diff options
| author | Johannes Ranke <jranke@uni-bremen.de> | 2021-03-31 19:42:17 +0200 |
|---|---|---|
| committer | Johannes Ranke <jranke@uni-bremen.de> | 2021-03-31 19:42:17 +0200 |
| commit | 3a5463672c297b37c3c9135c8b144c48744c05d0 (patch) | |
| tree | f28691df6359757133510ae44399ce71ecbfaa93 | |
| parent | 7bca4f58951c1c8cae63c4557102ae77a3aff616 (diff) | |
Bug fix in plot.mkinfit
In residual plots, use xlab and xlim if appropriate
128 files changed, 327 insertions, 248 deletions
@@ -1,9 +1,9 @@ # mkin 1.0.4 (Unreleased) -- 'plot.mixed.mmkin': Reset graphical parameters on exit - - All plotting functions setting graphical parameters: Use on.exit() for resetting graphical parameters +- 'plot.mkinfit': Use xlab and xlim for the residual plot if show_residuals is TRUE + # mkin 1.0.3 (2021-02-15) - Review and update README, the 'Introduction to mkin' vignette and some of the help pages diff --git a/R/plot.mkinfit.R b/R/plot.mkinfit.R index 2e319aae..1d4ea543 100644 --- a/R/plot.mkinfit.R +++ b/R/plot.mkinfit.R @@ -278,7 +278,7 @@ plot.mkinfit <- function(x, fit = x, if (show_residuals) { mkinresplot(fit, obs_vars = row_obs_vars, standardized = standardized, pch_obs = pch_obs[row_obs_vars], col_obs = col_obs[row_obs_vars], - legend = FALSE, frame = frame, xlab = xlab) + legend = FALSE, frame = frame, xlab = xlab, xlim = xlim) } # Show error model plot if requested @@ -6,5 +6,5 @@ * creating vignettes ... OK * checking for LF line-endings in source and make files and shell scripts * checking for empty or unneeded directories -* building ‘mkin_1.0.3.tar.gz’ +* building ‘mkin_1.0.4.tar.gz’ @@ -1,14 +1,16 @@ * using log directory ‘/home/jranke/git/mkin/mkin.Rcheck’ -* using R version 4.0.3 (2020-10-10) +* using R version 4.0.4 (2021-02-15) * using platform: x86_64-pc-linux-gnu (64-bit) * using session charset: UTF-8 * using options ‘--no-tests --as-cran’ * checking for file ‘mkin/DESCRIPTION’ ... OK * checking extension type ... Package -* this is package ‘mkin’ version ‘1.0.3’ +* this is package ‘mkin’ version ‘1.0.4’ * package encoding: UTF-8 -* checking CRAN incoming feasibility ... Note_to_CRAN_maintainers +* checking CRAN incoming feasibility ... NOTE Maintainer: ‘Johannes Ranke <jranke@uni-bremen.de>’ + +The Date field is over a month old. * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK @@ -67,5 +69,9 @@ Maintainer: ‘Johannes Ranke <jranke@uni-bremen.de>’ * checking for detritus in the temp directory ... OK * DONE -Status: OK +Status: 1 NOTE +See + ‘/home/jranke/git/mkin/mkin.Rcheck/00check.log’ +for details. + diff --git a/docs/articles/FOCUS_D.html b/docs/articles/FOCUS_D.html index 08acf58b..1bfba6e5 100644 --- a/docs/articles/FOCUS_D.html +++ b/docs/articles/FOCUS_D.html @@ -31,7 +31,7 @@ </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> @@ -94,13 +94,13 @@ - </header><script src="FOCUS_D_files/header-attrs-2.6/header-attrs.js"></script><script src="FOCUS_D_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> + </header><script src="FOCUS_D_files/header-attrs-2.7/header-attrs.js"></script><script src="FOCUS_D_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> <h1 data-toc-skip>Example evaluation of FOCUS Example Dataset D</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">Last change 31 January 2019 (rebuilt 2021-02-15)</h4> + <h4 class="date">Last change 31 January 2019 (rebuilt 2021-03-31)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/master/vignettes/FOCUS_D.rmd"><code>vignettes/FOCUS_D.rmd</code></a></small> <div class="hidden name"><code>FOCUS_D.rmd</code></div> @@ -185,10 +185,10 @@ <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 class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span></code></pre></div> -<pre><code>## mkin version used for fitting: 1.0.3 -## R version used for fitting: 4.0.3 -## Date of fit: Mon Feb 15 17:50:27 2021 -## Date of summary: Mon Feb 15 17:50:28 2021 +<pre><code>## mkin version used for fitting: 1.0.4 +## R version used for fitting: 4.0.4 +## Date of fit: Wed Mar 31 19:18:28 2021 +## Date of summary: Wed Mar 31 19:18:29 2021 ## ## Equations: ## d_parent/dt = - k_parent * parent @@ -196,7 +196,7 @@ ## ## Model predictions using solution type analytical ## -## Fitted using 401 model solutions performed in 0.161 s +## Fitted using 401 model solutions performed in 0.159 s ## ## Error model: Constant variance ## 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 abf26715..c3a5d56e 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 f4937894..42e48dee 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_D_files/header-attrs-2.7/header-attrs.js b/docs/articles/FOCUS_D_files/header-attrs-2.7/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/articles/FOCUS_D_files/header-attrs-2.7/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/FOCUS_L.html b/docs/articles/FOCUS_L.html index ab9739bc..7074816c 100644 --- a/docs/articles/FOCUS_L.html +++ b/docs/articles/FOCUS_L.html @@ -31,7 +31,7 @@ </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> @@ -94,13 +94,13 @@ - </header><script src="FOCUS_L_files/header-attrs-2.6/header-attrs.js"></script><script src="FOCUS_L_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> + </header><script src="FOCUS_L_files/header-attrs-2.7/header-attrs.js"></script><script src="FOCUS_L_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> <h1 data-toc-skip>Example evaluation of FOCUS Laboratory Data L1 to L3</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">Last change 17 November 2016 (rebuilt 2021-02-15)</h4> + <h4 class="date">Last change 17 November 2016 (rebuilt 2021-03-31)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/master/vignettes/FOCUS_L.rmd"><code>vignettes/FOCUS_L.rmd</code></a></small> <div class="hidden name"><code>FOCUS_L.rmd</code></div> @@ -126,10 +126,10 @@ <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r"> <code class="sourceCode R"><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 class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="va">m.L1.SFO</span><span class="op">)</span></code></pre></div> -<pre><code>## mkin version used for fitting: 1.0.3 -## R version used for fitting: 4.0.3 -## Date of fit: Mon Feb 15 17:50:30 2021 -## Date of summary: Mon Feb 15 17:50:30 2021 +<pre><code>## mkin version used for fitting: 1.0.4 +## R version used for fitting: 4.0.4 +## Date of fit: Wed Mar 31 19:18:31 2021 +## Date of summary: Wed Mar 