v-300h200q0 -36 -7 -57.5t-23.5 -30t-29.5 -10.5t-40 -2h-600q-41 0 -70.5 29.5t-29.5 70.5v800q0 41 29.5 70.5t70.5 29.5v-150q0 -62 44 -106t106 -44h300q62 0 106 44t44 106zM1212 250l-212 -212v162h-200v100h200v162z" />
<glyph unicode="&#xe209;" d="M658 1197l637 -1104q23 -38 7 -65.5t-60 -27.5h-1276q-44 0 -60 27.5t7 65.5l637 1104q22 39 54 39t54 -39zM704 800h-208q-20 0 -32 -14.5t-8 -34.5l58 -302q4 -20 21.5 -34.5t37.5 -14.5h54q20 0 37.5 14.5t21.5 34.5l58 302q4 20 -8 34.5t-32 14.5zM500 300v-100h200 v100h-200z" />
<glyph unicode="&#xe210;" d="M425 1100h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM425 800h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5 t17.5 7.5zM825 800h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM25 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150 q0 10 7.5 17.5t17.5 7.5zM425 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM825 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5 v150q0 10 7.5 17.5t17.5 7.5zM25 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM425 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5 t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM825 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe211;" d="M700 1200h100v-200h-100v-100h350q62 0 86.5 -39.5t-3.5 -94.5l-66 -132q-41 -83 -81 -134h-772q-40 51 -81 134l-66 132q-28 55 -3.5 94.5t86.5 39.5h350v100h-100v200h100v100h200v-100zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-12l137 -100 h-950l138 100h-13q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe212;" d="M600 1300q40 0 68.5 -29.5t28.5 -70.5h-194q0 41 28.5 70.5t68.5 29.5zM443 1100h314q18 -37 18 -75q0 -8 -3 -25h328q41 0 44.5 -16.5t-30.5 -38.5l-175 -145h-678l-178 145q-34 22 -29 38.5t46 16.5h328q-3 17 -3 25q0 38 18 75zM250 700h700q21 0 35.5 -14.5 t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-150v-200l275 -200h-950l275 200v200h-150q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe213;" d="M600 1181q75 0 128 -53t53 -128t-53 -128t-128 -53t-128 53t-53 128t53 128t128 53zM602 798h46q34 0 55.5 -28.5t21.5 -86.5q0 -76 39 -183h-324q39 107 39 183q0 58 21.5 86.5t56.5 28.5h45zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13 l138 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe214;" d="M600 1300q47 0 92.5 -53.5t71 -123t25.5 -123.5q0 -78 -55.5 -133.5t-133.5 -55.5t-133.5 55.5t-55.5 133.5q0 62 34 143l144 -143l111 111l-163 163q34 26 63 26zM602 798h46q34 0 55.5 -28.5t21.5 -86.5q0 -76 39 -183h-324q39 107 39 183q0 58 21.5 86.5t56.5 28.5h45 zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13l138 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe215;" d="M600 1200l300 -161v-139h-300q0 -57 18.5 -108t50 -91.5t63 -72t70 -67.5t57.5 -61h-530q-60 83 -90.5 177.5t-30.5 178.5t33 164.5t87.5 139.5t126 96.5t145.5 41.5v-98zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13l138 -100h-950l137 100 h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe216;" d="M600 1300q41 0 70.5 -29.5t29.5 -70.5v-78q46 -26 73 -72t27 -100v-50h-400v50q0 54 27 100t73 72v78q0 41 29.5 70.5t70.5 29.5zM400 800h400q54 0 100 -27t72 -73h-172v-100h200v-100h-200v-100h200v-100h-200v-100h200q0 -83 -58.5 -141.5t-141.5 -58.5h-400 q-83 0 -141.5 58.5t-58.5 141.5v400q0 83 58.5 141.5t141.5 58.5z" />
<glyph unicode="&#xe218;" d="M150 1100h900q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5zM125 400h950q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-283l224 -224q13 -13 13 -31.5t-13 -32 t-31.5 -13.5t-31.5 13l-88 88h-524l-87 -88q-13 -13 -32 -13t-32 13.5t-13 32t13 31.5l224 224h-289q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM541 300l-100 -100h324l-100 100h-124z" />
<glyph unicode="&#xe219;" d="M200 1100h800q83 0 141.5 -58.5t58.5 -141.5v-200h-100q0 41 -29.5 70.5t-70.5 29.5h-250q-41 0 -70.5 -29.5t-29.5 -70.5h-100q0 41 -29.5 70.5t-70.5 29.5h-250q-41 0 -70.5 -29.5t-29.5 -70.5h-100v200q0 83 58.5 141.5t141.5 58.5zM100 600h1000q41 0 70.5 -29.5 t29.5 -70.5v-300h-1200v300q0 41 29.5 70.5t70.5 29.5zM300 100v-50q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v50h200zM1100 100v-50q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v50h200z" />
<glyph unicode="&#xe221;" d="M480 1165l682 -683q31 -31 31 -75.5t-31 -75.5l-131 -131h-481l-517 518q-32 31 -32 75.5t32 75.5l295 296q31 31 75.5 31t76.5 -31zM108 794l342 -342l303 304l-341 341zM250 100h800q21 0 35.5 -14.5t14.5 -35.5v-50h-900v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe223;" d="M1057 647l-189 506q-8 19 -27.5 33t-40.5 14h-400q-21 0 -40.5 -14t-27.5 -33l-189 -506q-8 -19 1.5 -33t30.5 -14h625v-150q0 -21 14.5 -35.5t35.5 -14.5t35.5 14.5t14.5 35.5v150h125q21 0 30.5 14t1.5 33zM897 0h-595v50q0 21 14.5 35.5t35.5 14.5h50v50 q0 21 14.5 35.5t35.5 14.5h48v300h200v-300h47q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-50z" />
<glyph unicode="&#xe224;" d="M900 800h300v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-375v591l-300 300v84q0 10 7.5 17.5t17.5 7.5h375v-400zM1200 900h-200v200zM400 600h300v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-650q-10 0 -17.5 7.5t-7.5 17.5v950q0 10 7.5 17.5t17.5 7.5h375v-400zM700 700h-200v200z " />
<glyph unicode="&#xe225;" d="M484 1095h195q75 0 146 -32.5t124 -86t89.5 -122.5t48.5 -142q18 -14 35 -20q31 -10 64.5 6.5t43.5 48.5q10 34 -15 71q-19 27 -9 43q5 8 12.5 11t19 -1t23.5 -16q41 -44 39 -105q-3 -63 -46 -106.5t-104 -43.5h-62q-7 -55 -35 -117t-56 -100l-39 -234q-3 -20 -20 -34.5 t-38 -14.5h-100q-21 0 -33 14.5t-9 34.5l12 70q-49 -14 -91 -14h-195q-24 0 -65 8l-11 -64q-3 -20 -20 -34.5t-38 -14.5h-100q-21 0 -33 14.5t-9 34.5l26 157q-84 74 -128 175l-159 53q-19 7 -33 26t-14 40v50q0 21 14.5 35.5t35.5 14.5h124q11 87 56 166l-111 95 q-16 14 -12.5 23.5t24.5 9.5h203q116 101 250 101zM675 1000h-250q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h250q10 0 17.5 7.5t7.5 17.5v50q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe226;" d="M641 900l423 247q19 8 42 2.5t37 -21.5l32 -38q14 -15 12.5 -36t-17.5 -34l-139 -120h-390zM50 1100h106q67 0 103 -17t66 -71l102 -212h823q21 0 35.5 -14.5t14.5 -35.5v-50q0 -21 -14 -40t-33 -26l-737 -132q-23 -4 -40 6t-26 25q-42 67 -100 67h-300q-62 0 -106 44 t-44 106v200q0 62 44 106t106 44zM173 928h-80q-19 0 -28 -14t-9 -35v-56q0 -51 42 -51h134q16 0 21.5 8t5.5 24q0 11 -16 45t-27 51q-18 28 -43 28zM550 727q-32 0 -54.5 -22.5t-22.5 -54.5t22.5 -54.5t54.5 -22.5t54.5 22.5t22.5 54.5t-22.5 54.5t-54.5 22.5zM130 389 l152 130q18 19 34 24t31 -3.5t24.5 -17.5t25.5 -28q28 -35 50.5 -51t48.5 -13l63 5l48 -179q13 -61 -3.5 -97.5t-67.5 -79.5l-80 -69q-47 -40 -109 -35.5t-103 51.5l-130 151q-40 47 -35.5 109.5t51.5 102.5zM380 377l-102 -88q-31 -27 2 -65l37 -43q13 -15 27.5 -19.5 t31.5 6.5l61 53q19 16 14 49q-2 20 -12 56t-17 45q-11 12 -19 14t-23 -8z" />
<glyph unicode="&#xe227;" d="M625 1200h150q10 0 17.5 -7.5t7.5 -17.5v-109q79 -33 131 -87.5t53 -128.5q1 -46 -15 -84.5t-39 -61t-46 -38t-39 -21.5l-17 -6q6 0 15 -1.5t35 -9t50 -17.5t53 -30t50 -45t35.5 -64t14.5 -84q0 -59 -11.5 -105.5t-28.5 -76.5t-44 -51t-49.5 -31.5t-54.5 -16t-49.5 -6.5 t-43.5 -1v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-100v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-175q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h75v600h-75q-10 0 -17.5 7.5t-7.5 17.5v150 q0 10 7.5 17.5t17.5 7.5h175v75q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-75h100v75q0 10 7.5 17.5t17.5 7.5zM400 900v-200h263q28 0 48.5 10.5t30 25t15 29t5.5 25.5l1 10q0 4 -0.5 11t-6 24t-15 30t-30 24t-48.5 11h-263zM400 500v-200h363q28 0 48.5 10.5 t30 25t15 29t5.5 25.5l1 10q0 4 -0.5 11t-6 24t-15 30t-30 24t-48.5 11h-363z" />
<glyph unicode="&#xe230;" d="M212 1198h780q86 0 147 -61t61 -147v-416q0 -51 -18 -142.5t-36 -157.5l-18 -66q-29 -87 -93.5 -146.5t-146.5 -59.5h-572q-82 0 -147 59t-93 147q-8 28 -20 73t-32 143.5t-20 149.5v416q0 86 61 147t147 61zM600 1045q-70 0 -132.5 -11.5t-105.5 -30.5t-78.5 -41.5 t-57 -45t-36 -41t-20.5 -30.5l-6 -12l156 -243h560l156 243q-2 5 -6 12.5t-20 29.5t-36.5 42t-57 44.5t-79 42t-105 29.5t-132.5 12zM762 703h-157l195 261z" />
<glyph unicode="&#xe231;" d="M475 1300h150q103 0 189 -86t86 -189v-500q0 -41 -42 -83t-83 -42h-450q-41 0 -83 42t-42 83v500q0 103 86 189t189 86zM700 300v-225q0 -21 -27 -48t-48 -27h-150q-21 0 -48 27t-27 48v225h300z" />
<glyph unicode="&#xe232;" d="M475 1300h96q0 -150 89.5 -239.5t239.5 -89.5v-446q0 -41 -42 -83t-83 -42h-450q-41 0 -83 42t-42 83v500q0 103 86 189t189 86zM700 300v-225q0 -21 -27 -48t-48 -27h-150q-21 0 -48 27t-27 48v225h300z" />
<glyph unicode="&#xe233;" d="M1294 767l-638 -283l-378 170l-78 -60v-224l100 -150v-199l-150 148l-150 -149v200l100 150v250q0 4 -0.5 10.5t0 9.5t1 8t3 8t6.5 6l47 40l-147 65l642 283zM1000 380l-350 -166l-350 166v147l350 -165l350 165v-147z" />
<glyph unicode="&#xe234;" d="M250 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM650 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM1050 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44z" />
<glyph unicode="&#xe235;" d="M550 1100q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM550 700q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM550 300q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44z" />
<glyph unicode="&#xe236;" d="M125 1100h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM125 700h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5 t17.5 7.5zM125 300h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe237;" d="M350 1200h500q162 0 256 -93.5t94 -256.5v-500q0 -165 -93.5 -257.5t-256.5 -92.5h-500q-165 0 -257.5 92.5t-92.5 257.5v500q0 165 92.5 257.5t257.5 92.5zM900 1000h-600q-41 0 -70.5 -29.5t-29.5 -70.5v-600q0 -41 29.5 -70.5t70.5 -29.5h600q41 0 70.5 29.5 t29.5 70.5v600q0 41 -29.5 70.5t-70.5 29.5zM350 900h500q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -14.5 -35.5t-35.5 -14.5h-500q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 14.5 35.5t35.5 14.5zM400 800v-200h400v200h-400z" />
<glyph unicode="&#xe238;" d="M150 1100h1000q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5 t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe239;" d="M650 1187q87 -67 118.5 -156t0 -178t-118.5 -155q-87 66 -118.5 155t0 178t118.5 156zM300 800q124 0 212 -88t88 -212q-124 0 -212 88t-88 212zM1000 800q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM300 500q124 0 212 -88t88 -212q-124 0 -212 88t-88 212z M1000 500q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM700 199v-144q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v142q40 -4 43 -4q17 0 57 6z" />
<glyph unicode="&#xe240;" d="M745 878l69 19q25 6 45 -12l298 -295q11 -11 15 -26.5t-2 -30.5q-5 -14 -18 -23.5t-28 -9.5h-8q1 0 1 -13q0 -29 -2 -56t-8.5 -62t-20 -63t-33 -53t-51 -39t-72.5 -14h-146q-184 0 -184 288q0 24 10 47q-20 4 -62 4t-63 -4q11 -24 11 -47q0 -288 -184 -288h-142 q-48 0 -84.5 21t-56 51t-32 71.5t-16 75t-3.5 68.5q0 13 2 13h-7q-15 0 -27.5 9.5t-18.5 23.5q-6 15 -2 30.5t15 25.5l298 296q20 18 46 11l76 -19q20 -5 30.5 -22.5t5.5 -37.5t-22.5 -31t-37.5 -5l-51 12l-182 -193h891l-182 193l-44 -12q-20 -5 -37.5 6t-22.5 31t6 37.5 t31 22.5z" />
<glyph unicode="&#xe241;" d="M1200 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-850q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v850h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM500 450h-25q0 15 -4 24.5t-9 14.5t-17 7.5t-20 3t-25 0.5h-100v-425q0 -11 12.5 -17.5t25.5 -7.5h12v-50h-200v50q50 0 50 25v425h-100q-17 0 -25 -0.5t-20 -3t-17 -7.5t-9 -14.5t-4 -24.5h-25v150h500v-150z" />
<glyph unicode="&#xe242;" d="M1000 300v50q-25 0 -55 32q-14 14 -25 31t-16 27l-4 11l-289 747h-69l-300 -754q-18 -35 -39 -56q-9 -9 -24.5 -18.5t-26.5 -14.5l-11 -5v-50h273v50q-49 0 -78.5 21.5t-11.5 67.5l69 176h293l61 -166q13 -34 -3.5 -66.5t-55.5 -32.5v-50h312zM412 691l134 342l121 -342 h-255zM1100 150v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h1000q21 0 35.5 -14.5t14.5 -35.5z" />
<glyph unicode="&#xe243;" d="M50 1200h1100q21 0 35.5 -14.5t14.5 -35.5v-1100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v1100q0 21 14.5 35.5t35.5 14.5zM611 1118h-70q-13 0 -18 -12l-299 -753q-17 -32 -35 -51q-18 -18 -56 -34q-12 -5 -12 -18v-50q0 -8 5.5 -14t14.5 -6 h273q8 0 14 6t6 14v50q0 8 -6 14t-14 6q-55 0 -71 23q-10 14 0 39l63 163h266l57 -153q11 -31 -6 -55q-12 -17 -36 -17q-8 0 -14 -6t-6 -14v-50q0 -8 6 -14t14 -6h313q8 0 14 6t6 14v50q0 7 -5.5 13t-13.5 7q-17 0 -42 25q-25 27 -40 63h-1l-288 748q-5 12 -19 12zM639 611 h-197l103 264z" />
<glyph unicode="&#xe244;" d="M1200 1100h-1200v100h1200v-100zM50 1000h400q21 0 35.5 -14.5t14.5 -35.5v-900q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v900q0 21 14.5 35.5t35.5 14.5zM650 1000h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM700 900v-300h300v300h-300z" />
<glyph unicode="&#xe245;" d="M50 1200h400q21 0 35.5 -14.5t14.5 -35.5v-900q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v900q0 21 14.5 35.5t35.5 14.5zM650 700h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400 q0 21 14.5 35.5t35.5 14.5zM700 600v-300h300v300h-300zM1200 0h-1200v100h1200v-100z" />
<glyph unicode="&#xe246;" d="M50 1000h400q21 0 35.5 -14.5t14.5 -35.5v-350h100v150q0 21 14.5 35.5t35.5 14.5h400q21 0 35.5 -14.5t14.5 -35.5v-150h100v-100h-100v-150q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v150h-100v-350q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5zM700 700v-300h300v300h-300z" />
<glyph unicode="&#xe247;" d="M100 0h-100v1200h100v-1200zM250 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM300 1000v-300h300v300h-300zM250 500h900q21 0 35.5 -14.5t14.5 -35.5v-400 q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe248;" d="M600 1100h150q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-150v-100h450q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5h350v100h-150q-21 0 -35.5 14.5 t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5h150v100h100v-100zM400 1000v-300h300v300h-300z" />
<glyph unicode="&#xe249;" d="M1200 0h-100v1200h100v-1200zM550 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM600 1000v-300h300v300h-300zM50 500h900q21 0 35.5 -14.5t14.5 -35.5v-400 q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe250;" d="M865 565l-494 -494q-23 -23 -41 -23q-14 0 -22 13.5t-8 38.5v1000q0 25 8 38.5t22 13.5q18 0 41 -23l494 -494q14 -14 14 -35t-14 -35z" />
<glyph unicode="&#xe251;" d="M335 635l494 494q29 29 50 20.5t21 -49.5v-1000q0 -41 -21 -49.5t-50 20.5l-494 494q-14 14 -14 35t14 35z" />
<glyph unicode="&#xe252;" d="M100 900h1000q41 0 49.5 -21t-20.5 -50l-494 -494q-14 -14 -35 -14t-35 14l-494 494q-29 29 -20.5 50t49.5 21z" />
<glyph unicode="&#xe253;" d="M635 865l494 -494q29 -29 20.5 -50t-49.5 -21h-1000q-41 0 -49.5 21t20.5 50l494 494q14 14 35 14t35 -14z" />
<glyph unicode="&#xe254;" d="M700 741v-182l-692 -323v221l413 193l-413 193v221zM1200 0h-800v200h800v-200z" />
<glyph unicode="&#xe255;" d="M1200 900h-200v-100h200v-100h-300v300h200v100h-200v100h300v-300zM0 700h50q0 21 4 37t9.5 26.5t18 17.5t22 11t28.5 5.5t31 2t37 0.5h100v-550q0 -22 -25 -34.5t-50 -13.5l-25 -2v-100h400v100q-4 0 -11 0.5t-24 3t-30 7t-24 15t-11 24.5v550h100q25 0 37 -0.5t31 -2 t28.5 -5.5t22 -11t18 -17.5t9.5 -26.5t4 -37h50v300h-800v-300z" />
<glyph unicode="&#xe256;" d="M800 700h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-100v-550q0 -22 25 -34.5t50 -14.5l25 -1v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v550h-100q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h800v-300zM1100 200h-200v-100h200v-100h-300v300h200v100h-200v100h300v-300z" />
<glyph unicode="&#xe257;" d="M701 1098h160q16 0 21 -11t-7 -23l-464 -464l464 -464q12 -12 7 -23t-21 -11h-160q-13 0 -23 9l-471 471q-7 8 -7 18t7 18l471 471q10 9 23 9z" />
<glyph unicode="&#xe258;" d="M339 1098h160q13 0 23 -9l471 -471q7 -8 7 -18t-7 -18l-471 -471q-10 -9 -23 -9h-160q-16 0 -21 11t7 23l464 464l-464 464q-12 12 -7 23t21 11z" />
<glyph unicode="&#xe259;" d="M1087 882q11 -5 11 -21v-160q0 -13 -9 -23l-471 -471q-8 -7 -18 -7t-18 7l-471 471q-9 10 -9 23v160q0 16 11 21t23 -7l464 -464l464 464q12 12 23 7z" />
<glyph unicode="&#xe260;" d="M618 993l471 -471q9 -10 9 -23v-160q0 -16 -11 -21t-23 7l-464 464l-464 -464q-12 -12 -23 -7t-11 21v160q0 13 9 23l471 471q8 7 18 7t18 -7z" />
<glyph unicode="&#xf8ff;" d="M1000 1200q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM450 1000h100q21 0 40 -14t26 -33l79 -194q5 1 16 3q34 6 54 9.5t60 7t65.5 1t61 -10t56.5 -23t42.5 -42t29 -64t5 -92t-19.5 -121.5q-1 -7 -3 -19.5t-11 -50t-20.5 -73t-32.5 -81.5t-46.5 -83t-64 -70 t-82.5 -50q-13 -5 -42 -5t-65.5 2.5t-47.5 2.5q-14 0 -49.5 -3.5t-63 -3.5t-43.5 7q-57 25 -104.5 78.5t-75 111.5t-46.5 112t-26 90l-7 35q-15 63 -18 115t4.5 88.5t26 64t39.5 43.5t52 25.5t58.5 13t62.5 2t59.5 -4.5t55.5 -8l-147 192q-12 18 -5.5 30t27.5 12z" />
<glyph unicode="&#x1f511;" d="M250 1200h600q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-150v-500l-255 -178q-19 -9 -32 -1t-13 29v650h-150q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM400 1100v-100h300v100h-300z" />
<glyph unicode="&#x1f6aa;" d="M250 1200h750q39 0 69.5 -40.5t30.5 -84.5v-933l-700 -117v950l600 125h-700v-1000h-100v1025q0 23 15.5 49t34.5 26zM500 525v-100l100 20v100z" />
</font>
</defs></svg> ) format('svg')}.glyphicon{position:relative;top:1px;display:inline-block;font-family:'Glyphicons Halflings';font-style:normal;font-weight:400;line-height:1;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}.glyphicon-asterisk:before{content:"\2a"}.glyphicon-plus:before{content:"\2b"}.glyphicon-eur:before,.glyphicon-euro:before{content:"\20ac"}.glyphicon-minus:before{content:"\2212"}.glyphicon-cloud:before{content:"\2601"}.glyphicon-envelope:before{content:"\2709"}.glyphicon-pencil:before{content:"\270f"}.glyphicon-glass:before{content:"\e001"}.glyphicon-music:before{content:"\e002"}.glyphicon-search:before{content:"\e003"}.glyphicon-heart:before{content:"\e005"}.glyphicon-star:before{content:"\e006"}.glyphicon-star-empty:before{content:"\e007"}.glyphicon-user:before{content:"\e008"}.glyphicon-film:before{content:"\e009"}.glyphicon-th-large:before{content:"\e010"}.glyphicon-th:before{content:"\e011"}.glyphicon-th-list:before{content:"\e012"}.glyphicon-ok:before{content:"\e013"}.glyphicon-remove:before{content:"\e014"}.glyphicon-zoom-in:before{content:"\e015"}.glyphicon-zoom-out:before{content:"\e016"}.glyphicon-off:before{content:"\e017"}.glyphicon-signal:before{content:"\e018"}.glyphicon-cog:before{content:"\e019"}.glyphicon-trash:before{content:"\e020"}.glyphicon-home:before{content:"\e021"}.glyphicon-file:before{content:"\e022"}.glyphicon-time:before{content:"\e023"}.glyphicon-road:before{content:"\e024"}.glyphicon-download-alt:before{content:"\e025"}.glyphicon-download:before{content:"\e026"}.glyphicon-upload:before{content:"\e027"}.glyphicon-inbox:before{content:"\e028"}.glyphicon-play-circle:before{content:"\e029"}.glyphicon-repeat:before{content:"\e030"}.glyphicon-refresh:before{content:"\e031"}.glyphicon-list-alt:before{content:"\e032"}.glyphicon-lock:before{content:"\e033"}.glyphicon-flag:before{content:"\e034"}.glyphicon-headphones:before{content:"\e035"}.glyphicon-volume-off:before{content:"\e036"}.glyphicon-volume-down:before{content:"\e037"}.glyphicon-volume-up:before{content:"\e038"}.glyphicon-qrcode:before{content:"\e039"}.glyphicon-barcode:before{content:"\e040"}.glyphicon-tag:before{content:"\e041"}.glyphicon-tags:before{content:"\e042"}.glyphicon-book:before{content:"\e043"}.glyphicon-bookmark:before{content:"\e044"}.glyphicon-print:before{content:"\e045"}.glyphicon-camera:before{content:"\e046"}.glyphicon-font:before{content:"\e047"}.glyphicon-bold:before{content:"\e048"}.glyphicon-italic:before{content:"\e049"}.glyphicon-text-height:before{content:"\e050"}.glyphicon-text-width:before{content:"\e051"}.glyphicon-align-left:before{content:"\e052"}.glyphicon-align-center:before{content:"\e053"}.glyphicon-align-right:before{content:"\e054"}.glyphicon-align-justify:before{content:"\e055"}.glyphicon-list:before{content:"\e056"}.glyphicon-indent-left:before{content:"\e057"}.glyphicon-indent-right:before{content:"\e058"}.glyphicon-facetime-video:before{content:"\e059"}.glyphicon-picture:before{content:"\e060"}.glyphicon-map-marker:before{content:"\e062"}.glyphicon-adjust:before{content:"\e063"}.glyphicon-tint:before{content:"\e064"}.glyphicon-edit:before{content:"\e065"}.glyphicon-share:before{content:"\e066"}.glyphicon-check:before{content:"\e067"}.glyphicon-move:before{content:"\e068"}.glyphicon-step-backward:before{content:"\e069"}.glyphicon-fast-backward:before{content:"\e070"}.glyphicon-backward:before{content:"\e071"}.glyphicon-play:before{content:"\e072"}.glyphicon-pause:before{content:"\e073"}.glyphicon-stop:before{content:"\e074"}.glyphicon-forward:before{content:"\e075"}.glyphicon-fast-forward:before{content:"\e076"}.glyphicon-step-forward:before{content:"\e077"}.glyphicon-eject:before{content:"\e078"}.glyphicon-chevron-left:before{content:"\e079"}.glyphicon-chevron-right:before{content:"\e080"}.glyphicon-plus-sign:before{content:"\e081"}.glyphicon-minus-sign:before{content:"\e082"}.glyphicon-remove-sign:before{content:"\e083"}.glyphicon-ok-sign:before{content:"\e084"}.glyphicon-question-sign:before{content:"\e085"}.glyphicon-info-sign:before{content:"\e086"}.glyphicon-screenshot:before{content:"\e087"}.glyphicon-remove-circle:before{content:"\e088"}.glyphicon-ok-circle:before{content:"\e089"}.glyphicon-ban-circle:before{content:"\e090"}.glyphicon-arrow-left:before{content:"\e091"}.glyphicon-arrow-right:before{content:"\e092"}.glyphicon-arrow-up:before{content:"\e093"}.glyphicon-arrow-down:before{content:"\e094"}.glyphicon-share-alt:before{content:"\e095"}.glyphicon-resize-full:before{content:"\e096"}.glyphicon-resize-small:before{content:"\e097"}.glyphicon-exclamation-sign:before{content:"\e101"}.glyphicon-gift:before{content:"\e102"}.glyphicon-leaf:before{content:"\e103"}.glyphicon-fire:before{content:"\e104"}.glyphicon-eye-open:before{content:"\e105"}.glyphicon-eye-close:before{content:"\e106"}.glyphicon-warning-sign:before{content:"\e107"}.glyphicon-plane:before{content:"\e108"}.glyphicon-calendar:before{content:"\e109"}.glyphicon-random:before{content:"\e110"}.glyphicon-comment:before{content:"\e111"}.glyphicon-magnet:before{content:"\e112"}.glyphicon-chevron-up:before{content:"\e113"}.glyphicon-chevron-down:before{content:"\e114"}.glyphicon-retweet:before{content:"\e115"}.glyphicon-shopping-cart:before{content:"\e116"}.glyphicon-folder-close:before{content:"\e117"}.glyphicon-folder-open:before{content:"\e118"}.glyphicon-resize-vertical:before{content:"\e119"}.glyphicon-resize-horizontal:before{content:"\e120"}.glyphicon-hdd:before{content:"\e121"}.glyphicon-bullhorn:before{content:"\e122"}.glyphicon-bell:before{content:"\e123"}.glyphicon-certificate:before{content:"\e124"}.glyphicon-thumbs-up:before{content:"\e125"}.glyphicon-thumbs-down:before{content:"\e126"}.glyphicon-hand-right:before{content:"\e127"}.glyphicon-hand-left:before{content:"\e128"}.glyphicon-hand-up:before{content:"\e129"}.glyphicon-hand-down:before{content:"\e130"}.glyphicon-circle-arrow-right:before{content:"\e131"}.glyphicon-circle-arrow-left:before{content:"\e132"}.glyphicon-circle-arrow-up:before{content:"\e133"}.glyphicon-circle-arrow-down:before{content:"\e134"}.glyphicon-globe:before{content:"\e135"}.glyphicon-wrench:before{content:"\e136"}.glyphicon-tasks:before{content:"\e137"}.glyphicon-filter:before{content:"\e138"}.glyphicon-briefcase:before{content:"\e139"}.glyphicon-fullscreen:before{content:"\e140"}.glyphicon-dashboard:before{content:"\e141"}.glyphicon-paperclip:before{content:"\e142"}.glyphicon-heart-empty:before{content:"\e143"}.glyphicon-link:before{content:"\e144"}.glyphicon-phone:before{content:"\e145"}.glyphicon-pushpin:before{content:"\e146"}.glyphicon-usd:before{content:"\e148"}.glyphicon-gbp:before{content:"\e149"}.glyphicon-sort:before{content:"\e150"}.glyphicon-sort-by-alphabet:before{content:"\e151"}.glyphicon-sort-by-alphabet-alt:before{content:"\e152"}.glyphicon-sort-by-order:before{content:"\e153"}.glyphicon-sort-by-order-alt:before{content:"\e154"}.glyphicon-sort-by-attributes:before{content:"\e155"}.glyphicon-sort-by-attributes-alt:before{content:"\e156"}.glyphicon-unchecked:before{content:"\e157"}.glyphicon-expand:before{content:"\e158"}.glyphicon-collapse-down:before{content:"\e159"}.glyphicon-collapse-up:before{content:"\e160"}.glyphicon-log-in:before{content:"\e161"}.glyphicon-flash:before{content:"\e162"}.glyphicon-log-out:before{content:"\e163"}.glyphicon-new-window:before{content:"\e164"}.glyphicon-record:before{content:"\e165"}.glyphicon-save:before{content:"\e166"}.glyphicon-open:before{content:"\e167"}.glyphicon-saved:before{content:"\e168"}.glyphicon-import:before{content:"\e169"}.glyphicon-export:before{content:"\e170"}.glyphicon-send:before{content:"\e171"}.glyphicon-floppy-disk:before{content:"\e172"}.glyphicon-floppy-saved:before{content:"\e173"}.glyphicon-floppy-remove:before{content:"\e174"}.glyphicon-floppy-save:before{content:"\e175"}.glyphicon-floppy-open:before{content:"\e176"}.glyphicon-credit-card:before{content:"\e177"}.glyphicon-transfer:before{content:"\e178"}.glyphicon-cutlery:before{content:"\e179"}.glyphicon-header:before{content:"\e180"}.glyphicon-compressed:before{content:"\e181"}.glyphicon-earphone:before{content:"\e182"}.glyphicon-phone-alt:before{content:"\e183"}.glyphicon-tower:before{content:"\e184"}.glyphicon-stats:before{content:"\e185"}.glyphicon-sd-video:before{content:"\e186"}.glyphicon-hd-video:before{content:"\e187"}.glyphicon-subtitles:before{content:"\e188"}.glyphicon-sound-stereo:before{content:"\e189"}.glyphicon-sound-dolby:before{content:"\e190"}.glyphicon-sound-5-1:before{content:"\e191"}.glyphicon-sound-6-1:before{content:"\e192"}.glyphicon-sound-7-1:before{content:"\e193"}.glyphicon-copyright-mark:before{content:"\e194"}.glyphicon-registration-mark:before{content:"\e195"}.glyphicon-cloud-download:before{content:"\e197"}.glyphicon-cloud-upload:before{content:"\e198"}.glyphicon-tree-conifer:before{content:"\e199"}.glyphicon-tree-deciduous:before{content:"\e200"}.glyphicon-cd:before{content:"\e201"}.glyphicon-save-file:before{content:"\e202"}.glyphicon-open-file:before{content:"\e203"}.glyphicon-level-up:before{content:"\e204"}.glyphicon-copy:before{content:"\e205"}.glyphicon-paste:before{content:"\e206"}.glyphicon-alert:before{content:"\e209"}.glyphicon-equalizer:before{content:"\e210"}.glyphicon-king:before{content:"\e211"}.glyphicon-queen:before{content:"\e212"}.glyphicon-pawn:before{content:"\e213"}.glyphicon-bishop:before{content:"\e214"}.glyphicon-knight:before{content:"\e215"}.glyphicon-baby-formula:before{content:"\e216"}.glyphicon-tent:before{content:"\26fa"}.glyphicon-blackboard:before{content:"\e218"}.glyphicon-bed:before{content:"\e219"}.glyphicon-apple:before{content:"\f8ff"}.glyphicon-erase:before{content:"\e221"}.glyphicon-hourglass:before{content:"\231b"}.glyphicon-lamp:before{content:"\e223"}.glyphicon-duplicate:before{content:"\e224"}.glyphicon-piggy-bank:before{content:"\e225"}.glyphicon-scissors:before{content:"\e226"}.glyphicon-bitcoin:before{content:"\e227"}.glyphicon-btc:before{content:"\e227"}.glyphicon-xbt:before{content:"\e227"}.glyphicon-yen:before{content:"\00a5"}.glyphicon-jpy:before{content:"\00a5"}.glyphicon-ruble:before{content:"\20bd"}.glyphicon-rub:before{content:"\20bd"}.glyphicon-scale:before{content:"\e230"}.glyphicon-ice-lolly:before{content:"\e231"}.glyphicon-ice-lolly-tasted:before{content:"\e232"}.glyphicon-education:before{content:"\e233"}.glyphicon-option-horizontal:before{content:"\e234"}.glyphicon-option-vertical:before{content:"\e235"}.glyphicon-menu-hamburger:before{content:"\e236"}.glyphicon-modal-window:before{content:"\e237"}.glyphicon-oil:before{content:"\e238"}.glyphicon-grain:before{content:"\e239"}.glyphicon-sunglasses:before{content:"\e240"}.glyphicon-text-size:before{content:"\e241"}.glyphicon-text-color:before{content:"\e242"}.glyphicon-text-background:before{content:"\e243"}.glyphicon-object-align-top:before{content:"\e244"}.glyphicon-object-align-bottom:before{content:"\e245"}.glyphicon-object-align-horizontal:before{content:"\e246"}.glyphicon-object-align-left:before{content:"\e247"}.glyphicon-object-align-vertical:before{content:"\e248"}.glyphicon-object-align-right:before{content:"\e249"}.glyphicon-triangle-right:before{content:"\e250"}.glyphicon-triangle-left:before{content:"\e251"}.glyphicon-triangle-bottom:before{content:"\e252"}.glyphicon-triangle-top:before{content:"\e253"}.glyphicon-console:before{content:"\e254"}.glyphicon-superscript:before{content:"\e255"}.glyphicon-subscript:before{content:"\e256"}.glyphicon-menu-left:before{content:"\e257"}.glyphicon-menu-right:before{content:"\e258"}.glyphicon-menu-down:before{content:"\e259"}.glyphicon-menu-up:before{content:"\e260"}*{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}:after,:before{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}html{font-size:10px;-webkit-tap-highlight-color:rgba(0,0,0,0)}body{font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;line-height:1.42857143;color:#333;background-color:#fff}button,input,select,textarea{font-family:inherit;font-size:inherit;line-height:inherit}a{color:#337ab7;text-decoration:none}a:focus,a:hover{color:#23527c;text-decoration:underline}a:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}figure{margin:0}img{vertical-align:middle}.carousel-inner>.item>a>img,.carousel-inner>.item>img,.img-responsive,.thumbnail a>img,.thumbnail>img{display:block;max-width:100%;height:auto}.img-rounded{border-radius:6px}.img-thumbnail{display:inline-block;max-width:100%;height:auto;padding:4px;line-height:1.42857143;background-color:#fff;border:1px solid #ddd;border-radius:4px;-webkit-transition:all .2s ease-in-out;-o-transition:all .2s ease-in-out;transition:all .2s ease-in-out}.img-circle{border-radius:50%}hr{margin-top:20px;margin-bottom:20px;border:0;border-top:1px solid #eee}.sr-only{position:absolute;width:1px;height:1px;padding:0;margin:-1px;overflow:hidden;clip:rect(0,0,0,0);border:0}.sr-only-focusable:active,.sr-only-focusable:focus{position:static;width:auto;height:auto;margin:0;overflow:visible;clip:auto}[role=button]{cursor:pointer}.h1,.h2,.h3,.h4,.h5,.h6,h1,h2,h3,h4,h5,h6{font-family:inherit;font-weight:500;line-height:1.1;color:inherit}.h1 .small,.h1 small,.h2 .small,.h2 small,.h3 .small,.h3 small,.h4 .small,.h4 small,.h5 .small,.h5 small,.h6 .small,.h6 small,h1 .small,h1 small,h2 .small,h2 small,h3 .small,h3 small,h4 .small,h4 small,h5 .small,h5 small,h6 .small,h6 small{font-weight:400;line-height:1;color:#777}.h1,.h2,.h3,h1,h2,h3{margin-top:20px;margin-bottom:10px}.h1 .small,.h1 small,.h2 .small,.h2 small,.h3 .small,.h3 small,h1 .small,h1 small,h2 .small,h2 small,h3 .small,h3 small{font-size:65%}.h4,.h5,.h6,h4,h5,h6{margin-top:10px;margin-bottom:10px}.h4 .small,.h4 small,.h5 .small,.h5 small,.h6 .small,.h6 small,h4 .small,h4 small,h5 .small,h5 small,h6 .small,h6 small{font-size:75%}.h1,h1{font-size:36px}.h2,h2{font-size:30px}.h3,h3{font-size:24px}.h4,h4{font-size:18px}.h5,h5{font-size:14px}.h6,h6{font-size:12px}p{margin:0 0 10px}.lead{margin-bottom:20px;font-size:16px;font-weight:300;line-height:1.4}@media (min-width:768px){.lead{font-size:21px}}.small,small{font-size:85%}.mark,mark{padding:.2em;background-color:#fcf8e3}.text-left{text-align:left}.text-right{text-align:right}.text-center{text-align:center}.text-justify{text-align:justify}.text-nowrap{white-space:nowrap}.text-lowercase{text-transform:lowercase}.text-uppercase{text-transform:uppercase}.text-capitalize{text-transform:capitalize}.text-muted{color:#777}.text-primary{color:#337ab7}a.text-primary:focus,a.text-primary:hover{color:#286090}.text-success{color:#3c763d}a.text-success:focus,a.text-success:hover{color:#2b542c}.text-info{color:#31708f}a.text-info:focus,a.text-info:hover{color:#245269}.text-warning{color:#8a6d3b}a.text-warning:focus,a.text-warning:hover{color:#66512c}.text-danger{color:#a94442}a.text-danger:focus,a.text-danger:hover{color:#843534}.bg-primary{color:#fff;background-color:#337ab7}a.bg-primary:focus,a.bg-primary:hover{background-color:#286090}.bg-success{background-color:#dff0d8}a.bg-success:focus,a.bg-success:hover{background-color:#c1e2b3}.bg-info{background-color:#d9edf7}a.bg-info:focus,a.bg-info:hover{background-color:#afd9ee}.bg-warning{background-color:#fcf8e3}a.bg-warning:focus,a.bg-warning:hover{background-color:#f7ecb5}.bg-danger{background-color:#f2dede}a.bg-danger:focus,a.bg-danger:hover{background-color:#e4b9b9}.page-header{padding-bottom:9px;margin:40px 0 20px;border-bottom:1px solid #eee}ol,ul{margin-top:0;margin-bottom:10px}ol ol,ol ul,ul ol,ul ul{margin-bottom:0}.list-unstyled{padding-left:0;list-style:none}.list-inline{padding-left:0;margin-left:-5px;list-style:none}.list-inline>li{display:inline-block;padding-right:5px;padding-left:5px}dl{margin-top:0;margin-bottom:20px}dd,dt{line-height:1.42857143}dt{font-weight:700}dd{margin-left:0}@media (min-width:768px){.dl-horizontal dt{float:left;width:160px;overflow:hidden;clear:left;text-align:right;text-overflow:ellipsis;white-space:nowrap}.dl-horizontal dd{margin-left:180px}}abbr[data-original-title],abbr[title]{cursor:help;border-bottom:1px dotted #777}.initialism{font-size:90%;text-transform:uppercase}blockquote{padding:10px 20px;margin:0 0 20px;font-size:17.5px;border-left:5px solid #eee}blockquote ol:last-child,blockquote p:last-child,blockquote ul:last-child{margin-bottom:0}blockquote .small,blockquote footer,blockquote small{display:block;font-size:80%;line-height:1.42857143;color:#777}blockquote .small:before,blockquote footer:before,blockquote small:before{content:'\2014 \00A0'}.blockquote-reverse,blockquote.pull-right{padding-right:15px;padding-left:0;text-align:right;border-right:5px solid #eee;border-left:0}.blockquote-reverse .small:before,.blockquote-reverse footer:before,.blockquote-reverse small:before,blockquote.pull-right .small:before,blockquote.pull-right footer:before,blockquote.pull-right small:before{content:''}.blockquote-reverse .small:after,.blockquote-reverse footer:after,.blockquote-reverse small:after,blockquote.pull-right .small:after,blockquote.pull-right footer:after,blockquote.pull-right small:after{content:'\00A0 \2014'}address{margin-bottom:20px;font-style:normal;line-height:1.42857143}code,kbd,pre,samp{font-family:monospace}code{padding:2px 4px;font-size:90%;color:#c7254e;background-color:#f9f2f4;border-radius:4px}kbd{padding:2px 4px;font-size:90%;color:#fff;background-color:#333;border-radius:3px;-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,.25);box-shadow:inset 0 -1px 0 rgba(0,0,0,.25)}kbd kbd{padding:0;font-size:100%;font-weight:700;-webkit-box-shadow:none;box-shadow:none}pre{display:block;padding:9.5px;margin:0 0 10px;font-size:13px;line-height:1.42857143;color:#333;word-break:break-all;word-wrap:break-word;background-color:#f5f5f5;border:1px solid #ccc;border-radius:4px}pre code{padding:0;font-size:inherit;color:inherit;white-space:pre-wrap;background-color:transparent;border-radius:0}.pre-scrollable{max-height:340px;overflow-y:scroll}.container{padding-right:15px;padding-left:15px;margin-right:auto;margin-left:auto}@media (min-width:768px){.container{width:750px}}@media (min-width:992px){.container{width:970px}}@media (min-width:1200px){.container{width:1170px}}.container-fluid{padding-right:15px;padding-left:15px;margin-right:auto;margin-left:auto}.row{margin-right:-15px;margin-left:-15px}.col-lg-1,.col-lg-10,.col-lg-11,.col-lg-12,.col-lg-2,.col-lg-3,.col-lg-4,.col-lg-5,.col-lg-6,.col-lg-7,.col-lg-8,.col-lg-9,.col-md-1,.col-md-10,.col-md-11,.col-md-12,.col-md-2,.col-md-3,.col-md-4,.col-md-5,.col-md-6,.col-md-7,.col-md-8,.col-md-9,.col-sm-1,.col-sm-10,.col-sm-11,.col-sm-12,.col-sm-2,.col-sm-3,.col-sm-4,.col-sm-5,.col-sm-6,.col-sm-7,.col-sm-8,.col-sm-9,.col-xs-1,.col-xs-10,.col-xs-11,.col-xs-12,.col-xs-2,.col-xs-3,.col-xs-4,.col-xs-5,.col-xs-6,.col-xs-7,.col-xs-8,.col-xs-9{position:relative;min-height:1px;padding-right:15px;padding-left:15px}.col-xs-1,.col-xs-10,.col-xs-11,.col-xs-12,.col-xs-2,.col-xs-3,.col-xs-4,.col-xs-5,.col-xs-6,.col-xs-7,.col-xs-8,.col-xs-9{float:left}.col-xs-12{width:100%}.col-xs-11{width:91.66666667%}.col-xs-10{width:83.33333333%}.col-xs-9{width:75%}.col-xs-8{width:66.66666667%}.col-xs-7{width:58.33333333%}.col-xs-6{width:50%}.col-xs-5{width:41.66666667%}.col-xs-4{width:33.33333333%}.col-xs-3{width:25%}.col-xs-2{width:16.66666667%}.col-xs-1{width:8.33333333%}.col-xs-pull-12{right:100%}.col-xs-pull-11{right:91.66666667%}.col-xs-pull-10{right:83.33333333%}.col-xs-pull-9{right:75%}.col-xs-pull-8{right:66.66666667%}.col-xs-pull-7{right:58.33333333%}.col-xs-pull-6{right:50%}.col-xs-pull-5{right:41.66666667%}.col-xs-pull-4{right:33.33333333%}.col-xs-pull-3{right:25%}.col-xs-pull-2{right:16.66666667%}.col-xs-pull-1{right:8.33333333%}.col-xs-pull-0{right:auto}.col-xs-push-12{left:100%}.col-xs-push-11{left:91.66666667%}.col-xs-push-10{left:83.33333333%}.col-xs-push-9{left:75%}.col-xs-push-8{left:66.66666667%}.col-xs-push-7{left:58.33333333%}.col-xs-push-6{left:50%}.col-xs-push-5{left:41.66666667%}.col-xs-push-4{left:33.33333333%}.col-xs-push-3{left:25%}.col-xs-push-2{left:16.66666667%}.col-xs-push-1{left:8.33333333%}.col-xs-push-0{left:auto}.col-xs-offset-12{margin-left:100%}.col-xs-offset-11{margin-left:91.66666667%}.col-xs-offset-10{margin-left:83.33333333%}.col-xs-offset-9{margin-left:75%}.col-xs-offset-8{margin-left:66.66666667%}.col-xs-offset-7{margin-left:58.33333333%}.col-xs-offset-6{margin-left:50%}.col-xs-offset-5{margin-left:41.66666667%}.col-xs-offset-4{margin-left:33.33333333%}.col-xs-offset-3{margin-left:25%}.col-xs-offset-2{margin-left:16.66666667%}.col-xs-offset-1{margin-left:8.33333333%}.col-xs-offset-0{margin-left:0}@media (min-width:768px){.col-sm-1,.col-sm-10,.col-sm-11,.col-sm-12,.col-sm-2,.col-sm-3,.col-sm-4,.col-sm-5,.col-sm-6,.col-sm-7,.col-sm-8,.col-sm-9{float:left}.col-sm-12{width:100%}.col-sm-11{width:91.66666667%}.col-sm-10{width:83.33333333%}.col-sm-9{width:75%}.col-sm-8{width:66.66666667%}.col-sm-7{width:58.33333333%}.col-sm-6{width:50%}.col-sm-5{width:41.66666667%}.col-sm-4{width:33.33333333%}.col-sm-3{width:25%}.col-sm-2{width:16.66666667%}.col-sm-1{width:8.33333333%}.col-sm-pull-12{right:100%}.col-sm-pull-11{right:91.66666667%}.col-sm-pull-10{right:83.33333333%}.col-sm-pull-9{right:75%}.col-sm-pull-8{right:66.66666667%}.col-sm-pull-7{right:58.33333333%}.col-sm-pull-6{right:50%}.col-sm-pull-5{right:41.66666667%}.col-sm-pull-4{right:33.33333333%}.col-sm-pull-3{right:25%}.col-sm-pull-2{right:16.66666667%}.col-sm-pull-1{right:8.33333333%}.col-sm-pull-0{right:auto}.col-sm-push-12{left:100%}.col-sm-push-11{left:91.66666667%}.col-sm-push-10{left:83.33333333%}.col-sm-push-9{left:75%}.col-sm-push-8{left:66.66666667%}.col-sm-push-7{left:58.33333333%}.col-sm-push-6{left:50%}.col-sm-push-5{left:41.66666667%}.col-sm-push-4{left:33.33333333%}.col-sm-push-3{left:25%}.col-sm-push-2{left:16.66666667%}.col-sm-push-1{left:8.33333333%}.col-sm-push-0{left:auto}.col-sm-offset-12{margin-left:100%}.col-sm-offset-11{margin-left:91.66666667%}.col-sm-offset-10{margin-left:83.33333333%}.col-sm-offset-9{margin-left:75%}.col-sm-offset-8{margin-left:66.66666667%}.col-sm-offset-7{margin-left:58.33333333%}.col-sm-offset-6{margin-left:50%}.col-sm-offset-5{margin-left:41.66666667%}.col-sm-offset-4{margin-left:33.33333333%}.col-sm-offset-3{margin-left:25%}.col-sm-offset-2{margin-left:16.66666667%}.col-sm-offset-1{margin-left:8.33333333%}.col-sm-offset-0{margin-left:0}}@media (min-width:992px){.col-md-1,.col-md-10,.col-md-11,.col-md-12,.col-md-2,.col-md-3,.col-md-4,.col-md-5,.col-md-6,.col-md-7,.col-md-8,.col-md-9{float:left}.col-md-12{width:100%}.col-md-11{width:91.66666667%}.col-md-10{width:83.33333333%}.col-md-9{width:75%}.col-md-8{width:66.66666667%}.col-md-7{width:58.33333333%}.col-md-6{width:50%}.col-md-5{width:41.66666667%}.col-md-4{width:33.33333333%}.col-md-3{width:25%}.col-md-2{width:16.66666667%}.col-md-1{width:8.33333333%}.col-md-pull-12{right:100%}.col-md-pull-11{right:91.66666667%}.col-md-pull-10{right:83.33333333%}.col-md-pull-9{right:75%}.col-md-pull-8{right:66.66666667%}.col-md-pull-7{right:58.33333333%}.col-md-pull-6{right:50%}.col-md-pull-5{right:41.66666667%}.col-md-pull-4{right:33.33333333%}.col-md-pull-3{right:25%}.col-md-pull-2{right:16.66666667%}.col-md-pull-1{right:8.33333333%}.col-md-pull-0{right:auto}.col-md-push-12{left:100%}.col-md-push-11{left:91.66666667%}.col-md-push-10{left:83.33333333%}.col-md-push-9{left:75%}.col-md-push-8{left:66.66666667%}.col-md-push-7{left:58.33333333%}.col-md-push-6{left:50%}.col-md-push-5{left:41.66666667%}.col-md-push-4{left:33.33333333%}.col-md-push-3{left:25%}.col-md-push-2{left:16.66666667%}.col-md-push-1{left:8.33333333%}.col-md-push-0{left:auto}.col-md-offset-12{margin-left:100%}.col-md-offset-11{margin-left:91.66666667%}.col-md-offset-10{margin-left:83.33333333%}.col-md-offset-9{margin-left:75%}.col-md-offset-8{margin-left:66.66666667%}.col-md-offset-7{margin-left:58.33333333%}.col-md-offset-6{margin-left:50%}.col-md-offset-5{margin-left:41.66666667%}.col-md-offset-4{margin-left:33.33333333%}.col-md-offset-3{margin-left:25%}.col-md-offset-2{margin-left:16.66666667%}.col-md-offset-1{margin-left:8.33333333%}.col-md-offset-0{margin-left:0}}@media (min-width:1200px){.col-lg-1,.col-lg-10,.col-lg-11,.col-lg-12,.col-lg-2,.col-lg-3,.col-lg-4,.col-lg-5,.col-lg-6,.col-lg-7,.col-lg-8,.col-lg-9{float:left}.col-lg-12{width:100%}.col-lg-11{width:91.66666667%}.col-lg-10{width:83.33333333%}.col-lg-9{width:75%}.col-lg-8{width:66.66666667%}.col-lg-7{width:58.33333333%}.col-lg-6{width:50%}.col-lg-5{width:41.66666667%}.col-lg-4{width:33.33333333%}.col-lg-3{width:25%}.col-lg-2{width:16.66666667%}.col-lg-1{width:8.33333333%}.col-lg-pull-12{right:100%}.col-lg-pull-11{right:91.66666667%}.col-lg-pull-10{right:83.33333333%}.col-lg-pull-9{right:75%}.col-lg-pull-8{right:66.66666667%}.col-lg-pull-7{right:58.33333333%}.col-lg-pull-6{right:50%}.col-lg-pull-5{right:41.66666667%}.col-lg-pull-4{right:33.33333333%}.col-lg-pull-3{right:25%}.col-lg-pull-2{right:16.66666667%}.col-lg-pull-1{right:8.33333333%}.col-lg-pull-0{right:auto}.col-lg-push-12{left:100%}.col-lg-push-11{left:91.66666667%}.col-lg-push-10{left:83.33333333%}.col-lg-push-9{left:75%}.col-lg-push-8{left:66.66666667%}.col-lg-push-7{left:58.33333333%}.col-lg-push-6{left:50%}.col-lg-push-5{left:41.66666667%}.col-lg-push-4{left:33.33333333%}.col-lg-push-3{left:25%}.col-lg-push-2{left:16.66666667%}.col-lg-push-1{left:8.33333333%}.col-lg-push-0{left:auto}.col-lg-offset-12{margin-left:100%}.col-lg-offset-11{margin-left:91.66666667%}.col-lg-offset-10{margin-left:83.33333333%}.col-lg-offset-9{margin-left:75%}.col-lg-offset-8{margin-left:66.66666667%}.col-lg-offset-7{margin-left:58.33333333%}.col-lg-offset-6{margin-left:50%}.col-lg-offset-5{margin-left:41.66666667%}.col-lg-offset-4{margin-left:33.33333333%}.col-lg-offset-3{margin-left:25%}.col-lg-offset-2{margin-left:16.66666667%}.col-lg-offset-1{margin-left:8.33333333%}.col-lg-offset-0{margin-left:0}}table{background-color:transparent}caption{padding-top:8px;padding-bottom:8px;color:#777;text-align:left}th{}.table{width:100%;max-width:100%;margin-bottom:20px}.table>tbody>tr>td,.table>tbody>tr>th,.table>tfoot>tr>td,.table>tfoot>tr>th,.table>thead>tr>td,.table>thead>tr>th{padding:8px;line-height:1.42857143;vertical-align:top;border-top:1px solid #ddd}.table>thead>tr>th{vertical-align:bottom;border-bottom:2px solid #ddd}.table>caption+thead>tr:first-child>td,.table>caption+thead>tr:first-child>th,.table>colgroup+thead>tr:first-child>td,.table>colgroup+thead>tr:first-child>th,.table>thead:first-child>tr:first-child>td,.table>thead:first-child>tr:first-child>th{border-top:0}.table>tbody+tbody{border-top:2px solid #ddd}.table .table{background-color:#fff}.table-condensed>tbody>tr>td,.table-condensed>tbody>tr>th,.table-condensed>tfoot>tr>td,.table-condensed>tfoot>tr>th,.table-condensed>thead>tr>td,.table-condensed>thead>tr>th{padding:5px}.table-bordered{border:1px solid #ddd}.table-bordered>tbody>tr>td,.table-bordered>tbody>tr>th,.table-bordered>tfoot>tr>td,.table-bordered>tfoot>tr>th,.table-bordered>thead>tr>td,.table-bordered>thead>tr>th{border:1px solid #ddd}.table-bordered>thead>tr>td,.table-bordered>thead>tr>th{border-bottom-width:2px}.table-striped>tbody>tr:nth-of-type(odd){background-color:#f9f9f9}.table-hover>tbody>tr:hover{background-color:#f5f5f5}table col[class*=col-]{position:static;display:table-column;float:none}table td[class*=col-],table th[class*=col-]{position:static;display:table-cell;float:none}.table>tbody>tr.active>td,.table>tbody>tr.active>th,.table>tbody>tr>td.active,.table>tbody>tr>th.active,.table>tfoot>tr.active>td,.table>tfoot>tr.active>th,.table>tfoot>tr>td.active,.table>tfoot>tr>th.active,.table>thead>tr.active>td,.table>thead>tr.active>th,.table>thead>tr>td.active,.table>thead>tr>th.active{background-color:#f5f5f5}.table-hover>tbody>tr.active:hover>td,.table-hover>tbody>tr.active:hover>th,.table-hover>tbody>tr:hover>.active,.table-hover>tbody>tr>td.active:hover,.table-hover>tbody>tr>th.active:hover{background-color:#e8e8e8}.table>tbody>tr.success>td,.table>tbody>tr.success>th,.table>tbody>tr>td.success,.table>tbody>tr>th.success,.table>tfoot>tr.success>td,.table>tfoot>tr.success>th,.table>tfoot>tr>td.success,.table>tfoot>tr>th.success,.table>thead>tr.success>td,.table>thead>tr.success>th,.table>thead>tr>td.success,.table>thead>tr>th.success{background-color:#dff0d8}.table-hover>tbody>tr.success:hover>td,.table-hover>tbody>tr.success:hover>th,.table-hover>tbody>tr:hover>.success,.table-hover>tbody>tr>td.success:hover,.table-hover>tbody>tr>th.success:hover{background-color:#d0e9c6}.table>tbody>tr.info>td,.table>tbody>tr.info>th,.table>tbody>tr>td.info,.table>tbody>tr>th.info,.table>tfoot>tr.info>td,.table>tfoot>tr.info>th,.table>tfoot>tr>td.info,.table>tfoot>tr>th.info,.table>thead>tr.info>td,.table>thead>tr.info>th,.table>thead>tr>td.info,.table>thead>tr>th.info{background-color:#d9edf7}.table-hover>tbody>tr.info:hover>td,.table-hover>tbody>tr.info:hover>th,.table-hover>tbody>tr:hover>.info,.table-hover>tbody>tr>td.info:hover,.table-hover>tbody>tr>th.info:hover{background-color:#c4e3f3}.table>tbody>tr.warning>td,.table>tbody>tr.warning>th,.table>tbody>tr>td.warning,.table>tbody>tr>th.warning,.table>tfoot>tr.warning>td,.table>tfoot>tr.warning>th,.table>tfoot>tr>td.warning,.table>tfoot>tr>th.warning,.table>thead>tr.warning>td,.table>thead>tr.warning>th,.table>thead>tr>td.warning,.table>thead>tr>th.warning{background-color:#fcf8e3}.table-hover>tbody>tr.warning:hover>td,.table-hover>tbody>tr.warning:hover>th,.table-hover>tbody>tr:hover>.warning,.table-hover>tbody>tr>td.warning:hover,.table-hover>tbody>tr>th.warning:hover{background-color:#faf2cc}.table>tbody>tr.danger>td,.table>tbody>tr.danger>th,.table>tbody>tr>td.danger,.table>tbody>tr>th.danger,.table>tfoot>tr.danger>td,.table>tfoot>tr.danger>th,.table>tfoot>tr>td.danger,.table>tfoot>tr>th.danger,.table>thead>tr.danger>td,.table>thead>tr.danger>th,.table>thead>tr>td.danger,.table>thead>tr>th.danger{background-color:#f2dede}.table-hover>tbody>tr.danger:hover>td,.table-hover>tbody>tr.danger:hover>th,.table-hover>tbody>tr:hover>.danger,.table-hover>tbody>tr>td.danger:hover,.table-hover>tbody>tr>th.danger:hover{background-color:#ebcccc}.table-responsive{min-height:.01%;overflow-x:auto}@media screen and (max-width:767px){.table-responsive{width:100%;margin-bottom:15px;overflow-y:hidden;-ms-overflow-style:-ms-autohiding-scrollbar;border:1px solid #ddd}.table-responsive>.table{margin-bottom:0}.table-responsive>.table>tbody>tr>td,.table-responsive>.table>tbody>tr>th,.table-responsive>.table>tfoot>tr>td,.table-responsive>.table>tfoot>tr>th,.table-responsive>.table>thead>tr>td,.table-responsive>.table>thead>tr>th{white-space:nowrap}.table-responsive>.table-bordered{border:0}.table-responsive>.table-bordered>tbody>tr>td:first-child,.table-responsive>.table-bordered>tbody>tr>th:first-child,.table-responsive>.table-bordered>tfoot>tr>td:first-child,.table-responsive>.table-bordered>tfoot>tr>th:first-child,.table-responsive>.table-bordered>thead>tr>td:first-child,.table-responsive>.table-bordered>thead>tr>th:first-child{border-left:0}.table-responsive>.table-bordered>tbody>tr>td:last-child,.table-responsive>.table-bordered>tbody>tr>th:last-child,.table-responsive>.table-bordered>tfoot>tr>td:last-child,.table-responsive>.table-bordered>tfoot>tr>th:last-child,.table-responsive>.table-bordered>thead>tr>td:last-child,.table-responsive>.table-bordered>thead>tr>th:last-child{border-right:0}.table-responsive>.table-bordered>tbody>tr:last-child>td,.table-responsive>.table-bordered>tbody>tr:last-child>th,.table-responsive>.table-bordered>tfoot>tr:last-child>td,.table-responsive>.table-bordered>tfoot>tr:last-child>th{border-bottom:0}}fieldset{min-width:0;padding:0;margin:0;border:0}legend{display:block;width:100%;padding:0;margin-bottom:20px;font-size:21px;line-height:inherit;color:#333;border:0;border-bottom:1px solid #e5e5e5}label{display:inline-block;max-width:100%;margin-bottom:5px;font-weight:700}input[type=search]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}input[type=checkbox],input[type=radio]{margin:4px 0 0;margin-top:1px\9;line-height:normal}input[type=file]{display:block}input[type=range]{display:block;width:100%}select[multiple],select[size]{height:auto}input[type=file]:focus,input[type=checkbox]:focus,input[type=radio]:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}output{display:block;padding-top:7px;font-size:14px;line-height:1.42857143;color:#555}.form-control{display:block;width:100%;height:34px;padding:6px 12px;font-size:14px;line-height:1.42857143;color:#555;background-color:#fff;background-image:none;border:1px solid #ccc;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075);-webkit-transition:border-color ease-in-out .15s,-webkit-box-shadow ease-in-out .15s;-o-transition:border-color ease-in-out .15s,box-shadow ease-in-out .15s;transition:border-color ease-in-out .15s,box-shadow ease-in-out .15s}.form-control:focus{border-color:#66afe9;outline:0;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 8px rgba(102,175,233,.6);box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 8px rgba(102,175,233,.6)}.form-control::-moz-placeholder{color:#999;opacity:1}.form-control:-ms-input-placeholder{color:#999}.form-control::-webkit-input-placeholder{color:#999}.form-control[disabled],.form-control[readonly],fieldset[disabled] .form-control{background-color:#eee;opacity:1}.form-control[disabled],fieldset[disabled] .form-control{cursor:not-allowed}textarea.form-control{height:auto}input[type=search]{-webkit-appearance:none}@media screen and (-webkit-min-device-pixel-ratio:0){input[type=date].form-control,input[type=time].form-control,input[type=datetime-local].form-control,input[type=month].form-control{line-height:34px}.input-group-sm input[type=date],.input-group-sm input[type=time],.input-group-sm input[type=datetime-local],.input-group-sm input[type=month],input[type=date].input-sm,input[type=time].input-sm,input[type=datetime-local].input-sm,input[type=month].input-sm{line-height:30px}.input-group-lg input[type=date],.input-group-lg input[type=time],.input-group-lg input[type=datetime-local],.input-group-lg input[type=month],input[type=date].input-lg,input[type=time].input-lg,input[type=datetime-local].input-lg,input[type=month].input-lg{line-height:46px}}.form-group{margin-bottom:15px}.checkbox,.radio{position:relative;display:block;margin-top:10px;margin-bottom:10px}.checkbox label,.radio label{min-height:20px;padding-left:20px;margin-bottom:0;font-weight:400;cursor:pointer}.checkbox input[type=checkbox],.checkbox-inline input[type=checkbox],.radio input[type=radio],.radio-inline input[type=radio]{position:absolute;margin-top:4px\9;margin-left:-20px}.checkbox+.checkbox,.radio+.radio{margin-top:-5px}.checkbox-inline,.radio-inline{position:relative;display:inline-block;padding-left:20px;margin-bottom:0;font-weight:400;vertical-align:middle;cursor:pointer}.checkbox-inline+.checkbox-inline,.radio-inline+.radio-inline{margin-top:0;margin-left:10px}fieldset[disabled] input[type=checkbox],fieldset[disabled] input[type=radio],input[type=checkbox].disabled,input[type=checkbox][disabled],input[type=radio].disabled,input[type=radio][disabled]{cursor:not-allowed}.checkbox-inline.disabled,.radio-inline.disabled,fieldset[disabled] .checkbox-inline,fieldset[disabled] .radio-inline{cursor:not-allowed}.checkbox.disabled label,.radio.disabled label,fieldset[disabled] .checkbox label,fieldset[disabled] .radio label{cursor:not-allowed}.form-control-static{min-height:34px;padding-top:7px;padding-bottom:7px;margin-bottom:0}.form-control-static.input-lg,.form-control-static.input-sm{padding-right:0;padding-left:0}.input-sm{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}select.input-sm{height:30px;line-height:30px}select[multiple].input-sm,textarea.input-sm{height:auto}.form-group-sm .form-control{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}.form-group-sm select.form-control{height:30px;line-height:30px}.form-group-sm select[multiple].form-control,.form-group-sm textarea.form-control{height:auto}.form-group-sm .form-control-static{height:30px;min-height:32px;padding:6px 10px;font-size:12px;line-height:1.5}.input-lg{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}select.input-lg{height:46px;line-height:46px}select[multiple].input-lg,textarea.input-lg{height:auto}.form-group-lg .form-control{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}.form-group-lg select.form-control{height:46px;line-height:46px}.form-group-lg select[multiple].form-control,.form-group-lg textarea.form-control{height:auto}.form-group-lg .form-control-static{height:46px;min-height:38px;padding:11px 16px;font-size:18px;line-height:1.3333333}.has-feedback{position:relative}.has-feedback .form-control{padding-right:42.5px}.form-control-feedback{position:absolute;top:0;right:0;z-index:2;display:block;width:34px;height:34px;line-height:34px;text-align:center;pointer-events:none}.form-group-lg .form-control+.form-control-feedback,.input-group-lg+.form-control-feedback,.input-lg+.form-control-feedback{width:46px;height:46px;line-height:46px}.form-group-sm .form-control+.form-control-feedback,.input-group-sm+.form-control-feedback,.input-sm+.form-control-feedback{width:30px;height:30px;line-height:30px}.has-success .checkbox,.has-success .checkbox-inline,.has-success .control-label,.has-success .help-block,.has-success .radio,.has-success .radio-inline,.has-success.checkbox label,.has-success.checkbox-inline label,.has-success.radio label,.has-success.radio-inline label{color:#3c763d}.has-success .form-control{border-color:#3c763d;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-success .form-control:focus{border-color:#2b542c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #67b168;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #67b168}.has-success .input-group-addon{color:#3c763d;background-color:#dff0d8;border-color:#3c763d}.has-success .form-control-feedback{color:#3c763d}.has-warning .checkbox,.has-warning .checkbox-inline,.has-warning .control-label,.has-warning .help-block,.has-warning .radio,.has-warning .radio-inline,.has-warning.checkbox label,.has-warning.checkbox-inline label,.has-warning.radio label,.has-warning.radio-inline label{color:#8a6d3b}.has-warning .form-control{border-color:#8a6d3b;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-warning .form-control:focus{border-color:#66512c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #c0a16b;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #c0a16b}.has-warning .input-group-addon{color:#8a6d3b;background-color:#fcf8e3;border-color:#8a6d3b}.has-warning .form-control-feedback{color:#8a6d3b}.has-error .checkbox,.has-error .checkbox-inline,.has-error .control-label,.has-error .help-block,.has-error .radio,.has-error .radio-inline,.has-error.checkbox label,.has-error.checkbox-inline label,.has-error.radio label,.has-error.radio-inline label{color:#a94442}.has-error .form-control{border-color:#a94442;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-error .form-control:focus{border-color:#843534;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #ce8483;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #ce8483}.has-error .input-group-addon{color:#a94442;background-color:#f2dede;border-color:#a94442}.has-error .form-control-feedback{color:#a94442}.has-feedback label~.form-control-feedback{top:25px}.has-feedback label.sr-only~.form-control-feedback{top:0}.help-block{display:block;margin-top:5px;margin-bottom:10px;color:#737373}@media (min-width:768px){.form-inline .form-group{display:inline-block;margin-bottom:0;vertical-align:middle}.form-inline .form-control{display:inline-block;width:auto;vertical-align:middle}.form-inline .form-control-static{display:inline-block}.form-inline .input-group{display:inline-table;vertical-align:middle}.form-inline .input-group .form-control,.form-inline .input-group .input-group-addon,.form-inline .input-group .input-group-btn{width:auto}.form-inline .input-group>.form-control{width:100%}.form-inline .control-label{margin-bottom:0;vertical-align:middle}.form-inline .checkbox,.form-inline .radio{display:inline-block;margin-top:0;margin-bottom:0;vertical-align:middle}.form-inline .checkbox label,.form-inline .radio label{padding-left:0}.form-inline .checkbox input[type=checkbox],.form-inline .radio input[type=radio]{position:relative;margin-left:0}.form-inline .has-feedback .form-control-feedback{top:0}}.form-horizontal .checkbox,.form-horizontal .checkbox-inline,.form-horizontal .radio,.form-horizontal .radio-inline{padding-top:7px;margin-top:0;margin-bottom:0}.form-horizontal .checkbox,.form-horizontal .radio{min-height:27px}.form-horizontal .form-group{margin-right:-15px;margin-left:-15px}@media (min-width:768px){.form-horizontal .control-label{padding-top:7px;margin-bottom:0;text-align:right}}.form-horizontal .has-feedback .form-control-feedback{right:15px}@media (min-width:768px){.form-horizontal .form-group-lg .control-label{padding-top:14.33px;font-size:18px}}@media (min-width:768px){.form-horizontal .form-group-sm .control-label{padding-top:6px;font-size:12px}}.btn{display:inline-block;padding:6px 12px;margin-bottom:0;font-size:14px;font-weight:400;line-height:1.42857143;text-align:center;white-space:nowrap;vertical-align:middle;-ms-touch-action:manipulation;touch-action:manipulation;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;background-image:none;border:1px solid transparent;border-radius:4px}.btn.active.focus,.btn.active:focus,.btn.focus,.btn:active.focus,.btn:active:focus,.btn:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}.btn.focus,.btn:focus,.btn:hover{color:#333;text-decoration:none}.btn.active,.btn:active{background-image:none;outline:0;-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn.disabled,.btn[disabled],fieldset[disabled] .btn{cursor:not-allowed;filter:alpha(opacity=65);-webkit-box-shadow:none;box-shadow:none;opacity:.65}a.btn.disabled,fieldset[disabled] a.btn{pointer-events:none}.btn-default{color:#333;background-color:#fff;border-color:#ccc}.btn-default.focus,.btn-default:focus{color:#333;background-color:#e6e6e6;border-color:#8c8c8c}.btn-default:hover{color:#333;background-color:#e6e6e6;border-color:#adadad}.btn-default.active,.btn-default:active,.open>.dropdown-toggle.btn-default{color:#333;background-color:#e6e6e6;border-color:#adadad}.btn-default.active.focus,.btn-default.active:focus,.btn-default.active:hover,.btn-default:active.focus,.btn-default:active:focus,.btn-default:active:hover,.open>.dropdown-toggle.btn-default.focus,.open>.dropdown-toggle.btn-default:focus,.open>.dropdown-toggle.btn-default:hover{color:#333;background-color:#d4d4d4;border-color:#8c8c8c}.btn-default.active,.btn-default:active,.open>.dropdown-toggle.btn-default{background-image:none}.btn-default.disabled,.btn-default.disabled.active,.btn-default.disabled.focus,.btn-default.disabled:active,.btn-default.disabled:focus,.btn-default.disabled:hover,.btn-default[disabled],.btn-default[disabled].active,.btn-default[disabled].focus,.btn-default[disabled]:active,.btn-default[disabled]:focus,.btn-default[disabled]:hover,fieldset[disabled] .btn-default,fieldset[disabled] .btn-default.active,fieldset[disabled] .btn-default.focus,fieldset[disabled] .btn-default:active,fieldset[disabled] .btn-default:focus,fieldset[disabled] .btn-default:hover{background-color:#fff;border-color:#ccc}.btn-default .badge{color:#fff;background-color:#333}.btn-primary{color:#fff;background-color:#337ab7;border-color:#2e6da4}.btn-primary.focus,.btn-primary:focus{color:#fff;background-color:#286090;border-color:#122b40}.btn-primary:hover{color:#fff;background-color:#286090;border-color:#204d74}.btn-primary.active,.btn-primary:active,.open>.dropdown-toggle.btn-primary{color:#fff;background-color:#286090;border-color:#204d74}.btn-primary.active.focus,.btn-primary.active:focus,.btn-primary.active:hover,.btn-primary:active.focus,.btn-primary:active:focus,.btn-primary:active:hover,.open>.dropdown-toggle.btn-primary.focus,.open>.dropdown-toggle.btn-primary:focus,.open>.dropdown-toggle.btn-primary:hover{color:#fff;background-color:#204d74;border-color:#122b40}.btn-primary.active,.btn-primary:active,.open>.dropdown-toggle.btn-primary{background-image:none}.btn-primary.disabled,.btn-primary.disabled.active,.btn-primary.disabled.focus,.btn-primary.disabled:active,.btn-primary.disabled:focus,.btn-primary.disabled:hover,.btn-primary[disabled],.btn-primary[disabled].active,.btn-primary[disabled].focus,.btn-primary[disabled]:active,.btn-primary[disabled]:focus,.btn-primary[disabled]:hover,fieldset[disabled] .btn-primary,fieldset[disabled] .btn-primary.active,fieldset[disabled] .btn-primary.focus,fieldset[disabled] .btn-primary:active,fieldset[disabled] .btn-primary:focus,fieldset[disabled] .btn-primary:hover{background-color:#337ab7;border-color:#2e6da4}.btn-primary .badge{color:#337ab7;background-color:#fff}.btn-success{color:#fff;background-color:#5cb85c;border-color:#4cae4c}.btn-success.focus,.btn-success:focus{color:#fff;background-color:#449d44;border-color:#255625}.btn-success:hover{color:#fff;background-color:#449d44;border-color:#398439}.btn-success.active,.btn-success:active,.open>.dropdown-toggle.btn-success{color:#fff;background-color:#449d44;border-color:#398439}.btn-success.active.focus,.btn-success.active:focus,.btn-success.active:hover,.btn-success:active.focus,.btn-success:active:focus,.btn-success:active:hover,.open>.dropdown-toggle.btn-success.focus,.open>.dropdown-toggle.btn-success:focus,.open>.dropdown-toggle.btn-success:hover{color:#fff;background-color:#398439;border-color:#255625}.btn-success.active,.btn-success:active,.open>.dropdown-toggle.btn-success{background-image:none}.btn-success.disabled,.btn-success.disabled.active,.btn-success.disabled.focus,.btn-success.disabled:active,.btn-success.disabled:focus,.btn-success.disabled:hover,.btn-success[disabled],.btn-success[disabled].active,.btn-success[disabled].focus,.btn-success[disabled]:active,.btn-success[disabled]:focus,.btn-success[disabled]:hover,fieldset[disabled] .btn-success,fieldset[disabled] .btn-success.active,fieldset[disabled] .btn-success.focus,fieldset[disabled] .btn-success:active,fieldset[disabled] .btn-success:focus,fieldset[disabled] .btn-success:hover{background-color:#5cb85c;border-color:#4cae4c}.btn-success .badge{color:#5cb85c;background-color:#fff}.btn-info{color:#fff;background-color:#5bc0de;border-color:#46b8da}.btn-info.focus,.btn-info:focus{color:#fff;background-color:#31b0d5;border-color:#1b6d85}.btn-info:hover{color:#fff;background-color:#31b0d5;border-color:#269abc}.btn-info.active,.btn-info:active,.open>.dropdown-toggle.btn-info{color:#fff;background-color:#31b0d5;border-color:#269abc}.btn-info.active.focus,.btn-info.active:focus,.btn-info.active:hover,.btn-info:active.focus,.btn-info:active:focus,.btn-info:active:hover,.open>.dropdown-toggle.btn-info.focus,.open>.dropdown-toggle.btn-info:focus,.open>.dropdown-toggle.btn-info:hover{color:#fff;background-color:#269abc;border-color:#1b6d85}.btn-info.active,.btn-info:active,.open>.dropdown-toggle.btn-info{background-image:none}.btn-info.disabled,.btn-info.disabled.active,.btn-info.disabled.focus,.btn-info.disabled:active,.btn-info.disabled:focus,.btn-info.disabled:hover,.btn-info[disabled],.btn-info[disabled].active,.btn-info[disabled].focus,.btn-info[disabled]:active,.btn-info[disabled]:focus,.btn-info[disabled]:hover,fieldset[disabled] .btn-info,fieldset[disabled] .btn-info.active,fieldset[disabled] .btn-info.focus,fieldset[disabled] .btn-info:active,fieldset[disabled] .btn-info:focus,fieldset[disabled] .btn-info:hover{background-color:#5bc0de;border-color:#46b8da}.btn-info .badge{color:#5bc0de;background-color:#fff}.btn-warning{color:#fff;background-color:#f0ad4e;border-color:#eea236}.btn-warning.focus,.btn-warning:focus{color:#fff;background-color:#ec971f;border-color:#985f0d}.btn-warning:hover{color:#fff;background-color:#ec971f;border-color:#d58512}.btn-warning.active,.btn-warning:active,.open>.dropdown-toggle.btn-warning{color:#fff;background-color:#ec971f;border-color:#d58512}.btn-warning.active.focus,.btn-warning.active:focus,.btn-warning.active:hover,.btn-warning:active.focus,.btn-warning:active:focus,.btn-warning:active:hover,.open>.dropdown-toggle.btn-warning.focus,.open>.dropdown-toggle.btn-warning:focus,.open>.dropdown-toggle.btn-warning:hover{color:#fff;background-color:#d58512;border-color:#985f0d}.btn-warning.active,.btn-warning:active,.open>.dropdown-toggle.btn-warning{background-image:none}.btn-warning.disabled,.btn-warning.disabled.active,.btn-warning.disabled.focus,.btn-warning.disabled:active,.btn-warning.disabled:focus,.btn-warning.disabled:hover,.btn-warning[disabled],.btn-warning[disabled].active,.btn-warning[disabled].focus,.btn-warning[disabled]:active,.btn-warning[disabled]:focus,.btn-warning[disabled]:hover,fieldset[disabled] .btn-warning,fieldset[disabled] .btn-warning.active,fieldset[disabled] .btn-warning.focus,fieldset[disabled] .btn-warning:active,fieldset[disabled] .btn-warning:focus,fieldset[disabled] .btn-warning:hover{background-color:#f0ad4e;border-color:#eea236}.btn-warning .badge{color:#f0ad4e;background-color:#fff}.btn-danger{color:#fff;background-color:#d9534f;border-color:#d43f3a}.btn-danger.focus,.btn-danger:focus{color:#fff;background-color:#c9302c;border-color:#761c19}.btn-danger:hover{color:#fff;background-color:#c9302c;border-color:#ac2925}.btn-danger.active,.btn-danger:active,.open>.dropdown-toggle.btn-danger{color:#fff;background-color:#c9302c;border-color:#ac2925}.btn-danger.active.focus,.btn-danger.active:focus,.btn-danger.active:hover,.btn-danger:active.focus,.btn-danger:active:focus,.btn-danger:active:hover,.open>.dropdown-toggle.btn-danger.focus,.open>.dropdown-toggle.btn-danger:focus,.open>.dropdown-toggle.btn-danger:hover{color:#fff;background-color:#ac2925;border-color:#761c19}.btn-danger.active,.btn-danger:active,.open>.dropdown-toggle.btn-danger{background-image:none}.btn-danger.disabled,.btn-danger.disabled.active,.btn-danger.disabled.focus,.btn-danger.disabled:active,.btn-danger.disabled:focus,.btn-danger.disabled:hover,.btn-danger[disabled],.btn-danger[disabled].active,.btn-danger[disabled].focus,.btn-danger[disabled]:active,.btn-danger[disabled]:focus,.btn-danger[disabled]:hover,fieldset[disabled] .btn-danger,fieldset[disabled] .btn-danger.active,fieldset[disabled] .btn-danger.focus,fieldset[disabled] .btn-danger:active,fieldset[disabled] .btn-danger:focus,fieldset[disabled] .btn-danger:hover{background-color:#d9534f;border-color:#d43f3a}.btn-danger .badge{color:#d9534f;background-color:#fff}.btn-link{font-weight:400;color:#337ab7;border-radius:0}.btn-link,.btn-link.active,.btn-link:active,.btn-link[disabled],fieldset[disabled] .btn-link{background-color:transparent;-webkit-box-shadow:none;box-shadow:none}.btn-link,.btn-link:active,.btn-link:focus,.btn-link:hover{border-color:transparent}.btn-link:focus,.btn-link:hover{color:#23527c;text-decoration:underline;background-color:transparent}.btn-link[disabled]:focus,.btn-link[disabled]:hover,fieldset[disabled] .btn-link:focus,fieldset[disabled] .btn-link:hover{color:#777;text-decoration:none}.btn-group-lg>.btn,.btn-lg{padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}.btn-group-sm>.btn,.btn-sm{padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}.btn-group-xs>.btn,.btn-xs{padding:1px 5px;font-size:12px;line-height:1.5;border-radius:3px}.btn-block{display:block;width:100%}.btn-block+.btn-block{margin-top:5px}input[type=button].btn-block,input[type=reset].btn-block,input[type=submit].btn-block{width:100%}.fade{opacity:0;-webkit-transition:opacity .15s linear;-o-transition:opacity .15s linear;transition:opacity .15s linear}.fade.in{opacity:1}.collapse{display:none}.collapse.in{display:block}tr.collapse.in{display:table-row}tbody.collapse.in{display:table-row-group}.collapsing{position:relative;height:0;overflow:hidden;-webkit-transition-timing-function:ease;-o-transition-timing-function:ease;transition-timing-function:ease;-webkit-transition-duration:.35s;-o-transition-duration:.35s;transition-duration:.35s;-webkit-transition-property:height,visibility;-o-transition-property:height,visibility;transition-property:height,visibility}.caret{display:inline-block;width:0;height:0;margin-left:2px;vertical-align:middle;border-top:4px dashed;border-top:4px solid\9;border-right:4px solid transparent;border-left:4px solid transparent}.dropdown,.dropup{position:relative}.dropdown-toggle:focus{outline:0}.dropdown-menu{position:absolute;top:100%;left:0;z-index:1000;display:none;float:left;min-width:160px;padding:5px 0;margin:2px 0 0;font-size:14px;text-align:left;list-style:none;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,.15);border-radius:4px;-webkit-box-shadow:0 6px 12px rgba(0,0,0,.175);box-shadow:0 6px 12px rgba(0,0,0,.175)}.dropdown-menu.pull-right{right:0;left:auto}.dropdown-menu .divider{height:1px;margin:9px 0;overflow:hidden;background-color:#e5e5e5}.dropdown-menu>li>a{display:block;padding:3px 20px;clear:both;font-weight:400;line-height:1.42857143;color:#333;white-space:nowrap}.dropdown-menu>li>a:focus,.dropdown-menu>li>a:hover{color:#262626;text-decoration:none;background-color:#f5f5f5}.dropdown-menu>.active>a,.dropdown-menu>.active>a:focus,.dropdown-menu>.active>a:hover{color:#fff;text-decoration:none;background-color:#337ab7;outline:0}.dropdown-menu>.disabled>a,.dropdown-menu>.disabled>a:focus,.dropdown-menu>.disabled>a:hover{color:#777}.dropdown-menu>.disabled>a:focus,.dropdown-menu>.disabled>a:hover{text-decoration:none;cursor:not-allowed;background-color:transparent;background-image:none;filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.open>.dropdown-menu{display:block}.open>a{outline:0}.dropdown-menu-right{right:0;left:auto}.dropdown-menu-left{right:auto;left:0}.dropdown-header{display:block;padding:3px 20px;font-size:12px;line-height:1.42857143;color:#777;white-space:nowrap}.dropdown-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:990}.pull-right>.dropdown-menu{right:0;left:auto}.dropup .caret,.navbar-fixed-bottom .dropdown .caret{content:"";border-top:0;border-bottom:4px dashed;border-bottom:4px solid\9}.dropup .dropdown-menu,.navbar-fixed-bottom .dropdown .dropdown-menu{top:auto;bottom:100%;margin-bottom:2px}@media (min-width:768px){.navbar-right .dropdown-menu{right:0;left:auto}.navbar-right .dropdown-menu-left{right:auto;left:0}}.btn-group,.btn-group-vertical{position:relative;display:inline-block;vertical-align:middle}.btn-group-vertical>.btn,.btn-group>.btn{position:relative;float:left}.btn-group-vertical>.btn.active,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:hover,.btn-group>.btn.active,.btn-group>.btn:active,.btn-group>.btn:focus,.btn-group>.btn:hover{z-index:2}.btn-group .btn+.btn,.btn-group .btn+.btn-group,.btn-group .btn-group+.btn,.btn-group .btn-group+.btn-group{margin-left:-1px}.btn-toolbar{margin-left:-5px}.btn-toolbar .btn,.btn-toolbar .btn-group,.btn-toolbar .input-group{float:left}.btn-toolbar>.btn,.btn-toolbar>.btn-group,.btn-toolbar>.input-group{margin-left:5px}.btn-group>.btn:not(:first-child):not(:last-child):not(.dropdown-toggle){border-radius:0}.btn-group>.btn:first-child{margin-left:0}.btn-group>.btn:first-child:not(:last-child):not(.dropdown-toggle){border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:last-child:not(:first-child),.btn-group>.dropdown-toggle:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.btn-group>.btn-group{float:left}.btn-group>.btn-group:not(:first-child):not(:last-child)>.btn{border-radius:0}.btn-group>.btn-group:first-child:not(:last-child)>.btn:last-child,.btn-group>.btn-group:first-child:not(:last-child)>.dropdown-toggle{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn-group:last-child:not(:first-child)>.btn:first-child{border-top-left-radius:0;border-bottom-left-radius:0}.btn-group .dropdown-toggle:active,.btn-group.open .dropdown-toggle{outline:0}.btn-group>.btn+.dropdown-toggle{padding-right:8px;padding-left:8px}.btn-group>.btn-lg+.dropdown-toggle{padding-right:12px;padding-left:12px}.btn-group.open .dropdown-toggle{-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn-group.open .dropdown-toggle.btn-link{-webkit-box-shadow:none;box-shadow:none}.btn .caret{margin-left:0}.btn-lg .caret{border-width:5px 5px 0;border-bottom-width:0}.dropup .btn-lg .caret{border-width:0 5px 5px}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group,.btn-group-vertical>.btn-group>.btn{display:block;float:none;width:100%;max-width:100%}.btn-group-vertical>.btn-group>.btn{float:none}.btn-group-vertical>.btn+.btn,.btn-group-vertical>.btn+.btn-group,.btn-group-vertical>.btn-group+.btn,.btn-group-vertical>.btn-group+.btn-group{margin-top:-1px;margin-left:0}.btn-group-vertical>.btn:not(:first-child):not(:last-child){border-radius:0}.btn-group-vertical>.btn:first-child:not(:last-child){border-top-right-radius:4px;border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn:last-child:not(:first-child){border-top-left-radius:0;border-top-right-radius:0;border-bottom-left-radius:4px}.btn-group-vertical>.btn-group:not(:first-child):not(:last-child)>.btn{border-radius:0}.btn-group-vertical>.btn-group:first-child:not(:last-child)>.btn:last-child,.btn-group-vertical>.btn-group:first-child:not(:last-child)>.dropdown-toggle{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn-group:last-child:not(:first-child)>.btn:first-child{border-top-left-radius:0;border-top-right-radius:0}.btn-group-justified{display:table;width:100%;table-layout:fixed;border-collapse:separate}.btn-group-justified>.btn,.btn-group-justified>.btn-group{display:table-cell;float:none;width:1%}.btn-group-justified>.btn-group .btn{width:100%}.btn-group-justified>.btn-group .dropdown-menu{left:auto}[data-toggle=buttons]>.btn input[type=checkbox],[data-toggle=buttons]>.btn input[type=radio],[data-toggle=buttons]>.btn-group>.btn input[type=checkbox],[data-toggle=buttons]>.btn-group>.btn input[type=radio]{position:absolute;clip:rect(0,0,0,0);pointer-events:none}.input-group{position:relative;display:table;border-collapse:separate}.input-group[class*=col-]{float:none;padding-right:0;padding-left:0}.input-group .form-control{position:relative;z-index:2;float:left;width:100%;margin-bottom:0}.input-group-lg>.form-control,.input-group-lg>.input-group-addon,.input-group-lg>.input-group-btn>.btn{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}select.input-group-lg>.form-control,select.input-group-lg>.input-group-addon,select.input-group-lg>.input-group-btn>.btn{height:46px;line-height:46px}select[multiple].input-group-lg>.form-control,select[multiple].input-group-lg>.input-group-addon,select[multiple].input-group-lg>.input-group-btn>.btn,textarea.input-group-lg>.form-control,textarea.input-group-lg>.input-group-addon,textarea.input-group-lg>.input-group-btn>.btn{height:auto}.input-group-sm>.form-control,.input-group-sm>.input-group-addon,.input-group-sm>.input-group-btn>.btn{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}select.input-group-sm>.form-control,select.input-group-sm>.input-group-addon,select.input-group-sm>.input-group-btn>.btn{height:30px;line-height:30px}select[multiple].input-group-sm>.form-control,select[multiple].input-group-sm>.input-group-addon,select[multiple].input-group-sm>.input-group-btn>.btn,textarea.input-group-sm>.form-control,textarea.input-group-sm>.input-group-addon,textarea.input-group-sm>.input-group-btn>.btn{height:auto}.input-group .form-control,.input-group-addon,.input-group-btn{display:table-cell}.input-group .form-control:not(:first-child):not(:last-child),.input-group-addon:not(:first-child):not(:last-child),.input-group-btn:not(:first-child):not(:last-child){border-radius:0}.input-group-addon,.input-group-btn{width:1%;white-space:nowrap;vertical-align:middle}.input-group-addon{padding:6px 12px;font-size:14px;font-weight:400;line-height:1;color:#555;text-align:center;background-color:#eee;border:1px solid #ccc;border-radius:4px}.input-group-addon.input-sm{padding:5px 10px;font-size:12px;border-radius:3px}.input-group-addon.input-lg{padding:10px 16px;font-size:18px;border-radius:6px}.input-group-addon input[type=checkbox],.input-group-addon input[type=radio]{margin-top:0}.input-group .form-control:first-child,.input-group-addon:first-child,.input-group-btn:first-child>.btn,.input-group-btn:first-child>.btn-group>.btn,.input-group-btn:first-child>.dropdown-toggle,.input-group-btn:last-child>.btn-group:not(:last-child)>.btn,.input-group-btn:last-child>.btn:not(:last-child):not(.dropdown-toggle){border-top-right-radius:0;border-bottom-right-radius:0}.input-group-addon:first-child{border-right:0}.input-group .form-control:last-child,.input-group-addon:last-child,.input-group-btn:first-child>.btn-group:not(:first-child)>.btn,.input-group-btn:first-child>.btn:not(:first-child),.input-group-btn:last-child>.btn,.input-group-btn:last-child>.btn-group>.btn,.input-group-btn:last-child>.dropdown-toggle{border-top-left-radius:0;border-bottom-left-radius:0}.input-group-addon:last-child{border-left:0}.input-group-btn{position:relative;font-size:0;white-space:nowrap}.input-group-btn>.btn{position:relative}.input-group-btn>.btn+.btn{margin-left:-1px}.input-group-btn>.btn:active,.input-group-btn>.btn:focus,.input-group-btn>.btn:hover{z-index:2}.input-group-btn:first-child>.btn,.input-group-btn:first-child>.btn-group{margin-right:-1px}.input-group-btn:last-child>.btn,.input-group-btn:last-child>.btn-group{z-index:2;margin-left:-1px}.nav{padding-left:0;margin-bottom:0;list-style:none}.nav>li{position:relative;display:block}.nav>li>a{position:relative;display:block;padding:10px 15px}.nav>li>a:focus,.nav>li>a:hover{text-decoration:none;background-color:#eee}.nav>li.disabled>a{color:#777}.nav>li.disabled>a:focus,.nav>li.disabled>a:hover{color:#777;text-decoration:none;cursor:not-allowed;background-color:transparent}.nav .open>a,.nav .open>a:focus,.nav .open>a:hover{background-color:#eee;border-color:#337ab7}.nav .nav-divider{height:1px;margin:9px 0;overflow:hidden;background-color:#e5e5e5}.nav>li>a>img{max-width:none}.nav-tabs{border-bottom:1px solid #ddd}.nav-tabs>li{float:left;margin-bottom:-1px}.nav-tabs>li>a{margin-right:2px;line-height:1.42857143;border:1px solid transparent;border-radius:4px 4px 0 0}.nav-tabs>li>a:hover{border-color:#eee #eee #ddd}.nav-tabs>li.active>a,.nav-tabs>li.active>a:focus,.nav-tabs>li.active>a:hover{color:#555;cursor:default;background-color:#fff;border:1px solid #ddd;border-bottom-color:transparent}.nav-tabs.nav-justified{width:100%;border-bottom:0}.nav-tabs.nav-justified>li{float:none}.nav-tabs.nav-justified>li>a{margin-bottom:5px;text-align:center}.nav-tabs.nav-justified>.dropdown .dropdown-menu{top:auto;left:auto}@media (min-width:768px){.nav-tabs.nav-justified>li{display:table-cell;width:1%}.nav-tabs.nav-justified>li>a{margin-bottom:0}}.nav-tabs.nav-justified>li>a{margin-right:0;border-radius:4px}.nav-tabs.nav-justified>.active>a,.nav-tabs.nav-justified>.active>a:focus,.nav-tabs.nav-justified>.active>a:hover{border:1px solid #ddd}@media (min-width:768px){.nav-tabs.nav-justified>li>a{border-bottom:1px solid #ddd;border-radius:4px 4px 0 0}.nav-tabs.nav-justified>.active>a,.nav-tabs.nav-justified>.active>a:focus,.nav-tabs.nav-justified>.active>a:hover{border-bottom-color:#fff}}.nav-pills>li{float:left}.nav-pills>li>a{border-radius:4px}.nav-pills>li+li{margin-left:2px}.nav-pills>li.active>a,.nav-pills>li.active>a:focus,.nav-pills>li.active>a:hover{color:#fff;background-color:#337ab7}.nav-stacked>li{float:none}.nav-stacked>li+li{margin-top:2px;margin-left:0}.nav-justified{width:100%}.nav-justified>li{float:none}.nav-justified>li>a{margin-bottom:5px;text-align:center}.nav-justified>.dropdown .dropdown-menu{top:auto;left:auto}@media (min-width:768px){.nav-justified>li{display:table-cell;width:1%}.nav-justified>li>a{margin-bottom:0}}.nav-tabs-justified{border-bottom:0}.nav-tabs-justified>li>a{margin-right:0;border-radius:4px}.nav-tabs-justified>.active>a,.nav-tabs-justified>.active>a:focus,.nav-tabs-justified>.active>a:hover{border:1px solid #ddd}@media (min-width:768px){.nav-tabs-justified>li>a{border-bottom:1px solid #ddd;border-radius:4px 4px 0 0}.nav-tabs-justified>.active>a,.nav-tabs-justified>.active>a:focus,.nav-tabs-justified>.active>a:hover{border-bottom-color:#fff}}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.nav-tabs .dropdown-menu{margin-top:-1px;border-top-left-radius:0;border-top-right-radius:0}.navbar{position:relative;min-height:50px;margin-bottom:20px;border:1px solid transparent}@media (min-width:768px){.navbar{border-radius:4px}}@media (min-width:768px){.navbar-header{float:left}}.navbar-collapse{padding-right:15px;padding-left:15px;overflow-x:visible;-webkit-overflow-scrolling:touch;border-top:1px solid transparent;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1)}.navbar-collapse.in{overflow-y:auto}@media (min-width:768px){.navbar-collapse{width:auto;border-top:0;-webkit-box-shadow:none;box-shadow:none}.navbar-collapse.collapse{display:block!important;height:auto!important;padding-bottom:0;overflow:visible!important}.navbar-collapse.in{overflow-y:visible}.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse,.navbar-static-top .navbar-collapse{padding-right:0;padding-left:0}}.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse{max-height:340px}@media (max-device-width:480px) and (orientation:landscape){.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse{max-height:200px}}.container-fluid>.navbar-collapse,.container-fluid>.navbar-header,.container>.navbar-collapse,.container>.navbar-header{margin-right:-15px;margin-left:-15px}@media (min-width:768px){.container-fluid>.navbar-collapse,.container-fluid>.navbar-header,.container>.navbar-collapse,.container>.navbar-header{margin-right:0;margin-left:0}}.navbar-static-top{z-index:1000;border-width:0 0 1px}@media (min-width:768px){.navbar-static-top{border-radius:0}}.navbar-fixed-bottom,.navbar-fixed-top{position:fixed;right:0;left:0;z-index:1030}@media (min-width:768px){.navbar-fixed-bottom,.navbar-fixed-top{border-radius:0}}.navbar-fixed-top{top:0;border-width:0 0 1px}.navbar-fixed-bottom{bottom:0;margin-bottom:0;border-width:1px 0 0}.navbar-brand{float:left;height:50px;padding:15px 15px;font-size:18px;line-height:20px}.navbar-brand:focus,.navbar-brand:hover{text-decoration:none}.navbar-brand>img{display:block}@media (min-width:768px){.navbar>.container .navbar-brand,.navbar>.container-fluid .navbar-brand{margin-left:-15px}}.navbar-toggle{position:relative;float:right;padding:9px 10px;margin-top:8px;margin-right:15px;margin-bottom:8px;background-color:transparent;background-image:none;border:1px solid transparent;border-radius:4px}.navbar-toggle:focus{outline:0}.navbar-toggle .icon-bar{display:block;width:22px;height:2px;border-radius:1px}.navbar-toggle .icon-bar+.icon-bar{margin-top:4px}@media (min-width:768px){.navbar-toggle{display:none}}.navbar-nav{margin:7.5px -15px}.navbar-nav>li>a{padding-top:10px;padding-bottom:10px;line-height:20px}@media (max-width:767px){.navbar-nav .open .dropdown-menu{position:static;float:none;width:auto;margin-top:0;background-color:transparent;border:0;-webkit-box-shadow:none;box-shadow:none}.navbar-nav .open .dropdown-menu .dropdown-header,.navbar-nav .open .dropdown-menu>li>a{padding:5px 15px 5px 25px}.navbar-nav .open .dropdown-menu>li>a{line-height:20px}.navbar-nav .open .dropdown-menu>li>a:focus,.navbar-nav .open .dropdown-menu>li>a:hover{background-image:none}}@media (min-width:768px){.navbar-nav{float:left;margin:0}.navbar-nav>li{float:left}.navbar-nav>li>a{padding-top:15px;padding-bottom:15px}}.navbar-form{padding:10px 15px;margin-top:8px;margin-right:-15px;margin-bottom:8px;margin-left:-15px;border-top:1px solid transparent;border-bottom:1px solid transparent;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1),0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1),0 1px 0 rgba(255,255,255,.1)}@media (min-width:768px){.navbar-form .form-group{display:inline-block;margin-bottom:0;vertical-align:middle}.navbar-form .form-control{display:inline-block;width:auto;vertical-align:middle}.navbar-form .form-control-static{display:inline-block}.navbar-form .input-group{display:inline-table;vertical-align:middle}.navbar-form .input-group .form-control,.navbar-form .input-group .input-group-addon,.navbar-form .input-group .input-group-btn{width:auto}.navbar-form .input-group>.form-control{width:100%}.navbar-form .control-label{margin-bottom:0;vertical-align:middle}.navbar-form .checkbox,.navbar-form .radio{display:inline-block;margin-top:0;margin-bottom:0;vertical-align:middle}.navbar-form .checkbox label,.navbar-form .radio label{padding-left:0}.navbar-form .checkbox input[type=checkbox],.navbar-form .radio input[type=radio]{position:relative;margin-left:0}.navbar-form .has-feedback .form-control-feedback{top:0}}@media (max-width:767px){.navbar-form .form-group{margin-bottom:5px}.navbar-form .form-group:last-child{margin-bottom:0}}@media (min-width:768px){.navbar-form{width:auto;padding-top:0;padding-bottom:0;margin-right:0;margin-left:0;border:0;-webkit-box-shadow:none;box-shadow:none}}.navbar-nav>li>.dropdown-menu{margin-top:0;border-top-left-radius:0;border-top-right-radius:0}.navbar-fixed-bottom .navbar-nav>li>.dropdown-menu{margin-bottom:0;border-top-left-radius:4px;border-top-right-radius:4px;border-bottom-right-radius:0;border-bottom-left-radius:0}.navbar-btn{margin-top:8px;margin-bottom:8px}.navbar-btn.btn-sm{margin-top:10px;margin-bottom:10px}.navbar-btn.btn-xs{margin-top:14px;margin-bottom:14px}.navbar-text{margin-top:15px;margin-bottom:15px}@media (min-width:768px){.navbar-text{float:left;margin-right:15px;margin-left:15px}}@media (min-width:768px){.navbar-left{float:left!important}.navbar-right{float:right!important;margin-right:-15px}.navbar-right~.navbar-right{margin-right:0}}.navbar-default{background-color:#f8f8f8;border-color:#e7e7e7}.navbar-default .navbar-brand{color:#777}.navbar-default .navbar-brand:focus,.navbar-default .navbar-brand:hover{color:#5e5e5e;background-color:transparent}.navbar-default .navbar-text{color:#777}.navbar-default .navbar-nav>li>a{color:#777}.navbar-default .navbar-nav>li>a:focus,.navbar-default .navbar-nav>li>a:hover{color:#333;background-color:transparent}.navbar-default .navbar-nav>.active>a,.navbar-default .navbar-nav>.active>a:focus,.navbar-default .navbar-nav>.active>a:hover{color:#555;background-color:#e7e7e7}.navbar-default .navbar-nav>.disabled>a,.navbar-default .navbar-nav>.disabled>a:focus,.navbar-default .navbar-nav>.disabled>a:hover{color:#ccc;background-color:transparent}.navbar-default .navbar-toggle{border-color:#ddd}.navbar-default .navbar-toggle:focus,.navbar-default .navbar-toggle:hover{background-color:#ddd}.navbar-default .navbar-toggle .icon-bar{background-color:#888}.navbar-default .navbar-collapse,.navbar-default .navbar-form{border-color:#e7e7e7}.navbar-default .navbar-nav>.open>a,.navbar-default .navbar-nav>.open>a:focus,.navbar-default .navbar-nav>.open>a:hover{color:#555;background-color:#e7e7e7}@media (max-width:767px){.navbar-default .navbar-nav .open .dropdown-menu>li>a{color:#777}.navbar-default .navbar-nav .open .dropdown-menu>li>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>li>a:hover{color:#333;background-color:transparent}.navbar-default .navbar-nav .open .dropdown-menu>.active>a,.navbar-default .navbar-nav .open .dropdown-menu>.active>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>.active>a:hover{color:#555;background-color:#e7e7e7}.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a,.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a:hover{color:#ccc;background-color:transparent}}.navbar-default .navbar-link{color:#777}.navbar-default .navbar-link:hover{color:#333}.navbar-default .btn-link{color:#777}.navbar-default .btn-link:focus,.navbar-default .btn-link:hover{color:#333}.navbar-default .btn-link[disabled]:focus,.navbar-default .btn-link[disabled]:hover,fieldset[disabled] .navbar-default .btn-link:focus,fieldset[disabled] .navbar-default .btn-link:hover{color:#ccc}.navbar-inverse{background-color:#222;border-color:#080808}.navbar-inverse .navbar-brand{color:#9d9d9d}.navbar-inverse .navbar-brand:focus,.navbar-inverse .navbar-brand:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-text{color:#9d9d9d}.navbar-inverse .navbar-nav>li>a{color:#9d9d9d}.navbar-inverse .navbar-nav>li>a:focus,.navbar-inverse .navbar-nav>li>a:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-nav>.active>a,.navbar-inverse .navbar-nav>.active>a:focus,.navbar-inverse .navbar-nav>.active>a:hover{color:#fff;background-color:#080808}.navbar-inverse .navbar-nav>.disabled>a,.navbar-inverse .navbar-nav>.disabled>a:focus,.navbar-inverse .navbar-nav>.disabled>a:hover{color:#444;background-color:transparent}.navbar-inverse .navbar-toggle{border-color:#333}.navbar-inverse .navbar-toggle:focus,.navbar-inverse .navbar-toggle:hover{background-color:#333}.navbar-inverse .navbar-toggle .icon-bar{background-color:#fff}.navbar-inverse .navbar-collapse,.navbar-inverse .navbar-form{border-color:#101010}.navbar-inverse .navbar-nav>.open>a,.navbar-inverse .navbar-nav>.open>a:focus,.navbar-inverse .navbar-nav>.open>a:hover{color:#fff;background-color:#080808}@media (max-width:767px){.navbar-inverse .navbar-nav .open .dropdown-menu>.dropdown-header{border-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu .divider{background-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu>li>a{color:#9d9d9d}.navbar-inverse .navbar-nav .open .dropdown-menu>li>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>li>a:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a,.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a:hover{color:#fff;background-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a,.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a:hover{color:#444;background-color:transparent}}.navbar-inverse .navbar-link{color:#9d9d9d}.navbar-inverse .navbar-link:hover{color:#fff}.navbar-inverse .btn-link{color:#9d9d9d}.navbar-inverse .btn-link:focus,.navbar-inverse .btn-link:hover{color:#fff}.navbar-inverse .btn-link[disabled]:focus,.navbar-inverse .btn-link[disabled]:hover,fieldset[disabled] .navbar-inverse .btn-link:focus,fieldset[disabled] .navbar-inverse .btn-link:hover{color:#444}.breadcrumb{padding:8px 15px;margin-bottom:20px;list-style:none;background-color:#f5f5f5;border-radius:4px}.breadcrumb>li{display:inline-block}.breadcrumb>li+li:before{padding:0 5px;color:#ccc;content:"/\00a0"}.breadcrumb>.active{color:#777}.pagination{display:inline-block;padding-left:0;margin:20px 0;border-radius:4px}.pagination>li{display:inline}.pagination>li>a,.pagination>li>span{position:relative;float:left;padding:6px 12px;margin-left:-1px;line-height:1.42857143;color:#337ab7;text-decoration:none;background-color:#fff;border:1px solid #ddd}.pagination>li:first-child>a,.pagination>li:first-child>span{margin-left:0;border-top-left-radius:4px;border-bottom-left-radius:4px}.pagination>li:last-child>a,.pagination>li:last-child>span{border-top-right-radius:4px;border-bottom-right-radius:4px}.pagination>li>a:focus,.pagination>li>a:hover,.pagination>li>span:focus,.pagination>li>span:hover{z-index:3;color:#23527c;background-color:#eee;border-color:#ddd}.pagination>.active>a,.pagination>.active>a:focus,.pagination>.active>a:hover,.pagination>.active>span,.pagination>.active>span:focus,.pagination>.active>span:hover{z-index:2;color:#fff;cursor:default;background-color:#337ab7;border-color:#337ab7}.pagination>.disabled>a,.pagination>.disabled>a:focus,.pagination>.disabled>a:hover,.pagination>.disabled>span,.pagination>.disabled>span:focus,.pagination>.disabled>span:hover{color:#777;cursor:not-allowed;background-color:#fff;border-color:#ddd}.pagination-lg>li>a,.pagination-lg>li>span{padding:10px 16px;font-size:18px;line-height:1.3333333}.pagination-lg>li:first-child>a,.pagination-lg>li:first-child>span{border-top-left-radius:6px;border-bottom-left-radius:6px}.pagination-lg>li:last-child>a,.pagination-lg>li:last-child>span{border-top-right-radius:6px;border-bottom-right-radius:6px}.pagination-sm>li>a,.pagination-sm>li>span{padding:5px 10px;font-size:12px;line-height:1.5}.pagination-sm>li:first-child>a,.pagination-sm>li:first-child>span{border-top-left-radius:3px;border-bottom-left-radius:3px}.pagination-sm>li:last-child>a,.pagination-sm>li:last-child>span{border-top-right-radius:3px;border-bottom-right-radius:3px}.pager{padding-left:0;margin:20px 0;text-align:center;list-style:none}.pager li{display:inline}.pager li>a,.pager li>span{display:inline-block;padding:5px 14px;background-color:#fff;border:1px solid #ddd;border-radius:15px}.pager li>a:focus,.pager li>a:hover{text-decoration:none;background-color:#eee}.pager .next>a,.pager .next>span{float:right}.pager .previous>a,.pager .previous>span{float:left}.pager .disabled>a,.pager .disabled>a:focus,.pager .disabled>a:hover,.pager .disabled>span{color:#777;cursor:not-allowed;background-color:#fff}.label{display:inline;padding:.2em .6em .3em;font-size:75%;font-weight:700;line-height:1;color:#fff;text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:.25em}a.label:focus,a.label:hover{color:#fff;text-decoration:none;cursor:pointer}.label:empty{display:none}.btn .label{position:relative;top:-1px}.label-default{background-color:#777}.label-default[href]:focus,.label-default[href]:hover{background-color:#5e5e5e}.label-primary{background-color:#337ab7}.label-primary[href]:focus,.label-primary[href]:hover{background-color:#286090}.label-success{background-color:#5cb85c}.label-success[href]:focus,.label-success[href]:hover{background-color:#449d44}.label-info{background-color:#5bc0de}.label-info[href]:focus,.label-info[href]:hover{background-color:#31b0d5}.label-warning{background-color:#f0ad4e}.label-warning[href]:focus,.label-warning[href]:hover{background-color:#ec971f}.label-danger{background-color:#d9534f}.label-danger[href]:focus,.label-danger[href]:hover{background-color:#c9302c}.badge{display:inline-block;min-width:10px;padding:3px 7px;font-size:12px;font-weight:700;line-height:1;color:#fff;text-align:center;white-space:nowrap;vertical-align:middle;background-color:#777;border-radius:10px}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.btn-group-xs>.btn .badge,.btn-xs .badge{top:0;padding:1px 5px}a.badge:focus,a.badge:hover{color:#fff;text-decoration:none;cursor:pointer}.list-group-item.active>.badge,.nav-pills>.active>a>.badge{color:#337ab7;background-color:#fff}.list-group-item>.badge{float:right}.list-group-item>.badge+.badge{margin-right:5px}.nav-pills>li>a>.badge{margin-left:3px}.jumbotron{padding-top:30px;padding-bottom:30px;margin-bottom:30px;color:inherit;background-color:#eee}.jumbotron .h1,.jumbotron h1{color:inherit}.jumbotron p{margin-bottom:15px;font-size:21px;font-weight:200}.jumbotron>hr{border-top-color:#d5d5d5}.container .jumbotron,.container-fluid .jumbotron{border-radius:6px}.jumbotron .container{max-width:100%}@media screen and (min-width:768px){.jumbotron{padding-top:48px;padding-bottom:48px}.container .jumbotron,.container-fluid .jumbotron{padding-right:60px;padding-left:60px}.jumbotron .h1,.jumbotron h1{font-size:63px}}.thumbnail{display:block;padding:4px;margin-bottom:20px;line-height:1.42857143;background-color:#fff;border:1px solid #ddd;border-radius:4px;-webkit-transition:border .2s ease-in-out;-o-transition:border .2s ease-in-out;transition:border .2s ease-in-out}.thumbnail a>img,.thumbnail>img{margin-right:auto;margin-left:auto}a.thumbnail.active,a.thumbnail:focus,a.thumbnail:hover{border-color:#337ab7}.thumbnail .caption{padding:9px;color:#333}.alert{padding:15px;margin-bottom:20px;border:1px solid transparent;border-radius:4px}.alert h4{margin-top:0;color:inherit}.alert .alert-link{font-weight:700}.alert>p,.alert>ul{margin-bottom:0}.alert>p+p{margin-top:5px}.alert-dismissable,.alert-dismissible{padding-right:35px}.alert-dismissable .close,.alert-dismissible .close{position:relative;top:-2px;right:-21px;color:inherit}.alert-success{color:#3c763d;background-color:#dff0d8;border-color:#d6e9c6}.alert-success hr{border-top-color:#c9e2b3}.alert-success .alert-link{color:#2b542c}.alert-info{color:#31708f;background-color:#d9edf7;border-color:#bce8f1}.alert-info hr{border-top-color:#a6e1ec}.alert-info .alert-link{color:#245269}.alert-warning{color:#8a6d3b;background-color:#fcf8e3;border-color:#faebcc}.alert-warning hr{border-top-color:#f7e1b5}.alert-warning .alert-link{color:#66512c}.alert-danger{color:#a94442;background-color:#f2dede;border-color:#ebccd1}.alert-danger hr{border-top-color:#e4b9c0}.alert-danger .alert-link{color:#843534}@-webkit-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@-o-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}.progress{height:20px;margin-bottom:20px;overflow:hidden;background-color:#f5f5f5;border-radius:4px;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,.1);box-shadow:inset 0 1px 2px rgba(0,0,0,.1)}.progress-bar{float:left;width:0;height:100%;font-size:12px;line-height:20px;color:#fff;text-align:center;background-color:#337ab7;-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,.15);box-shadow:inset 0 -1px 0 rgba(0,0,0,.15);-webkit-transition:width .6s ease;-o-transition:width .6s ease;transition:width .6s ease}.progress-bar-striped,.progress-striped .progress-bar{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);-webkit-background-size:40px 40px;background-size:40px 40px}.progress-bar.active,.progress.active .progress-bar{-webkit-animation:progress-bar-stripes 2s linear infinite;-o-animation:progress-bar-stripes 2s linear infinite;animation:progress-bar-stripes 2s linear infinite}.progress-bar-success{background-color:#5cb85c}.progress-striped .progress-bar-success{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-info{background-color:#5bc0de}.progress-striped .progress-bar-info{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-warning{background-color:#f0ad4e}.progress-striped .progress-bar-warning{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-danger{background-color:#d9534f}.progress-striped .progress-bar-danger{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.media{margin-top:15px}.media:first-child{margin-top:0}.media,.media-body{overflow:hidden;zoom:1}.media-body{width:10000px}.media-object{display:block}.media-object.img-thumbnail{max-width:none}.media-right,.media>.pull-right{padding-left:10px}.media-left,.media>.pull-left{padding-right:10px}.media-body,.media-left,.media-right{display:table-cell;vertical-align:top}.media-middle{vertical-align:middle}.media-bottom{vertical-align:bottom}.media-heading{margin-top:0;margin-bottom:5px}.media-list{padding-left:0;list-style:none}.list-group{padding-left:0;margin-bottom:20px}.list-group-item{position:relative;display:block;padding:10px 15px;margin-bottom:-1px;background-color:#fff;border:1px solid #ddd}.list-group-item:first-child{border-top-left-radius:4px;border-top-right-radius:4px}.list-group-item:last-child{margin-bottom:0;border-bottom-right-radius:4px;border-bottom-left-radius:4px}a.list-group-item,button.list-group-item{color:#555}a.list-group-item .list-group-item-heading,button.list-group-item .list-group-item-heading{color:#333}a.list-group-item:focus,a.list-group-item:hover,button.list-group-item:focus,button.list-group-item:hover{color:#555;text-decoration:none;background-color:#f5f5f5}button.list-group-item{width:100%;text-align:left}.list-group-item.disabled,.list-group-item.disabled:focus,.list-group-item.disabled:hover{color:#777;cursor:not-allowed;background-color:#eee}.list-group-item.disabled .list-group-item-heading,.list-group-item.disabled:focus .list-group-item-heading,.list-group-item.disabled:hover .list-group-item-heading{color:inherit}.list-group-item.disabled .list-group-item-text,.list-group-item.disabled:focus .list-group-item-text,.list-group-item.disabled:hover .list-group-item-text{color:#777}.list-group-item.active,.list-group-item.active:focus,.list-group-item.active:hover{z-index:2;color:#fff;background-color:#337ab7;border-color:#337ab7}.list-group-item.active .list-group-item-heading,.list-group-item.active .list-group-item-heading>.small,.list-group-item.active .list-group-item-heading>small,.list-group-item.active:focus .list-group-item-heading,.list-group-item.active:focus .list-group-item-heading>.small,.list-group-item.active:focus .list-group-item-heading>small,.list-group-item.active:hover .list-group-item-heading,.list-group-item.active:hover .list-group-item-heading>.small,.list-group-item.active:hover .list-group-item-heading>small{color:inherit}.list-group-item.active .list-group-item-text,.list-group-item.active:focus .list-group-item-text,.list-group-item.active:hover .list-group-item-text{color:#c7ddef}.list-group-item-success{color:#3c763d;background-color:#dff0d8}a.list-group-item-success,button.list-group-item-success{color:#3c763d}a.list-group-item-success .list-group-item-heading,button.list-group-item-success .list-group-item-heading{color:inherit}a.list-group-item-success:focus,a.list-group-item-success:hover,button.list-group-item-success:focus,button.list-group-item-success:hover{color:#3c763d;background-color:#d0e9c6}a.list-group-item-success.active,a.list-group-item-success.active:focus,a.list-group-item-success.active:hover,button.list-group-item-success.active,button.list-group-item-success.active:focus,button.list-group-item-success.active:hover{color:#fff;background-color:#3c763d;border-color:#3c763d}.list-group-item-info{color:#31708f;background-color:#d9edf7}a.list-group-item-info,button.list-group-item-info{color:#31708f}a.list-group-item-info .list-group-item-heading,button.list-group-item-info .list-group-item-heading{color:inherit}a.list-group-item-info:focus,a.list-group-item-info:hover,button.list-group-item-info:focus,button.list-group-item-info:hover{color:#31708f;background-color:#c4e3f3}a.list-group-item-info.active,a.list-group-item-info.active:focus,a.list-group-item-info.active:hover,button.list-group-item-info.active,button.list-group-item-info.active:focus,button.list-group-item-info.active:hover{color:#fff;background-color:#31708f;border-color:#31708f}.list-group-item-warning{color:#8a6d3b;background-color:#fcf8e3}a.list-group-item-warning,button.list-group-item-warning{color:#8a6d3b}a.list-group-item-warning .list-group-item-heading,button.list-group-item-warning .list-group-item-heading{color:inherit}a.list-group-item-warning:focus,a.list-group-item-warning:hover,button.list-group-item-warning:focus,button.list-group-item-warning:hover{color:#8a6d3b;background-color:#faf2cc}a.list-group-item-warning.active,a.list-group-item-warning.active:focus,a.list-group-item-warning.active:hover,button.list-group-item-warning.active,button.list-group-item-warning.active:focus,button.list-group-item-warning.active:hover{color:#fff;background-color:#8a6d3b;border-color:#8a6d3b}.list-group-item-danger{color:#a94442;background-color:#f2dede}a.list-group-item-danger,button.list-group-item-danger{color:#a94442}a.list-group-item-danger .list-group-item-heading,button.list-group-item-danger .list-group-item-heading{color:inherit}a.list-group-item-danger:focus,a.list-group-item-danger:hover,button.list-group-item-danger:focus,button.list-group-item-danger:hover{color:#a94442;background-color:#ebcccc}a.list-group-item-danger.active,a.list-group-item-danger.active:focus,a.list-group-item-danger.active:hover,button.list-group-item-danger.active,button.list-group-item-danger.active:focus,button.list-group-item-danger.active:hover{color:#fff;background-color:#a94442;border-color:#a94442}.list-group-item-heading{margin-top:0;margin-bottom:5px}.list-group-item-text{margin-bottom:0;line-height:1.3}.panel{margin-bottom:20px;background-color:#fff;border:1px solid transparent;border-radius:4px;-webkit-box-shadow:0 1px 1px rgba(0,0,0,.05);box-shadow:0 1px 1px rgba(0,0,0,.05)}.panel-body{padding:15px}.panel-heading{padding:10px 15px;border-bottom:1px solid transparent;border-top-left-radius:3px;border-top-right-radius:3px}.panel-heading>.dropdown .dropdown-toggle{color:inherit}.panel-title{margin-top:0;margin-bottom:0;font-size:16px;color:inherit}.panel-title>.small,.panel-title>.small>a,.panel-title>a,.panel-title>small,.panel-title>small>a{color:inherit}.panel-footer{padding:10px 15px;background-color:#f5f5f5;border-top:1px solid #ddd;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.list-group,.panel>.panel-collapse>.list-group{margin-bottom:0}.panel>.list-group .list-group-item,.panel>.panel-collapse>.list-group .list-group-item{border-width:1px 0;border-radius:0}.panel>.list-group:first-child .list-group-item:first-child,.panel>.panel-collapse>.list-group:first-child .list-group-item:first-child{border-top:0;border-top-left-radius:3px;border-top-right-radius:3px}.panel>.list-group:last-child .list-group-item:last-child,.panel>.panel-collapse>.list-group:last-child .list-group-item:last-child{border-bottom:0;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.panel-heading+.panel-collapse>.list-group .list-group-item:first-child{border-top-left-radius:0;border-top-right-radius:0}.panel-heading+.list-group .list-group-item:first-child{border-top-width:0}.list-group+.panel-footer{border-top-width:0}.panel>.panel-collapse>.table,.panel>.table,.panel>.table-responsive>.table{margin-bottom:0}.panel>.panel-collapse>.table caption,.panel>.table caption,.panel>.table-responsive>.table caption{padding-right:15px;padding-left:15px}.panel>.table-responsive:first-child>.table:first-child,.panel>.table:first-child{border-top-left-radius:3px;border-top-right-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child,.panel>.table:first-child>thead:first-child>tr:first-child{border-top-left-radius:3px;border-top-right-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child td:first-child,.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child th:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child td:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child th:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child td:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child th:first-child,.panel>.table:first-child>thead:first-child>tr:first-child td:first-child,.panel>.table:first-child>thead:first-child>tr:first-child th:first-child{border-top-left-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child td:last-child,.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child th:last-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child td:last-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child th:last-child,.panel>.table:first-child>tbody:first-child>tr:first-child td:last-child,.panel>.table:first-child>tbody:first-child>tr:first-child th:last-child,.panel>.table:first-child>thead:first-child>tr:first-child td:last-child,.panel>.table:first-child>thead:first-child>tr:first-child th:last-child{border-top-right-radius:3px}.panel>.table-responsive:last-child>.table:last-child,.panel>.table:last-child{border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child{border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child td:first-child,.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child th:first-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child td:first-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child th:first-child,.panel>.table:last-child>tbody:last-child>tr:last-child td:first-child,.panel>.table:last-child>tbody:last-child>tr:last-child th:first-child,.panel>.table:last-child>tfoot:last-child>tr:last-child td:first-child,.panel>.table:last-child>tfoot:last-child>tr:last-child th:first-child{border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child td:last-child,.