31 19:18:31 2021 ## ## Equations: ## d_parent/dt = - k_parent * parent @@ -232,10 +232,10 @@ <pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> <pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is ## doubtful</code></pre> -<pre><code>## mkin version used for fitting: 1.0.3 -## R version used for fitting: 4.0.3 -## Date of fit: Mon Feb 15 17:50:31 2021 -## Date of summary: Mon Feb 15 17:50:31 2021 +<pre><code>## mkin version used for fitting: 1.0.4 +## R version used for fitting: 4.0.4 +## Date of fit: Wed Mar 31 19:18:32 2021 +## Date of summary: Wed Mar 31 19:18:32 2021 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent @@ -344,10 +344,10 @@ <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 class="fu"><a href="https://rdrr.io/r/base/summary.html">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></code></pre></div> -<pre><code>## mkin version used for fitting: 1.0.3 -## R version used for fitting: 4.0.3 -## Date of fit: Mon Feb 15 17:50:31 2021 -## Date of summary: Mon Feb 15 17:50:31 2021 +<pre><code>## mkin version used for fitting: 1.0.4 +## R version used for fitting: 4.0.4 +## Date of fit: Wed Mar 31 19:18:32 2021 +## Date of summary: Wed Mar 31 19:18:32 2021 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent @@ -425,10 +425,10 @@ <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 class="fu"><a href="https://rdrr.io/r/base/summary.html">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></code></pre></div> -<pre><code>## mkin version used for fitting: 1.0.3 -## R version used for fitting: 4.0.3 -## Date of fit: Mon Feb 15 17:50:32 2021 -## Date of summary: Mon Feb 15 17:50:32 2021 +<pre><code>## mkin version used for fitting: 1.0.4 +## R version used for fitting: 4.0.4 +## Date of fit: Wed Mar 31 19:18:33 2021 +## Date of summary: Wed Mar 31 19:18:33 2021 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -531,10 +531,10 @@ <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 class="fu"><a href="https://rdrr.io/r/base/summary.html">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></code></pre></div> -<pre><code>## mkin version used for fitting: 1.0.3 -## R version used for fitting: 4.0.3 -## Date of fit: Mon Feb 15 17:50:32 2021 -## Date of summary: Mon Feb 15 17:50:32 2021 +<pre><code>## mkin version used for fitting: 1.0.4 +## R version used for fitting: 4.0.4 +## Date of fit: Wed Mar 31 19:18:33 2021 +## Date of summary: Wed Mar 31 19:18:33 2021 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -644,10 +644,10 @@ <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 class="fu"><a href="https://rdrr.io/r/base/summary.html">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></code></pre></div> -<pre><code>## mkin version used for fitting: 1.0.3 -## R version used for fitting: 4.0.3 -## Date of fit: Mon Feb 15 17:50:33 2021 -## Date of summary: Mon Feb 15 17:50:33 2021 +<pre><code>## mkin version used for fitting: 1.0.4 +## R version used for fitting: 4.0.4 +## Date of fit: Wed Mar 31 19:18:34 2021 +## Date of summary: Wed Mar 31 19:18:34 2021 ## ## Equations: ## d_parent/dt = - k_parent * parent @@ -709,10 +709,10 @@ ## parent 106 352</code></pre> <div class="sourceCode" id="cb31"><pre class="downlit sourceCode r"> <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/summary.html">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></code></pre></div> -<pre><code>## mkin version used for fitting: 1.0.3 -## R version used for fitting: 4.0.3 -## Date of fit: Mon Feb 15 17:50:33 2021 -## Date of summary: Mon Feb 15 17:50:33 2021 +<pre><code>## mkin version used for fitting: 1.0.4 +## R version used for fitting: 4.0.4 +## Date of fit: Wed Mar 31 19:18:34 2021 +## Date of summary: Wed Mar 31 19:18:34 2021 ## ## Equations: ## d_parent/dt = - 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We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/mkin.html b/docs/articles/mkin.html index 6dbb093d..a0fccc16 100644 --- a/docs/articles/mkin.html +++ b/docs/articles/mkin.html @@ -31,7 +31,7 @@ </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> @@ -94,13 +94,13 @@ - </header><script src="mkin_files/header-attrs-2.6/header-attrs.js"></script><script src="mkin_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> + </header><script src="mkin_files/header-attrs-2.7/header-attrs.js"></script><script src="mkin_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> <h1 data-toc-skip>Introduction to mkin</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">Last change 15 February 2021 (rebuilt 2021-02-15)</h4> + <h4 class="date">Last change 15 February 2021 (rebuilt 2021-03-31)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/master/vignettes/mkin.rmd"><code>vignettes/mkin.rmd</code></a></small> <div class="hidden name"><code>mkin.rmd</code></div> 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 bf38fdd7..8a182047 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/mkin_files/header-attrs-2.7/header-attrs.js b/docs/articles/mkin_files/header-attrs-2.7/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/articles/mkin_files/header-attrs-2.7/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/twa.html b/docs/articles/twa.html index 2e3d2f96..167f60d3 100644 --- a/docs/articles/twa.html +++ b/docs/articles/twa.html @@ -31,7 +31,7 @@ </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> @@ -94,13 +94,13 @@ - </header><script src="twa_files/header-attrs-2.6/header-attrs.js"></script><script src="twa_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> + </header><script src="twa_files/header-attrs-2.7/header-attrs.js"></script><script src="twa_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> <h1 data-toc-skip>Calculation of time weighted average concentrations with mkin</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">Last change 18 September 2019 (rebuilt 2021-02-15)</h4> + <h4 class="date">Last change 18 September 2019 (rebuilt 2021-03-31)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/master/vignettes/twa.rmd"><code>vignettes/twa.rmd</code></a></small> <div class="hidden name"><code>twa.rmd</code></div> diff --git a/docs/articles/twa_files/header-attrs-2.7/header-attrs.js b/docs/articles/twa_files/header-attrs-2.7/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/articles/twa_files/header-attrs-2.7/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/web_only/FOCUS_Z.html b/docs/articles/web_only/FOCUS_Z.html