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child th:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child td:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child th:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child td:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child th:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child td:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child th:last-child{border-bottom-right-radius:3px}.panel>.panel-body+.table,.panel>.panel-body+.table-responsive,.panel>.table+.panel-body,.panel>.table-responsive+.panel-body{border-top:1px solid #ddd}.panel>.table>tbody:first-child>tr:first-child td,.panel>.table>tbody:first-child>tr:first-child th{border-top:0}.panel>.table-bordered,.panel>.table-responsive>.table-bordered{border:0}.panel>.table-bordered>tbody>tr>td:first-child,.panel>.table-bordered>tbody>tr>th:first-child,.panel>.table-bordered>tfoot>tr>td:first-child,.panel>.table-bordered>tfoot>tr>th:first-child,.panel>.table-bordered>thead>tr>td:first-child,.panel>.table-bordered>thead>tr>th:first-child,.panel>.table-responsive>.table-bordered>tbody>tr>td:first-child,.panel>.table-responsive>.table-bordered>tbody>tr>th:first-child,.panel>.table-responsive>.table-bordered>tfoot>tr>td:first-child,.panel>.table-responsive>.table-bordered>tfoot>tr>th:first-child,.panel>.table-responsive>.table-bordered>thead>tr>td:first-child,.panel>.table-responsive>.table-bordered>thead>tr>th:first-child{border-left:0}.panel>.table-bordered>tbody>tr>td:last-child,.panel>.table-bordered>tbody>tr>th:last-child,.panel>.table-bordered>tfoot>tr>td:last-child,.panel>.table-bordered>tfoot>tr>th:last-child,.panel>.table-bordered>thead>tr>td:last-child,.panel>.table-bordered>thead>tr>th:last-child,.panel>.table-responsive>.table-bordered>tbody>tr>td:last-child,.panel>.table-responsive>.table-bordered>tbody>tr>th:last-child,.panel>.table-responsive>.table-bordered>tfoot>tr>td:last-child,.panel>.table-responsive>.table-bordered>tfoot>tr>th:last-child,.panel>.table-responsive>.table-bordered>thead>tr>td:last-child,.panel>.table-responsive>.table-bordered>thead>tr>th:last-child{border-right:0}.panel>.table-bordered>tbody>tr:first-child>td,.panel>.table-bordered>tbody>tr:first-child>th,.panel>.table-bordered>thead>tr:first-child>td,.panel>.table-bordered>thead>tr:first-child>th,.panel>.table-responsive>.table-bordered>tbody>tr:first-child>td,.panel>.table-responsive>.table-bordered>tbody>tr:first-child>th,.panel>.table-responsive>.table-bordered>thead>tr:first-child>td,.panel>.table-responsive>.table-bordered>thead>tr:first-child>th{border-bottom:0}.panel>.table-bordered>tbody>tr:last-child>td,.panel>.table-bordered>tbody>tr:last-child>th,.panel>.table-bordered>tfoot>tr:last-child>td,.panel>.table-bordered>tfoot>tr:last-child>th,.panel>.table-responsive>.table-bordered>tbody>tr:last-child>td,.panel>.table-responsive>.table-bordered>tbody>tr:last-child>th,.panel>.table-responsive>.table-bordered>tfoot>tr:last-child>td,.panel>.table-responsive>.table-bordered>tfoot>tr:last-child>th{border-bottom:0}.panel>.table-responsive{margin-bottom:0;border:0}.panel-group{margin-bottom:20px}.panel-group .panel{margin-bottom:0;border-radius:4px}.panel-group .panel+.panel{margin-top:5px}.panel-group .panel-heading{border-bottom:0}.panel-group .panel-heading+.panel-collapse>.list-group,.panel-group .panel-heading+.panel-collapse>.panel-body{border-top:1px solid #ddd}.panel-group .panel-footer{border-top:0}.panel-group .panel-footer+.panel-collapse .panel-body{border-bottom:1px solid #ddd}.panel-default{border-color:#ddd}.panel-default>.panel-heading{color:#333;background-color:#f5f5f5;border-color:#ddd}.panel-default>.panel-heading+.panel-collapse>.panel-body{border-top-color:#ddd}.panel-default>.panel-heading .badge{color:#f5f5f5;background-color:#333}.panel-default>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#ddd}.panel-primary{border-color:#337ab7}.panel-primary>.panel-heading{color:#fff;background-color:#337ab7;border-color:#337ab7}.panel-primary>.panel-heading+.panel-collapse>.panel-body{border-top-color:#337ab7}.panel-primary>.panel-heading .badge{color:#337ab7;background-color:#fff}.panel-primary>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#337ab7}.panel-success{border-color:#d6e9c6}.panel-success>.panel-heading{color:#3c763d;background-color:#dff0d8;border-color:#d6e9c6}.panel-success>.panel-heading+.panel-collapse>.panel-body{border-top-color:#d6e9c6}.panel-success>.panel-heading .badge{color:#dff0d8;background-color:#3c763d}.panel-success>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#d6e9c6}.panel-info{border-color:#bce8f1}.panel-info>.panel-heading{color:#31708f;background-color:#d9edf7;border-color:#bce8f1}.panel-info>.panel-heading+.panel-collapse>.panel-body{border-top-color:#bce8f1}.panel-info>.panel-heading .badge{color:#d9edf7;background-color:#31708f}.panel-info>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#bce8f1}.panel-warning{border-color:#faebcc}.panel-warning>.panel-heading{color:#8a6d3b;background-color:#fcf8e3;border-color:#faebcc}.panel-warning>.panel-heading+.panel-collapse>.panel-body{border-top-color:#faebcc}.panel-warning>.panel-heading .badge{color:#fcf8e3;background-color:#8a6d3b}.panel-warning>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#faebcc}.panel-danger{border-color:#ebccd1}.panel-danger>.panel-heading{color:#a94442;background-color:#f2dede;border-color:#ebccd1}.panel-danger>.panel-heading+.panel-collapse>.panel-body{border-top-color:#ebccd1}.panel-danger>.panel-heading .badge{color:#f2dede;background-color:#a94442}.panel-danger>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#ebccd1}.embed-responsive{position:relative;display:block;height:0;padding:0;overflow:hidden}.embed-responsive .embed-responsive-item,.embed-responsive embed,.embed-responsive iframe,.embed-responsive object,.embed-responsive video{position:absolute;top:0;bottom:0;left:0;width:100%;height:100%;border:0}.embed-responsive-16by9{padding-bottom:56.25%}.embed-responsive-4by3{padding-bottom:75%}.well{min-height:20px;padding:19px;margin-bottom:20px;background-color:#f5f5f5;border:1px solid #e3e3e3;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.05);box-shadow:inset 0 1px 1px rgba(0,0,0,.05)}.well blockquote{border-color:#ddd;border-color:rgba(0,0,0,.15)}.well-lg{padding:24px;border-radius:6px}.well-sm{padding:9px;border-radius:3px}.close{float:right;font-size:21px;font-weight:700;line-height:1;color:#000;text-shadow:0 1px 0 #fff;filter:alpha(opacity=20);opacity:.2}.close:focus,.close:hover{color:#000;text-decoration:none;cursor:pointer;filter:alpha(opacity=50);opacity:.5}button.close{-webkit-appearance:none;padding:0;cursor:pointer;background:0 0;border:0}.modal-open{overflow:hidden}.modal{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1050;display:none;overflow:hidden;-webkit-overflow-scrolling:touch;outline:0}.modal.fade .modal-dialog{-webkit-transition:-webkit-transform .3s ease-out;-o-transition:-o-transform .3s ease-out;transition:transform .3s ease-out;-webkit-transform:translate(0,-25%);-ms-transform:translate(0,-25%);-o-transform:translate(0,-25%);transform:translate(0,-25%)}.modal.in .modal-dialog{-webkit-transform:translate(0,0);-ms-transform:translate(0,0);-o-transform:translate(0,0);transform:translate(0,0)}.modal-open .modal{overflow-x:hidden;overflow-y:auto}.modal-dialog{position:relative;width:auto;margin:10px}.modal-content{position:relative;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #999;border:1px solid rgba(0,0,0,.2);border-radius:6px;outline:0;-webkit-box-shadow:0 3px 9px rgba(0,0,0,.5);box-shadow:0 3px 9px rgba(0,0,0,.5)}.modal-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1040;background-color:#000}.modal-backdrop.fade{filter:alpha(opacity=0);opacity:0}.modal-backdrop.in{filter:alpha(opacity=50);opacity:.5}.modal-header{min-height:16.43px;padding:15px;border-bottom:1px solid #e5e5e5}.modal-header .close{margin-top:-2px}.modal-title{margin:0;line-height:1.42857143}.modal-body{position:relative;padding:15px}.modal-footer{padding:15px;text-align:right;border-top:1px solid #e5e5e5}.modal-footer .btn+.btn{margin-bottom:0;margin-left:5px}.modal-footer .btn-group .btn+.btn{margin-left:-1px}.modal-footer .btn-block+.btn-block{margin-left:0}.modal-scrollbar-measure{position:absolute;top:-9999px;width:50px;height:50px;overflow:scroll}@media (min-width:768px){.modal-dialog{width:600px;margin:30px auto}.modal-content{-webkit-box-shadow:0 5px 15px rgba(0,0,0,.5);box-shadow:0 5px 15px rgba(0,0,0,.5)}.modal-sm{width:300px}}@media (min-width:992px){.modal-lg{width:900px}}.tooltip{position:absolute;z-index:1070;display:block;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:12px;font-style:normal;font-weight:400;line-height:1.42857143;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;word-spacing:normal;word-wrap:normal;white-space:normal;filter:alpha(opacity=0);opacity:0;line-break:auto}.tooltip.in{filter:alpha(opacity=90);opacity:.9}.tooltip.top{padding:5px 0;margin-top:-3px}.tooltip.right{padding:0 5px;margin-left:3px}.tooltip.bottom{padding:5px 0;margin-top:3px}.tooltip.left{padding:0 5px;margin-left:-3px}.tooltip-inner{max-width:200px;padding:3px 8px;color:#fff;text-align:center;background-color:#000;border-radius:4px}.tooltip-arrow{position:absolute;width:0;height:0;border-color:transparent;border-style:solid}.tooltip.top .tooltip-arrow{bottom:0;left:50%;margin-left:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.top-left .tooltip-arrow{right:5px;bottom:0;margin-bottom:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.top-right .tooltip-arrow{bottom:0;left:5px;margin-bottom:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.right .tooltip-arrow{top:50%;left:0;margin-top:-5px;border-width:5px 5px 5px 0;border-right-color:#000}.tooltip.left .tooltip-arrow{top:50%;right:0;margin-top:-5px;border-width:5px 0 5px 5px;border-left-color:#000}.tooltip.bottom .tooltip-arrow{top:0;left:50%;margin-left:-5px;border-width:0 5px 5px;border-bottom-color:#000}.tooltip.bottom-left .tooltip-arrow{top:0;right:5px;margin-top:-5px;border-width:0 5px 5px;border-bottom-color:#000}.tooltip.bottom-right .tooltip-arrow{top:0;left:5px;margin-top:-5px;border-width:0 5px 5px;border-bottom-color:#000}.popover{position:absolute;top:0;left:0;z-index:1060;display:none;max-width:276px;padding:1px;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;font-style:normal;font-weight:400;line-height:1.42857143;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;word-spacing:normal;word-wrap:normal;white-space:normal;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,.2);border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,.2);box-shadow:0 5px 10px rgba(0,0,0,.2);line-break:auto}.popover.top{margin-top:-10px}.popover.right{margin-left:10px}.popover.bottom{margin-top:10px}.popover.left{margin-left:-10px}.popover-title{padding:8px 14px;margin:0;font-size:14px;background-color:#f7f7f7;border-bottom:1px solid #ebebeb;border-radius:5px 5px 0 0}.popover-content{padding:9px 14px}.popover>.arrow,.popover>.arrow:after{position:absolute;display:block;width:0;height:0;border-color:transparent;border-style:solid}.popover>.arrow{border-width:11px}.popover>.arrow:after{content:"";border-width:10px}.popover.top>.arrow{bottom:-11px;left:50%;margin-left:-11px;border-top-color:#999;border-top-color:rgba(0,0,0,.25);border-bottom-width:0}.popover.top>.arrow:after{bottom:1px;margin-left:-10px;content:" ";border-top-color:#fff;border-bottom-width:0}.popover.right>.arrow{top:50%;left:-11px;margin-top:-11px;border-right-color:#999;border-right-color:rgba(0,0,0,.25);border-left-width:0}.popover.right>.arrow:after{bottom:-10px;left:1px;content:" ";border-right-color:#fff;border-left-width:0}.popover.bottom>.arrow{top:-11px;left:50%;margin-left:-11px;border-top-width:0;border-bottom-color:#999;border-bottom-color:rgba(0,0,0,.25)}.popover.bottom>.arrow:after{top:1px;margin-left:-10px;content:" ";border-top-width:0;border-bottom-color:#fff}.popover.left>.arrow{top:50%;right:-11px;margin-top:-11px;border-right-width:0;border-left-color:#999;border-left-color:rgba(0,0,0,.25)}.popover.left>.arrow:after{right:1px;bottom:-10px;content:" ";border-right-width:0;border-left-color:#fff}.carousel{position:relative}.carousel-inner{position:relative;width:100%;overflow:hidden}.carousel-inner>.item{position:relative;display:none;-webkit-transition:.6s ease-in-out left;-o-transition:.6s ease-in-out left;transition:.6s ease-in-out left}.carousel-inner>.item>a>img,.carousel-inner>.item>img{line-height:1}@media all and (transform-3d),(-webkit-transform-3d){.carousel-inner>.item{-webkit-transition:-webkit-transform .6s ease-in-out;-o-transition:-o-transform .6s ease-in-out;transition:transform .6s ease-in-out;-webkit-backface-visibility:hidden;backface-visibility:hidden;-webkit-perspective:1000px;perspective:1000px}.carousel-inner>.item.active.right,.carousel-inner>.item.next{left:0;-webkit-transform:translate3d(100%,0,0);transform:translate3d(100%,0,0)}.carousel-inner>.item.active.left,.carousel-inner>.item.prev{left:0;-webkit-transform:translate3d(-100%,0,0);transform:translate3d(-100%,0,0)}.carousel-inner>.item.active,.carousel-inner>.item.next.left,.carousel-inner>.item.prev.right{left:0;-webkit-transform:translate3d(0,0,0);transform:translate3d(0,0,0)}}.carousel-inner>.active,.carousel-inner>.next,.carousel-inner>.prev{display:block}.carousel-inner>.active{left:0}.carousel-inner>.next,.carousel-inner>.prev{position:absolute;top:0;width:100%}.carousel-inner>.next{left:100%}.carousel-inner>.prev{left:-100%}.carousel-inner>.next.left,.carousel-inner>.prev.right{left:0}.carousel-inner>.active.left{left:-100%}.carousel-inner>.active.right{left:100%}.carousel-control{position:absolute;top:0;bottom:0;left:0;width:15%;font-size:20px;color:#fff;text-align:center;text-shadow:0 1px 2px rgba(0,0,0,.6);filter:alpha(opacity=50);opacity:.5}.carousel-control.left{background-image:-webkit-linear-gradient(left,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);background-image:-o-linear-gradient(left,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);background-image:-webkit-gradient(linear,left top,right top,from(rgba(0,0,0,.5)),to(rgba(0,0,0,.0001)));background-image:linear-gradient(to right,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);background-repeat:repeat-x}.carousel-control.right{right:0;left:auto;background-image:-webkit-linear-gradient(left,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);background-image:-o-linear-gradient(left,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);background-image:-webkit-gradient(linear,left top,right top,from(rgba(0,0,0,.0001)),to(rgba(0,0,0,.5)));background-image:linear-gradient(to right,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);background-repeat:repeat-x}.carousel-control:focus,.carousel-control:hover{color:#fff;text-decoration:none;filter:alpha(opacity=90);outline:0;opacity:.9}.carousel-control .glyphicon-chevron-left,.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next,.carousel-control .icon-prev{position:absolute;top:50%;z-index:5;display:inline-block;margin-top:-10px}.carousel-control .glyphicon-chevron-left,.carousel-control .icon-prev{left:50%;margin-left:-10px}.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next{right:50%;margin-right:-10px}.carousel-control .icon-next,.carousel-control .icon-prev{width:20px;height:20px;font-family:serif;line-height:1}.carousel-control .icon-prev:before{content:'\2039'}.carousel-control .icon-next:before{content:'\203a'}.carousel-indicators{position:absolute;bottom:10px;left:50%;z-index:15;width:60%;padding-left:0;margin-left:-30%;text-align:center;list-style:none}.carousel-indicators li{display:inline-block;width:10px;height:10px;margin:1px;text-indent:-999px;cursor:pointer;background-color:#000\9;background-color:rgba(0,0,0,0);border:1px solid #fff;border-radius:10px}.carousel-indicators .active{width:12px;height:12px;margin:0;background-color:#fff}.carousel-caption{position:absolute;right:15%;bottom:20px;left:15%;z-index:10;padding-top:20px;padding-bottom:20px;color:#fff;text-align:center;text-shadow:0 1px 2px rgba(0,0,0,.6)}.carousel-caption .btn{text-shadow:none}@media screen and (min-width:768px){.carousel-control .glyphicon-chevron-left,.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next,.carousel-control .icon-prev{width:30px;height:30px;margin-top:-15px;font-size:30px}.carousel-control .glyphicon-chevron-left,.carousel-control .icon-prev{margin-left:-15px}.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next{margin-right:-15px}.carousel-caption{right:20%;left:20%;padding-bottom:30px}.carousel-indicators{bottom:20px}}.btn-group-vertical>.btn-group:after,.btn-group-vertical>.btn-group:before,.btn-toolbar:after,.btn-toolbar:before,.clearfix:after,.clearfix:before,.container-fluid:after,.container-fluid:before,.container:after,.container:before,.dl-horizontal dd:after,.dl-horizontal dd:before,.form-horizontal .form-group:after,.form-horizontal .form-group:before,.modal-footer:after,.modal-footer:before,.nav:after,.nav:before,.navbar-collapse:after,.navbar-collapse:before,.navbar-header:after,.navbar-header:before,.navbar:after,.navbar:before,.pager:after,.pager:before,.panel-body:after,.panel-body:before,.row:after,.row:before{display:table;content:" "}.btn-group-vertical>.btn-group:after,.btn-toolbar:after,.clearfix:after,.container-fluid:after,.container:after,.dl-horizontal dd:after,.form-horizontal .form-group:after,.modal-footer:after,.nav:after,.navbar-collapse:after,.navbar-header:after,.navbar:after,.pager:after,.panel-body:after,.row:after{clear:both}.center-block{display:block;margin-right:auto;margin-left:auto}.pull-right{float:right!important}.pull-left{float:left!important}.hide{display:none!important}.show{display:block!important}.invisible{visibility:hidden}.text-hide{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0}.hidden{display:none!important}.affix{position:fixed}@-ms-viewport{width:device-width}.visible-lg,.visible-md,.visible-sm,.visible-xs{display:none!important}.visible-lg-block,.visible-lg-inline,.visible-lg-inline-block,.visible-md-block,.visible-md-inline,.visible-md-inline-block,.visible-sm-block,.visible-sm-inline,.visible-sm-inline-block,.visible-xs-block,.visible-xs-inline,.visible-xs-inline-block{display:none!important}@media (max-width:767px){.visible-xs{display:block!important}table.visible-xs{display:table!important}tr.visible-xs{display:table-row!important}td.visible-xs,th.visible-xs{display:table-cell!important}}@media (max-width:767px){.visible-xs-block{display:block!important}}@media (max-width:767px){.visible-xs-inline{display:inline!important}}@media (max-width:767px){.visible-xs-inline-block{display:inline-block!important}}@media (min-width:768px) and (max-width:991px){.visible-sm{display:block!important}table.visible-sm{display:table!important}tr.visible-sm{display:table-row!important}td.visible-sm,th.visible-sm{display:table-cell!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-block{display:block!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-inline{display:inline!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-inline-block{display:inline-block!important}}@media (min-width:992px) and (max-width:1199px){.visible-md{display:block!important}table.visible-md{display:table!important}tr.visible-md{display:table-row!important}td.visible-md,th.visible-md{display:table-cell!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-block{display:block!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-inline{display:inline!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-inline-block{display:inline-block!important}}@media (min-width:1200px){.visible-lg{display:block!important}table.visible-lg{display:table!important}tr.visible-lg{display:table-row!important}td.visible-lg,th.visible-lg{display:table-cell!important}}@media (min-width:1200px){.visible-lg-block{display:block!important}}@media (min-width:1200px){.visible-lg-inline{display:inline!important}}@media (min-width:1200px){.visible-lg-inline-block{display:inline-block!important}}@media (max-width:767px){.hidden-xs{display:none!important}}@media (min-width:768px) and (max-width:991px){.hidden-sm{display:none!important}}@media (min-width:992px) and (max-width:1199px){.hidden-md{display:none!important}}@media (min-width:1200px){.hidden-lg{display:none!important}}.visible-print{display:none!important}@media print{.visible-print{display:block!important}table.visible-print{display:table!important}tr.visible-print{display:table-row!important}td.visible-print,th.visible-print{display:table-cell!important}}.visible-print-block{display:none!important}@media print{.visible-print-block{display:block!important}}.visible-print-inline{display:none!important}@media print{.visible-print-inline{display:inline!important}}.visible-print-inline-block{display:none!important}@media print{.visible-print-inline-block{display:inline-block!important}}@media print{.hidden-print{display:none!important}} +<style type="text/css">html{font-family:sans-serif;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}body{margin:0}article,aside,details,figcaption,figure,footer,header,hgroup,main,menu,nav,section,summary{display:block}audio,canvas,progress,video{display:inline-block;vertical-align:baseline}audio:not([controls]){display:none;height:0}[hidden],template{display:none}a{background-color:transparent}a:active,a:hover{outline:0}abbr[title]{border-bottom:1px dotted}b,strong{font-weight:700}dfn{font-style:italic}h1{margin:.67em 0;font-size:2em}mark{color:#000;background:#ff0}small{font-size:80%}sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}sup{top:-.5em}sub{bottom:-.25em}img{border:0}svg:not(:root){overflow:hidden}figure{margin:1em 40px}hr{height:0;-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box}pre{overflow:auto}code,kbd,pre,samp{font-family:monospace,monospace;font-size:1em}button,input,optgroup,select,textarea{margin:0;font:inherit;color:inherit}button{overflow:visible}button,select{text-transform:none}button,html input[type=button],input[type=reset],input[type=submit]{-webkit-appearance:button;cursor:pointer}button[disabled],html input[disabled]{cursor:default}button::-moz-focus-inner,input::-moz-focus-inner{padding:0;border:0}input{line-height:normal}input[type=checkbox],input[type=radio]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;padding:0}input[type=number]::-webkit-inner-spin-button,input[type=number]::-webkit-outer-spin-button{height:auto}input[type=search]{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;-webkit-appearance:textfield}input[type=search]::-webkit-search-cancel-button,input[type=search]::-webkit-search-decoration{-webkit-appearance:none}fieldset{padding:.35em .625em .75em;margin:0 2px;border:1px solid silver}legend{padding:0;border:0}textarea{overflow:auto}optgroup{font-weight:700}table{border-spacing:0;border-collapse:collapse}td,th{padding:0}@media print{*,:after,:before{color:#000!important;text-shadow:none!important;background:0 0!important;-webkit-box-shadow:none!important;box-shadow:none!important}a,a:visited{text-decoration:underline}a[href]:after{content:" (" attr(href) ")"}abbr[title]:after{content:" (" attr(title) ")"}a[href^="javascript:"]:after,a[href^="#"]:after{content:""}blockquote,pre{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}img,tr{page-break-inside:avoid}img{max-width:100%!important}h2,h3,p{orphans:3;widows:3}h2,h3{page-break-after:avoid}.navbar{display:none}.btn>.caret,.dropup>.btn>.caret{border-top-color:#000!important}.label{border:1px solid #000}.table{border-collapse:collapse!important}.table td,.table th{background-color:#fff!important}.table-bordered td,.table-bordered th{border:1px solid #ddd!important}}@font-face{font-family:'Glyphicons Halflings';src:url(data:application/vnd.ms-fontobject;base64,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);src:url(data:application/vnd.ms-fontobject;base64,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) format('embedded-opentype'),url(data:application/font-woff;base64,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) format('woff'),url(data:application/font-sfnt;base64,AAEAAAAPAIAAAwBwRkZUTW0ql9wAAAD8AAAAHEdERUYBRAAEAAABGAAAACBPUy8yZ7lriQAAATgAAABgY21hcNqt44EAAAGYAAAGcmN2dCAAKAL4AAAIDAAAAARnYXNw//8AAwAACBAAAAAIZ2x5Zn1dwm8AAAgYAACUpGhlYWQFTS/YAACcvAAAADZoaGVhCkQEEQAAnPQAAAAkaG10eNLHIGAAAJ0YAAADdGxvY2Fv+5XOAACgjAAAAjBtYXhwAWoA2AAAorwAAAAgbmFtZbMsoJsAAKLcAAADonBvc3S6o+U1AACmgAAACtF3ZWJmwxhUUAAAsVQAAAAGAAAAAQAAAADMPaLPAAAAANB2gXUAAAAA0HZzlwABAAAADgAAABgAAAAAAAIAAQABARYAAQAEAAAAAgAAAAMEiwGQAAUABAMMAtAAAABaAwwC0AAAAaQAMgK4AAAAAAUAAAAAAAAAAAAAAAIAAAAAAAAAAAAAAFVLV04AQAAg//8DwP8QAAAFFAB7AAAAAQAAAAAAAAAAAAAAIAABAAAABQAAAAMAAAAsAAAACgAAAdwAAQAAAAAEaAADAAEAAAAsAAMACgAAAdwABAGwAAAAaABAAAUAKAAgACsAoAClIAogLyBfIKwgvSISIxsl/CYBJvonCScP4APgCeAZ4CngOeBJ4FngYOBp4HngieCX4QnhGeEp4TnhRuFJ4VnhaeF54YnhleGZ4gbiCeIW4hniIeIn4jniSeJZ4mD4////AAAAIAAqAKAApSAAIC8gXyCsIL0iEiMbJfwmASb6JwknD+AB4AXgEOAg4DDgQOBQ4GDgYuBw4IDgkOEB4RDhIOEw4UDhSOFQ4WDhcOGA4ZDhl+IA4gniEOIY4iHiI+Iw4kDiUOJg+P/////j/9r/Zv9i4Ajf5N+132nfWd4F3P3aHdoZ2SHZE9kOIB0gHCAWIBAgCiAEH/4f+B/3H/Ef6x/lH3wfdh9wH2ofZB9jH10fVx9RH0sfRR9EHt4e3B7WHtUezh7NHsUevx65HrMIFQABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADAAAAAACjAAAAAAAAAA1AAAAIAAAACAAAAADAAAAKgAAACsAAAAEAAAAoAAAAKAAAAAGAAAApQAAAKUAAAAHAAAgAAAAIAoAAAAIAAAgLwAAIC8AAAATAAAgXwAAIF8AAAAUAAAgrAAAIKwAAAAVAAAgvQAAIL0AAAAWAAAiEgAAIhIAAAAXAAAjGwAAIxsAAAAYAAAl/AAAJfwAAAAZAAAmAQAAJgEAAAAaAAAm+gAAJvoAAAAbAAAnCQAAJwkAAAAcAAAnDwAAJw8AAAAdAADgAQAA4AMAAAAeAADgBQAA4AkAAAAhAADgEAAA4BkAAAAmAADgIAAA4CkAAAAwAADgMAAA4DkAAAA6AADgQAAA4EkAAABEAADgUAAA4FkAAABOAADgYAAA4GAAAABYAADgYgAA4GkAAABZAADgcAAA4HkAAABhAADggAAA4IkAAABrAADgkAAA4JcAAAB1AADhAQAA4QkAAAB9AADhEAAA4RkAAACGAADhIAAA4SkAAACQAADhMAAA4TkAAACaAADhQAAA4UYAAACkAADhSAAA4UkAAACrAADhUAAA4VkAAACtAADhYAAA4WkAAAC3AADhcAAA4XkAAADBAADhgAAA4YkAAADLAADhkAAA4ZUAAADVAADhlwAA4ZkAAADbAADiAAAA4gYAAADeAADiCQAA4gkAAADlAADiEAAA4hYAAADmAADiGAAA4hkAAADtAADiIQAA4iEAAADvAADiIwAA4icAAADwAADiMAAA4jkAAAD1AADiQAAA4kkAAAD/AADiUAAA4lkAAAEJAADiYAAA4mAAAAETAAD4/wAA+P8AAAEUAAH1EQAB9REAAAEVAAH2qgAB9qoAAAEWAAYCCgAAAAABAAABAAAAAAAAAAAAAAAAAAAAAQACAAAAAAAAAAIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQAAAAAAAwAAAAAAAAAAAAAAAAAAAAAAAAAEAAUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAHAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAVAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAKAL4AAAAAf//AAIAAgAoAAABaAMgAAMABwAusQEALzyyBwQA7TKxBgXcPLIDAgDtMgCxAwAvPLIFBADtMrIHBgH8PLIBAgDtMjMRIRElMxEjKAFA/ujw8AMg/OAoAtAAAQBkAGQETARMAFsAAAEyFh8BHgEdATc+AR8BFgYPATMyFhcWFRQGDwEOASsBFx4BDwEGJi8BFRQGBwYjIiYvAS4BPQEHDgEvASY2PwEjIiYnJjU0Nj8BPgE7AScuAT8BNhYfATU0Njc2AlgPJgsLCg+eBxYIagcCB57gChECBgMCAQIRCuCeBwIHaggWB54PCikiDyYLCwoPngcWCGoHAgee4AoRAgYDAgECEQrgngcCB2oIFgeeDwopBEwDAgECEQrgngcCB2oIFgeeDwopIg8mCwsKD54HFghqBwIHnuAKEQIGAwIBAhEK4J4HAgdqCBYHng8KKSIPJgsLCg+eBxYIagcCB57gChECBgAAAAABAAAAAARMBEwAIwAAATMyFhURITIWHQEUBiMhERQGKwEiJjURISImPQE0NjMhETQ2AcLIFR0BXhUdHRX+oh0VyBUd/qIVHR0VAV4dBEwdFf6iHRXIFR3+ohUdHRUBXh0VyBUdAV4VHQAAAAABAHAAAARABEwARQAAATMyFgcBBgchMhYPAQ4BKwEVITIWDwEOASsBFRQGKwEiJj0BISImPwE+ATsBNSEiJj8BPgE7ASYnASY2OwEyHwEWMj8BNgM5+goFCP6UBgUBDAoGBngGGAp9ARMKBgZ4BhgKfQ8LlAsP/u0KBgZ4BhgKff7tCgYGeAYYCnYFBv6UCAUK+hkSpAgUCKQSBEwKCP6UBgwMCKAIDGQMCKAIDK4LDw8LrgwIoAgMZAwIoAgMDAYBbAgKEqQICKQSAAABAGQABQSMBK4AOwAAATIXFhcjNC4DIyIOAwchByEGFSEHIR4EMzI+AzUzBgcGIyInLgEnIzczNjcjNzM+ATc2AujycDwGtSM0QDkXEys4MjAPAXtk/tQGAZZk/tQJMDlCNBUWOUA0I64eYmunznYkQgzZZHABBdpkhhQ+H3UErr1oaS1LMCEPCx4uTzJkMjJkSnRCKw8PIjBKK6trdZ4wqndkLzVkV4UljQAAAgB7AAAETASwAD4ARwAAASEyHgUVHAEVFA4FKwEHITIWDwEOASsBFRQGKwEiJj0BISImPwE+ATsBNSEiJj8BPgE7ARE0NhcRMzI2NTQmIwGsAV5DakIwFgwBAQwWMEJqQ7ICASAKBgZ4BhgKigsKlQoP/vUKBgZ4BhgKdf71CgYGeAYYCnUPtstALS1ABLAaJD8yTyokCwsLJCpQMkAlGmQMCKAIDK8LDg8KrwwIoAgMZAwIoAgMAdsKD8j+1EJWVEAAAAEAyAGQBEwCvAAPAAATITIWHQEUBiMhIiY9ATQ2+gMgFR0dFfzgFR0dArwdFcgVHR0VyBUdAAAAAgDIAAAD6ASwACUAQQAAARUUBisBFRQGBx4BHQEzMhYdASE1NDY7ATU0NjcuAT0BIyImPQEXFRQWFx4BFAYHDgEdASE1NCYnLgE0Njc+AT0BA+gdFTJjUVFjMhUd/OAdFTJjUVFjMhUdyEE3HCAgHDdBAZBBNxwgIBw3QQSwlhUdZFuVIyOVW5YdFZaWFR2WW5UjI5VbZB0VlshkPGMYDDI8MgwYYzyWljxjGAwyPDIMGGM8ZAAAAAEAAAAAAAAAAAAAAAAxAAAB//IBLATCBEEAFgAAATIWFzYzMhYVFAYjISImNTQ2NyY1NDYB9261LCwueKqqeP0ST3FVQgLYBEF3YQ6teHmtclBFaw4MGZnXAAAAAgAAAGQEsASvABoAHgAAAB4BDwEBMzIWHQEhNTQ2OwEBJyY+ARYfATc2AyEnAwL2IAkKiAHTHhQe+1AeFB4B1IcKCSAkCm9wCXoBebbDBLMTIxC7/RYlFSoqFSUC6rcQJBQJEJSWEPwecAIWAAAAAAQAAABkBLAETAALABcAIwA3AAATITIWBwEGIicBJjYXARYUBwEGJjURNDYJATYWFREUBicBJjQHARYGIyEiJjcBNjIfARYyPwE2MhkEfgoFCP3MCBQI/cwIBQMBCAgI/vgICgoDjAEICAoKCP74CFwBbAgFCvuCCgUIAWwIFAikCBQIpAgUBEwKCP3JCAgCNwgK2v74CBQI/vgIBQoCJgoF/vABCAgFCv3aCgUIAQgIFID+lAgKCggBbAgIpAgIpAgAAAAD//D/8AS6BLoACQANABAAAAAyHwEWFA8BJzcTAScJAQUTA+AmDpkNDWPWXyL9mdYCZv4f/rNuBLoNmQ4mDlzWYP50/ZrWAmb8anABTwAAAAEAAAAABLAEsAAPAAABETMyFh0BITU0NjsBEQEhArz6FR384B0V+v4MBLACiv3aHRUyMhUdAiYCJgAAAAEADgAIBEwEnAAfAAABJTYWFREUBgcGLgE2NzYXEQURFAYHBi4BNjc2FxE0NgFwAoUnMFNGT4gkV09IQv2oWEFPiCRXT0hCHQP5ow8eIvzBN1EXGSltchkYEAIJm/2iKmAVGilucRoYEQJ/JioAAAACAAn/+AS7BKcAHQApAAAAMh4CFQcXFAcBFgYPAQYiJwEGIycHIi4CND4BBCIOARQeATI+ATQmAZDItoNOAQFOARMXARY7GikT/u13jgUCZLaDTk6DAXKwlFZWlLCUVlYEp06DtmQCBY15/u4aJRg6FBQBEk0BAU6Dtsi2g1tWlLCUVlaUsJQAAQBkAFgErwREABkAAAE+Ah4CFRQOAwcuBDU0PgIeAQKJMHt4dVg2Q3mEqD4+p4V4Qzhadnh5A7VESAUtU3ZAOXmAf7JVVbJ/gHk5QHZTLQVIAAAAAf/TAF4EewSUABgAAAETNjIXEyEyFgcFExYGJyUFBiY3EyUmNjMBl4MHFQeBAaUVBhH+qoIHDxH+qf6qEQ8Hgv6lEQYUAyABYRMT/p8RDPn+bxQLDPb3DAsUAZD7DBEAAv/TAF4EewSUABgAIgAAARM2MhcTITIWBwUTFgYnJQUGJjcTJSY2MwUjFwc3Fyc3IycBl4MHFQeBAaUVBhH+qoIHDxH+qf6qEQ8Hgv6lEQYUAfPwxUrBw0rA6k4DIAFhExP+nxEM+f5vFAsM9vcMCxQBkPsMEWSO4ouM5YzTAAABAAAAAASwBLAAJgAAATIWHQEUBiMVFBYXBR4BHQEUBiMhIiY9ATQ2NyU+AT0BIiY9ATQ2Alh8sD4mDAkBZgkMDwr7ggoPDAkBZgkMJj6wBLCwfPouaEsKFwbmBRcKXQoPDwpdChcF5gYXCktoLvp8sAAAAA0AAAAABLAETAAPABMAIwAnACsALwAzADcARwBLAE8AUwBXAAATITIWFREUBiMhIiY1ETQ2FxUzNSkBIgYVERQWMyEyNjURNCYzFTM1BRUzNSEVMzUFFTM1IRUzNQchIgYVERQWMyEyNjURNCYFFTM1IRUzNQUVMzUhFTM1GQR+Cg8PCvuCCg8PVWQCo/3aCg8PCgImCg8Pc2T8GGQDIGT8GGQDIGTh/doKDw8KAiYKDw/872QDIGT8GGQDIGQETA8K++YKDw8KBBoKD2RkZA8K/qIKDw8KAV4KD2RkyGRkZGTIZGRkZGQPCv6iCg8PCgFeCg9kZGRkZMhkZGRkAAAEAAAAAARMBEwADwAfAC8APwAAEyEyFhURFAYjISImNRE0NikBMhYVERQGIyEiJjURNDYBITIWFREUBiMhIiY1ETQ2KQEyFhURFAYjISImNRE0NjIBkBUdHRX+cBUdHQJtAZAVHR0V/nAVHR39vQGQFR0dFf5wFR0dAm0BkBUdHRX+cBUdHQRMHRX+cBUdHRUBkBUdHRX+cBUdHRUBkBUd/agdFf5wFR0dFQGQFR0dFf5wFR0dFQGQFR0AAAkAAAAABEwETAAPAB8ALwA/AE8AXwBvAH8AjwAAEzMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2ATMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2ATMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2MsgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR389cgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR389cgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR0ETB0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHf5wHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHR0VyBUd/nAdFcgVHR0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0ABgAAAAAEsARMAA8AHwAvAD8ATwBfAAATMzIWHQEUBisBIiY9ATQ2KQEyFh0BFAYjISImPQE0NgEzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2ATMyFh0BFAYrASImPQE0NikBMhYdARQGIyEiJj0BNDYyyBUdHRXIFR0dAaUCvBUdHRX9RBUdHf6FyBUdHRXIFR0dAaUCvBUdHRX9RBUdHf6FyBUdHRXIFR0dAaUCvBUdHRX9RBUdHQRMHRXIFR0dFcgVHR0VyBUdHRXIFR3+cB0VyBUdHRXIFR0dFcgVHR0VyBUd/nAdFcgVHR0VyBUdHRXIFR0dFcgVHQAAAAABACYALAToBCAAFwAACQE2Mh8BFhQHAQYiJwEmND8BNjIfARYyAdECOwgUB7EICPzxBxUH/oAICLEHFAirBxYB3QI7CAixBxQI/PAICAGACBQHsQgIqwcAAQBuAG4EQgRCACMAAAEXFhQHCQEWFA8BBiInCQEGIi8BJjQ3CQEmND8BNjIXCQE2MgOIsggI/vUBCwgIsggVB/70/vQHFQiyCAgBC/71CAiyCBUHAQwBDAcVBDuzCBUH/vT+9AcVCLIICAEL/vUICLIIFQcBDAEMBxUIsggI/vUBDAcAAwAX/+sExQSZABkAJQBJAAAAMh4CFRQHARYUDwEGIicBBiMiLgI0PgEEIg4BFB4BMj4BNCYFMzIWHQEzMhYdARQGKwEVFAYrASImPQEjIiY9ATQ2OwE1NDYBmcSzgk1OASwICG0HFQj+1HeOYrSBTU2BAW+zmFhYmLOZWFj+vJYKD0sKDw8KSw8KlgoPSwoPDwpLDwSZTYKzYo15/tUIFQhsCAgBK01NgbTEs4JNWJmzmFhYmLOZIw8KSw8KlgoPSwoPDwpLDwqWCg9LCg8AAAMAF//rBMUEmQAZACUANQAAADIeAhUUBwEWFA8BBiInAQYjIi4CND4BBCIOARQeATI+ATQmBSEyFh0BFAYjISImPQE0NgGZxLOCTU4BLAgIbQcVCP7Ud45itIFNTYEBb7OYWFiYs5lYWP5YAV4KDw8K/qIKDw8EmU2Cs2KNef7VCBUIbAgIAStNTYG0xLOCTViZs5hYWJizmYcPCpYKDw8KlgoPAAAAAAIAFwAXBJkEsAAPAC0AAAEzMhYVERQGKwEiJjURNDYFNRYSFRQOAiIuAjU0EjcVDgEVFB4BMj4BNTQmAiZkFR0dFWQVHR0BD6fSW5vW6tabW9KnZ3xyxejFcnwEsB0V/nAVHR0VAZAVHeGmPv7ZuHXWm1tbm9Z1uAEnPqY3yHh0xXJyxXR4yAAEAGQAAASwBLAADwAfAC8APwAAATMyFhURFAYrASImNRE0NgEzMhYVERQGKwEiJjURNDYBMzIWFREUBisBIiY1ETQ2BTMyFh0BFAYrASImPQE0NgQBlgoPDwqWCg8P/t6WCg8PCpYKDw/+3pYKDw8KlgoPD/7elgoPDwqWCg8PBLAPCvuCCg8PCgR+Cg/+cA8K/RIKDw8KAu4KD/7UDwr+PgoPDwoBwgoPyA8K+goPDwr6Cg8AAAAAAgAaABsElgSWAEcATwAAATIfAhYfATcWFwcXFh8CFhUUDwIGDwEXBgcnBwYPAgYjIi8CJi8BByYnNycmLwImNTQ/AjY/ASc2Nxc3Nj8CNhIiBhQWMjY0AlghKSYFMS0Fhj0rUAMZDgGYBQWYAQ8YA1AwOIYFLDIFJisfISkmBTEtBYY8LFADGQ0ClwYGlwINGQNQLzqFBS0xBSYreLJ+frJ+BJYFmAEOGQJQMDmGBSwxBiYrHiIoJgYxLAWGPSxRAxkOApcFBZcCDhkDUTA5hgUtMAYmKiAhKCYGMC0Fhj0sUAIZDgGYBf6ZfrF+frEABwBkAAAEsAUUABMAFwAhACUAKQAtADEAAAEhMhYdASEyFh0BITU0NjMhNTQ2FxUhNQERFAYjISImNREXETMRMxEzETMRMxEzETMRAfQBLCk7ARMKD/u0DwoBEzspASwBLDsp/UQpO2RkZGRkZGRkBRQ7KWQPCktLCg9kKTtkZGT+1PzgKTs7KQMgZP1EArz9RAK8/UQCvP1EArwAAQAMAAAFCATRAB8AABMBNjIXARYGKwERFAYrASImNREhERQGKwEiJjURIyImEgJsCBUHAmAIBQqvDwr6Cg/+1A8K+goPrwoFAmoCYAcH/aAICv3BCg8PCgF3/okKDw8KAj8KAAIAZAAAA+gEsAARABcAAAERFBYzIREUBiMhIiY1ETQ2MwEjIiY9AQJYOykBLB0V/OAVHR0VA1L6FR0EsP5wKTv9dhUdHRUETBUd/nAdFfoAAwAXABcEmQSZAA8AGwAwAAAAMh4CFA4CIi4CND4BBCIOARQeATI+ATQmBTMyFhURMzIWHQEUBisBIiY1ETQ2AePq1ptbW5vW6tabW1ubAb/oxXJyxejFcnL+fDIKD68KDw8K+goPDwSZW5vW6tabW1ub1urWmztyxejFcnLF6MUNDwr+7Q8KMgoPDwoBXgoPAAAAAAL/nAAABRQEsAALAA8AACkBAyMDIQEzAzMDMwEDMwMFFP3mKfIp/eYBr9EVohTQ/p4b4BsBkP5wBLD+1AEs/nD+1AEsAAAAAAIAZAAABLAEsAAVAC8AAAEzMhYVETMyFgcBBiInASY2OwERNDYBMzIWFREUBiMhIiY1ETQ2OwEyFh0BITU0NgImyBUdvxQLDf65DSYN/rkNCxS/HQJUMgoPDwr75goPDwoyCg8DhA8EsB0V/j4XEP5wEBABkBAXAcIVHfzgDwr+ogoPDwoBXgoPDwqvrwoPAAMAFwAXBJkEmQAPABsAMQAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JgUzMhYVETMyFgcDBiInAyY2OwERNDYB4+rWm1tbm9bq1ptbW5sBv+jFcnLF6MVycv58lgoPiRUKDd8NJg3fDQoViQ8EmVub1urWm1tbm9bq1ps7csXoxXJyxejFDQ8K/u0XEP7tEBABExAXARMKDwAAAAMAFwAXBJkEmQAPABsAMQAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JiUTFgYrAREUBisBIiY1ESMiJjcTNjIB4+rWm1tbm9bq1ptbW5sBv+jFcnLF6MVycv7n3w0KFYkPCpYKD4kVCg3fDSYEmVub1urWm1tbm9bq1ps7csXoxXJyxejFAf7tEBf+7QoPDwoBExcQARMQAAAAAAIAAAAABLAEsAAZADkAABMhMhYXExYVERQGBwYjISImJyY1EzQ3Ez4BBSEiBgcDBhY7ATIWHwEeATsBMjY/AT4BOwEyNicDLgHhAu4KEwO6BwgFDBn7tAweAgYBB7kDEwKX/dQKEgJXAgwKlgoTAiYCEwr6ChMCJgITCpYKDAJXAhIEsA4K/XQYGf5XDB4CBggEDRkBqRkYAowKDsgOC/4+Cw4OCpgKDg4KmAoODgsBwgsOAAMAFwAXBJkEmQAPABsAJwAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JgUXFhQPAQYmNRE0NgHj6tabW1ub1urWm1tbmwG/6MVycsXoxXJy/ov9ERH9EBgYBJlbm9bq1ptbW5vW6tabO3LF6MVycsXoxV2+DCQMvgwLFQGQFQsAAQAXABcEmQSwACgAAAE3NhYVERQGIyEiJj8BJiMiDgEUHgEyPgE1MxQOAiIuAjQ+AjMyA7OHBwsPCv6WCwQHhW2BdMVycsXoxXKWW5vW6tabW1ub1nXABCSHBwQL/pYKDwsHhUxyxejFcnLFdHXWm1tbm9bq1ptbAAAAAAIAFwABBJkEsAAaADUAAAE3NhYVERQGIyEiJj8BJiMiDgEVIzQ+AjMyEzMUDgIjIicHBiY1ETQ2MyEyFg8BFjMyPgEDs4cHCw8L/pcLBAeGboF0xXKWW5vWdcDrllub1nXAnIYHCw8LAWgKBQiFboJ0xXIEJIcHBAv+lwsPCweGS3LFdHXWm1v9v3XWm1t2hggFCgFoCw8LB4VMcsUAAAAKAGQAAASwBLAADwAfAC8APwBPAF8AbwB/AI8AnwAAEyEyFhURFAYjISImNRE0NgUhIgYVERQWMyEyNjURNCYFMzIWHQEUBisBIiY9ATQ2MyEyFh0BFAYjISImPQE0NgczMhYdARQGKwEiJj0BNDYzITIWHQEUBiMhIiY9ATQ2BzMyFh0BFAYrASImPQE0NjMhMhYdARQGIyEiJj0BNDYHMzIWHQEUBisBIiY9ATQ2MyEyFh0BFAYjISImPQE0Nn0EGgoPDwr75goPDwPA/K4KDw8KA1IKDw/9CDIKDw8KMgoPD9IBwgoPDwr+PgoPD74yCg8PCjIKDw/SAcIKDw8K/j4KDw++MgoPDwoyCg8P0gHCCg8PCv4+Cg8PvjIKDw8KMgoPD9IBwgoPDwr+PgoPDwSwDwr7ggoPDwoEfgoPyA8K/K4KDw8KA1IKD2QPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKDwAAAAACAAAAAARMBLAAGQAjAAABNTQmIyEiBh0BIyIGFREUFjMhMjY1ETQmIyE1NDY7ATIWHQEDhHVT/tRSdmQpOzspA4QpOzsp/ageFMgUHgMgyFN1dlLIOyn9qCk7OykCWCk7lhUdHRWWAAIAZAAABEwETAAJADcAABMzMhYVESMRNDYFMhcWFREUBw4DIyIuAScuAiMiBwYjIicmNRE+ATc2HgMXHgIzMjc2fTIKD2QPA8AEBRADIUNAMRwaPyonKSxHHlVLBwgGBQ4WeDsXKC4TOQQpLUUdZ1AHBEwPCvvNBDMKDzACBhH+WwYGO1AkDQ0ODg8PDzkFAwcPAbY3VwMCAwsGFAEODg5XCAAAAwAAAAAEsASXACEAMQBBAAAAMh4CFREUBisBIiY1ETQuASAOARURFAYrASImNRE0PgEDMzIWFREUBisBIiY1ETQ2ITMyFhURFAYrASImNRE0NgHk6N6jYw8KMgoPjeT++uSNDwoyCg9joyqgCAwMCKAIDAwCYKAIDAwIoAgMDASXY6PedP7UCg8PCgEsf9FyctF//tQKDw8KASx03qP9wAwI/jQIDAwIAcwIDAwI/jQIDAwIAcwIDAAAAAACAAAA0wRHA90AFQA5AAABJTYWFREUBiclJisBIiY1ETQ2OwEyBTc2Mh8BFhQPARcWFA8BBiIvAQcGIi8BJjQ/AScmND8BNjIXAUEBAgkMDAn+/hUZ+goPDwr6GQJYeAcUByIHB3h4BwciBxQHeHgHFAciBwd3dwcHIgcUBwMurAYHCv0SCgcGrA4PCgFeCg+EeAcHIgcUB3h4BxQHIgcHd3cHByIHFAd4eAcUByIICAAAAAACAAAA0wNyA90AFQAvAAABJTYWFREUBiclJisBIiY1ETQ2OwEyJTMWFxYVFAcGDwEiLwEuATc2NTQnJjY/ATYBQQECCQwMCf7+FRn6Cg8PCvoZAdIECgZgWgYLAwkHHQcDBkhOBgMIHQcDLqwGBwr9EgoHBqwODwoBXgoPZAEJgaGafwkBAQYXBxMIZ36EaggUBxYFAAAAAAMAAADEBGID7AAbADEASwAAATMWFxYVFAYHBgcjIi8BLgE3NjU0JicmNj8BNgUlNhYVERQGJyUmKwEiJjURNDY7ATIlMxYXFhUUBwYPASIvAS4BNzY1NCcmNj8BNgPHAwsGh0RABwoDCQcqCAIGbzs3BgIJKgf9ggECCQwMCf7+FRn6Cg8PCvoZAdIECgZgWgYLAwkHHQcDBkhOBgMIHQcD7AEJs9lpy1QJAQYiBhQIlrJarEcJFAYhBb6sBgcK/RIKBwasDg8KAV4KD2QBCYGhmn8JAQEGFwcTCGd+hGoIFQYWBQAAAAANAAAAAASwBLAACQAVABkAHQAhACUALQA7AD8AQwBHAEsATwAAATMVIxUhFSMRIQEjFTMVIREjESM1IQURIREhESERBSM1MwUjNTMBMxEhETM1MwEzFSMVIzUjNTM1IzUhBREhEQcjNTMFIzUzASM1MwUhNSEB9GRk/nBkAfQCvMjI/tTIZAJY+7QBLAGQASz84GRkArxkZP1EyP4MyGQB9MhkyGRkyAEs/UQBLGRkZAOEZGT+DGRkAfT+1AEsA4RkZGQCWP4MZMgBLAEsyGT+1AEs/tQBLMhkZGT+DP4MAfRk/tRkZGRkyGTI/tQBLMhkZGT+1GRkZAAAAAAJAAAAAASwBLAAAwAHAAsADwATABcAGwAfACMAADcjETMTIxEzASMRMxMjETMBIxEzASE1IRcjNTMXIzUzBSM1M2RkZMhkZAGQyMjIZGQBLMjI/OD+1AEsyGRkyGRkASzIyMgD6PwYA+j8GAPo/BgD6PwYA+j7UGRkW1tbW1sAAAIAAAAKBKYEsAANABUAAAkBFhQHAQYiJwETNDYzBCYiBhQWMjYB9AKqCAj+MAgUCP1WAQ8KAUM7Uzs7UzsEsP1WCBQI/jAICAKqAdsKD807O1Q7OwAAAAADAAAACgXSBLAADQAZACEAAAkBFhQHAQYiJwETNDYzIQEWFAcBBiIvAQkBBCYiBhQWMjYB9AKqCAj+MAgUCP1WAQ8KAwYCqggI/jAIFAg4Aaj9RP7TO1M7O1M7BLD9VggUCP4wCAgCqgHbCg/9VggUCP4wCAg4AaoCvM07O1Q7OwAAAAABAGQAAASwBLAAJgAAASEyFREUDwEGJjURNCYjISIPAQYWMyEyFhURFAYjISImNRE0PwE2ASwDOUsSQAgKDwr9RBkSQAgFCgK8Cg8PCvyuCg8SixIEsEv8fBkSQAgFCgO2Cg8SQAgKDwr8SgoPDwoDzxkSixIAAAABAMj//wRMBLAACgAAEyEyFhURCQERNDb6AyAVHf4+/j4dBLAdFfuCAbz+QwR/FR0AAAAAAwAAAAAEsASwABUARQBVAAABISIGBwMGHwEeATMhMjY/ATYnAy4BASMiBg8BDgEjISImLwEuASsBIgYVERQWOwEyNj0BNDYzITIWHQEUFjsBMjY1ETQmASEiBg8BBhYzITI2LwEuAQM2/kQLEAFOBw45BhcKAcIKFwY+DgdTARABVpYKFgROBBYK/doKFgROBBYKlgoPDwqWCg8PCgLuCg8PCpYKDw/+sf4MChMCJgILCgJYCgsCJgITBLAPCv7TGBVsCQwMCWwVGAEtCg/+cA0JnAkNDQmcCQ0PCv12Cg8PCpYKDw8KlgoPDwoCigoP/agOCpgKDg4KmAoOAAAAAAQAAABkBLAETAAdACEAKQAxAAABMzIeAh8BMzIWFREUBiMhIiY1ETQ2OwE+BAEVMzUEIgYUFjI2NCQyFhQGIiY0AfTIOF00JAcGlik7Oyn8GCk7OymWAgknM10ByGT+z76Hh76H/u9WPDxWPARMKTs7FRQ7Kf2oKTs7KQJYKTsIG0U1K/7UZGRGh76Hh74IPFY8PFYAAAAAAgA1AAAEsASvACAAIwAACQEWFx4BHwEVITUyNi8BIQYHBh4CMxUhNTY3PgE/AQEDIQMCqQGBFCgSJQkK/l81LBFS/nk6IgsJKjIe/pM4HAwaBwcBj6wBVKIEr/waMioTFQECQkJXLd6RWSIuHAxCQhgcDCUNDQPu/VoByQAAAAADAGQAAAPwBLAAJwAyADsAAAEeBhUUDgMjITU+ATURNC4EJzUFMh4CFRQOAgclMzI2NTQuAisBETMyNjU0JisBAvEFEzUwOyodN1htbDD+DCk7AQYLFyEaAdc5dWM+Hy0tEP6Pi05pESpTPnbYUFJ9Xp8CgQEHGB0zOlIuQ3VONxpZBzMoAzsYFBwLEAkHRwEpSXNDM1s6KwkxYUopOzQb/K5lUFqBAAABAMgAAANvBLAAGQAAARcOAQcDBhYXFSE1NjcTNjQuBCcmJzUDbQJTQgeECSxK/gy6Dq0DAw8MHxUXDQYEsDkTNSj8uTEoBmFhEFIDQBEaExAJCwYHAwI5AAAAAAL/tQAABRQEsAAlAC8AAAEjNC4FKwERFBYfARUhNTI+AzURIyIOBRUjESEFIxEzByczESM3BRQyCAsZEyYYGcgyGRn+cAQOIhoWyBkYJhMZCwgyA+j7m0tLfX1LS30DhBUgFQ4IAwH8rhYZAQJkZAEFCRUOA1IBAwgOFSAVASzI/OCnpwMgpwACACH/tQSPBLAAJQAvAAABIzQuBSsBERQWHwEVITUyPgM1ESMiDgUVIxEhEwc1IRUnNxUhNQRMMggLGRMmGBnIMhkZ/nAEDiIaFsgZGCYTGQsIMgPoQ6f84KenAyADhBUgFQ4IAwH9dhYZAQJkZAEFCRUOAooBAwgOFSAVASz7gn1LS319S0sABAAAAAAEsARMAA8AHwAvAD8AABMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYyAlgVHR0V/agVHR0VA+gVHR0V/BgVHR0VAyAVHR0V/OAVHR0VBEwVHR0V+7QVHR0ETB0VZBUdHRVkFR3+1B0VZBUdHRVkFR3+1B0VZBUdHRVkFR3+1B0VZBUdHRVkFR0ABAAAAAAEsARMAA8AHwAvAD8AABMhMhYdARQGIyEiJj0BNDYDITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NgMhMhYdARQGIyEiJj0BNDb6ArwVHR0V/UQVHR2zBEwVHR0V+7QVHR3dArwVHR0V/UQVHR2zBEwVHR0V+7QVHR0ETB0VZBUdHRVkFR3+1B0VZBUdHRVkFR3+1B0VZBUdHRVkFR3+1B0VZBUdHRVkFR0ABAAAAAAEsARMAA8AHwAvAD8AAAE1NDYzITIWHQEUBiMhIiYBNTQ2MyEyFh0BFAYjISImEzU0NjMhMhYdARQGIyEiJgE1NDYzITIWHQEUBiMhIiYB9B0VAlgVHR0V/agVHf5wHRUD6BUdHRX8GBUdyB0VAyAVHR0V/OAVHf7UHRUETBUdHRX7tBUdA7ZkFR0dFWQVHR3+6WQVHR0VZBUdHf7pZBUdHRVkFR0d/ulkFR0dFWQVHR0AAAQAAAAABLAETAAPAB8ALwA/AAATITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2MgRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dBEwdFWQVHR0VZBUd/tQdFWQVHR0VZBUd/tQdFWQVHR0VZBUd/tQdFWQVHR0VZBUdAAgAAAAABLAETAAPAB8ALwA/AE8AXwBvAH8AABMzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2ATMyFh0BFAYrASImPQE0NikBMhYdARQGIyEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2KQEyFh0BFAYjISImPQE0NgEzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2MmQVHR0VZBUdHQFBAyAVHR0V/OAVHR3+6WQVHR0VZBUdHQFBAyAVHR0V/OAVHR3+6WQVHR0VZBUdHQFBAyAVHR0V/OAVHR3+6WQVHR0VZBUdHQFBAyAVHR0V/OAVHR0ETB0VZBUdHRVkFR0dFWQVHR0VZBUd/tQdFWQVHR0VZBUdHRVkFR0dFWQVHf7UHRVkFR0dFWQVHR0VZBUdHRVkFR3+1B0VZBUdHRVkFR0dFWQVHR0VZBUdAAAG/5wAAASwBEwAAwATACMAKgA6AEoAACEjETsCMhYdARQGKwEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2BQc1IzUzNQUhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2AZBkZJZkFR0dFWQVHR0VAfQVHR0V/gwVHR3++qfIyAHCASwVHR0V/tQVHR0VAlgVHR0V/agVHR0ETB0VZBUdHRVkFR3+1B0VZBUdHRVkFR36fUtkS68dFWQVHR0VZBUd/tQdFWQVHR0VZBUdAAAABgAAAAAFFARMAA8AEwAjACoAOgBKAAATMzIWHQEUBisBIiY9ATQ2ASMRMwEhMhYdARQGIyEiJj0BNDYFMxUjFSc3BSEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYyZBUdHRVkFR0dA2dkZPyuAfQVHR0V/gwVHR0EL8jIp6f75gEsFR0dFf7UFR0dFQJYFR0dFf2oFR0dBEwdFWQVHR0VZBUd+7QETP7UHRVkFR0dFWQVHchkS319rx0VZBUdHRVkFR3+1B0VZBUdHRVkFR0AAAAAAgAAAMgEsAPoAA8AEgAAEyEyFhURFAYjISImNRE0NgkCSwLuHywsH/0SHywsBIT+1AEsA+gsH/12HywsHwKKHyz9RAEsASwAAwAAAAAEsARMAA8AFwAfAAATITIWFREUBiMhIiY1ETQ2FxE3BScBExEEMhYUBiImNCwEWBIaGhL7qBIaGkr3ASpKASXs/NJwTk5wTgRMGhL8DBIaGhID9BIaZP0ftoOcAT7+4AH0dE5vT09vAAAAAAIA2wAFBDYEkQAWAB4AAAEyHgEVFAcOAQ8BLgQnJjU0PgIWIgYUFjI2NAKIdcZzRkWyNjYJIV5YbSk8RHOft7eCgreCBJF4ynVzj23pPz4IIWZomEiEdVijeUjDgriBgbgAAAACABcAFwSZBJkADwAXAAAAMh4CFA4CIi4CND4BAREiDgEUHgEB4+rWm1tbm9bq1ptbW5sBS3TFcnLFBJlbm9bq1ptbW5vW6tab/G8DVnLF6MVyAAACAHUAAwPfBQ8AGgA1AAABHgYVFA4DBy4DNTQ+BQMOAhceBBcWNj8BNiYnLgInJjc2IyYCKhVJT1dOPiUzVnB9P1SbfEokP0xXUEm8FykoAwEbITEcExUWAgYCCQkFEikMGiACCAgFD0iPdXdzdYdFR4BeRiYEBTpjl1lFh3ZzeHaQ/f4hS4I6JUEnIw4IBwwQIgoYBwQQQSlZtgsBAAAAAwAAAAAEywRsAAwAKgAvAAABNz4CHgEXHgEPAiUhMhcHISIGFREUFjMhMjY9ATcRFAYjISImNRE0NgkBBzcBA+hsAgYUFR0OFgoFBmz9BQGQMje7/pApOzspAfQpO8i7o/5wpbm5Azj+lqE3AWMD9XMBAgIEDw4WKgsKc8gNuzsp/gwpOzsptsj+tKW5uaUBkKW5/tf+ljKqAWMAAgAAAAAEkwRMABsANgAAASEGByMiBhURFBYzITI2NTcVFAYjISImNRE0NgUBFhQHAQYmJzUmDgMHPgY3NT4BAV4BaaQ0wyk7OykB9Ck7yLml/nClubkCfwFTCAj+rAcLARo5ZFRYGgouOUlARioTAQsETJI2Oyn+DCk7OymZZ6W5uaUBkKW5G/7TBxUH/s4GBAnLAQINFjAhO2JBNB0UBwHSCgUAAAAAAgAAAAAEnQRMAB0ANQAAASEyFwchIgYVERQWMyEyNj0BNxUUBiMhIiY1ETQ2CQE2Mh8BFhQHAQYiLwEmND8BNjIfARYyAV4BXjxDsv6jKTs7KQH0KTvIuaX+cKW5uQHKAYsHFQdlBwf97QcVB/gHB2UHFQdvCBQETBexOyn+DCk7OylFyNulubmlAZCluf4zAYsHB2UHFQf97AcH+AcVB2UHB28HAAAAAQAKAAoEpgSmADsAAAkBNjIXARYGKwEVMzU0NhcBFhQHAQYmPQEjFTMyFgcBBiInASY2OwE1IxUUBicBJjQ3ATYWHQEzNSMiJgE+AQgIFAgBBAcFCqrICggBCAgI/vgICsiqCgUH/vwIFAj++AgFCq/ICgj++AgIAQgICsivCgUDlgEICAj++AgKyK0KBAf+/AcVB/73BwQKrcgKCP74CAgBCAgKyK0KBAcBCQcVBwEEBwQKrcgKAAEAyAAAA4QETAAZAAATMzIWFREBNhYVERQGJwERFAYrASImNRE0NvpkFR0B0A8VFQ/+MB0VZBUdHQRMHRX+SgHFDggV/BgVCA4Bxf5KFR0dFQPoFR0AAAABAAAAAASwBEwAIwAAEzMyFhURATYWFREBNhYVERQGJwERFAYnAREUBisBIiY1ETQ2MmQVHQHQDxUB0A8VFQ/+MBUP/jAdFWQVHR0ETB0V/koBxQ4IFf5KAcUOCBX8GBUIDgHF/koVCA4Bxf5KFR0dFQPoFR0AAAABAJ0AGQSwBDMAFQAAAREUBicBERQGJwEmNDcBNhYVEQE2FgSwFQ/+MBUP/hQPDwHsDxUB0A8VBBr8GBUIDgHF/koVCA4B4A4qDgHgDggV/koBxQ4IAAAAAQDIABYEMwQ2AAsAABMBFhQHAQYmNRE0NvMDLhIS/NISGRkEMv4OCx4L/g4LDhUD6BUOAAIAyABkA4QD6AAPAB8AABMzMhYVERQGKwEiJjURNDYhMzIWFREUBisBIiY1ETQ2+sgVHR0VyBUdHQGlyBUdHRXIFR0dA+gdFfzgFR0dFQMgFR0dFfzgFR0dFQMgFR0AAAEAyABkBEwD6AAPAAABERQGIyEiJjURNDYzITIWBEwdFfzgFR0dFQMgFR0DtvzgFR0dFQMgFR0dAAAAAAEAAAAZBBMEMwAVAAABETQ2FwEWFAcBBiY1EQEGJjURNDYXAfQVDwHsDw/+FA8V/jAPFRUPAmQBthUIDv4gDioO/iAOCBUBtv47DggVA+gVCA4AAAH//gACBLMETwAjAAABNzIWFRMUBiMHIiY1AwEGJjUDAQYmNQM0NhcBAzQ2FwEDNDYEGGQUHgUdFWQVHQL+MQ4VAv4yDxUFFQ8B0gIVDwHSAh0ETgEdFfwYFR0BHRUBtf46DwkVAbX+OQ4JFAPoFQkP/j4BthQJDv49AbYVHQAAAQEsAAAD6ARMABkAAAEzMhYVERQGKwEiJjURAQYmNRE0NhcBETQ2A1JkFR0dFWQVHf4wDxUVDwHQHQRMHRX8GBUdHRUBtv47DggVA+gVCA7+OwG2FR0AAAIAZADIBLAESAALABsAAAkBFgYjISImNwE2MgEhMhYdARQGIyEiJj0BNDYCrgH1DwkW++4WCQ8B9Q8q/fcD6BUdHRX8GBUdHQQ5/eQPFhYPAhwP/UgdFWQVHR0VZBUdAAEAiP/8A3UESgAFAAAJAgcJAQN1/qABYMX92AIoA4T+n/6fxgIoAiYAAAAAAQE7//wEKARKAAUAAAkBJwkBNwQo/dnGAWH+n8YCI/3ZxgFhAWHGAAIAFwAXBJkEmQAPADMAAAAyHgIUDgIiLgI0PgEFIyIGHQEjIgYdARQWOwEVFBY7ATI2PQEzMjY9ATQmKwE1NCYB4+rWm1tbm9bq1ptbW5sBfWQVHZYVHR0Vlh0VZBUdlhUdHRWWHQSZW5vW6tabW1ub1urWm7odFZYdFWQVHZYVHR0Vlh0VZBUdlhUdAAAAAAIAFwAXBJkEmQAPAB8AAAAyHgIUDgIiLgI0PgEBISIGHQEUFjMhMjY9ATQmAePq1ptbW5vW6tabW1ubAkX+DBUdHRUB9BUdHQSZW5vW6tabW1ub1urWm/5+HRVkFR0dFWQVHQACABcAFwSZBJkADwAzAAAAMh4CFA4CIi4CND4BBCIPAScmIg8BBhQfAQcGFB8BFjI/ARcWMj8BNjQvATc2NC8BAePq1ptbW5vW6tabW1ubAeUZCXh4CRkJjQkJeHgJCY0JGQl4eAkZCY0JCXh4CQmNBJlbm9bq1ptbW5vW6tabrQl4eAkJjQkZCXh4CRkJjQkJeHgJCY0JGQl4eAkZCY0AAgAXABcEmQSZAA8AJAAAADIeAhQOAiIuAjQ+AQEnJiIPAQYUHwEWMjcBNjQvASYiBwHj6tabW1ub1urWm1tbmwEVVAcVCIsHB/IHFQcBdwcHiwcVBwSZW5vW6tabW1ub1urWm/4xVQcHiwgUCPEICAF3BxUIiwcHAAAAAAMAFwAXBJkEmQAPADsASwAAADIeAhQOAiIuAjQ+AQUiDgMVFDsBFjc+ATMyFhUUBgciDgUHBhY7ATI+AzU0LgMTIyIGHQEUFjsBMjY9ATQmAePq1ptbW5vW6tabW1ubAT8dPEIyIRSDHgUGHR8UFw4TARkOGhITDAIBDQ6tBx4oIxgiM0Q8OpYKDw8KlgoPDwSZW5vW6tabW1ub1urWm5ELHi9PMhkFEBQQFRIXFgcIBw4UHCoZCBEQKDhcNi9IKhsJ/eMPCpYKDw8KlgoPAAADABcAFwSZBJkADwAfAD4AAAAyHgIUDgIiLgI0PgEFIyIGHQEUFjsBMjY9ATQmAyMiBh0BFBY7ARUjIgYdARQWMyEyNj0BNCYrARE0JgHj6tabW1ub1urWm1tbmwGWlgoPDwqWCg8PCvoKDw8KS0sKDw8KAV4KDw8KSw8EmVub1urWm1tbm9bq1ptWDwqWCg8PCpYKD/7UDwoyCg/IDwoyCg8PCjIKDwETCg8AAgAAAAAEsASwAC8AXwAAATMyFh0BHgEXMzIWHQEUBisBDgEHFRQGKwEiJj0BLgEnIyImPQE0NjsBPgE3NTQ2ExUUBisBIiY9AQ4BBzMyFh0BFAYrAR4BFzU0NjsBMhYdAT4BNyMiJj0BNDY7AS4BAg2WCg9nlxvCCg8PCsIbl2cPCpYKD2eXG8IKDw8KwhuXZw+5DwqWCg9EZheoCg8PCqgXZkQPCpYKD0RmF6gKDw8KqBdmBLAPCsIbl2cPCpYKD2eXG8IKDw8KwhuXZw8KlgoPZ5cbwgoP/s2oCg8PCqgXZkQPCpYKD0RmF6gKDw8KqBdmRA8KlgoPRGYAAwAXABcEmQSZAA8AGwA/AAAAMh4CFA4CIi4CND4BBCIOARQeATI+ATQmBxcWFA8BFxYUDwEGIi8BBwYiLwEmND8BJyY0PwE2Mh8BNzYyAePq1ptbW5vW6tabW1ubAb/oxXJyxejFcnKaQAcHfHwHB0AHFQd8fAcVB0AHB3x8BwdABxUHfHwHFQSZW5vW6tabW1ub1urWmztyxejFcnLF6MVaQAcVB3x8BxUHQAcHfHwHB0AHFQd8fAcVB0AHB3x8BwAAAAMAFwAXBJkEmQAPABsAMAAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JgcXFhQHAQYiLwEmND8BNjIfATc2MgHj6tabW1ub1urWm1tbmwG/6MVycsXoxXJyg2oHB/7ACBQIyggIagcVB0/FBxUEmVub1urWm1tbm9bq1ps7csXoxXJyxejFfWoHFQf+vwcHywcVB2oICE/FBwAAAAMAFwAXBJkEmQAPABgAIQAAADIeAhQOAiIuAjQ+AQUiDgEVFBcBJhcBFjMyPgE1NAHj6tabW1ub1urWm1tbmwFLdMVyQQJLafX9uGhzdMVyBJlbm9bq1ptbW5vW6tabO3LFdHhpAktB0P24PnLFdHMAAAAAAQAXAFMEsAP5ABUAABMBNhYVESEyFh0BFAYjIREUBicBJjQnAgoQFwImFR0dFf3aFxD99hACRgGrDQoV/t0dFcgVHf7dFQoNAasNJgAAAAABAAAAUwSZA/kAFQAACQEWFAcBBiY1ESEiJj0BNDYzIRE0NgJ/AgoQEP32EBf92hUdHRUCJhcD8f5VDSYN/lUNChUBIx0VyBUdASMVCgAAAAEAtwAABF0EmQAVAAAJARYGIyERFAYrASImNREhIiY3ATYyAqoBqw0KFf7dHRXIFR3+3RUKDQGrDSYEif32EBf92hUdHRUCJhcQAgoQAAAAAQC3ABcEXQSwABUAAAEzMhYVESEyFgcBBiInASY2MyERNDYCJsgVHQEjFQoN/lUNJg3+VQ0KFQEjHQSwHRX92hcQ/fYQEAIKEBcCJhUdAAABAAAAtwSZBF0AFwAACQEWFAcBBiY1EQ4DBz4ENxE0NgJ/AgoQEP32EBdesKWBJAUsW4fHfhcEVf5VDSYN/lUNChUBIwIkRHVNabGdcUYHAQYVCgACAAAAAASwBLAAFQArAAABITIWFREUBi8BBwYiLwEmND8BJyY2ASEiJjURNDYfATc2Mh8BFhQPARcWBgNSASwVHRUOXvkIFAhqBwf5Xg4I/iH+1BUdFQ5e+QgUCGoHB/leDggEsB0V/tQVCA5e+QcHaggUCPleDhX7UB0VASwVCA5e+QcHaggUCPleDhUAAAACAEkASQRnBGcAFQArAAABFxYUDwEXFgYjISImNRE0Nh8BNzYyASEyFhURFAYvAQcGIi8BJjQ/AScmNgP2agcH+V4OCBX+1BUdFQ5e+QgU/QwBLBUdFQ5e+QgUCGoHB/leDggEYGoIFAj5Xg4VHRUBLBUIDl75B/3xHRX+1BUIDl75BwdqCBQI+V4OFQAAAAADABcAFwSZBJkADwAfAC8AAAAyHgIUDgIiLgI0PgEFIyIGFxMeATsBMjY3EzYmAyMiBh0BFBY7ATI2PQE0JgHj6tabW1ub1urWm1tbmwGz0BQYBDoEIxQ2FCMEOgQYMZYKDw8KlgoPDwSZW5vW6tabW1ub1urWm7odFP7SFB0dFAEuFB3+DA8KlgoPDwqWCg8AAAAABQAAAAAEsASwAEkAVQBhAGgAbwAAATIWHwEWHwEWFxY3Nj8BNjc2MzIWHwEWHwIeATsBMhYdARQGKwEiBh0BIREjESE1NCYrASImPQE0NjsBMjY1ND8BNjc+BAUHBhY7ATI2LwEuAQUnJgYPAQYWOwEyNhMhIiY1ESkBERQGIyERAQQJFAUFFhbEFQ8dCAsmxBYXERUXMA0NDgQZCAEPCj0KDw8KMgoP/nDI/nAPCjIKDw8KPQsOCRkFDgIGFRYfAp2mBwQK2woKAzMDEP41sQgQAzMDCgrnCwMe/okKDwGQAlgPCv6JBLAEAgIKDXYNCxUJDRZ2DQoHIREQFRh7LAkLDwoyCg8PCq8BLP7UrwoPDwoyCg8GBQQwgBkUAwgWEQ55ogcKDgqVCgSqnQcECo8KDgr8cg8KAXf+iQoPAZAAAAAAAgAAAAwErwSmACsASQAAATYWFQYCDgQuAScmByYOAQ8BBiY1NDc+ATc+AScuAT4BNz4GFyYGBw4BDwEOBAcOARY2Nz4CNz4DNz4BBI0IGgItQmxhi2KORDg9EQQRMxuZGhYqCFUYEyADCQIQOjEnUmFch3vAJQgdHyaiPT44XHRZUhcYDhItIRmKcVtGYWtbKRYEBKYDEwiy/t3IlVgxEQgLCwwBAQIbG5kYEyJAJghKFRE8Hzdff4U/M0o1JSMbL0QJGCYvcSEhHjZST2c1ODwEJygeW0AxJUBff1UyFAABAF0AHgRyBM8ATwAAAQ4BHgQXLgc+ATceAwYHDgQHBicmNzY3PgQuAScWDgMmJy4BJyY+BDcGHgM3PgEuAicmPgMCjScfCic4R0IgBBsKGAoQAwEJEg5gikggBhANPkpTPhZINx8SBgsNJysiCRZOQQoVNU1bYC9QZwICBAUWITsoCAYdJzIYHw8YIiYHDyJJYlkEz0OAZVxEOSQMBzgXOB42IzElKRIqg5Gnl0o3Z0c6IAYWCwYNAwQFIDhHXGF1OWiqb0sdBxUknF0XNTQ8PEUiNWNROBYJDS5AQVUhVZloUSkAAAAAA//cAGoE1ARGABsAPwBRAAAAMh4FFA4FIi4FND4EBSYGFxYVFAYiJjU0NzYmBwYHDgEXHgQyPgM3NiYnJgUHDgEXFhcWNj8BNiYnJicuAQIGpJ17bk85HBw6T257naKde25POhwcOU9uewIPDwYIGbD4sBcIBw5GWg0ECxYyWl+DiINfWjIWCwQMWv3/Iw8JCSU4EC0OIw4DDywtCyIERi1JXGJcSSpJXGJcSS0tSVxiXEkqSVxiXEncDwYTOT58sLB8OzcTBg9FcxAxEiRGXkQxMEVeRSQSMRF1HiQPLxJEMA0EDyIPJQ8sSRIEAAAABP/cAAAE1ASwABQAJwA7AEwAACEjNy4ENTQ+BTMyFzczEzceARUUDgMHNz4BNzYmJyYlBgcOARceBBc3LgE1NDc2JhcHDgEXFhcWNj8CJyYnLgECUJQfW6l2WSwcOU9ue51SPUEglCYvbIknUGqYUi5NdiYLBAw2/VFGWg0ECxIqSExoNSlrjxcIB3wjDwkJJTgQLQ4MFgMsLQsieBRhdHpiGxVJXGJcSS0Pef5StVXWNBpacm5jGq0xiD8SMRFGckVzEDESHjxRQTkNmhKnbjs3EwZwJA8vEkQwDQQPC1YELEkSBAAAAAP/ngAABRIEqwALABgAKAAAJwE2FhcBFgYjISImJSE1NDY7ATIWHQEhAQczMhYPAQ4BKwEiJi8BJjZaAoIUOBQCghUbJfryJRsBCgFZDwqWCg8BWf5DaNAUGAQ6BCMUNhQjBDoEGGQEKh8FIfvgIEdEhEsKDw8KSwLT3x0U/BQdHRT8FB0AAAABAGQAFQSwBLAAKAAAADIWFREBHgEdARQGJyURFh0BFAYvAQcGJj0BNDcRBQYmPQE0NjcBETQCTHxYAWsPFhgR/plkGhPNzRMaZP6ZERgWDwFrBLBYPv6t/rsOMRQpFA0M+f75XRRAFRAJgIAJEBVAFF0BB/kMDRQpFDEOAUUBUz4AAAARAAAAAARMBLAAHQAnACsALwAzADcAOwA/AEMARwBLAE8AUwBXAFsAXwBjAAABMzIWHQEzMhYdASE1NDY7ATU0NjsBMhYdASE1NDYBERQGIyEiJjURFxUzNTMVMzUzFTM1MxUzNTMVMzUFFTM1MxUzNTMVMzUzFTM1MxUzNQUVMzUzFTM1MxUzNTMVMzUzFTM1A1JkFR0yFR37tB0VMh0VZBUdAfQdAQ8dFfwYFR1kZGRkZGRkZGRk/HxkZGRkZGRkZGT8fGRkZGRkZGRkZASwHRUyHRWWlhUdMhUdHRUyMhUd/nD9EhUdHRUC7shkZGRkZGRkZGRkyGRkZGRkZGRkZGTIZGRkZGRkZGRkZAAAAAMAAAAZBXcElwAZACUANwAAARcWFA8BBiY9ASMBISImPQE0NjsBATM1NDYBBycjIiY9ATQ2MyEBFxYUDwEGJj0BIyc3FzM1NDYEb/kPD/kOFZ/9qP7dFR0dFdECWPEV/amNetEVHR0VASMDGvkPD/kOFfG1jXqfFQSN5g4qDuYOCBWW/agdFWQVHQJYlhUI/piNeh0VZBUd/k3mDioO5g4IFZa1jXqWFQgAAAABAAAAAASwBEwAEgAAEyEyFhURFAYjIQERIyImNRE0NmQD6Ck7Oyn9rP7QZCk7OwRMOyn9qCk7/tQBLDspAlgpOwAAAAMAZAAABEwEsAAJABMAPwAAEzMyFh0BITU0NiEzMhYdASE1NDYBERQOBSIuBTURIRUUFRwBHgYyPgYmNTQ9AZbIFR3+1B0C0cgVHf7UHQEPBhgoTGacwJxmTCgYBgEsAwcNFB8nNkI2Jx8TDwUFAQSwHRX6+hUdHRX6+hUd/nD+1ClJalZcPigoPlxWakkpASz6CRIVKyclIRsWEAgJEBccISUnKhURCPoAAAAB//8A1ARMA8IABQAAAQcJAScBBEzG/p/+n8UCJwGbxwFh/p/HAicAAQAAAO4ETQPcAAUAAAkCNwkBBE392v3ZxgFhAWEDFf3ZAifH/p8BYQAAAAAC/1EAZAVfA+gAFAApAAABITIWFREzMhYPAQYiLwEmNjsBESElFxYGKwERIRchIiY1ESMiJj8BNjIBlALqFR2WFQgO5g4qDuYOCBWW/oP+HOYOCBWWAYHX/RIVHZYVCA7mDioD6B0V/dkVDvkPD/kOFQGRuPkOFf5wyB0VAiYVDvkPAAABAAYAAASeBLAAMAAAEzMyFh8BITIWBwMOASMhFyEyFhQGKwEVFAYiJj0BIRUUBiImPQEjIiYvAQMjIiY0NjheERwEJgOAGB4FZAUsIf2HMAIXFR0dFTIdKh3+1B0qHR8SHQYFyTYUHh4EsBYQoiUY/iUVK8gdKh0yFR0dFTIyFR0dFTIUCQoDwR0qHQAAAAACAAAAAASwBEwACwAPAAABFSE1MzQ2MyEyFhUFIREhBLD7UMg7KQEsKTv9RASw+1AD6GRkKTs7Kcj84AACAAAAAAXcBEwADAAQAAATAxEzNDYzITIWFSEVBQEhAcjIyDspASwqOgH0ASz+1PtQASwDIP5wAlgpOzspyGT9RAK8AAEBRQAAA2sErwAbAAABFxYGKwERMzIWDwEGIi8BJjY7AREjIiY/ATYyAnvmDggVlpYVCA7mDioO5g4IFZaWFQgO5g4qBKD5DhX9pxUO+Q8P+Q4VAlkVDvkPAAAAAQABAUQErwNrABsAAAEXFhQPAQYmPQEhFRQGLwEmND8BNhYdASE1NDYDqPkODvkPFf2oFQ/5Dg75DxUCWBUDYOUPKQ/lDwkUl5cUCQ/lDykP5Q8JFZWVFQkAAAAEAAAAAASwBLAACQAZAB0AIQAAAQMuASMhIgYHAwUhIgYdARQWMyEyNj0BNCYFNTMVMzUzFQSRrAUkFP1gFCQFrAQt/BgpOzspA+gpOzv+q2RkZAGQAtwXLSgV/R1kOylkKTs7KWQpO8hkZGRkAAAAA/+cAGQEsARMAAsAIwAxAAAAMhYVERQGIiY1ETQDJSMTFgYjIisBIiYnAj0BNDU0PgE7ASUBFSIuAz0BND4CNwRpKh0dKh1k/V0mLwMRFQUCVBQdBDcCCwzIAqP8GAQOIhoWFR0dCwRMHRX8rhUdHRUDUhX8mcj+7BAIHBUBUQ76AgQQDw36/tT6AQsTKRwyGigUDAEAAAACAEoAAARmBLAALAA1AAABMzIWDwEeARcTFzMyFhQGBw4EIyIuBC8BLgE0NjsBNxM+ATcnJjYDFjMyNw4BIiYCKV4UEgYSU3oPP3YRExwaEggeZGqfTzl0XFU+LwwLEhocExF2Pw96UxIGEyQyNDUxDDdGOASwFRMlE39N/rmtHSkoBwQLHBYSCg4REg4FBAgoKR2tAUdNfhQgExr7vgYGMT09AAEAFAAUBJwEnAAXAAABNwcXBxcHFycHJwcnBzcnNyc3Jxc3FzcDIOBO6rS06k7gLZubLeBO6rS06k7gLZubA7JO4C2bmy3gTuq0tOpO4C2bmy3gTuq0tAADAAAAZASwBLAAIQAtAD0AAAEzMhYdAQchMhYdARQHAw4BKwEiJi8BIyImNRE0PwI+ARcPAREzFzMTNSE3NQEzMhYVERQGKwEiJjURNDYCijIoPBwBSCg8He4QLBf6B0YfHz0tNxSRYA0xG2SWZIjW+v4+Mv12ZBUdHRVkFR0dBLBRLJZ9USxkLR3+qBghMhkZJCcBkCQbxMYcKGTU1f6JZAF3feGv/tQdFf4MFR0dFQH0FR0AAAAAAwAAAAAEsARMACAAMAA8AAABMzIWFxMWHQEUBiMhFh0BFAYrASImLwImNRE0NjsBNgUzMhYVERQGKwEiJjURNDYhByMRHwEzNSchNQMCWPoXLBDuHTwo/rgcPCgyGzENYJEUNy09fP3pZBUdHRVkFR0dAl+IZJZkMjIBwvoETCEY/qgdLWQsUXYHlixRKBzGxBskAZAnJGRkHRX+DBUdHRUB9BUdZP6J1dSv4X0BdwADAAAAZAUOBE8AGwA3AEcAAAElNh8BHgEPASEyFhQGKwEDDgEjISImNRE0NjcXERchEz4BOwEyNiYjISoDLgQnJj8BJwUzMhYVERQGKwEiJjURNDYBZAFrHxZuDQEMVAEuVGxuVGqDBhsP/qoHphwOOmQBJYMGGw/LFRMSFv44AgoCCQMHAwUDAQwRklb9T2QVHR0VZBUdHQNp5hAWcA0mD3lMkE7+rRUoog0CDRElCkj+CVkBUxUoMjIBAgIDBQIZFrdT5B0V/gwVHR0VAfQVHQAAAAP/nABkBLAETwAdADYARgAAAQUeBBURFAYjISImJwMjIiY0NjMhJyY2PwE2BxcWBw4FKgIjIRUzMhYXEyE3ESUFMzIWFREUBisBIiY1ETQ2AdsBbgIIFBANrAf+qg8bBoNqVW1sVAEuVQsBDW4WSpIRDAIDBQMHAwkDCgH+Jd0PHAaCASZq/qoCUGQVHR0VZBUdHQRP5gEFEBEXC/3zDaIoFQFTTpBMeQ8mDXAWrrcWGQIFAwICAWQoFf6tWQH37OQdFf4MFR0dFQH0FR0AAAADAGEAAARMBQ4AGwA3AEcAAAAyFh0BBR4BFREUBiMhIiYvAQMmPwE+AR8BETQXNTQmBhURHAMOBAcGLwEHEyE3ESUuAQMhMhYdARQGIyEiJj0BNDYB3pBOAVMVKKIN/fMRJQoJ5hAWcA0mD3nGMjIBAgIDBQIZFrdT7AH3Wf6tFSiWAfQVHR0V/gwVHR0FDm5UaoMGGw/+qgemHA4OAWsfFm4NAQxUAS5U1ssVExIW/jgCCgIJAwcDBQMBDBGSVv6tZAElgwYb/QsdFWQVHR0VZBUdAAP//QAGA+gFFAAPAC0ASQAAASEyNj0BNCYjISIGHQEUFgEVFAYiJjURBwYmLwEmNxM+BDMhMhYVERQGBwEDFzc2Fx4FHAIVERQWNj0BNDY3JREnAV4B9BUdHRX+DBUdHQEPTpBMeQ8mDXAWEOYBBRARFwsCDQ2iKBX9iexTtxYZAgUDAgIBMjIoFQFTWQRMHRVkFR0dFWQVHfzmalRubFQBLlQMAQ1uFh8BawIIEw8Mpgf+qg8bBgHP/q1WkhEMAQMFAwcDCQIKAv44FhITFcsPGwaDASVkAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEBJSYGHQEhIgYdARQWMyEVFBY3JTY0AeLs1ptbW5vW7NabW1ubAob+7RAX/u0KDw8KARMXEAETEASaW5vW7NabW1ub1uzWm/453w0KFYkPCpYKD4kVCg3fDSYAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgENAQYUFwUWNj0BITI2PQE0JiMhNTQmAeLs1ptbW5vW7NabW1ubASX+7RAQARMQFwETCg8PCv7tFwSaW5vW7NabW1ub1uzWm+jfDSYN3w0KFYkPCpYKD4kVCgAAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEBAyYiBwMGFjsBERQWOwEyNjURMzI2AeLs1ptbW5vW7NabW1ubAkvfDSYN3w0KFYkPCpYKD4kVCgSaW5vW7NabW1ub1uzWm/5AARMQEP7tEBf+7QoPDwoBExcAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEFIyIGFREjIgYXExYyNxM2JisBETQmAeLs1ptbW5vW7NabW1ubAZeWCg+JFQoN3w0mDd8NChWJDwSaW5vW7NabW1ub1uzWm7sPCv7tFxD+7RAQARMQFwETCg8AAAMAGAAYBJgEmAAPAJYApgAAADIeAhQOAiIuAjQ+ASUOAwcGJgcOAQcGFgcOAQcGFgcUFgcyHgEXHgIXHgI3Fg4BFx4CFxQGFBcWNz4CNy4BJy4BJyIOAgcGJyY2NS4BJzYuAQYHBicmNzY3HgIXHgMfAT4CJyY+ATc+AzcmNzIWMjY3LgMnND4CJiceAT8BNi4CJwYHFB4BFS4CJz4BNxYyPgEB5OjVm1xcm9Xo1ZtcXJsBZA8rHDoKDz0PFD8DAxMBAzEFCRwGIgEMFhkHECIvCxU/OR0HFBkDDRQjEwcFaHUeISQDDTAMD0UREi4oLBAzDwQBBikEAQMLGhIXExMLBhAGKBsGBxYVEwYFAgsFAwMNFwQGCQcYFgYQCCARFwkKKiFBCwQCAQMDHzcLDAUdLDgNEiEQEgg/KhADGgMKEgoRBJhcm9Xo1ZtcXJvV6NWbEQwRBwkCAwYFBycPCxcHInIWInYcCUcYChQECA4QBAkuHgQPJioRFRscBAcSCgwCch0kPiAIAQcHEAsBAgsLIxcBMQENCQIPHxkCFBkdHB4QBgEBBwoMGBENBAMMJSAQEhYXDQ4qFBkKEhIDCQsXJxQiBgEOCQwHAQ0DBAUcJAwSCwRnETIoAwEJCwsLJQcKDBEAAAAAAQAAAAIErwSFABYAAAE2FwUXNxYGBw4BJwEGIi8BJjQ3ASY2AvSkjv79kfsGUE08hjv9rA8rD28PDwJYIk8EhVxliuh+WYcrIgsW/awQEG4PKxACV2XJAAYAAABgBLAErAAPABMAIwAnADcAOwAAEyEyFh0BFAYjISImPQE0NgUjFTMFITIWHQEUBiMhIiY9ATQ2BSEVIQUhMhYdARQGIyEiJj0BNDYFIRUhZAPoKTs7KfwYKTs7BBHIyPwYA+gpOzsp/BgpOzsEEf4MAfT8GAPoKTs7KfwYKTs7BBH+1AEsBKw7KWQpOzspZCk7ZGTIOylkKTs7KWQpO2RkyDspZCk7OylkKTtkZAAAAAIAZAAABEwEsAALABEAABMhMhYUBiMhIiY0NgERBxEBIZYDhBUdHRX8fBUdHQI7yP6iA4QEsB0qHR0qHf1E/tTIAfQB9AAAAAMAAABkBLAEsAAXABsAJQAAATMyFh0BITIWFREhNSMVIRE0NjMhNTQ2FxUzNQEVFAYjISImPQEB9MgpOwEsKTv+DMj+DDspASw7KcgB9Dsp/BgpOwSwOylkOyn+cGRkAZApO2QpO2RkZP1EyCk7OynIAAAABAAAAAAEsASwABUAKwBBAFcAABMhMhYPARcWFA8BBiIvAQcGJjURNDYpATIWFREUBi8BBwYiLwEmND8BJyY2ARcWFA8BFxYGIyEiJjURNDYfATc2MgU3NhYVERQGIyEiJj8BJyY0PwE2MhcyASwVCA5exwcHaggUCMdeDhUdAzUBLBUdFQ5exwgUCGoHB8deDgj+L2oHB8deDggV/tQVHRUOXscIFALLXg4VHRX+1BUIDl7HBwdqCBQIBLAVDl7HCBQIagcHx14OCBUBLBUdHRX+1BUIDl7HBwdqCBQIx14OFf0maggUCMdeDhUdFQEsFQgOXscHzl4OCBX+1BUdFQ5exwgUCGoHBwAAAAYAAAAABKgEqAAPABsAIwA7AEMASwAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JiQyFhQGIiY0JDIWFAYjIicHFhUUBiImNTQ2PwImNTQEMhYUBiImNCQyFhQGIiY0Advy3Z9fX5/d8t2gXl6gAcbgv29vv+C/b2/+LS0gIC0gAUwtICAWDg83ETNIMykfegEJ/octICAtIAIdLSAgLSAEqF+f3fLdoF5eoN3y3Z9Xb7/gv29vv+C/BiAtISEtICAtIQqRFxwkMzMkIDEFfgEODhekIC0gIC0gIC0gIC0AAf/YAFoEuQS8AFsAACUBNjc2JicmIyIOAwcABw4EFx4BMzI3ATYnLgEjIgcGBwEOASY0NwA3PgEzMhceARcWBgcOBgcGIyImJyY2NwE2NzYzMhceARcWBgcBDgEnLgECIgHVWwgHdl8WGSJBMD8hIP6IDx4eLRMNBQlZN0ozAiQkEAcdEhoYDRr+qw8pHA4BRyIjQS4ODyw9DQ4YIwwod26La1YOOEBGdiIwGkQB/0coW2tQSE5nDxE4Qv4eDyoQEAOtAdZbZWKbEQQUGjIhH/6JDxsdNSg3HT5CMwIkJCcQFBcMGv6uDwEcKQ4BTSIjIQEINykvYyMLKnhuiWZMBxtAOU6+RAH/SBg3ISSGV121Qv4kDwIPDyYAAAACAGQAWASvBEQAGQBEAAABPgIeAhUUDgMHLgQ1ND4CHgEFIg4DIi4DIyIGFRQeAhcWFx4EMj4DNzY3PgQ1NCYCiTB7eHVYNkN5hKg+PqeFeEM4WnZ4eQEjIT8yLSohJyktPyJDbxtBMjMPBw86KzEhDSIzKUAMBAgrKT8dF2oDtURIBS1TdkA5eYB/slVVsn+AeTlAdlMtBUgtJjY1JiY1NiZvTRc4SjQxDwcOPCouGBgwKEALBAkpKkQqMhNPbQACADn/8gR3BL4AFwAuAAAAMh8BFhUUBg8BJi8BNycBFwcvASY0NwEDNxYfARYUBwEGIi8BJjQ/ARYfAQcXAQKru0KNQjgiHR8uEl/3/nvUaRONQkIBGxJpCgmNQkL+5UK6Qo1CQjcdLhJf9wGFBL5CjUJeKmsiHTUuEl/4/nvUahKNQrpCARv+RmkICY1CukL+5UJCjUK7Qjc3LxFf+AGFAAAAAAMAyAAAA+gEsAARABUAHQAAADIeAhURFAYjISImNRE0PgEHESERACIGFBYyNjQCBqqaZDo7Kf2oKTs8Zj4CWP7/Vj09Vj0EsB4uMhX8Ryk7OykDuRUzLar9RAK8/RY9Vj09VgABAAAAAASwBLAAFgAACQEWFAYiLwEBEScBBRMBJyEBJyY0NjIDhgEbDx0qDiT+6dT+zP7oywEz0gEsAQsjDx0qBKH+5g8qHQ8j/vX+1NL+zcsBGAE01AEXJA4qHQAAAAADAScAEQQJBOAAMgBAAEsAAAEVHgQXIy4DJxEXHgQVFAYHFSM1JicuASczHgEXEScuBDU0PgI3NRkBDgMVFB4DFxYXET4ENC4CArwmRVI8LAKfBA0dMydAIjxQNyiym2SWVygZA4sFV0obLkJOMCAyVWg6HSoqFQ4TJhkZCWgWKTEiGBkzNwTgTgUTLD9pQiQuLBsH/s0NBxMtPGQ+i6oMTU8QVyhrVk1iEAFPCA4ZLzlYNkZwSCoGTf4SARIEDh02Jh0rGRQIBgPQ/soCCRYgNEM0JRkAAAABAGQAZgOUBK0ASgAAATIeARUjNC4CIyIGBwYVFB4BFxYXMxUjFgYHBgc+ATM2FjMyNxcOAyMiLgEHDgEPASc+BTc+AScjNTMmJy4CPgE3NgIxVJlemSc8OxolVBQpGxoYBgPxxQgVFS02ImIWIIwiUzUyHzY4HCAXanQmJ1YYFzcEGAcTDBEJMAwk3aYXFQcKAg4tJGEErVCLTig/IhIdFSw5GkowKgkFZDKCHj4yCg8BIh6TExcIASIfBAMaDAuRAxAFDQsRCjePR2QvORQrREFMIVgAAAACABn//wSXBLAADwAfAAABMzIWDwEGIi8BJjY7AREzBRcWBisBESMRIyImPwE2MgGQlhUIDuYOKg7mDggVlsgCF+YOCBWWyJYVCA7mDioBLBYO+g8P+g4WA4QQ+Q4V/HwDhBUO+Q8AAAQAGf//A+gEsAAHABcAGwAlAAABIzUjFSMRIQEzMhYPAQYiLwEmNjsBETMFFTM1EwczFSE1NyM1IQPoZGRkASz9qJYVCA7mDioO5g4IFZbIAZFkY8jI/tTIyAEsArxkZAH0/HwWDvoPD/oOFgOEZMjI/RL6ZJb6ZAAAAAAEABn//wPoBLAADwAZACEAJQAAATMyFg8BBiIvASY2OwERMwUHMxUhNTcjNSERIzUjFSMRIQcVMzUBkJYVCA7mDioO5g4IFZbIAljIyP7UyMgBLGRkZAEsx2QBLBYO+g8P+g4WA4SW+mSW+mT7UGRkAfRkyMgAAAAEABn//wRMBLAADwAVABsAHwAAATMyFg8BBiIvASY2OwERMwEjESM1MxMjNSMRIQcVMzUBkJYVCA7mDioO5g4IFZbIAlhkZMhkZMgBLMdkASwWDvoPD/oOFgOE/gwBkGT7UGQBkGTIyAAAAAAEABn//wRMBLAADwAVABkAHwAAATMyFg8BBiIvASY2OwERMwEjNSMRIQcVMzUDIxEjNTMBkJYVCA7mDioO5g4IFZbIArxkyAEsx2QBZGTIASwWDvoPD/oOFgOE/gxkAZBkyMj7tAGQZAAAAAAFABn//wSwBLAADwATABcAGwAfAAABMzIWDwEGIi8BJjY7AREzBSM1MxMhNSETITUhEyE1IQGQlhUIDuYOKg7mDggVlsgB9MjIZP7UASxk/nABkGT+DAH0ASwWDvoPD/oOFgOEyMj+DMj+DMj+DMgABQAZ//8EsASwAA8AEwAXABsAHwAAATMyFg8BBiIvASY2OwERMwUhNSEDITUhAyE1IQMjNTMBkJYVCA7mDioO5g4IFZbIAyD+DAH0ZP5wAZBk/tQBLGTIyAEsFg76Dw/6DhYDhMjI/gzI/gzI/gzIAAIAAAAABEwETAAPAB8AAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmAV4BkKK8u6P+cKW5uQJn/gwpOzspAfQpOzsETLuj/nClubmlAZClucg7Kf4MKTs7KQH0KTsAAAAAAwAAAAAETARMAA8AHwArAAABITIWFREUBiMhIiY1ETQ2BSEiBhURFBYzITI2NRE0JgUXFhQPAQYmNRE0NgFeAZClubml/nCju7wCZP4MKTs7KQH0KTs7/m/9ERH9EBgYBEy5pf5wpbm5pQGQo7vIOyn+DCk7OykB9Ck7gr4MJAy+DAsVAZAVCwAAAAADAAAAAARMBEwADwAfACsAAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmBSEyFg8BBiIvASY2AV4BkKO7uaX+cKW5uQJn/gwpOzspAfQpOzv+FQGQFQsMvgwkDL4MCwRMvKL+cKW5uaUBkKO7yDsp/gwpOzspAfQpO8gYEP0REf0QGAAAAAMAAAAABEwETAAPAB8AKwAAASEyFhURFAYjISImNRE0NgUhIgYVERQWMyEyNjURNCYFFxYGIyEiJj8BNjIBXgGQpbm5pf5wo7u5Amf+DCk7OykB9Ck7O/77vgwLFf5wFQsMvgwkBEy5pf5wo7u8ogGQpbnIOyn+DCk7OykB9Ck7z/0QGBgQ/REAAAAAAgAAAAAFFARMAB8ANQAAASEyFhURFAYjISImPQE0NjMhMjY1ETQmIyEiJj0BNDYHARYUBwEGJj0BIyImPQE0NjsBNTQ2AiYBkKW5uaX+cBUdHRUBwik7Oyn+PhUdHb8BRBAQ/rwQFvoVHR0V+hYETLml/nCluR0VZBUdOykB9Ck7HRVkFR3p/uQOJg7+5A4KFZYdFcgVHZYVCgAAAQDZAAID1wSeACMAAAEXFgcGAgclMhYHIggBBwYrAScmNz4BPwEhIicmNzYANjc2MwMZCQgDA5gCASwYEQ4B/vf+8wQMDgkJCQUCUCcn/tIXCAoQSwENuwUJEASeCQoRC/5TBwEjEv7K/sUFDwgLFQnlbm4TFRRWAS/TBhAAAAACAAAAAAT+BEwAHwA1AAABITIWHQEUBiMhIgYVERQWMyEyFh0BFAYjISImNRE0NgUBFhQHAQYmPQEjIiY9ATQ2OwE1NDYBXgGQFR0dFf4+KTs7KQHCFR0dFf5wpbm5AvEBRBAQ/rwQFvoVHR0V+hYETB0VZBUdOyn+DCk7HRVkFR25pQGQpbnp/uQOJg7+5A4KFZYdFcgVHZYVCgACAAAAAASwBLAAFQAxAAABITIWFREUBi8BAQYiLwEmNDcBJyY2ASMiBhURFBYzITI2PQE3ERQGIyEiJjURNDYzIQLuAZAVHRUObf7IDykPjQ8PAThtDgj+75wpOzspAfQpO8i7o/5wpbm5pQEsBLAdFf5wFQgObf7IDw+NDykPAThtDhX+1Dsp/gwpOzsplMj+1qW5uaUBkKW5AAADAA4ADgSiBKIADwAbACMAAAAyHgIUDgIiLgI0PgEEIg4BFB4BMj4BNCYEMhYUBiImNAHh7tmdXV2d2e7ZnV1dnQHD5sJxccLmwnFx/nugcnKgcgSiXZ3Z7tmdXV2d2e7ZnUdxwubCcXHC5sJzcqBycqAAAAMAAAAABEwEsAAVAB8AIwAAATMyFhURMzIWBwEGIicBJjY7ARE0NgEhMhYdASE1NDYFFTM1AcLIFR31FAoO/oEOJw3+hQ0JFfod/oUD6BUd+7QdA2dkBLAdFf6iFg/+Vg8PAaoPFgFeFR38fB0V+voVHWQyMgAAAAMAAAAABEwErAAVAB8AIwAACQEWBisBFRQGKwEiJj0BIyImNwE+AQEhMhYdASE1NDYFFTM1AkcBeg4KFfQiFsgUGPoUCw4Bfw4n/fkD6BUd+7QdA2dkBJ7+TQ8g+hQeHRX6IQ8BrxAC/H8dFfr6FR1kMjIAAwAAAAAETARLABQAHgAiAAAJATYyHwEWFAcBBiInASY0PwE2MhcDITIWHQEhNTQ2BRUzNQGMAXEHFQeLBwf98wcVB/7cBweLCBUH1APoFR37tB0DZ2QC0wFxBweLCBUH/fMICAEjCBQIiwcH/dIdFfr6FR1kMjIABAAAAAAETASbAAkAGQAjACcAABM3NjIfAQcnJjQFNzYWFQMOASMFIiY/ASc3ASEyFh0BITU0NgUVMzWHjg4qDk3UTQ4CFtIOFQIBHRX9qxUIDtCa1P49A+gVHfu0HQNnZAP/jg4OTdRMDyqa0g4IFf2pFB4BFQ7Qm9T9Oh0V+voVHWQyMgAAAAQAAAAABEwEsAAPABkAIwAnAAABBR4BFRMUBi8BByc3JyY2EwcGIi8BJjQ/AQEhMhYdASE1NDYFFTM1AV4CVxQeARUO0JvUm9IOCMNMDyoOjg4OTf76A+gVHfu0HQNnZASwAgEdFf2rFQgO0JrUmtIOFf1QTQ4Ojg4qDk3+WB0V+voVHWQyMgACAAT/7ASwBK8ABQAIAAAlCQERIQkBFQEEsP4d/sb+cQSs/TMCq2cBFP5xAacDHPz55gO5AAAAAAIAAABkBEwEsAAVABkAAAERFAYrAREhESMiJjURNDY7AREhETMHIzUzBEwdFZb9RJYVHR0V+gH0ZMhkZAPo/K4VHQGQ/nAdFQPoFB7+1AEsyMgAAAMAAABFBN0EsAAWABoALwAAAQcBJyYiDwEhESMiJjURNDY7AREhETMHIzUzARcWFAcBBiIvASY0PwE2Mh8BATYyBEwC/tVfCRkJlf7IlhUdHRX6AfRkyGRkAbBqBwf+XAgUCMoICGoHFQdPASkHFQPolf7VXwkJk/5wHRUD6BQe/tQBLMjI/c5qBxUH/lsHB8sHFQdqCAhPASkHAAMAAAANBQcEsAAWABoAPgAAAREHJy4BBwEhESMiJjURNDY7AREhETMHIzUzARcWFA8BFxYUDwEGIi8BBwYiLwEmND8BJyY0PwE2Mh8BNzYyBExnhg8lEP72/reWFR0dFfoB9GTIZGQB9kYPD4ODDw9GDykPg4MPKQ9GDw+Dgw8PRg8pD4ODDykD6P7zZ4YPAw7+9v5wHRUD6BQe/tQBLMjI/YxGDykPg4MPKQ9GDw+Dgw8PRg8pD4ODDykPRg8Pg4MPAAADAAAAFQSXBLAAFQAZAC8AAAERISIGHQEhESMiJjURNDY7AREhETMHIzUzEzMyFh0BMzIWDwEGIi8BJjY7ATU0NgRM/qIVHf4MlhUdHRX6AfRkyGRklmQVHZYVCA7mDioO5g4IFZYdA+j+1B0Vlv5wHRUD6BQe/tQBLMjI/agdFfoVDuYODuYOFfoVHQAAAAADAAAAAASXBLAAFQAZAC8AAAERJyYiBwEhESMiJjURNDY7AREhETMHIzUzExcWBisBFRQGKwEiJj0BIyImPwE2MgRMpQ4qDv75/m6WFR0dFfoB9GTIZGTr5g4IFZYdFWQVHZYVCA7mDioD6P5wpQ8P/vf+cB0VA+gUHv7UASzIyP2F5Q8V+hQeHhT6FQ/lDwADAAAAyASwBEwACQATABcAABMhMhYdASE1NDYBERQGIyEiJjURExUhNTIETBUd+1AdBJMdFfu0FR1kAZAETB0VlpYVHf7U/doVHR0VAib+1MjIAAAGAAMAfQStBJcADwAZAB0ALQAxADsAAAEXFhQPAQYmPQEhNSE1NDYBIyImPQE0NjsBFyM1MwE3NhYdASEVIRUUBi8BJjQFIzU7AjIWHQEUBisBA6f4Dg74DhX+cAGQFf0vMhUdHRUyyGRk/oL3DhUBkP5wFQ73DwOBZGRkMxQdHRQzBI3mDioO5g4IFZbIlhUI/oUdFWQVHcjI/cvmDggVlsiWFQgO5g4qecgdFWQVHQAAAAACAGQAAASwBLAAFgBRAAABJTYWFREUBisBIiY1ES4ENRE0NiUyFh8BERQOAg8BERQGKwEiJjURLgQ1ETQ+AzMyFh8BETMRPAE+AjMyFh8BETMRND4DA14BFBklHRXIFR0EDiIaFiX+4RYZAgEVHR0LCh0VyBUdBA4iGhYBBwoTDRQZAgNkBQkVDxcZAQFkAQUJFQQxdBIUH/uuFR0dFQGNAQgbHzUeAWcfRJEZDA3+Phw/MSkLC/5BFR0dFQG/BA8uLkAcAcICBxENCxkMDf6iAV4CBxENCxkMDf6iAV4CBxENCwABAGQAAASwBEwAMwAAARUiDgMVERQWHwEVITUyNjURIREUFjMVITUyPgM1ETQmLwE1IRUiBhURIRE0JiM1BLAEDiIaFjIZGf5wSxn+DBlL/nAEDiIaFjIZGQGQSxkB9BlLBEw4AQUKFA78iBYZAQI4OA0lAYr+diUNODgBBQoUDgN4FhkBAjg4DSX+dgGKJQ04AAAABgAAAAAETARMAAwAHAAgACQAKAA0AAABITIWHQEjBTUnITchBSEyFhURFAYjISImNRE0NhcVITUBBTUlBRUhNQUVFAYjIQchJyE3MwKjAXcVHWn+2cj+cGQBd/4lASwpOzsp/tQpOzspASwCvP5wAZD8GAEsArwdFf6JZP6JZAGQyGkD6B0VlmJiyGTIOyn+DCk7OykB9Ck7ZMjI/veFo4XGyMhm+BUdZGTIAAEAEAAQBJ8EnwAmAAATNzYWHwEWBg8BHgEXNz4BHwEeAQ8BBiIuBicuBTcRohEuDosOBhF3ZvyNdxEzE8ATBxGjAw0uMUxPZWZ4O0p3RjITCwED76IRBhPCFDERdo78ZXYRBA6IDi8RogEECBUgNUNjO0qZfHNVQBAAAAACAAAAAASwBEwAIwBBAAAAMh4EHwEVFAYvAS4BPQEmIAcVFAYPAQYmPQE+BRIyHgIfARUBHgEdARQGIyEiJj0BNDY3ATU0PgIB/LimdWQ/LAkJHRTKFB2N/sKNHRTKFB0DDTE7ZnTKcFImFgEBAW0OFR0V+7QVHRUOAW0CFiYETBUhKCgiCgrIFRgDIgMiFZIYGJIVIgMiAxgVyAQNJyQrIP7kExwcCgoy/tEPMhTUFR0dFdQUMg8BLzIEDSEZAAADAAAAAASwBLAADQAdACcAAAEHIScRMxUzNTMVMzUzASEyFhQGKwEXITcjIiY0NgMhMhYdASE1NDYETMj9qMjIyMjIyPyuArwVHR0VDIn8SokMFR0dswRMFR37UB0CvMjIAfTIyMjI/OAdKh1kZB0qHf7UHRUyMhUdAAAAAwBkAAAEsARMAAkAEwAdAAABIyIGFREhETQmASMiBhURIRE0JgEhETQ2OwEyFhUCvGQpOwEsOwFnZCk7ASw7/Rv+1DspZCk7BEw7KfwYA+gpO/7UOyn9RAK8KTv84AGQKTs7KQAAAAAF/5wAAASwBEwADwATAB8AJQApAAATITIWFREUBiMhIiY1ETQ2FxEhEQUjFTMRITUzNSMRIQURByMRMwcRMxHIArx8sLB8/UR8sLAYA4T+DMjI/tTIyAEsAZBkyMhkZARMsHz+DHywsHwB9HywyP1EArzIZP7UZGQBLGT+1GQB9GT+1AEsAAAABf+cAAAEsARMAA8AEwAfACUAKQAAEyEyFhURFAYjISImNRE0NhcRIREBIzUjFSMRMxUzNTMFEQcjETMHETMRyAK8fLCwfP1EfLCwGAOE/gxkZGRkZGQBkGTIyGRkBEywfP4MfLCwfAH0fLDI/UQCvP2oyMgB9MjIZP7UZAH0ZP7UASwABP+cAAAEsARMAA8AEwAbACMAABMhMhYVERQGIyEiJjURNDYXESERBSMRMxUhESEFIxEzFSERIcgCvHywsHz9RHywsBgDhP4MyMj+1AEsAZDIyP7UASwETLB8/gx8sLB8AfR8sMj9RAK8yP7UZAH0ZP7UZAH0AAAABP+cAAAEsARMAA8AEwAWABkAABMhMhYVERQGIyEiJjURNDYXESERAS0BDQERyAK8fLCwfP1EfLCwGAOE/gz+1AEsAZD+1ARMsHz+DHywsHwB9HywyP1EArz+DJaWlpYBLAAAAAX/nAAABLAETAAPABMAFwAgACkAABMhMhYVERQGIyEiJjURNDYXESERAyERIQcjIgYVFBY7AQERMzI2NTQmI8gCvHywsHz9RHywsBgDhGT9RAK8ZIImOTYpgv4Mgik2OSYETLB8/gx8sLB8AfR8sMj9RAK8/agB9GRWQUFUASz+1FRBQVYAAAAF/5wAAASwBEwADwATAB8AJQApAAATITIWFREUBiMhIiY1ETQ2FxEhEQUjFTMRITUzNSMRIQEjESM1MwMjNTPIArx8sLB8/UR8sLAYA4T+DMjI/tTIyAEsAZBkZMjIZGQETLB8/gx8sLB8AfR8sMj9RAK8yGT+1GRkASz+DAGQZP4MZAAG/5wAAASwBEwADwATABkAHwAjACcAABMhMhYVERQGIyEiJjURNDYXESERBTMRIREzASMRIzUzBRUzNQEjNTPIArx8sLB8/UR8sLAYA4T9RMj+1GQCWGRkyP2oZAEsZGQETLB8/gx8sLB8AfR8sMj9RAK8yP5wAfT+DAGQZMjIyP7UZAAF/5wAAASwBEwADwATABwAIgAmAAATITIWFREUBiMhIiY1ETQ2FxEhEQEHIzU3NSM1IQEjESM1MwMjNTPIArx8sLB8/UR8sLAYA4T+DMdkx8gBLAGQZGTIx2RkBEywfP4MfLCwfAH0fLDI/UQCvP5wyDLIlmT+DAGQZP4MZAAAAAMACQAJBKcEpwAPABsAJQAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JgchFSEVISc1NyEB4PDbnl5entvw255eXp4BxeTCcXHC5MJxcWz+1AEs/tRkZAEsBKdentvw255eXp7b8NueTHHC5MJxccLkwtDIZGTIZAAAAAAEAAkACQSnBKcADwAbACcAKwAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JgcVBxcVIycjFSMRIQcVMzUB4PDbnl5entvw255eXp4BxeTCcXHC5MJxcWwyZGRklmQBLMjIBKdentvw255eXp7b8NueTHHC5MJxccLkwtBkMmQyZGQBkGRkZAAAAv/y/50EwgRBACAANgAAATIWFzYzMhYUBisBNTQmIyEiBh0BIyImNTQ2NyY1ND4BEzMyFhURMzIWDwEGIi8BJjY7ARE0NgH3brUsLC54qqp4gB0V/tQVHd5QcFZBAmKqepYKD4kVCg3fDSYN3w0KFYkPBEF3YQ6t8a36FR0dFfpzT0VrDhMSZKpi/bMPCv7tFxD0EBD0EBcBEwoPAAAAAAL/8v+cBMMEQQAcADMAAAEyFhc2MzIWFxQGBwEmIgcBIyImNTQ2NyY1ND4BExcWBisBERQGKwEiJjURIyImNzY3NjIB9m62LCsueaoBeFr+hg0lDf6DCU9xVkECYqnm3w0KFYkPCpYKD4kVCg3HGBMZBEF3YQ+teGOkHAFoEBD+k3NPRWsOExNkqWP9kuQQF/7tCg8PCgETFxDMGBMAAAABAGQAAARMBG0AGAAAJTUhATMBMwkBMwEzASEVIyIGHQEhNTQmIwK8AZD+8qr+8qr+1P7Uqv7yqv7yAZAyFR0BkB0VZGQBLAEsAU3+s/7U/tRkHRUyMhUdAAAAAAEAeQAABDcEmwAvAAABMhYXHgEVFAYHFhUUBiMiJxUyFh0BITU0NjM1BiMiJjU0Ny4BNTQ2MzIXNCY1NDYCWF6TGll7OzIJaUo3LRUd/tQdFS03SmkELzlpSgUSAqMEm3FZBoNaPWcfHRpKaR77HRUyMhUd+x5pShIUFVg1SmkCAhAFdKMAAAAGACcAFASJBJwAEQAqAEIASgBiAHsAAAEWEgIHDgEiJicmAhI3PgEyFgUiBw4BBwYWHwEWMzI3Njc2Nz4BLwEmJyYXIgcOAQcGFh8BFjMyNz4BNz4BLwEmJyYWJiIGFBYyNjciBw4BBw4BHwEWFxYzMjc+ATc2Ji8BJhciBwYHBgcOAR8BFhcWMzI3PgE3NiYvASYD8m9PT29T2dzZU29PT29T2dzZ/j0EBHmxIgQNDCQDBBcGG0dGYAsNAwkDCwccBAVQdRgEDA0iBAQWBhJROQwMAwkDCwf5Y4xjY4xjVhYGElE6CwwDCQMLBwgEBVB1GAQNDCIEjRcGG0dGYAsNAwkDCwcIBAR5sSIEDQwkAwPyb/7V/tVvU1dXU28BKwErb1NXVxwBIrF5DBYDCQEWYEZHGwMVDCMNBgSRAhh1UA0WAwkBFTpREgMVCyMMBwT6Y2OMY2MVFTpREQQVCyMMBwQCGHVQDRYDCQEkFmBGRxsDFQwjDQYEASKxeQwWAwkBAAAABQBkAAAD6ASwAAwADwAWABwAIgAAASERIzUhFSERNDYzIQEjNQMzByczNTMDISImNREFFRQGKwECvAEstP6s/oQPCgI/ASzIZKLU1KJktP51Cg8DhA8KwwMg/oTIyALzCg/+1Mj84NTUyP4MDwoBi8jDCg8AAAAABQBkAAAD6ASwAAkADAATABoAIQAAASERCQERNDYzIQEjNRMjFSM1IzcDISImPQEpARUUBisBNQK8ASz+ov3aDwoCPwEsyD6iZKLUqv6dCg8BfAIIDwqbAyD9+AFe/doERwoP/tTI/HzIyNT+ZA8KNzcKD1AAAAAAAwAAAAAEsAP0AAgAGQAfAAABIxUzFyERIzcFMzIeAhUhFSEDETM0PgIBMwMhASEEiqJkZP7UotT9EsgbGiEOASz9qMhkDiEaAnPw8PzgASwB9AMgyGQBLNTUBBErJGT+ogHCJCsRBP5w/nAB9AAAAAMAAAAABEwETAAZADIAOQAAATMyFh0BMzIWHQEUBiMhIiY9ATQ2OwE1NDYFNTIWFREUBiMhIic3ARE0NjMVFBYzITI2AQc1IzUzNQKKZBUdMhUdHRX+1BUdHRUyHQFzKTs7Kf2oARP2/ro7KVg+ASw+WP201MjIBEwdFTIdFWQVHR0VZBUdMhUd+pY7KfzgKTsE9gFGAUQpO5Y+WFj95tSiZKIAAwBkAAAEvARMABkANgA9AAABMzIWHQEzMhYdARQGIyEiJj0BNDY7ATU0NgU1MhYVESMRMxQOAiMhIiY1ETQ2MxUUFjMhMjYBBzUjNTM1AcJkFR0yFR0dFf7UFR0dFTIdAXMpO8jIDiEaG/2oKTs7KVg+ASw+WAGc1MjIBEwdFTIdFWQVHR0VZBUdMhUd+pY7Kf4M/tQkKxEEOykDICk7lj5YWP3m1KJkogAAAAP/ogAABRYE1AALABsAHwAACQEWBiMhIiY3ATYyEyMiBhcTHgE7ATI2NxM2JgMVMzUCkgJ9FyAs+wQsIBcCfRZARNAUGAQ6BCMUNhQjBDoEGODIBK37sCY3NyYEUCf+TB0U/tIUHR0UAS4UHf4MZGQAAAAACQAAAAAETARMAA8AHwAvAD8ATwBfAG8AfwCPAAABMzIWHQEUBisBIiY9ATQ2EzMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2ITMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2ITMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBqfoKDw8K+goPDwr6Cg8PCvoKDw8BmvoKDw8K+goPD/zq+goPDwr6Cg8PAZr6Cg8PCvoKDw8BmvoKDw8K+goPD/zq+goPDwr6Cg8PAZr6Cg8PCvoKDw8BmvoKDw8K+goPDwRMDwqWCg8PCpYKD/7UDwqWCg8PCpYKDw8KlgoPDwqWCg/+1A8KlgoPDwqWCg8PCpYKDw8KlgoPDwqWCg8PCpYKD/7UDwqWCg8PCpYKDw8KlgoPDwqWCg8PCpYKDw8KlgoPAAAAAwAAAAAEsAUUABkAKQAzAAABMxUjFSEyFg8BBgchJi8BJjYzITUjNTM1MwEhMhYUBisBFyE3IyImNDYDITIWHQEhNTQ2ArxkZAFePjEcQiko/PwoKUIcMT4BXmRkyP4+ArwVHR0VDIn8SooNFR0dswRMFR37UB0EsMhkTzeEUzMzU4Q3T2TIZPx8HSodZGQdKh3+1B0VMjIVHQAABAAAAAAEsAUUAAUAGQArADUAAAAyFhUjNAchFhUUByEyFg8BIScmNjMhJjU0AyEyFhQGKwEVBSElNSMiJjQ2AyEyFh0BITU0NgIwUDnCPAE6EgMBSCkHIq/9WrIiCikBSAOvArwVHR0VlgET/EoBE5YVHR2zBEwVHftQHQUUOykpjSUmCBEhFpGRFiERCCb+lR0qHcjIyMgdKh39qB0VMjIVHQAEAAAAAASwBJ0ABwAUACQALgAAADIWFAYiJjQTMzIWFRQXITY1NDYzASEyFhQGKwEXITcjIiY0NgMhMhYdASE1NDYCDZZqapZqty4iKyf+vCcrI/7NArwVHR0VDYr8SokMFR0dswRMFR37UB0EnWqWamqW/us5Okxra0w6Of5yHSodZGQdKh3+1B0VMjIVHQAEAAAAAASwBRQADwAcACwANgAAATIeARUUBiImNTQ3FzcnNhMzMhYVFBchNjU0NjMBITIWFAYrARchNyMiJjQ2AyEyFh0BITU0NgJYL1szb5xvIpBvoyIfLiIrJ/68Jysj/s0CvBUdHRUNivxKiQwVHR2zBEwVHftQHQUUa4s2Tm9vTj5Rj2+jGv4KOTpMa2tMOjn+ch0qHWRkHSod/tQdFTIyFR0AAAADAAAAAASwBRIAEgAiACwAAAEFFSEUHgMXIS4BNTQ+AjcBITIWFAYrARchNyMiJjQ2AyEyFh0BITU0NgJYASz+1CU/P00T/e48PUJtj0r+ogK8FR0dFQ2K/EqJDBUdHbMETBUd+1AdBLChizlmUT9IGVO9VFShdksE/H4dKh1kZB0qHf7UHRUyMhUdAAIAyAAAA+gFFAAPACkAAAAyFh0BHgEdASE1NDY3NTQDITIWFyMVMxUjFTMVIxUzFAYjISImNRE0NgIvUjsuNv5wNi5kAZA2XBqsyMjIyMh1U/5wU3V1BRQ7KU4aXDYyMjZcGk4p/kc2LmRkZGRkU3V1UwGQU3UAAAMAZP//BEwETAAPAC8AMwAAEyEyFhURFAYjISImNRE0NgMhMhYdARQGIyEXFhQGIi8BIQcGIiY0PwEhIiY9ATQ2BQchJ5YDhBUdHRX8fBUdHQQDtgoPDwr+5eANGiUNWP30Vw0mGg3g/t8KDw8BqmQBRGQETB0V/gwVHR0VAfQVHf1EDwoyCg/gDSUbDVhYDRslDeAPCjIKD2RkZAAAAAAEAAAAAASwBEwAGQAjAC0ANwAAEyEyFh0BIzQmKwEiBhUjNCYrASIGFSM1NDYDITIWFREhETQ2ExUUBisBIiY9ASEVFAYrASImPQHIAyBTdWQ7KfopO2Q7KfopO2R1EQPoKTv7UDvxHRVkFR0D6B0VZBUdBEx1U8gpOzspKTs7KchTdf4MOyn+1AEsKTv+DDIVHR0VMjIVHR0VMgADAAEAAASpBKwADQARABsAAAkBFhQPASEBJjQ3ATYyCQMDITIWHQEhNTQ2AeACqh8fg/4f/fsgIAEnH1n+rAFWAS/+q6IDIBUd/HwdBI39VR9ZH4MCBh9ZHwEoH/5u/qoBMAFV/BsdFTIyFR0AAAAAAgCPAAAEIQSwABcALwAAAQMuASMhIgYHAwYWMyEVFBYyNj0BMzI2AyE1NDY7ATU0NjsBETMRMzIWHQEzMhYVBCG9CCcV/nAVJwi9CBMVAnEdKh19FROo/a0dFTIdFTDILxUdMhUdAocB+hMcHBP+BhMclhUdHRWWHP2MMhUdMhUdASz+1B0VMh0VAAAEAAAAAASwBLAADQAQAB8AIgAAASERFAYjIREBNTQ2MyEBIzUBIREUBiMhIiY1ETQ2MyEBIzUDhAEsDwr+if7UDwoBdwEsyP2oASwPCv12Cg8PCgF3ASzIAyD9wQoPAk8BLFQKD/7UyP4M/cEKDw8KA7YKD/7UyAAC/5wAZAUUBEcARgBWAAABMzIeAhcWFxY2NzYnJjc+ARYXFgcOASsBDgEPAQ4BKwEiJj8BBisBIicHDgErASImPwEmLwEuAT0BNDY7ATY3JyY2OwE2BSMiBh0BFBY7ATI2PQE0JgHkw0uOakkMEhEfQwoKGRMKBQ8XDCkCA1Y9Pgc4HCcDIhVkFRgDDDEqwxgpCwMiFWQVGAMaVCyfExwdFXwLLW8QBxXLdAFF+goPDwr6Cg8PBEdBa4pJDgYKISAiJRsQCAYIDCw9P1c3fCbqFB0dFEYOCEAUHR0UnUplNQcmFTIVHVdPXw4TZV8PCjIKDw8KMgoPAAb/nP/mBRQEfgAJACQANAA8AFIAYgAAASU2Fh8BFgYPASUzMhYfASEyFh0BFAYHBQYmJyYjISImPQE0NhcjIgYdARQ7ATI2NTQmJyYEIgYUFjI2NAE3PgEeARceAT8BFxYGDwEGJi8BJjYlBwYfAR4BPwE2Jy4BJy4BAoEBpxMuDiAOAxCL/CtqQ0geZgM3FR0cE/0fFyIJKjr+1D5YWLlQExIqhhALIAsSAYBALS1ALf4PmBIgHhMQHC0aPzANITNQL3wpgigJASlmHyElDR0RPRMFAhQHCxADhPcICxAmDyoNeMgiNtQdFTIVJgeEBBQPQ1g+yD5YrBwVODMQEAtEERzJLUAtLUD+24ITChESEyMgAwWzPUkrRSgJL5cvfRxYGyYrDwkLNRAhFEgJDAQAAAAAAwBkAAAEOQSwAFEAYABvAAABMzIWHQEeARcWDgIPATIeBRUUDgUjFRQGKwEiJj0BIxUUBisBIiY9ASMiJj0BNDY7AREjIiY9ATQ2OwE1NDY7ATIWHQEzNTQ2AxUhMj4CNTc0LgMjARUhMj4CNTc0LgMjAnGWCg9PaAEBIC4uEBEGEjQwOiodFyI2LUAjGg8KlgoPZA8KlgoPrwoPDwpLSwoPDwqvDwqWCg9kD9cBBxwpEwsBAQsTKRz++QFrHCkTCwEBCxMpHASwDwptIW1KLk0tHwYGAw8UKDJOLTtdPCoVCwJLCg8PCktLCg8PCksPCpYKDwJYDwqWCg9LCg8PCktLCg/+1MgVHR0LCgQOIhoW/nDIFR0dCwoEDiIaFgAAAwAEAAIEsASuABcAKQAsAAATITIWFREUBg8BDgEjISImJy4CNRE0NgQiDgQPARchNy4FAyMT1AMMVnokEhIdgVL9xFKCHAgYKHoCIIx9VkcrHQYGnAIwnAIIIClJVSGdwwSuelb+YDO3QkJXd3ZYHFrFMwGgVnqZFyYtLSUMDPPzBQ8sKDEj/sIBBQACAMgAAAOEBRQADwAZAAABMzIWFREUBiMhIiY1ETQ2ARUUBisBIiY9AQHblmesVCn+PilUrAFINhWWFTYFFKxn/gwpVFQpAfRnrPwY4RU2NhXhAAACAMgAAAOEBRQADwAZAAABMxQWMxEUBiMhIiY1ETQ2ARUUBisBIiY9AQHbYLOWVCn+PilUrAFINhWWFTYFFJaz/kIpVFQpAfRnrPwY4RU2NhXhAAACAAAAFAUOBBoAFAAaAAAJASUHFRcVJwc1NzU0Jj4CPwEnCQEFJTUFJQUO/YL+hk5klpZkAQEBBQQvkwKCAVz+ov6iAV4BXgL//uWqPOCWx5SVyJb6BA0GCgYDKEEBG/1ipqaTpaUAAAMAZAH0BLADIAAHAA8AFwAAEjIWFAYiJjQkMhYUBiImNCQyFhQGIiY0vHxYWHxYAeh8WFh8WAHofFhYfFgDIFh8WFh8WFh8WFh8WFh8WFh8AAAAAAMBkAAAArwETAAHAA8AFwAAADIWFAYiJjQSMhYUBiImNBIyFhQGIiY0Aeh8WFh8WFh8WFh8WFh8WFh8WARMWHxYWHz+yFh8WFh8/shYfFhYfAAAAAMAZABkBEwETAAPAB8ALwAAEyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2fQO2Cg8PCvxKCg8PCgO2Cg8PCvxKCg8PCgO2Cg8PCvxKCg8PBEwPCpYKDw8KlgoP/nAPCpYKDw8KlgoP/nAPCpYKDw8KlgoPAAAABAAAAAAEsASwAA8AHwAvADMAAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmBSEyFhURFAYjISImNRE0NhcVITUBXgH0ory7o/4Mpbm5Asv9qCk7OykCWCk7O/2xAfQVHR0V/gwVHR1HAZAEsLuj/gylubmlAfSlucg7Kf2oKTs7KQJYKTtkHRX+1BUdHRUBLBUdZMjIAAAAAAEAZABkBLAETAA7AAATITIWFAYrARUzMhYUBisBFTMyFhQGKwEVMzIWFAYjISImNDY7ATUjIiY0NjsBNSMiJjQ2OwE1IyImNDaWA+gVHR0VMjIVHR0VMjIVHR0VMjIVHR0V/BgVHR0VMjIVHR0VMjIVHR0VMjIVHR0ETB0qHcgdKh3IHSodyB0qHR0qHcgdKh3IHSodyB0qHQAAAAYBLAAFA+gEowAHAA0AEwAZAB8AKgAAAR4BBgcuATYBMhYVIiYlFAYjNDYBMhYVIiYlFAYjNDYDFRQGIiY9ARYzMgKKVz8/V1c/P/75fLB8sAK8sHyw/cB8sHywArywfLCwHSodKAMRBKNDsrJCQrKy/sCwfLB8fLB8sP7UsHywfHywfLD+05AVHR0VjgQAAAH/tQDIBJQDgQBCAAABNzYXAR4BBw4BKwEyFRQOBCsBIhE0NyYiBxYVECsBIi4DNTQzIyImJyY2NwE2HwEeAQ4BLwEHIScHBi4BNgLpRRkUASoLCAYFGg8IAQQNGyc/KZK4ChRUFQu4jjBJJxkHAgcPGQYGCAsBKhQaTBQVCiMUM7YDe7YsFCMKFgNuEwYS/tkLHw8OEw0dNkY4MhwBIBgXBAQYF/7gKjxTQyMNEw4PHwoBKBIHEwUjKBYGDMHBDAUWKCMAAAAAAgAAAAAEsASwACUAQwAAASM0LgUrAREUFh8BFSE1Mj4DNREjIg4FFSMRIQEjNC4DKwERFBYXMxUjNTI1ESMiDgMVIzUhBLAyCAsZEyYYGcgyGRn+cAQOIhoWyBkYJhMZCwgyA+j9RBkIChgQEWQZDQzIMmQREBgKCBkB9AOEFSAVDggDAfyuFhkBAmRkAQUJFQ4DUgEDCA4VIBUBLP0SDxMKBQH+VwsNATIyGQGpAQUKEw+WAAAAAAMAAAAABEwErgAdACAAMAAAATUiJy4BLwEBIwEGBw4BDwEVITUiJj8BIRcWBiMVARsBARUUBiMhIiY9ATQ2MyEyFgPoGR4OFgUE/t9F/tQSFQkfCwsBETE7EkUBJT0NISf+7IZ5AbEdFfwYFR0dFQPoFR0BLDIgDiIKCwLr/Q4jFQkTBQUyMisusKYiQTIBhwFW/qr942QVHR0VZBUdHQADAAAAAASwBLAADwBHAEoAABMhMhYVERQGIyEiJjURNDYFIyIHAQYHBgcGHQEUFjMhMjY9ATQmIyInJj8BIRcWBwYjIgYdARQWMyEyNj0BNCYnIicmJyMBJhMjEzIETBUdHRX7tBUdHQJGRg0F/tUREhImDAsJAREIDAwINxAKCj8BCjkLEQwYCAwMCAE5CAwLCBEZGQ8B/uAFDsVnBLAdFfu0FR0dFQRMFR1SDP0PIBMSEAUNMggMDAgyCAwXDhmjmR8YEQwIMggMDAgyBwwBGRskAuwM/gUBCAAABAAAAAAEsASwAAMAEwAjACcAAAEhNSEFITIWFREUBiMhIiY1ETQ2KQEyFhURFAYjISImNRE0NhcRIREEsPtQBLD7ggGQFR0dFf5wFR0dAm0BkBUdHRX+cBUdHUcBLARMZMgdFfx8FR0dFQOEFR0dFf5wFR0dFQGQFR1k/tQBLAAEAAAAAASwBLAADwAfACMAJwAAEyEyFhURFAYjISImNRE0NgEhMhYVERQGIyEiJjURNDYXESEREyE1ITIBkBUdHRX+cBUdHQJtAZAVHR0V/nAVHR1HASzI+1AEsASwHRX8fBUdHRUDhBUd/gwdFf5wFR0dFQGQFR1k/tQBLP2oZAAAAAACAAAAZASwA+gAJwArAAATITIWFREzNTQ2MyEyFh0BMxUjFRQGIyEiJj0BIxEUBiMhIiY1ETQ2AREhETIBkBUdZB0VAZAVHWRkHRX+cBUdZB0V/nAVHR0CnwEsA+gdFf6ilhUdHRWWZJYVHR0Vlv6iFR0dFQMgFR3+1P7UASwAAAQAAAAABLAEsAADABMAFwAnAAAzIxEzFyEyFhURFAYjISImNRE0NhcRIREBITIWFREUBiMhIiY1ETQ2ZGRklgGQFR0dFf5wFR0dRwEs/qIDhBUdHRX8fBUdHQSwZB0V/nAVHR0VAZAVHWT+1AEs/gwdFf5wFR0dFQGQFR0AAAAAAgBkAAAETASwACcAKwAAATMyFhURFAYrARUhMhYVERQGIyEiJjURNDYzITUjIiY1ETQ2OwE1MwcRIRECWJYVHR0VlgHCFR0dFfx8FR0dFQFelhUdHRWWZMgBLARMHRX+cBUdZB0V/nAVHR0VAZAVHWQdFQGQFR1kyP7UASwAAAAEAAAAAASwBLAAAwATABcAJwAAISMRMwUhMhYVERQGIyEiJjURNDYXESERASEyFhURFAYjISImNRE0NgSwZGT9dgGQFR0dFf5wFR0dRwEs/K4DhBUdHRX8fBUdHQSwZB0V/nAVHR0VAZAVHWT+1AEs/gwdFf5wFR0dFQGQFR0AAAEBLAAwA28EgAAPAAAJAQYjIiY1ETQ2MzIXARYUA2H+EhcSDhAQDhIXAe4OAjX+EhcbGQPoGRsX/hIOKgAAAAABAUEAMgOEBH4ACwAACQE2FhURFAYnASY0AU8B7h0qKh3+Eg4CewHuHREp/BgpER0B7g4qAAAAAAEAMgFBBH4DhAALAAATITIWBwEGIicBJjZkA+gpER3+Eg4qDv4SHREDhCod/hIODgHuHSoAAAAAAQAyASwEfgNvAAsAAAkBFgYjISImNwE2MgJ7Ae4dESn8GCkRHQHuDioDYf4SHSoqHQHuDgAAAAACAAgAAASwBCgABgAKAAABFQE1LQE1ASE1IQK8/UwBnf5jBKj84AMgAuW2/r3dwcHd+9jIAAAAAAIAAABkBLAEsAALADEAAAEjFTMVIREzNSM1IQEzND4FOwERFAYPARUhNSIuAzURMzIeBRUzESEEsMjI/tTIyAEs+1AyCAsZEyYYGWQyGRkBkAQOIhoWZBkYJhMZCwgy/OADhGRkASxkZP4MFSAVDggDAf3aFhkBAmRkAQUJFQ4CJgEDCA4VIBUBLAAAAgAAAAAETAPoACUAMQAAASM0LgUrAREUFh8BFSE1Mj4DNREjIg4FFSMRIQEjFTMVIREzNSM1IQMgMggLGRMmGBlkMhkZ/nAEDiIaFmQZGCYTGQsIMgMgASzIyP7UyMgBLAK8FSAVDggDAf3aFhkCAWRkAQUJFQ4CJgEDCA4VIBUBLPzgZGQBLGRkAAABAMgAZgNyBEoAEgAAATMyFgcJARYGKwEiJwEmNDcBNgK9oBAKDP4wAdAMChCgDQr+KQcHAdcKBEoWDP4w/jAMFgkB1wgUCAHXCQAAAQE+AGYD6ARKABIAAAEzMhcBFhQHAQYrASImNwkBJjYBU6ANCgHXBwf+KQoNoBAKDAHQ/jAMCgRKCf4pCBQI/ikJFgwB0AHQDBYAAAEAZgDIBEoDcgASAAAAFh0BFAcBBiInASY9ATQ2FwkBBDQWCf4pCBQI/ikJFgwB0AHQA3cKEKANCv4pBwcB1woNoBAKDP4wAdAAAAABAGYBPgRKA+gAEgAACQEWHQEUBicJAQYmPQE0NwE2MgJqAdcJFgz+MP4wDBYJAdcIFAPh/ikKDaAQCgwB0P4wDAoQoA0KAdcHAAAAAgDZ//kEPQSwAAUAOgAAARQGIzQ2BTMyFh8BNjc+Ah4EBgcOBgcGIiYjIgYiJy4DLwEuAT4EHgEXJyY2A+iwfLD+VmQVJgdPBQsiKFAzRyorDwURAQQSFyozTSwNOkkLDkc3EDlfNyYHBw8GDyUqPjdGMR+TDA0EsHywfLDIHBPCAQIGBwcFDx81S21DBxlLR1xKQhEFBQcHGWt0bCQjP2hJNyATBwMGBcASGAAAAAACAMgAFQOEBLAAFgAaAAATITIWFREUBisBEQcGJjURIyImNRE0NhcVITX6AlgVHR0Vlv8TGpYVHR2rASwEsB0V/nAVHf4MsgkQFQKKHRUBkBUdZGRkAAAAAgDIABkETASwAA4AEgAAEyEyFhURBRElIREjETQ2ARU3NfoC7ic9/UQCWP1EZB8BDWQEsFEs/Ft1A7Z9/BgEARc0/V1kFGQAAQAAAAECTW/DBF9fDzz1AB8EsAAAAADQdnOXAAAAANB2c5f/Uf+cBdwFFAAAAAgAAgAAAAAAAAABAAAFFP+FAAAFFP9R/tQF3AABAAAAAAAAAAAAAAAAAAAAowG4ACgAAAAAAZAAAASwAAAEsABkBLAAAASwAAAEsABwAooAAAUUAAACigAABRQAAAGxAAABRQAAANgAAADYAAAAogAAAQQAAABIAAABBAAAAUUAAASwAGQEsAB7BLAAyASwAMgB9AAABLD/8gSwAAAEsAAABLD/8ASwAAAEsAAOBLAACQSwAGQEsP/TBLD/0wSwAAAEsAAABLAAAASwAAAEsAAABLAAJgSwAG4EsAAXBLAAFwSwABcEsABkBLAAGgSwAGQEsAAMBLAAZASwABcEsP+cBLAAZASwABcEsAAXBLAAAASwABcEsAAXBLAAFwSwAGQEsAAABLAAZASwAAAEsAAABLAAAASwAAAEsAAABLAAAASwAAAEsAAABLAAZASwAMgEsAAABLAAAASwADUEsABkBLAAyASw/7UEsAAhBLAAAASwAAAEsAAABLAAAASwAAAEsP+cBLAAAASwAAAEsAAABLAA2wSwABcEsAB1BLAAAASwAAAEsAAABLAACgSwAMgEsAAABLAAnQSwAMgEsADIBLAAyASwAAAEsP/+BLABLASwAGQEsACIBLABOwSwABcEsAAXBLAAFwSwABcEsAAXBLAAFwSwAAAEsAAXBLAAFwSwABcEsAAXBLAAAASwALcEsAC3BLAAAASwAAAEsABJBLAAFwSwAAAEsAAABLAAXQSw/9wEsP/cBLD/nwSwAGQEsAAABLAAAASwAAAEsABkBLD//wSwAAAEsP9RBLAABgSwAAAEsAAABLABRQSwAAEEsAAABLD/nASwAEoEsAAUBLAAAASwAAAEsAAABLD/nASwAGEEsP/9BLAAFgSwABYEsAAWBLAAFgSwABgEsAAABMQAAASwAGQAAAAAAAD/2ABkADkAyAAAAScAZAAZABkAGQAZABkAGQAZAAAAAAAAAAAAAADZAAAAAAAOAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAMAZABkAAAAEAAAAAAAZP+c/5z/nP+c/5z/nP+c/5wACQAJ//L/8gBkAHkAJwBkAGQAAAAAAGT/ogAAAAAAAAAAAAAAAADIAGQAAAABAI8AAP+c/5wAZAAEAMgAyAAAAGQBkABkAAAAZAEs/7UAAAAAAAAAAAAAAAAAAABkAAABLAFBADIAMgAIAAAAAADIAT4AZgBmANkAyADIAAAAKgAqACoAKgCyAOgA6AFOAU4BTgFOAU4BTgFOAU4BTgFOAU4BTgFOAU4BpAIGAiICfgKGAqwC5ANGA24DjAPEBAgEMgRiBKIE3AVcBboGcgb0ByAHYgfKCB4IYgi+CTYJhAm2Cd4KKApMCpQK4gswC4oLygwIDFgNKg1eDbAODg5oDrQPKA+mD+YQEhBUEJAQqhEqEXYRthIKEjgSfBLAExoTdBPQFCoU1BU8FagVzBYEFjYWYBawFv4XUhemGAIYLhhqGJYYsBjgGP4ZKBloGZQZxBnaGe4aNhpoGrga9hteG7QcMhyUHOIdHB1EHWwdlB28HeYeLh52HsAfYh/SIEYgviEyIXYhuCJAIpYiuCMOIyIjOCN6I8Ij4CQCJDAkXiSWJOIlNCVgJbwmFCZ+JuYnUCe8J/goNChwKKwpoCnMKiYqSiqEKworeiwILGgsuizsLRwtiC30LiguZi6iLtgvDi9GL34vsi/4MD4whDDSMRIxYDGuMegyJDJeMpoy3jMiMz4zaDO2NBg0YDSoNNI1LDWeNeg2PjZ8Ntw3GjdON5I31DgQOEI4hjjIOQo5SjmIOcw6HDpsOpo63jugO9w8GDxQPKI8+D0yPew+Oj6MPtQ/KD9uP6o/+kBIQIBAxkECQX5CGEKoQu5DGENCQ3ZDoEPKRBBEYESuRPZFWkW2RgZGdEa0RvZHNkd2R7ZH9kgWSDJITkhqSIZIzEkSSThJXkmESapKAkouSlIAAQAAARcApwARAAAAAAACAAAAAQABAAAAQAAuAAAAAAAAABAAxgABAAAAAAATABIAAAADAAEECQAAAGoAEgADAAEECQABACgAfAADAAEECQACAA4ApAADAAEECQADAEwAsgADAAEECQAEADgA/gADAAEECQAFAHgBNgADAAEECQAGADYBrgADAAEECQAIABYB5AADAAEECQAJABYB+gADAAEECQALACQCEAADAAEECQAMACQCNAADAAEECQATACQCWAADAAEECQDIABYCfAADAAEECQDJADACkgADAAEECdkDABoCwnd3dy5nbHlwaGljb25zLmNvbQBDAG8AcAB5AHIAaQBnAGgAdAAgAKkAIAAyADAAMQA0ACAAYgB5ACAASgBhAG4AIABLAG8AdgBhAHIAaQBrAC4AIABBAGwAbAAgAHIAaQBnAGgAdABzACAAcgBlAHMAZQByAHYAZQBkAC4ARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzAFIAZQBnAHUAbABhAHIAMQAuADAAMAA5ADsAVQBLAFcATgA7AEcATABZAFAASABJAEMATwBOAFMASABhAGwAZgBsAGkAbgBnAHMALQBSAGUAZwB1AGwAYQByAEcATABZAFAASABJAEMATwBOAFMAIABIAGEAbABmAGwAaQBuAGcAcwAgAFIAZQBnAHUAbABhAHIAVgBlAHIAcwBpAG8AbgAgADEALgAwADAAOQA7AFAAUwAgADAAMAAxAC4AMAAwADkAOwBoAG8AdABjAG8AbgB2ACAAMQAuADAALgA3ADAAOwBtAGEAawBlAG8AdABmAC4AbABpAGIAMgAuADUALgA1ADgAMwAyADkARwBMAFkAUABIAEkAQwBPAE4AUwBIAGEAbABmAGwAaQBuAGcAcwAtAFIAZQBnAHUAbABhAHIASgBhAG4AIABLAG8AdgBhAHIAaQBrAEoAYQBuACAASwBvAHYAYQByAGkAawB3AHcAdwAuAGcAbAB5AHAAaABpAGMAbwBuAHMALgBjAG8AbQB3AHcAdwAuAGcAbAB5AHAAaABpAGMAbwBuAHMALgBjAG8AbQB3AHcAdwAuAGcAbAB5AHAAaABpAGMAbwBuAHMALgBjAG8AbQBXAGUAYgBmAG8AbgB0ACAAMQAuADAAVwBlAGQAIABPAGMAdAAgADIAOQAgADAANgA6ADMANgA6ADAANwAgADIAMAAxADQARgBvAG4AdAAgAFMAcQB1AGkAcgByAGUAbAAAAAIAAAAAAAD/tQAyAAAAAAAAAAAAAAAAAAAAAAAAAAABFwAAAQIBAwADAA0ADgEEAJYBBQEGAQcBCAEJAQoBCwEMAQ0BDgEPARABEQESARMA7wEUARUBFgEXARgBGQEaARsBHAEdAR4BHwEgASEBIgEjASQBJQEmAScBKAEpASoBKwEsAS0BLgEvATABMQEyATMBNAE1ATYBNwE4ATkBOgE7ATwBPQE+AT8BQAFBAUIBQwFEAUUBRgFHAUgBSQFKAUsBTAFNAU4BTwFQAVEBUgFTAVQBVQFWAVcBWAFZAVoBWwFcAV0BXgFfAWABYQFiAWMBZAFlAWYBZwFoAWkBagFrAWwBbQFuAW8BcAFxAXIBcwF0AXUBdgF3AXgBeQF6AXsBfAF9AX4BfwGAAYEBggGDAYQBhQGGAYcBiAGJAYoBiwGMAY0BjgGPAZABkQGSAZMBlAGVAZYBlwGYAZkBmgGbAZwBnQGeAZ8BoAGhAaIBowGkAaUBpgGnAagBqQGqAasBrAGtAa4BrwGwAbEBsgGzAbQBtQG2AbcBuAG5AboBuwG8Ab0BvgG/AcABwQHCAcMBxAHFAcYBxwHIAckBygHLAcwBzQHOAc8B0AHRAdIB0wHUAdUB1gHXAdgB2QHaAdsB3AHdAd4B3wHgAeEB4gHjAeQB5QHmAecB6AHpAeoB6wHsAe0B7gHvAfAB8QHyAfMB9AH1AfYB9wH4AfkB+gH7AfwB/QH+Af8CAAIBAgICAwIEAgUCBgIHAggCCQIKAgsCDAINAg4CDwIQAhECEgZnbHlwaDEGZ2x5cGgyB3VuaTAwQTAHdW5pMjAwMAd1bmkyMDAxB3VuaTIwMDIHdW5pMjAwMwd1bmkyMDA0B3VuaTIwMDUHdW5pMjAwNgd1bmkyMDA3B3VuaTIwMDgHdW5pMjAwOQd1bmkyMDBBB3VuaTIwMkYHdW5pMjA1RgRFdXJvB3VuaTIwQkQHdW5pMjMxQgd1bmkyNUZDB3VuaTI2MDEHdW5pMjZGQQd1bmkyNzA5B3VuaTI3MEYHdW5pRTAwMQd1bmlFMDAyB3VuaUUwMDMHdW5pRTAwNQd1bmlFMDA2B3VuaUUwMDcHdW5pRTAwOAd1bmlFMDA5B3VuaUUwMTAHdW5pRTAxMQd1bmlFMDEyB3VuaUUwMTMHdW5pRTAxNAd1bmlFMDE1B3VuaUUwMTYHdW5pRTAxNwd1bmlFMDE4B3VuaUUwMTkHdW5pRTAyMAd1bmlFMDIxB3VuaUUwMjIHdW5pRTAyMwd1bmlFMDI0B3VuaUUwMjUHdW5pRTAyNgd1bmlFMDI3B3VuaUUwMjgHdW5pRTAyOQd1bmlFMDMwB3VuaUUwMzEHdW5pRTAzMgd1bmlFMDMzB3VuaUUwMzQHdW5pRTAzNQd1bmlFMDM2B3VuaUUwMzcHdW5pRTAzOAd1bmlFMDM5B3VuaUUwNDAHdW5pRTA0MQd1bmlFMDQyB3VuaUUwNDMHdW5pRTA0NAd1bmlFMDQ1B3VuaUUwNDYHdW5pRTA0Nwd1bmlFMDQ4B3VuaUUwNDkHdW5pRTA1MAd1bmlFMDUxB3VuaUUwNTIHdW5pRTA1Mwd1bmlFMDU0B3VuaUUwNTUHdW5pRTA1Ngd1bmlFMDU3B3VuaUUwNTgHdW5pRTA1OQd1bmlFMDYwB3VuaUUwNjIHdW5pRTA2Mwd1bmlFMDY0B3VuaUUwNjUHdW5pRTA2Ngd1bmlFMDY3B3VuaUUwNjgHdW5pRTA2OQd1bmlFMDcwB3VuaUUwNzEHdW5pRTA3Mgd1bmlFMDczB3VuaUUwNzQHdW5pRTA3NQd1bmlFMDc2B3VuaUUwNzcHdW5pRTA3OAd1bmlFMDc5B3VuaUUwODAHdW5pRTA4MQd1bmlFMDgyB3VuaUUwODMHdW5pRTA4NAd1bmlFMDg1B3VuaUUwODYHdW5pRTA4Nwd1bmlFMDg4B3VuaUUwODkHdW5pRTA5MAd1bmlFMDkxB3VuaUUwOTIHdW5pRTA5Mwd1bmlFMDk0B3VuaUUwOTUHdW5pRTA5Ngd1bmlFMDk3B3VuaUUxMDEHdW5pRTEwMgd1bmlFMTAzB3VuaUUxMDQHdW5pRTEwNQd1bmlFMTA2B3VuaUUxMDcHdW5pRTEwOAd1bmlFMTA5B3VuaUUxMTAHdW5pRTExMQd1bmlFMTEyB3VuaUUxMTMHdW5pRTExNAd1bmlFMTE1B3VuaUUxMTYHdW5pRTExNwd1bmlFMTE4B3VuaUUxMTkHdW5pRTEyMAd1bmlFMTIxB3VuaUUxMjIHdW5pRTEyMwd1bmlFMTI0B3VuaUUxMjUHdW5pRTEyNgd1bmlFMTI3B3VuaUUxMjgHdW5pRTEyOQd1bmlFMTMwB3VuaUUxMzEHdW5pRTEzMgd1bmlFMTMzB3VuaUUxMzQHdW5pRTEzNQd1bmlFMTM2B3VuaUUxMzcHdW5pRTEzOAd1bmlFMTM5B3VuaUUxNDAHdW5pRTE0MQd1bmlFMTQyB3VuaUUxNDMHdW5pRTE0NAd1bmlFMTQ1B3VuaUUxNDYHdW5pRTE0OAd1bmlFMTQ5B3VuaUUxNTAHdW5pRTE1MQd1bmlFMTUyB3VuaUUxNTMHdW5pRTE1NAd1bmlFMTU1B3VuaUUxNTYHdW5pRTE1Nwd1bmlFMTU4B3VuaUUxNTkHdW5pRTE2MAd1bmlFMTYxB3VuaUUxNjIHdW5pRTE2Mwd1bmlFMTY0B3VuaUUxNjUHdW5pRTE2Ngd1bmlFMTY3B3VuaUUxNjgHdW5pRTE2OQd1bmlFMTcwB3VuaUUxNzEHdW5pRTE3Mgd1bmlFMTczB3VuaUUxNzQHdW5pRTE3NQd1bmlFMTc2B3VuaUUxNzcHdW5pRTE3OAd1bmlFMTc5B3VuaUUxODAHdW5pRTE4MQd1bmlFMTgyB3VuaUUxODMHdW5pRTE4NAd1bmlFMTg1B3VuaUUxODYHdW5pRTE4Nwd1bmlFMTg4B3VuaUUxODkHdW5pRTE5MAd1bmlFMTkxB3VuaUUxOTIHdW5pRTE5Mwd1bmlFMTk0B3VuaUUxOTUHdW5pRTE5Nwd1bmlFMTk4B3VuaUUxOTkHdW5pRTIwMAd1bmlFMjAxB3VuaUUyMDIHdW5pRTIwMwd1bmlFMjA0B3VuaUUyMDUHdW5pRTIwNgd1bmlFMjA5B3VuaUUyMTAHdW5pRTIxMQd1bmlFMjEyB3VuaUUyMTMHdW5pRTIxNAd1bmlFMjE1B3VuaUUyMTYHdW5pRTIxOAd1bmlFMjE5B3VuaUUyMjEHdW5pRTIyMwd1bmlFMjI0B3VuaUUyMjUHdW5pRTIyNgd1bmlFMjI3B3VuaUUyMzAHdW5pRTIzMQd1bmlFMjMyB3VuaUUyMzMHdW5pRTIzNAd1bmlFMjM1B3VuaUUyMzYHdW5pRTIzNwd1bmlFMjM4B3VuaUUyMzkHdW5pRTI0MAd1bmlFMjQxB3VuaUUyNDIHdW5pRTI0Mwd1bmlFMjQ0B3VuaUUyNDUHdW5pRTI0Ngd1bmlFMjQ3B3VuaUUyNDgHdW5pRTI0OQd1bmlFMjUwB3VuaUUyNTEHdW5pRTI1Mgd1bmlFMjUzB3VuaUUyNTQHdW5pRTI1NQd1bmlFMjU2B3VuaUUyNTcHdW5pRTI1OAd1bmlFMjU5B3VuaUUyNjAHdW5pRjhGRgZ1MUY1MTEGdTFGNkFBAAAAAAFUUMMXAAA=) format('truetype'),url(data:image/svg+xml;base64,<?xml version="1.0" standalone="no"?>
<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd" >
<svg xmlns="http://www.w3.org/2000/svg">
<metadata></metadata>
<defs>
<font id="glyphicons_halflingsregular" horiz-adv-x="1200" >
<font-face units-per-em="1200" ascent="960" descent="-240" />
<missing-glyph horiz-adv-x="500" />
<glyph horiz-adv-x="0" />
<glyph horiz-adv-x="400" />
<glyph unicode=" " />
<glyph unicode="*" d="M600 1100q15 0 34 -1.5t30 -3.5l11 -1q10 -2 17.5 -10.5t7.5 -18.5v-224l158 158q7 7 18 8t19 -6l106 -106q7 -8 6 -19t-8 -18l-158 -158h224q10 0 18.5 -7.5t10.5 -17.5q6 -41 6 -75q0 -15 -1.5 -34t-3.5 -30l-1 -11q-2 -10 -10.5 -17.5t-18.5 -7.5h-224l158 -158 q7 -7 8 -18t-6 -19l-106 -106q-8 -7 -19 -6t-18 8l-158 158v-224q0 -10 -7.5 -18.5t-17.5 -10.5q-41 -6 -75 -6q-15 0 -34 1.5t-30 3.5l-11 1q-10 2 -17.5 10.5t-7.5 18.5v224l-158 -158q-7 -7 -18 -8t-19 6l-106 106q-7 8 -6 19t8 18l158 158h-224q-10 0 -18.5 7.5 t-10.5 17.5q-6 41 -6 75q0 15 1.5 34t3.5 30l1 11q2 10 10.5 17.5t18.5 7.5h224l-158 158q-7 7 -8 18t6 19l106 106q8 7 19 6t18 -8l158 -158v224q0 10 7.5 18.5t17.5 10.5q41 6 75 6z" />
<glyph unicode="+" d="M450 1100h200q21 0 35.5 -14.5t14.5 -35.5v-350h350q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-350v-350q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v350h-350q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5 h350v350q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xa0;" />
<glyph unicode="&#xa5;" d="M825 1100h250q10 0 12.5 -5t-5.5 -13l-364 -364q-6 -6 -11 -18h268q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-125v-100h275q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-125v-174q0 -11 -7.5 -18.5t-18.5 -7.5h-148q-11 0 -18.5 7.5t-7.5 18.5v174 h-275q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h125v100h-275q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h118q-5 12 -11 18l-364 364q-8 8 -5.5 13t12.5 5h250q25 0 43 -18l164 -164q8 -8 18 -8t18 8l164 164q18 18 43 18z" />
<glyph unicode="&#x2000;" horiz-adv-x="650" />
<glyph unicode="&#x2001;" horiz-adv-x="1300" />
<glyph unicode="&#x2002;" horiz-adv-x="650" />
<glyph unicode="&#x2003;" horiz-adv-x="1300" />
<glyph unicode="&#x2004;" horiz-adv-x="433" />
<glyph unicode="&#x2005;" horiz-adv-x="325" />
<glyph unicode="&#x2006;" horiz-adv-x="216" />
<glyph unicode="&#x2007;" horiz-adv-x="216" />
<glyph unicode="&#x2008;" horiz-adv-x="162" />
<glyph unicode="&#x2009;" horiz-adv-x="260" />
<glyph unicode="&#x200a;" horiz-adv-x="72" />
<glyph unicode="&#x202f;" horiz-adv-x="260" />
<glyph unicode="&#x205f;" horiz-adv-x="325" />
<glyph unicode="&#x20ac;" d="M744 1198q242 0 354 -189q60 -104 66 -209h-181q0 45 -17.5 82.5t-43.5 61.5t-58 40.5t-60.5 24t-51.5 7.5q-19 0 -40.5 -5.5t-49.5 -20.5t-53 -38t-49 -62.5t-39 -89.5h379l-100 -100h-300q-6 -50 -6 -100h406l-100 -100h-300q9 -74 33 -132t52.5 -91t61.5 -54.5t59 -29 t47 -7.5q22 0 50.5 7.5t60.5 24.5t58 41t43.5 61t17.5 80h174q-30 -171 -128 -278q-107 -117 -274 -117q-206 0 -324 158q-36 48 -69 133t-45 204h-217l100 100h112q1 47 6 100h-218l100 100h134q20 87 51 153.5t62 103.5q117 141 297 141z" />
<glyph unicode="&#x20bd;" d="M428 1200h350q67 0 120 -13t86 -31t57 -49.5t35 -56.5t17 -64.5t6.5 -60.5t0.5 -57v-16.5v-16.5q0 -36 -0.5 -57t-6.5 -61t-17 -65t-35 -57t-57 -50.5t-86 -31.5t-120 -13h-178l-2 -100h288q10 0 13 -6t-3 -14l-120 -160q-6 -8 -18 -14t-22 -6h-138v-175q0 -11 -5.5 -18 t-15.5 -7h-149q-10 0 -17.5 7.5t-7.5 17.5v175h-267q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h117v100h-267q-10 0 -13 6t3 14l120 160q6 8 18 14t22 6h117v475q0 10 7.5 17.5t17.5 7.5zM600 1000v-300h203q64 0 86.5 33t22.5 119q0 84 -22.5 116t-86.5 32h-203z" />
<glyph unicode="&#x2212;" d="M250 700h800q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#x231b;" d="M1000 1200v-150q0 -21 -14.5 -35.5t-35.5 -14.5h-50v-100q0 -91 -49.5 -165.5t-130.5 -109.5q81 -35 130.5 -109.5t49.5 -165.5v-150h50q21 0 35.5 -14.5t14.5 -35.5v-150h-800v150q0 21 14.5 35.5t35.5 14.5h50v150q0 91 49.5 165.5t130.5 109.5q-81 35 -130.5 109.5 t-49.5 165.5v100h-50q-21 0 -35.5 14.5t-14.5 35.5v150h800zM400 1000v-100q0 -60 32.5 -109.5t87.5 -73.5q28 -12 44 -37t16 -55t-16 -55t-44 -37q-55 -24 -87.5 -73.5t-32.5 -109.5v-150h400v150q0 60 -32.5 109.5t-87.5 73.5q-28 12 -44 37t-16 55t16 55t44 37 q55 24 87.5 73.5t32.5 109.5v100h-400z" />
<glyph unicode="&#x25fc;" horiz-adv-x="500" d="M0 0z" />
<glyph unicode="&#x2601;" d="M503 1089q110 0 200.5 -59.5t134.5 -156.5q44 14 90 14q120 0 205 -86.5t85 -206.5q0 -121 -85 -207.5t-205 -86.5h-750q-79 0 -135.5 57t-56.5 137q0 69 42.5 122.5t108.5 67.5q-2 12 -2 37q0 153 108 260.5t260 107.5z" />
<glyph unicode="&#x26fa;" d="M774 1193.5q16 -9.5 20.5 -27t-5.5 -33.5l-136 -187l467 -746h30q20 0 35 -18.5t15 -39.5v-42h-1200v42q0 21 15 39.5t35 18.5h30l468 746l-135 183q-10 16 -5.5 34t20.5 28t34 5.5t28 -20.5l111 -148l112 150q9 16 27 20.5t34 -5zM600 200h377l-182 112l-195 534v-646z " />
<glyph unicode="&#x2709;" d="M25 1100h1150q10 0 12.5 -5t-5.5 -13l-564 -567q-8 -8 -18 -8t-18 8l-564 567q-8 8 -5.5 13t12.5 5zM18 882l264 -264q8 -8 8 -18t-8 -18l-264 -264q-8 -8 -13 -5.5t-5 12.5v550q0 10 5 12.5t13 -5.5zM918 618l264 264q8 8 13 5.5t5 -12.5v-550q0 -10 -5 -12.5t-13 5.5 l-264 264q-8 8 -8 18t8 18zM818 482l364 -364q8 -8 5.5 -13t-12.5 -5h-1150q-10 0 -12.5 5t5.5 13l364 364q8 8 18 8t18 -8l164 -164q8 -8 18 -8t18 8l164 164q8 8 18 8t18 -8z" />
<glyph unicode="&#x270f;" d="M1011 1210q19 0 33 -13l153 -153q13 -14 13 -33t-13 -33l-99 -92l-214 214l95 96q13 14 32 14zM1013 800l-615 -614l-214 214l614 614zM317 96l-333 -112l110 335z" />
<glyph unicode="&#xe001;" d="M700 650v-550h250q21 0 35.5 -14.5t14.5 -35.5v-50h-800v50q0 21 14.5 35.5t35.5 14.5h250v550l-500 550h1200z" />
<glyph unicode="&#xe002;" d="M368 1017l645 163q39 15 63 0t24 -49v-831q0 -55 -41.5 -95.5t-111.5 -63.5q-79 -25 -147 -4.5t-86 75t25.5 111.5t122.5 82q72 24 138 8v521l-600 -155v-606q0 -42 -44 -90t-109 -69q-79 -26 -147 -5.5t-86 75.5t25.5 111.5t122.5 82.5q72 24 138 7v639q0 38 14.5 59 t53.5 34z" />
<glyph unicode="&#xe003;" d="M500 1191q100 0 191 -39t156.5 -104.5t104.5 -156.5t39 -191l-1 -2l1 -5q0 -141 -78 -262l275 -274q23 -26 22.5 -44.5t-22.5 -42.5l-59 -58q-26 -20 -46.5 -20t-39.5 20l-275 274q-119 -77 -261 -77l-5 1l-2 -1q-100 0 -191 39t-156.5 104.5t-104.5 156.5t-39 191 t39 191t104.5 156.5t156.5 104.5t191 39zM500 1022q-88 0 -162 -43t-117 -117t-43 -162t43 -162t117 -117t162 -43t162 43t117 117t43 162t-43 162t-117 117t-162 43z" />
<glyph unicode="&#xe005;" d="M649 949q48 68 109.5 104t121.5 38.5t118.5 -20t102.5 -64t71 -100.5t27 -123q0 -57 -33.5 -117.5t-94 -124.5t-126.5 -127.5t-150 -152.5t-146 -174q-62 85 -145.5 174t-150 152.5t-126.5 127.5t-93.5 124.5t-33.5 117.5q0 64 28 123t73 100.5t104 64t119 20 t120.5 -38.5t104.5 -104z" />
<glyph unicode="&#xe006;" d="M407 800l131 353q7 19 17.5 19t17.5 -19l129 -353h421q21 0 24 -8.5t-14 -20.5l-342 -249l130 -401q7 -20 -0.5 -25.5t-24.5 6.5l-343 246l-342 -247q-17 -12 -24.5 -6.5t-0.5 25.5l130 400l-347 251q-17 12 -14 20.5t23 8.5h429z" />
<glyph unicode="&#xe007;" d="M407 800l131 353q7 19 17.5 19t17.5 -19l129 -353h421q21 0 24 -8.5t-14 -20.5l-342 -249l130 -401q7 -20 -0.5 -25.5t-24.5 6.5l-343 246l-342 -247q-17 -12 -24.5 -6.5t-0.5 25.5l130 400l-347 251q-17 12 -14 20.5t23 8.5h429zM477 700h-240l197 -142l-74 -226 l193 139l195 -140l-74 229l192 140h-234l-78 211z" />
<glyph unicode="&#xe008;" d="M600 1200q124 0 212 -88t88 -212v-250q0 -46 -31 -98t-69 -52v-75q0 -10 6 -21.5t15 -17.5l358 -230q9 -5 15 -16.5t6 -21.5v-93q0 -10 -7.5 -17.5t-17.5 -7.5h-1150q-10 0 -17.5 7.5t-7.5 17.5v93q0 10 6 21.5t15 16.5l358 230q9 6 15 17.5t6 21.5v75q-38 0 -69 52 t-31 98v250q0 124 88 212t212 88z" />
<glyph unicode="&#xe009;" d="M25 1100h1150q10 0 17.5 -7.5t7.5 -17.5v-1050q0 -10 -7.5 -17.5t-17.5 -7.5h-1150q-10 0 -17.5 7.5t-7.5 17.5v1050q0 10 7.5 17.5t17.5 7.5zM100 1000v-100h100v100h-100zM875 1000h-550q-10 0 -17.5 -7.5t-7.5 -17.5v-350q0 -10 7.5 -17.5t17.5 -7.5h550 q10 0 17.5 7.5t7.5 17.5v350q0 10 -7.5 17.5t-17.5 7.5zM1000 1000v-100h100v100h-100zM100 800v-100h100v100h-100zM1000 800v-100h100v100h-100zM100 600v-100h100v100h-100zM1000 600v-100h100v100h-100zM875 500h-550q-10 0 -17.5 -7.5t-7.5 -17.5v-350q0 -10 7.5 -17.5 t17.5 -7.5h550q10 0 17.5 7.5t7.5 17.5v350q0 10 -7.5 17.5t-17.5 7.5zM100 400v-100h100v100h-100zM1000 400v-100h100v100h-100zM100 200v-100h100v100h-100zM1000 200v-100h100v100h-100z" />
<glyph unicode="&#xe010;" d="M50 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM650 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400 q0 21 14.5 35.5t35.5 14.5zM50 500h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM650 500h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe011;" d="M50 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200 q0 21 14.5 35.5t35.5 14.5zM850 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200 q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM850 700h200q21 0 35.5 -14.5t14.5 -35.5v-200 q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 300h200 q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM850 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5 t35.5 14.5z" />
<glyph unicode="&#xe012;" d="M50 1100h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 1100h700q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v200 q0 21 14.5 35.5t35.5 14.5zM50 700h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 700h700q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-700 q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM50 300h200q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5zM450 300h700q21 0 35.5 -14.5t14.5 -35.5v-200 q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe013;" d="M465 477l571 571q8 8 18 8t17 -8l177 -177q8 -7 8 -17t-8 -18l-783 -784q-7 -8 -17.5 -8t-17.5 8l-384 384q-8 8 -8 18t8 17l177 177q7 8 17 8t18 -8l171 -171q7 -7 18 -7t18 7z" />
<glyph unicode="&#xe014;" d="M904 1083l178 -179q8 -8 8 -18.5t-8 -17.5l-267 -268l267 -268q8 -7 8 -17.5t-8 -18.5l-178 -178q-8 -8 -18.5 -8t-17.5 8l-268 267l-268 -267q-7 -8 -17.5 -8t-18.5 8l-178 178q-8 8 -8 18.5t8 17.5l267 268l-267 268q-8 7 -8 17.5t8 18.5l178 178q8 8 18.5 8t17.5 -8 l268 -267l268 268q7 7 17.5 7t18.5 -7z" />
<glyph unicode="&#xe015;" d="M507 1177q98 0 187.5 -38.5t154.5 -103.5t103.5 -154.5t38.5 -187.5q0 -141 -78 -262l300 -299q8 -8 8 -18.5t-8 -18.5l-109 -108q-7 -8 -17.5 -8t-18.5 8l-300 299q-119 -77 -261 -77q-98 0 -188 38.5t-154.5 103t-103 154.5t-38.5 188t38.5 187.5t103 154.5 t154.5 103.5t188 38.5zM506.5 1023q-89.5 0 -165.5 -44t-120 -120.5t-44 -166t44 -165.5t120 -120t165.5 -44t166 44t120.5 120t44 165.5t-44 166t-120.5 120.5t-166 44zM425 900h150q10 0 17.5 -7.5t7.5 -17.5v-75h75q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5 t-17.5 -7.5h-75v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-75q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h75v75q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe016;" d="M507 1177q98 0 187.5 -38.5t154.5 -103.5t103.5 -154.5t38.5 -187.5q0 -141 -78 -262l300 -299q8 -8 8 -18.5t-8 -18.5l-109 -108q-7 -8 -17.5 -8t-18.5 8l-300 299q-119 -77 -261 -77q-98 0 -188 38.5t-154.5 103t-103 154.5t-38.5 188t38.5 187.5t103 154.5 t154.5 103.5t188 38.5zM506.5 1023q-89.5 0 -165.5 -44t-120 -120.5t-44 -166t44 -165.5t120 -120t165.5 -44t166 44t120.5 120t44 165.5t-44 166t-120.5 120.5t-166 44zM325 800h350q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-350q-10 0 -17.5 7.5 t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe017;" d="M550 1200h100q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM800 975v166q167 -62 272 -209.5t105 -331.5q0 -117 -45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5 t-184.5 123t-123 184.5t-45.5 224q0 184 105 331.5t272 209.5v-166q-103 -55 -165 -155t-62 -220q0 -116 57 -214.5t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5q0 120 -62 220t-165 155z" />
<glyph unicode="&#xe018;" d="M1025 1200h150q10 0 17.5 -7.5t7.5 -17.5v-1150q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v1150q0 10 7.5 17.5t17.5 7.5zM725 800h150q10 0 17.5 -7.5t7.5 -17.5v-750q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v750 q0 10 7.5 17.5t17.5 7.5zM425 500h150q10 0 17.5 -7.5t7.5 -17.5v-450q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v450q0 10 7.5 17.5t17.5 7.5zM125 300h150q10 0 17.5 -7.5t7.5 -17.5v-250q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5 v250q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe019;" d="M600 1174q33 0 74 -5l38 -152l5 -1q49 -14 94 -39l5 -2l134 80q61 -48 104 -105l-80 -134l3 -5q25 -44 39 -93l1 -6l152 -38q5 -43 5 -73q0 -34 -5 -74l-152 -38l-1 -6q-15 -49 -39 -93l-3 -5l80 -134q-48 -61 -104 -105l-134 81l-5 -3q-44 -25 -94 -39l-5 -2l-38 -151 q-43 -5 -74 -5q-33 0 -74 5l-38 151l-5 2q-49 14 -94 39l-5 3l-134 -81q-60 48 -104 105l80 134l-3 5q-25 45 -38 93l-2 6l-151 38q-6 42 -6 74q0 33 6 73l151 38l2 6q13 48 38 93l3 5l-80 134q47 61 105 105l133 -80l5 2q45 25 94 39l5 1l38 152q43 5 74 5zM600 815 q-89 0 -152 -63t-63 -151.5t63 -151.5t152 -63t152 63t63 151.5t-63 151.5t-152 63z" />
<glyph unicode="&#xe020;" d="M500 1300h300q41 0 70.5 -29.5t29.5 -70.5v-100h275q10 0 17.5 -7.5t7.5 -17.5v-75h-1100v75q0 10 7.5 17.5t17.5 7.5h275v100q0 41 29.5 70.5t70.5 29.5zM500 1200v-100h300v100h-300zM1100 900v-800q0 -41 -29.5 -70.5t-70.5 -29.5h-700q-41 0 -70.5 29.5t-29.5 70.5 v800h900zM300 800v-700h100v700h-100zM500 800v-700h100v700h-100zM700 800v-700h100v700h-100zM900 800v-700h100v700h-100z" />
<glyph unicode="&#xe021;" d="M18 618l620 608q8 7 18.5 7t17.5 -7l608 -608q8 -8 5.5 -13t-12.5 -5h-175v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v375h-300v-375q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v575h-175q-10 0 -12.5 5t5.5 13z" />
<glyph unicode="&#xe022;" d="M600 1200v-400q0 -41 29.5 -70.5t70.5 -29.5h300v-650q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v1100q0 21 14.5 35.5t35.5 14.5h450zM1000 800h-250q-21 0 -35.5 14.5t-14.5 35.5v250z" />
<glyph unicode="&#xe023;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM525 900h50q10 0 17.5 -7.5t7.5 -17.5v-275h175q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe024;" d="M1300 0h-538l-41 400h-242l-41 -400h-538l431 1200h209l-21 -300h162l-20 300h208zM515 800l-27 -300h224l-27 300h-170z" />
<glyph unicode="&#xe025;" d="M550 1200h200q21 0 35.5 -14.5t14.5 -35.5v-450h191q20 0 25.5 -11.5t-7.5 -27.5l-327 -400q-13 -16 -32 -16t-32 16l-327 400q-13 16 -7.5 27.5t25.5 11.5h191v450q0 21 14.5 35.5t35.5 14.5zM1125 400h50q10 0 17.5 -7.5t7.5 -17.5v-350q0 -10 -7.5 -17.5t-17.5 -7.5 h-1050q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h50q10 0 17.5 -7.5t7.5 -17.5v-175h900v175q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe026;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM525 900h150q10 0 17.5 -7.5t7.5 -17.5v-275h137q21 0 26 -11.5t-8 -27.5l-223 -275q-13 -16 -32 -16t-32 16l-223 275q-13 16 -8 27.5t26 11.5h137v275q0 10 7.5 17.5t17.5 7.5z " />
<glyph unicode="&#xe027;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM632 914l223 -275q13 -16 8 -27.5t-26 -11.5h-137v-275q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v275h-137q-21 0 -26 11.5t8 27.5l223 275q13 16 32 16 t32 -16z" />
<glyph unicode="&#xe028;" d="M225 1200h750q10 0 19.5 -7t12.5 -17l186 -652q7 -24 7 -49v-425q0 -12 -4 -27t-9 -17q-12 -6 -37 -6h-1100q-12 0 -27 4t-17 8q-6 13 -6 38l1 425q0 25 7 49l185 652q3 10 12.5 17t19.5 7zM878 1000h-556q-10 0 -19 -7t-11 -18l-87 -450q-2 -11 4 -18t16 -7h150 q10 0 19.5 -7t11.5 -17l38 -152q2 -10 11.5 -17t19.5 -7h250q10 0 19.5 7t11.5 17l38 152q2 10 11.5 17t19.5 7h150q10 0 16 7t4 18l-87 450q-2 11 -11 18t-19 7z" />
<glyph unicode="&#xe029;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM540 820l253 -190q17 -12 17 -30t-17 -30l-253 -190q-16 -12 -28 -6.5t-12 26.5v400q0 21 12 26.5t28 -6.5z" />
<glyph unicode="&#xe030;" d="M947 1060l135 135q7 7 12.5 5t5.5 -13v-362q0 -10 -7.5 -17.5t-17.5 -7.5h-362q-11 0 -13 5.5t5 12.5l133 133q-109 76 -238 76q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5h150q0 -117 -45.5 -224 t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5q192 0 347 -117z" />
<glyph unicode="&#xe031;" d="M947 1060l135 135q7 7 12.5 5t5.5 -13v-361q0 -11 -7.5 -18.5t-18.5 -7.5h-361q-11 0 -13 5.5t5 12.5l134 134q-110 75 -239 75q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5h-150q0 117 45.5 224t123 184.5t184.5 123t224 45.5q192 0 347 -117zM1027 600h150 q0 -117 -45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5q-192 0 -348 118l-134 -134q-7 -8 -12.5 -5.5t-5.5 12.5v360q0 11 7.5 18.5t18.5 7.5h360q10 0 12.5 -5.5t-5.5 -12.5l-133 -133q110 -76 240 -76q116 0 214.5 57t155.5 155.5t57 214.5z" />
<glyph unicode="&#xe032;" d="M125 1200h1050q10 0 17.5 -7.5t7.5 -17.5v-1150q0 -10 -7.5 -17.5t-17.5 -7.5h-1050q-10 0 -17.5 7.5t-7.5 17.5v1150q0 10 7.5 17.5t17.5 7.5zM1075 1000h-850q-10 0 -17.5 -7.5t-7.5 -17.5v-850q0 -10 7.5 -17.5t17.5 -7.5h850q10 0 17.5 7.5t7.5 17.5v850 q0 10 -7.5 17.5t-17.5 7.5zM325 900h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 900h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 700h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 700h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 500h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 500h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5zM325 300h50q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM525 300h450q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-450q-10 0 -17.5 7.5t-7.5 17.5v50 q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe033;" d="M900 800v200q0 83 -58.5 141.5t-141.5 58.5h-300q-82 0 -141 -59t-59 -141v-200h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-600q0 -41 29.5 -70.5t70.5 -29.5h900q41 0 70.5 29.5t29.5 70.5v600q0 41 -29.5 70.5t-70.5 29.5h-100zM400 800v150q0 21 15 35.5t35 14.5h200 q20 0 35 -14.5t15 -35.5v-150h-300z" />
<glyph unicode="&#xe034;" d="M125 1100h50q10 0 17.5 -7.5t7.5 -17.5v-1075h-100v1075q0 10 7.5 17.5t17.5 7.5zM1075 1052q4 0 9 -2q16 -6 16 -23v-421q0 -6 -3 -12q-33 -59 -66.5 -99t-65.5 -58t-56.5 -24.5t-52.5 -6.5q-26 0 -57.5 6.5t-52.5 13.5t-60 21q-41 15 -63 22.5t-57.5 15t-65.5 7.5 q-85 0 -160 -57q-7 -5 -15 -5q-6 0 -11 3q-14 7 -14 22v438q22 55 82 98.5t119 46.5q23 2 43 0.5t43 -7t32.5 -8.5t38 -13t32.5 -11q41 -14 63.5 -21t57 -14t63.5 -7q103 0 183 87q7 8 18 8z" />
<glyph unicode="&#xe035;" d="M600 1175q116 0 227 -49.5t192.5 -131t131 -192.5t49.5 -227v-300q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v300q0 127 -70.5 231.5t-184.5 161.5t-245 57t-245 -57t-184.5 -161.5t-70.5 -231.5v-300q0 -10 -7.5 -17.5t-17.5 -7.5h-50 q-10 0 -17.5 7.5t-7.5 17.5v300q0 116 49.5 227t131 192.5t192.5 131t227 49.5zM220 500h160q8 0 14 -6t6 -14v-460q0 -8 -6 -14t-14 -6h-160q-8 0 -14 6t-6 14v460q0 8 6 14t14 6zM820 500h160q8 0 14 -6t6 -14v-460q0 -8 -6 -14t-14 -6h-160q-8 0 -14 6t-6 14v460 q0 8 6 14t14 6z" />
<glyph unicode="&#xe036;" d="M321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM900 668l120 120q7 7 17 7t17 -7l34 -34q7 -7 7 -17t-7 -17l-120 -120l120 -120q7 -7 7 -17 t-7 -17l-34 -34q-7 -7 -17 -7t-17 7l-120 119l-120 -119q-7 -7 -17 -7t-17 7l-34 34q-7 7 -7 17t7 17l119 120l-119 120q-7 7 -7 17t7 17l34 34q7 8 17 8t17 -8z" />
<glyph unicode="&#xe037;" d="M321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM766 900h4q10 -1 16 -10q96 -129 96 -290q0 -154 -90 -281q-6 -9 -17 -10l-3 -1q-9 0 -16 6 l-29 23q-7 7 -8.5 16.5t4.5 17.5q72 103 72 229q0 132 -78 238q-6 8 -4.5 18t9.5 17l29 22q7 5 15 5z" />
<glyph unicode="&#xe038;" d="M967 1004h3q11 -1 17 -10q135 -179 135 -396q0 -105 -34 -206.5t-98 -185.5q-7 -9 -17 -10h-3q-9 0 -16 6l-42 34q-8 6 -9 16t5 18q111 150 111 328q0 90 -29.5 176t-84.5 157q-6 9 -5 19t10 16l42 33q7 5 15 5zM321 814l258 172q9 6 15 2.5t6 -13.5v-750q0 -10 -6 -13.5 t-15 2.5l-258 172q-21 14 -46 14h-250q-10 0 -17.5 7.5t-7.5 17.5v350q0 10 7.5 17.5t17.5 7.5h250q25 0 46 14zM766 900h4q10 -1 16 -10q96 -129 96 -290q0 -154 -90 -281q-6 -9 -17 -10l-3 -1q-9 0 -16 6l-29 23q-7 7 -8.5 16.5t4.5 17.5q72 103 72 229q0 132 -78 238 q-6 8 -4.5 18.5t9.5 16.5l29 22q7 5 15 5z" />
<glyph unicode="&#xe039;" d="M500 900h100v-100h-100v-100h-400v-100h-100v600h500v-300zM1200 700h-200v-100h200v-200h-300v300h-200v300h-100v200h600v-500zM100 1100v-300h300v300h-300zM800 1100v-300h300v300h-300zM300 900h-100v100h100v-100zM1000 900h-100v100h100v-100zM300 500h200v-500 h-500v500h200v100h100v-100zM800 300h200v-100h-100v-100h-200v100h-100v100h100v200h-200v100h300v-300zM100 400v-300h300v300h-300zM300 200h-100v100h100v-100zM1200 200h-100v100h100v-100zM700 0h-100v100h100v-100zM1200 0h-300v100h300v-100z" />
<glyph unicode="&#xe040;" d="M100 200h-100v1000h100v-1000zM300 200h-100v1000h100v-1000zM700 200h-200v1000h200v-1000zM900 200h-100v1000h100v-1000zM1200 200h-200v1000h200v-1000zM400 0h-300v100h300v-100zM600 0h-100v91h100v-91zM800 0h-100v91h100v-91zM1100 0h-200v91h200v-91z" />
<glyph unicode="&#xe041;" d="M500 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-682 682l1 475q0 10 7.5 17.5t17.5 7.5h474zM319.5 1024.5q-29.5 29.5 -71 29.5t-71 -29.5t-29.5 -71.5t29.5 -71.5t71 -29.5t71 29.5t29.5 71.5t-29.5 71.5z" />
<glyph unicode="&#xe042;" d="M500 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-682 682l1 475q0 10 7.5 17.5t17.5 7.5h474zM800 1200l682 -682q8 -8 8 -18t-8 -18l-464 -464q-8 -8 -18 -8t-18 8l-56 56l424 426l-700 700h150zM319.5 1024.5q-29.5 29.5 -71 29.5t-71 -29.5 t-29.5 -71.5t29.5 -71.5t71 -29.5t71 29.5t29.5 71.5t-29.5 71.5z" />
<glyph unicode="&#xe043;" d="M300 1200h825q75 0 75 -75v-900q0 -25 -18 -43l-64 -64q-8 -8 -13 -5.5t-5 12.5v950q0 10 -7.5 17.5t-17.5 7.5h-700q-25 0 -43 -18l-64 -64q-8 -8 -5.5 -13t12.5 -5h700q10 0 17.5 -7.5t7.5 -17.5v-950q0 -10 -7.5 -17.5t-17.5 -7.5h-850q-10 0 -17.5 7.5t-7.5 17.5v975 q0 25 18 43l139 139q18 18 43 18z" />
<glyph unicode="&#xe044;" d="M250 1200h800q21 0 35.5 -14.5t14.5 -35.5v-1150l-450 444l-450 -445v1151q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe045;" d="M822 1200h-444q-11 0 -19 -7.5t-9 -17.5l-78 -301q-7 -24 7 -45l57 -108q6 -9 17.5 -15t21.5 -6h450q10 0 21.5 6t17.5 15l62 108q14 21 7 45l-83 301q-1 10 -9 17.5t-19 7.5zM1175 800h-150q-10 0 -21 -6.5t-15 -15.5l-78 -156q-4 -9 -15 -15.5t-21 -6.5h-550 q-10 0 -21 6.5t-15 15.5l-78 156q-4 9 -15 15.5t-21 6.5h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-650q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h750q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5 t7.5 17.5v650q0 10 -7.5 17.5t-17.5 7.5zM850 200h-500q-10 0 -19.5 -7t-11.5 -17l-38 -152q-2 -10 3.5 -17t15.5 -7h600q10 0 15.5 7t3.5 17l-38 152q-2 10 -11.5 17t-19.5 7z" />
<glyph unicode="&#xe046;" d="M500 1100h200q56 0 102.5 -20.5t72.5 -50t44 -59t25 -50.5l6 -20h150q41 0 70.5 -29.5t29.5 -70.5v-600q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v600q0 41 29.5 70.5t70.5 29.5h150q2 8 6.5 21.5t24 48t45 61t72 48t102.5 21.5zM900 800v-100 h100v100h-100zM600 730q-95 0 -162.5 -67.5t-67.5 -162.5t67.5 -162.5t162.5 -67.5t162.5 67.5t67.5 162.5t-67.5 162.5t-162.5 67.5zM600 603q43 0 73 -30t30 -73t-30 -73t-73 -30t-73 30t-30 73t30 73t73 30z" />
<glyph unicode="&#xe047;" d="M681 1199l385 -998q20 -50 60 -92q18 -19 36.5 -29.5t27.5 -11.5l10 -2v-66h-417v66q53 0 75 43.5t5 88.5l-82 222h-391q-58 -145 -92 -234q-11 -34 -6.5 -57t25.5 -37t46 -20t55 -6v-66h-365v66q56 24 84 52q12 12 25 30.5t20 31.5l7 13l399 1006h93zM416 521h340 l-162 457z" />
<glyph unicode="&#xe048;" d="M753 641q5 -1 14.5 -4.5t36 -15.5t50.5 -26.5t53.5 -40t50.5 -54.5t35.5 -70t14.5 -87q0 -67 -27.5 -125.5t-71.5 -97.5t-98.5 -66.5t-108.5 -40.5t-102 -13h-500v89q41 7 70.5 32.5t29.5 65.5v827q0 24 -0.5 34t-3.5 24t-8.5 19.5t-17 13.5t-28 12.5t-42.5 11.5v71 l471 -1q57 0 115.5 -20.5t108 -57t80.5 -94t31 -124.5q0 -51 -15.5 -96.5t-38 -74.5t-45 -50.5t-38.5 -30.5zM400 700h139q78 0 130.5 48.5t52.5 122.5q0 41 -8.5 70.5t-29.5 55.5t-62.5 39.5t-103.5 13.5h-118v-350zM400 200h216q80 0 121 50.5t41 130.5q0 90 -62.5 154.5 t-156.5 64.5h-159v-400z" />
<glyph unicode="&#xe049;" d="M877 1200l2 -57q-83 -19 -116 -45.5t-40 -66.5l-132 -839q-9 -49 13 -69t96 -26v-97h-500v97q186 16 200 98l173 832q3 17 3 30t-1.5 22.5t-9 17.5t-13.5 12.5t-21.5 10t-26 8.5t-33.5 10q-13 3 -19 5v57h425z" />
<glyph unicode="&#xe050;" d="M1300 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-850q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v850h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM175 1000h-75v-800h75l-125 -167l-125 167h75v800h-75l125 167z" />
<glyph unicode="&#xe051;" d="M1100 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-650q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v650h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM1167 50l-167 -125v75h-800v-75l-167 125l167 125v-75h800v75z" />
<glyph unicode="&#xe052;" d="M50 1100h600q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 500h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe053;" d="M250 1100h700q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM250 500h700q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-700q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe054;" d="M500 950v100q0 21 14.5 35.5t35.5 14.5h600q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5zM100 650v100q0 21 14.5 35.5t35.5 14.5h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000 q-21 0 -35.5 14.5t-14.5 35.5zM300 350v100q0 21 14.5 35.5t35.5 14.5h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5zM0 50v100q0 21 14.5 35.5t35.5 14.5h1100q21 0 35.5 -14.5t14.5 -35.5v-100 q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5z" />
<glyph unicode="&#xe055;" d="M50 1100h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 800h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 500h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h1100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe056;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 1100h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 800h800q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 500h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 500h800q21 0 35.5 -14.5t14.5 -35.5v-100 q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM350 200h800 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe057;" d="M400 0h-100v1100h100v-1100zM550 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM550 800h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM267 550l-167 -125v75h-200v100h200v75zM550 500h300q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM550 200h600 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe058;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM900 0h-100v1100h100v-1100zM50 800h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM1100 600h200v-100h-200v-75l-167 125l167 125v-75zM50 500h300q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5zM50 200h600 q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-600q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe059;" d="M75 1000h750q31 0 53 -22t22 -53v-650q0 -31 -22 -53t-53 -22h-750q-31 0 -53 22t-22 53v650q0 31 22 53t53 22zM1200 300l-300 300l300 300v-600z" />
<glyph unicode="&#xe060;" d="M44 1100h1112q18 0 31 -13t13 -31v-1012q0 -18 -13 -31t-31 -13h-1112q-18 0 -31 13t-13 31v1012q0 18 13 31t31 13zM100 1000v-737l247 182l298 -131l-74 156l293 318l236 -288v500h-1000zM342 884q56 0 95 -39t39 -94.5t-39 -95t-95 -39.5t-95 39.5t-39 95t39 94.5 t95 39z" />
<glyph unicode="&#xe062;" d="M648 1169q117 0 216 -60t156.5 -161t57.5 -218q0 -115 -70 -258q-69 -109 -158 -225.5t-143 -179.5l-54 -62q-9 8 -25.5 24.5t-63.5 67.5t-91 103t-98.5 128t-95.5 148q-60 132 -60 249q0 88 34 169.5t91.5 142t137 96.5t166.5 36zM652.5 974q-91.5 0 -156.5 -65 t-65 -157t65 -156.5t156.5 -64.5t156.5 64.5t65 156.5t-65 157t-156.5 65z" />
<glyph unicode="&#xe063;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 173v854q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57z" />
<glyph unicode="&#xe064;" d="M554 1295q21 -72 57.5 -143.5t76 -130t83 -118t82.5 -117t70 -116t49.5 -126t18.5 -136.5q0 -71 -25.5 -135t-68.5 -111t-99 -82t-118.5 -54t-125.5 -23q-84 5 -161.5 34t-139.5 78.5t-99 125t-37 164.5q0 69 18 136.5t49.5 126.5t69.5 116.5t81.5 117.5t83.5 119 t76.5 131t58.5 143zM344 710q-23 -33 -43.5 -70.5t-40.5 -102.5t-17 -123q1 -37 14.5 -69.5t30 -52t41 -37t38.5 -24.5t33 -15q21 -7 32 -1t13 22l6 34q2 10 -2.5 22t-13.5 19q-5 4 -14 12t-29.5 40.5t-32.5 73.5q-26 89 6 271q2 11 -6 11q-8 1 -15 -10z" />
<glyph unicode="&#xe065;" d="M1000 1013l108 115q2 1 5 2t13 2t20.5 -1t25 -9.5t28.5 -21.5q22 -22 27 -43t0 -32l-6 -10l-108 -115zM350 1100h400q50 0 105 -13l-187 -187h-368q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v182l200 200v-332 q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5zM1009 803l-362 -362l-161 -50l55 170l355 355z" />
<glyph unicode="&#xe066;" d="M350 1100h361q-164 -146 -216 -200h-195q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5l200 153v-103q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5z M824 1073l339 -301q8 -7 8 -17.5t-8 -17.5l-340 -306q-7 -6 -12.5 -4t-6.5 11v203q-26 1 -54.5 0t-78.5 -7.5t-92 -17.5t-86 -35t-70 -57q10 59 33 108t51.5 81.5t65 58.5t68.5 40.5t67 24.5t56 13.5t40 4.5v210q1 10 6.5 12.5t13.5 -4.5z" />
<glyph unicode="&#xe067;" d="M350 1100h350q60 0 127 -23l-178 -177h-349q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v69l200 200v-219q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5z M643 639l395 395q7 7 17.5 7t17.5 -7l101 -101q7 -7 7 -17.5t-7 -17.5l-531 -532q-7 -7 -17.5 -7t-17.5 7l-248 248q-7 7 -7 17.5t7 17.5l101 101q7 7 17.5 7t17.5 -7l111 -111q8 -7 18 -7t18 7z" />
<glyph unicode="&#xe068;" d="M318 918l264 264q8 8 18 8t18 -8l260 -264q7 -8 4.5 -13t-12.5 -5h-170v-200h200v173q0 10 5 12t13 -5l264 -260q8 -7 8 -17.5t-8 -17.5l-264 -265q-8 -7 -13 -5t-5 12v173h-200v-200h170q10 0 12.5 -5t-4.5 -13l-260 -264q-8 -8 -18 -8t-18 8l-264 264q-8 8 -5.5 13 t12.5 5h175v200h-200v-173q0 -10 -5 -12t-13 5l-264 265q-8 7 -8 17.5t8 17.5l264 260q8 7 13 5t5 -12v-173h200v200h-175q-10 0 -12.5 5t5.5 13z" />
<glyph unicode="&#xe069;" d="M250 1100h100q21 0 35.5 -14.5t14.5 -35.5v-438l464 453q15 14 25.5 10t10.5 -25v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v1000q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe070;" d="M50 1100h100q21 0 35.5 -14.5t14.5 -35.5v-438l464 453q15 14 25.5 10t10.5 -25v-438l464 453q15 14 25.5 10t10.5 -25v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5 t-14.5 35.5v1000q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe071;" d="M1200 1050v-1000q0 -21 -10.5 -25t-25.5 10l-464 453v-438q0 -21 -10.5 -25t-25.5 10l-492 480q-15 14 -15 35t15 35l492 480q15 14 25.5 10t10.5 -25v-438l464 453q15 14 25.5 10t10.5 -25z" />