index 57fc3545..e4a6cb52 100644 --- a/docs/articles/web_only/FOCUS_Z.html +++ b/docs/articles/web_only/FOCUS_Z.html @@ -31,7 +31,7 @@ </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> @@ -94,13 +94,13 @@ - </header><script src="FOCUS_Z_files/header-attrs-2.6/header-attrs.js"></script><script src="FOCUS_Z_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> + </header><script src="FOCUS_Z_files/header-attrs-2.7/header-attrs.js"></script><script src="FOCUS_Z_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> <h1 data-toc-skip>Example evaluation of FOCUS dataset Z</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">Last change 16 January 2018 (rebuilt 2021-02-15)</h4> + <h4 class="date">Last change 16 January 2018 (rebuilt 2021-03-31)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/master/vignettes/web_only/FOCUS_Z.rmd"><code>vignettes/web_only/FOCUS_Z.rmd</code></a></small> <div class="hidden name"><code>FOCUS_Z.rmd</code></div> diff --git 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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 e7501cbb..0a83f8ea 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/FOCUS_Z_files/header-attrs-2.7/header-attrs.js b/docs/articles/web_only/FOCUS_Z_files/header-attrs-2.7/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/articles/web_only/FOCUS_Z_files/header-attrs-2.7/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/web_only/NAFTA_examples.html b/docs/articles/web_only/NAFTA_examples.html index e79375b3..65a71b56 100644 --- a/docs/articles/web_only/NAFTA_examples.html +++ b/docs/articles/web_only/NAFTA_examples.html @@ -31,7 +31,7 @@ </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> @@ -94,13 +94,13 @@ - </header><script src="NAFTA_examples_files/header-attrs-2.6/header-attrs.js"></script><script src="NAFTA_examples_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> + </header><script src="NAFTA_examples_files/header-attrs-2.7/header-attrs.js"></script><script src="NAFTA_examples_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> <h1 data-toc-skip>Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">26 February 2019 (rebuilt 2021-02-15)</h4> + <h4 class="date">26 February 2019 (rebuilt 2021-03-31)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/master/vignettes/web_only/NAFTA_examples.rmd"><code>vignettes/web_only/NAFTA_examples.rmd</code></a></small> <div class="hidden name"><code>NAFTA_examples.rmd</code></div> 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 f5420ce8..b1c874cc 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 0ae4bd9f..9dfa26fb 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 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b/docs/articles/web_only/NAFTA_examples_files/header-attrs-2.7/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/articles/web_only/NAFTA_examples_files/header-attrs-2.7/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/web_only/benchmarks.html b/docs/articles/web_only/benchmarks.html index 9908c224..290906c1 100644 --- a/docs/articles/web_only/benchmarks.html +++ b/docs/articles/web_only/benchmarks.html @@ -31,7 +31,7 @@ </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> @@ -94,13 +94,13 @@ - </header><script src="benchmarks_files/header-attrs-2.6/header-attrs.js"></script><script src="benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> + </header><script src="benchmarks_files/header-attrs-2.7/header-attrs.js"></script><script src="benchmarks_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row"> <div class="col-md-9 contents"> <div class="page-header toc-ignore"> <h1 data-toc-skip>Benchmark timings for mkin</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">Last change 13 May 2020 (rebuilt 2021-02-15)</h4> + <h4 class="date">Last change 13 May 2020 (rebuilt 2021-03-31)</h4> <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/master/vignettes/web_only/benchmarks.rmd"><code>vignettes/web_only/benchmarks.rmd</code></a></small> <div class="hidden name"><code>benchmarks.rmd</code></div> @@ -232,6 +232,11 @@ <td align="right">1.881</td> <td align="right">3.504</td> </tr> +<tr class="even"> +<td align="left">1.0.4</td> +<td align="right">1.867</td> +<td align="right">3.450</td> +</tr> </tbody> </table> </div> @@ -301,6 +306,12 @@ <td align="right">6.344</td> <td align="right">2.798</td> </tr> +<tr class="even"> +<td align="left">1.0.4</td> +<td align="right">1.415</td> +<td align="right">6.364</td> +<td align="right">2.820</td> +</tr> </tbody> </table> </div> @@ -400,6 +411,15 @@ <td align="right">1.923</td> <td align="right">2.839</td> </tr> +<tr class="even"> +<td align="left">1.0.4</td> +<td align="right">0.785</td> +<td align="right">1.252</td> +<td align="right">1.466</td> +<td align="right">3.091</td> +<td align="right">1.936</td> +<td align="right">2.826</td> +</tr> </tbody> </table> </div> diff --git a/docs/articles/web_only/benchmarks_files/header-attrs-2.7/header-attrs.js b/docs/articles/web_only/benchmarks_files/header-attrs-2.7/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/articles/web_only/benchmarks_files/header-attrs-2.7/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/news/index.html b/docs/news/index.html index 3a52a093..fae6503a 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -145,8 +145,8 @@ <h1 class="page-header" data-toc-text="1.0.4"> <a href="#mkin-104-unreleased" class="anchor"></a>mkin 1.0.4 (Unreleased)</h1> <ul> -<li><p>‘plot.mixed.mmkin’: Reset graphical parameters on exit</p></li> <li><p>All plotting functions setting graphical parameters: Use on.exit() for resetting graphical parameters</p></li> +<li><p>‘plot.mkinfit’: Use xlab and xlim for the residual plot if show_residuals is TRUE</p></li> </ul> </div> <div id="mkin-103-2021-02-15" class="section level1"> diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 49dceae1..df454e62 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -10,7 +10,7 @@ articles: web_only/NAFTA_examples: NAFTA_examples.html web_only/benchmarks: benchmarks.html web_only/compiled_models: compiled_models.html -last_built: 2021-02-24T14:04Z +last_built: 2021-03-31T17:15Z urls: reference: https://pkgdown.jrwb.de/mkin/reference article: https://pkgdown.jrwb.de/mkin/articles diff --git a/docs/reference/D24_2014.html b/docs/reference/D24_2014.html index e2d47f1b..c59bfad6 100644 --- a/docs/reference/D24_2014.html +++ b/docs/reference/D24_2014.html @@ -77,7 +77,7 @@ constrained by data protection regulations." /> </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> diff --git a/docs/reference/Extract.mmkin.html b/docs/reference/Extract.mmkin.html index 5a99cf1b..b1c73ba5 100644 --- a/docs/reference/Extract.mmkin.html +++ b/docs/reference/Extract.mmkin.html @@ -72,7 +72,7 @@ </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> diff --git a/docs/reference/Rplot001.png b/docs/reference/Rplot001.png Binary files differindex 1cbcb153..17a35806 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 745d583d..808f1968 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 19198739..97f650e2 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 1028a9c4..21ad6783 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 aa844051..4828f5b2 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 81525882..4aed2c87 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 10b7455a..3405a171 100644 --- a/docs/reference/Rplot007.png +++ b/docs/reference/Rplot007.png diff --git a/docs/reference/add_err-1.png b/docs/reference/add_err-1.png Binary files differindex 9ba106db..70118923 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 3088c40e..69b820c2 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 493a761a..1de78fa7 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 6fbecd14..18ca517e 100644 --- a/docs/reference/add_err.html +++ b/docs/reference/add_err.html @@ -74,7 +74,7 @@ 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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> diff --git a/docs/reference/confint.mkinfit.html b/docs/reference/confint.mkinfit.html index 06e78459..e81e0c7b 100644 --- a/docs/reference/confint.mkinfit.html +++ b/docs/reference/confint.mkinfit.html @@ -79,7 +79,7 @@ method of Venzon and Moolgavkar (1988)." /> </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> @@ -285,13 +285,13 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, <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'>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 class='fu'><a href='https://rdrr.io/r/base/system.time.html'>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'>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> </div><div class='output co'>#> user system elapsed -#> 4.255 1.029 3.937 </div><div class='input'><span class='co'># Using more cores does not save much time here, as parent_0 takes up most of the time</span> +#> 3.871 0.000 3.871 </div><div class='input'><span class='co'># Using more cores does not save much time here, as parent_0 takes up most of the time</span> <span class='co'># If we additionally exclude parent_0 (the confidence of which is often of</span> <span class='co'># minor interest), we get a nice performance improvement if we use at least 4 cores</span> <span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span><span class='op'>(</span><span class='va'>ci_profile_no_parent_0</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span><span class='op'>(</span><span class='va'>f_d_1</span>, method <span class='op'>=</span> <span class='st'>"profile"</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>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> </div><div class='output co'>#> <span class='message'>Profiling the likelihood</span></div><div class='output co'>#> user system elapsed -#> 1.469 0.092 0.911 </div><div class='input'><span class='va'>ci_profile</span> +#> 1.484 0.116 0.923 </div><div class='input'><span class='va'>ci_profile</span> </div><div class='output co'>#> 2.5% 97.5% #> parent_0 96.456003640 1.027703e+02 #> k_parent_sink 0.040762501 5.549764e-02 diff --git a/docs/reference/dimethenamid_2018.html b/docs/reference/dimethenamid_2018.html index 6845f74f..3b6ae721 100644 --- a/docs/reference/dimethenamid_2018.html +++ b/docs/reference/dimethenamid_2018.html @@ -77,7 +77,7 @@ constrained by data protection regulations." /> </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> diff --git a/docs/reference/experimental_data_for_UBA-1.png b/docs/reference/experimental_data_for_UBA-1.png Binary files differindex 33946ded..5527c07f 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 77f75678..4da8d6c3 100644 --- a/docs/reference/experimental_data_for_UBA.html +++ b/docs/reference/experimental_data_for_UBA.html @@ -100,7 +100,7 @@ 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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> diff --git a/docs/reference/f_time_norm_focus.html b/docs/reference/f_time_norm_focus.html index aa494b27..a0899ac8 100644 --- a/docs/reference/f_time_norm_focus.html +++ b/docs/reference/f_time_norm_focus.html @@ -73,7 +73,7 @@ in Appendix 8 to the FOCUS kinetics guidance (FOCUS 2014, p. 369)." /> </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> diff --git a/docs/reference/focus_soil_moisture.html b/docs/reference/focus_soil_moisture.html index 61f235db..bbacc554 100644 --- a/docs/reference/focus_soil_moisture.html +++ b/docs/reference/focus_soil_moisture.html @@ -73,7 +73,7 @@ corresponds to pF2, MWHC to pF 1 and 1/3 bar to pF 2.5." /> </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> diff --git a/docs/reference/logLik.mkinfit.html b/docs/reference/logLik.mkinfit.html index 9e5b4069..83fb4e48 100644 --- a/docs/reference/logLik.mkinfit.html +++ b/docs/reference/logLik.mkinfit.html @@ -76,7 +76,7 @@ the error model." /> </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> diff --git a/docs/reference/mccall81_245T-1.png b/docs/reference/mccall81_245T-1.png Binary files differindex 91fe060e..478462ae 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 b7dca4a7..4f8d3fa0 100644 --- a/docs/reference/mccall81_245T.html +++ b/docs/reference/mccall81_245T.html @@ -74,7 +74,7 @@ </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> diff --git a/docs/reference/mixed-1.png b/docs/reference/mixed-1.png Binary files differindex 28a376f4..b9454c86 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 23d955e3..b4f8db16 100644 --- a/docs/reference/mixed.html +++ b/docs/reference/mixed.html @@ -72,7 +72,7 @@ </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> diff --git a/docs/reference/mkinds.html b/docs/reference/mkinds.html index 5111a9e0..fe89012c 100644 --- a/docs/reference/mkinds.html +++ b/docs/reference/mkinds.html @@ -75,7 +75,7 @@ provided by this package come as mkinds objects nevertheless." /> </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> diff --git a/docs/reference/mkindsg.html b/docs/reference/mkindsg.html index 003e5e8f..a9686e4c 100644 --- a/docs/reference/mkindsg.html +++ b/docs/reference/mkindsg.html @@ -75,7 +75,7 @@ dataset if no data are supplied." /> </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> diff --git a/docs/reference/mkinfit-1.png b/docs/reference/mkinfit-1.png Binary files differindex de2a90a9..e1d0f2f4 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 180f2ee7..2c162e49 100644 --- a/docs/reference/mkinfit.html +++ b/docs/reference/mkinfit.html @@ -80,7 +80,7 @@ likelihood function." /> </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> @@ -431,10 +431,10 @@ doi: <a href='https://doi.org/10.3390/environments6120124'>10.3390/environments6 <span class='co'># Use shorthand notation for parent only degradation</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 class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>fit</span><span class='op'>)</span> -</div><div class='output co'>#> mkin version used for fitting: 1.0.3 -#> R version used for fitting: 4.0.3 -#> Date of fit: Mon Feb 15 13:43:26 2021 -#> Date of summary: Mon Feb 15 13:43:26 2021 +</div><div class='output co'>#> mkin version used for fitting: 1.0.4 +#> R version used for fitting: 4.0.4 +#> Date of fit: Wed Mar 31 19:15:41 2021 +#> Date of summary: Wed Mar 31 19:15:41 2021 #> #> Equations: #> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent @@ -574,10 +574,10 @@ doi: <a href='https://doi.org/10.3390/environments6120124'>10.3390/environments6 analytical <span class='op'>=</span> <span class='fu'>mkinfit</span><span class='op'>(</span><span class='va'>SFO_SFO</span>, <span class='va'>FOCUS_D</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span>, error_model <span class='op'>=</span> <span class='st'>"tc"</span>, solution_type <span class='op'>=</span> <span class='st'>"analytical"</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>}</span> -</div><div class='output co'>#> test relative elapsed -#> 3 analytical 1.000 0.550 -#> 1 deSolve_compiled 1.731 0.952 -#> 2 eigen 2.662 1.464</div><div class='input'><span class='co'># }</span> +</div><div class='output co'>#> <span class='message'>Loading required package: rbenchmark</span></div><div class='output co'>#> test relative elapsed +#> 3 analytical 1.000 0.547 +#> 1 deSolve_compiled 1.717 0.939 +#> 2 eigen 2.644 1.446</div><div class='input'><span class='co'># }</span> <span class='co'># Use stepwise fitting, using optimised parameters from parent only fit, FOMC-SFO</span> <span class='co'># \dontrun{</span> @@ -598,10 +598,10 @@ doi: <a href='https://doi.org/10.3390/environments6120124'>10.3390/environments6 #> 2 6 -64.983 -1 0.3075 0.5792</div><div class='input'><span class='co'># Also, the missing standard error for log_beta and the t-tests for alpha</span> <span class='co'># and beta indicate overparameterisation</span> <span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>fit.FOMC_SFO.tc</span>, data <span class='op'>=</span> <span class='cn'>FALSE</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='warning'>Warning: NaNs produced</span></div><div class='output co'>#> <span class='warning'>Warning: NaNs produced</span></div><div class='output co'>#> <span class='warning'>Warning: diag(.) had 0 or NA entries; non-finite result is doubtful</span></div><div class='output co'>#> mkin version used for fitting: 1.0.3 -#> R version used for fitting: 4.0.3 -#> Date of fit: Mon Feb 15 13:43:38 2021 -#> Date of summary: Mon Feb 15 13:43:38 2021 +</div><div class='output co'>#> <span class='warning'>Warning: NaNs produced</span></div><div class='output co'>#> <span class='warning'>Warning: NaNs produced</span></div><div class='output co'>#> <span class='warning'>Warning: diag(.) had 0 or NA entries; non-finite result is doubtful</span></div><div class='output co'>#> mkin version used for fitting: 1.0.4 +#> R version used for fitting: 4.0.4 +#> Date of fit: Wed Mar 31 19:15:53 2021 +#> Date of summary: Wed Mar 31 19:15:53 2021 #> #> Equations: #> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent @@ -610,7 +610,7 @@ doi: <a href='https://doi.org/10.3390/environments6120124'>10.3390/environments6 #> #> Model predictions using solution type deSolve #> -#> Fitted using 3729 model solutions performed in 2.802 s +#> Fitted using 3729 model solutions performed in 2.798 s #> #> Error model: Two-component variance function #> diff --git a/docs/reference/mkinmod.html b/docs/reference/mkinmod.html index 4ce9468a..1e8ad60d 100644 --- a/docs/reference/mkinmod.html +++ b/docs/reference/mkinmod.html @@ -78,7 +78,7 @@ mkinmod." /> </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> @@ -348,7 +348,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p> 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>, 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>, 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> -</div><div class='output co'>#> <span class='message'>Copied DLL from /tmp/RtmpiJ2M4Z/filee097a4a94a921.so to /home/jranke/.local/share/mkin/SFO_SFO.so</span></div><div class='input'><span class='co'># Now we can save the model and restore it in a new session</span> +</div><div class='output co'>#> <span class='message'>Copied DLL from /tmp/Rtmp17EVw2/file269cfd5cc541a9.so to /home/jranke/.local/share/mkin/SFO_SFO.so</span></div><div class='input'><span class='co'># Now we can save the model and restore it in a new session</span> <span class='fu'><a href='https://rdrr.io/r/base/readRDS.html'>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 class='co'># Terminate the R session here if you would like to check, and then do</span> <span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'><a href='https://pkgdown.jrwb.de/mkin/'>mkin</a></span><span class='op'>)</span> @@ -397,7 +397,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p> #> }) #> return(predicted) #> } -#> <environment: 0x55555b0c2760></div><div class='input'> +#> <environment: 0x55555c17ae90></div><div class='input'> <span class='co'># If we have several parallel metabolites</span> <span class='co'># (compare tests/testthat/test_synthetic_data_for_UBA_2014.R)</span> <span class='va'>m_synth_DFOP_par</span> <span class='op'><-</span> <span class='fu'>mkinmod</span><span class='op'>(</span> diff --git a/docs/reference/mkinpredict.html b/docs/reference/mkinpredict.html index 25e26419..5775ba62 100644 --- a/docs/reference/mkinpredict.html +++ b/docs/reference/mkinpredict.html @@ -74,7 +74,7 @@ kinetic parameters and initial values for the state variables." /> </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> @@ -410,8 +410,8 @@ as these always return mapped output.