<glyph unicode="&#xe072;" d="M243 1074l814 -498q18 -11 18 -26t-18 -26l-814 -498q-18 -11 -30.5 -4t-12.5 28v1000q0 21 12.5 28t30.5 -4z" />
<glyph unicode="&#xe073;" d="M250 1000h200q21 0 35.5 -14.5t14.5 -35.5v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5zM650 1000h200q21 0 35.5 -14.5t14.5 -35.5v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v800 q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe074;" d="M1100 950v-800q0 -21 -14.5 -35.5t-35.5 -14.5h-800q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5h800q21 0 35.5 -14.5t14.5 -35.5z" />
<glyph unicode="&#xe075;" d="M500 612v438q0 21 10.5 25t25.5 -10l492 -480q15 -14 15 -35t-15 -35l-492 -480q-15 -14 -25.5 -10t-10.5 25v438l-464 -453q-15 -14 -25.5 -10t-10.5 25v1000q0 21 10.5 25t25.5 -10z" />
<glyph unicode="&#xe076;" d="M1048 1102l100 1q20 0 35 -14.5t15 -35.5l5 -1000q0 -21 -14.5 -35.5t-35.5 -14.5l-100 -1q-21 0 -35.5 14.5t-14.5 35.5l-2 437l-463 -454q-14 -15 -24.5 -10.5t-10.5 25.5l-2 437l-462 -455q-15 -14 -25.5 -9.5t-10.5 24.5l-5 1000q0 21 10.5 25.5t25.5 -10.5l466 -450 l-2 438q0 20 10.5 24.5t25.5 -9.5l466 -451l-2 438q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe077;" d="M850 1100h100q21 0 35.5 -14.5t14.5 -35.5v-1000q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v438l-464 -453q-15 -14 -25.5 -10t-10.5 25v1000q0 21 10.5 25t25.5 -10l464 -453v438q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe078;" d="M686 1081l501 -540q15 -15 10.5 -26t-26.5 -11h-1042q-22 0 -26.5 11t10.5 26l501 540q15 15 36 15t36 -15zM150 400h1000q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe079;" d="M885 900l-352 -353l352 -353l-197 -198l-552 552l552 550z" />
<glyph unicode="&#xe080;" d="M1064 547l-551 -551l-198 198l353 353l-353 353l198 198z" />
<glyph unicode="&#xe081;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM650 900h-100q-21 0 -35.5 -14.5t-14.5 -35.5v-150h-150 q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5t35.5 -14.5h150v-150q0 -21 14.5 -35.5t35.5 -14.5h100q21 0 35.5 14.5t14.5 35.5v150h150q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5h-150v150q0 21 -14.5 35.5t-35.5 14.5z" />
<glyph unicode="&#xe082;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM850 700h-500q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5 t35.5 -14.5h500q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5z" />
<glyph unicode="&#xe083;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM741.5 913q-12.5 0 -21.5 -9l-120 -120l-120 120q-9 9 -21.5 9 t-21.5 -9l-141 -141q-9 -9 -9 -21.5t9 -21.5l120 -120l-120 -120q-9 -9 -9 -21.5t9 -21.5l141 -141q9 -9 21.5 -9t21.5 9l120 120l120 -120q9 -9 21.5 -9t21.5 9l141 141q9 9 9 21.5t-9 21.5l-120 120l120 120q9 9 9 21.5t-9 21.5l-141 141q-9 9 -21.5 9z" />
<glyph unicode="&#xe084;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM546 623l-84 85q-7 7 -17.5 7t-18.5 -7l-139 -139q-7 -8 -7 -18t7 -18 l242 -241q7 -8 17.5 -8t17.5 8l375 375q7 7 7 17.5t-7 18.5l-139 139q-7 7 -17.5 7t-17.5 -7z" />
<glyph unicode="&#xe085;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM588 941q-29 0 -59 -5.5t-63 -20.5t-58 -38.5t-41.5 -63t-16.5 -89.5 q0 -25 20 -25h131q30 -5 35 11q6 20 20.5 28t45.5 8q20 0 31.5 -10.5t11.5 -28.5q0 -23 -7 -34t-26 -18q-1 0 -13.5 -4t-19.5 -7.5t-20 -10.5t-22 -17t-18.5 -24t-15.5 -35t-8 -46q-1 -8 5.5 -16.5t20.5 -8.5h173q7 0 22 8t35 28t37.5 48t29.5 74t12 100q0 47 -17 83 t-42.5 57t-59.5 34.5t-64 18t-59 4.5zM675 400h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe086;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM675 1000h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5 t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5zM675 700h-250q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h75v-200h-75q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h350q10 0 17.5 7.5t7.5 17.5v50q0 10 -7.5 17.5 t-17.5 7.5h-75v275q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe087;" d="M525 1200h150q10 0 17.5 -7.5t7.5 -17.5v-194q103 -27 178.5 -102.5t102.5 -178.5h194q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-194q-27 -103 -102.5 -178.5t-178.5 -102.5v-194q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v194 q-103 27 -178.5 102.5t-102.5 178.5h-194q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h194q27 103 102.5 178.5t178.5 102.5v194q0 10 7.5 17.5t17.5 7.5zM700 893v-168q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v168q-68 -23 -119 -74 t-74 -119h168q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-168q23 -68 74 -119t119 -74v168q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-168q68 23 119 74t74 119h-168q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h168 q-23 68 -74 119t-119 74z" />
<glyph unicode="&#xe088;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM759 823l64 -64q7 -7 7 -17.5t-7 -17.5l-124 -124l124 -124q7 -7 7 -17.5t-7 -17.5l-64 -64q-7 -7 -17.5 -7t-17.5 7l-124 124l-124 -124q-7 -7 -17.5 -7t-17.5 7l-64 64 q-7 7 -7 17.5t7 17.5l124 124l-124 124q-7 7 -7 17.5t7 17.5l64 64q7 7 17.5 7t17.5 -7l124 -124l124 124q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe089;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5t57 -214.5 t155.5 -155.5t214.5 -57t214.5 57t155.5 155.5t57 214.5t-57 214.5t-155.5 155.5t-214.5 57zM782 788l106 -106q7 -7 7 -17.5t-7 -17.5l-320 -321q-8 -7 -18 -7t-18 7l-202 203q-8 7 -8 17.5t8 17.5l106 106q7 8 17.5 8t17.5 -8l79 -79l197 197q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe090;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM600 1027q-116 0 -214.5 -57t-155.5 -155.5t-57 -214.5q0 -120 65 -225 l587 587q-105 65 -225 65zM965 819l-584 -584q104 -62 219 -62q116 0 214.5 57t155.5 155.5t57 214.5q0 115 -62 219z" />
<glyph unicode="&#xe091;" d="M39 582l522 427q16 13 27.5 8t11.5 -26v-291h550q21 0 35.5 -14.5t14.5 -35.5v-200q0 -21 -14.5 -35.5t-35.5 -14.5h-550v-291q0 -21 -11.5 -26t-27.5 8l-522 427q-16 13 -16 32t16 32z" />
<glyph unicode="&#xe092;" d="M639 1009l522 -427q16 -13 16 -32t-16 -32l-522 -427q-16 -13 -27.5 -8t-11.5 26v291h-550q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h550v291q0 21 11.5 26t27.5 -8z" />
<glyph unicode="&#xe093;" d="M682 1161l427 -522q13 -16 8 -27.5t-26 -11.5h-291v-550q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v550h-291q-21 0 -26 11.5t8 27.5l427 522q13 16 32 16t32 -16z" />
<glyph unicode="&#xe094;" d="M550 1200h200q21 0 35.5 -14.5t14.5 -35.5v-550h291q21 0 26 -11.5t-8 -27.5l-427 -522q-13 -16 -32 -16t-32 16l-427 522q-13 16 -8 27.5t26 11.5h291v550q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe095;" d="M639 1109l522 -427q16 -13 16 -32t-16 -32l-522 -427q-16 -13 -27.5 -8t-11.5 26v291q-94 -2 -182 -20t-170.5 -52t-147 -92.5t-100.5 -135.5q5 105 27 193.5t67.5 167t113 135t167 91.5t225.5 42v262q0 21 11.5 26t27.5 -8z" />
<glyph unicode="&#xe096;" d="M850 1200h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94l-249 -249q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l249 249l-94 94q-14 14 -10 24.5t25 10.5zM350 0h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l249 249 q8 7 18 7t18 -7l106 -106q7 -8 7 -18t-7 -18l-249 -249l94 -94q14 -14 10 -24.5t-25 -10.5z" />
<glyph unicode="&#xe097;" d="M1014 1120l106 -106q7 -8 7 -18t-7 -18l-249 -249l94 -94q14 -14 10 -24.5t-25 -10.5h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l249 249q8 7 18 7t18 -7zM250 600h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94 l-249 -249q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l249 249l-94 94q-14 14 -10 24.5t25 10.5z" />
<glyph unicode="&#xe101;" d="M600 1177q117 0 224 -45.5t184.5 -123t123 -184.5t45.5 -224t-45.5 -224t-123 -184.5t-184.5 -123t-224 -45.5t-224 45.5t-184.5 123t-123 184.5t-45.5 224t45.5 224t123 184.5t184.5 123t224 45.5zM704 900h-208q-20 0 -32 -14.5t-8 -34.5l58 -302q4 -20 21.5 -34.5 t37.5 -14.5h54q20 0 37.5 14.5t21.5 34.5l58 302q4 20 -8 34.5t-32 14.5zM675 400h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe102;" d="M260 1200q9 0 19 -2t15 -4l5 -2q22 -10 44 -23l196 -118q21 -13 36 -24q29 -21 37 -12q11 13 49 35l196 118q22 13 45 23q17 7 38 7q23 0 47 -16.5t37 -33.5l13 -16q14 -21 18 -45l25 -123l8 -44q1 -9 8.5 -14.5t17.5 -5.5h61q10 0 17.5 -7.5t7.5 -17.5v-50 q0 -10 -7.5 -17.5t-17.5 -7.5h-50q-10 0 -17.5 -7.5t-7.5 -17.5v-175h-400v300h-200v-300h-400v175q0 10 -7.5 17.5t-17.5 7.5h-50q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5h61q11 0 18 3t7 8q0 4 9 52l25 128q5 25 19 45q2 3 5 7t13.5 15t21.5 19.5t26.5 15.5 t29.5 7zM915 1079l-166 -162q-7 -7 -5 -12t12 -5h219q10 0 15 7t2 17l-51 149q-3 10 -11 12t-15 -6zM463 917l-177 157q-8 7 -16 5t-11 -12l-51 -143q-3 -10 2 -17t15 -7h231q11 0 12.5 5t-5.5 12zM500 0h-375q-10 0 -17.5 7.5t-7.5 17.5v375h400v-400zM1100 400v-375 q0 -10 -7.5 -17.5t-17.5 -7.5h-375v400h400z" />
<glyph unicode="&#xe103;" d="M1165 1190q8 3 21 -6.5t13 -17.5q-2 -178 -24.5 -323.5t-55.5 -245.5t-87 -174.5t-102.5 -118.5t-118 -68.5t-118.5 -33t-120 -4.5t-105 9.5t-90 16.5q-61 12 -78 11q-4 1 -12.5 0t-34 -14.5t-52.5 -40.5l-153 -153q-26 -24 -37 -14.5t-11 43.5q0 64 42 102q8 8 50.5 45 t66.5 58q19 17 35 47t13 61q-9 55 -10 102.5t7 111t37 130t78 129.5q39 51 80 88t89.5 63.5t94.5 45t113.5 36t129 31t157.5 37t182 47.5zM1116 1098q-8 9 -22.5 -3t-45.5 -50q-38 -47 -119 -103.5t-142 -89.5l-62 -33q-56 -30 -102 -57t-104 -68t-102.5 -80.5t-85.5 -91 t-64 -104.5q-24 -56 -31 -86t2 -32t31.5 17.5t55.5 59.5q25 30 94 75.5t125.5 77.5t147.5 81q70 37 118.5 69t102 79.5t99 111t86.5 148.5q22 50 24 60t-6 19z" />
<glyph unicode="&#xe104;" d="M653 1231q-39 -67 -54.5 -131t-10.5 -114.5t24.5 -96.5t47.5 -80t63.5 -62.5t68.5 -46.5t65 -30q-4 7 -17.5 35t-18.5 39.5t-17 39.5t-17 43t-13 42t-9.5 44.5t-2 42t4 43t13.5 39t23 38.5q96 -42 165 -107.5t105 -138t52 -156t13 -159t-19 -149.5q-13 -55 -44 -106.5 t-68 -87t-78.5 -64.5t-72.5 -45t-53 -22q-72 -22 -127 -11q-31 6 -13 19q6 3 17 7q13 5 32.5 21t41 44t38.5 63.5t21.5 81.5t-6.5 94.5t-50 107t-104 115.5q10 -104 -0.5 -189t-37 -140.5t-65 -93t-84 -52t-93.5 -11t-95 24.5q-80 36 -131.5 114t-53.5 171q-2 23 0 49.5 t4.5 52.5t13.5 56t27.5 60t46 64.5t69.5 68.5q-8 -53 -5 -102.5t17.5 -90t34 -68.5t44.5 -39t49 -2q31 13 38.5 36t-4.5 55t-29 64.5t-36 75t-26 75.5q-15 85 2 161.5t53.5 128.5t85.5 92.5t93.5 61t81.5 25.5z" />
<glyph unicode="&#xe105;" d="M600 1094q82 0 160.5 -22.5t140 -59t116.5 -82.5t94.5 -95t68 -95t42.5 -82.5t14 -57.5t-14 -57.5t-43 -82.5t-68.5 -95t-94.5 -95t-116.5 -82.5t-140 -59t-159.5 -22.5t-159.5 22.5t-140 59t-116.5 82.5t-94.5 95t-68.5 95t-43 82.5t-14 57.5t14 57.5t42.5 82.5t68 95 t94.5 95t116.5 82.5t140 59t160.5 22.5zM888 829q-15 15 -18 12t5 -22q25 -57 25 -119q0 -124 -88 -212t-212 -88t-212 88t-88 212q0 59 23 114q8 19 4.5 22t-17.5 -12q-70 -69 -160 -184q-13 -16 -15 -40.5t9 -42.5q22 -36 47 -71t70 -82t92.5 -81t113 -58.5t133.5 -24.5 t133.5 24t113 58.5t92.5 81.5t70 81.5t47 70.5q11 18 9 42.5t-14 41.5q-90 117 -163 189zM448 727l-35 -36q-15 -15 -19.5 -38.5t4.5 -41.5q37 -68 93 -116q16 -13 38.5 -11t36.5 17l35 34q14 15 12.5 33.5t-16.5 33.5q-44 44 -89 117q-11 18 -28 20t-32 -12z" />
<glyph unicode="&#xe106;" d="M592 0h-148l31 120q-91 20 -175.5 68.5t-143.5 106.5t-103.5 119t-66.5 110t-22 76q0 21 14 57.5t42.5 82.5t68 95t94.5 95t116.5 82.5t140 59t160.5 22.5q61 0 126 -15l32 121h148zM944 770l47 181q108 -85 176.5 -192t68.5 -159q0 -26 -19.5 -71t-59.5 -102t-93 -112 t-129 -104.5t-158 -75.5l46 173q77 49 136 117t97 131q11 18 9 42.5t-14 41.5q-54 70 -107 130zM310 824q-70 -69 -160 -184q-13 -16 -15 -40.5t9 -42.5q18 -30 39 -60t57 -70.5t74 -73t90 -61t105 -41.5l41 154q-107 18 -178.5 101.5t-71.5 193.5q0 59 23 114q8 19 4.5 22 t-17.5 -12zM448 727l-35 -36q-15 -15 -19.5 -38.5t4.5 -41.5q37 -68 93 -116q16 -13 38.5 -11t36.5 17l12 11l22 86l-3 4q-44 44 -89 117q-11 18 -28 20t-32 -12z" />
<glyph unicode="&#xe107;" d="M-90 100l642 1066q20 31 48 28.5t48 -35.5l642 -1056q21 -32 7.5 -67.5t-50.5 -35.5h-1294q-37 0 -50.5 34t7.5 66zM155 200h345v75q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-75h345l-445 723zM496 700h208q20 0 32 -14.5t8 -34.5l-58 -252 q-4 -20 -21.5 -34.5t-37.5 -14.5h-54q-20 0 -37.5 14.5t-21.5 34.5l-58 252q-4 20 8 34.5t32 14.5z" />
<glyph unicode="&#xe108;" d="M650 1200q62 0 106 -44t44 -106v-339l363 -325q15 -14 26 -38.5t11 -44.5v-41q0 -20 -12 -26.5t-29 5.5l-359 249v-263q100 -93 100 -113v-64q0 -21 -13 -29t-32 1l-205 128l-205 -128q-19 -9 -32 -1t-13 29v64q0 20 100 113v263l-359 -249q-17 -12 -29 -5.5t-12 26.5v41 q0 20 11 44.5t26 38.5l363 325v339q0 62 44 106t106 44z" />
<glyph unicode="&#xe109;" d="M850 1200h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-150h-1100v150q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5h100q21 0 35.5 -14.5t14.5 -35.5v-50h500v50q0 21 14.5 35.5t35.5 14.5zM1100 800v-750q0 -21 -14.5 -35.5 t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v750h1100zM100 600v-100h100v100h-100zM300 600v-100h100v100h-100zM500 600v-100h100v100h-100zM700 600v-100h100v100h-100zM900 600v-100h100v100h-100zM100 400v-100h100v100h-100zM300 400v-100h100v100h-100zM500 400 v-100h100v100h-100zM700 400v-100h100v100h-100zM900 400v-100h100v100h-100zM100 200v-100h100v100h-100zM300 200v-100h100v100h-100zM500 200v-100h100v100h-100zM700 200v-100h100v100h-100zM900 200v-100h100v100h-100z" />
<glyph unicode="&#xe110;" d="M1135 1165l249 -230q15 -14 15 -35t-15 -35l-249 -230q-14 -14 -24.5 -10t-10.5 25v150h-159l-600 -600h-291q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h209l600 600h241v150q0 21 10.5 25t24.5 -10zM522 819l-141 -141l-122 122h-209q-21 0 -35.5 14.5 t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h291zM1135 565l249 -230q15 -14 15 -35t-15 -35l-249 -230q-14 -14 -24.5 -10t-10.5 25v150h-241l-181 181l141 141l122 -122h159v150q0 21 10.5 25t24.5 -10z" />
<glyph unicode="&#xe111;" d="M100 1100h1000q41 0 70.5 -29.5t29.5 -70.5v-600q0 -41 -29.5 -70.5t-70.5 -29.5h-596l-304 -300v300h-100q-41 0 -70.5 29.5t-29.5 70.5v600q0 41 29.5 70.5t70.5 29.5z" />
<glyph unicode="&#xe112;" d="M150 1200h200q21 0 35.5 -14.5t14.5 -35.5v-250h-300v250q0 21 14.5 35.5t35.5 14.5zM850 1200h200q21 0 35.5 -14.5t14.5 -35.5v-250h-300v250q0 21 14.5 35.5t35.5 14.5zM1100 800v-300q0 -41 -3 -77.5t-15 -89.5t-32 -96t-58 -89t-89 -77t-129 -51t-174 -20t-174 20 t-129 51t-89 77t-58 89t-32 96t-15 89.5t-3 77.5v300h300v-250v-27v-42.5t1.5 -41t5 -38t10 -35t16.5 -30t25.5 -24.5t35 -19t46.5 -12t60 -4t60 4.5t46.5 12.5t35 19.5t25 25.5t17 30.5t10 35t5 38t2 40.5t-0.5 42v25v250h300z" />
<glyph unicode="&#xe113;" d="M1100 411l-198 -199l-353 353l-353 -353l-197 199l551 551z" />
<glyph unicode="&#xe114;" d="M1101 789l-550 -551l-551 551l198 199l353 -353l353 353z" />
<glyph unicode="&#xe115;" d="M404 1000h746q21 0 35.5 -14.5t14.5 -35.5v-551h150q21 0 25 -10.5t-10 -24.5l-230 -249q-14 -15 -35 -15t-35 15l-230 249q-14 14 -10 24.5t25 10.5h150v401h-381zM135 984l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-400h385l215 -200h-750q-21 0 -35.5 14.5 t-14.5 35.5v550h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe116;" d="M56 1200h94q17 0 31 -11t18 -27l38 -162h896q24 0 39 -18.5t10 -42.5l-100 -475q-5 -21 -27 -42.5t-55 -21.5h-633l48 -200h535q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-50q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v50h-300v-50 q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v50h-31q-18 0 -32.5 10t-20.5 19l-5 10l-201 961h-54q-20 0 -35 14.5t-15 35.5t15 35.5t35 14.5z" />
<glyph unicode="&#xe117;" d="M1200 1000v-100h-1200v100h200q0 41 29.5 70.5t70.5 29.5h300q41 0 70.5 -29.5t29.5 -70.5h500zM0 800h1200v-800h-1200v800z" />
<glyph unicode="&#xe118;" d="M200 800l-200 -400v600h200q0 41 29.5 70.5t70.5 29.5h300q42 0 71 -29.5t29 -70.5h500v-200h-1000zM1500 700l-300 -700h-1200l300 700h1200z" />
<glyph unicode="&#xe119;" d="M635 1184l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-601h150q21 0 25 -10.5t-10 -24.5l-230 -249q-14 -15 -35 -15t-35 15l-230 249q-14 14 -10 24.5t25 10.5h150v601h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe120;" d="M936 864l249 -229q14 -15 14 -35.5t-14 -35.5l-249 -229q-15 -15 -25.5 -10.5t-10.5 24.5v151h-600v-151q0 -20 -10.5 -24.5t-25.5 10.5l-249 229q-14 15 -14 35.5t14 35.5l249 229q15 15 25.5 10.5t10.5 -25.5v-149h600v149q0 21 10.5 25.5t25.5 -10.5z" />
<glyph unicode="&#xe121;" d="M1169 400l-172 732q-5 23 -23 45.5t-38 22.5h-672q-20 0 -38 -20t-23 -41l-172 -739h1138zM1100 300h-1000q-41 0 -70.5 -29.5t-29.5 -70.5v-100q0 -41 29.5 -70.5t70.5 -29.5h1000q41 0 70.5 29.5t29.5 70.5v100q0 41 -29.5 70.5t-70.5 29.5zM800 100v100h100v-100h-100 zM1000 100v100h100v-100h-100z" />
<glyph unicode="&#xe122;" d="M1150 1100q21 0 35.5 -14.5t14.5 -35.5v-850q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v850q0 21 14.5 35.5t35.5 14.5zM1000 200l-675 200h-38l47 -276q3 -16 -5.5 -20t-29.5 -4h-7h-84q-20 0 -34.5 14t-18.5 35q-55 337 -55 351v250v6q0 16 1 23.5t6.5 14 t17.5 6.5h200l675 250v-850zM0 750v-250q-4 0 -11 0.5t-24 6t-30 15t-24 30t-11 48.5v50q0 26 10.5 46t25 30t29 16t25.5 7z" />
<glyph unicode="&#xe123;" d="M553 1200h94q20 0 29 -10.5t3 -29.5l-18 -37q83 -19 144 -82.5t76 -140.5l63 -327l118 -173h17q19 0 33 -14.5t14 -35t-13 -40.5t-31 -27q-8 -4 -23 -9.5t-65 -19.5t-103 -25t-132.5 -20t-158.5 -9q-57 0 -115 5t-104 12t-88.5 15.5t-73.5 17.5t-54.5 16t-35.5 12l-11 4 q-18 8 -31 28t-13 40.5t14 35t33 14.5h17l118 173l63 327q15 77 76 140t144 83l-18 32q-6 19 3.5 32t28.5 13zM498 110q50 -6 102 -6q53 0 102 6q-12 -49 -39.5 -79.5t-62.5 -30.5t-63 30.5t-39 79.5z" />
<glyph unicode="&#xe124;" d="M800 946l224 78l-78 -224l234 -45l-180 -155l180 -155l-234 -45l78 -224l-224 78l-45 -234l-155 180l-155 -180l-45 234l-224 -78l78 224l-234 45l180 155l-180 155l234 45l-78 224l224 -78l45 234l155 -180l155 180z" />
<glyph unicode="&#xe125;" d="M650 1200h50q40 0 70 -40.5t30 -84.5v-150l-28 -125h328q40 0 70 -40.5t30 -84.5v-100q0 -45 -29 -74l-238 -344q-16 -24 -38 -40.5t-45 -16.5h-250q-7 0 -42 25t-66 50l-31 25h-61q-45 0 -72.5 18t-27.5 57v400q0 36 20 63l145 196l96 198q13 28 37.5 48t51.5 20z M650 1100l-100 -212l-150 -213v-375h100l136 -100h214l250 375v125h-450l50 225v175h-50zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe126;" d="M600 1100h250q23 0 45 -16.5t38 -40.5l238 -344q29 -29 29 -74v-100q0 -44 -30 -84.5t-70 -40.5h-328q28 -118 28 -125v-150q0 -44 -30 -84.5t-70 -40.5h-50q-27 0 -51.5 20t-37.5 48l-96 198l-145 196q-20 27 -20 63v400q0 39 27.5 57t72.5 18h61q124 100 139 100z M50 1000h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5zM636 1000l-136 -100h-100v-375l150 -213l100 -212h50v175l-50 225h450v125l-250 375h-214z" />
<glyph unicode="&#xe127;" d="M356 873l363 230q31 16 53 -6l110 -112q13 -13 13.5 -32t-11.5 -34l-84 -121h302q84 0 138 -38t54 -110t-55 -111t-139 -39h-106l-131 -339q-6 -21 -19.5 -41t-28.5 -20h-342q-7 0 -90 81t-83 94v525q0 17 14 35.5t28 28.5zM400 792v-503l100 -89h293l131 339 q6 21 19.5 41t28.5 20h203q21 0 30.5 25t0.5 50t-31 25h-456h-7h-6h-5.5t-6 0.5t-5 1.5t-5 2t-4 2.5t-4 4t-2.5 4.5q-12 25 5 47l146 183l-86 83zM50 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v500 q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe128;" d="M475 1103l366 -230q2 -1 6 -3.5t14 -10.5t18 -16.5t14.5 -20t6.5 -22.5v-525q0 -13 -86 -94t-93 -81h-342q-15 0 -28.5 20t-19.5 41l-131 339h-106q-85 0 -139.5 39t-54.5 111t54 110t138 38h302l-85 121q-11 15 -10.5 34t13.5 32l110 112q22 22 53 6zM370 945l146 -183 q17 -22 5 -47q-2 -2 -3.5 -4.5t-4 -4t-4 -2.5t-5 -2t-5 -1.5t-6 -0.5h-6h-6.5h-6h-475v-100h221q15 0 29 -20t20 -41l130 -339h294l106 89v503l-342 236zM1050 800h100q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5 v500q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe129;" d="M550 1294q72 0 111 -55t39 -139v-106l339 -131q21 -6 41 -19.5t20 -28.5v-342q0 -7 -81 -90t-94 -83h-525q-17 0 -35.5 14t-28.5 28l-9 14l-230 363q-16 31 6 53l112 110q13 13 32 13.5t34 -11.5l121 -84v302q0 84 38 138t110 54zM600 972v203q0 21 -25 30.5t-50 0.5 t-25 -31v-456v-7v-6v-5.5t-0.5 -6t-1.5 -5t-2 -5t-2.5 -4t-4 -4t-4.5 -2.5q-25 -12 -47 5l-183 146l-83 -86l236 -339h503l89 100v293l-339 131q-21 6 -41 19.5t-20 28.5zM450 200h500q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-500 q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe130;" d="M350 1100h500q21 0 35.5 14.5t14.5 35.5v100q0 21 -14.5 35.5t-35.5 14.5h-500q-21 0 -35.5 -14.5t-14.5 -35.5v-100q0 -21 14.5 -35.5t35.5 -14.5zM600 306v-106q0 -84 -39 -139t-111 -55t-110 54t-38 138v302l-121 -84q-15 -12 -34 -11.5t-32 13.5l-112 110 q-22 22 -6 53l230 363q1 2 3.5 6t10.5 13.5t16.5 17t20 13.5t22.5 6h525q13 0 94 -83t81 -90v-342q0 -15 -20 -28.5t-41 -19.5zM308 900l-236 -339l83 -86l183 146q22 17 47 5q2 -1 4.5 -2.5t4 -4t2.5 -4t2 -5t1.5 -5t0.5 -6v-5.5v-6v-7v-456q0 -22 25 -31t50 0.5t25 30.5 v203q0 15 20 28.5t41 19.5l339 131v293l-89 100h-503z" />
<glyph unicode="&#xe131;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM914 632l-275 223q-16 13 -27.5 8t-11.5 -26v-137h-275 q-10 0 -17.5 -7.5t-7.5 -17.5v-150q0 -10 7.5 -17.5t17.5 -7.5h275v-137q0 -21 11.5 -26t27.5 8l275 223q16 13 16 32t-16 32z" />
<glyph unicode="&#xe132;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM561 855l-275 -223q-16 -13 -16 -32t16 -32l275 -223q16 -13 27.5 -8 t11.5 26v137h275q10 0 17.5 7.5t7.5 17.5v150q0 10 -7.5 17.5t-17.5 7.5h-275v137q0 21 -11.5 26t-27.5 -8z" />
<glyph unicode="&#xe133;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM855 639l-223 275q-13 16 -32 16t-32 -16l-223 -275q-13 -16 -8 -27.5 t26 -11.5h137v-275q0 -10 7.5 -17.5t17.5 -7.5h150q10 0 17.5 7.5t7.5 17.5v275h137q21 0 26 11.5t-8 27.5z" />
<glyph unicode="&#xe134;" d="M600 1178q118 0 225 -45.5t184.5 -123t123 -184.5t45.5 -225t-45.5 -225t-123 -184.5t-184.5 -123t-225 -45.5t-225 45.5t-184.5 123t-123 184.5t-45.5 225t45.5 225t123 184.5t184.5 123t225 45.5zM675 900h-150q-10 0 -17.5 -7.5t-7.5 -17.5v-275h-137q-21 0 -26 -11.5 t8 -27.5l223 -275q13 -16 32 -16t32 16l223 275q13 16 8 27.5t-26 11.5h-137v275q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe135;" d="M600 1176q116 0 222.5 -46t184 -123.5t123.5 -184t46 -222.5t-46 -222.5t-123.5 -184t-184 -123.5t-222.5 -46t-222.5 46t-184 123.5t-123.5 184t-46 222.5t46 222.5t123.5 184t184 123.5t222.5 46zM627 1101q-15 -12 -36.5 -20.5t-35.5 -12t-43 -8t-39 -6.5 q-15 -3 -45.5 0t-45.5 -2q-20 -7 -51.5 -26.5t-34.5 -34.5q-3 -11 6.5 -22.5t8.5 -18.5q-3 -34 -27.5 -91t-29.5 -79q-9 -34 5 -93t8 -87q0 -9 17 -44.5t16 -59.5q12 0 23 -5t23.5 -15t19.5 -14q16 -8 33 -15t40.5 -15t34.5 -12q21 -9 52.5 -32t60 -38t57.5 -11 q7 -15 -3 -34t-22.5 -40t-9.5 -38q13 -21 23 -34.5t27.5 -27.5t36.5 -18q0 -7 -3.5 -16t-3.5 -14t5 -17q104 -2 221 112q30 29 46.5 47t34.5 49t21 63q-13 8 -37 8.5t-36 7.5q-15 7 -49.5 15t-51.5 19q-18 0 -41 -0.5t-43 -1.5t-42 -6.5t-38 -16.5q-51 -35 -66 -12 q-4 1 -3.5 25.5t0.5 25.5q-6 13 -26.5 17.5t-24.5 6.5q1 15 -0.5 30.5t-7 28t-18.5 11.5t-31 -21q-23 -25 -42 4q-19 28 -8 58q6 16 22 22q6 -1 26 -1.5t33.5 -4t19.5 -13.5q7 -12 18 -24t21.5 -20.5t20 -15t15.5 -10.5l5 -3q2 12 7.5 30.5t8 34.5t-0.5 32q-3 18 3.5 29 t18 22.5t15.5 24.5q6 14 10.5 35t8 31t15.5 22.5t34 22.5q-6 18 10 36q8 0 24 -1.5t24.5 -1.5t20 4.5t20.5 15.5q-10 23 -31 42.5t-37.5 29.5t-49 27t-43.5 23q0 1 2 8t3 11.5t1.5 10.5t-1 9.5t-4.5 4.5q31 -13 58.5 -14.5t38.5 2.5l12 5q5 28 -9.5 46t-36.5 24t-50 15 t-41 20q-18 -4 -37 0zM613 994q0 -17 8 -42t17 -45t9 -23q-8 1 -39.5 5.5t-52.5 10t-37 16.5q3 11 16 29.5t16 25.5q10 -10 19 -10t14 6t13.5 14.5t16.5 12.5z" />
<glyph unicode="&#xe136;" d="M756 1157q164 92 306 -9l-259 -138l145 -232l251 126q6 -89 -34 -156.5t-117 -110.5q-60 -34 -127 -39.5t-126 16.5l-596 -596q-15 -16 -36.5 -16t-36.5 16l-111 110q-15 15 -15 36.5t15 37.5l600 599q-34 101 5.5 201.5t135.5 154.5z" />
<glyph unicode="&#xe137;" horiz-adv-x="1220" d="M100 1196h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 1096h-200v-100h200v100zM100 796h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000 q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 696h-500v-100h500v100zM100 396h1000q41 0 70.5 -29.5t29.5 -70.5v-100q0 -41 -29.5 -70.5t-70.5 -29.5h-1000q-41 0 -70.5 29.5t-29.5 70.5v100q0 41 29.5 70.5t70.5 29.5zM1100 296h-300v-100h300v100z " />
<glyph unicode="&#xe138;" d="M150 1200h900q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM700 500v-300l-200 -200v500l-350 500h900z" />
<glyph unicode="&#xe139;" d="M500 1200h200q41 0 70.5 -29.5t29.5 -70.5v-100h300q41 0 70.5 -29.5t29.5 -70.5v-400h-500v100h-200v-100h-500v400q0 41 29.5 70.5t70.5 29.5h300v100q0 41 29.5 70.5t70.5 29.5zM500 1100v-100h200v100h-200zM1200 400v-200q0 -41 -29.5 -70.5t-70.5 -29.5h-1000 q-41 0 -70.5 29.5t-29.5 70.5v200h1200z" />
<glyph unicode="&#xe140;" d="M50 1200h300q21 0 25 -10.5t-10 -24.5l-94 -94l199 -199q7 -8 7 -18t-7 -18l-106 -106q-8 -7 -18 -7t-18 7l-199 199l-94 -94q-14 -14 -24.5 -10t-10.5 25v300q0 21 14.5 35.5t35.5 14.5zM850 1200h300q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -10.5 -25t-24.5 10l-94 94 l-199 -199q-8 -7 -18 -7t-18 7l-106 106q-7 8 -7 18t7 18l199 199l-94 94q-14 14 -10 24.5t25 10.5zM364 470l106 -106q7 -8 7 -18t-7 -18l-199 -199l94 -94q14 -14 10 -24.5t-25 -10.5h-300q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 10.5 25t24.5 -10l94 -94l199 199 q8 7 18 7t18 -7zM1071 271l94 94q14 14 24.5 10t10.5 -25v-300q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -25 10.5t10 24.5l94 94l-199 199q-7 8 -7 18t7 18l106 106q8 7 18 7t18 -7z" />
<glyph unicode="&#xe141;" d="M596 1192q121 0 231.5 -47.5t190 -127t127 -190t47.5 -231.5t-47.5 -231.5t-127 -190.5t-190 -127t-231.5 -47t-231.5 47t-190.5 127t-127 190.5t-47 231.5t47 231.5t127 190t190.5 127t231.5 47.5zM596 1010q-112 0 -207.5 -55.5t-151 -151t-55.5 -207.5t55.5 -207.5 t151 -151t207.5 -55.5t207.5 55.5t151 151t55.5 207.5t-55.5 207.5t-151 151t-207.5 55.5zM454.5 905q22.5 0 38.5 -16t16 -38.5t-16 -39t-38.5 -16.5t-38.5 16.5t-16 39t16 38.5t38.5 16zM754.5 905q22.5 0 38.5 -16t16 -38.5t-16 -39t-38 -16.5q-14 0 -29 10l-55 -145 q17 -23 17 -51q0 -36 -25.5 -61.5t-61.5 -25.5t-61.5 25.5t-25.5 61.5q0 32 20.5 56.5t51.5 29.5l122 126l1 1q-9 14 -9 28q0 23 16 39t38.5 16zM345.5 709q22.5 0 38.5 -16t16 -38.5t-16 -38.5t-38.5 -16t-38.5 16t-16 38.5t16 38.5t38.5 16zM854.5 709q22.5 0 38.5 -16 t16 -38.5t-16 -38.5t-38.5 -16t-38.5 16t-16 38.5t16 38.5t38.5 16z" />
<glyph unicode="&#xe142;" d="M546 173l469 470q91 91 99 192q7 98 -52 175.5t-154 94.5q-22 4 -47 4q-34 0 -66.5 -10t-56.5 -23t-55.5 -38t-48 -41.5t-48.5 -47.5q-376 -375 -391 -390q-30 -27 -45 -41.5t-37.5 -41t-32 -46.5t-16 -47.5t-1.5 -56.5q9 -62 53.5 -95t99.5 -33q74 0 125 51l548 548 q36 36 20 75q-7 16 -21.5 26t-32.5 10q-26 0 -50 -23q-13 -12 -39 -38l-341 -338q-15 -15 -35.5 -15.5t-34.5 13.5t-14 34.5t14 34.5q327 333 361 367q35 35 67.5 51.5t78.5 16.5q14 0 29 -1q44 -8 74.5 -35.5t43.5 -68.5q14 -47 2 -96.5t-47 -84.5q-12 -11 -32 -32 t-79.5 -81t-114.5 -115t-124.5 -123.5t-123 -119.5t-96.5 -89t-57 -45q-56 -27 -120 -27q-70 0 -129 32t-93 89q-48 78 -35 173t81 163l511 511q71 72 111 96q91 55 198 55q80 0 152 -33q78 -36 129.5 -103t66.5 -154q17 -93 -11 -183.5t-94 -156.5l-482 -476 q-15 -15 -36 -16t-37 14t-17.5 34t14.5 35z" />
<glyph unicode="&#xe143;" d="M649 949q48 68 109.5 104t121.5 38.5t118.5 -20t102.5 -64t71 -100.5t27 -123q0 -57 -33.5 -117.5t-94 -124.5t-126.5 -127.5t-150 -152.5t-146 -174q-62 85 -145.5 174t-150 152.5t-126.5 127.5t-93.5 124.5t-33.5 117.5q0 64 28 123t73 100.5t104 64t119 20 t120.5 -38.5t104.5 -104zM896 972q-33 0 -64.5 -19t-56.5 -46t-47.5 -53.5t-43.5 -45.5t-37.5 -19t-36 19t-40 45.5t-43 53.5t-54 46t-65.5 19q-67 0 -122.5 -55.5t-55.5 -132.5q0 -23 13.5 -51t46 -65t57.5 -63t76 -75l22 -22q15 -14 44 -44t50.5 -51t46 -44t41 -35t23 -12 t23.5 12t42.5 36t46 44t52.5 52t44 43q4 4 12 13q43 41 63.5 62t52 55t46 55t26 46t11.5 44q0 79 -53 133.5t-120 54.5z" />
<glyph unicode="&#xe144;" d="M776.5 1214q93.5 0 159.5 -66l141 -141q66 -66 66 -160q0 -42 -28 -95.5t-62 -87.5l-29 -29q-31 53 -77 99l-18 18l95 95l-247 248l-389 -389l212 -212l-105 -106l-19 18l-141 141q-66 66 -66 159t66 159l283 283q65 66 158.5 66zM600 706l105 105q10 -8 19 -17l141 -141 q66 -66 66 -159t-66 -159l-283 -283q-66 -66 -159 -66t-159 66l-141 141q-66 66 -66 159.5t66 159.5l55 55q29 -55 75 -102l18 -17l-95 -95l247 -248l389 389z" />
<glyph unicode="&#xe145;" d="M603 1200q85 0 162 -15t127 -38t79 -48t29 -46v-953q0 -41 -29.5 -70.5t-70.5 -29.5h-600q-41 0 -70.5 29.5t-29.5 70.5v953q0 21 30 46.5t81 48t129 37.5t163 15zM300 1000v-700h600v700h-600zM600 254q-43 0 -73.5 -30.5t-30.5 -73.5t30.5 -73.5t73.5 -30.5t73.5 30.5 t30.5 73.5t-30.5 73.5t-73.5 30.5z" />
<glyph unicode="&#xe146;" d="M902 1185l283 -282q15 -15 15 -36t-14.5 -35.5t-35.5 -14.5t-35 15l-36 35l-279 -267v-300l-212 210l-308 -307l-280 -203l203 280l307 308l-210 212h300l267 279l-35 36q-15 14 -15 35t14.5 35.5t35.5 14.5t35 -15z" />
<glyph unicode="&#xe148;" d="M700 1248v-78q38 -5 72.5 -14.5t75.5 -31.5t71 -53.5t52 -84t24 -118.5h-159q-4 36 -10.5 59t-21 45t-40 35.5t-64.5 20.5v-307l64 -13q34 -7 64 -16.5t70 -32t67.5 -52.5t47.5 -80t20 -112q0 -139 -89 -224t-244 -97v-77h-100v79q-150 16 -237 103q-40 40 -52.5 93.5 t-15.5 139.5h139q5 -77 48.5 -126t117.5 -65v335l-27 8q-46 14 -79 26.5t-72 36t-63 52t-40 72.5t-16 98q0 70 25 126t67.5 92t94.5 57t110 27v77h100zM600 754v274q-29 -4 -50 -11t-42 -21.5t-31.5 -41.5t-10.5 -65q0 -29 7 -50.5t16.5 -34t28.5 -22.5t31.5 -14t37.5 -10 q9 -3 13 -4zM700 547v-310q22 2 42.5 6.5t45 15.5t41.5 27t29 42t12 59.5t-12.5 59.5t-38 44.5t-53 31t-66.5 24.5z" />
<glyph unicode="&#xe149;" d="M561 1197q84 0 160.5 -40t123.5 -109.5t47 -147.5h-153q0 40 -19.5 71.5t-49.5 48.5t-59.5 26t-55.5 9q-37 0 -79 -14.5t-62 -35.5q-41 -44 -41 -101q0 -26 13.5 -63t26.5 -61t37 -66q6 -9 9 -14h241v-100h-197q8 -50 -2.5 -115t-31.5 -95q-45 -62 -99 -112 q34 10 83 17.5t71 7.5q32 1 102 -16t104 -17q83 0 136 30l50 -147q-31 -19 -58 -30.5t-55 -15.5t-42 -4.5t-46 -0.5q-23 0 -76 17t-111 32.5t-96 11.5q-39 -3 -82 -16t-67 -25l-23 -11l-55 145q4 3 16 11t15.5 10.5t13 9t15.5 12t14.5 14t17.5 18.5q48 55 54 126.5 t-30 142.5h-221v100h166q-23 47 -44 104q-7 20 -12 41.5t-6 55.5t6 66.5t29.5 70.5t58.5 71q97 88 263 88z" />
<glyph unicode="&#xe150;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM935 1184l230 -249q14 -14 10 -24.5t-25 -10.5h-150v-900h-200v900h-150q-21 0 -25 10.5t10 24.5l230 249q14 15 35 15t35 -15z" />
<glyph unicode="&#xe151;" d="M1000 700h-100v100h-100v-100h-100v500h300v-500zM400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM801 1100v-200h100v200h-100zM1000 350l-200 -250h200v-100h-300v150l200 250h-200v100h300v-150z " />
<glyph unicode="&#xe152;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1000 1050l-200 -250h200v-100h-300v150l200 250h-200v100h300v-150zM1000 0h-100v100h-100v-100h-100v500h300v-500zM801 400v-200h100v200h-100z " />
<glyph unicode="&#xe153;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1000 700h-100v400h-100v100h200v-500zM1100 0h-100v100h-200v400h300v-500zM901 400v-200h100v200h-100z" />
<glyph unicode="&#xe154;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1100 700h-100v100h-200v400h300v-500zM901 1100v-200h100v200h-100zM1000 0h-100v400h-100v100h200v-500z" />
<glyph unicode="&#xe155;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM900 1000h-200v200h200v-200zM1000 700h-300v200h300v-200zM1100 400h-400v200h400v-200zM1200 100h-500v200h500v-200z" />
<glyph unicode="&#xe156;" d="M400 300h150q21 0 25 -11t-10 -25l-230 -250q-14 -15 -35 -15t-35 15l-230 250q-14 14 -10 25t25 11h150v900h200v-900zM1200 1000h-500v200h500v-200zM1100 700h-400v200h400v-200zM1000 400h-300v200h300v-200zM900 100h-200v200h200v-200z" />
<glyph unicode="&#xe157;" d="M350 1100h400q162 0 256 -93.5t94 -256.5v-400q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5z" />
<glyph unicode="&#xe158;" d="M350 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-163 0 -256.5 92.5t-93.5 257.5v400q0 163 94 256.5t256 93.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM440 770l253 -190q17 -12 17 -30t-17 -30l-253 -190q-16 -12 -28 -6.5t-12 26.5v400q0 21 12 26.5t28 -6.5z" />
<glyph unicode="&#xe159;" d="M350 1100h400q163 0 256.5 -94t93.5 -256v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 163 92.5 256.5t257.5 93.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM350 700h400q21 0 26.5 -12t-6.5 -28l-190 -253q-12 -17 -30 -17t-30 17l-190 253q-12 16 -6.5 28t26.5 12z" />
<glyph unicode="&#xe160;" d="M350 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -163 -92.5 -256.5t-257.5 -93.5h-400q-163 0 -256.5 94t-93.5 256v400q0 165 92.5 257.5t257.5 92.5zM800 900h-500q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5 v500q0 41 -29.5 70.5t-70.5 29.5zM580 693l190 -253q12 -16 6.5 -28t-26.5 -12h-400q-21 0 -26.5 12t6.5 28l190 253q12 17 30 17t30 -17z" />
<glyph unicode="&#xe161;" d="M550 1100h400q165 0 257.5 -92.5t92.5 -257.5v-400q0 -165 -92.5 -257.5t-257.5 -92.5h-400q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h450q41 0 70.5 29.5t29.5 70.5v500q0 41 -29.5 70.5t-70.5 29.5h-450q-21 0 -35.5 14.5t-14.5 35.5v100 q0 21 14.5 35.5t35.5 14.5zM338 867l324 -284q16 -14 16 -33t-16 -33l-324 -284q-16 -14 -27 -9t-11 26v150h-250q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h250v150q0 21 11 26t27 -9z" />
<glyph unicode="&#xe162;" d="M793 1182l9 -9q8 -10 5 -27q-3 -11 -79 -225.5t-78 -221.5l300 1q24 0 32.5 -17.5t-5.5 -35.5q-1 0 -133.5 -155t-267 -312.5t-138.5 -162.5q-12 -15 -26 -15h-9l-9 8q-9 11 -4 32q2 9 42 123.5t79 224.5l39 110h-302q-23 0 -31 19q-10 21 6 41q75 86 209.5 237.5 t228 257t98.5 111.5q9 16 25 16h9z" />
<glyph unicode="&#xe163;" d="M350 1100h400q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-450q-41 0 -70.5 -29.5t-29.5 -70.5v-500q0 -41 29.5 -70.5t70.5 -29.5h450q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400 q0 165 92.5 257.5t257.5 92.5zM938 867l324 -284q16 -14 16 -33t-16 -33l-324 -284q-16 -14 -27 -9t-11 26v150h-250q-21 0 -35.5 14.5t-14.5 35.5v200q0 21 14.5 35.5t35.5 14.5h250v150q0 21 11 26t27 -9z" />
<glyph unicode="&#xe164;" d="M750 1200h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -10.5 -25t-24.5 10l-109 109l-312 -312q-15 -15 -35.5 -15t-35.5 15l-141 141q-15 15 -15 35.5t15 35.5l312 312l-109 109q-14 14 -10 24.5t25 10.5zM456 900h-156q-41 0 -70.5 -29.5t-29.5 -70.5v-500 q0 -41 29.5 -70.5t70.5 -29.5h500q41 0 70.5 29.5t29.5 70.5v148l200 200v-298q0 -165 -93.5 -257.5t-256.5 -92.5h-400q-165 0 -257.5 92.5t-92.5 257.5v400q0 165 92.5 257.5t257.5 92.5h300z" />
<glyph unicode="&#xe165;" d="M600 1186q119 0 227.5 -46.5t187 -125t125 -187t46.5 -227.5t-46.5 -227.5t-125 -187t-187 -125t-227.5 -46.5t-227.5 46.5t-187 125t-125 187t-46.5 227.5t46.5 227.5t125 187t187 125t227.5 46.5zM600 1022q-115 0 -212 -56.5t-153.5 -153.5t-56.5 -212t56.5 -212 t153.5 -153.5t212 -56.5t212 56.5t153.5 153.5t56.5 212t-56.5 212t-153.5 153.5t-212 56.5zM600 794q80 0 137 -57t57 -137t-57 -137t-137 -57t-137 57t-57 137t57 137t137 57z" />
<glyph unicode="&#xe166;" d="M450 1200h200q21 0 35.5 -14.5t14.5 -35.5v-350h245q20 0 25 -11t-9 -26l-383 -426q-14 -15 -33.5 -15t-32.5 15l-379 426q-13 15 -8.5 26t25.5 11h250v350q0 21 14.5 35.5t35.5 14.5zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5z M900 200v-50h100v50h-100z" />
<glyph unicode="&#xe167;" d="M583 1182l378 -435q14 -15 9 -31t-26 -16h-244v-250q0 -20 -17 -35t-39 -15h-200q-20 0 -32 14.5t-12 35.5v250h-250q-20 0 -25.5 16.5t8.5 31.5l383 431q14 16 33.5 17t33.5 -14zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5z M900 200v-50h100v50h-100z" />
<glyph unicode="&#xe168;" d="M396 723l369 369q7 7 17.5 7t17.5 -7l139 -139q7 -8 7 -18.5t-7 -17.5l-525 -525q-7 -8 -17.5 -8t-17.5 8l-292 291q-7 8 -7 18t7 18l139 139q8 7 18.5 7t17.5 -7zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50 h-100z" />
<glyph unicode="&#xe169;" d="M135 1023l142 142q14 14 35 14t35 -14l77 -77l-212 -212l-77 76q-14 15 -14 36t14 35zM655 855l210 210q14 14 24.5 10t10.5 -25l-2 -599q-1 -20 -15.5 -35t-35.5 -15l-597 -1q-21 0 -25 10.5t10 24.5l208 208l-154 155l212 212zM50 300h1000q21 0 35.5 -14.5t14.5 -35.5 v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50h-100z" />
<glyph unicode="&#xe170;" d="M350 1200l599 -2q20 -1 35 -15.5t15 -35.5l1 -597q0 -21 -10.5 -25t-24.5 10l-208 208l-155 -154l-212 212l155 154l-210 210q-14 14 -10 24.5t25 10.5zM524 512l-76 -77q-15 -14 -36 -14t-35 14l-142 142q-14 14 -14 35t14 35l77 77zM50 300h1000q21 0 35.5 -14.5 t14.5 -35.5v-250h-1100v250q0 21 14.5 35.5t35.5 14.5zM900 200v-50h100v50h-100z" />
<glyph unicode="&#xe171;" d="M1200 103l-483 276l-314 -399v423h-399l1196 796v-1096zM483 424v-230l683 953z" />
<glyph unicode="&#xe172;" d="M1100 1000v-850q0 -21 -14.5 -35.5t-35.5 -14.5h-150v400h-700v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200z" />
<glyph unicode="&#xe173;" d="M1100 1000l-2 -149l-299 -299l-95 95q-9 9 -21.5 9t-21.5 -9l-149 -147h-312v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM1132 638l106 -106q7 -7 7 -17.5t-7 -17.5l-420 -421q-8 -7 -18 -7 t-18 7l-202 203q-8 7 -8 17.5t8 17.5l106 106q7 8 17.5 8t17.5 -8l79 -79l297 297q7 7 17.5 7t17.5 -7z" />
<glyph unicode="&#xe174;" d="M1100 1000v-269l-103 -103l-134 134q-15 15 -33.5 16.5t-34.5 -12.5l-266 -266h-329v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM1202 572l70 -70q15 -15 15 -35.5t-15 -35.5l-131 -131 l131 -131q15 -15 15 -35.5t-15 -35.5l-70 -70q-15 -15 -35.5 -15t-35.5 15l-131 131l-131 -131q-15 -15 -35.5 -15t-35.5 15l-70 70q-15 15 -15 35.5t15 35.5l131 131l-131 131q-15 15 -15 35.5t15 35.5l70 70q15 15 35.5 15t35.5 -15l131 -131l131 131q15 15 35.5 15 t35.5 -15z" />
<glyph unicode="&#xe175;" d="M1100 1000v-300h-350q-21 0 -35.5 -14.5t-14.5 -35.5v-150h-500v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM850 600h100q21 0 35.5 -14.5t14.5 -35.5v-250h150q21 0 25 -10.5t-10 -24.5 l-230 -230q-14 -14 -35 -14t-35 14l-230 230q-14 14 -10 24.5t25 10.5h150v250q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe176;" d="M1100 1000v-400l-165 165q-14 15 -35 15t-35 -15l-263 -265h-402v-400h-150q-21 0 -35.5 14.5t-14.5 35.5v1000q0 20 14.5 35t35.5 15h250v-300h500v300h100zM700 1000h-100v200h100v-200zM935 565l230 -229q14 -15 10 -25.5t-25 -10.5h-150v-250q0 -20 -14.5 -35 t-35.5 -15h-100q-21 0 -35.5 15t-14.5 35v250h-150q-21 0 -25 10.5t10 25.5l230 229q14 15 35 15t35 -15z" />
<glyph unicode="&#xe177;" d="M50 1100h1100q21 0 35.5 -14.5t14.5 -35.5v-150h-1200v150q0 21 14.5 35.5t35.5 14.5zM1200 800v-550q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v550h1200zM100 500v-200h400v200h-400z" />
<glyph unicode="&#xe178;" d="M935 1165l248 -230q14 -14 14 -35t-14 -35l-248 -230q-14 -14 -24.5 -10t-10.5 25v150h-400v200h400v150q0 21 10.5 25t24.5 -10zM200 800h-50q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v-200zM400 800h-100v200h100v-200zM18 435l247 230 q14 14 24.5 10t10.5 -25v-150h400v-200h-400v-150q0 -21 -10.5 -25t-24.5 10l-247 230q-15 14 -15 35t15 35zM900 300h-100v200h100v-200zM1000 500h51q20 0 34.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-34.5 -14.5h-51v200z" />
<glyph unicode="&#xe179;" d="M862 1073l276 116q25 18 43.5 8t18.5 -41v-1106q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v397q-4 1 -11 5t-24 17.5t-30 29t-24 42t-11 56.5v359q0 31 18.5 65t43.5 52zM550 1200q22 0 34.5 -12.5t14.5 -24.5l1 -13v-450q0 -28 -10.5 -59.5 t-25 -56t-29 -45t-25.5 -31.5l-10 -11v-447q0 -21 -14.5 -35.5t-35.5 -14.5h-200q-21 0 -35.5 14.5t-14.5 35.5v447q-4 4 -11 11.5t-24 30.5t-30 46t-24 55t-11 60v450q0 2 0.5 5.5t4 12t8.5 15t14.5 12t22.5 5.5q20 0 32.5 -12.5t14.5 -24.5l3 -13v-350h100v350v5.5t2.5 12 t7 15t15 12t25.5 5.5q23 0 35.5 -12.5t13.5 -24.5l1 -13v-350h100v350q0 2 0.5 5.5t3 12t7 15t15 12t24.5 5.5z" />
<glyph unicode="&#xe180;" d="M1200 1100v-56q-4 0 -11 -0.5t-24 -3t-30 -7.5t-24 -15t-11 -24v-888q0 -22 25 -34.5t50 -13.5l25 -2v-56h-400v56q75 0 87.5 6.5t12.5 43.5v394h-500v-394q0 -37 12.5 -43.5t87.5 -6.5v-56h-400v56q4 0 11 0.5t24 3t30 7.5t24 15t11 24v888q0 22 -25 34.5t-50 13.5 l-25 2v56h400v-56q-75 0 -87.5 -6.5t-12.5 -43.5v-394h500v394q0 37 -12.5 43.5t-87.5 6.5v56h400z" />
<glyph unicode="&#xe181;" d="M675 1000h375q21 0 35.5 -14.5t14.5 -35.5v-150h-105l-295 -98v98l-200 200h-400l100 100h375zM100 900h300q41 0 70.5 -29.5t29.5 -70.5v-500q0 -41 -29.5 -70.5t-70.5 -29.5h-300q-41 0 -70.5 29.5t-29.5 70.5v500q0 41 29.5 70.5t70.5 29.5zM100 800v-200h300v200 h-300zM1100 535l-400 -133v163l400 133v-163zM100 500v-200h300v200h-300zM1100 398v-248q0 -21 -14.5 -35.5t-35.5 -14.5h-375l-100 -100h-375l-100 100h400l200 200h105z" />
<glyph unicode="&#xe182;" d="M17 1007l162 162q17 17 40 14t37 -22l139 -194q14 -20 11 -44.5t-20 -41.5l-119 -118q102 -142 228 -268t267 -227l119 118q17 17 42.5 19t44.5 -12l192 -136q19 -14 22.5 -37.5t-13.5 -40.5l-163 -162q-3 -1 -9.5 -1t-29.5 2t-47.5 6t-62.5 14.5t-77.5 26.5t-90 42.5 t-101.5 60t-111 83t-119 108.5q-74 74 -133.5 150.5t-94.5 138.5t-60 119.5t-34.5 100t-15 74.5t-4.5 48z" />
<glyph unicode="&#xe183;" d="M600 1100q92 0 175 -10.5t141.5 -27t108.5 -36.5t81.5 -40t53.5 -37t31 -27l9 -10v-200q0 -21 -14.5 -33t-34.5 -9l-202 34q-20 3 -34.5 20t-14.5 38v146q-141 24 -300 24t-300 -24v-146q0 -21 -14.5 -38t-34.5 -20l-202 -34q-20 -3 -34.5 9t-14.5 33v200q3 4 9.5 10.5 t31 26t54 37.5t80.5 39.5t109 37.5t141 26.5t175 10.5zM600 795q56 0 97 -9.5t60 -23.5t30 -28t12 -24l1 -10v-50l365 -303q14 -15 24.5 -40t10.5 -45v-212q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v212q0 20 10.5 45t24.5 40l365 303v50 q0 4 1 10.5t12 23t30 29t60 22.5t97 10z" />
<glyph unicode="&#xe184;" d="M1100 700l-200 -200h-600l-200 200v500h200v-200h200v200h200v-200h200v200h200v-500zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-12l137 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5 t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe185;" d="M700 1100h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-1000h300v1000q0 41 -29.5 70.5t-70.5 29.5zM1100 800h-100q-41 0 -70.5 -29.5t-29.5 -70.5v-700h300v700q0 41 -29.5 70.5t-70.5 29.5zM400 0h-300v400q0 41 29.5 70.5t70.5 29.5h100q41 0 70.5 -29.5t29.5 -70.5v-400z " />
<glyph unicode="&#xe186;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-100h200v-300h-300v100h200v100h-200v300h300v-100zM900 700v-300l-100 -100h-200v500h200z M700 700v-300h100v300h-100z" />
<glyph unicode="&#xe187;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 300h-100v200h-100v-200h-100v500h100v-200h100v200h100v-500zM900 700v-300l-100 -100h-200v500h200z M700 700v-300h100v300h-100z" />
<glyph unicode="&#xe188;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-300h200v-100h-300v500h300v-100zM900 700h-200v-300h200v-100h-300v500h300v-100z" />
<glyph unicode="&#xe189;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 400l-300 150l300 150v-300zM900 550l-300 -150v300z" />
<glyph unicode="&#xe190;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM900 300h-700v500h700v-500zM800 700h-130q-38 0 -66.5 -43t-28.5 -108t27 -107t68 -42h130v300zM300 700v-300 h130q41 0 68 42t27 107t-28.5 108t-66.5 43h-130z" />
<glyph unicode="&#xe191;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 700h-200v-100h200v-300h-300v100h200v100h-200v300h300v-100zM900 300h-100v400h-100v100h200v-500z M700 300h-100v100h100v-100z" />
<glyph unicode="&#xe192;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM300 700h200v-400h-300v500h100v-100zM900 300h-100v400h-100v100h200v-500zM300 600v-200h100v200h-100z M700 300h-100v100h100v-100z" />
<glyph unicode="&#xe193;" d="M200 1100h700q124 0 212 -88t88 -212v-500q0 -124 -88 -212t-212 -88h-700q-124 0 -212 88t-88 212v500q0 124 88 212t212 88zM100 900v-700h900v700h-900zM500 500l-199 -200h-100v50l199 200v150h-200v100h300v-300zM900 300h-100v400h-100v100h200v-500zM701 300h-100 v100h100v-100z" />
<glyph unicode="&#xe194;" d="M600 1191q120 0 229.5 -47t188.5 -126t126 -188.5t47 -229.5t-47 -229.5t-126 -188.5t-188.5 -126t-229.5 -47t-229.5 47t-188.5 126t-126 188.5t-47 229.5t47 229.5t126 188.5t188.5 126t229.5 47zM600 1021q-114 0 -211 -56.5t-153.5 -153.5t-56.5 -211t56.5 -211 t153.5 -153.5t211 -56.5t211 56.5t153.5 153.5t56.5 211t-56.5 211t-153.5 153.5t-211 56.5zM800 700h-300v-200h300v-100h-300l-100 100v200l100 100h300v-100z" />
<glyph unicode="&#xe195;" d="M600 1191q120 0 229.5 -47t188.5 -126t126 -188.5t47 -229.5t-47 -229.5t-126 -188.5t-188.5 -126t-229.5 -47t-229.5 47t-188.5 126t-126 188.5t-47 229.5t47 229.5t126 188.5t188.5 126t229.5 47zM600 1021q-114 0 -211 -56.5t-153.5 -153.5t-56.5 -211t56.5 -211 t153.5 -153.5t211 -56.5t211 56.5t153.5 153.5t56.5 211t-56.5 211t-153.5 153.5t-211 56.5zM800 700v-100l-50 -50l100 -100v-50h-100l-100 100h-150v-100h-100v400h300zM500 700v-100h200v100h-200z" />
<glyph unicode="&#xe197;" d="M503 1089q110 0 200.5 -59.5t134.5 -156.5q44 14 90 14q120 0 205 -86.5t85 -207t-85 -207t-205 -86.5h-128v250q0 21 -14.5 35.5t-35.5 14.5h-300q-21 0 -35.5 -14.5t-14.5 -35.5v-250h-222q-80 0 -136 57.5t-56 136.5q0 69 43 122.5t108 67.5q-2 19 -2 37q0 100 49 185 t134 134t185 49zM525 500h150q10 0 17.5 -7.5t7.5 -17.5v-275h137q21 0 26 -11.5t-8 -27.5l-223 -244q-13 -16 -32 -16t-32 16l-223 244q-13 16 -8 27.5t26 11.5h137v275q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe198;" d="M502 1089q110 0 201 -59.5t135 -156.5q43 15 89 15q121 0 206 -86.5t86 -206.5q0 -99 -60 -181t-150 -110l-378 360q-13 16 -31.5 16t-31.5 -16l-381 -365h-9q-79 0 -135.5 57.5t-56.5 136.5q0 69 43 122.5t108 67.5q-2 19 -2 38q0 100 49 184.5t133.5 134t184.5 49.5z M632 467l223 -228q13 -16 8 -27.5t-26 -11.5h-137v-275q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v275h-137q-21 0 -26 11.5t8 27.5q199 204 223 228q19 19 31.5 19t32.5 -19z" />
<glyph unicode="&#xe199;" d="M700 100v100h400l-270 300h170l-270 300h170l-300 333l-300 -333h170l-270 -300h170l-270 -300h400v-100h-50q-21 0 -35.5 -14.5t-14.5 -35.5v-50h400v50q0 21 -14.5 35.5t-35.5 14.5h-50z" />
<glyph unicode="&#xe200;" d="M600 1179q94 0 167.5 -56.5t99.5 -145.5q89 -6 150.5 -71.5t61.5 -155.5q0 -61 -29.5 -112.5t-79.5 -82.5q9 -29 9 -55q0 -74 -52.5 -126.5t-126.5 -52.5q-55 0 -100 30v-251q21 0 35.5 -14.5t14.5 -35.5v-50h-300v50q0 21 14.5 35.5t35.5 14.5v251q-45 -30 -100 -30 q-74 0 -126.5 52.5t-52.5 126.5q0 18 4 38q-47 21 -75.5 65t-28.5 97q0 74 52.5 126.5t126.5 52.5q5 0 23 -2q0 2 -1 10t-1 13q0 116 81.5 197.5t197.5 81.5z" />
<glyph unicode="&#xe201;" d="M1010 1010q111 -111 150.5 -260.5t0 -299t-150.5 -260.5q-83 -83 -191.5 -126.5t-218.5 -43.5t-218.5 43.5t-191.5 126.5q-111 111 -150.5 260.5t0 299t150.5 260.5q83 83 191.5 126.5t218.5 43.5t218.5 -43.5t191.5 -126.5zM476 1065q-4 0 -8 -1q-121 -34 -209.5 -122.5 t-122.5 -209.5q-4 -12 2.5 -23t18.5 -14l36 -9q3 -1 7 -1q23 0 29 22q27 96 98 166q70 71 166 98q11 3 17.5 13.5t3.5 22.5l-9 35q-3 13 -14 19q-7 4 -15 4zM512 920q-4 0 -9 -2q-80 -24 -138.5 -82.5t-82.5 -138.5q-4 -13 2 -24t19 -14l34 -9q4 -1 8 -1q22 0 28 21 q18 58 58.5 98.5t97.5 58.5q12 3 18 13.5t3 21.5l-9 35q-3 12 -14 19q-7 4 -15 4zM719.5 719.5q-49.5 49.5 -119.5 49.5t-119.5 -49.5t-49.5 -119.5t49.5 -119.5t119.5 -49.5t119.5 49.5t49.5 119.5t-49.5 119.5zM855 551q-22 0 -28 -21q-18 -58 -58.5 -98.5t-98.5 -57.5 q-11 -4 -17 -14.5t-3 -21.5l9 -35q3 -12 14 -19q7 -4 15 -4q4 0 9 2q80 24 138.5 82.5t82.5 138.5q4 13 -2.5 24t-18.5 14l-34 9q-4 1 -8 1zM1000 515q-23 0 -29 -22q-27 -96 -98 -166q-70 -71 -166 -98q-11 -3 -17.5 -13.5t-3.5 -22.5l9 -35q3 -13 14 -19q7 -4 15 -4 q4 0 8 1q121 34 209.5 122.5t122.5 209.5q4 12 -2.5 23t-18.5 14l-36 9q-3 1 -7 1z" />
<glyph unicode="&#xe202;" d="M700 800h300v-380h-180v200h-340v-200h-380v755q0 10 7.5 17.5t17.5 7.5h575v-400zM1000 900h-200v200zM700 300h162l-212 -212l-212 212h162v200h100v-200zM520 0h-395q-10 0 -17.5 7.5t-7.5 17.5v395zM1000 220v-195q0 -10 -7.5 -17.5t-17.5 -7.5h-195z" />
<glyph unicode="&#xe203;" d="M700 800h300v-520l-350 350l-550 -550v1095q0 10 7.5 17.5t17.5 7.5h575v-400zM1000 900h-200v200zM862 200h-162v-200h-100v200h-162l212 212zM480 0h-355q-10 0 -17.5 7.5t-7.5 17.5v55h380v-80zM1000 80v-55q0 -10 -7.5 -17.5t-17.5 -7.5h-155v80h180z" />
<glyph unicode="&#xe204;" d="M1162 800h-162v-200h100l100 -100h-300v300h-162l212 212zM200 800h200q27 0 40 -2t29.5 -10.5t23.5 -30t7 -57.5h300v-100h-600l-200 -350v450h100q0 36 7 57.5t23.5 30t29.5 10.5t40 2zM800 400h240l-240 -400h-800l300 500h500v-100z" />
<glyph unicode="&#xe205;" d="M650 1100h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5zM1000 850v150q41 0 70.5 -29.5t29.5 -70.5v-800 q0 -41 -29.5 -70.5t-70.5 -29.5h-600q-1 0 -20 4l246 246l-326 326v324q0 41 29.5 70.5t70.5 29.5v-150q0 -62 44 -106t106 -44h300q62 0 106 44t44 106zM412 250l-212 -212v162h-200v100h200v162z" />
<glyph unicode="&#xe206;" d="M450 1100h100q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-300q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h50v50q0 21 14.5 35.5t35.5 14.5zM800 850v150q41 0 70.5 -29.5t29.5 -70.5v-500 h-200v-300h200q0 -36 -7 -57.5t-23.5 -30t-29.5 -10.5t-40 -2h-600q-41 0 -70.5 29.5t-29.5 70.5v800q0 41 29.5 70.5t70.5 29.5v-150q0 -62 44 -106t106 -44h300q62 0 106 44t44 106zM1212 250l-212 -212v162h-200v100h200v162z" />
<glyph unicode="&#xe209;" d="M658 1197l637 -1104q23 -38 7 -65.5t-60 -27.5h-1276q-44 0 -60 27.5t7 65.5l637 1104q22 39 54 39t54 -39zM704 800h-208q-20 0 -32 -14.5t-8 -34.5l58 -302q4 -20 21.5 -34.5t37.5 -14.5h54q20 0 37.5 14.5t21.5 34.5l58 302q4 20 -8 34.5t-32 14.5zM500 300v-100h200 v100h-200z" />
<glyph unicode="&#xe210;" d="M425 1100h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM425 800h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5 t17.5 7.5zM825 800h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM25 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150 q0 10 7.5 17.5t17.5 7.5zM425 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM825 500h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5 v150q0 10 7.5 17.5t17.5 7.5zM25 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM425 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5 t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM825 200h250q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-250q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe211;" d="M700 1200h100v-200h-100v-100h350q62 0 86.5 -39.5t-3.5 -94.5l-66 -132q-41 -83 -81 -134h-772q-40 51 -81 134l-66 132q-28 55 -3.5 94.5t86.5 39.5h350v100h-100v200h100v100h200v-100zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-12l137 -100 h-950l138 100h-13q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe212;" d="M600 1300q40 0 68.5 -29.5t28.5 -70.5h-194q0 41 28.5 70.5t68.5 29.5zM443 1100h314q18 -37 18 -75q0 -8 -3 -25h328q41 0 44.5 -16.5t-30.5 -38.5l-175 -145h-678l-178 145q-34 22 -29 38.5t46 16.5h328q-3 17 -3 25q0 38 18 75zM250 700h700q21 0 35.5 -14.5 t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-150v-200l275 -200h-950l275 200v200h-150q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe213;" d="M600 1181q75 0 128 -53t53 -128t-53 -128t-128 -53t-128 53t-53 128t53 128t128 53zM602 798h46q34 0 55.5 -28.5t21.5 -86.5q0 -76 39 -183h-324q39 107 39 183q0 58 21.5 86.5t56.5 28.5h45zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13 l138 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe214;" d="M600 1300q47 0 92.5 -53.5t71 -123t25.5 -123.5q0 -78 -55.5 -133.5t-133.5 -55.5t-133.5 55.5t-55.5 133.5q0 62 34 143l144 -143l111 111l-163 163q34 26 63 26zM602 798h46q34 0 55.5 -28.5t21.5 -86.5q0 -76 39 -183h-324q39 107 39 183q0 58 21.5 86.5t56.5 28.5h45 zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13l138 -100h-950l137 100h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe215;" d="M600 1200l300 -161v-139h-300q0 -57 18.5 -108t50 -91.5t63 -72t70 -67.5t57.5 -61h-530q-60 83 -90.5 177.5t-30.5 178.5t33 164.5t87.5 139.5t126 96.5t145.5 41.5v-98zM250 400h700q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-13l138 -100h-950l137 100 h-12q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5zM50 100h1100q21 0 35.5 -14.5t14.5 -35.5v-50h-1200v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe216;" d="M600 1300q41 0 70.5 -29.5t29.5 -70.5v-78q46 -26 73 -72t27 -100v-50h-400v50q0 54 27 100t73 72v78q0 41 29.5 70.5t70.5 29.5zM400 800h400q54 0 100 -27t72 -73h-172v-100h200v-100h-200v-100h200v-100h-200v-100h200q0 -83 -58.5 -141.5t-141.5 -58.5h-400 q-83 0 -141.5 58.5t-58.5 141.5v400q0 83 58.5 141.5t141.5 58.5z" />
<glyph unicode="&#xe218;" d="M150 1100h900q21 0 35.5 -14.5t14.5 -35.5v-500q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v500q0 21 14.5 35.5t35.5 14.5zM125 400h950q10 0 17.5 -7.5t7.5 -17.5v-50q0 -10 -7.5 -17.5t-17.5 -7.5h-283l224 -224q13 -13 13 -31.5t-13 -32 t-31.5 -13.5t-31.5 13l-88 88h-524l-87 -88q-13 -13 -32 -13t-32 13.5t-13 32t13 31.5l224 224h-289q-10 0 -17.5 7.5t-7.5 17.5v50q0 10 7.5 17.5t17.5 7.5zM541 300l-100 -100h324l-100 100h-124z" />
<glyph unicode="&#xe219;" d="M200 1100h800q83 0 141.5 -58.5t58.5 -141.5v-200h-100q0 41 -29.5 70.5t-70.5 29.5h-250q-41 0 -70.5 -29.5t-29.5 -70.5h-100q0 41 -29.5 70.5t-70.5 29.5h-250q-41 0 -70.5 -29.5t-29.5 -70.5h-100v200q0 83 58.5 141.5t141.5 58.5zM100 600h1000q41 0 70.5 -29.5 t29.5 -70.5v-300h-1200v300q0 41 29.5 70.5t70.5 29.5zM300 100v-50q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v50h200zM1100 100v-50q0 -21 -14.5 -35.5t-35.5 -14.5h-100q-21 0 -35.5 14.5t-14.5 35.5v50h200z" />
<glyph unicode="&#xe221;" d="M480 1165l682 -683q31 -31 31 -75.5t-31 -75.5l-131 -131h-481l-517 518q-32 31 -32 75.5t32 75.5l295 296q31 31 75.5 31t76.5 -31zM108 794l342 -342l303 304l-341 341zM250 100h800q21 0 35.5 -14.5t14.5 -35.5v-50h-900v50q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe223;" d="M1057 647l-189 506q-8 19 -27.5 33t-40.5 14h-400q-21 0 -40.5 -14t-27.5 -33l-189 -506q-8 -19 1.5 -33t30.5 -14h625v-150q0 -21 14.5 -35.5t35.5 -14.5t35.5 14.5t14.5 35.5v150h125q21 0 30.5 14t1.5 33zM897 0h-595v50q0 21 14.5 35.5t35.5 14.5h50v50 q0 21 14.5 35.5t35.5 14.5h48v300h200v-300h47q21 0 35.5 -14.5t14.5 -35.5v-50h50q21 0 35.5 -14.5t14.5 -35.5v-50z" />
<glyph unicode="&#xe224;" d="M900 800h300v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-375v591l-300 300v84q0 10 7.5 17.5t17.5 7.5h375v-400zM1200 900h-200v200zM400 600h300v-575q0 -10 -7.5 -17.5t-17.5 -7.5h-650q-10 0 -17.5 7.5t-7.5 17.5v950q0 10 7.5 17.5t17.5 7.5h375v-400zM700 700h-200v200z " />
<glyph unicode="&#xe225;" d="M484 1095h195q75 0 146 -32.5t124 -86t89.5 -122.5t48.5 -142q18 -14 35 -20q31 -10 64.5 6.5t43.5 48.5q10 34 -15 71q-19 27 -9 43q5 8 12.5 11t19 -1t23.5 -16q41 -44 39 -105q-3 -63 -46 -106.5t-104 -43.5h-62q-7 -55 -35 -117t-56 -100l-39 -234q-3 -20 -20 -34.5 t-38 -14.5h-100q-21 0 -33 14.5t-9 34.5l12 70q-49 -14 -91 -14h-195q-24 0 -65 8l-11 -64q-3 -20 -20 -34.5t-38 -14.5h-100q-21 0 -33 14.5t-9 34.5l26 157q-84 74 -128 175l-159 53q-19 7 -33 26t-14 40v50q0 21 14.5 35.5t35.5 14.5h124q11 87 56 166l-111 95 q-16 14 -12.5 23.5t24.5 9.5h203q116 101 250 101zM675 1000h-250q-10 0 -17.5 -7.5t-7.5 -17.5v-50q0 -10 7.5 -17.5t17.5 -7.5h250q10 0 17.5 7.5t7.5 17.5v50q0 10 -7.5 17.5t-17.5 7.5z" />
<glyph unicode="&#xe226;" d="M641 900l423 247q19 8 42 2.5t37 -21.5l32 -38q14 -15 12.5 -36t-17.5 -34l-139 -120h-390zM50 1100h106q67 0 103 -17t66 -71l102 -212h823q21 0 35.5 -14.5t14.5 -35.5v-50q0 -21 -14 -40t-33 -26l-737 -132q-23 -4 -40 6t-26 25q-42 67 -100 67h-300q-62 0 -106 44 t-44 106v200q0 62 44 106t106 44zM173 928h-80q-19 0 -28 -14t-9 -35v-56q0 -51 42 -51h134q16 0 21.5 8t5.5 24q0 11 -16 45t-27 51q-18 28 -43 28zM550 727q-32 0 -54.5 -22.5t-22.5 -54.5t22.5 -54.5t54.5 -22.5t54.5 22.5t22.5 54.5t-22.5 54.5t-54.5 22.5zM130 389 l152 130q18 19 34 24t31 -3.5t24.5 -17.5t25.5 -28q28 -35 50.5 -51t48.5 -13l63 5l48 -179q13 -61 -3.5 -97.5t-67.5 -79.5l-80 -69q-47 -40 -109 -35.5t-103 51.5l-130 151q-40 47 -35.5 109.5t51.5 102.5zM380 377l-102 -88q-31 -27 2 -65l37 -43q13 -15 27.5 -19.5 t31.5 6.5l61 53q19 16 14 49q-2 20 -12 56t-17 45q-11 12 -19 14t-23 -8z" />
<glyph unicode="&#xe227;" d="M625 1200h150q10 0 17.5 -7.5t7.5 -17.5v-109q79 -33 131 -87.5t53 -128.5q1 -46 -15 -84.5t-39 -61t-46 -38t-39 -21.5l-17 -6q6 0 15 -1.5t35 -9t50 -17.5t53 -30t50 -45t35.5 -64t14.5 -84q0 -59 -11.5 -105.5t-28.5 -76.5t-44 -51t-49.5 -31.5t-54.5 -16t-49.5 -6.5 t-43.5 -1v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-100v-75q0 -10 -7.5 -17.5t-17.5 -7.5h-150q-10 0 -17.5 7.5t-7.5 17.5v75h-175q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5h75v600h-75q-10 0 -17.5 7.5t-7.5 17.5v150 q0 10 7.5 17.5t17.5 7.5h175v75q0 10 7.5 17.5t17.5 7.5h150q10 0 17.5 -7.5t7.5 -17.5v-75h100v75q0 10 7.5 17.5t17.5 7.5zM400 900v-200h263q28 0 48.5 10.5t30 25t15 29t5.5 25.5l1 10q0 4 -0.5 11t-6 24t-15 30t-30 24t-48.5 11h-263zM400 500v-200h363q28 0 48.5 10.5 t30 25t15 29t5.5 25.5l1 10q0 4 -0.5 11t-6 24t-15 30t-30 24t-48.5 11h-363z" />
<glyph unicode="&#xe230;" d="M212 1198h780q86 0 147 -61t61 -147v-416q0 -51 -18 -142.5t-36 -157.5l-18 -66q-29 -87 -93.5 -146.5t-146.5 -59.5h-572q-82 0 -147 59t-93 147q-8 28 -20 73t-32 143.5t-20 149.5v416q0 86 61 147t147 61zM600 1045q-70 0 -132.5 -11.5t-105.5 -30.5t-78.5 -41.5 t-57 -45t-36 -41t-20.5 -30.5l-6 -12l156 -243h560l156 243q-2 5 -6 12.5t-20 29.5t-36.5 42t-57 44.5t-79 42t-105 29.5t-132.5 12zM762 703h-157l195 261z" />
<glyph unicode="&#xe231;" d="M475 1300h150q103 0 189 -86t86 -189v-500q0 -41 -42 -83t-83 -42h-450q-41 0 -83 42t-42 83v500q0 103 86 189t189 86zM700 300v-225q0 -21 -27 -48t-48 -27h-150q-21 0 -48 27t-27 48v225h300z" />