</p></td> </div><div class='output co'>#> test relative elapsed #> 2 deSolve_compiled 1.0 0.005 #> 4 analytical 1.0 0.005 -#> 1 eigen 4.4 0.022 -#> 3 deSolve 47.0 0.235</div><div class='input'> +#> 1 eigen 4.0 0.020 +#> 3 deSolve 46.2 0.231</div><div class='input'> <span class='co'># \dontrun{</span> <span class='co'># Predict from a fitted model</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> diff --git a/docs/reference/mkinresplot-1.png b/docs/reference/mkinresplot-1.png Binary files differindex ffd34f6f..963aa5b7 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 04ff15b8..fe3150e7 100644 --- a/docs/reference/mkinresplot.html +++ b/docs/reference/mkinresplot.html @@ -75,7 +75,7 @@ argument show_residuals = TRUE." /> </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> diff --git a/docs/reference/mmkin-1.png b/docs/reference/mmkin-1.png Binary files differindex 0db3379f..26586ab9 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 024a9892..dbcdd8ee 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 a23d7cb9..80245fc6 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 89975db5..328aa564 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 a2f34983..9ce5e919 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 c9800fe7..c73d14bf 100644 --- a/docs/reference/mmkin.html +++ b/docs/reference/mmkin.html @@ -75,7 +75,7 @@ datasets specified in its first two arguments." /> </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> @@ -234,9 +234,9 @@ plotting.</p></div> <span class='va'>time_default</span> </div><div class='output co'>#> user system elapsed -#> 4.630 0.415 1.717 </div><div class='input'><span class='va'>time_1</span> +#> 5.387 0.413 1.864 </div><div class='input'><span class='va'>time_1</span> </div><div class='output co'>#> user system elapsed -#> 5.694 0.000 5.694 </div><div class='input'> +#> 5.786 0.008 5.794 </div><div class='input'> <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> </div><div class='output co'>#> $ff #> parent_M1 parent_sink M1_M2 M1_sink diff --git a/docs/reference/nlme-1.png b/docs/reference/nlme-1.png Binary files differindex 728cc557..cca4ce0a 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 e8167455..c0d8e857 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 7b0c6a97..c6b43aab 100644 --- a/docs/reference/nlme.html +++ b/docs/reference/nlme.html @@ -75,7 +75,7 @@ datasets. They are used internally by the nlme.mmkin() method." /> </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> diff --git a/docs/reference/nlme.mmkin-1.png b/docs/reference/nlme.mmkin-1.png Binary files differindex 9186c135..90ede880 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 d395fe02..0d140fd1 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 40518a59..8a60b52b 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 189e34ef..03448606 100644 --- a/docs/reference/nlme.mmkin.html +++ b/docs/reference/nlme.mmkin.html @@ -74,7 +74,7 @@ have been obtained by fitting the same model to a list of datasets." /> </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> diff --git a/docs/reference/parms.html b/docs/reference/parms.html index e45d6a5c..b4346b13 100644 --- a/docs/reference/parms.html +++ b/docs/reference/parms.html @@ -74,7 +74,7 @@ considering the error structure that was assumed for the fit." /> </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> diff --git a/docs/reference/plot.mkinfit-1.png b/docs/reference/plot.mkinfit-1.png Binary files differindex e5da9f1c..54e5c46f 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 a11d1680..ff8418a3 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 c976d4b1..54f2b981 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 c8bc00fe..7a7bfc6c 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 6631aa68..6a6741e7 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 946b20c5..c4d0b9c7 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 10807ea8..802b00ef 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 b80c672d..ff6da93e 100644 --- a/docs/reference/plot.mkinfit.html +++ b/docs/reference/plot.mkinfit.html @@ -74,7 +74,7 @@ observed data together with the solution of the fitted model." /> </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> diff --git a/docs/reference/plot.mmkin-1.png b/docs/reference/plot.mmkin-1.png Binary files differindex 647dfb8a..d06d683c 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 1bc1c9db..d3678aca 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 50d6ffac..f84d5782 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 e049fa16..9919dacb 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 2421995b..945b863f 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 20f9033d..3348e050 100644 --- a/docs/reference/plot.mmkin.html +++ b/docs/reference/plot.mmkin.html @@ -76,7 +76,7 @@ the fit of at least one model to the same dataset is shown." /> </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> diff --git a/docs/reference/reexports.html b/docs/reference/reexports.html index 864c4ff9..c6d716a1 100644 --- a/docs/reference/reexports.html +++ b/docs/reference/reexports.html @@ -79,7 +79,7 @@ below to see their documentation. </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> diff --git a/docs/reference/sigma_twocomp-1.png b/docs/reference/sigma_twocomp-1.png Binary files differindex 6e61684e..fddb86a7 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 397582f0..1b4e45e4 100644 --- a/docs/reference/sigma_twocomp.html +++ b/docs/reference/sigma_twocomp.html @@ -73,7 +73,7 @@ dependence of the measured value \(y\):" /> </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> diff --git a/docs/reference/summary.nlme.mmkin.html b/docs/reference/summary.nlme.mmkin.html index d6840425..8df9011d 100644 --- a/docs/reference/summary.nlme.mmkin.html +++ b/docs/reference/summary.nlme.mmkin.html @@ -76,7 +76,7 @@ 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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> @@ -265,10 +265,10 @@ José Pinheiro and Douglas Bates for the components inherited from nlme</p> #> <span class='warning'>iteration limit reached without convergence (10)</span></div><div class='input'><span class='va'>f_nlme</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span><span class='op'>(</span><span class='va'>f_mmkin</span><span class='op'>)</span> </div><div class='output co'>#> <span class='warning'>Warning: Iteration 4, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>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> </div><div class='output co'>#> nlme version used for fitting: 3.1.152 -#> mkin version used for pre-fitting: 1.0.3 -#> R version used for fitting: 4.0.3 -#> Date of fit: Mon Feb 15 13:46:13 2021 -#> Date of summary: Mon Feb 15 13:46:13 2021 +#> mkin version used for pre-fitting: 1.0.4 +#> R version used for fitting: 4.0.4 +#> Date of fit: Wed Mar 31 19:18:24 2021 +#> Date of summary: Wed Mar 31 19:18:24 2021 #> #> Equations: #> d_parent/dt = - k_parent * parent @@ -278,7 +278,7 @@ José Pinheiro and Douglas Bates for the components inherited from nlme</p> #> #> Model predictions using solution type analytical #> -#> Fitted in 0.553 s using 4 iterations +#> Fitted in 0.537 s using 4 iterations #> #> Variance model: Two-component variance function #> diff --git a/docs/reference/transform_odeparms.html b/docs/reference/transform_odeparms.html index c3c756f6..bbaad91e 100644 --- a/docs/reference/transform_odeparms.html +++ b/docs/reference/transform_odeparms.html @@ -77,7 +77,7 @@ the ilr transformation is used." /> </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">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.4</span> </span> </div> @@ -3,14 +3,14 @@ Loading required package: parallel Testing mkin ✔ | OK F W S | Context ✔ | 5 | AIC calculation -✔ | 2 | Export dataset for reading into CAKE [0.2 s] +✔ | 2 | Export dataset for reading into CAKE ✔ | 14 | Results for FOCUS D established in expertise for UBA (Ranke 2014) [1.0 s] ✔ | 4 | Calculation of FOCUS chi2 error levels [0.5 s] -✔ | 7 | Fitting the SFORB model [3.4 s] +✔ | 7 | Fitting the SFORB model [3.5 s] ✔ | 5 | Analytical solutions for coupled models [3.1 s] ✔ | 5 | Calculation of Akaike weights -✔ | 12 | Confidence intervals and p-values [1.1 s] -✔ | 14 | Error model fitting [4.1 s] +✔ | 12 | Confidence intervals and p-values [1.0 s] +✔ | 14 | Error model fitting [4.2 s] ✔ | 5 | Time step normalisation ✔ | 4 | Test fitting the decline of metabolites from their maximum [0.3 s] ✔ | 1 | Fitting the logistic model [0.2 s] @@ -19,10 +19,27 @@ Testing mkin ✔ | 1 | mkinfit features [0.3 s] ✔ | 10 | Special cases of mkinfit calls [0.3 s] ✔ | 8 | mkinmod model generation and printing [0.2 s] -✔ | 3 | Model predictions with mkinpredict [0.3 s] +✔ | 3 | Model predictions with mkinpredict [0.4 s] ✔ | 16 | Evaluations according to 2015 NAFTA guidance [1.6 s] ✔ | 9 | Nonlinear mixed-effects models [7.9 s] -✔ | 14 | Plotting [1.7 s] +✖ | 10 4 | Plotting [1.7 s] +──────────────────────────────────────────────────────────────────────────────── +Failure (test_plot.R:20:3): Plotting mkinfit, mmkin and mixed model objects is reproducible +Figures don't match: mkinfit-plot-for-focus-c-with-residuals-like-in-gmkin.svg + + +Failure (test_plot.R:21:3): Plotting mkinfit, mmkin and mixed model objects is reproducible +Figures don't match: plot-res-for-focus-c.svg + + +Failure (test_plot.R:22:3): Plotting mkinfit, mmkin and mixed model objects is reproducible +Figures don't match: mkinfit-plot-for-focus-c-with-sep-true.svg + + +Failure (test_plot.R:30:3): Plotting mkinfit, mmkin and mixed model objects is reproducible +Figures don't match: plot-res-for-focus-d.svg + +──────────────────────────────────────────────────────────────────────────────── ✔ | 4 | Residuals extracted from mkinfit models ✔ | 2 | Complex test case from Schaefer et al. (2007) Piacenza paper [1.5 s] ✔ | 4 | Summary [0.1 s] @@ -32,6 +49,6 @@ Testing mkin ✔ | 4 | Calculation of maximum time weighted average concentrations (TWAs) [2.5 s] ══ Results ═════════════════════════════════════════════════════════════════════ -Duration: 40.9 s +Duration: 41.0 s -[ FAIL 0 | WARN 0 | SKIP 0 | PASS 174 ] +[ FAIL 4 | WARN 0 | SKIP 0 | PASS 170 ] diff --git a/vignettes/FOCUS_D.html b/vignettes/FOCUS_D.html index f1f078db..f740cfc0 100644 --- a/vignettes/FOCUS_D.html +++ b/vignettes/FOCUS_D.html @@ -60,6 +60,15 @@ if (!!window.navigator.userAgent.match("MSIE 8")) { !function(a){"use strict";a.matchMedia=a.matchMedia||function(a){var b,c=a.documentElement,d=c.firstElementChild||c.firstChild,e=a.createElement("body"),f=a.createElement("div");return f.id="mq-test-1",f.style.cssText="position:absolute;top:-100em",e.style.background="none",e.appendChild(f),function(a){return f.innerHTML='­<style media="'+a+'"> #mq-test-1 { width: 42px; 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type="text/javascript"> if (window.hljs) { hljs.configure({languages: []}); @@ -249,32 +253,6 @@ if (window.hljs) { -<style type="text/css"> -h1 { - font-size: 34px; -} -h1.title { - font-size: 38px; -} -h2 { - font-size: 30px; -} -h3 { - font-size: 24px; -} -h4 { - font-size: 18px; -} -h5 { - font-size: 16px; -} -h6 { - font-size: 12px; -} -.table th:not([align]) { - text-align: left; -} -</style> @@ -286,10 +264,6 @@ h6 { margin-left: auto; margin-right: auto; } -code { - color: inherit; - background-color: rgba(0, 0, 0, 0.04); -} img { max-width:100%; } @@ -305,6 +279,9 @@ button.code-folding-btn:focus { summary { display: list-item; } +pre code { + padding: 0; +} </style> @@ -317,7 +294,6 @@ summary { max-height: 500px; min-height: 44px; overflow-y: auto; - background: white; border: 1px solid #ddd; border-radius: 4px; } @@ -381,13 +357,13 @@ summary { -<div class="fluid-row" id="header"> +<div id="header"> <h1 class="title toc-ignore">Example evaluation of FOCUS Example Dataset D</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">Last change 31 January 2019 (rebuilt 2021-02-15)</h4> +<h4 class="date">Last change 31 January 2019 (rebuilt 2021-03-31)</h4> </div> @@ -455,16 +431,16 @@ print(FOCUS_2006_D)</code></pre> ## of zero were removed from the data</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 class="r"><code>plot_sep(fit, lpos = c("topright", "bottomright"))</code></pre> -<p><img 