<glyph unicode="&#xe232;" d="M475 1300h96q0 -150 89.5 -239.5t239.5 -89.5v-446q0 -41 -42 -83t-83 -42h-450q-41 0 -83 42t-42 83v500q0 103 86 189t189 86zM700 300v-225q0 -21 -27 -48t-48 -27h-150q-21 0 -48 27t-27 48v225h300z" />
<glyph unicode="&#xe233;" d="M1294 767l-638 -283l-378 170l-78 -60v-224l100 -150v-199l-150 148l-150 -149v200l100 150v250q0 4 -0.5 10.5t0 9.5t1 8t3 8t6.5 6l47 40l-147 65l642 283zM1000 380l-350 -166l-350 166v147l350 -165l350 165v-147z" />
<glyph unicode="&#xe234;" d="M250 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM650 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM1050 800q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44z" />
<glyph unicode="&#xe235;" d="M550 1100q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM550 700q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44zM550 300q62 0 106 -44t44 -106t-44 -106t-106 -44t-106 44t-44 106t44 106t106 44z" />
<glyph unicode="&#xe236;" d="M125 1100h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5zM125 700h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5 t17.5 7.5zM125 300h950q10 0 17.5 -7.5t7.5 -17.5v-150q0 -10 -7.5 -17.5t-17.5 -7.5h-950q-10 0 -17.5 7.5t-7.5 17.5v150q0 10 7.5 17.5t17.5 7.5z" />
<glyph unicode="&#xe237;" d="M350 1200h500q162 0 256 -93.5t94 -256.5v-500q0 -165 -93.5 -257.5t-256.5 -92.5h-500q-165 0 -257.5 92.5t-92.5 257.5v500q0 165 92.5 257.5t257.5 92.5zM900 1000h-600q-41 0 -70.5 -29.5t-29.5 -70.5v-600q0 -41 29.5 -70.5t70.5 -29.5h600q41 0 70.5 29.5 t29.5 70.5v600q0 41 -29.5 70.5t-70.5 29.5zM350 900h500q21 0 35.5 -14.5t14.5 -35.5v-300q0 -21 -14.5 -35.5t-35.5 -14.5h-500q-21 0 -35.5 14.5t-14.5 35.5v300q0 21 14.5 35.5t35.5 14.5zM400 800v-200h400v200h-400z" />
<glyph unicode="&#xe238;" d="M150 1100h1000q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5t-35.5 -14.5h-50v-200h50q21 0 35.5 -14.5t14.5 -35.5t-14.5 -35.5 t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5h50v200h-50q-21 0 -35.5 14.5t-14.5 35.5t14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe239;" d="M650 1187q87 -67 118.5 -156t0 -178t-118.5 -155q-87 66 -118.5 155t0 178t118.5 156zM300 800q124 0 212 -88t88 -212q-124 0 -212 88t-88 212zM1000 800q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM300 500q124 0 212 -88t88 -212q-124 0 -212 88t-88 212z M1000 500q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM700 199v-144q0 -21 -14.5 -35.5t-35.5 -14.5t-35.5 14.5t-14.5 35.5v142q40 -4 43 -4q17 0 57 6z" />
<glyph unicode="&#xe240;" d="M745 878l69 19q25 6 45 -12l298 -295q11 -11 15 -26.5t-2 -30.5q-5 -14 -18 -23.5t-28 -9.5h-8q1 0 1 -13q0 -29 -2 -56t-8.5 -62t-20 -63t-33 -53t-51 -39t-72.5 -14h-146q-184 0 -184 288q0 24 10 47q-20 4 -62 4t-63 -4q11 -24 11 -47q0 -288 -184 -288h-142 q-48 0 -84.5 21t-56 51t-32 71.5t-16 75t-3.5 68.5q0 13 2 13h-7q-15 0 -27.5 9.5t-18.5 23.5q-6 15 -2 30.5t15 25.5l298 296q20 18 46 11l76 -19q20 -5 30.5 -22.5t5.5 -37.5t-22.5 -31t-37.5 -5l-51 12l-182 -193h891l-182 193l-44 -12q-20 -5 -37.5 6t-22.5 31t6 37.5 t31 22.5z" />
<glyph unicode="&#xe241;" d="M1200 900h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-200v-850q0 -22 25 -34.5t50 -13.5l25 -2v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v850h-200q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h1000v-300zM500 450h-25q0 15 -4 24.5t-9 14.5t-17 7.5t-20 3t-25 0.5h-100v-425q0 -11 12.5 -17.5t25.5 -7.5h12v-50h-200v50q50 0 50 25v425h-100q-17 0 -25 -0.5t-20 -3t-17 -7.5t-9 -14.5t-4 -24.5h-25v150h500v-150z" />
<glyph unicode="&#xe242;" d="M1000 300v50q-25 0 -55 32q-14 14 -25 31t-16 27l-4 11l-289 747h-69l-300 -754q-18 -35 -39 -56q-9 -9 -24.5 -18.5t-26.5 -14.5l-11 -5v-50h273v50q-49 0 -78.5 21.5t-11.5 67.5l69 176h293l61 -166q13 -34 -3.5 -66.5t-55.5 -32.5v-50h312zM412 691l134 342l121 -342 h-255zM1100 150v-100q0 -21 -14.5 -35.5t-35.5 -14.5h-1000q-21 0 -35.5 14.5t-14.5 35.5v100q0 21 14.5 35.5t35.5 14.5h1000q21 0 35.5 -14.5t14.5 -35.5z" />
<glyph unicode="&#xe243;" d="M50 1200h1100q21 0 35.5 -14.5t14.5 -35.5v-1100q0 -21 -14.5 -35.5t-35.5 -14.5h-1100q-21 0 -35.5 14.5t-14.5 35.5v1100q0 21 14.5 35.5t35.5 14.5zM611 1118h-70q-13 0 -18 -12l-299 -753q-17 -32 -35 -51q-18 -18 -56 -34q-12 -5 -12 -18v-50q0 -8 5.5 -14t14.5 -6 h273q8 0 14 6t6 14v50q0 8 -6 14t-14 6q-55 0 -71 23q-10 14 0 39l63 163h266l57 -153q11 -31 -6 -55q-12 -17 -36 -17q-8 0 -14 -6t-6 -14v-50q0 -8 6 -14t14 -6h313q8 0 14 6t6 14v50q0 7 -5.5 13t-13.5 7q-17 0 -42 25q-25 27 -40 63h-1l-288 748q-5 12 -19 12zM639 611 h-197l103 264z" />
<glyph unicode="&#xe244;" d="M1200 1100h-1200v100h1200v-100zM50 1000h400q21 0 35.5 -14.5t14.5 -35.5v-900q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v900q0 21 14.5 35.5t35.5 14.5zM650 1000h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM700 900v-300h300v300h-300z" />
<glyph unicode="&#xe245;" d="M50 1200h400q21 0 35.5 -14.5t14.5 -35.5v-900q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v900q0 21 14.5 35.5t35.5 14.5zM650 700h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400 q0 21 14.5 35.5t35.5 14.5zM700 600v-300h300v300h-300zM1200 0h-1200v100h1200v-100z" />
<glyph unicode="&#xe246;" d="M50 1000h400q21 0 35.5 -14.5t14.5 -35.5v-350h100v150q0 21 14.5 35.5t35.5 14.5h400q21 0 35.5 -14.5t14.5 -35.5v-150h100v-100h-100v-150q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v150h-100v-350q0 -21 -14.5 -35.5t-35.5 -14.5h-400 q-21 0 -35.5 14.5t-14.5 35.5v800q0 21 14.5 35.5t35.5 14.5zM700 700v-300h300v300h-300z" />
<glyph unicode="&#xe247;" d="M100 0h-100v1200h100v-1200zM250 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM300 1000v-300h300v300h-300zM250 500h900q21 0 35.5 -14.5t14.5 -35.5v-400 q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe248;" d="M600 1100h150q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-150v-100h450q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5h350v100h-150q-21 0 -35.5 14.5 t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5h150v100h100v-100zM400 1000v-300h300v300h-300z" />
<glyph unicode="&#xe249;" d="M1200 0h-100v1200h100v-1200zM550 1100h400q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-400q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM600 1000v-300h300v300h-300zM50 500h900q21 0 35.5 -14.5t14.5 -35.5v-400 q0 -21 -14.5 -35.5t-35.5 -14.5h-900q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5z" />
<glyph unicode="&#xe250;" d="M865 565l-494 -494q-23 -23 -41 -23q-14 0 -22 13.5t-8 38.5v1000q0 25 8 38.5t22 13.5q18 0 41 -23l494 -494q14 -14 14 -35t-14 -35z" />
<glyph unicode="&#xe251;" d="M335 635l494 494q29 29 50 20.5t21 -49.5v-1000q0 -41 -21 -49.5t-50 20.5l-494 494q-14 14 -14 35t14 35z" />
<glyph unicode="&#xe252;" d="M100 900h1000q41 0 49.5 -21t-20.5 -50l-494 -494q-14 -14 -35 -14t-35 14l-494 494q-29 29 -20.5 50t49.5 21z" />
<glyph unicode="&#xe253;" d="M635 865l494 -494q29 -29 20.5 -50t-49.5 -21h-1000q-41 0 -49.5 21t20.5 50l494 494q14 14 35 14t35 -14z" />
<glyph unicode="&#xe254;" d="M700 741v-182l-692 -323v221l413 193l-413 193v221zM1200 0h-800v200h800v-200z" />
<glyph unicode="&#xe255;" d="M1200 900h-200v-100h200v-100h-300v300h200v100h-200v100h300v-300zM0 700h50q0 21 4 37t9.5 26.5t18 17.5t22 11t28.5 5.5t31 2t37 0.5h100v-550q0 -22 -25 -34.5t-50 -13.5l-25 -2v-100h400v100q-4 0 -11 0.5t-24 3t-30 7t-24 15t-11 24.5v550h100q25 0 37 -0.5t31 -2 t28.5 -5.5t22 -11t18 -17.5t9.5 -26.5t4 -37h50v300h-800v-300z" />
<glyph unicode="&#xe256;" d="M800 700h-50q0 21 -4 37t-9.5 26.5t-18 17.5t-22 11t-28.5 5.5t-31 2t-37 0.5h-100v-550q0 -22 25 -34.5t50 -14.5l25 -1v-100h-400v100q4 0 11 0.5t24 3t30 7t24 15t11 24.5v550h-100q-25 0 -37 -0.5t-31 -2t-28.5 -5.5t-22 -11t-18 -17.5t-9.5 -26.5t-4 -37h-50v300 h800v-300zM1100 200h-200v-100h200v-100h-300v300h200v100h-200v100h300v-300z" />
<glyph unicode="&#xe257;" d="M701 1098h160q16 0 21 -11t-7 -23l-464 -464l464 -464q12 -12 7 -23t-21 -11h-160q-13 0 -23 9l-471 471q-7 8 -7 18t7 18l471 471q10 9 23 9z" />
<glyph unicode="&#xe258;" d="M339 1098h160q13 0 23 -9l471 -471q7 -8 7 -18t-7 -18l-471 -471q-10 -9 -23 -9h-160q-16 0 -21 11t7 23l464 464l-464 464q-12 12 -7 23t21 11z" />
<glyph unicode="&#xe259;" d="M1087 882q11 -5 11 -21v-160q0 -13 -9 -23l-471 -471q-8 -7 -18 -7t-18 7l-471 471q-9 10 -9 23v160q0 16 11 21t23 -7l464 -464l464 464q12 12 23 7z" />
<glyph unicode="&#xe260;" d="M618 993l471 -471q9 -10 9 -23v-160q0 -16 -11 -21t-23 7l-464 464l-464 -464q-12 -12 -23 -7t-11 21v160q0 13 9 23l471 471q8 7 18 7t18 -7z" />
<glyph unicode="&#xf8ff;" d="M1000 1200q0 -124 -88 -212t-212 -88q0 124 88 212t212 88zM450 1000h100q21 0 40 -14t26 -33l79 -194q5 1 16 3q34 6 54 9.5t60 7t65.5 1t61 -10t56.5 -23t42.5 -42t29 -64t5 -92t-19.5 -121.5q-1 -7 -3 -19.5t-11 -50t-20.5 -73t-32.5 -81.5t-46.5 -83t-64 -70 t-82.5 -50q-13 -5 -42 -5t-65.5 2.5t-47.5 2.5q-14 0 -49.5 -3.5t-63 -3.5t-43.5 7q-57 25 -104.5 78.5t-75 111.5t-46.5 112t-26 90l-7 35q-15 63 -18 115t4.5 88.5t26 64t39.5 43.5t52 25.5t58.5 13t62.5 2t59.5 -4.5t55.5 -8l-147 192q-12 18 -5.5 30t27.5 12z" />
<glyph unicode="&#x1f511;" d="M250 1200h600q21 0 35.5 -14.5t14.5 -35.5v-400q0 -21 -14.5 -35.5t-35.5 -14.5h-150v-500l-255 -178q-19 -9 -32 -1t-13 29v650h-150q-21 0 -35.5 14.5t-14.5 35.5v400q0 21 14.5 35.5t35.5 14.5zM400 1100v-100h300v100h-300z" />
<glyph unicode="&#x1f6aa;" d="M250 1200h750q39 0 69.5 -40.5t30.5 -84.5v-933l-700 -117v950l600 125h-700v-1000h-100v1025q0 23 15.5 49t34.5 26zM500 525v-100l100 20v100z" />
</font>
</defs></svg> ) format('svg')}.glyphicon{position:relative;top:1px;display:inline-block;font-family:'Glyphicons Halflings';font-style:normal;font-weight:400;line-height:1;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}.glyphicon-asterisk:before{content:"\2a"}.glyphicon-plus:before{content:"\2b"}.glyphicon-eur:before,.glyphicon-euro:before{content:"\20ac"}.glyphicon-minus:before{content:"\2212"}.glyphicon-cloud:before{content:"\2601"}.glyphicon-envelope:before{content:"\2709"}.glyphicon-pencil:before{content:"\270f"}.glyphicon-glass:before{content:"\e001"}.glyphicon-music:before{content:"\e002"}.glyphicon-search:before{content:"\e003"}.glyphicon-heart:before{content:"\e005"}.glyphicon-star:before{content:"\e006"}.glyphicon-star-empty:before{content:"\e007"}.glyphicon-user:before{content:"\e008"}.glyphicon-film:before{content:"\e009"}.glyphicon-th-large:before{content:"\e010"}.glyphicon-th:before{content:"\e011"}.glyphicon-th-list:before{content:"\e012"}.glyphicon-ok:before{content:"\e013"}.glyphicon-remove:before{content:"\e014"}.glyphicon-zoom-in:before{content:"\e015"}.glyphicon-zoom-out:before{content:"\e016"}.glyphicon-off:before{content:"\e017"}.glyphicon-signal:before{content:"\e018"}.glyphicon-cog:before{content:"\e019"}.glyphicon-trash:before{content:"\e020"}.glyphicon-home:before{content:"\e021"}.glyphicon-file:before{content:"\e022"}.glyphicon-time:before{content:"\e023"}.glyphicon-road:before{content:"\e024"}.glyphicon-download-alt:before{content:"\e025"}.glyphicon-download:before{content:"\e026"}.glyphicon-upload:before{content:"\e027"}.glyphicon-inbox:before{content:"\e028"}.glyphicon-play-circle:before{content:"\e029"}.glyphicon-repeat:before{content:"\e030"}.glyphicon-refresh:before{content:"\e031"}.glyphicon-list-alt:before{content:"\e032"}.glyphicon-lock:before{content:"\e033"}.glyphicon-flag:before{content:"\e034"}.glyphicon-headphones:before{content:"\e035"}.glyphicon-volume-off:before{content:"\e036"}.glyphicon-volume-down:before{content:"\e037"}.glyphicon-volume-up:before{content:"\e038"}.glyphicon-qrcode:before{content:"\e039"}.glyphicon-barcode:before{content:"\e040"}.glyphicon-tag:before{content:"\e041"}.glyphicon-tags:before{content:"\e042"}.glyphicon-book:before{content:"\e043"}.glyphicon-bookmark:before{content:"\e044"}.glyphicon-print:before{content:"\e045"}.glyphicon-camera:before{content:"\e046"}.glyphicon-font:before{content:"\e047"}.glyphicon-bold:before{content:"\e048"}.glyphicon-italic:before{content:"\e049"}.glyphicon-text-height:before{content:"\e050"}.glyphicon-text-width:before{content:"\e051"}.glyphicon-align-left:before{content:"\e052"}.glyphicon-align-center:before{content:"\e053"}.glyphicon-align-right:before{content:"\e054"}.glyphicon-align-justify:before{content:"\e055"}.glyphicon-list:before{content:"\e056"}.glyphicon-indent-left:before{content:"\e057"}.glyphicon-indent-right:before{content:"\e058"}.glyphicon-facetime-video:before{content:"\e059"}.glyphicon-picture:before{content:"\e060"}.glyphicon-map-marker:before{content:"\e062"}.glyphicon-adjust:before{content:"\e063"}.glyphicon-tint:before{content:"\e064"}.glyphicon-edit:before{content:"\e065"}.glyphicon-share:before{content:"\e066"}.glyphicon-check:before{content:"\e067"}.glyphicon-move:before{content:"\e068"}.glyphicon-step-backward:before{content:"\e069"}.glyphicon-fast-backward:before{content:"\e070"}.glyphicon-backward:before{content:"\e071"}.glyphicon-play:before{content:"\e072"}.glyphicon-pause:before{content:"\e073"}.glyphicon-stop:before{content:"\e074"}.glyphicon-forward:before{content:"\e075"}.glyphicon-fast-forward:before{content:"\e076"}.glyphicon-step-forward:before{content:"\e077"}.glyphicon-eject:before{content:"\e078"}.glyphicon-chevron-left:before{content:"\e079"}.glyphicon-chevron-right:before{content:"\e080"}.glyphicon-plus-sign:before{content:"\e081"}.glyphicon-minus-sign:before{content:"\e082"}.glyphicon-remove-sign:before{content:"\e083"}.glyphicon-ok-sign:before{content:"\e084"}.glyphicon-question-sign:before{content:"\e085"}.glyphicon-info-sign:before{content:"\e086"}.glyphicon-screenshot:before{content:"\e087"}.glyphicon-remove-circle:before{content:"\e088"}.glyphicon-ok-circle:before{content:"\e089"}.glyphicon-ban-circle:before{content:"\e090"}.glyphicon-arrow-left:before{content:"\e091"}.glyphicon-arrow-right:before{content:"\e092"}.glyphicon-arrow-up:before{content:"\e093"}.glyphicon-arrow-down:before{content:"\e094"}.glyphicon-share-alt:before{content:"\e095"}.glyphicon-resize-full:before{content:"\e096"}.glyphicon-resize-small:before{content:"\e097"}.glyphicon-exclamation-sign:before{content:"\e101"}.glyphicon-gift:before{content:"\e102"}.glyphicon-leaf:before{content:"\e103"}.glyphicon-fire:before{content:"\e104"}.glyphicon-eye-open:before{content:"\e105"}.glyphicon-eye-close:before{content:"\e106"}.glyphicon-warning-sign:before{content:"\e107"}.glyphicon-plane:before{content:"\e108"}.glyphicon-calendar:before{content:"\e109"}.glyphicon-random:before{content:"\e110"}.glyphicon-comment:before{content:"\e111"}.glyphicon-magnet:before{content:"\e112"}.glyphicon-chevron-up:before{content:"\e113"}.glyphicon-chevron-down:before{content:"\e114"}.glyphicon-retweet:before{content:"\e115"}.glyphicon-shopping-cart:before{content:"\e116"}.glyphicon-folder-close:before{content:"\e117"}.glyphicon-folder-open:before{content:"\e118"}.glyphicon-resize-vertical:before{content:"\e119"}.glyphicon-resize-horizontal:before{content:"\e120"}.glyphicon-hdd:before{content:"\e121"}.glyphicon-bullhorn:before{content:"\e122"}.glyphicon-bell:before{content:"\e123"}.glyphicon-certificate:before{content:"\e124"}.glyphicon-thumbs-up:before{content:"\e125"}.glyphicon-thumbs-down:before{content:"\e126"}.glyphicon-hand-right:before{content:"\e127"}.glyphicon-hand-left:before{content:"\e128"}.glyphicon-hand-up:before{content:"\e129"}.glyphicon-hand-down:before{content:"\e130"}.glyphicon-circle-arrow-right:before{content:"\e131"}.glyphicon-circle-arrow-left:before{content:"\e132"}.glyphicon-circle-arrow-up:before{content:"\e133"}.glyphicon-circle-arrow-down:before{content:"\e134"}.glyphicon-globe:before{content:"\e135"}.glyphicon-wrench:before{content:"\e136"}.glyphicon-tasks:before{content:"\e137"}.glyphicon-filter:before{content:"\e138"}.glyphicon-briefcase:before{content:"\e139"}.glyphicon-fullscreen:before{content:"\e140"}.glyphicon-dashboard:before{content:"\e141"}.glyphicon-paperclip:before{content:"\e142"}.glyphicon-heart-empty:before{content:"\e143"}.glyphicon-link:before{content:"\e144"}.glyphicon-phone:before{content:"\e145"}.glyphicon-pushpin:before{content:"\e146"}.glyphicon-usd:before{content:"\e148"}.glyphicon-gbp:before{content:"\e149"}.glyphicon-sort:before{content:"\e150"}.glyphicon-sort-by-alphabet:before{content:"\e151"}.glyphicon-sort-by-alphabet-alt:before{content:"\e152"}.glyphicon-sort-by-order:before{content:"\e153"}.glyphicon-sort-by-order-alt:before{content:"\e154"}.glyphicon-sort-by-attributes:before{content:"\e155"}.glyphicon-sort-by-attributes-alt:before{content:"\e156"}.glyphicon-unchecked:before{content:"\e157"}.glyphicon-expand:before{content:"\e158"}.glyphicon-collapse-down:before{content:"\e159"}.glyphicon-collapse-up:before{content:"\e160"}.glyphicon-log-in:before{content:"\e161"}.glyphicon-flash:before{content:"\e162"}.glyphicon-log-out:before{content:"\e163"}.glyphicon-new-window:before{content:"\e164"}.glyphicon-record:before{content:"\e165"}.glyphicon-save:before{content:"\e166"}.glyphicon-open:before{content:"\e167"}.glyphicon-saved:before{content:"\e168"}.glyphicon-import:before{content:"\e169"}.glyphicon-export:before{content:"\e170"}.glyphicon-send:before{content:"\e171"}.glyphicon-floppy-disk:before{content:"\e172"}.glyphicon-floppy-saved:before{content:"\e173"}.glyphicon-floppy-remove:before{content:"\e174"}.glyphicon-floppy-save:before{content:"\e175"}.glyphicon-floppy-open:before{content:"\e176"}.glyphicon-credit-card:before{content:"\e177"}.glyphicon-transfer:before{content:"\e178"}.glyphicon-cutlery:before{content:"\e179"}.glyphicon-header:before{content:"\e180"}.glyphicon-compressed:before{content:"\e181"}.glyphicon-earphone:before{content:"\e182"}.glyphicon-phone-alt:before{content:"\e183"}.glyphicon-tower:before{content:"\e184"}.glyphicon-stats:before{content:"\e185"}.glyphicon-sd-video:before{content:"\e186"}.glyphicon-hd-video:before{content:"\e187"}.glyphicon-subtitles:before{content:"\e188"}.glyphicon-sound-stereo:before{content:"\e189"}.glyphicon-sound-dolby:before{content:"\e190"}.glyphicon-sound-5-1:before{content:"\e191"}.glyphicon-sound-6-1:before{content:"\e192"}.glyphicon-sound-7-1:before{content:"\e193"}.glyphicon-copyright-mark:before{content:"\e194"}.glyphicon-registration-mark:before{content:"\e195"}.glyphicon-cloud-download:before{content:"\e197"}.glyphicon-cloud-upload:before{content:"\e198"}.glyphicon-tree-conifer:before{content:"\e199"}.glyphicon-tree-deciduous:before{content:"\e200"}.glyphicon-cd:before{content:"\e201"}.glyphicon-save-file:before{content:"\e202"}.glyphicon-open-file:before{content:"\e203"}.glyphicon-level-up:before{content:"\e204"}.glyphicon-copy:before{content:"\e205"}.glyphicon-paste:before{content:"\e206"}.glyphicon-alert:before{content:"\e209"}.glyphicon-equalizer:before{content:"\e210"}.glyphicon-king:before{content:"\e211"}.glyphicon-queen:before{content:"\e212"}.glyphicon-pawn:before{content:"\e213"}.glyphicon-bishop:before{content:"\e214"}.glyphicon-knight:before{content:"\e215"}.glyphicon-baby-formula:before{content:"\e216"}.glyphicon-tent:before{content:"\26fa"}.glyphicon-blackboard:before{content:"\e218"}.glyphicon-bed:before{content:"\e219"}.glyphicon-apple:before{content:"\f8ff"}.glyphicon-erase:before{content:"\e221"}.glyphicon-hourglass:before{content:"\231b"}.glyphicon-lamp:before{content:"\e223"}.glyphicon-duplicate:before{content:"\e224"}.glyphicon-piggy-bank:before{content:"\e225"}.glyphicon-scissors:before{content:"\e226"}.glyphicon-bitcoin:before{content:"\e227"}.glyphicon-btc:before{content:"\e227"}.glyphicon-xbt:before{content:"\e227"}.glyphicon-yen:before{content:"\00a5"}.glyphicon-jpy:before{content:"\00a5"}.glyphicon-ruble:before{content:"\20bd"}.glyphicon-rub:before{content:"\20bd"}.glyphicon-scale:before{content:"\e230"}.glyphicon-ice-lolly:before{content:"\e231"}.glyphicon-ice-lolly-tasted:before{content:"\e232"}.glyphicon-education:before{content:"\e233"}.glyphicon-option-horizontal:before{content:"\e234"}.glyphicon-option-vertical:before{content:"\e235"}.glyphicon-menu-hamburger:before{content:"\e236"}.glyphicon-modal-window:before{content:"\e237"}.glyphicon-oil:before{content:"\e238"}.glyphicon-grain:before{content:"\e239"}.glyphicon-sunglasses:before{content:"\e240"}.glyphicon-text-size:before{content:"\e241"}.glyphicon-text-color:before{content:"\e242"}.glyphicon-text-background:before{content:"\e243"}.glyphicon-object-align-top:before{content:"\e244"}.glyphicon-object-align-bottom:before{content:"\e245"}.glyphicon-object-align-horizontal:before{content:"\e246"}.glyphicon-object-align-left:before{content:"\e247"}.glyphicon-object-align-vertical:before{content:"\e248"}.glyphicon-object-align-right:before{content:"\e249"}.glyphicon-triangle-right:before{content:"\e250"}.glyphicon-triangle-left:before{content:"\e251"}.glyphicon-triangle-bottom:before{content:"\e252"}.glyphicon-triangle-top:before{content:"\e253"}.glyphicon-console:before{content:"\e254"}.glyphicon-superscript:before{content:"\e255"}.glyphicon-subscript:before{content:"\e256"}.glyphicon-menu-left:before{content:"\e257"}.glyphicon-menu-right:before{content:"\e258"}.glyphicon-menu-down:before{content:"\e259"}.glyphicon-menu-up:before{content:"\e260"}*{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}:after,:before{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}html{font-size:10px;-webkit-tap-highlight-color:rgba(0,0,0,0)}body{font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;line-height:1.42857143;color:#333;background-color:#fff}button,input,select,textarea{font-family:inherit;font-size:inherit;line-height:inherit}a{color:#337ab7;text-decoration:none}a:focus,a:hover{color:#23527c;text-decoration:underline}a:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}figure{margin:0}img{vertical-align:middle}.carousel-inner>.item>a>img,.carousel-inner>.item>img,.img-responsive,.thumbnail a>img,.thumbnail>img{display:block;max-width:100%;height:auto}.img-rounded{border-radius:6px}.img-thumbnail{display:inline-block;max-width:100%;height:auto;padding:4px;line-height:1.42857143;background-color:#fff;border:1px solid #ddd;border-radius:4px;-webkit-transition:all .2s ease-in-out;-o-transition:all .2s ease-in-out;transition:all .2s ease-in-out}.img-circle{border-radius:50%}hr{margin-top:20px;margin-bottom:20px;border:0;border-top:1px solid #eee}.sr-only{position:absolute;width:1px;height:1px;padding:0;margin:-1px;overflow:hidden;clip:rect(0,0,0,0);border:0}.sr-only-focusable:active,.sr-only-focusable:focus{position:static;width:auto;height:auto;margin:0;overflow:visible;clip:auto}[role=button]{cursor:pointer}.h1,.h2,.h3,.h4,.h5,.h6,h1,h2,h3,h4,h5,h6{font-family:inherit;font-weight:500;line-height:1.1;color:inherit}.h1 .small,.h1 small,.h2 .small,.h2 small,.h3 .small,.h3 small,.h4 .small,.h4 small,.h5 .small,.h5 small,.h6 .small,.h6 small,h1 .small,h1 small,h2 .small,h2 small,h3 .small,h3 small,h4 .small,h4 small,h5 .small,h5 small,h6 .small,h6 small{font-weight:400;line-height:1;color:#777}.h1,.h2,.h3,h1,h2,h3{margin-top:20px;margin-bottom:10px}.h1 .small,.h1 small,.h2 .small,.h2 small,.h3 .small,.h3 small,h1 .small,h1 small,h2 .small,h2 small,h3 .small,h3 small{font-size:65%}.h4,.h5,.h6,h4,h5,h6{margin-top:10px;margin-bottom:10px}.h4 .small,.h4 small,.h5 .small,.h5 small,.h6 .small,.h6 small,h4 .small,h4 small,h5 .small,h5 small,h6 .small,h6 small{font-size:75%}.h1,h1{font-size:36px}.h2,h2{font-size:30px}.h3,h3{font-size:24px}.h4,h4{font-size:18px}.h5,h5{font-size:14px}.h6,h6{font-size:12px}p{margin:0 0 10px}.lead{margin-bottom:20px;font-size:16px;font-weight:300;line-height:1.4}@media (min-width:768px){.lead{font-size:21px}}.small,small{font-size:85%}.mark,mark{padding:.2em;background-color:#fcf8e3}.text-left{text-align:left}.text-right{text-align:right}.text-center{text-align:center}.text-justify{text-align:justify}.text-nowrap{white-space:nowrap}.text-lowercase{text-transform:lowercase}.text-uppercase{text-transform:uppercase}.text-capitalize{text-transform:capitalize}.text-muted{color:#777}.text-primary{color:#337ab7}a.text-primary:focus,a.text-primary:hover{color:#286090}.text-success{color:#3c763d}a.text-success:focus,a.text-success:hover{color:#2b542c}.text-info{color:#31708f}a.text-info:focus,a.text-info:hover{color:#245269}.text-warning{color:#8a6d3b}a.text-warning:focus,a.text-warning:hover{color:#66512c}.text-danger{color:#a94442}a.text-danger:focus,a.text-danger:hover{color:#843534}.bg-primary{color:#fff;background-color:#337ab7}a.bg-primary:focus,a.bg-primary:hover{background-color:#286090}.bg-success{background-color:#dff0d8}a.bg-success:focus,a.bg-success:hover{background-color:#c1e2b3}.bg-info{background-color:#d9edf7}a.bg-info:focus,a.bg-info:hover{background-color:#afd9ee}.bg-warning{background-color:#fcf8e3}a.bg-warning:focus,a.bg-warning:hover{background-color:#f7ecb5}.bg-danger{background-color:#f2dede}a.bg-danger:focus,a.bg-danger:hover{background-color:#e4b9b9}.page-header{padding-bottom:9px;margin:40px 0 20px;border-bottom:1px solid #eee}ol,ul{margin-top:0;margin-bottom:10px}ol ol,ol ul,ul ol,ul ul{margin-bottom:0}.list-unstyled{padding-left:0;list-style:none}.list-inline{padding-left:0;margin-left:-5px;list-style:none}.list-inline>li{display:inline-block;padding-right:5px;padding-left:5px}dl{margin-top:0;margin-bottom:20px}dd,dt{line-height:1.42857143}dt{font-weight:700}dd{margin-left:0}@media (min-width:768px){.dl-horizontal dt{float:left;width:160px;overflow:hidden;clear:left;text-align:right;text-overflow:ellipsis;white-space:nowrap}.dl-horizontal dd{margin-left:180px}}abbr[data-original-title],abbr[title]{cursor:help;border-bottom:1px dotted #777}.initialism{font-size:90%;text-transform:uppercase}blockquote{padding:10px 20px;margin:0 0 20px;font-size:17.5px;border-left:5px solid #eee}blockquote ol:last-child,blockquote p:last-child,blockquote ul:last-child{margin-bottom:0}blockquote .small,blockquote footer,blockquote small{display:block;font-size:80%;line-height:1.42857143;color:#777}blockquote .small:before,blockquote footer:before,blockquote small:before{content:'\2014 \00A0'}.blockquote-reverse,blockquote.pull-right{padding-right:15px;padding-left:0;text-align:right;border-right:5px solid #eee;border-left:0}.blockquote-reverse .small:before,.blockquote-reverse footer:before,.blockquote-reverse small:before,blockquote.pull-right .small:before,blockquote.pull-right footer:before,blockquote.pull-right small:before{content:''}.blockquote-reverse .small:after,.blockquote-reverse footer:after,.blockquote-reverse small:after,blockquote.pull-right .small:after,blockquote.pull-right footer:after,blockquote.pull-right small:after{content:'\00A0 \2014'}address{margin-bottom:20px;font-style:normal;line-height:1.42857143}code,kbd,pre,samp{font-family:monospace}code{padding:2px 4px;font-size:90%;color:#c7254e;background-color:#f9f2f4;border-radius:4px}kbd{padding:2px 4px;font-size:90%;color:#fff;background-color:#333;border-radius:3px;-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,.25);box-shadow:inset 0 -1px 0 rgba(0,0,0,.25)}kbd kbd{padding:0;font-size:100%;font-weight:700;-webkit-box-shadow:none;box-shadow:none}pre{display:block;padding:9.5px;margin:0 0 10px;font-size:13px;line-height:1.42857143;color:#333;word-break:break-all;word-wrap:break-word;background-color:#f5f5f5;border:1px solid #ccc;border-radius:4px}pre code{padding:0;font-size:inherit;color:inherit;white-space:pre-wrap;background-color:transparent;border-radius:0}.pre-scrollable{max-height:340px;overflow-y:scroll}.container{padding-right:15px;padding-left:15px;margin-right:auto;margin-left:auto}@media (min-width:768px){.container{width:750px}}@media (min-width:992px){.container{width:970px}}@media (min-width:1200px){.container{width:1170px}}.container-fluid{padding-right:15px;padding-left:15px;margin-right:auto;margin-left:auto}.row{margin-right:-15px;margin-left:-15px}.col-lg-1,.col-lg-10,.col-lg-11,.col-lg-12,.col-lg-2,.col-lg-3,.col-lg-4,.col-lg-5,.col-lg-6,.col-lg-7,.col-lg-8,.col-lg-9,.col-md-1,.col-md-10,.col-md-11,.col-md-12,.col-md-2,.col-md-3,.col-md-4,.col-md-5,.col-md-6,.col-md-7,.col-md-8,.col-md-9,.col-sm-1,.col-sm-10,.col-sm-11,.col-sm-12,.col-sm-2,.col-sm-3,.col-sm-4,.col-sm-5,.col-sm-6,.col-sm-7,.col-sm-8,.col-sm-9,.col-xs-1,.col-xs-10,.col-xs-11,.col-xs-12,.col-xs-2,.col-xs-3,.col-xs-4,.col-xs-5,.col-xs-6,.col-xs-7,.col-xs-8,.col-xs-9{position:relative;min-height:1px;padding-right:15px;padding-left:15px}.col-xs-1,.col-xs-10,.col-xs-11,.col-xs-12,.col-xs-2,.col-xs-3,.col-xs-4,.col-xs-5,.col-xs-6,.col-xs-7,.col-xs-8,.col-xs-9{float:left}.col-xs-12{width:100%}.col-xs-11{width:91.66666667%}.col-xs-10{width:83.33333333%}.col-xs-9{width:75%}.col-xs-8{width:66.66666667%}.col-xs-7{width:58.33333333%}.col-xs-6{width:50%}.col-xs-5{width:41.66666667%}.col-xs-4{width:33.33333333%}.col-xs-3{width:25%}.col-xs-2{width:16.66666667%}.col-xs-1{width:8.33333333%}.col-xs-pull-12{right:100%}.col-xs-pull-11{right:91.66666667%}.col-xs-pull-10{right:83.33333333%}.col-xs-pull-9{right:75%}.col-xs-pull-8{right:66.66666667%}.col-xs-pull-7{right:58.33333333%}.col-xs-pull-6{right:50%}.col-xs-pull-5{right:41.66666667%}.col-xs-pull-4{right:33.33333333%}.col-xs-pull-3{right:25%}.col-xs-pull-2{right:16.66666667%}.col-xs-pull-1{right:8.33333333%}.col-xs-pull-0{right:auto}.col-xs-push-12{left:100%}.col-xs-push-11{left:91.66666667%}.col-xs-push-10{left:83.33333333%}.col-xs-push-9{left:75%}.col-xs-push-8{left:66.66666667%}.col-xs-push-7{left:58.33333333%}.col-xs-push-6{left:50%}.col-xs-push-5{left:41.66666667%}.col-xs-push-4{left:33.33333333%}.col-xs-push-3{left:25%}.col-xs-push-2{left:16.66666667%}.col-xs-push-1{left:8.33333333%}.col-xs-push-0{left:auto}.col-xs-offset-12{margin-left:100%}.col-xs-offset-11{margin-left:91.66666667%}.col-xs-offset-10{margin-left:83.33333333%}.col-xs-offset-9{margin-left:75%}.col-xs-offset-8{margin-left:66.66666667%}.col-xs-offset-7{margin-left:58.33333333%}.col-xs-offset-6{margin-left:50%}.col-xs-offset-5{margin-left:41.66666667%}.col-xs-offset-4{margin-left:33.33333333%}.col-xs-offset-3{margin-left:25%}.col-xs-offset-2{margin-left:16.66666667%}.col-xs-offset-1{margin-left:8.33333333%}.col-xs-offset-0{margin-left:0}@media (min-width:768px){.col-sm-1,.col-sm-10,.col-sm-11,.col-sm-12,.col-sm-2,.col-sm-3,.col-sm-4,.col-sm-5,.col-sm-6,.col-sm-7,.col-sm-8,.col-sm-9{float:left}.col-sm-12{width:100%}.col-sm-11{width:91.66666667%}.col-sm-10{width:83.33333333%}.col-sm-9{width:75%}.col-sm-8{width:66.66666667%}.col-sm-7{width:58.33333333%}.col-sm-6{width:50%}.col-sm-5{width:41.66666667%}.col-sm-4{width:33.33333333%}.col-sm-3{width:25%}.col-sm-2{width:16.66666667%}.col-sm-1{width:8.33333333%}.col-sm-pull-12{right:100%}.col-sm-pull-11{right:91.66666667%}.col-sm-pull-10{right:83.33333333%}.col-sm-pull-9{right:75%}.col-sm-pull-8{right:66.66666667%}.col-sm-pull-7{right:58.33333333%}.col-sm-pull-6{right:50%}.col-sm-pull-5{right:41.66666667%}.col-sm-pull-4{right:33.33333333%}.col-sm-pull-3{right:25%}.col-sm-pull-2{right:16.66666667%}.col-sm-pull-1{right:8.33333333%}.col-sm-pull-0{right:auto}.col-sm-push-12{left:100%}.col-sm-push-11{left:91.66666667%}.col-sm-push-10{left:83.33333333%}.col-sm-push-9{left:75%}.col-sm-push-8{left:66.66666667%}.col-sm-push-7{left:58.33333333%}.col-sm-push-6{left:50%}.col-sm-push-5{left:41.66666667%}.col-sm-push-4{left:33.33333333%}.col-sm-push-3{left:25%}.col-sm-push-2{left:16.66666667%}.col-sm-push-1{left:8.33333333%}.col-sm-push-0{left:auto}.col-sm-offset-12{margin-left:100%}.col-sm-offset-11{margin-left:91.66666667%}.col-sm-offset-10{margin-left:83.33333333%}.col-sm-offset-9{margin-left:75%}.col-sm-offset-8{margin-left:66.66666667%}.col-sm-offset-7{margin-left:58.33333333%}.col-sm-offset-6{margin-left:50%}.col-sm-offset-5{margin-left:41.66666667%}.col-sm-offset-4{margin-left:33.33333333%}.col-sm-offset-3{margin-left:25%}.col-sm-offset-2{margin-left:16.66666667%}.col-sm-offset-1{margin-left:8.33333333%}.col-sm-offset-0{margin-left:0}}@media (min-width:992px){.col-md-1,.col-md-10,.col-md-11,.col-md-12,.col-md-2,.col-md-3,.col-md-4,.col-md-5,.col-md-6,.col-md-7,.col-md-8,.col-md-9{float:left}.col-md-12{width:100%}.col-md-11{width:91.66666667%}.col-md-10{width:83.33333333%}.col-md-9{width:75%}.col-md-8{width:66.66666667%}.col-md-7{width:58.33333333%}.col-md-6{width:50%}.col-md-5{width:41.66666667%}.col-md-4{width:33.33333333%}.col-md-3{width:25%}.col-md-2{width:16.66666667%}.col-md-1{width:8.33333333%}.col-md-pull-12{right:100%}.col-md-pull-11{right:91.66666667%}.col-md-pull-10{right:83.33333333%}.col-md-pull-9{right:75%}.col-md-pull-8{right:66.66666667%}.col-md-pull-7{right:58.33333333%}.col-md-pull-6{right:50%}.col-md-pull-5{right:41.66666667%}.col-md-pull-4{right:33.33333333%}.col-md-pull-3{right:25%}.col-md-pull-2{right:16.66666667%}.col-md-pull-1{right:8.33333333%}.col-md-pull-0{right:auto}.col-md-push-12{left:100%}.col-md-push-11{left:91.66666667%}.col-md-push-10{left:83.33333333%}.col-md-push-9{left:75%}.col-md-push-8{left:66.66666667%}.col-md-push-7{left:58.33333333%}.col-md-push-6{left:50%}.col-md-push-5{left:41.66666667%}.col-md-push-4{left:33.33333333%}.col-md-push-3{left:25%}.col-md-push-2{left:16.66666667%}.col-md-push-1{left:8.33333333%}.col-md-push-0{left:auto}.col-md-offset-12{margin-left:100%}.col-md-offset-11{margin-left:91.66666667%}.col-md-offset-10{margin-left:83.33333333%}.col-md-offset-9{margin-left:75%}.col-md-offset-8{margin-left:66.66666667%}.col-md-offset-7{margin-left:58.33333333%}.col-md-offset-6{margin-left:50%}.col-md-offset-5{margin-left:41.66666667%}.col-md-offset-4{margin-left:33.33333333%}.col-md-offset-3{margin-left:25%}.col-md-offset-2{margin-left:16.66666667%}.col-md-offset-1{margin-left:8.33333333%}.col-md-offset-0{margin-left:0}}@media (min-width:1200px){.col-lg-1,.col-lg-10,.col-lg-11,.col-lg-12,.col-lg-2,.col-lg-3,.col-lg-4,.col-lg-5,.col-lg-6,.col-lg-7,.col-lg-8,.col-lg-9{float:left}.col-lg-12{width:100%}.col-lg-11{width:91.66666667%}.col-lg-10{width:83.33333333%}.col-lg-9{width:75%}.col-lg-8{width:66.66666667%}.col-lg-7{width:58.33333333%}.col-lg-6{width:50%}.col-lg-5{width:41.66666667%}.col-lg-4{width:33.33333333%}.col-lg-3{width:25%}.col-lg-2{width:16.66666667%}.col-lg-1{width:8.33333333%}.col-lg-pull-12{right:100%}.col-lg-pull-11{right:91.66666667%}.col-lg-pull-10{right:83.33333333%}.col-lg-pull-9{right:75%}.col-lg-pull-8{right:66.66666667%}.col-lg-pull-7{right:58.33333333%}.col-lg-pull-6{right:50%}.col-lg-pull-5{right:41.66666667%}.col-lg-pull-4{right:33.33333333%}.col-lg-pull-3{right:25%}.col-lg-pull-2{right:16.66666667%}.col-lg-pull-1{right:8.33333333%}.col-lg-pull-0{right:auto}.col-lg-push-12{left:100%}.col-lg-push-11{left:91.66666667%}.col-lg-push-10{left:83.33333333%}.col-lg-push-9{left:75%}.col-lg-push-8{left:66.66666667%}.col-lg-push-7{left:58.33333333%}.col-lg-push-6{left:50%}.col-lg-push-5{left:41.66666667%}.col-lg-push-4{left:33.33333333%}.col-lg-push-3{left:25%}.col-lg-push-2{left:16.66666667%}.col-lg-push-1{left:8.33333333%}.col-lg-push-0{left:auto}.col-lg-offset-12{margin-left:100%}.col-lg-offset-11{margin-left:91.66666667%}.col-lg-offset-10{margin-left:83.33333333%}.col-lg-offset-9{margin-left:75%}.col-lg-offset-8{margin-left:66.66666667%}.col-lg-offset-7{margin-left:58.33333333%}.col-lg-offset-6{margin-left:50%}.col-lg-offset-5{margin-left:41.66666667%}.col-lg-offset-4{margin-left:33.33333333%}.col-lg-offset-3{margin-left:25%}.col-lg-offset-2{margin-left:16.66666667%}.col-lg-offset-1{margin-left:8.33333333%}.col-lg-offset-0{margin-left:0}}table{background-color:transparent}caption{padding-top:8px;padding-bottom:8px;color:#777;text-align:left}th{}.table{width:100%;max-width:100%;margin-bottom:20px}.table>tbody>tr>td,.table>tbody>tr>th,.table>tfoot>tr>td,.table>tfoot>tr>th,.table>thead>tr>td,.table>thead>tr>th{padding:8px;line-height:1.42857143;vertical-align:top;border-top:1px solid #ddd}.table>thead>tr>th{vertical-align:bottom;border-bottom:2px solid #ddd}.table>caption+thead>tr:first-child>td,.table>caption+thead>tr:first-child>th,.table>colgroup+thead>tr:first-child>td,.table>colgroup+thead>tr:first-child>th,.table>thead:first-child>tr:first-child>td,.table>thead:first-child>tr:first-child>th{border-top:0}.table>tbody+tbody{border-top:2px solid #ddd}.table .table{background-color:#fff}.table-condensed>tbody>tr>td,.table-condensed>tbody>tr>th,.table-condensed>tfoot>tr>td,.table-condensed>tfoot>tr>th,.table-condensed>thead>tr>td,.table-condensed>thead>tr>th{padding:5px}.table-bordered{border:1px solid #ddd}.table-bordered>tbody>tr>td,.table-bordered>tbody>tr>th,.table-bordered>tfoot>tr>td,.table-bordered>tfoot>tr>th,.table-bordered>thead>tr>td,.table-bordered>thead>tr>th{border:1px solid #ddd}.table-bordered>thead>tr>td,.table-bordered>thead>tr>th{border-bottom-width:2px}.table-striped>tbody>tr:nth-of-type(odd){background-color:#f9f9f9}.table-hover>tbody>tr:hover{background-color:#f5f5f5}table col[class*=col-]{position:static;display:table-column;float:none}table td[class*=col-],table th[class*=col-]{position:static;display:table-cell;float:none}.table>tbody>tr.active>td,.table>tbody>tr.active>th,.table>tbody>tr>td.active,.table>tbody>tr>th.active,.table>tfoot>tr.active>td,.table>tfoot>tr.active>th,.table>tfoot>tr>td.active,.table>tfoot>tr>th.active,.table>thead>tr.active>td,.table>thead>tr.active>th,.table>thead>tr>td.active,.table>thead>tr>th.active{background-color:#f5f5f5}.table-hover>tbody>tr.active:hover>td,.table-hover>tbody>tr.active:hover>th,.table-hover>tbody>tr:hover>.active,.table-hover>tbody>tr>td.active:hover,.table-hover>tbody>tr>th.active:hover{background-color:#e8e8e8}.table>tbody>tr.success>td,.table>tbody>tr.success>th,.table>tbody>tr>td.success,.table>tbody>tr>th.success,.table>tfoot>tr.success>td,.table>tfoot>tr.success>th,.table>tfoot>tr>td.success,.table>tfoot>tr>th.success,.table>thead>tr.success>td,.table>thead>tr.success>th,.table>thead>tr>td.success,.table>thead>tr>th.success{background-color:#dff0d8}.table-hover>tbody>tr.success:hover>td,.table-hover>tbody>tr.success:hover>th,.table-hover>tbody>tr:hover>.success,.table-hover>tbody>tr>td.success:hover,.table-hover>tbody>tr>th.success:hover{background-color:#d0e9c6}.table>tbody>tr.info>td,.table>tbody>tr.info>th,.table>tbody>tr>td.info,.table>tbody>tr>th.info,.table>tfoot>tr.info>td,.table>tfoot>tr.info>th,.table>tfoot>tr>td.info,.table>tfoot>tr>th.info,.table>thead>tr.info>td,.table>thead>tr.info>th,.table>thead>tr>td.info,.table>thead>tr>th.info{background-color:#d9edf7}.table-hover>tbody>tr.info:hover>td,.table-hover>tbody>tr.info:hover>th,.table-hover>tbody>tr:hover>.info,.table-hover>tbody>tr>td.info:hover,.table-hover>tbody>tr>th.info:hover{background-color:#c4e3f3}.table>tbody>tr.warning>td,.table>tbody>tr.warning>th,.table>tbody>tr>td.warning,.table>tbody>tr>th.warning,.table>tfoot>tr.warning>td,.table>tfoot>tr.warning>th,.table>tfoot>tr>td.warning,.table>tfoot>tr>th.warning,.table>thead>tr.warning>td,.table>thead>tr.warning>th,.table>thead>tr>td.warning,.table>thead>tr>th.warning{background-color:#fcf8e3}.table-hover>tbody>tr.warning:hover>td,.table-hover>tbody>tr.warning:hover>th,.table-hover>tbody>tr:hover>.warning,.table-hover>tbody>tr>td.warning:hover,.table-hover>tbody>tr>th.warning:hover{background-color:#faf2cc}.table>tbody>tr.danger>td,.table>tbody>tr.danger>th,.table>tbody>tr>td.danger,.table>tbody>tr>th.danger,.table>tfoot>tr.danger>td,.table>tfoot>tr.danger>th,.table>tfoot>tr>td.danger,.table>tfoot>tr>th.danger,.table>thead>tr.danger>td,.table>thead>tr.danger>th,.table>thead>tr>td.danger,.table>thead>tr>th.danger{background-color:#f2dede}.table-hover>tbody>tr.danger:hover>td,.table-hover>tbody>tr.danger:hover>th,.table-hover>tbody>tr:hover>.danger,.table-hover>tbody>tr>td.danger:hover,.table-hover>tbody>tr>th.danger:hover{background-color:#ebcccc}.table-responsive{min-height:.01%;overflow-x:auto}@media screen and (max-width:767px){.table-responsive{width:100%;margin-bottom:15px;overflow-y:hidden;-ms-overflow-style:-ms-autohiding-scrollbar;border:1px solid #ddd}.table-responsive>.table{margin-bottom:0}.table-responsive>.table>tbody>tr>td,.table-responsive>.table>tbody>tr>th,.table-responsive>.table>tfoot>tr>td,.table-responsive>.table>tfoot>tr>th,.table-responsive>.table>thead>tr>td,.table-responsive>.table>thead>tr>th{white-space:nowrap}.table-responsive>.table-bordered{border:0}.table-responsive>.table-bordered>tbody>tr>td:first-child,.table-responsive>.table-bordered>tbody>tr>th:first-child,.table-responsive>.table-bordered>tfoot>tr>td:first-child,.table-responsive>.table-bordered>tfoot>tr>th:first-child,.table-responsive>.table-bordered>thead>tr>td:first-child,.table-responsive>.table-bordered>thead>tr>th:first-child{border-left:0}.table-responsive>.table-bordered>tbody>tr>td:last-child,.table-responsive>.table-bordered>tbody>tr>th:last-child,.table-responsive>.table-bordered>tfoot>tr>td:last-child,.table-responsive>.table-bordered>tfoot>tr>th:last-child,.table-responsive>.table-bordered>thead>tr>td:last-child,.table-responsive>.table-bordered>thead>tr>th:last-child{border-right:0}.table-responsive>.table-bordered>tbody>tr:last-child>td,.table-responsive>.table-bordered>tbody>tr:last-child>th,.table-responsive>.table-bordered>tfoot>tr:last-child>td,.table-responsive>.table-bordered>tfoot>tr:last-child>th{border-bottom:0}}fieldset{min-width:0;padding:0;margin:0;border:0}legend{display:block;width:100%;padding:0;margin-bottom:20px;font-size:21px;line-height:inherit;color:#333;border:0;border-bottom:1px solid #e5e5e5}label{display:inline-block;max-width:100%;margin-bottom:5px;font-weight:700}input[type=search]{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}input[type=checkbox],input[type=radio]{margin:4px 0 0;margin-top:1px\9;line-height:normal}input[type=file]{display:block}input[type=range]{display:block;width:100%}select[multiple],select[size]{height:auto}input[type=file]:focus,input[type=checkbox]:focus,input[type=radio]:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}output{display:block;padding-top:7px;font-size:14px;line-height:1.42857143;color:#555}.form-control{display:block;width:100%;height:34px;padding:6px 12px;font-size:14px;line-height:1.42857143;color:#555;background-color:#fff;background-image:none;border:1px solid #ccc;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075);-webkit-transition:border-color ease-in-out .15s,-webkit-box-shadow ease-in-out .15s;-o-transition:border-color ease-in-out .15s,box-shadow ease-in-out .15s;transition:border-color ease-in-out .15s,box-shadow ease-in-out .15s}.form-control:focus{border-color:#66afe9;outline:0;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 8px rgba(102,175,233,.6);box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 8px rgba(102,175,233,.6)}.form-control::-moz-placeholder{color:#999;opacity:1}.form-control:-ms-input-placeholder{color:#999}.form-control::-webkit-input-placeholder{color:#999}.form-control[disabled],.form-control[readonly],fieldset[disabled] .form-control{background-color:#eee;opacity:1}.form-control[disabled],fieldset[disabled] .form-control{cursor:not-allowed}textarea.form-control{height:auto}input[type=search]{-webkit-appearance:none}@media screen and (-webkit-min-device-pixel-ratio:0){input[type=date].form-control,input[type=time].form-control,input[type=datetime-local].form-control,input[type=month].form-control{line-height:34px}.input-group-sm input[type=date],.input-group-sm input[type=time],.input-group-sm input[type=datetime-local],.input-group-sm input[type=month],input[type=date].input-sm,input[type=time].input-sm,input[type=datetime-local].input-sm,input[type=month].input-sm{line-height:30px}.input-group-lg input[type=date],.input-group-lg input[type=time],.input-group-lg input[type=datetime-local],.input-group-lg input[type=month],input[type=date].input-lg,input[type=time].input-lg,input[type=datetime-local].input-lg,input[type=month].input-lg{line-height:46px}}.form-group{margin-bottom:15px}.checkbox,.radio{position:relative;display:block;margin-top:10px;margin-bottom:10px}.checkbox label,.radio label{min-height:20px;padding-left:20px;margin-bottom:0;font-weight:400;cursor:pointer}.checkbox input[type=checkbox],.checkbox-inline input[type=checkbox],.radio input[type=radio],.radio-inline input[type=radio]{position:absolute;margin-top:4px\9;margin-left:-20px}.checkbox+.checkbox,.radio+.radio{margin-top:-5px}.checkbox-inline,.radio-inline{position:relative;display:inline-block;padding-left:20px;margin-bottom:0;font-weight:400;vertical-align:middle;cursor:pointer}.checkbox-inline+.checkbox-inline,.radio-inline+.radio-inline{margin-top:0;margin-left:10px}fieldset[disabled] input[type=checkbox],fieldset[disabled] input[type=radio],input[type=checkbox].disabled,input[type=checkbox][disabled],input[type=radio].disabled,input[type=radio][disabled]{cursor:not-allowed}.checkbox-inline.disabled,.radio-inline.disabled,fieldset[disabled] .checkbox-inline,fieldset[disabled] .radio-inline{cursor:not-allowed}.checkbox.disabled label,.radio.disabled label,fieldset[disabled] .checkbox label,fieldset[disabled] .radio label{cursor:not-allowed}.form-control-static{min-height:34px;padding-top:7px;padding-bottom:7px;margin-bottom:0}.form-control-static.input-lg,.form-control-static.input-sm{padding-right:0;padding-left:0}.input-sm{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}select.input-sm{height:30px;line-height:30px}select[multiple].input-sm,textarea.input-sm{height:auto}.form-group-sm .form-control{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}.form-group-sm select.form-control{height:30px;line-height:30px}.form-group-sm select[multiple].form-control,.form-group-sm textarea.form-control{height:auto}.form-group-sm .form-control-static{height:30px;min-height:32px;padding:6px 10px;font-size:12px;line-height:1.5}.input-lg{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}select.input-lg{height:46px;line-height:46px}select[multiple].input-lg,textarea.input-lg{height:auto}.form-group-lg .form-control{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}.form-group-lg select.form-control{height:46px;line-height:46px}.form-group-lg select[multiple].form-control,.form-group-lg textarea.form-control{height:auto}.form-group-lg .form-control-static{height:46px;min-height:38px;padding:11px 16px;font-size:18px;line-height:1.3333333}.has-feedback{position:relative}.has-feedback .form-control{padding-right:42.5px}.form-control-feedback{position:absolute;top:0;right:0;z-index:2;display:block;width:34px;height:34px;line-height:34px;text-align:center;pointer-events:none}.form-group-lg .form-control+.form-control-feedback,.input-group-lg+.form-control-feedback,.input-lg+.form-control-feedback{width:46px;height:46px;line-height:46px}.form-group-sm .form-control+.form-control-feedback,.input-group-sm+.form-control-feedback,.input-sm+.form-control-feedback{width:30px;height:30px;line-height:30px}.has-success .checkbox,.has-success .checkbox-inline,.has-success .control-label,.has-success .help-block,.has-success .radio,.has-success .radio-inline,.has-success.checkbox label,.has-success.checkbox-inline label,.has-success.radio label,.has-success.radio-inline label{color:#3c763d}.has-success .form-control{border-color:#3c763d;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-success .form-control:focus{border-color:#2b542c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #67b168;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #67b168}.has-success .input-group-addon{color:#3c763d;background-color:#dff0d8;border-color:#3c763d}.has-success .form-control-feedback{color:#3c763d}.has-warning .checkbox,.has-warning .checkbox-inline,.has-warning .control-label,.has-warning .help-block,.has-warning .radio,.has-warning .radio-inline,.has-warning.checkbox label,.has-warning.checkbox-inline label,.has-warning.radio label,.has-warning.radio-inline label{color:#8a6d3b}.has-warning .form-control{border-color:#8a6d3b;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-warning .form-control:focus{border-color:#66512c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #c0a16b;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #c0a16b}.has-warning .input-group-addon{color:#8a6d3b;background-color:#fcf8e3;border-color:#8a6d3b}.has-warning .form-control-feedback{color:#8a6d3b}.has-error .checkbox,.has-error .checkbox-inline,.has-error .control-label,.has-error .help-block,.has-error .radio,.has-error .radio-inline,.has-error.checkbox label,.has-error.checkbox-inline label,.has-error.radio label,.has-error.radio-inline label{color:#a94442}.has-error .form-control{border-color:#a94442;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075);box-shadow:inset 0 1px 1px rgba(0,0,0,.075)}.has-error .form-control:focus{border-color:#843534;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #ce8483;box-shadow:inset 0 1px 1px rgba(0,0,0,.075),0 0 6px #ce8483}.has-error .input-group-addon{color:#a94442;background-color:#f2dede;border-color:#a94442}.has-error .form-control-feedback{color:#a94442}.has-feedback label~.form-control-feedback{top:25px}.has-feedback label.sr-only~.form-control-feedback{top:0}.help-block{display:block;margin-top:5px;margin-bottom:10px;color:#737373}@media (min-width:768px){.form-inline .form-group{display:inline-block;margin-bottom:0;vertical-align:middle}.form-inline .form-control{display:inline-block;width:auto;vertical-align:middle}.form-inline .form-control-static{display:inline-block}.form-inline .input-group{display:inline-table;vertical-align:middle}.form-inline .input-group .form-control,.form-inline .input-group .input-group-addon,.form-inline .input-group .input-group-btn{width:auto}.form-inline .input-group>.form-control{width:100%}.form-inline .control-label{margin-bottom:0;vertical-align:middle}.form-inline .checkbox,.form-inline .radio{display:inline-block;margin-top:0;margin-bottom:0;vertical-align:middle}.form-inline .checkbox label,.form-inline .radio label{padding-left:0}.form-inline .checkbox input[type=checkbox],.form-inline .radio input[type=radio]{position:relative;margin-left:0}.form-inline .has-feedback .form-control-feedback{top:0}}.form-horizontal .checkbox,.form-horizontal .checkbox-inline,.form-horizontal .radio,.form-horizontal .radio-inline{padding-top:7px;margin-top:0;margin-bottom:0}.form-horizontal .checkbox,.form-horizontal .radio{min-height:27px}.form-horizontal .form-group{margin-right:-15px;margin-left:-15px}@media (min-width:768px){.form-horizontal .control-label{padding-top:7px;margin-bottom:0;text-align:right}}.form-horizontal .has-feedback .form-control-feedback{right:15px}@media (min-width:768px){.form-horizontal .form-group-lg .control-label{padding-top:14.33px;font-size:18px}}@media (min-width:768px){.form-horizontal .form-group-sm .control-label{padding-top:6px;font-size:12px}}.btn{display:inline-block;padding:6px 12px;margin-bottom:0;font-size:14px;font-weight:400;line-height:1.42857143;text-align:center;white-space:nowrap;vertical-align:middle;-ms-touch-action:manipulation;touch-action:manipulation;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;background-image:none;border:1px solid transparent;border-radius:4px}.btn.active.focus,.btn.active:focus,.btn.focus,.btn:active.focus,.btn:active:focus,.btn:focus{outline:thin dotted;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}.btn.focus,.btn:focus,.btn:hover{color:#333;text-decoration:none}.btn.active,.btn:active{background-image:none;outline:0;-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn.disabled,.btn[disabled],fieldset[disabled] .btn{cursor:not-allowed;filter:alpha(opacity=65);-webkit-box-shadow:none;box-shadow:none;opacity:.65}a.btn.disabled,fieldset[disabled] a.btn{pointer-events:none}.btn-default{color:#333;background-color:#fff;border-color:#ccc}.btn-default.focus,.btn-default:focus{color:#333;background-color:#e6e6e6;border-color:#8c8c8c}.btn-default:hover{color:#333;background-color:#e6e6e6;border-color:#adadad}.btn-default.active,.btn-default:active,.open>.dropdown-toggle.btn-default{color:#333;background-color:#e6e6e6;border-color:#adadad}.btn-default.active.focus,.btn-default.active:focus,.btn-default.active:hover,.btn-default:active.focus,.btn-default:active:focus,.btn-default:active:hover,.open>.dropdown-toggle.btn-default.focus,.open>.dropdown-toggle.btn-default:focus,.open>.dropdown-toggle.btn-default:hover{color:#333;background-color:#d4d4d4;border-color:#8c8c8c}.btn-default.active,.btn-default:active,.open>.dropdown-toggle.btn-default{background-image:none}.btn-default.disabled,.btn-default.disabled.active,.btn-default.disabled.focus,.btn-default.disabled:active,.btn-default.disabled:focus,.btn-default.disabled:hover,.btn-default[disabled],.btn-default[disabled].active,.btn-default[disabled].focus,.btn-default[disabled]:active,.btn-default[disabled]:focus,.btn-default[disabled]:hover,fieldset[disabled] .btn-default,fieldset[disabled] .btn-default.active,fieldset[disabled] .btn-default.focus,fieldset[disabled] .btn-default:active,fieldset[disabled] .btn-default:focus,fieldset[disabled] .btn-default:hover{background-color:#fff;border-color:#ccc}.btn-default .badge{color:#fff;background-color:#333}.btn-primary{color:#fff;background-color:#337ab7;border-color:#2e6da4}.btn-primary.focus,.btn-primary:focus{color:#fff;background-color:#286090;border-color:#122b40}.btn-primary:hover{color:#fff;background-color:#286090;border-color:#204d74}.btn-primary.active,.btn-primary:active,.open>.dropdown-toggle.btn-primary{color:#fff;background-color:#286090;border-color:#204d74}.btn-primary.active.focus,.btn-primary.active:focus,.btn-primary.active:hover,.btn-primary:active.focus,.btn-primary:active:focus,.btn-primary:active:hover,.open>.dropdown-toggle.btn-primary.focus,.open>.dropdown-toggle.btn-primary:focus,.open>.dropdown-toggle.btn-primary:hover{color:#fff;background-color:#204d74;border-color:#122b40}.btn-primary.active,.btn-primary:active,.open>.dropdown-toggle.btn-primary{background-image:none}.btn-primary.disabled,.btn-primary.disabled.active,.btn-primary.disabled.focus,.btn-primary.disabled:active,.btn-primary.disabled:focus,.btn-primary.disabled:hover,.btn-primary[disabled],.btn-primary[disabled].active,.btn-primary[disabled].focus,.btn-primary[disabled]:active,.btn-primary[disabled]:focus,.btn-primary[disabled]:hover,fieldset[disabled] .btn-primary,fieldset[disabled] .btn-primary.active,fieldset[disabled] .btn-primary.focus,fieldset[disabled] .btn-primary:active,fieldset[disabled] .btn-primary:focus,fieldset[disabled] .btn-primary:hover{background-color:#337ab7;border-color:#2e6da4}.btn-primary .badge{color:#337ab7;background-color:#fff}.btn-success{color:#fff;background-color:#5cb85c;border-color:#4cae4c}.btn-success.focus,.btn-success:focus{color:#fff;background-color:#449d44;border-color:#255625}.btn-success:hover{color:#fff;background-color:#449d44;border-color:#398439}.btn-success.active,.btn-success:active,.open>.dropdown-toggle.btn-success{color:#fff;background-color:#449d44;border-color:#398439}.btn-success.active.focus,.btn-success.active:focus,.btn-success.active:hover,.btn-success:active.focus,.btn-success:active:focus,.btn-success:active:hover,.open>.dropdown-toggle.btn-success.focus,.open>.dropdown-toggle.btn-success:focus,.open>.dropdown-toggle.btn-success:hover{color:#fff;background-color:#398439;border-color:#255625}.btn-success.active,.btn-success:active,.open>.dropdown-toggle.btn-success{background-image:none}.btn-success.disabled,.btn-success.disabled.active,.btn-success.disabled.focus,.btn-success.disabled:active,.btn-success.disabled:focus,.btn-success.disabled:hover,.btn-success[disabled],.btn-success[disabled].active,.btn-success[disabled].focus,.btn-success[disabled]:active,.btn-success[disabled]:focus,.btn-success[disabled]:hover,fieldset[disabled] .btn-success,fieldset[disabled] .btn-success.active,fieldset[disabled] .btn-success.focus,fieldset[disabled] .btn-success:active,fieldset[disabled] .btn-success:focus,fieldset[disabled] .btn-success:hover{background-color:#5cb85c;border-color:#4cae4c}.btn-success .badge{color:#5cb85c;background-color:#fff}.btn-info{color:#fff;background-color:#5bc0de;border-color:#46b8da}.btn-info.focus,.btn-info:focus{color:#fff;background-color:#31b0d5;border-color:#1b6d85}.btn-info:hover{color:#fff;background-color:#31b0d5;border-color:#269abc}.btn-info.active,.btn-info:active,.open>.dropdown-toggle.btn-info{color:#fff;background-color:#31b0d5;border-color:#269abc}.btn-info.active.focus,.btn-info.active:focus,.btn-info.active:hover,.btn-info:active.focus,.btn-info:active:focus,.btn-info:active:hover,.open>.dropdown-toggle.btn-info.focus,.open>.dropdown-toggle.btn-info:focus,.open>.dropdown-toggle.btn-info:hover{color:#fff;background-color:#269abc;border-color:#1b6d85}.btn-info.active,.btn-info:active,.open>.dropdown-toggle.btn-info{background-image:none}.btn-info.disabled,.btn-info.disabled.active,.btn-info.disabled.focus,.btn-info.disabled:active,.btn-info.disabled:focus,.btn-info.disabled:hover,.btn-info[disabled],.btn-info[disabled].active,.btn-info[disabled].focus,.btn-info[disabled]:active,.btn-info[disabled]:focus,.btn-info[disabled]:hover,fieldset[disabled] .btn-info,fieldset[disabled] .btn-info.active,fieldset[disabled] .btn-info.focus,fieldset[disabled] .btn-info:active,fieldset[disabled] .btn-info:focus,fieldset[disabled] .btn-info:hover{background-color:#5bc0de;border-color:#46b8da}.btn-info .badge{color:#5bc0de;background-color:#fff}.btn-warning{color:#fff;background-color:#f0ad4e;border-color:#eea236}.btn-warning.focus,.btn-warning:focus{color:#fff;background-color:#ec971f;border-color:#985f0d}.btn-warning:hover{color:#fff;background-color:#ec971f;border-color:#d58512}.btn-warning.active,.btn-warning:active,.open>.dropdown-toggle.btn-warning{color:#fff;background-color:#ec971f;border-color:#d58512}.btn-warning.active.focus,.btn-warning.active:focus,.btn-warning.active:hover,.btn-warning:active.focus,.btn-warning:active:focus,.btn-warning:active:hover,.open>.dropdown-toggle.btn-warning.focus,.open>.dropdown-toggle.btn-warning:focus,.open>.dropdown-toggle.btn-warning:hover{color:#fff;background-color:#d58512;border-color:#985f0d}.btn-warning.active,.btn-warning:active,.open>.dropdown-toggle.btn-warning{background-image:none}.btn-warning.disabled,.btn-warning.disabled.active,.btn-warning.disabled.focus,.btn-warning.disabled:active,.btn-warning.disabled:focus,.btn-warning.disabled:hover,.btn-warning[disabled],.btn-warning[disabled].active,.btn-warning[disabled].focus,.btn-warning[disabled]:active,.btn-warning[disabled]:focus,.btn-warning[disabled]:hover,fieldset[disabled] .btn-warning,fieldset[disabled] .btn-warning.active,fieldset[disabled] .btn-warning.focus,fieldset[disabled] .btn-warning:active,fieldset[disabled] .btn-warning:focus,fieldset[disabled] .btn-warning:hover{background-color:#f0ad4e;border-color:#eea236}.btn-warning .badge{color:#f0ad4e;background-color:#fff}.btn-danger{color:#fff;background-color:#d9534f;border-color:#d43f3a}.btn-danger.focus,.btn-danger:focus{color:#fff;background-color:#c9302c;border-color:#761c19}.btn-danger:hover{color:#fff;background-color:#c9302c;border-color:#ac2925}.btn-danger.active,.btn-danger:active,.open>.dropdown-toggle.btn-danger{color:#fff;background-color:#c9302c;border-color:#ac2925}.btn-danger.active.focus,.btn-danger.active:focus,.btn-danger.active:hover,.btn-danger:active.focus,.btn-danger:active:focus,.btn-danger:active:hover,.open>.dropdown-toggle.btn-danger.focus,.open>.dropdown-toggle.btn-danger:focus,.open>.dropdown-toggle.btn-danger:hover{color:#fff;background-color:#ac2925;border-color:#761c19}.btn-danger.active,.btn-danger:active,.open>.dropdown-toggle.btn-danger{background-image:none}.btn-danger.disabled,.btn-danger.disabled.active,.btn-danger.disabled.focus,.btn-danger.disabled:active,.btn-danger.disabled:focus,.btn-danger.disabled:hover,.btn-danger[disabled],.btn-danger[disabled].active,.btn-danger[disabled].focus,.btn-danger[disabled]:active,.btn-danger[disabled]:focus,.btn-danger[disabled]:hover,fieldset[disabled] .btn-danger,fieldset[disabled] .btn-danger.active,fieldset[disabled] .btn-danger.focus,fieldset[disabled] .btn-danger:active,fieldset[disabled] .btn-danger:focus,fieldset[disabled] .btn-danger:hover{background-color:#d9534f;border-color:#d43f3a}.btn-danger .badge{color:#d9534f;background-color:#fff}.btn-link{font-weight:400;color:#337ab7;border-radius:0}.btn-link,.btn-link.active,.btn-link:active,.btn-link[disabled],fieldset[disabled] .btn-link{background-color:transparent;-webkit-box-shadow:none;box-shadow:none}.btn-link,.btn-link:active,.btn-link:focus,.btn-link:hover{border-color:transparent}.btn-link:focus,.btn-link:hover{color:#23527c;text-decoration:underline;background-color:transparent}.btn-link[disabled]:focus,.btn-link[disabled]:hover,fieldset[disabled] .btn-link:focus,fieldset[disabled] .btn-link:hover{color:#777;text-decoration:none}.btn-group-lg>.btn,.btn-lg{padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}.btn-group-sm>.btn,.btn-sm{padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}.btn-group-xs>.btn,.btn-xs{padding:1px 5px;font-size:12px;line-height:1.5;border-radius:3px}.btn-block{display:block;width:100%}.btn-block+.btn-block{margin-top:5px}input[type=button].btn-block,input[type=reset].btn-block,input[type=submit].btn-block{width:100%}.fade{opacity:0;-webkit-transition:opacity .15s linear;-o-transition:opacity .15s linear;transition:opacity .15s linear}.fade.in{opacity:1}.collapse{display:none}.collapse.in{display:block}tr.collapse.in{display:table-row}tbody.collapse.in{display:table-row-group}.collapsing{position:relative;height:0;overflow:hidden;-webkit-transition-timing-function:ease;-o-transition-timing-function:ease;transition-timing-function:ease;-webkit-transition-duration:.35s;-o-transition-duration:.35s;transition-duration:.35s;-webkit-transition-property:height,visibility;-o-transition-property:height,visibility;transition-property:height,visibility}.caret{display:inline-block;width:0;height:0;margin-left:2px;vertical-align:middle;border-top:4px dashed;border-top:4px solid\9;border-right:4px solid transparent;border-left:4px solid transparent}.dropdown,.dropup{position:relative}.dropdown-toggle:focus{outline:0}.dropdown-menu{position:absolute;top:100%;left:0;z-index:1000;display:none;float:left;min-width:160px;padding:5px 0;margin:2px 0 0;font-size:14px;text-align:left;list-style:none;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,.15);border-radius:4px;-webkit-box-shadow:0 6px 12px rgba(0,0,0,.175);box-shadow:0 6px 12px rgba(0,0,0,.175)}.dropdown-menu.pull-right{right:0;left:auto}.dropdown-menu .divider{height:1px;margin:9px 0;overflow:hidden;background-color:#e5e5e5}.dropdown-menu>li>a{display:block;padding:3px 20px;clear:both;font-weight:400;line-height:1.42857143;color:#333;white-space:nowrap}.dropdown-menu>li>a:focus,.dropdown-menu>li>a:hover{color:#262626;text-decoration:none;background-color:#f5f5f5}.dropdown-menu>.active>a,.dropdown-menu>.active>a:focus,.dropdown-menu>.active>a:hover{color:#fff;text-decoration:none;background-color:#337ab7;outline:0}.dropdown-menu>.disabled>a,.dropdown-menu>.disabled>a:focus,.dropdown-menu>.disabled>a:hover{color:#777}.dropdown-menu>.disabled>a:focus,.dropdown-menu>.disabled>a:hover{text-decoration:none;cursor:not-allowed;background-color:transparent;background-image:none;filter:progid:DXImageTransform.Microsoft.gradient(enabled=false)}.open>.dropdown-menu{display:block}.open>a{outline:0}.dropdown-menu-right{right:0;left:auto}.dropdown-menu-left{right:auto;left:0}.dropdown-header{display:block;padding:3px 20px;font-size:12px;line-height:1.42857143;color:#777;white-space:nowrap}.dropdown-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:990}.pull-right>.dropdown-menu{right:0;left:auto}.dropup .caret,.navbar-fixed-bottom .dropdown .caret{content:"";border-top:0;border-bottom:4px dashed;border-bottom:4px solid\9}.dropup .dropdown-menu,.navbar-fixed-bottom .dropdown .dropdown-menu{top:auto;bottom:100%;margin-bottom:2px}@media (min-width:768px){.navbar-right .dropdown-menu{right:0;left:auto}.navbar-right .dropdown-menu-left{right:auto;left:0}}.btn-group,.btn-group-vertical{position:relative;display:inline-block;vertical-align:middle}.btn-group-vertical>.btn,.btn-group>.btn{position:relative;float:left}.btn-group-vertical>.btn.active,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:hover,.btn-group>.btn.active,.btn-group>.btn:active,.btn-group>.btn:focus,.btn-group>.btn:hover{z-index:2}.btn-group .btn+.btn,.btn-group .btn+.btn-group,.btn-group .btn-group+.btn,.btn-group .btn-group+.btn-group{margin-left:-1px}.btn-toolbar{margin-left:-5px}.btn-toolbar .btn,.btn-toolbar .btn-group,.btn-toolbar .input-group{float:left}.btn-toolbar>.btn,.btn-toolbar>.btn-group,.btn-toolbar>.input-group{margin-left:5px}.btn-group>.btn:not(:first-child):not(:last-child):not(.dropdown-toggle){border-radius:0}.btn-group>.btn:first-child{margin-left:0}.btn-group>.btn:first-child:not(:last-child):not(.dropdown-toggle){border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:last-child:not(:first-child),.btn-group>.dropdown-toggle:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.btn-group>.btn-group{float:left}.btn-group>.btn-group:not(:first-child):not(:last-child)>.btn{border-radius:0}.btn-group>.btn-group:first-child:not(:last-child)>.btn:last-child,.btn-group>.btn-group:first-child:not(:last-child)>.dropdown-toggle{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn-group:last-child:not(:first-child)>.btn:first-child{border-top-left-radius:0;border-bottom-left-radius:0}.btn-group .dropdown-toggle:active,.btn-group.open .dropdown-toggle{outline:0}.btn-group>.btn+.dropdown-toggle{padding-right:8px;padding-left:8px}.btn-group>.btn-lg+.dropdown-toggle{padding-right:12px;padding-left:12px}.btn-group.open .dropdown-toggle{-webkit-box-shadow:inset 0 3px 5px rgba(0,0,0,.125);box-shadow:inset 0 3px 5px rgba(0,0,0,.125)}.btn-group.open .dropdown-toggle.btn-link{-webkit-box-shadow:none;box-shadow:none}.btn .caret{margin-left:0}.btn-lg .caret{border-width:5px 5px 0;border-bottom-width:0}.dropup .btn-lg .caret{border-width:0 5px 5px}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group,.btn-group-vertical>.btn-group>.btn{display:block;float:none;width:100%;max-width:100%}.btn-group-vertical>.btn-group>.btn{float:none}.btn-group-vertical>.btn+.btn,.btn-group-vertical>.btn+.btn-group,.btn-group-vertical>.btn-group+.btn,.btn-group-vertical>.btn-group+.btn-group{margin-top:-1px;margin-left:0}.btn-group-vertical>.btn:not(:first-child):not(:last-child){border-radius:0}.btn-group-vertical>.btn:first-child:not(:last-child){border-top-right-radius:4px;border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn:last-child:not(:first-child){border-top-left-radius:0;border-top-right-radius:0;border-bottom-left-radius:4px}.btn-group-vertical>.btn-group:not(:first-child):not(:last-child)>.btn{border-radius:0}.btn-group-vertical>.btn-group:first-child:not(:last-child)>.btn:last-child,.btn-group-vertical>.btn-group:first-child:not(:last-child)>.dropdown-toggle{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn-group:last-child:not(:first-child)>.btn:first-child{border-top-left-radius:0;border-top-right-radius:0}.btn-group-justified{display:table;width:100%;table-layout:fixed;border-collapse:separate}.btn-group-justified>.btn,.btn-group-justified>.btn-group{display:table-cell;float:none;width:1%}.btn-group-justified>.btn-group .btn{width:100%}.btn-group-justified>.btn-group .dropdown-menu{left:auto}[data-toggle=buttons]>.btn input[type=checkbox],[data-toggle=buttons]>.btn input[type=radio],[data-toggle=buttons]>.btn-group>.btn input[type=checkbox],[data-toggle=buttons]>.btn-group>.btn input[type=radio]{position:absolute;clip:rect(0,0,0,0);pointer-events:none}.input-group{position:relative;display:table;border-collapse:separate}.input-group[class*=col-]{float:none;padding-right:0;padding-left:0}.input-group .form-control{position:relative;z-index:2;float:left;width:100%;margin-bottom:0}.input-group-lg>.form-control,.input-group-lg>.input-group-addon,.input-group-lg>.input-group-btn>.btn{height:46px;padding:10px 16px;font-size:18px;line-height:1.3333333;border-radius:6px}select.input-group-lg>.form-control,select.input-group-lg>.input-group-addon,select.input-group-lg>.input-group-btn>.btn{height:46px;line-height:46px}select[multiple].input-group-lg>.form-control,select[multiple].input-group-lg>.input-group-addon,select[multiple].input-group-lg>.input-group-btn>.btn,textarea.input-group-lg>.form-control,textarea.input-group-lg>.input-group-addon,textarea.input-group-lg>.input-group-btn>.btn{height:auto}.input-group-sm>.form-control,.input-group-sm>.input-group-addon,.input-group-sm>.input-group-btn>.btn{height:30px;padding:5px 10px;font-size:12px;line-height:1.5;border-radius:3px}select.input-group-sm>.form-control,select.input-group-sm>.input-group-addon,select.input-group-sm>.input-group-btn>.btn{height:30px;line-height:30px}select[multiple].input-group-sm>.form-control,select[multiple].input-group-sm>.input-group-addon,select[multiple].input-group-sm>.input-group-btn>.btn,textarea.input-group-sm>.form-control,textarea.input-group-sm>.input-group-addon,textarea.input-group-sm>.input-group-btn>.btn{height:auto}.input-group .form-control,.input-group-addon,.input-group-btn{display:table-cell}.input-group .form-control:not(:first-child):not(:last-child),.input-group-addon:not(:first-child):not(:last-child),.input-group-btn:not(:first-child):not(:last-child){border-radius:0}.input-group-addon,.input-group-btn{width:1%;white-space:nowrap;vertical-align:middle}.input-group-addon{padding:6px 12px;font-size:14px;font-weight:400;line-height:1;color:#555;text-align:center;background-color:#eee;border:1px solid #ccc;border-radius:4px}.input-group-addon.input-sm{padding:5px 10px;font-size:12px;border-radius:3px}.input-group-addon.input-lg{padding:10px 16px;font-size:18px;border-radius:6px}.input-group-addon input[type=checkbox],.input-group-addon input[type=radio]{margin-top:0}.input-group .form-control:first-child,.input-group-addon:first-child,.input-group-btn:first-child>.btn,.input-group-btn:first-child>.btn-group>.btn,.input-group-btn:first-child>.dropdown-toggle,.input-group-btn:last-child>.btn-group:not(:last-child)>.btn,.input-group-btn:last-child>.btn:not(:last-child):not(.dropdown-toggle){border-top-right-radius:0;border-bottom-right-radius:0}.input-group-addon:first-child{border-right:0}.input-group .form-control:last-child,.input-group-addon:last-child,.input-group-btn:first-child>.btn-group:not(:first-child)>.btn,.input-group-btn:first-child>.btn:not(:first-child),.input-group-btn:last-child>.btn,.input-group-btn:last-child>.btn-group>.btn,.input-group-btn:last-child>.dropdown-toggle{border-top-left-radius:0;border-bottom-left-radius:0}.input-group-addon:last-child{border-left:0}.input-group-btn{position:relative;font-size:0;white-space:nowrap}.input-group-btn>.btn{position:relative}.input-group-btn>.btn+.btn{margin-left:-1px}.input-group-btn>.btn:active,.input-group-btn>.btn:focus,.input-group-btn>.btn:hover{z-index:2}.input-group-btn:first-child>.btn,.input-group-btn:first-child>.btn-group{margin-right:-1px}.input-group-btn:last-child>.btn,.input-group-btn:last-child>.btn-group{z-index:2;margin-left:-1px}.nav{padding-left:0;margin-bottom:0;list-style:none}.nav>li{position:relative;display:block}.nav>li>a{position:relative;display:block;padding:10px 15px}.nav>li>a:focus,.nav>li>a:hover{text-decoration:none;background-color:#eee}.nav>li.disabled>a{color:#777}.nav>li.disabled>a:focus,.nav>li.disabled>a:hover{color:#777;text-decoration:none;cursor:not-allowed;background-color:transparent}.nav .open>a,.nav .open>a:focus,.nav .open>a:hover{background-color:#eee;border-color:#337ab7}.nav .nav-divider{height:1px;margin:9px 0;overflow:hidden;background-color:#e5e5e5}.nav>li>a>img{max-width:none}.nav-tabs{border-bottom:1px solid #ddd}.nav-tabs>li{float:left;margin-bottom:-1px}.nav-tabs>li>a{margin-right:2px;line-height:1.42857143;border:1px solid transparent;border-radius:4px 4px 0 0}.nav-tabs>li>a:hover{border-color:#eee #eee #ddd}.nav-tabs>li.active>a,.nav-tabs>li.active>a:focus,.nav-tabs>li.active>a:hover{color:#555;cursor:default;background-color:#fff;border:1px solid #ddd;border-bottom-color:transparent}.nav-tabs.nav-justified{width:100%;border-bottom:0}.nav-tabs.nav-justified>li{float:none}.nav-tabs.nav-justified>li>a{margin-bottom:5px;text-align:center}.nav-tabs.nav-justified>.dropdown .dropdown-menu{top:auto;left:auto}@media (min-width:768px){.nav-tabs.nav-justified>li{display:table-cell;width:1%}.nav-tabs.nav-justified>li>a{margin-bottom:0}}.nav-tabs.nav-justified>li>a{margin-right:0;border-radius:4px}.nav-tabs.nav-justified>.active>a,.nav-tabs.nav-justified>.active>a:focus,.nav-tabs.nav-justified>.active>a:hover{border:1px solid #ddd}@media (min-width:768px){.nav-tabs.nav-justified>li>a{border-bottom:1px solid #ddd;border-radius:4px 4px 0 0}.nav-tabs.nav-justified>.active>a,.nav-tabs.nav-justified>.active>a:focus,.nav-tabs.nav-justified>.active>a:hover{border-bottom-color:#fff}}.nav-pills>li{float:left}.nav-pills>li>a{border-radius:4px}.nav-pills>li+li{margin-left:2px}.nav-pills>li.active>a,.nav-pills>li.active>a:focus,.nav-pills>li.active>a:hover{color:#fff;background-color:#337ab7}.nav-stacked>li{float:none}.nav-stacked>li+li{margin-top:2px;margin-left:0}.nav-justified{width:100%}.nav-justified>li{float:none}.nav-justified>li>a{margin-bottom:5px;text-align:center}.nav-justified>.dropdown .dropdown-menu{top:auto;left:auto}@media (min-width:768px){.nav-justified>li{display:table-cell;width:1%}.nav-justified>li>a{margin-bottom:0}}.nav-tabs-justified{border-bottom:0}.nav-tabs-justified>li>a{margin-right:0;border-radius:4px}.nav-tabs-justified>.active>a,.nav-tabs-justified>.active>a:focus,.nav-tabs-justified>.active>a:hover{border:1px solid #ddd}@media (min-width:768px){.nav-tabs-justified>li>a{border-bottom:1px solid #ddd;border-radius:4px 4px 0 0}.nav-tabs-justified>.active>a,.nav-tabs-justified>.active>a:focus,.nav-tabs-justified>.active>a:hover{border-bottom-color:#fff}}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.nav-tabs .dropdown-menu{margin-top:-1px;border-top-left-radius:0;border-top-right-radius:0}.navbar{position:relative;min-height:50px;margin-bottom:20px;border:1px solid transparent}@media (min-width:768px){.navbar{border-radius:4px}}@media (min-width:768px){.navbar-header{float:left}}.navbar-collapse{padding-right:15px;padding-left:15px;overflow-x:visible;-webkit-overflow-scrolling:touch;border-top:1px solid transparent;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1)}.navbar-collapse.in{overflow-y:auto}@media (min-width:768px){.navbar-collapse{width:auto;border-top:0;-webkit-box-shadow:none;box-shadow:none}.navbar-collapse.collapse{display:block!important;height:auto!important;padding-bottom:0;overflow:visible!important}.navbar-collapse.in{overflow-y:visible}.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse,.navbar-static-top .navbar-collapse{padding-right:0;padding-left:0}}.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse{max-height:340px}@media (max-device-width:480px) and (orientation:landscape){.navbar-fixed-bottom .navbar-collapse,.navbar-fixed-top .navbar-collapse{max-height:200px}}.container-fluid>.navbar-collapse,.container-fluid>.navbar-header,.container>.navbar-collapse,.container>.navbar-header{margin-right:-15px;margin-left:-15px}@media (min-width:768px){.container-fluid>.navbar-collapse,.container-fluid>.navbar-header,.container>.navbar-collapse,.container>.navbar-header{margin-right:0;margin-left:0}}.navbar-static-top{z-index:1000;border-width:0 0 1px}@media (min-width:768px){.navbar-static-top{border-radius:0}}.navbar-fixed-bottom,.navbar-fixed-top{position:fixed;right:0;left:0;z-index:1030}@media (min-width:768px){.navbar-fixed-bottom,.navbar-fixed-top{border-radius:0}}.navbar-fixed-top{top:0;border-width:0 0 1px}.navbar-fixed-bottom{bottom:0;margin-bottom:0;border-width:1px 0 0}.navbar-brand{float:left;height:50px;padding:15px 15px;font-size:18px;line-height:20px}.navbar-brand:focus,.navbar-brand:hover{text-decoration:none}.navbar-brand>img{display:block}@media (min-width:768px){.navbar>.container .navbar-brand,.navbar>.container-fluid .navbar-brand{margin-left:-15px}}.navbar-toggle{position:relative;float:right;padding:9px 10px;margin-top:8px;margin-right:15px;margin-bottom:8px;background-color:transparent;background-image:none;border:1px solid transparent;border-radius:4px}.navbar-toggle:focus{outline:0}.navbar-toggle .icon-bar{display:block;width:22px;height:2px;border-radius:1px}.navbar-toggle .icon-bar+.icon-bar{margin-top:4px}@media (min-width:768px){.navbar-toggle{display:none}}.navbar-nav{margin:7.5px -15px}.navbar-nav>li>a{padding-top:10px;padding-bottom:10px;line-height:20px}@media (max-width:767px){.navbar-nav .open .dropdown-menu{position:static;float:none;width:auto;margin-top:0;background-color:transparent;border:0;-webkit-box-shadow:none;box-shadow:none}.navbar-nav .open .dropdown-menu .dropdown-header,.navbar-nav .open .dropdown-menu>li>a{padding:5px 15px 5px 25px}.navbar-nav .open .dropdown-menu>li>a{line-height:20px}.navbar-nav .open .dropdown-menu>li>a:focus,.navbar-nav .open .dropdown-menu>li>a:hover{background-image:none}}@media (min-width:768px){.navbar-nav{float:left;margin:0}.navbar-nav>li{float:left}.navbar-nav>li>a{padding-top:15px;padding-bottom:15px}}.navbar-form{padding:10px 15px;margin-top:8px;margin-right:-15px;margin-bottom:8px;margin-left:-15px;border-top:1px solid transparent;border-bottom:1px solid transparent;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1),0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1),0 1px 0 rgba(255,255,255,.1)}@media (min-width:768px){.navbar-form .form-group{display:inline-block;margin-bottom:0;vertical-align:middle}.navbar-form .form-control{display:inline-block;width:auto;vertical-align:middle}.navbar-form .form-control-static{display:inline-block}.navbar-form .input-group{display:inline-table;vertical-align:middle}.navbar-form .input-group .form-control,.navbar-form .input-group .input-group-addon,.navbar-form .input-group .input-group-btn{width:auto}.navbar-form .input-group>.form-control{width:100%}.navbar-form .control-label{margin-bottom:0;vertical-align:middle}.navbar-form .checkbox,.navbar-form .radio{display:inline-block;margin-top:0;margin-bottom:0;vertical-align:middle}.navbar-form .checkbox label,.navbar-form .radio label{padding-left:0}.navbar-form .checkbox input[type=checkbox],.navbar-form .radio input[type=radio]{position:relative;margin-left:0}.navbar-form .has-feedback .form-control-feedback{top:0}}@media (max-width:767px){.navbar-form .form-group{margin-bottom:5px}.navbar-form .form-group:last-child{margin-bottom:0}}@media (min-width:768px){.navbar-form{width:auto;padding-top:0;padding-bottom:0;margin-right:0;margin-left:0;border:0;-webkit-box-shadow:none;box-shadow:none}}.navbar-nav>li>.dropdown-menu{margin-top:0;border-top-left-radius:0;border-top-right-radius:0}.navbar-fixed-bottom .navbar-nav>li>.dropdown-menu{margin-bottom:0;border-top-left-radius:4px;border-top-right-radius:4px;border-bottom-right-radius:0;border-bottom-left-radius:0}.navbar-btn{margin-top:8px;margin-bottom:8px}.navbar-btn.btn-sm{margin-top:10px;margin-bottom:10px}.navbar-btn.btn-xs{margin-top:14px;margin-bottom:14px}.navbar-text{margin-top:15px;margin-bottom:15px}@media (min-width:768px){.navbar-text{float:left;margin-right:15px;margin-left:15px}}@media (min-width:768px){.navbar-left{float:left!important}.navbar-right{float:right!important;margin-right:-15px}.navbar-right~.navbar-right{margin-right:0}}.navbar-default{background-color:#f8f8f8;border-color:#e7e7e7}.navbar-default .navbar-brand{color:#777}.navbar-default .navbar-brand:focus,.navbar-default .navbar-brand:hover{color:#5e5e5e;background-color:transparent}.navbar-default .navbar-text{color:#777}.navbar-default .navbar-nav>li>a{color:#777}.navbar-default .navbar-nav>li>a:focus,.navbar-default .navbar-nav>li>a:hover{color:#333;background-color:transparent}.navbar-default .navbar-nav>.active>a,.navbar-default .navbar-nav>.active>a:focus,.navbar-default .navbar-nav>.active>a:hover{color:#555;background-color:#e7e7e7}.navbar-default .navbar-nav>.disabled>a,.navbar-default .navbar-nav>.disabled>a:focus,.navbar-default .navbar-nav>.disabled>a:hover{color:#ccc;background-color:transparent}.navbar-default .navbar-toggle{border-color:#ddd}.navbar-default .navbar-toggle:focus,.navbar-default .navbar-toggle:hover{background-color:#ddd}.navbar-default .navbar-toggle .icon-bar{background-color:#888}.navbar-default .navbar-collapse,.navbar-default .navbar-form{border-color:#e7e7e7}.navbar-default .navbar-nav>.open>a,.navbar-default .navbar-nav>.open>a:focus,.navbar-default .navbar-nav>.open>a:hover{color:#555;background-color:#e7e7e7}@media (max-width:767px){.navbar-default .navbar-nav .open .dropdown-menu>li>a{color:#777}.navbar-default .navbar-nav .open .dropdown-menu>li>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>li>a:hover{color:#333;background-color:transparent}.navbar-default .navbar-nav .open .dropdown-menu>.active>a,.navbar-default .navbar-nav .open .dropdown-menu>.active>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>.active>a:hover{color:#555;background-color:#e7e7e7}.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a,.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a:focus,.navbar-default .navbar-nav .open .dropdown-menu>.disabled>a:hover{color:#ccc;background-color:transparent}}.navbar-default .navbar-link{color:#777}.navbar-default .navbar-link:hover{color:#333}.navbar-default .btn-link{color:#777}.navbar-default .btn-link:focus,.navbar-default .btn-link:hover{color:#333}.navbar-default .btn-link[disabled]:focus,.navbar-default .btn-link[disabled]:hover,fieldset[disabled] .navbar-default .btn-link:focus,fieldset[disabled] .navbar-default .btn-link:hover{color:#ccc}.navbar-inverse{background-color:#222;border-color:#080808}.navbar-inverse .navbar-brand{color:#9d9d9d}.navbar-inverse .navbar-brand:focus,.navbar-inverse .navbar-brand:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-text{color:#9d9d9d}.navbar-inverse .navbar-nav>li>a{color:#9d9d9d}.navbar-inverse .navbar-nav>li>a:focus,.navbar-inverse .navbar-nav>li>a:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-nav>.active>a,.navbar-inverse .navbar-nav>.active>a:focus,.navbar-inverse .navbar-nav>.active>a:hover{color:#fff;background-color:#080808}.navbar-inverse .navbar-nav>.disabled>a,.navbar-inverse .navbar-nav>.disabled>a:focus,.navbar-inverse .navbar-nav>.disabled>a:hover{color:#444;background-color:transparent}.navbar-inverse .navbar-toggle{border-color:#333}.navbar-inverse .navbar-toggle:focus,.navbar-inverse .navbar-toggle:hover{background-color:#333}.navbar-inverse .navbar-toggle .icon-bar{background-color:#fff}.navbar-inverse .navbar-collapse,.navbar-inverse .navbar-form{border-color:#101010}.navbar-inverse .navbar-nav>.open>a,.navbar-inverse .navbar-nav>.open>a:focus,.navbar-inverse .navbar-nav>.open>a:hover{color:#fff;background-color:#080808}@media (max-width:767px){.navbar-inverse .navbar-nav .open .dropdown-menu>.dropdown-header{border-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu .divider{background-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu>li>a{color:#9d9d9d}.navbar-inverse .navbar-nav .open .dropdown-menu>li>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>li>a:hover{color:#fff;background-color:transparent}.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a,.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>.active>a:hover{color:#fff;background-color:#080808}.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a,.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a:focus,.navbar-inverse .navbar-nav .open .dropdown-menu>.disabled>a:hover{color:#444;background-color:transparent}}.navbar-inverse .navbar-link{color:#9d9d9d}.navbar-inverse .navbar-link:hover{color:#fff}.navbar-inverse .btn-link{color:#9d9d9d}.navbar-inverse .btn-link:focus,.navbar-inverse .btn-link:hover{color:#fff}.navbar-inverse .btn-link[disabled]:focus,.navbar-inverse .btn-link[disabled]:hover,fieldset[disabled] .navbar-inverse .btn-link:focus,fieldset[disabled] .navbar-inverse .btn-link:hover{color:#444}.breadcrumb{padding:8px 15px;margin-bottom:20px;list-style:none;background-color:#f5f5f5;border-radius:4px}.breadcrumb>li{display:inline-block}.breadcrumb>li+li:before{padding:0 5px;color:#ccc;content:"/\00a0"}.breadcrumb>.active{color:#777}.pagination{display:inline-block;padding-left:0;margin:20px 0;border-radius:4px}.pagination>li{display:inline}.pagination>li>a,.pagination>li>span{position:relative;float:left;padding:6px 12px;margin-left:-1px;line-height:1.42857143;color:#337ab7;text-decoration:none;background-color:#fff;border:1px solid #ddd}.pagination>li:first-child>a,.pagination>li:first-child>span{margin-left:0;border-top-left-radius:4px;border-bottom-left-radius:4px}.pagination>li:last-child>a,.pagination>li:last-child>span{border-top-right-radius:4px;border-bottom-right-radius:4px}.pagination>li>a:focus,.pagination>li>a:hover,.pagination>li>span:focus,.pagination>li>span:hover{z-index:3;color:#23527c;background-color:#eee;border-color:#ddd}.pagination>.active>a,.pagination>.active>a:focus,.pagination>.active>a:hover,.pagination>.active>span,.pagination>.active>span:focus,.pagination>.active>span:hover{z-index:2;color:#fff;cursor:default;background-color:#337ab7;border-color:#337ab7}.pagination>.disabled>a,.pagination>.disabled>a:focus,.pagination>.disabled>a:hover,.pagination>.disabled>span,.pagination>.disabled>span:focus,.pagination>.disabled>span:hover{color:#777;cursor:not-allowed;background-color:#fff;border-color:#ddd}.pagination-lg>li>a,.pagination-lg>li>span{padding:10px 16px;font-size:18px;line-height:1.3333333}.pagination-lg>li:first-child>a,.pagination-lg>li:first-child>span{border-top-left-radius:6px;border-bottom-left-radius:6px}.pagination-lg>li:last-child>a,.pagination-lg>li:last-child>span{border-top-right-radius:6px;border-bottom-right-radius:6px}.pagination-sm>li>a,.pagination-sm>li>span{padding:5px 10px;font-size:12px;line-height:1.5}.pagination-sm>li:first-child>a,.pagination-sm>li:first-child>span{border-top-left-radius:3px;border-bottom-left-radius:3px}.pagination-sm>li:last-child>a,.pagination-sm>li:last-child>span{border-top-right-radius:3px;border-bottom-right-radius:3px}.pager{padding-left:0;margin:20px 0;text-align:center;list-style:none}.pager li{display:inline}.pager li>a,.pager li>span{display:inline-block;padding:5px 14px;background-color:#fff;border:1px solid #ddd;border-radius:15px}.pager li>a:focus,.pager li>a:hover{text-decoration:none;background-color:#eee}.pager .next>a,.pager .next>span{float:right}.pager .previous>a,.pager .previous>span{float:left}.pager .disabled>a,.pager .disabled>a:focus,.pager .disabled>a:hover,.pager .disabled>span{color:#777;cursor:not-allowed;background-color:#fff}.label{display:inline;padding:.2em .6em .3em;font-size:75%;font-weight:700;line-height:1;color:#fff;text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:.25em}a.label:focus,a.label:hover{color:#fff;text-decoration:none;cursor:pointer}.label:empty{display:none}.btn .label{position:relative;top:-1px}.label-default{background-color:#777}.label-default[href]:focus,.label-default[href]:hover{background-color:#5e5e5e}.label-primary{background-color:#337ab7}.label-primary[href]:focus,.label-primary[href]:hover{background-color:#286090}.label-success{background-color:#5cb85c}.label-success[href]:focus,.label-success[href]:hover{background-color:#449d44}.label-info{background-color:#5bc0de}.label-info[href]:focus,.label-info[href]:hover{background-color:#31b0d5}.label-warning{background-color:#f0ad4e}.label-warning[href]:focus,.label-warning[href]:hover{background-color:#ec971f}.label-danger{background-color:#d9534f}.label-danger[href]:focus,.label-danger[href]:hover{background-color:#c9302c}.badge{display:inline-block;min-width:10px;padding:3px 7px;font-size:12px;font-weight:700;line-height:1;color:#fff;text-align:center;white-space:nowrap;vertical-align:middle;background-color:#777;border-radius:10px}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.btn-group-xs>.btn .badge,.btn-xs .badge{top:0;padding:1px 5px}a.badge:focus,a.badge:hover{color:#fff;text-decoration:none;cursor:pointer}.list-group-item.active>.badge,.nav-pills>.active>a>.badge{color:#337ab7;background-color:#fff}.list-group-item>.badge{float:right}.list-group-item>.badge+.badge{margin-right:5px}.nav-pills>li>a>.badge{margin-left:3px}.jumbotron{padding-top:30px;padding-bottom:30px;margin-bottom:30px;color:inherit;background-color:#eee}.jumbotron .h1,.jumbotron h1{color:inherit}.jumbotron p{margin-bottom:15px;font-size:21px;font-weight:200}.jumbotron>hr{border-top-color:#d5d5d5}.container .jumbotron,.container-fluid .jumbotron{border-radius:6px}.jumbotron .container{max-width:100%}@media screen and (min-width:768px){.jumbotron{padding-top:48px;padding-bottom:48px}.container .jumbotron,.container-fluid .jumbotron{padding-right:60px;padding-left:60px}.jumbotron .h1,.jumbotron h1{font-size:63px}}.thumbnail{display:block;padding:4px;margin-bottom:20px;line-height:1.42857143;background-color:#fff;border:1px solid #ddd;border-radius:4px;-webkit-transition:border .2s ease-in-out;-o-transition:border .2s ease-in-out;transition:border .2s ease-in-out}.thumbnail a>img,.thumbnail>img{margin-right:auto;margin-left:auto}a.thumbnail.active,a.thumbnail:focus,a.thumbnail:hover{border-color:#337ab7}.thumbnail .caption{padding:9px;color:#333}.alert{padding:15px;margin-bottom:20px;border:1px solid transparent;border-radius:4px}.alert h4{margin-top:0;color:inherit}.alert .alert-link{font-weight:700}.alert>p,.alert>ul{margin-bottom:0}.alert>p+p{margin-top:5px}.alert-dismissable,.alert-dismissible{padding-right:35px}.alert-dismissable .close,.alert-dismissible .close{position:relative;top:-2px;right:-21px;color:inherit}.alert-success{color:#3c763d;background-color:#dff0d8;border-color:#d6e9c6}.alert-success hr{border-top-color:#c9e2b3}.alert-success .alert-link{color:#2b542c}.alert-info{color:#31708f;background-color:#d9edf7;border-color:#bce8f1}.alert-info hr{border-top-color:#a6e1ec}.alert-info .alert-link{color:#245269}.alert-warning{color:#8a6d3b;background-color:#fcf8e3;border-color:#faebcc}.alert-warning hr{border-top-color:#f7e1b5}.alert-warning .alert-link{color:#66512c}.alert-danger{color:#a94442;background-color:#f2dede;border-color:#ebccd1}.alert-danger hr{border-top-color:#e4b9c0}.alert-danger .alert-link{color:#843534}@-webkit-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@-o-keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}@keyframes progress-bar-stripes{from{background-position:40px 0}to{background-position:0 0}}.progress{height:20px;margin-bottom:20px;overflow:hidden;background-color:#f5f5f5;border-radius:4px;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,.1);box-shadow:inset 0 1px 2px rgba(0,0,0,.1)}.progress-bar{float:left;width:0;height:100%;font-size:12px;line-height:20px;color:#fff;text-align:center;background-color:#337ab7;-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,.15);box-shadow:inset 0 -1px 0 rgba(0,0,0,.15);-webkit-transition:width .6s ease;-o-transition:width .6s ease;transition:width .6s ease}.progress-bar-striped,.progress-striped .progress-bar{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);-webkit-background-size:40px 40px;background-size:40px 40px}.progress-bar.active,.progress.active .progress-bar{-webkit-animation:progress-bar-stripes 2s linear infinite;-o-animation:progress-bar-stripes 2s linear infinite;animation:progress-bar-stripes 2s linear infinite}.progress-bar-success{background-color:#5cb85c}.progress-striped .progress-bar-success{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-info{background-color:#5bc0de}.progress-striped .progress-bar-info{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-warning{background-color:#f0ad4e}.progress-striped .progress-bar-warning{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.progress-bar-danger{background-color:#d9534f}.progress-striped .progress-bar-danger{background-image:-webkit-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:-o-linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent)}.media{margin-top:15px}.media:first-child{margin-top:0}.media,.media-body{overflow:hidden;zoom:1}.media-body{width:10000px}.media-object{display:block}.media-object.img-thumbnail{max-width:none}.media-right,.media>.pull-right{padding-left:10px}.media-left,.media>.pull-left{padding-right:10px}.media-body,.media-left,.media-right{display:table-cell;vertical-align:top}.media-middle{vertical-align:middle}.media-bottom{vertical-align:bottom}.media-heading{margin-top:0;margin-bottom:5px}.media-list{padding-left:0;list-style:none}.list-group{padding-left:0;margin-bottom:20px}.list-group-item{position:relative;display:block;padding:10px 15px;margin-bottom:-1px;background-color:#fff;border:1px solid #ddd}.list-group-item:first-child{border-top-left-radius:4px;border-top-right-radius:4px}.list-group-item:last-child{margin-bottom:0;border-bottom-right-radius:4px;border-bottom-left-radius:4px}a.list-group-item,button.list-group-item{color:#555}a.list-group-item .list-group-item-heading,button.list-group-item .list-group-item-heading{color:#333}a.list-group-item:focus,a.list-group-item:hover,button.list-group-item:focus,button.list-group-item:hover{color:#555;text-decoration:none;background-color:#f5f5f5}button.list-group-item{width:100%;text-align:left}.list-group-item.disabled,.list-group-item.disabled:focus,.list-group-item.disabled:hover{color:#777;cursor:not-allowed;background-color:#eee}.list-group-item.disabled .list-group-item-heading,.list-group-item.disabled:focus .list-group-item-heading,.list-group-item.disabled:hover .list-group-item-heading{color:inherit}.list-group-item.disabled .list-group-item-text,.list-group-item.disabled:focus .list-group-item-text,.list-group-item.disabled:hover .list-group-item-text{color:#777}.list-group-item.active,.list-group-item.active:focus,.list-group-item.active:hover{z-index:2;color:#fff;background-color:#337ab7;border-color:#337ab7}.list-group-item.active .list-group-item-heading,.list-group-item.active .list-group-item-heading>.small,.list-group-item.active .list-group-item-heading>small,.list-group-item.active:focus .list-group-item-heading,.list-group-item.active:focus .list-group-item-heading>.small,.list-group-item.active:focus .list-group-item-heading>small,.list-group-item.active:hover .list-group-item-heading,.list-group-item.active:hover .list-group-item-heading>.small,.list-group-item.active:hover .list-group-item-heading>small{color:inherit}.list-group-item.active .list-group-item-text,.list-group-item.active:focus .list-group-item-text,.list-group-item.active:hover .list-group-item-text{color:#c7ddef}.list-group-item-success{color:#3c763d;background-color:#dff0d8}a.list-group-item-success,button.list-group-item-success{color:#3c763d}a.list-group-item-success .list-group-item-heading,button.list-group-item-success .list-group-item-heading{color:inherit}a.list-group-item-success:focus,a.list-group-item-success:hover,button.list-group-item-success:focus,button.list-group-item-success:hover{color:#3c763d;background-color:#d0e9c6}a.list-group-item-success.active,a.list-group-item-success.active:focus,a.list-group-item-success.active:hover,button.list-group-item-success.active,button.list-group-item-success.active:focus,button.list-group-item-success.active:hover{color:#fff;background-color:#3c763d;border-color:#3c763d}.list-group-item-info{color:#31708f;background-color:#d9edf7}a.list-group-item-info,button.list-group-item-info{color:#31708f}a.list-group-item-info .list-group-item-heading,button.list-group-item-info .list-group-item-heading{color:inherit}a.list-group-item-info:focus,a.list-group-item-info:hover,button.list-group-item-info:focus,button.list-group-item-info:hover{color:#31708f;background-color:#c4e3f3}a.list-group-item-info.active,a.list-group-item-info.active:focus,a.list-group-item-info.active:hover,button.list-group-item-info.active,button.list-group-item-info.active:focus,button.list-group-item-info.active:hover{color:#fff;background-color:#31708f;border-color:#31708f}.list-group-item-warning{color:#8a6d3b;background-color:#fcf8e3}a.list-group-item-warning,button.list-group-item-warning{color:#8a6d3b}a.list-group-item-warning .list-group-item-heading,button.list-group-item-warning .list-group-item-heading{color:inherit}a.list-group-item-warning:focus,a.list-group-item-warning:hover,button.list-group-item-warning:focus,button.list-group-item-warning:hover{color:#8a6d3b;background-color:#faf2cc}a.list-group-item-warning.active,a.list-group-item-warning.active:focus,a.list-group-item-warning.active:hover,button.list-group-item-warning.active,button.list-group-item-warning.active:focus,button.list-group-item-warning.active:hover{color:#fff;background-color:#8a6d3b;border-color:#8a6d3b}.list-group-item-danger{color:#a94442;background-color:#f2dede}a.list-group-item-danger,button.list-group-item-danger{color:#a94442}a.list-group-item-danger .list-group-item-heading,button.list-group-item-danger .list-group-item-heading{color:inherit}a.list-group-item-danger:focus,a.list-group-item-danger:hover,button.list-group-item-danger:focus,button.list-group-item-danger:hover{color:#a94442;background-color:#ebcccc}a.list-group-item-danger.active,a.list-group-item-danger.active:focus,a.list-group-item-danger.active:hover,button.list-group-item-danger.active,button.list-group-item-danger.active:focus,button.list-group-item-danger.active:hover{color:#fff;background-color:#a94442;border-color:#a94442}.list-group-item-heading{margin-top:0;margin-bottom:5px}.list-group-item-text{margin-bottom:0;line-height:1.3}.panel{margin-bottom:20px;background-color:#fff;border:1px solid transparent;border-radius:4px;-webkit-box-shadow:0 1px 1px rgba(0,0,0,.05);box-shadow:0 1px 1px rgba(0,0,0,.05)}.panel-body{padding:15px}.panel-heading{padding:10px 15px;border-bottom:1px solid transparent;border-top-left-radius:3px;border-top-right-radius:3px}.panel-heading>.dropdown .dropdown-toggle{color:inherit}.panel-title{margin-top:0;margin-bottom:0;font-size:16px;color:inherit}.panel-title>.small,.panel-title>.small>a,.panel-title>a,.panel-title>small,.panel-title>small>a{color:inherit}.panel-footer{padding:10px 15px;background-color:#f5f5f5;border-top:1px solid #ddd;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.list-group,.panel>.panel-collapse>.list-group{margin-bottom:0}.panel>.list-group .list-group-item,.panel>.panel-collapse>.list-group .list-group-item{border-width:1px 0;border-radius:0}.panel>.list-group:first-child .list-group-item:first-child,.panel>.panel-collapse>.list-group:first-child .list-group-item:first-child{border-top:0;border-top-left-radius:3px;border-top-right-radius:3px}.panel>.list-group:last-child .list-group-item:last-child,.panel>.panel-collapse>.list-group:last-child .list-group-item:last-child{border-bottom:0;border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.panel-heading+.panel-collapse>.list-group .list-group-item:first-child{border-top-left-radius:0;border-top-right-radius:0}.panel-heading+.list-group .list-group-item:first-child{border-top-width:0}.list-group+.panel-footer{border-top-width:0}.panel>.panel-collapse>.table,.panel>.table,.panel>.table-responsive>.table{margin-bottom:0}.panel>.panel-collapse>.table caption,.panel>.table caption,.panel>.table-responsive>.table caption{padding-right:15px;padding-left:15px}.panel>.table-responsive:first-child>.table:first-child,.panel>.table:first-child{border-top-left-radius:3px;border-top-right-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child,.panel>.table:first-child>thead:first-child>tr:first-child{border-top-left-radius:3px;border-top-right-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child td:first-child,.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child th:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child td:first-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child th:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child td:first-child,.panel>.table:first-child>tbody:first-child>tr:first-child th:first-child,.panel>.table:first-child>thead:first-child>tr:first-child td:first-child,.panel>.table:first-child>thead:first-child>tr:first-child th:first-child{border-top-left-radius:3px}.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child td:last-child,.panel>.table-responsive:first-child>.table:first-child>tbody:first-child>tr:first-child th:last-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child td:last-child,.panel>.table-responsive:first-child>.table:first-child>thead:first-child>tr:first-child th:last-child,.panel>.table:first-child>tbody:first-child>tr:first-child td:last-child,.panel>.table:first-child>tbody:first-child>tr:first-child th:last-child,.panel>.table:first-child>thead:first-child>tr:first-child td:last-child,.panel>.table:first-child>thead:first-child>tr:first-child th:last-child{border-top-right-radius:3px}.panel>.table-responsive:last-child>.table:last-child,.panel>.table:last-child{border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child{border-bottom-right-radius:3px;border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child td:first-child,.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child th:first-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child td:first-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child th:first-child,.panel>.table:last-child>tbody:last-child>tr:last-child td:first-child,.panel>.table:last-child>tbody:last-child>tr:last-child th:first-child,.panel>.table:last-child>tfoot:last-child>tr:last-child td:first-child,.panel>.table:last-child>tfoot:last-child>tr:last-child th:first-child{border-bottom-left-radius:3px}.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child td:last-child,.panel>.table-responsive:last-child>.table:last-child>tbody:last-child>tr:last-child th:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child td:last-child,.panel>.table-responsive:last-child>.table:last-child>tfoot:last-child>tr:last-child th:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child td:last-child,.panel>.table:last-child>tbody:last-child>tr:last-child th:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child td:last-child,.panel>.table:last-child>tfoot:last-child>tr:last-child th:last-child{border-bottom-right-radius:3px}.panel>.panel-body+.table,.panel>.panel-body+.table-responsive,.panel>.table+.panel-body,.panel>.table-responsive+.panel-body{border-top:1px solid #ddd}.panel>.table>tbody:first-child>tr:first-child td,.panel>.table>tbody:first-child>tr:first-child th{border-top:0}.panel>.table-bordered,.panel>.table-responsive>.table-bordered{border:0}.panel>.table-bordered>tbody>tr>td:first-child,.panel>.table-bordered>tbody>tr>th:first-child,.panel>.table-bordered>tfoot>tr>td:first-child,.panel>.table-bordered>tfoot>tr>th:first-child,.panel>.table-bordered>thead>tr>td:first-child,.panel>.table-bordered>thead>tr>th:first-child,.panel>.table-responsive>.table-bordered>tbody>tr>td:first-child,.panel>.table-responsive>.table-bordered>tbody>tr>th:first-child,.panel>.table-responsive>.table-bordered>tfoot>tr>td:first-child,.panel>.table-responsive>.table-bordered>tfoot>tr>th:first-child,.panel>.table-responsive>.table-bordered>thead>tr>td:first-child,.panel>.table-responsive>.table-bordered>thead>tr>th:first-child{border-left:0}.panel>.table-bordered>tbody>tr>td:last-child,.panel>.table-bordered>tbody>tr>th:last-child,.panel>.table-bordered>tfoot>tr>td:last-child,.panel>.table-bordered>tfoot>tr>th:last-child,.panel>.table-bordered>thead>tr>td:last-child,.panel>.table-bordered>thead>tr>th:last-child,.panel>.table-responsive>.table-bordered>tbody>tr>td:last-child,.panel>.table-responsive>.table-bordered>tbody>tr>th:last-child,.panel>.table-responsive>.table-bordered>tfoot>tr>td:last-child,.panel>.table-responsive>.table-bordered>tfoot>tr>th:last-child,.panel>.table-responsive>.table-bordered>thead>tr>td:last-child,.panel>.table-responsive>.table-bordered>thead>tr>th:last-child{border-right:0}.panel>.table-bordered>tbody>tr:first-child>td,.panel>.table-bordered>tbody>tr:first-child>th,.panel>.table-bordered>thead>tr:first-child>td,.panel>.table-bordered>thead>tr:first-child>th,.panel>.table-responsive>.table-bordered>tbody>tr:first-child>td,.panel>.table-responsive>.table-bordered>tbody>tr:first-child>th,.panel>.table-responsive>.table-bordered>thead>tr:first-child>td,.panel>.table-responsive>.table-bordered>thead>tr:first-child>th{border-bottom:0}.panel>.table-bordered>tbody>tr:last-child>td,.panel>.table-bordered>tbody>tr:last-child>th,.panel>.table-bordered>tfoot>tr:last-child>td,.panel>.table-bordered>tfoot>tr:last-child>th,.panel>.table-responsive>.table-bordered>tbody>tr:last-child>td,.panel>.table-responsive>.table-bordered>tbody>tr:last-child>th,.panel>.table-responsive>.table-bordered>tfoot>tr:last-child>td,.panel>.table-responsive>.table-bordered>tfoot>tr:last-child>th{border-bottom:0}.panel>.table-responsive{margin-bottom:0;border:0}.panel-group{margin-bottom:20px}.panel-group .panel{margin-bottom:0;border-radius:4px}.panel-group .panel+.panel{margin-top:5px}.panel-group .panel-heading{border-bottom:0}.panel-group .panel-heading+.panel-collapse>.list-group,.panel-group .panel-heading+.panel-collapse>.panel-body{border-top:1px solid #ddd}.panel-group .panel-footer{border-top:0}.panel-group .panel-footer+.panel-collapse .panel-body{border-bottom:1px solid #ddd}.panel-default{border-color:#ddd}.panel-default>.panel-heading{color:#333;background-color:#f5f5f5;border-color:#ddd}.panel-default>.panel-heading+.panel-collapse>.panel-body{border-top-color:#ddd}.panel-default>.panel-heading .badge{color:#f5f5f5;background-color:#333}.panel-default>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#ddd}.panel-primary{border-color:#337ab7}.panel-primary>.panel-heading{color:#fff;background-color:#337ab7;border-color:#337ab7}.panel-primary>.panel-heading+.panel-collapse>.panel-body{border-top-color:#337ab7}.panel-primary>.panel-heading .badge{color:#337ab7;background-color:#fff}.panel-primary>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#337ab7}.panel-success{border-color:#d6e9c6}.panel-success>.panel-heading{color:#3c763d;background-color:#dff0d8;border-color:#d6e9c6}.panel-success>.panel-heading+.panel-collapse>.panel-body{border-top-color:#d6e9c6}.panel-success>.panel-heading .badge{color:#dff0d8;background-color:#3c763d}.panel-success>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#d6e9c6}.panel-info{border-color:#bce8f1}.panel-info>.panel-heading{color:#31708f;background-color:#d9edf7;border-color:#bce8f1}.panel-info>.panel-heading+.panel-collapse>.panel-body{border-top-color:#bce8f1}.panel-info>.panel-heading .badge{color:#d9edf7;background-color:#31708f}.panel-info>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#bce8f1}.panel-warning{border-color:#faebcc}.panel-warning>.panel-heading{color:#8a6d3b;background-color:#fcf8e3;border-color:#faebcc}.panel-warning>.panel-heading+.panel-collapse>.panel-body{border-top-color:#faebcc}.panel-warning>.panel-heading .badge{color:#fcf8e3;background-color:#8a6d3b}.panel-warning>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#faebcc}.panel-danger{border-color:#ebccd1}.panel-danger>.panel-heading{color:#a94442;background-color:#f2dede;border-color:#ebccd1}.panel-danger>.panel-heading+.panel-collapse>.panel-body{border-top-color:#ebccd1}.panel-danger>.panel-heading .badge{color:#f2dede;background-color:#a94442}.panel-danger>.panel-footer+.panel-collapse>.panel-body{border-bottom-color:#ebccd1}.embed-responsive{position:relative;display:block;height:0;padding:0;overflow:hidden}.embed-responsive .embed-responsive-item,.embed-responsive embed,.embed-responsive iframe,.embed-responsive object,.embed-responsive video{position:absolute;top:0;bottom:0;left:0;width:100%;height:100%;border:0}.embed-responsive-16by9{padding-bottom:56.25%}.embed-responsive-4by3{padding-bottom:75%}.well{min-height:20px;padding:19px;margin-bottom:20px;background-color:#f5f5f5;border:1px solid #e3e3e3;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.05);box-shadow:inset 0 1px 1px rgba(0,0,0,.05)}.well blockquote{border-color:#ddd;border-color:rgba(0,0,0,.15)}.well-lg{padding:24px;border-radius:6px}.well-sm{padding:9px;border-radius:3px}.close{float:right;font-size:21px;font-weight:700;line-height:1;color:#000;text-shadow:0 1px 0 #fff;filter:alpha(opacity=20);opacity:.2}.close:focus,.close:hover{color:#000;text-decoration:none;cursor:pointer;filter:alpha(opacity=50);opacity:.5}button.close{-webkit-appearance:none;padding:0;cursor:pointer;background:0 0;border:0}.modal-open{overflow:hidden}.modal{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1050;display:none;overflow:hidden;-webkit-overflow-scrolling:touch;outline:0}.modal.fade .modal-dialog{-webkit-transition:-webkit-transform .3s ease-out;-o-transition:-o-transform .3s ease-out;transition:transform .3s ease-out;-webkit-transform:translate(0,-25%);-ms-transform:translate(0,-25%);-o-transform:translate(0,-25%);transform:translate(0,-25%)}.modal.in .modal-dialog{-webkit-transform:translate(0,0);-ms-transform:translate(0,0);-o-transform:translate(0,0);transform:translate(0,0)}.modal-open .modal{overflow-x:hidden;overflow-y:auto}.modal-dialog{position:relative;width:auto;margin:10px}.modal-content{position:relative;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #999;border:1px solid rgba(0,0,0,.2);border-radius:6px;outline:0;-webkit-box-shadow:0 3px 9px rgba(0,0,0,.5);box-shadow:0 3px 9px rgba(0,0,0,.5)}.modal-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1040;background-color:#000}.modal-backdrop.fade{filter:alpha(opacity=0);opacity:0}.modal-backdrop.in{filter:alpha(opacity=50);opacity:.5}.modal-header{min-height:16.43px;padding:15px;border-bottom:1px solid #e5e5e5}.modal-header .close{margin-top:-2px}.modal-title{margin:0;line-height:1.42857143}.modal-body{position:relative;padding:15px}.modal-footer{padding:15px;text-align:right;border-top:1px solid #e5e5e5}.modal-footer .btn+.btn{margin-bottom:0;margin-left:5px}.modal-footer .btn-group .btn+.btn{margin-left:-1px}.modal-footer .btn-block+.btn-block{margin-left:0}.modal-scrollbar-measure{position:absolute;top:-9999px;width:50px;height:50px;overflow:scroll}@media (min-width:768px){.modal-dialog{width:600px;margin:30px auto}.modal-content{-webkit-box-shadow:0 5px 15px rgba(0,0,0,.5);box-shadow:0 5px 15px rgba(0,0,0,.5)}.modal-sm{width:300px}}@media (min-width:992px){.modal-lg{width:900px}}.tooltip{position:absolute;z-index:1070;display:block;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:12px;font-style:normal;font-weight:400;line-height:1.42857143;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;word-spacing:normal;word-wrap:normal;white-space:normal;filter:alpha(opacity=0);opacity:0;line-break:auto}.tooltip.in{filter:alpha(opacity=90);opacity:.9}.tooltip.top{padding:5px 0;margin-top:-3px}.tooltip.right{padding:0 5px;margin-left:3px}.tooltip.bottom{padding:5px 0;margin-top:3px}.tooltip.left{padding:0 5px;margin-left:-3px}.tooltip-inner{max-width:200px;padding:3px 8px;color:#fff;text-align:center;background-color:#000;border-radius:4px}.tooltip-arrow{position:absolute;width:0;height:0;border-color:transparent;border-style:solid}.tooltip.top .tooltip-arrow{bottom:0;left:50%;margin-left:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.top-left .tooltip-arrow{right:5px;bottom:0;margin-bottom:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.top-right .tooltip-arrow{bottom:0;left:5px;margin-bottom:-5px;border-width:5px 5px 0;border-top-color:#000}.tooltip.right .tooltip-arrow{top:50%;left:0;margin-top:-5px;border-width:5px 5px 5px 0;border-right-color:#000}.tooltip.left .tooltip-arrow{top:50%;right:0;margin-top:-5px;border-width:5px 0 5px 5px;border-left-color:#000}.tooltip.bottom .tooltip-arrow{top:0;left:50%;margin-left:-5px;border-width:0 5px 5px;border-bottom-color:#000}.tooltip.bottom-left .tooltip-arrow{top:0;right:5px;margin-top:-5px;border-width:0 5px 5px;border-bottom-color:#000}.tooltip.bottom-right .tooltip-arrow{top:0;left:5px;margin-top:-5px;border-width:0 5px 5px;border-bottom-color:#000}.popover{position:absolute;top:0;left:0;z-index:1060;display:none;max-width:276px;padding:1px;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;font-style:normal;font-weight:400;line-height:1.42857143;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;word-spacing:normal;word-wrap:normal;white-space:normal;background-color:#fff;-webkit-background-clip:padding-box;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,.2);border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,.2);box-shadow:0 5px 10px rgba(0,0,0,.2);line-break:auto}.popover.top{margin-top:-10px}.popover.right{margin-left:10px}.popover.bottom{margin-top:10px}.popover.left{margin-left:-10px}.popover-title{padding:8px 14px;margin:0;font-size:14px;background-color:#f7f7f7;border-bottom:1px solid #ebebeb;border-radius:5px 5px 0 0}.popover-content{padding:9px 14px}.popover>.arrow,.popover>.arrow:after{position:absolute;display:block;width:0;height:0;border-color:transparent;border-style:solid}.popover>.arrow{border-width:11px}.popover>.arrow:after{content:"";border-width:10px}.popover.top>.arrow{bottom:-11px;left:50%;margin-left:-11px;border-top-color:#999;border-top-color:rgba(0,0,0,.25);border-bottom-width:0}.popover.top>.arrow:after{bottom:1px;margin-left:-10px;content:" ";border-top-color:#fff;border-bottom-width:0}.popover.right>.arrow{top:50%;left:-11px;margin-top:-11px;border-right-color:#999;border-right-color:rgba(0,0,0,.25);border-left-width:0}.popover.right>.arrow:after{bottom:-10px;left:1px;content:" ";border-right-color:#fff;border-left-width:0}.popover.bottom>.arrow{top:-11px;left:50%;margin-left:-11px;border-top-width:0;border-bottom-color:#999;border-bottom-color:rgba(0,0,0,.25)}.popover.bottom>.arrow:after{top:1px;margin-left:-10px;content:" ";border-top-width:0;border-bottom-color:#fff}.popover.left>.arrow{top:50%;right:-11px;margin-top:-11px;border-right-width:0;border-left-color:#999;border-left-color:rgba(0,0,0,.25)}.popover.left>.arrow:after{right:1px;bottom:-10px;content:" ";border-right-width:0;border-left-color:#fff}.carousel{position:relative}.carousel-inner{position:relative;width:100%;overflow:hidden}.carousel-inner>.item{position:relative;display:none;-webkit-transition:.6s ease-in-out left;-o-transition:.6s ease-in-out left;transition:.6s ease-in-out left}.carousel-inner>.item>a>img,.carousel-inner>.item>img{line-height:1}@media all and (transform-3d),(-webkit-transform-3d){.carousel-inner>.item{-webkit-transition:-webkit-transform .6s ease-in-out;-o-transition:-o-transform .6s ease-in-out;transition:transform .6s ease-in-out;-webkit-backface-visibility:hidden;backface-visibility:hidden;-webkit-perspective:1000px;perspective:1000px}.carousel-inner>.item.active.right,.carousel-inner>.item.next{left:0;-webkit-transform:translate3d(100%,0,0);transform:translate3d(100%,0,0)}.carousel-inner>.item.active.left,.carousel-inner>.item.prev{left:0;-webkit-transform:translate3d(-100%,0,0);transform:translate3d(-100%,0,0)}.carousel-inner>.item.active,.carousel-inner>.item.next.left,.carousel-inner>.item.prev.right{left:0;-webkit-transform:translate3d(0,0,0);transform:translate3d(0,0,0)}}.carousel-inner>.active,.carousel-inner>.next,.carousel-inner>.prev{display:block}.carousel-inner>.active{left:0}.carousel-inner>.next,.carousel-inner>.prev{position:absolute;top:0;width:100%}.carousel-inner>.next{left:100%}.carousel-inner>.prev{left:-100%}.carousel-inner>.next.left,.carousel-inner>.prev.right{left:0}.carousel-inner>.active.left{left:-100%}.carousel-inner>.active.right{left:100%}.carousel-control{position:absolute;top:0;bottom:0;left:0;width:15%;font-size:20px;color:#fff;text-align:center;text-shadow:0 1px 2px rgba(0,0,0,.6);filter:alpha(opacity=50);opacity:.5}.carousel-control.left{background-image:-webkit-linear-gradient(left,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);background-image:-o-linear-gradient(left,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);background-image:-webkit-gradient(linear,left top,right top,from(rgba(0,0,0,.5)),to(rgba(0,0,0,.0001)));background-image:linear-gradient(to right,rgba(0,0,0,.5) 0,rgba(0,0,0,.0001) 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);background-repeat:repeat-x}.carousel-control.right{right:0;left:auto;background-image:-webkit-linear-gradient(left,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);background-image:-o-linear-gradient(left,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);background-image:-webkit-gradient(linear,left top,right top,from(rgba(0,0,0,.0001)),to(rgba(0,0,0,.5)));background-image:linear-gradient(to right,rgba(0,0,0,.0001) 0,rgba(0,0,0,.5) 100%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);background-repeat:repeat-x}.carousel-control:focus,.carousel-control:hover{color:#fff;text-decoration:none;filter:alpha(opacity=90);outline:0;opacity:.9}.carousel-control .glyphicon-chevron-left,.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next,.carousel-control .icon-prev{position:absolute;top:50%;z-index:5;display:inline-block;margin-top:-10px}.carousel-control .glyphicon-chevron-left,.carousel-control .icon-prev{left:50%;margin-left:-10px}.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next{right:50%;margin-right:-10px}.carousel-control .icon-next,.carousel-control .icon-prev{width:20px;height:20px;font-family:serif;line-height:1}.carousel-control .icon-prev:before{content:'\2039'}.carousel-control .icon-next:before{content:'\203a'}.carousel-indicators{position:absolute;bottom:10px;left:50%;z-index:15;width:60%;padding-left:0;margin-left:-30%;text-align:center;list-style:none}.carousel-indicators li{display:inline-block;width:10px;height:10px;margin:1px;text-indent:-999px;cursor:pointer;background-color:#000\9;background-color:rgba(0,0,0,0);border:1px solid #fff;border-radius:10px}.carousel-indicators .active{width:12px;height:12px;margin:0;background-color:#fff}.carousel-caption{position:absolute;right:15%;bottom:20px;left:15%;z-index:10;padding-top:20px;padding-bottom:20px;color:#fff;text-align:center;text-shadow:0 1px 2px rgba(0,0,0,.6)}.carousel-caption .btn{text-shadow:none}@media screen and (min-width:768px){.carousel-control .glyphicon-chevron-left,.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next,.carousel-control .icon-prev{width:30px;height:30px;margin-top:-15px;font-size:30px}.carousel-control .glyphicon-chevron-left,.carousel-control .icon-prev{margin-left:-15px}.carousel-control .glyphicon-chevron-right,.carousel-control .icon-next{margin-right:-15px}.carousel-caption{right:20%;left:20%;padding-bottom:30px}.carousel-indicators{bottom:20px}}.btn-group-vertical>.btn-group:after,.btn-group-vertical>.btn-group:before,.btn-toolbar:after,.btn-toolbar:before,.clearfix:after,.clearfix:before,.container-fluid:after,.container-fluid:before,.container:after,.container:before,.dl-horizontal dd:after,.dl-horizontal dd:before,.form-horizontal .form-group:after,.form-horizontal .form-group:before,.modal-footer:after,.modal-footer:before,.nav:after,.nav:before,.navbar-collapse:after,.navbar-collapse:before,.navbar-header:after,.navbar-header:before,.navbar:after,.navbar:before,.pager:after,.pager:before,.panel-body:after,.panel-body:before,.row:after,.row:before{display:table;content:" "}.btn-group-vertical>.btn-group:after,.btn-toolbar:after,.clearfix:after,.container-fluid:after,.container:after,.dl-horizontal dd:after,.form-horizontal .form-group:after,.modal-footer:after,.nav:after,.navbar-collapse:after,.navbar-header:after,.navbar:after,.pager:after,.panel-body:after,.row:after{clear:both}.center-block{display:block;margin-right:auto;margin-left:auto}.pull-right{float:right!important}.pull-left{float:left!important}.hide{display:none!important}.show{display:block!important}.invisible{visibility:hidden}.text-hide{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0}.hidden{display:none!important}.affix{position:fixed}@-ms-viewport{width:device-width}.visible-lg,.visible-md,.visible-sm,.visible-xs{display:none!important}.visible-lg-block,.visible-lg-inline,.visible-lg-inline-block,.visible-md-block,.visible-md-inline,.visible-md-inline-block,.visible-sm-block,.visible-sm-inline,.visible-sm-inline-block,.visible-xs-block,.visible-xs-inline,.visible-xs-inline-block{display:none!important}@media (max-width:767px){.visible-xs{display:block!important}table.visible-xs{display:table!important}tr.visible-xs{display:table-row!important}td.visible-xs,th.visible-xs{display:table-cell!important}}@media (max-width:767px){.visible-xs-block{display:block!important}}@media (max-width:767px){.visible-xs-inline{display:inline!important}}@media (max-width:767px){.visible-xs-inline-block{display:inline-block!important}}@media (min-width:768px) and (max-width:991px){.visible-sm{display:block!important}table.visible-sm{display:table!important}tr.visible-sm{display:table-row!important}td.visible-sm,th.visible-sm{display:table-cell!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-block{display:block!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-inline{display:inline!important}}@media (min-width:768px) and (max-width:991px){.visible-sm-inline-block{display:inline-block!important}}@media (min-width:992px) and (max-width:1199px){.visible-md{display:block!important}table.visible-md{display:table!important}tr.visible-md{display:table-row!important}td.visible-md,th.visible-md{display:table-cell!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-block{display:block!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-inline{display:inline!important}}@media (min-width:992px) and (max-width:1199px){.visible-md-inline-block{display:inline-block!important}}@media (min-width:1200px){.visible-lg{display:block!important}table.visible-lg{display:table!important}tr.visible-lg{display:table-row!important}td.visible-lg,th.visible-lg{display:table-cell!important}}@media (min-width:1200px){.visible-lg-block{display:block!important}}@media (min-width:1200px){.visible-lg-inline{display:inline!important}}@media (min-width:1200px){.visible-lg-inline-block{display:inline-block!important}}@media (max-width:767px){.hidden-xs{display:none!important}}@media (min-width:768px) and (max-width:991px){.hidden-sm{display:none!important}}@media (min-width:992px) and (max-width:1199px){.hidden-md{display:none!important}}@media (min-width:1200px){.hidden-lg{display:none!important}}.visible-print{display:none!important}@media print{.visible-print{display:block!important}table.visible-print{display:table!important}tr.visible-print{display:table-row!important}td.visible-print,th.visible-print{display:table-cell!important}}.visible-print-block{display:none!important}@media print{.visible-print-block{display:block!important}}.visible-print-inline{display:none!important}@media print{.visible-print-inline{display:inline!important}}.visible-print-inline-block{display:none!important}@media print{.visible-print-inline-block{display:inline-block!important}}@media print{.hidden-print{display:none!important}} </style> <script>/*! * Bootstrap v3.3.5 (http://getbootstrap.com) @@ -299,8 +299,8 @@ pre code { border-radius: 4px; } -.tabset-dropdown > .nav-tabs > li.active:before { - content: ""; +.tabset-dropdown > .nav-tabs > li.active:before, .tabset-dropdown > .nav-tabs.nav-tabs-open:before { + content: "\e259"; font-family: 'Glyphicons Halflings'; display: inline-block; padding: 10px; @@ -308,16 +308,9 @@ pre code { } .tabset-dropdown > .nav-tabs.nav-tabs-open > li.active:before { - content: ""; - border: none; -} - -.tabset-dropdown > .nav-tabs.nav-tabs-open:before { - content: ""; + content: "\e258"; font-family: 'Glyphicons Halflings'; - display: inline-block; - padding: 10px; - border-right: 1px solid #ddd; + border: none; } .tabset-dropdown > .nav-tabs > li.active { @@ -364,12 +357,13 @@ pre code { <h1 class="title toc-ignore">Short demo of the multistart method</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">Last change 26 September 2022 (rebuilt 2022-10-26)</h4> +<h4 class="date">Last change 20 April 2023 (rebuilt 2023-04-20)</h4> </div> -<p>The dimethenamid data from 2018 from seven soils is used as example data in this vignette.</p> +<p>The dimethenamid data from 2018 from seven soils is used as example +data in this vignette.</p> <pre class="r"><code>library(mkin) dmta_ds <- lapply(1:7, function(i) { ds_i <- dimethenamid_2018$ds[[i]]$data @@ -380,37 +374,47 @@ dmta_ds <- lapply(1:7, function(i) { names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title) dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]]) dmta_ds[["Elliot 1"]] <- dmta_ds[["Elliot 2"]] <- NULL</code></pre> -<p>First, we check the DFOP model with the two-component error model and random effects for all degradation parameters.</p> +<p>First, we check the DFOP model with the two-component error model and +random effects for all degradation parameters.</p> <pre class="r"><code>f_mmkin <- mmkin("DFOP", dmta_ds, error_model = "tc", cores = 7, quiet = TRUE) f_saem_full <- saem(f_mmkin) illparms(f_saem_full)</code></pre> <pre><code>## [1] "sd(log_k2)"</code></pre> -<p>We see that not all variability parameters are identifiable. The <code>illparms</code> function tells us that the confidence interval for the standard deviation of ‘log_k2’ includes zero. We check this assessment using multiple runs with different starting values.</p> +<p>We see that not all variability parameters are identifiable. The +<code>illparms</code> function tells us that the confidence interval for +the standard deviation of ‘log_k2’ includes zero. We check this +assessment using multiple runs with different starting values.</p> <pre class="r"><code>f_saem_full_multi <- multistart(f_saem_full, n = 16, cores = 16) -parhist(f_saem_full_multi)</code></pre> -<p><img src="data:image/png;base64,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" width="672" /></p> -<p>This confirms that the variance of k2 is the most problematic parameter, so we reduce the parameter distribution model by removing the intersoil variability for k2.</p> +parplot(f_saem_full_multi, lpos = "topleft")</code></pre> +<p><img src="data:image/png;base64,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" width="672" /></p> +<p>This confirms that the variance of k2 is the most problematic +parameter, so we reduce the parameter distribution model by removing the +intersoil variability for k2.</p> <pre class="r"><code>f_saem_reduced <- update(f_saem_full, no_random_effect = "log_k2") -illparms(f_saem_reduced)</code></pre> -<pre><code>## character(0)</code></pre> -<pre class="r"><code>f_saem_reduced_multi <- multistart(f_saem_reduced, n = 16, cores = 16) -parhist(f_saem_reduced_multi, lpos = "topright")</code></pre> -<p><img src="data:image/png;base64,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" width="672" /></p> -<p>The results confirm that all remaining parameters can be determined with sufficient certainty.</p> -<p>We can also analyse the log-likelihoods obtained in the multiple runs:</p> +illparms(f_saem_reduced) +f_saem_reduced_multi <- multistart(f_saem_reduced, n = 16, cores = 16) +parplot(f_saem_reduced_multi, lpos = "topright", ylim = c(0.5, 2))</code></pre> +<p><img src="data:image/png;base64,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" width="672" /></p> +<p>The results confirm that all remaining parameters can be determined +with sufficient certainty.</p> +<p>We can also analyse the log-likelihoods obtained in the multiple +runs:</p> <pre class="r"><code>llhist(f_saem_reduced_multi)</code></pre> -<p><img src="data:image/png;base64,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" width="672" /></p> -<p>The parameter histograms can be further improved by excluding the result with the low likelihood.</p> -<pre class="r"><code>parhist(f_saem_reduced_multi, lpos = "topright", llmin = -326, ylim = c(0.5, 2))</code></pre> -<p><img src="data:image/png;base64,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" width="672" /></p> -<p>We can use the <code>anova</code> method to compare the models, including a likelihood ratio test if the models are nested.</p> -<pre class="r"><code>anova(f_saem_full, best(f_saem_reduced_multi), test = TRUE)</code></pre> +<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUAAAAPACAIAAAB7BESOAAAACXBIWXMAAB2HAAAdhwGP5fFlAAAgAElEQVR4nOzdf5zNdd74/9fxa5gxqKH8mhoy+bGxJIWlbKUtKrt11Ra7S6Uou/2wrO0HSl3WkqQtrN2ytW3ltm1XKnFltatoJb9HbCTEFOvnIDEy8/3jfG5z+SLGmWG8Z+73v97zPq9zXk/9cd2ux77f531i+fn5AQAAADi1lSvpAQAAAIBjE/AAAAAQAQIeAAAAIkDAAwAAQAQIeAAAAIgAAQ8AAAARIOABAAAgAgQ8AAAARICABwAAgAgQ8AAAABABAh4AAAAiQMADAABABAh4AAAAiAABDwAAABEg4AEAACACBDwAAABEgIAHAACACBDwAAAAEAECHgAAACJAwAMAAEAECHgAAACIAAEPAAAAESDgAQAAIAIEPAAAAESAgAcAAIAIEPAAAAAQAQIeAAAAIkDAAwAAQAQIeAAAAIgAAQ8AAAARIOABAAAgAgQ8AAAARICABwAAgAgQ8AAAABABAh4AAAAiQMADAABABAh4AAAAiAABDwAAABEg4AEAACACBDwAAABEgIAHAACACBDwAAAAEAECHgAAACJAwAMAAEAECHgAAACIAAEPAAAAESDgAQAAIAIEPAAAAESAgAcAAIAIEPAAAAAQAQIeAAAAIkDAAwAAQAQIeAAAAIgAAQ8AAAARIOABAAAgAgQ8AAAARICABwAAgAgQ8AAAABABAh4AAAAiQMADAABABAh4AAAAiAABDwAAABEg4AEAACACBDwAAABEgIAHAACACBDwAAAAEAECHgAAACJAwAMAAEAECHgAAACIAAEPAAAAESDgAQAAIAIEPAAAAESAgAcAAIAIEPAAAAAQAQIeAAAAIkDAAwAAQAQIeAAAAIgAAQ8AAAARIOABAAAgAgQ8AAAARICABwAAgAgQ8AAAABABAh4AAAAiQMADAABABAh4AAAAiAABDwAAABEg4AEAACACBDwAAABEgIAHAACACBDwAAAAEAECHgAAACJAwAMAAEAECHgAAACIAAEPAAAAESDgAQAAIAIEPAAAAESAgAcAAIAIEPAAAAAQAQIeAAAAIkDAAwAAQAQIeAAAAIgAAQ8AAAARIOABAAAgAgQ8AAAARICABwAAgAgQ8AAAABABAh4AAAAiQMADAABABAh4AAAAiAABDwAAABEg4AEAACACBDwAAABEgIAHAACACBDwAAAAEAECHgAAACJAwAMAAEAECHgAAACIAAEPAAAAESDgAQAAIAIEPAAAAESAgAcAAIAIEPAAAAAQAQIeAAAAIkDAAwAAQAQIeAAAAIgAAQ8AAAARIOABAAAgAgQ8AAAARICABwAAgAgQ8AAAABABAh4AAAAiQMADAABABAh4AAAAiAABDwAAABEg4AEAACACBDwAAABEgIAHAACACBDwAAAAEAECHgAAACJAwAMAAEAECHgAAACIAAEPAAAAESDgAQAAIAIEPAAAAESAgAcAAIAIEPAAAAAQAQIeAAAAIkDAAwAAQAQIeAAAAIgAAQ8AAAARIOABAAAgAgQ8AAAARICABwAAgAgQ8AAAABABAh4AAAAiQMADAABABAh4AAA4mt0z/7X9xSn7v9xc0oMAZZ2ABwCAo/nqgwU5r8/45j9bS3oQoKwT8AAAABABAh4AAAAiQMADAABABAh4AAAAiAABDwAAABEg4AEAACACBDwAAABEgIAHAACACBDwAAAAEAECvkhmz559yy23tG3btnr16mefffZVV101ePDgbdu2lfRcAAAAlDYVSnqAqNq+fXu/fv1efvnlgjM7d+78/PPPp0+fPn78+JEjR956660lOB4AAACljIBPUM+ePd98880QQoUKFa644oomTZrk5OT861//Wr58+datW3v37l27du0uXbqU9JgAAACUEgI+ERMmTIjX+znnnPPmm282bdo0fj4vL2/s2LH9+/fPz8/v1avXqlWrqlevXqKTAgAAUEr4DnwiJk6cGD947rnnCuo9hFCuXLn77rvv+uuvDyFs3rz5X//6V8nMBwAAQKkj4BOxfPnyEEJKSkqHDh0Of7Xgzvn58+ef1LEAAAAovQT8ccvPz8/LywshVKxYsVy5I/wHTElJiR9s3br1pE4GAABA6SXgj1ssFsvMzAwh7NixY9myZYcveO+99+IH55577kmdDAAAgNJLwCfinnvuiR/cdttt27dvP/il6dOn//73vw8hpKWl3XzzzSUwHAAAAKWRp9An4o477lixYsWTTz45b968zMzMn/zkJ40bN965c+ecOXPiT6c/7bTTpk6dWqNGjZKeFAAAgFIilp+fX9IzRNWzzz7bu3fvw883bdp0xowZ9erVK+Ln5+fn79ixI7H37t+//4wzzijiAADAibN3796vv/66pKegUL4a86dvlq9Oua9XhWbnlPQsx6dcuXJ+1RhKEwGfoOnTp/ft23fdunVHfLVHjx5jx45NS0sryhZ33nnnhAkTEn77o48++tBDDxVlAADgBNm0aVPjxo1zcnJKehAK5c8dr+1wRvpP339j9n/Wl/Qsx23s2LF33313SU8BFA+30Cdi8uTJPXr0OHDgQOXKlQcMGNCtW7dzzz03JycnKyvrN7/5zezZs//yl79kZWW9++67RWn4hg0bpqWlxZ94f1y+/vrrvXv3btu2LeGtAYATasOGDTk5OeXKlatatWpJz8KxVahQIYSQnJxcrVq1kp7lOOTm5u7du/fjjz8u6UGAYuMK/HFbv359o0aNcnNzq1Sp8uGHHzZv3vyQBffee+/YsWNDCDfeeOPkyZNP/oT9+/cfM2bME088cd9995383QGAY1qwYMEFF1zQrFmzEvl/FTheVf78VvnVG77+6dUHzqlf0rMch1dfffWRRx6544474o9YBkoBT6E/bk899VRubm4IYciQIYfXewhh5MiRDRs2DCG8+uqra9euPcnjAQAAUCoJ+OO2ePHi+MFll112xAWVKlW65JJLQgh5eXlLly49eZMBAABQegn441bwZPijPOa94CUPpwEAAKBYCPjjlp6eHj9YsmTJt60peOmss846GTMBAABQ2gn443bVVVfFDx5//PEjPiJ+4cKFM2fODCFUr169Xbt2J3U4AAAASikBf9x69uzZsmXLEML7779/yy23HPJrbe+9995//dd/7d+/P4Tw29/+tlKlSiUzJQAAAKWL34E/bpUqVXr55Zdbt269Z8+eF1544a233rr44osbN268ffv2rKysf/3rX/Fl1113XZ8+fUp2VAAAAEoNAZ+IJk2azJ49++c///kHH3ywbdu2119//eBXk5KSfvnLXz744IMlNR4AAAClj4BPUKtWrWbPnv3SSy9Nnjx5wYIFW7ZsqVy5cv369Tt16nTfffc1atSopAcEAACgVBHwiYvFYj169OjRo0dJDwIAAEDp5yF2AAAAEAECHgAAACJAwAMAAEAECHgAAACIAAEPAAAAESDgAQAAIAIEPAAAAESAgAcAAIAIEPAAAAAQAQIeAAAAIkDAAwAAQAQIeAAAAIgAAQ8AAAARIOABAAAgAgQ8AAAARICABwAAgAgQ8AAAABABAh4AAAAiQMADAABABAh4AAAAiAABDwAAABEg4AEAACACBDwAAABEgIAHjk96enosFmvTps0p9VGJad68eSwWy8zMLPxb9uzZM3To0O985zvJycnp6em7du2Kn7/qqqtisdhpp512YiYFAIBQoaQHAIiSm2666c0334wfb9iwIS8v7yiLly9f/rvf/S6E0LNnz7Zt256M+QAAKL0EPEBhLV68OF7v9erVu+22284999zk5OSjrF+/fv2ECRNCCG3bthXwAAAUkYAHjs8TTzyxZ8+etLS0U+qjTo6PP/44fjB06NDbb7/94Jd++ctf3nTTTZUqVSqJuQAAKBMEPHB8brjhhlPwo06OvXv3xg8aNmx4yEuXX375SR8HAICyxUPsgGPbs2fP1q1bS3D3gnIGAIAyS8BDKXfgwIEXX3yxW7du6enpSUlJaWlp559//qBBg9auXXvE9W3atInFYjfffHMIYc+ePYMHD87IyKhaterixYvjCzIzM7/t0fF79+594oknLrzwwurVq9eoUeOiiy4aO3Zsbm7u1q1bY7FYLBZ79tlnD15/xI/q2LFjLBa75pprQgjz58+/+OKLq1WrVqVKlZSUlJYtW95+++3fNnkIIScnZ/To0VdccUXdunWTkpJq1arVqlWrG2+8ccaMGcf1H+1wt9xySywW6927d/zPyy+/PP4vysnJiZ855Cn09913XywWu/LKK+N/9urVK75+3bp1RZwEAIAyyy30UJqtXbv2hz/84ZIlSwrObNu2bdu2bYsWLRo7duxvfvOb++6779veu2fPni5dusyaNauQe3366addu3ZduXJlwZl58+bNmzdv0qRJf/rTnxIYftq0aT/60Y/27dtXMM+SJUuWLFnywgsvvPLKKz/60Y8OWT99+vTu3btv37694MyWLVu2bNmyePHiv/71r5deeum0adN8Rx0AgOgS8FBq7dy5s2PHjhs2bAghVK1atV27dueff/6XX345d+7clStX7tu3r3///klJSXfdddcR396vX79Zs2adc845V155ZUZGRrNmzY6yV05OzqWXXrp+/foQQnp6+iWXXFK3bt358+fPnj17yZIlXbp0Od7hN2zY8JOf/GTfvn3NmjW76qqrmjZtum7dukmTJm3YsCE3N/fWW29t27ZtnTp1CtZ/+umnP/zhD+O137BhwyuuuKJ27dq7du1avnz5jBkzvvnmm3fffbd///5PP/308U4SN2zYsHvvvfe1114bNmxYCGHChAnxp8qnpqYecf3AgQN79eo1Z86cfv36xd9+7bXXhhDq1q2b2AAAACDgodQaMWJEvN5btmz52muvNWjQIH4+Pz//v//7v4cOHZqXlzdkyJAePXpUr179kPfOmjXryy+/HDBgwKOPPlq5cuVj7jV69Oh4vd94443PP/98wVs++OCDH/7wh19++eXxDh+/Y/+uu+4aM2ZMwWXzQYMGdezYcdGiRTt27Jg5c+ZPfvKTgvX//d//Ha/3e++9d/To0eXK/d/3g5YuXXrxxRfn5OS8/vrrCQd8enp6enr6/Pnz4382atTou9/97lHW161bt27duhs3boz/edZZZx19PQAAHJOAp1TJXZu9deIr+d8cKOlBCqtCrdNr9b8lVr58sX/y+vXrx4wZE0KoUaPGP/7xjxo1ahS8FIvFHnrooZycnMcff3zr1q0jRoz4zW9+c8jbv/zyy3bt2o0aNaowe23duvXJJ58MITRt2vSVV16JxWIFL7Vv337SpElXX311Av+ENm3aPP300wd/WkpKymOPPda1a9cQwuLFiw8O+PjXBKpWrfrb3/724HoPIbRo0eKaa6558cUXs7Ozt23bdvrppycwDAAAlDgBT6myP3vTvpVrSnqK4/DNF5vy9+XGkqsU+ye/9NJL8Se333vvvQfXe4EBAwY888wzX3/99XPPPXd4wIcQBg8eXMi9Xn755V27doUQ+vfvf3Bvx3Xp0qVx48affPLJ8f0DQnj44YcP/7QWLVrEDwq+Gx/3xz/+cf/+/SkpKUf8lntKSkr8YP/+/cc7BgAAnCIEPKVKyvfOr9SgXv7efcdeemqoUCut3Amo9xDC3Llz4weHP+wt7swzz2zfvv3MmTP/85//rFmzpuAG+wItW7Ys5F7//ve/4wdXXHHF4a/GYrHLL788gYC/4IILDj9ZpcqR/3Odf/75Rzz/zTff/P3vf//b3/52vLsDAMCpRsBT2lSse2ZJj3BKyM7Ojh8cXuYFCl764osvDlmWlJRUu3btQu61atWqEEK5cuW+7Qlt6enphfyoAlWrVj3jjDOO91379u374IMPsrKyVq1atWbNmnXr1q1cuTI3N/d4PwcAAE5BAh5Kp/g97SkpKd/2mPQQQr169eIHO3bsOOSlmjVrHn77+rf57LPPQginn356hQpH/j8phf/fAgoU5sl5B8vNzR01atSoUaMKfpg9rnz58u3atdu3b9/ChQuPdwYAADilCHgoneLd/tVXX+3evbtq1apHXFPwjPTDFxS+3kMI8a+db9++/cCBA+WP9EC+LVu2FP7TEpCfn3/DDTe88cYbIYSKFSteddVVF110UYsWLRo2bHjOOeckJSXdd999Ah4AgKgT8FA6FfxG+po1a5o3b37ENatXrz5kcWLOPPPM5cuXHzhw4Isvvjji3fLr1q0ryucf09SpU+P13rx587fffrt+/fondDsAACgR5Y69BIigtm3bxg/iZXu4rVu3xh90d/rpp2dmZhZlr/bt28cPZs6cecQF//znP4vy+cf07rvvxg9Gjx59xHo//DsCAAAQOQIeSqebb745KSkphDB69OidO3cevmDkyJG7d+8OIfTs2fO4bpg/3I033hg/GD16dH5+/iGvTp8+PSsrqyiff0xfffVV/CAtLe3wV7ds2TJjxowTOgAAAJwEAh5Kp4yMjLvvvjuEsH379ksvvfTzzz8veCk/P3/EiBGPP/54CCEtLW3IkCFF3KtFixbXXXddCGHZsmU9e/Y8+Knv8+bNu+WWW4r4+cdU8It3kyZNOuSlefPmde7cueCZ/AWpf5Lt3bu3RPYFAKA08R14KLUefPDBF1988csvv1ywYEHz5s07duzYqlWrjRs3fvDBB8uXL4+vGT58eI0aNYq+18SJExcuXLh27do///nPc+bM6dSpU+3atRcuXPiPf/wjNze3R48ef/nLX8K3/4p7EV1zzTUPPvjg9u3bn3766eXLl1977bWnn376Z599tmDBgrfeeis/P79x48bxH6IfOHBg7969v//97x/vU+4TU/DvHT9+fHJycm5u7g033FCtWrWTsDUAAKWPgIdSq3r16rNnz+7WrduyZct27tw5derUqVOnFrxauXLlkSNH3nHHHcWyV1pa2j/+8Y8f/OAHK1eu/Oyzz+I/LBdCKF++/O9+97vU1NR4wCcnJxfLdoeoX7/+pEmTbr755q+//vrdd98t+Ep8CKFGjRpjx45t2rTphRdeGEJ47bXXXnvttQ0bNhT8hN4J1aJFi+rVq+fk5CxZsuRnP/tZCOHyyy8X8AAAJMYt9FCaNWzYcPHixc8///w111xTr169SpUq1ahRo2XLloMGDfrkk09+8YtfFONeGRkZS5YsGTFiRIsWLapUqVKrVq2rr7561qxZ/fr1K/ht9iN+R71YdOvWbeXKlX379j3//PNTU1OrV69+/vnnP/zww59++unPfvazNm3ajBw5sk6dOhUrVszMzIw/HeAkqFGjxptvvvm9730vNTW1UqVKGRkZ8Z/cAwCABLgCD6Vc+fLlf/azn8Uv/xbGRx99dPQFq1at+raXKleuPGjQoEGDBn3bW84999xjftT7779/lN3T0tIOf05eXP369cePH/9tbxw4cODAgQMPOXm8T9e77bbbbrvttiO+NG3atCOe79ix4+zZs49rFwAAOCJX4IGiWrx4catWrVq1ajVx4sTDX/3mm2/+9re/hRDOOuusM88886RPBwAApYSAB4qqWbNmq1atWrx48bhx4w5+BH3cc88998UXX4QQfvzjH5fEdAAAUEoIeKCoKlWq1Lt37xDCkiVLrrnmmtmzZ+/YsWPHjh0ffvhh//79+/btG0JIS0s7/A52AACg8HwHHigGjz/++Mcff/z3v//9nXfeeeeddw55tVq1aq+++mqtWrVKZDYAACgdXIEHikGFChWmT5/+/PPPN2nSJBaLFZw/88wz77rrro8//rhTp04lNx0AAJQGrsADxaPgcfd79uz57LPP9uzZc8YZZ5x99tkH9zwAAJAwAQ8Us+Tk5PPOO6+kpwAAgNLGLfQAAAAQAQIeAAAAIkDAAwAAQAQIeAAAAIgAAQ8AAAARIOABAAAgAgQ8AAAARICABwAAgAgQ8AAAABABAh4AAAAiQMADAABABAh4AAAAiAABDwAAABEg4AEAACACBDwAAABEgIAHAACACBDwAAAAEAECHgAAACKgQrF/4oEDB55//vl58+addtppHTp0uPLKK8uXL1/suwAAAECZUqSAf+uttyZMmPDvf//7008/jZ/Jzc297LLLZs+eXbDmqquumjx5cmpqapHGBAAAgLIt8Vvohw0bdu21106dOnXTpk0FJ0eOHHlwvYcQpk2b9uMf/zjxAQEAAICEA37FihWPPPJIfn5+CCEtLS1+Mi8v76mnngoh1K9f/+9///ucOXM6duwYQpg2bdr8+fOLaWAAAAAoixIM+McffzwvLy+E8Ic//GHt2rXxkx988MHmzZtDCCNHjrzsssvat2//5ptvVq1aNYQwfvz44pkXAAAAyqQEA37p0qUhhEsuuaR3794FJ2fMmBFCSEpKuvbaa+Nnqlevfs0114QQPvnkk6JOCgAAAGVYggH/2WefhRAuvPDCg0/OnTs3hHDBBRekpKQUnDznnHMK1gMAAACJSTDgv/nmmxBClSpVCs7k5eV9+OGHIYT27dsfvDJ+p31OTk7iMwIAAECZl2DAx6+rF3z7PYTw0UcfxSv94osvPnjlqlWrQgjp6ekJjwgAAAAkGPDnnntuCGHKlCkFl9afe+65EELlypUvvfTSgmVbtmz53//93xDC2WefXdRJAQAAoAxLMOD79u0bQsjJybnsssteeOGFIUOG/PGPfwwhXHrppcnJyfE1a9as6dWr186dO0MInTt3LqaBAQAAoCyqkNjbOnXq1Llz5xkzZixYsKBnz57xk7FYbMiQIfHjMWPG9O/fP35cq1atO++8s+izAgAAQJmV4BX4EML//M///OhHP/q/DypX7tFHH73ooovif+bm5sYPzjjjjFdeeeXg59IDAAAAxyvBK/AhhJSUlL/97W/Lli374IMPQgjt2rVr0aJFwavVq1fv3LnzBRdccPfdd9euXbsYJgUAAIAyLPGADyHEYrHmzZs3b9788Jf69u0b/548AAAAUHSJ30IPAAAAnDQCHgAAACKgULfQH/Em+eOVlZVV9A8BAACAsqlQAb9s2bITPQcAAABwFIUK+PPOO++I5zdt2rR58+aCP8uVK1evXr19+/b95z//KTjZtWvXYrmADwAAAGVZoQL+iHe/v/fee926dQshnHbaaXfddVevXr3OPvvsihUrhhB27Ngxbdq0wYMHr169+v333//lL3/5/e9/v3jnBgAAgDIlwYfYbdq06frrr9+xY8d3vvOd1atXP/bYY40aNYrXewihRo0aN99884oVKzp16rRz584bb7zxiy++KL6ZAQAAoMxJMOCfeuqpLVu2JCcnT5069bTTTjvimooVK06ePLlGjRpbtmwZO3ZsEYYEAACAsi7BgH/jjTdCCO3atTv77LOPsuyMM8743ve+F0KYMmVKYhsBAAAAIeGAX7t2bQihRYsWx1zZrFmzEML69esT2wgAAAAICQd83MqVK4+5ZsWKFSGESpUqFWUjAAAAKOMSDPiMjIwQwgcffJCTk3OUZTk5OR988EHBegAAACAxCQZ8165dQwjbt2/v0aNHfn7+Edfk5+f/9Kc/3bZtW8F6AAAAIDEJBvzdd99dtWrVEMLUqVM7duz43nvvHbJg9uzZl1xyyZtvvhlCSE1N7devXxEHBQAAgLKsQmJvq1u37sSJE3/yk5/k5eXNmTPnkksuqVevXmZmZr169b744otVq1Zt2LAhvrJcuXITJ06sU6dO8c0MAAAAZU6CAR9CuPnmm0877bT+/fvHH1OXnZ2dnZ19yJrzzjtv1KhRV155ZZFmBAAAgDKvSE+hv/LKK7OysiZMmHDhhRdWqPD//reA8uXLN2jQoHPnzi+88MKSJUvUOwAAABRd4lfg48qXL9+nT58+ffrs27dv8+bN+/btS09P96NxAAAAULyKGvAFkpKS6tevX1yfBgAAABys2AJ+zZo12dnZOTk5qamptWvXzszMjMVixfXhAAAAUMYVNeBzcnJGjBjx7LPPbt68+eDzaWlpPXv2HDx4cI0aNYq4BQAAAFCkh9g988wzDRs2HDFixCH1HkLYunXrE0880ahRo4kTJxZlCwAAACAU5Qr86NGjBwwYED9OTk5u165dgwYN6tWrt3HjxtWrV8+dO3f37t1bt27t06dPbm7uz3/+82IaGAAAAMqiBAN+zZo1DzzwQAihXLly/fv3Hzhw4BlnnHHwgi1btowcOXL06NF5eXkDBw7s1q1benp6McwLAAAAZVKCt9CPGzcuNzc3hDBs2LBRo0YdUu8hhJo1a44cOfLRRx8NIezdu3f8+PFFHBQAAADKsgQDfsaMGSGExo0bx6/Df5v777+/adOmIYTp06cnthEAAAAQEg74devWhRDatWt39N+Ki8Vi7dq1K1gPAAAAJCbBgN+zZ08IoXbt2sdcWadOnRDCV199ldhGAAAAQEg44GvWrBlCWLRo0TFXLly4MIRQq1atxDYCAAAAQsIBf/7554cQ5syZs3bt2qMsW7du3ezZs0MIrVu3TmwjAAAAICQc8DfccEMIYffu3dddd11OTs4R1+zcufP666/ftWtXCOH6669PeMRTWV5e3v/8z/9069atQYMGycnJLVq06N69+9y5c0t6LgAAAEqbBAO+R48eLVq0CCEsWrQoIyNj2LBhS5cujbf67t27s7KyHnvssYyMjAULFoQQmjdv3r1792Ic+hSxYcOGSy+99LrrrnvjjTfWrl379ddfZ2Vlvfzyy+3bt+/bt29eXl5JDwgAAEDpUSGxt5UvX37KlCkdOnTIzs7esWPH0KFDhw4dGkJISUk55Hl1devWnTJlSvny5Yth2FPJli1bOnXqtHr16hBC3bp1u3btWq1atWXLlr3zzjv5+fm///3v69atO2TIkJIeEwAAgFIiwSvwIYSMjIzly5fff//9lStXLjh5cL0nJSX96le/Wr58eYMGDYo04ympV69e8Xrv03nIXXoAACAASURBVKfP2rVrJ06c+Pjjj0+fPn327NlVq1YNITzyyCNLly4t6TEBAAAoJRK8Ah9XrVq14cOH33nnnS+99NLcuXOzs7N37dqVmppap06dtm3bdu/e/eyzzy6uQU8p//jHP6ZOnRpC6Ny58/jx42OxWMFL7du3f/zxx+O30L/66qvxLxoAAABAERUp4OPS09MHDRpU9M+JkKeffjp+MGLEiIPrPa5Hjx4TJkz45ptvVq5cedJHAwAAoHQqhoAva77++uvp06eHEJo2bRr/Ob1DVK1addGiRSd9LgAAAEqzYgv4NWvWZGdn5+TkpKam1q5dOzMz8/BL06XDokWL9uzZE0K4+OKLS3oWAAAAyoqiBnxOTs6IESOeffbZzZs3H3w+LS2tZ8+egwcPrlGjRhG3ONX8+9//jh/Ur18/frBgwYLFixdv27bt9NNPv/DCC5s3b15y0wEAAFA6FSngn3nmmSFDhmzbtu3wl7Zu3frEE088//zzw4cPv+OOO4qyy6lmzZo18YPatWvPmTOnT58+H3/88cEL2rZtO2nSpCZNmpTEdAAAAJROiQf86NGjBwwYED9OTk5u165dgwYN6tWrt3HjxtWrV8+dO3f37t1bt27t06dPbm7uz3/+82IauOTt3r07frBo0aK77rpr//79hyyYO3duq1atJkyY0LNnz6JsNGPGjGeffTYvL+9437hkyZIQws6dO4uyOwAAAKeUBAN+zZo1DzzwQAihXLly/fv3Hzhw4BlnnHHwgi1btowcOXL06NF5eXkDBw7s1q1benp6Mcx7Coh/AT6EMG7cuBBC27ZtH3rooZYtW1auXDkrK2vUqFFvv/323r1777rrro4dOzZs2DDhjcaNG/f6668n/HbPwAcAAChNEgz4cePG5ebmhhCGDRv24IMPHr6gZs2aI0eOrFGjxoMPPrh3797x48cPHz68SJOeMg4cOFBwfPvtt0+YMKFcuXLxPzt16tSpU6d777137Nixe/bs6du37zvvvJPwRuPGjevevXsCb3z++eenTp16xCfkAwAAEFEJBvyMGTNCCI0bN45fh/82999//4svvrhixYrp06eXmoBPTk6OH6Snp48ZM6ag3gs89thjkydP3rhx44wZMzZs2FDwrLvjVadOnRtuuCGBN/7rX/+aOnXq4YMBAAAQXQk23rp160II7dq1O/pvxcVisXbt2hWsLx2qVq0aP7jiiitSUlKOuKBjx47x4wULFpy8yQAAACi9Egz4+PfAa9eufcyVderUCSF89dVXiW10CmrQoEH84CjPmc/MzIwfHPER/QAAAHC8Egz4mjVrhhAWLVp0zJULFy4MIdSqVSuxjU5BTZs2jR9kZ2d/25rNmzfHD0477bSTMRMAAAClXYIBH39A2pw5c9auXXuUZevWrZs9e3YIoXXr1oltdAq68MILa9SoEUKYO3fut62ZP39+/OC88847SWMBAABQqiUY8PGHq+3evfu6667Lyck54pqdO3def/31u3btCiFcf/31CY94qqlUqVL8nzN37ty//vWvhy/461//Gr83oVWrVo0aNTrZ8wEAAFAaJRjwPXr0aNGiRQhh0aJFGRkZw4YNW7p0abzVd+/enZWV9dhjj2VkZMQf4da8efPEfg7tlDVkyJDKlSuHEO64445DGv7111/v27dvCKFcuXLjx48vmfkAAAAodRL8Gbny5ctPmTKlQ4cO2dnZO3bsGDp06NChQ0MIKSkphzyvrm7dulOmTClfvnwxDHvKOOuss5544om77rprx44dN954Y+vWrS+66KJYLPbRRx/NmzcvvqZ///4XXXRRyc4JAABAqZFgwIcQMjIyli9fPmLEiDFjxuzduzd+8uB6T0pKuueeex544IHq1asXdcxTz5133rlz587Bgwfv379/wYIFB/9cXNWqVUeOHBm/Dg8AAADFIvGADyFUq1Zt+PDhd95550svvTR37tzs7Oxdu3alpqbWqVOnbdu23bt3P/vss4tr0FPQoEGDunbtOnHixHfeeSf+RPr09PTLL7+8f//+GRkZJT0dAAAApUqRAj4uPT190KBBRf+cKDrvvPOeeuqpkp4CAACA0i/Bh9gBAAAAJ5OABwAAgAgo0i30GzdufO+995YuXbpv375jLh41alRR9gIAAICyLPGAf+2112677bYdO3YUcr2ABwAAgIQlGPCrV6++6aab9u/fX7zTAAAAAEeUYMA/9thj8Xpv2bLlsGHDmjVrVrly5WIdDAAAAPg/CQb8ggULQgj169efM2dOcnJysY4EAAAAHCrBp9CvXr06hHDLLbeodwAAADgJEgz4pKSkEEJGRkZxzgIAAAB8iwQDPjMzM4TwySefFOswAAAAwJElGPA333xzCGHatGkeRA8AAAAnQYIB369fv/PPPz8rK+u+++4r3oEAAACAwyUY8BUrVnz77bdbtWr1zDPPtGnT5q233tq8eXPxTgYAAAAUKNTPyKWnpx/x/N69e0MI8+fPv+aaa0IIqamp1atX/7YPWb9+fUITAgAAAIUL+A0bNhRm2a5du3bt2lW0eQAAAIAjKFTAd+jQ4UTPAQAAABxFoQL+/fffP9FzAAAAAEeR4EPsAAAAgJNJwAMAAEAECHgAAACIgEJ9B75Nmzbxg6FDh1599dUHnym8jz766HjfAgAAAMQVKuDnz58fP9i6deshZwAAAICToFABX79+/fhBcnJy/KBRo0YnaiIAAADgMIUK+PXr1x9yZtWqVSdgGAAAAODIPMQOAAAAIkDAAwAAQAQIeAAAAIiAQn0HPjMzs+g7+do8AAAAJKxQAf/pp5+e6DkAAACAoyhUwJ933nkneg4AAADgKAoV8FlZWSd6DgAAAOAoPMQOAAAAIkDAAwAAQAQU6hb6Y9qyZcvChQs3b968b9++bt26paWl5efnx2KxYvlwAAAAoKgBP3Xq1CFDhixatCg/Pz9+pnXr1mlpab/61a82bNjQu3fvyy67rMhDAgAAQFmX+C30+fn5d9xxx9VXX71w4cKCei+wd+/eV1555fLLL3/wwQcPfxUAAAA4LokH/NChQ//whz+EEGKx2MUXX3zPPfcc/GqDBg3it9APHz7817/+dRGnBAAAgDIuwYBfs2bN8OHDQwj16tWbOXPmrFmznnzyyYMX9O/ff968eXXq1AkhPPnkk+vWrSv6rAAAAFBmJRjwv/vd7w4cOBBCeOGFF77//e8fcc0FF1wwbdq0WCyWm5v7xBNPJD4jAAAAlHkJBvzs2bNDCB07drz00kuPsuy73/3uVVddFUJYvHhxYhsBAAAAIeGAX716dQihVatWx1z53e9+N4SwcuXKxDYCAAAAQsIBv3fv3hBCUlLSMVdWrFgxhJCTk5PYRgAAAEBIOODr1q0bQli2bNkxVy5dujSEULt27cQ2AgAAAELCAd+pU6cQwsyZM1esWHGUZVlZWdOmTQshdOjQIbGNAAAAgJBwwN9yyy0hhNzc3J/+9KdffPHFEdds2LChS5cu+/btCyH06NEj4REBAACABAO+ffv2P/3pT0MICxYsaN68+SOPPPLee+/FX9q0adNHH300YsSI1q1bb9iwIYTQpUuXH/zgB8U1MQAAAJRBFRJ+5x/+8IcdO3a8+eab27Zte/jhhx9++OH4+UNavW3bti+99FJRRgQAAAASvAIfQkhKSnrjjTemTJnSqFGjIy6oU6fOc889N2fOnOrVqye8CwAAABCKcgU+7tprr73yyiunT5/+/vvvr1+/PicnJzU1tV69eu3bt+/SpUtKSkqxTAkAAABlXFEDPoRQqVKla6+99tprr/22BStWrLjzzjv/+c9/Fn0vAAAAKJsSvIX+17/+dWGWff311w899FDLli1nzZqV2EYAAABASPgK/G9/+9u8vLyRI0ceZc20adP69eu3Zs2axLYAAAAACiT+ELtRo0YNGDDgiC9lZ2ffeOONXbp0idd7hQoVvm0lAAAAUBgJBnzLli1DCKNHj+7fv//B5w8cODB27NimTZv+9a9/jZ9p3779woULR40aVcRBAQAAoCxLMOBnzpzZqlWrEMKYMWPuvffe+Ml58+a1adPm3nvv3bVrVwjh9NNP/8Mf/jB79uzmzZsX17gAAABQNiX4HfjTTz995syZnTt3XrBgwdixY/fv3x9CmDBhQl5eXnxBr169Ro0aVbNmzWKbFAAAAMqwxH9G7rTTTvv73//+gx/8YN68eePGjSs436xZs/Hjx1988cXFMR4AAAAQQlEeYhdCqFGjxowZM9q2bRv/Mykpafjw4YsXL1bvAAAAULyKFPAhhGrVqr3zzjvt27cPIeTm5taqVatixYrFMRgAAADwf4oa8CGE1NTU//3f/+3QoUN+fv4dd9zxxz/+seifCQAAABysUN+Bf+SRR465pm3bth9++OH+/fvvuOOOJUuWHP74uqFDhyYyIAAAAFDIgH/44YcL/4n5+flPP/304ecFPAAAACSsGG6hBwAAAE60Ql2B//e//32i5wAAAACOolAB37hx4xM9BwAAAHAUbqEHAACACBDwAAAAEAGFuoW+TZs28YOhQ4deffXVB58pvI8++uh43wIAAADEFSrg58+fHz/YunXrIWcAAACAk6BQAV+/fv34QXJycvygUaNGJ2oiAAAA4DCFCvj169cfcmbVqlUnYBgAAADgyDzEDgAAACJAwAMAAEAECHgAAACIgEJ9Bz4zM7PoO/naPAAAACSsUAH/6aefnug5AAAAgKMoVMCfd955J3oOAAAA4CgKFfBZWVkneg4AAADgKDzEDgAAACJAwAMAAEAECHgAAACIAAEPAAAAESDgAQAAIAIEPAAAAESAgAcAAIAIEPAAAAAQAQIeAAAAIkDAAwAAQAQIeAAAAIgAAQ8AAAARUCGxtz377LOFXFmlSpXk5OS0tLRzzz33zDPPTGw7AAAAKOMSDPjevXsn8K6zzjrrxz/+8a9+9auaNWsmti8AAACUTSf1FvrPP/981KhRTZo0mTVr1sncFwAAAKIuwSvwW7ZsWb9+/VVXXbVx48YQQpMmTS677LL09PTatWtv3rz5888//+c//5mVlRVC6NGjxxVXXJGTk7Nq1apXX331yy+/3Lp1a7du3T7++ON69eoV5z8FAAAASq8EAz41NbVnz54bN26sVavWpEmTunTpEovFDlkzffr0Xr16vfzyy+3bt//FL34RQhg9evRzzz3Xt2/fnJyc4cOHP/PMM0UdHwAAAMqGBG+hHzdu3NKlS5OSkqZMmdK1a9fD6z2EcOWVV06ZMqVixYp33333nDlzQggVK1bs06fPPffcE0J47bXXijI3AAAAlCkJBvxf/vKXEEKHDh3atWt3lGUXXXTRJZdccuDAgd/+9rcFJ2+66aYQwsaNG9etW5fY7gAAAFDWJBjwK1euDCG0bt36mCsvuOCCEMKHH35YcKZJkybxg//85z+J7Q4AAABlTYIBn5+fH0L48ssvj7kyvmb37t2HvDeEkJycnNjuAAAAUNYkGPDnnntuCGHWrFn79u07yrLc3Nz4L8bF18ctW7YshBCLxRo2bJjY7gAAAFDWJBjw8e+xf/755z179iy4on6I/Pz822677bPPPgsh3HzzzfGTubm5jz76aAihSZMmVapUSWx3AAAAKGsSDPh+/fplZmaGECZPnvyd73znj3/84+eff/7NN9+EEL755pv169f/6U9/atGixYsvvhhCaNy48d133x1CmDhxYps2bWbMmBFCeOCBB4rtHwEAAAClXYK/A1+lSpWpU6dedtll69evX7Fixe233x5CKFeu3Omnn759+/YDBw4UrMzIyJg+fXrlypVDCOPHj1+6dGkIoXPnzt27dy+O+QEAAKBMSPAKfAghMzMzKytr8ODBqamp8TN5eXlbtmw5uN6vu+66BQsWZGRkFJxJSUm5995733777XLlEt8aAAAAypoEr8DHVa9efdiwYXfffffLL7/8ySefrFq16rPPPqtUqVLt2rXbtWt34403tmjR4uD1kyZNatasWaVKlYo2MwAAAJQ5RQr4uJo1a/7iF78ozMqWLVsWfTsAAAAog9zHDgAAABFQDFfg49asWZOdnZ2Tk5Oamlq7du3MzMxYLFZcHw4AAABlXFEDPicnZ8SIEc8+++zmzZsPPp+WltazZ8/BgwfXqFGjiFsAAAAARbqF/plnnmnYsOGIESMOqfcQwtatW5944olGjRpNnDixKFsAAAAAoShX4EePHj1gwID4cXJycrt27Ro0aFCvXr2NGzeuXr167ty5u3fv3rp1a58+fXJzc3/+858X08AAAABQFiUY8GvWrHnggQdCCOXKlevfv//AgQPPOOOMgxds2bJl5MiRo0ePzsvLGzhwYLdu3dLT04thXgAAACiTEryFfty4cbm5uSGEYcOGjRo16pB6DyHUrFlz5MiRjz76aAhh796948ePL+KgAAAAUJYlGPAzZswIITRu3Dh+Hf7b3H///U2bNg0hTJ8+PbGNAAAAgJBwwK9bty6E0K5du6P/VlwsFmvXrl3BegAAACAxCQb8nj17Qgi1a9c+5so6deqEEL766qvENgIAAABCwgFfs2bNEMKiRYuOuXLhwoUhhFq1aiW2UbQsWrQoKSkpFovNnDmzpGcBAACgVEkw4M8///wQwpw5c9auXXuUZevWrZs9e3YIoXXr1oltFCFff/11jx494s/2AwAAgOKVYMDfcMMNIYTdu3dfd911OTk5R1yzc+fO66+/fteuXSGE66+/PuERo2LAgAErVqwo6SkAAAAonRIM+B49erRo0SKEsGjRooyMjGHDhi1dujTe6rt3787KynrssccyMjIWLFgQQmjevHn37t2LcehT0Ntvvz1u3LiSngIAAIBSq0JibytfvvyUKVM6dOiQnZ29Y8eOoUOHDh06NISQkpJyyPPq6tatO2XKlPLlyxfDsKeqzZs333rrrSGE8uXLHzhwoKTHAQAAoBRK8Ap8CCEjI2P58uX3339/5cqVC04eXO9JSUm/+tWvli9f3qBBgyLNeMq79dZbN23aVK9evZ49e5b0LAAAAJROCV6Bj6tWrdrw4cPvvPPOl156ae7cudnZ2bt27UpNTa1Tp07btm27d+9+9tlnF9egp6zx48e/9dZbsVhs0qRJ77zzTkmPAwAAQOlUpICPS09PHzRoUNE/J4o++eSTAQMGhBB+8YtfdO7cWcADAABwgiR+Cz379+/v3r37nj17mjRpMmLEiJIeBwAAgNJMwCdu8ODBCxcurFix4osvvlilSpWSHgcAAIDSrFC30GdmZhZ9p1WrVhX9Q04ds2bNGjVqVAhh6NChrVu3PhFb7Nu3b9myZQm8cdOmTcU+DECZsmXLlnXr1pX0FJRmK1asKOkRAIieQgX8p59+eqLniJYdO3b87Gc/y8vLa9u27a9//esTtMutt9760ksvJfz2pUuXFuMwAGVKq1atNmzYUNJTAAD8/xQq4M8777wTPUe03HXXXZ9//nlKSsqf//znE/cT95dffvknn3ySwBs3bNiwadOmM888s9hHAigj4vXerFmzkh6EUisnJyc7O7ukpwAgYgoV8FlZWSd6jgh58cUXX3755RDCmDFjGjVqdOI2uuWWW2655ZYE3ti/f/8xY8YIeIAimjx5ckmPQKn11ltv3X///SU9BQAR4yF2x2fnzp39+vULIXTt2vX2228v6XEAAAAoK4rhd+DLlF27du3cuTOEMHXq1Fgs9m3LLr/88vhBr169Jk2adJKGAwAAoPRyBR4AAAAiwBX445OamnqUx86/++678+bNCyHcdNNNGRkZIYQT9AtzAAAAlDUC/vhUq1btN7/5zbe9OnDgwHjA9+7d+7LLLjuJcwEAAFDKuYUeAAAAIkDAAwAAQAQIeAAAAIgAAQ8AAAARIOCL06hRo/Lz8/Pz8z3BDgAAgOJVqKfQf/TRR3v27GnSpMmZZ54ZPzNr1qwQwgUXXJCSknICpwMAAABCCIW8At+1a9dOnTrNmDGj4EynTp06der06aefnrDBAAAAgP9TqIDfs2dPCGHRokUneBgAAADgyAp1C/255567aNGiCRMmfPXVVw0aNCg4/9xzz9WtW7eQOw0aNCiRAQEAAIBCBnzfvn379OmzZ8+e3//+9weff+qppwq/k4AHAACAhBXqFvrevXsPHTo0KSnpRE8DAAAAHFGhrsCXK1fu4Ycffuihh1avXr13794QQsuWLUMIkydPbty48YkdEAAAAChkwP+/pRUqHJLrjRs3/u53v1vcIwEAAACHOo6AP9iTTz4ZQqhXr16xDgMAAAAcWYIBf8899xTvHAAAAMBRJBjwh1uzZk12dnZOTk5qamrt2rUzMzNjsdj/196dB1ZR3v3DvsMaQBbZQ2RXARWoghsVrIq1okLFWldaxK1V+/Crba27Vlvqoy1qcWnF2qdWKbaPIlIEW9EqoBQEkVVRQTAgyhqEEALJ+f0x75s3L4QIk5Aw51zXX8eZ+575ot/c+MnMmamqgwMAAECGq2yAz8/Pv++++/74xz+uW7eu7PYWLVp8//vfv+OOO5o1a1bJUwAAAAD79Bq5vXn00Ue7dOly33337ZbeQwgbNmwYPXr04Ycf/sQTT1TmFAAAAECozBX43/72tz/96U+jzw0bNjz55JM7d+6cm5u7du3ajz/+eNasWVu3bt2wYcO1115bVFR0ww03VFHBAAAAkIliBvgVK1bceuutIYRatWrdeOONP/vZz1q3bl12wPr16++///7f/va3JSUlP/vZz4YMGdK+ffsqqBcAAAAyUsxb6B977LGioqIQwj333PPAAw/slt5DCC1btrz//vvvvffeEEJhYeHjjz9eyUIBAAAgk8UM8P/6179CCN26dYuuw+/NLbfc0qNHjxDC1KlT450IAAAACLED/MqVK0MIJ598csXvisvKyjr55JNLxwMAAADxxAzwBQUFIYS2bdt+5cicnJwQwrZt2+KdCAAAAAixA3zLli1DCO++++5Xjpw3b14IoVWrVvFOBAAAAITYAf64444LIcycOfOTTz6pYNjKlStnzJgRQujTp0+8EwEAAAAhdoC/8MILQwhbt24dOnRofn5+uWO2bNlywQUXfPnllyGECy64IHaJAAAAQMwAf9lll/Xq1SuE8O6773bq1Omee+5ZsGBBlNW3bt26cOHCX/7yl506dZo7d24IoWfPnpdeemkVFg0AAACZpk68abVr1544ceIpp5yyevXqzZs333XXXXfddVcIoVGjRrs9r65du3YTJ06sXbt2FRQLAAAAmSrmFfgQQqdOnZYsWXLLLbdkZ2eXbiyb3uvXr3/TTTctWbKkc+fOlaoRAAAAMl7MK/CRJk2ajBo16oc//OG4ceNmzZq1evXqL7/8snHjxjk5OSeddNKll17asWPHqioUAAAAMlmlAnykffv2P//5zyt/HAAAAGBv4t9CDwAAAFQbAR4AAAASQIAHAACABBDgAQAAIAEEeAAAAEgAAR4AAAASQIAHAACABBDgAQAAIAEEeAAAAEiAmAF+9erVVVsHAAAAUIGYAb5Dhw4DBw7885///OWXX1ZtQQAAAMCeYgb4kpKSadOmDR8+vG3btpdddtnUqVOLi4urtjIAAACgVMwA//Wvfz0rKyuEUFBQMG7cuLPPPjs3N/fGG2+cN29elZYHAAAAhBA7wM+YMWP58uW//vWve/bsGW35/PPPH3zwwT59+hx99NH33Xffp59+WnVFAgAAQKaL/xT6Tp063XzzzQsWLFi4cOEtt9zSqVOnaPuSJUtuueWWjh07nn766X/605+2bNlSNZUCAABABquC18gdc8wxo0aNWr58+cyZM2+44YbWrVuHEFKp1Ouvvz5ixIi2bdtefPHFkydP3rVrV+XPBQAAAJmpyt4Dn5WV1a9fvzFjxqxevXrq1Knf+973GjduHELYvn37c889d+655+bm5o4cOfKdd96pqjMCAABA5qiyAF+qTp06Z5111o9+9KMRI0bUqVOndPsXX3zxu9/97vjjjz/mmGPGjRtXUlJS5acGAACAdFXnq4fss5KSkrfeeuv5559/4YUXVq1aVXZXhw4datWq9cknn4QQFi9efNlll73wwgt//etf69atW4UFAAAAQLqqgivwu3btmjZt2nXXXZebm9u/f/+HHnqoNL136NDhxhtvfPvttz/55JPly5dPmzbt0ksvjUL7888/P2bMmMqfHQAAADJB/CvwRUVFr7766vPPPz9x4sQNGzaU3dWhQ4cLL7zwwgsvPPHEE8tuP/30008//fRbb721b9++hYWFTz755I033hi7AAAAAMgcMQP85ZdfPmnSpN1eEdexY8fvfOc7e+b23Rx99NGnnnrqK6+84l3xAAAAsI9iBvhnn3229HPHjh2j6+0nnHDCPk6P7qLv3bt3vLMDAABApol/C32nTp2i6+37nttL3XrrrTfccENubm7sswMAAEBGiRngZ8+effzxx8c+68knnxx7LgAAAGSgmE+h3y297/ZS94KCgnXr1sUvCgAAAPj/q9Rr5LZv337//ff37dv32GOPLbv9ww8/bNOmTc+ePX/5y1/u2rWrchUCAAAAlfgO/OrVq88444wPPvgghNC9e/fd9qZSqUWLFi1atGjSpEl///vfO3ToUKkyAQAAILPFvAJfUlIyZMiQKL3XrVu3b9++Zffm5ORccsklTZo0CSHMnj17+PDhla4TAAAAMlrMAP/ss8/OnTs3hNC/f/+VK1f+5S9/Kbu3devW48aN++ijj84888wQwuuvv/63v/2t8rUCAABAxooZ4MePHx9CaNas2YQJE3Jycsod06pVq7///e+tWrUKITz//POxSwQAAABiBvj58+eHEAYPHtyiRYsKhjVt2nTw4MEhhKVLl8Y7EQAAABBiB/j169eHEDp27PiVI9u3bx9C+OijyajAqwAAIABJREFUj+KdCAAAAAixA/yhhx4aQogeYlex999/P4TQuHHjeCcCAAAAQuwAf8wxx4QQpk+fvm3btgqGbd++fcaMGSGEHj16xDsRAAAAEGIH+AsvvDCE8Nlnnw0bNiyVSu1t2NVXX52XlxdCOP/88+OdCAAAAAixA/yIESO6desWQpgwYcJxxx33t7/9rbCwsHTvrl27/vnPf/br1+/ZZ58NIRx22GFXX311lZQLAAAAmalOvGl169Z98cUXBwwYsG7duvnz51900UVZWVlt2rTJzc3dsGHDp59+WlxcHI1s1KjR888/37Bhw6qrGQAAADJOzCvwIYTu3bu/995711xzTe3atUMIqVRq7dq1c+fO/eSTT0rT+5AhQ959990TTjihaooFAACATBU/wIcQcnJy/vCHPyxcuPCGG2742te+duihh2ZlZTVq1Ojwww8fNmzY66+//uKLLx5xxBFVVSsAAABkrJi30JfVo0ePMWPGVP44AAAAwN5U6go8AAAAUD0EeAAAAEiASt1Cv2bNmtdee23+/Pk7duz4ysFuswcAAIDY4gf4CRMmXHHFFfn5+fs4XoAHAACA2GIG+BUrVlx88cVFRUVVWw0AAABQrpgB/pe//GWU3o877rh77723V69eDRo0qNLCAAAAgP9PzAA/Z86cEEL79u2nT5/esGHDKi0JAAAA2F3Mp9B/9NFHIYQrrrhCegcAAIBqECfAFxQUbN++PYTQoUOHqq4HAAAAKEecAJ9KpWrVqhVCeP/996u6HgAAAKAccQJ8o0aN+vXrF0KYMmXKzp07q7okAAAAYHcxvwM/evToevXqLV68+Mc//nEqlaramgAAAIDdxAzwxx9//IQJE5o3b/7oo4+edNJJL7/88hdffFG1lQEAAAClYr5G7rzzzgshtG/ffuPGjbNnzz7nnHNCCIccckh2dvbepqxbty7euQAAAICYAf4f//jHnhu3bt26devWytUDAAAAlCNmgD/llFOqtg4AAACgAjED/PTp06u2DgAAAKACMR9iBwAAAFQnAR4AAAASIOYt9LtZv379vHnz1q1bt2PHjiFDhrRo0SKVSmVlZVXJwQEAAIDKBvjJkyffeeed7777biqVirb06dOnRYsWN910U15e3lVXXXXGGWdUukgAAADIdPFvoU+lUtdcc8255547b9680vReqrCwcPz48QMHDrztttv23AsAAADsl/gB/q677ho7dmwIISsra8CAASNHjiy7t3PnztEt9KNGjbr55psrWSUAAABkuJgBfsWKFaNGjQoh5ObmTps27Y033njooYfKDrjxxhtnz56dk5MTQnjooYdWrlxZ+VoBAAAgY8UM8GPGjCkuLg4hPP3006eddlq5Y/r27TtlypSsrKyioqLRo0fHrxEAAAAyXswAP2PGjBBC//79Tz/99AqG9e7d++yzzw4hzJ8/P96JAAAAgBA7wH/88cchhGOPPfYrR/bu3TuEsGzZsngnAgAAAELsAF9YWBhCqF+//leOrFu3bgghPz8/3okAAACAEDvAt2vXLoSwaNGirxy5YMGCEELbtm3jnQgAAAAIsQP8N77xjRDCtGnTli5dWsGwhQsXTpkyJYRwyimnxDsRAAAAEGIH+CuuuCKEUFRUNGzYsDVr1pQ7Ji8vb9CgQTt27AghXHbZZbFLBAAAAGIG+H79+g0bNiyEMHfu3J49e/7iF7948803o12ff/75nDlz7rvvvj59+uTl5YUQBg0adNZZZ1VVxQAAAJCB6sSeOXbs2M2bN0+aNGnjxo1333333XffHW3fLaufdNJJ48aNq0yJAAAAQMwr8CGE+vXrv/TSSxMnTjz88MPLHZCTk/PUU0/NnDmzadOmsc8CAAAAhMpcgY8MHjz4W9/61tSpU6dPn/7pp5/m5+c3btw4Nze3X79+gwYNatSoUZVUCQAAABmusgE+hFCvXr3BgwcPHjy48ocCAAAAyhX/FnoAAACg2gjwAAAAkAAxb6F/9NFH93fK9ddfH+9cAAAAQMwAf8MNN+zvFAEeAAAAYquCh9hVLCsrq3bt2gf6LAAAAJDeYgb4GTNm7G1XcXHxihUr3n333SeffHLbtm0NGjR45plnzj///LgVAgAAAHED/Ne//vUK9g4YMOD73//+TTfddNFFF82YMeM73/nOpEmTBg0aFO9cAAAAwAF8Cn27du2ee+65li1blpSUXH311SUlJQfuXAAAAJDeDuxr5Nq1a3fBBReEENasWbNkyZIDei4AAABIYwf8PfDHHXdc9MGj7AAAACC2Ax7gCwoKQgi1a9fu2rXrgT4XAAAApKsDHuCnTp0aQujUqVO9evUO9LkAAAAgXR3A98CXlJT893//9yuvvBJC6Nat24E7EQAAAKS9mAH+zDPPrHjAjh07li5dun79+ugfr7/++ngnOsi9+uqr48aNe+uttz777LPatWt369atW7duV1111SmnnFLTpQEAAJBWYgb4V199dd8HX3rppen3EvjNmzdfeeWVL7zwQtmNs2bNmjVr1p///Odhw4Y9+OCDLVq0qKnyAAAASDMxA3zLli2/cky9evV69ux5/vnnX3vttfHOctDatWvXhRdeGP0WIysr67TTTjv++OMLCwvnzZs3ffr0EMJf/vKXL774YsqUKVlZWTVdLAAAAOkgZoBft25d1daRLH/605+i9F6vXr0JEyaUvb/g1VdfHTZs2Nq1a1955ZVHH330hhtuqLkyAQAASB8H/Cn0ael3v/td9GHcuHG7fTtg4MCBf/7zn6ML73fddVdxcXEN1AcAAEDaEeD327JlyxYtWhRC6NWr1wUXXLDngG9+85t9+/YNIWzcuHHevHnVXR8AAADpSIDfb++//370oX///nsbc+yxx0Yf1qxZUx01AQAAkO5ifge+Z8+elT/3mWeeOXr06Mofp5rl5+dnZ2eHELp06bK3Mdu3b48+tG7duprKAgAAIK3FDPDRPeSV9J3vfKfyB6l+w4YNGzZsWAUDdu7c+frrr4cQsrKyjjzyyOqqCwAAgHQWM8DffPPNJSUljz766LZt20o3Zmdn5+bmfv7551u3bi3d2KZNm1atWpV7kH79+sU7+0HuN7/5TV5eXghh6NChXgUPAABAlYgZ4H/9618/8sgjUXrPzc392c9+duGFF+bk5ERPX1+7du2ECRNGjRqVl5dXUlLy5JNPnnjiiVVZ9UHs0Ucfve2220II9evXv/POOytzqL/+9a9PPfVUjInLli0LIaxfv74yZ4eD1v/8z/88++yzNV0FAABUt5gBftmyZTfddFMI4aSTTnrttdcaNGhQdm/btm1/+MMfXnHFFaeffvrbb789ePDghQsXpv23wZcvX/6jH/3o5ZdfDiHUqlXrmWee6dWrV2UO+OKLL0Zvm48nugsA0s+jjz76zjvv1HQVAABQ3WIG+EceeWT79u1NmjR58cUXd0vvpbKzs1944YVu3bp98cUXDz300KhRoypR50Ft27Zt99133wMPPLBjx44QQvPmzceOHTt06NBKHvaPf/zjtddeW1JSsr8Tf//73z///PO9e/euZAFwcEqlUiGE22+/vWPHjjVdC2nr6quvrukSAAB2FzPA//Of/wwhfP3rX2/Tpk0Fw9q2bXvKKae8/PLLL730UroG+AkTJowcOfLTTz+N/vHb3/72448/3rZt28of+ZBDDjn99NNjTIzuAoi+zgDpqmfPnkcddVRNVwEAANUn5nvgo7zao0ePrxx59NFHhxBWrVoV70QHsy1btlx00UVDhw6N/m0cd9xxr7322oQJE6okvQMAAEBZMa/A16tXr6CgYOnSpV85MhoT3fKaTjZv3jxgwICFCxeGEJo3bz569Ojvfe97LnoDAABwgMS8At+lS5cQwttvv7158+YKhuXn57/11lshhPbt28c70cGpsLAwejJfCOGss85avHjx97//fekdAACAAydmgB8yZEgIYfPmzZdddtnenrKWSqWGDRu2cePGEMJZZ50Vu8SD0J133jl9+vQQwqWXXvqPf/zDPfMAAAAcaDED/A033NCyZcsQwssvv9y/f/8333xztwEzZsw49dRTJ02aFEKoV6/eddddV8lCDx4FBQVPPvlkCKFr165PPvlknToxv4YAAAAA+y5m+GzevPmzzz47ePDgHTt2vPXWW6eeempubu4RRxyRm5u7Zs2aDz/8sOxLyB944IEjjjiiigquec8999ymTZtCCD/4wQ/29go9AAAAqFrxrx5/85vffOONN37yk5/MnDkzhLB69erVq1fvNuawww4bNWrUsGHDKlXjQWbOnDnRh6effnry5MkVD37yySe7du164IsCAAAgzVXq9u8TTzxxxowZkyZN+t3vfjdr1qytW7dG21u3bn3EEUcMHjz4v/7rv7Kzs6uizoPI8uXLow/RQ+wqVvrvBAAAACqjCr6/fd5555133nnFxcUbNmzYvHlz27ZtmzRpUvnDHrRKAzwAAABUmyp7AFvt2rVbt27dunXrqjrgQWvZsmU1XQIAAAAZp2oC/Pr16+fNm7du3bodO3YMGTKkRYsWqVTKe9EBAACgqlQ2wE+ePPnOO+989913U6lUtKVPnz4tWrS46aab8vLyrrrqqjPOOKPSRQIAAECmi/ke+BBCKpW65pprzj333Hnz5pWm91KFhYXjx48fOHDgbbfdtudeAAAAYL/ED/B33XXX2LFjQwhZWVkDBgwYOXJk2b2dO3eObqEfNWrUzTffXMkqAQAAIMPFDPArVqwYNWpUCCE3N3fatGlvvPHGQw89VHbAjTfeOHv27JycnBDCQw89tHLlysrXCgAAABkrZoAfM2ZMcXFxCOHpp58+7bTTyh3Tt2/fKVOmZGVlFRUVjR49On6NAAAAkPFiBvgZM2aEEPr373/66adXMKx3795nn312CGH+/PnxTgQAAACE2AH+448/DiEce+yxXzmyd+/ewbvTAQAAoHJiBvjCwsIQQv369b9yZN26dUMI+fn58U4EAAAAhNgBvl27diGERYsWfeXIBQsWhBDatm0b70QAAABAiB3gv/GNb4QQpk2btnTp0gqGLVy4cMqUKSGEU045Jd6JAAAAgBA7wF9xxRUhhKKiomHDhq1Zs6bcMXl5eYMGDdqxY0cI4bLLLotdIgAAABAzwPfr12/YsGEhhLlz5/bs2fMXv/jFm2++Ge36/PPP58yZc9999/Xp0ycvLy+EMGjQoLPOOquqKgYAAIAMVCf2zLFjx27evHnSpEkbN268++6777777mj7bln9pJNOGjduXGVKBAAAAGJegQ8h1K9f/6WXXpo4ceLhhx9e7oCcnJynnnpq5syZTZs2jX0WAAAAIFTmCnxk8ODB3/rWt6ZOnTp9+vRPP/00Pz+/cePGubm5/fr1GzRoUKNGjaqkSgAAAMhwcQL8+vXr33vvvRDCUUcdlZOTU69evcGDBw8ePLiqawMAAAD+H3FuoW/QoMGZZ545cOBAX24HAACA6hEnwDdq1Kh9+/YhhJUrV1Z1PQAAAEA5Yj7ELnqH3JQpUwoLC6u0HgAAAKAcMQP8rbfeeuqpp3700Ud33nln1RYEAAAA7ClmgG/YsOHkyZOHDx/+wAMP9OvXb8qUKZ999lkqlara4gAAAIBIzNfIXXLJJdGHnJyct99+e9CgQSGE+vXrt2zZMisrq9wpn376abxzAQAAADED/Pjx4/fcuGPHjtWrV1euHgAAAKAcMQN83759q7YOAAAAoAIxA/ycOXOqtg4AAACgAvsU4O+4444QwrHHHjt06NADXA8AAABQjn0K8L/85S9DCMOHDxfgAQAAoEbEfI1cWXXr1q1bt+6VV15Z+UMBAAAA5Yr5Hfiydu3aFUIoKSmp/KEAAACAclXBFXgAAADgQBPgAQAAIAEEeAAAAEgAAR4AAAASQIAHAACABBDgAQAAIAEEeAAAAEgAAR4AAAASQIAHAACABKiz70MnTJgwa9aseHtDCEuXLt2PugAAAIAy9iPA5+fn5+fnx9sLAAAAVIZb6AEAACAB9ukK/CeffHKAywAAAAAqsk8BvmPHjge6DgAAAKACbqEHAACABBDgAQAAIAEEeAAAAEgAAR4AAAASQIAHAACABBDgAQAAIAEEeAAAAEgAAR4AAAASQIAHAACABBDgAQAAIAEEeAAAAEgAAR4AAAASoE5NF0BaWbly5RNPPFFcXFzThZDOVq9eXdMlAEACFBUVhRBmz559880313QtpLNGjRr9+Mc/PuSQQ2q6kIwgwFOVfvOb3zzyyCM1XQUAAGHp0qUhhPnz58+fP7+mayHNtW/ffvjw4TVdRUYQ4KlK0S96Bw4c2LNnz5quhbQ1ZsyYXbt21XQVAHCwi/667Nmz58CBA2u6FtLWtGnTFixYsGPHjpouJFMI8FS9r3/969/5zndqugrS1uOPPy7AA8A+6tat24gRI2q6CtJWXl7eggULarqKDOIhdgAAAJAAAjwAAAAkgAAPAAAACSDAAwAAQAII8AAAAJAAAjwAAAAkgAAPAAAACSDAAwAAQAII8AAAAJAAAjwAAAAkgAAPAAAACSDAAwAAQAII8AAAAJAAAjwAAAAkgAAPAAAACSDAAwAAQAII8AAAAJAAAjwAAAAkgAAPAAAACSDAAwAAQAII8AAAAJAAAjwAAAAkgAAPAAAACSDAAwAAQAII8AAAAJAAAjwAAAAkgAAPAAAACSDAAwAAQAII8AAAAJAAAjwAAAAkgAAPAAAACSDAAwAAQAII8AAAAJAAAjwAAAAkgAAPAAAACSDAAwAAQAII8AAAAJAAAjwAAAAkgAAPAAAACSDAAwAAQAII8AAAAJAAAjwAAAAkgAAPAAAACSDAAwAAQAII8AAAAJAAAjwAAAAkgAAPAAAACSDAAwAAQAII8AAAAJAAAjwAAAAkgAAPAAAACSDAAwAAQAII8AAAAJAAAjwAAAAkgAAPAAAACSDAAwAAQAII8JWyfPnyn/zkJz169GjUqFGrVq0GDBjw2GOPFRUV1XRdAAAApJs6NV1Ago0dO3bkyJHbt2+P/rGgoGD69OnTp09/7LHHJk2a1Llz55otDwAAgHQiwMc0bty4a6+9NpVKhRAGDBhwwgknfPnllxMnTly7du3ixYsHDhw4d+7cZs2a1XSZAAAApAm30MexadOmkSNHplKp2rVrjx8//o033njggQd+//vfL1++/JxzzgkhLF++/O67767pMgEAAEgfAnwcjzzyyPr160MIP/7xjy+66KLS7Q0aNHj22Wfbtm0bQnj00Uc///zzGisRAACA9CLAx/Hcc8+FEOrUqXPjjTfutqtp06bXXnttCGHXrl0vvPBCDRQHAABAOhLg99uqVasWL14cQjjhhBNycnL2HDBkyJDow+TJk6u1MgAAANKXAL/fFi1aFH048cQTyx3Qq1evBg0ahBCinA8AAACVJ8DvtyVLlkQfunTpUu6A2rVrd+jQIYSwcuXKgoKC6qsMAACA9CXA77cNGzZEH8q9f77srlQqtXHjxmoqCwAAgLTmPfD7bevWrdGHRo0a7W1Mw4YNdxscw4YNG/7973+XlJTs78Rly5aFEGJMrCqLFy9u0qRJTZ2dtBf19qxZs/Ly8mq6FtLcP//5z5ougbS1cOHCEMKWLVu0WSKcsmFD6xDmzp37xcdLarqW/bBmzZoQQl5enjbjwPn0009ruoQMk2I/jRgxIvpX99prr+1tzPnnnx+NmTNnTuwTDR06tDL/ZYcNGxb71LH94Ac/qGxHAgAcZP7Sf/CKC64/pXX7mi4EDlJjx46t/uiRmVyB32/Z2dnRh507d+5tTFFRUfShfv36sU80fPjw2rVrx5iYn5//0UcfXX/99bFPHdtVV121adOmGrz4TybIy8tbt25d7969a9XyJSAOlA8++CCE0K1bt5ouhLRVUlLy3nvvtWrV6rDDDqvpWvhqbVLNQioMGDAgJ6uopmvZD4WFhQsXLjz88MMPPfTQmq6FdNagQYNzzjmnpqvIFAL8fjvkkEOiDxU8oK50V+PGjWOf6LzzzjvvvPNiT68Rffr0GT9+fE1XAQBQlT6/95Ht771/++23N+jdvaZrATKa61f7rVWrVtGHtWvX7m1M6a7mzZtXR00AAACkOwF+v5XeUfnJJ5+UOyCVSq1atSqE0K5dO89yAwAAoEoI8PutZ8+e0Yc5c+aUO2Dp0qXbtm0rOxIAAAAqSYDfb506derevXsI4a233tq8efOeA6ZOnRp9GDRoULVWBgAAQPoS4OO48MILQwiFhYVjx47dbdfOnTv/8Ic/hBDq1KlT+jI5AAAAqCQBPo6RI0dGj5e/55575s6dW7o9lUr95Cc/WbZsWQhhxIgR7dt7WSgAAABVw2vk4mjRosXDDz88YsSIrVu3Dhgw4Morr+zbt29+fv748ePfeuutEELnzp1/9atf1XSZAAAApA8BPqYrrrhiw4YNt9xyS0FBwZgxY8ru6t69+4QJE1q2bFlTtQEAAJB+3EIf309/+tN33nnnmmuu6dq1a3Z2dosWLU488cQHH3xw7ty50VPuAAAAoKq4Al8pvXv3jh5ZBwAAAAeUK/AAAACQAAI8AAAAJIAADwAAAAkgwAMAAEACCPAAAACQAAI8AAAAJIAADwAAAAkgwAMAAEACCPAAAACQAFmpVKqmayB93Hvvvb/+9a9btmxZt27dmq6FtJWfn799+/Y2bdpkZWXVdC2krfXr14cQWrZsWdOFkLZSqdTnn3/eoEGDpk2b1nQtfLX7Dz++T+OWN300Z+6X62u6lv2wc+fOdevWNWvWrGHDhjVdC2mroKBg69atL7zwwplnnlnTtWQEAZ6qdOKJJ86ePbumqwAAqEqtsxtm167zRWFBYfGumq4FDkb/5//8nwcffLCmq8gIdWq6ANLK1772tdmzZ//qV7+6+OKLa7oW0taQIUMWLVo0ceLEY445pqZrIW117do1hPDxxx/XdCGkrUWLFg0ZMuSYY46ZOHFiTddC2ho/fvxtt9128cUX/+pXv6rpWkhbt9122/jx43v06FHThWQKAZ6q17Jlyy5dutR0FaSt+vXrhxByc3O1GQeaHuPA2bRpUwihfv362owDJ/oeUJMmTbQZB06TJk1quoTM4iF2AAAAkAACPAAAACSAAA8AAAAJIMADAABAAgjwAAAAkAACPAAAACSAAA8AAAAJIMADAABAAgjwAAAAkAACPAAAACSAAA8AAAAJIMADAABAAgjwAAAAkAACPAAAACSAAA8AAAAJIMADAABAAtSp6QJIK02aNAkhNG3atKYLIZ01bdo0KysrajY4QKxjHGhNmjTJysrSaRxQUYP5G5MDyv//V7OsVCpV0zWQPr788ss333zzW9/6Vu3atWu6FtJWXl7e8uXLBwwYUNOFkM4WLlwYQujZs2dNF0I6e/PNN7t06XLYYYfVdCGkreLi4qlTpw4YMKBx48Y1XQtpy///VzMBHgAAABLAd+ABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEODT1s6dOx9++OHvfve7PXv2bNSo0eGHH37OOefceeedmzdvrnjiq6++OmLEiO7duzdt2rR58+Ynn3zy8OHDZ8yYcSDOVYHly5f/5Cc/6dGjR6NGjVq1ajVgwIDHHnusqKhob+NvvPHGrK/yn//8J3Y9lCvT2qwys4itOtss9qwKTJw48YILLjjssMOys7NzcnJOPfXUMWPGFBQU7G281axGZFqbRaxm1SzpbTZjxowrrrjipJNOatq0aceOHc8+++w77rhj48aNextvNat+mdZjkYxbylKko//85z/HHHNMuf/FW7Ro8dRTT5U7a9OmTUOHDt1bqwwbNmz9+vVVda6KPfHEEw0aNNjzgEcfffTy5cvLnXLeeed9ZbfPmjUrRjHsTQa2WexZxFadbRZvVgW++OKL7373u+Ue7fDDD583b165s6xm1S8D2yxlNat2iW6zjRs3XnLJJXsr/o9//GO5s6xm1SwDeyyVkUtZViqV+qqfLBJm/fr1Rx555KZNm0IIbdq0Offccw877LDVq1e/8cYbH374YQghKytr2rRpp512WtlZu3btOvvss1999dVowGmnnXb88ccXFhbOmzdv+vTp0ZizzjprypQpWVlZlTxXxcaNG3f55ZdHnTlgwIATTjjhyy+/nDhx4tq1a0MIXbp0mTt3brNmzXabddRRRy1duvSQQw7p3r373o78pz/9aW/rGvsrM9ss3ixiq842izerAlu2bOnVq9fKlStDCM2aNfvGN75x1FFHffzxx2+++eZnn30WQmjduvWsWbM6d+6820SrWTXLzDazmlWzRLdZCGHw4MGTJk0KIdSpU+eb3/xm9+7d8/Pz33777SVLlkRn+cc//jFo0KDdZlnNqlNm9liGLmU1+dsDDowrr7wy+o87fPjw7du3l27fuXPnz3/+82hXhw4dCgsLy8564oknol316tWbPHly2V3/+te/2rZtG+0dM2ZM5c9VgY0bN7Zs2TKEULt27fHjx5duLygoOOecc6IDjhw5crdZxcXF9evXDyFccMEF+3giKikD2yzeLCqjOtss3qwK/OAHP4imnHHGGWvWrCndvnXr1hEjRkS7zj777N1mWc2qXwa2mdWs+iW6zR5//PFoSteuXZcsWVK6vbi4ePTo0dGuVq1abd68uewsq1k1y8Aey9ilTIBPN/n5+dHvunr06LFz587d9hYXFx9//PFRQ8+dO7fsrtJff/7v//7vnod95ZVXosPGvMxhAAARuklEQVQ2b958165dlTxXBe65555oyk9/+tPddm3evDlaEerUqbN27dqyu6KLDyGEm2++eR9PRGVkZpvFm0Vs1dlmsWftzZw5c6Lxubm5W7Zs2XPAwIEDo9NNnz697HarWTXLzDazmlWzRLdZKpU69thjowO+8cYbe+694IILor1Tpkwpu91qVp0ys8cydikT4NPNrFmzola+++67yx1wxx13RAPKfhPmgw8+iDb26tVrb0cu/cmfPXt2Zc5VsaOPPjr6YSt7JaHUXXfdFR3wscceK7t92rRp0fYKviFDFcrMNos3i9iqs83izapA6eWOvV1/eO+996IB1113XdntVrNqlpltZjWrZolus1QqFV1Ib9SoUXFx8Z57//jHP0ZHu/fee8tut5pVp8zssYxdyjyFPt1EXxQJIez5hbdI8+bNow+ff/556cb3338/+tC/f/+9Hbn0d2Nr1qypzLkqsGrVqsWLF4cQTjjhhJycnD0HDBkyJPowefLksts/+uij6MORRx65LyeikjKwzWI3J7FVZ5vFm1WBhQsXRh/69u1b7oDo+cAhhKlTp5bdbjWrZhnYZlaz6pfoNkulUiUlJSGEunXr1qpVTnCIeiyEsGHDhrLbrWbVKQN7LJOXsjo1XQBV7Mwzz4xe3rC354WUvq6ja9eupRvz8/Ozs7NDCF26dNnbkbdv3x59aN26dWXOVYFFixZFH0488cRyB/Tq1atBgwbbt2+PfmJLlf4lccQRR2zbtu2dd95ZtmzZ1q1b27VrN2DAgHJ/qqmMDGyz2M1JbNXZZvFmVSB6jFAIIfp63p6ysrIOO+ywDz74YMWKFQUFBQ0bNoy2W82qWQa2mdWs+iW6zbKyso444oglS5Zs3rx50aJFez5w7s0334w+7BbUrWbVKQN7LKOXspq+BYBq9eqrrzZu3DiE0LJly23btu37xKKiosMOOyyEkJWVtY+vhYhxrgceeCBqywoeetGtW7eojLLHPP/880MIDRo0ePjhh3dbKbKysi6//PINGzbsSwFUibRss9jNyQFSnW0WY1bpk3J3+8Je2QPWq1cvGrNo0aLS7Vazg0patpnV7GBzkLdZKpX6wx/+EPXMCSecsHHjxrK7pkyZUrt27RBCixYtNm3aVHaX1ezgkZY9lslLmSvw6W/mzJlbtmxZvXr1zJkzn3nmmV27dtWvX//ZZ58tveCzL37zm9/k5eWFEIYOHdqiRYsDdK7SG2Mq+L1sTk7OBx98kEqlNm7cuNs1q+3bt48cOXK38alU6plnnvnXv/713HPPnXrqqftSBjGkfZvFbk6qUHW2WSVn9e3b9+WXXw4hTJky5Vvf+taeA15++eWioqLo89atW0u3W81qXNq3mdXsYJCgNgshXHPNNUuXLn3ooYdmz559xBFHXH755d26dduyZcvMmTOj934deuihkydP3u1lXVazmpX2PZbRS1nN/e6AarLbenr88ccvXrx4v47wyCOPRA+TrF+//nvvvXfgznXDDTdEE/d2MSFV5oLD0qVLoy0lJSWlP5MtWrQYM2bMqlWrohtmnnrqqdzc3GjX3h7SS5VI+zaLN4uqVZ1tVslZH3zwQXTls06dOnu+H2HNmjWlS1MI4dVXX422W80OBmnfZlazg0GC2qzUk08+GcrTo0ePvLy83QZbzWpc2vdYJi9lAnz62+0HuFatWueff/7y5cv3Ze7HH39c2vq1atX6+9//fuDOlUqlSl9a+9prr+1tTHRHVghhzpw50Zbol3whhC5duqxatWq38Zs2bTrppJOiAddff/0+VsL+Svs2izeLqlWdbVaZWZHS19tkZ2ffd999Cxcu3LZt26JFi373u98deuihZf8gr7/+ejTFanYwSPs2s5odDJLVZqlUasqUKR07dgx7cdlll+12p7TVrMalfY9l8lImwKe/wsLCbdu2LVu27KWXXjrttNOiPs7JyVm2bFkFs7Zu3Xr77bdHL3UIITRv3vz5558/QOcqdd1110VTXnnllb2NOeecc6IxCxYsiLbs2rVr/fr169ev3759e7lT3n333eiZlvXq1du6deu+VML+Svs2izeLqlWdbRZvVllFRUXl3tUc6dOnT+kfofR/LKxmB4O0bzOr2cEgWW02fvz46EvI2dnZt99++5w5c/Lz81etWjV58uRTTjklOmyvXr3K5iurWY1L+x7L5KVMgE+Ym2++udy/pPf9wRI//OEPoykXXXTR3sa88MIL7du3Lz34t7/97c8++yxGtftyrrJuuummaPyECRP2NqZ0DVqxYsW+V1L6ootp06bt+6yMpc32bLMD15wZ62Bus6pqzlQq9cQTTzRp0qTsH7BWrVpXX311fn7+ySefHG3Zr1v7rGb7RZvt2WZWsyqX3m22atWq6JsaDRo0KDcFlX7F/bvf/e5+Hdlqtu/02J49lslLmQCfMJX/AS4oKIje/VC7du2dO3futjc/P/+73/1u6WGPO+64Cu5LqeS59lT6PMnHH398b2N69OgRjcnPz9/3Sq6++upo1l/+8pd9n5WxtNmebXbgmjNjHZxtVrXNGdm6deukSZMefPDB++677+mnn167dm20vVOnTiGErKysgoKCfT+a1Wy/aLM928xqVuXSu81++tOfRkf49a9/Xe6AHTt2RK8Tq1Wr1n7FJKvZvtNje/ZYJi9lnkKfMMOGDSv91lBZ0cshQgizZ89OpVLZ2dm9e/cu9wgNGjQ46qij5s2bV1xcvHr16rLfNtm8efOAAQMWLlwYQmjevPno0aO/973vRQ+iKFdlzlWu6GUPIYRPPvmk3AGpVGrVqlUhhHbt2u12taFibdq0iT5U8MehlDbbs80OXHNmrIOqzSoz6ys1atTo3HPP3W1jSUnJ2rVrQwidOnVq0KDBvh/NarZftNmebWY1q3Lp3Wbz58+PPpxxxhnlDqhXr96pp566fPnykpKSBQsWRL8z2hdWs32nx/bssYxeymrk1wYcONEPZKNGjYqLi/c2pnQJ+PDDD0s3bt++vfReprPOOmtf7n6Jfa69WbFiRTT49NNPL3fA4sWLSyss3fjOO+/MmDFj5syZFRz5kksuiSaWPoaXysjANos3i8qozjaLPSu2t99+OzrXZZddVrrRalb9MrDNrGbVL9Ft1rdv3+hQn3zyyd7G/PznP4/GPP3009EWq1k1y8Aey+SlTIBPN9/85jejZn3//ffLHVBSUhL9uq527drbtm0r3f6zn/0smnjppZfuy33IlTlXBbp37x5CyM7O3rRp0557f/vb30ZnfPjhh0s3lj6/5z//+U+5xywuLj7iiCNCCHXr1vW2kiqRgW0WexaxVWebxZ61Nx988EHfvn379u17++23lzug9P03L7zwQulGq1n1y8A2S1nNql2i26z0Od4TJ07c25jStevf//73blusZtUjA3sslcFLmQCfbu6///6oWa+++upyB/zhD3+IBpx66qmlG7dt2xa9b6Zr1677/m3MeOeq2B133BFNuf/++3fbVVRUdOSRR4YQ6tSpU/aVJA899FA0ZciQIeUe87HHHitdZfaxDCqWgW0WexaxVWebxZtVgZKSkrZt24YQmjVrtnnz5t32fvjhh9Ebkjt06FBYWFi63WpW/TKwzVJWs2qX6DZ74oknotr69+9f7tXduXPn1q1bN4TQtGnTHTt2RButZtUsA3sslcFLmQCfbr744ovoL+ysrKyHH354165dZff+7W9/K/3129tvv126/amnnop+AB544IEDfa5UKrVx48YX/1+7vV9k/fr10axDDjnknXfeKd1eUlLyox/9KCrymmuuKTslPz+/devW0a4f//jHZQ9YUlLyxBNPHHLIISGEQw89tPShPlRSBrZZ7FnEVp1tFm9WqsI2+6//+q/omGeffXbZ60sff/xx6WN1Sm8FjFjNql8GtlnKalbtEt1mO3bs+NrXvhYd83vf+96GDRvKznrjjTc6d+4c7f39739fut1qVs0ysMdSGbyUZaVSqUB6eeqpp6688sro85FHHnnCCSd06NBh7dq18+bNK31KxN13333XXXeVTrnuuusef/zxEELPnj1btGhR8fGffPLJrl27xj5XCOE///lP6fdw8vLycnNzy+7905/+NGLEiBBCw4YNr7zyyr59++bn548fP/6tt94KIXTu3Hn27NktW7YsO+XFF1+88MILd+3aFULIzc0dOHBgbm7uqlWr3nnnnffff7/0sMOHD/+qf3nsqwxss9iziK3a2ix2c1bQZl9++WXfvn2XLVsWQmjTps25557bpk2bZcuWTZo0aceOHSGEq666auzYsbsd3GpW/TKwzYLVrNolus3ef//9Pn36FBQUhBCaN28+YMCAbt26bdq0aeHChaXPWRg6dOjzzz9f9uBWs2qWgT0WMnYpq+nfIHBAPPzww/Xr1y/3v3izZs32/M3cWWedte89M3/+/MqcK5VKzZo1q3RMXl7envU/8MADdeqU84qE7t277+2FyRMnTozuJNxTkyZN9rz+QOVlYJvFnkVs1dNmsZuz4jZbvHjxUUcdtecRatWqdcstt+zte4NWs+qXgW2WsppVu0S32bx58/r161fuQerXr3/rrbeW+wwaq1k1y8AeS2XkUibAp62PPvrorrvu6tu3b9u2bbOzs7t37/7tb3/7zjvvXLdu3Z6Do+eIxPhRjHGu1D4kq1QqNX/+/GuuuaZr167Z2dktWrQ48cQTH3zwwYofUbZly5YxY8ZEv+KtV69eixYtTjrppHvvvXffX5LJ/srANos9i9iqoc1iN+dXtllRUdHo0aPPPPPMdu3aNWrUqEePHj/60Y8WLFhQ8R/Zalb9MrDNUlazapfoNispKXnmmWfOO++8du3a1atXr0mTJkcdddR1111X8ftfrGbVLAN7LJV5S5lb6AEAACABatV0AQAAAMBXE+ABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAQR4AAAASAABHgAAABJAgAcAAIAEEOABAAAgAf4vDO388rK8L5IAAAAASUVORK5CYII=" width="672" /></p> +<p>We can use the <code>anova</code> method to compare the models.</p> +<pre class="r"><code>anova(f_saem_full, best(f_saem_full_multi), + f_saem_reduced, best(f_saem_reduced_multi))</code></pre> <pre><code>## Data: 155 observations of 1 variable(s) grouped in 6 datasets ## -## npar AIC BIC Lik Chisq Df Pr(>Chisq) -## best(f_saem_reduced_multi) 9 663.81 661.93 -322.90 -## f_saem_full 10 668.27 666.19 -324.13 0 1 1</code></pre> -<p>While AIC and BIC are lower for the reduced model, the likelihood ratio test does not indicate a significant difference between the fits.</p> +## npar AIC BIC Lik +## f_saem_reduced 9 663.73 661.86 -322.86 +## best(f_saem_reduced_multi) 9 663.69 661.82 -322.85 +## f_saem_full 10 669.77 667.69 -324.89 +## best(f_saem_full_multi) 10 665.56 663.48 -322.78</code></pre> +<p>The reduced model gives the lowest information criteria and similar +likelihoods as the best variant of the full model. The multistart method +leads to a much lower improvement of the likelihood for the reduced +model, indicating that it is fitted more efficiently.</p> diff --git a/vignettes/web_only/saem_benchmarks.rda b/vignettes/web_only/saem_benchmarks.rda Binary files differindex a9621825..d041d600 100644 --- a/vignettes/web_only/saem_benchmarks.rda +++ b/vignettes/web_only/saem_benchmarks.rda |