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5L056udHUwauJs28jBp4G7awMOkoYdKbVzzMAAAAAAAikQDCPLQlpamfvNDcdKdsvyC/8fefcdFcad/AH9mZ9tsoSwgiFgQUHq3R1GJvcQWY3LGqCmXckku/VLuNBdz6cnvcsmlGcVyJkawi7F3RaV3AUGQXrawZWbr/P5YQ4waAwo7y/K8X/4Bs9+Z+awFd5/9fp9vv9f+zHUcAACrUq3PyDWcy2FKKzuaCvEosSg8WBw6VDgkQDjYn1R49NDd7S2BBAF+Nz9k0xnMjS3m+mZLfZO5vtlc32xuaLa0qiytKjqn2D6GIEnBQD9h4EDhkABhYIAwMIBHiXsoKkIIIYQQQgihXgcLQIgb2v0nzLWNAGC4WEDnFFNx4Vwlsbbr9Ccv6jNyjJeq7HUfQigQR4SII4aJI0OEgQMJksdVNjueTCIKHiwKHvzrIZY1N7Waq+tNVxvMNfWmmnpzfbPpSp3pSt21AQQhGOArChosDB4kChkiHDyAEOA/doQQQgghhBDqu/A9IeKAVatTb/sZAKj4CDq7SLlhu390qOPrLMbyK9qfT+rPZrNmCwAQIiEVFy4dE0clRPLEIgeH6aA/my3o308YeNtd6glC4Ocj8PORjIqxH2CNJlNNvamq1lR11VRVa7pSa65tNNc2wonzAEAI+MLAgaJhgaLhgeLhgT03j6mDub6JJ5eScllP3wghhBDqXYxlVZpdhxWPLOD38+I6C0IIob4FC0CIA+of9tr0Bip6eL9Xn6h/8V1zbaP2wCm3mUmOuTtrNutPZ2l/Pmm8XAMAwONJEqOkSSMl8RGc78VOZxe1fLqOdJMN+M8q+7bxnUSIhKKQIaKQIfZvWYvVdKXWVFFtvFxjLL9irmsyllUZy6pgLwAA39tTFBYkHj5UFB4sHNgfCKJ7n4Wppr7htQ/5vt7+H7+OnYkQQgihDqzF2vrFJnN9M2u2+L7xFNdxEEII9S1YAEKOZr7aoD1yFkie5/KFBJ/0fHhe8wffqrfuk45P6OkJI6zFqjt8Rr39gFWpAQBSLpMlj5FPvcdJPoJjLVZlynYAsLbr1Nv2K5YvuONLEXzSvmpMDgAANpoxllUZL1UZy68Yy6osrSrLqUz9qUwA4Ekl4tChorBgcXiQMGhwt8zDUqWksWaLubaxPf24+9zku78gQggh5Bra9x0z1zcDAJ1dRGcXUfERXCdCCCHUh2ABCDmaMiUNrDa32ZOEg/wBQDIimooNo3NL1FvTvR5b3FN3tdl0Jy+qf0q3NLcBgHDoQLeZE6Xj4gmBoKfu2HXa/SfM9U18b0+LUqPdf0I+dZzA37dbrsyjxFRMGBUTBgDAsubaRqb0srHkMlNy2dKiNGQVGrIKAYAnFolCh4rDQ8QRwcLgIXdWDDJczKfzLxEiIWs0aVJ/liWNJN3l3fIsEEIIoV7NqtFq0g4AgGRktOFCvnLDdv+YUILEqbIIIYQcBAtAyKEMGbl0XilPJvFYOL3joGL5wvqX3tMePC2feo+9KtTtN1X9uNfec1owsL/nktmSkdHdvu7pLlk1WnXqfgDw+vODhgt52kNnlN9v8/37X7r/TgQhGNhfMLC/fMo9AGBpUxuLK5jicqa4wlzXROeW0LklYC8GhQdTkcPEkcOEgQGd/O1iLVbVxh0AoHh4Hp1TbMgqVG/Z4/XUQ93/LBBCCKHeRr1lj81ASxIifV56tP6Ff5nrmrT7T7jNnsx1LoQQQn1FHyoAaTSa0tLSyspKnU6n1+vFYrFMJgsMDAwPD/f09OQ6XZ/Ami2qzbsAwPOhuTy5tOO4IMBPPvWe9v0nlOtT/VY91413tDS3tX231b5XOt/P22PxLNn4RGcr/dipf9hr09NUQiQVFy4MGqQ/m03nlTpgcjjfy4M/PlE6PhEArOp2pqiCKS5nisrNtY322ekAwJNJqKjh4qjh4ujhAj+f21ytfc8Rc0OLIMBPNmUcFRtO55dqj56TTb1HFDSoR58FQggh5ORMVbXaYxkEn/R8ZD5BkopH5je997V6a7r0nkTSw6277mJpbDVV13XsEYEQQghdz/ULQBaLZe3atRs3bszIyGBZ9uYBBEGMGjVq2bJljz76qFDIcQ9g19a++4i5sUUwsL8seewND3ksmaU/ncUUlBku5EtGRnfDzWy29n3HVT/uZY0mnkzi+dBcWfIYp51lbbpSqz16zv5yEABIN5nHohnKDduV61L9o0Md1keZ9HCTjouXjosHAKtKwxSW0QVlTGGZpblNfy5Hfy4HAPj9vKio4eLoUCp6+PVVPACwqrWa7QcBQLF8IUGSfD9v+Yyk9t1HlOtS+695wTnrbgghhJBjKNengs3mNjvZvr7b/pEPnVOs3pru9ecl3XIL1mxpWvOlubHF58WV0rHx3XJNhBBCrsTFC0D19fVTpkwpLi6+/iBBEFKp1Gw2G41GAGBZNiMjIyMj47PPPtu9e3doaChHYV2cVd2u2XkIrlUHbmwuw5NK3BfPUH6/TblhOxUXdpeteUyVV1u//sFUWQMA0nsSFSsWOnkbGuW6NLDZ5HOTO5r+yGcmaQ+dMdc3aX/mZnI46ekuHT9COn4EAFia2+j8UqbgEpNfZmlu0x45qz1yFghCNHSgOCaMig0VDRtK8EnV/3bZaEYyMpqKDbNfxOP+GfqTF42XKvVnc+x1JYQQQqgP0p/JYoorSHe5+6LfLoHPv6Q9fEY+ZaxwaDdMlW3fd9zc2AIAqo07JIlRhNCJGh0ihBByBt2w44/Toml63rx5xcXFAoFg7ty5a9euLSkpaWlpMZvNWq2WYRij0VhRUbFhw4bFixdLJJLy8vLJkye3trZyHdw1qTbttNGMZHQsFXPrEpvbtPHCQf6Wptb2vcfu/DYsq07dX//6R6bKGn4/L983n/b563Inr/7oz2YzxeWku/z6vkgds4HUW9Ot6nbu0gEA8Pt5ye8d5/PCyoHr3vP/6G+eD8+joocTfL7xco1m+4HGf/z76vJXG//xf7rj54HP83x4PgCwZjPYbDxK7LFkFgCoNm5njSZun0WvxLLq1J/1py5ynQMhhNCdY01m+xJ4j4fm8CRUx3HBAF/5jCRg2bZ1qXCrWepdYlW3a9J+BgBS4W5pVWl2Hb7LCyKEEHI9rlwAWrNmzcWLFxUKxaFDh3bt2vXoo4+GhoZ6e3uTv6wDEgqFQUFBy5Yt27p1a0ZGxqBBgxoaGj744ANuY7skU2WN7uRFQsD3XHrf7w7i8RQrFgGAJu2AVaW5g7tYtbqmf32l/nEfsOA2Z/KAz96k4sLvOLNj/Pqi8MHZPCl1/UP2yeE2mlFvTeco3U0IQhgY4H7fvb7/eHbQhg9933rGbW6ycJC/jTEyxRXAsmCxNb37Zct/NtWsfL1x9efAsvLksaKgQZY2Nb4SvQP605nqH/e2frHZXNfEdRaEEEJ3SLPzkKVFKRwSIJ80+oaHPO6fQbrJjKWV+ozcu7yL6n+77fNw+730KBCEZsdBS4vyLq+JEELIxbhyASg1NRUA3n333aSkpD8cHBUV9c033wDArl27ejxZX/PLR1tuc5Jv30JYHDVMMiLaxhhVW/Z09SbGsqqGVz6gc4pJd7nvW88oHllAiHpBRyfNrsOW5jbhkAD55DE3P2pvpqM9fMa+nM2pEEIBFRumWDbf/9M3vB69HwAIAZ8npSyNrfoT51maYYor6l96r33fMbc5k4Eg7C9/uU7dm7BGk+p/uwGAtVqV3//EdRyEEEJ3ouMjEMXKhcC7eQk85bFkNgCoNtzVVFlT5VXd8fMEn/R8eL5o+FDpmDjWZFZt2X03yRFCCLkeV+4BVF1dDQATJ07s5PikpCSSJK9evdqDmfok/eksY2klABgu5Nm3GL8Nm04PALrj591mJAmHDuzkLdr3HVdt2sFarOLQIJ8XV5AKj7vM7BhWpfpaX6RbvSiEa5PDJ7TvPaZcl+b3zl+ds48yazLbX9p6PbFEljRSdyyj9ast9odMNfXKlO0AQIiELGNq/c9G3zef7hWFOWeg2XHI0qoSDhlgaVHS+ZccsCUcQgihbqfatJM1mqTj4sXhIbccIJ8yTnv4rKmyRrPnqMd1HYK6gGWV61OBZd1mTxb09wEAz2XzDVmF+tNZ8qnjxWFBd5MfIYSQK3HlGUDu7u4AcPny5U6Or66utlqt9rNQNzJdbbB/Ya5tNFXW3P6XpbkNAIBlTbWNnbk4a7a0/F+Kcn0qa7W5zU32ffv53lL9AQCl/UXh2N99UQgAHvfPJN1kTOnlu58c3kM02w9aWlXCoQNlE0cBQWgPnQEAj0XTRUMHAYBwsD/pLmeNJiCAKa6oeeTVpnf/255+wtKIzbZux9Kq0uw5AgSheGyxx/0zAEC5LpU1W7jOhRBCqAuMZVX6M1mEUOD5p99fAk8QipULry3aalXdwV30pzOZksukh9x9wVT7Eb63p/vcZGBZ5bptd99dCCGEejeWbfvuJ/XWfVzncAquPANowoQJqampn3/++eTJkymKuv1gq9X60UcfAcDIkSMdkq4P8XhgpnR0LEBXXn/w+cKB/f9wlI1mmj/8jim4xJNQ3n95uHv2j3cUY1mV/nQWIRTcri/SL5PD2779UbVhuyQ+wtmmz/xap1ixCAhCdyzDWFFNKjzc50+l4iMa3vzUXN/s/+kbNj2t2riDKa5gLRY6p5jOKVau2ybw96USIyVxEaLwIIJ00Fb3vYV9LYB0/AhxaJAoJFB75Jz5aoP255NuczjYEg4hhNCdYFnlulRgWff77uX387rNQHFokGRUjCEjV/3DHu9nl3XtJiazfb2w55/uu77DtPv8qbrj501Vtbrj52U39R5CCKG+Q3figvbASQAQh4eIo4ZxHYdjrlwAeuutt9LT0w8ePJiUlLR69erJkyeLxeKbh5nN5lOnTr3//vuHDh0iCOL55593fFTXRpBk5xdzdZ5Vo2169ytTZQ3p6e771tPCwQO6/RY96JcXhXwfhfbg6T8YbLMRJGlpVbXvPea+cJpD8nWWatMO1mgSDOxvVbfrTpy3L/iSjo4xZBcBgDh0KFNyufU/m9zmTJZNHmMsq2ItVlnyWJYx0rkl5vom8+6m9t1HeJSYig2j4iOo+Agn37LNMezdQAmhwPNPcwGAIHmK5Qub3vlC/VO6dHwi6eHGdUCEEEJ/THfyorGiGgD0GbmGrKLbD7bpDfZT5DOSRMGDO3+X38zDvQ4hFHg+NLfl3ymq/+2SjIq5vjaEEEJ9h41m7FVyAGj7/if/T94gSFdeBfWHXLkAFBMTk5KS8sADD1y8eHHWrFkSiSQxMdHHx8fLy0sul+t0OqVS2drampWV1d5+bZvtd955Jzk5mdvYqDMszW1N73xhbmgR+PfzfeuZ23+w5oTMja32F4XmuiZN3aFOnqU/k+VUBSBLm1p/NgcAzFcbWj75vuN4e/oJSD/R8a2xrOr6R41lVQM+e5O12oyXKunsIjq7yFRTrz+Xoz+XAwQhChpEJUZK4iOFgQHO2fOox/3SNN19wVS+t6f9GBUTSsVH0NlF6q37vP78ILcBEUIIdYalodn+hfmXtfB/jGUtjS2dLwDdMA/3hkel9yRoD55iSi5rth+8/XRjhBByVZrtB60qjShkiFWjNdc26g6fkU8bz3UoLrlyAQgA7r//foVC8dJLL+Xl5RkMhpMnT/7eyKioqPfee2/WrFmOjIfujOlKXdO7/7WqNKKgQf3eeKo3zhkR9PfxfvZhq6q9S2eJw4N7KM+dIT3c3GYmWZUaAGAZoyGvBGysOGo4KZN0jDHVNpqvNhASMRUdZn9pKo4JBQCC5InDg8XhwZ5L77O0KOmsQkNWIVNUbqyoNlZUq3/cx/fyoOIjqcRIKmo4IRRw9BQ5YN/0je/t6T7nN8VoxcpF9fml2sNn5VPGCYcO4ioeQgihTvJYPFMyKhZYW+dPIQQCQSeWwHf4db3wLTs9E4RixaL61z5s33tUNnmMwL9f56+MEEIuwNLU2r73GBCE4tH7rW2q5o/Wqn7YIx0bz5NLuY7GGRcvAAFAcnJybm5uXl7egQMHCgsLq6qqdDqdwWCgKEoulwcGBkZEREydOjUuLo7rpKhTTJU1jW//x6anqejhPq88zqNusayvV5AljfrjQc6NIHmKFYvsXzd/8C3YWNmk0d7PLL1+DGs21z2/xtLcRkUPl0+955bX4fso5NMnyKdPYI0muuASnVVEZxda2tTaQ6e1h04TIiEVNZxKiJQkRJIKF+/RbqMZ9dZ0APB8ZMEN/Z4Efj7y6Unte4+2rUvt/84LfXR6FEII9SI8njAwoOcuf8N64VuyLw3THctQbdrZ77Unei4MQgg5IeWG7azZLJs0WhQ8GIIHUzGhdF6pOnV/x1uYPsj1C0B2MTExMTExjrmXVqt94oknWls7u8lRfX09AJSWlvZkKBdhrmtqWvNfm56Wjonzfn45wcfOwU6BKSgzXMzniUWeD8254SFCIPB8eF7LJ9+rtuyWjom7fbmdEAkliVGSxCgAMFXVGrIK6MxC4+UaQ2aBIbOgjSCEgQMliZGSxEhh4ECXrICot6Zb1e2i0KHS0bE3P+rxwEz96Uz7K37pGKxZI4RQH3ar9cK35Pmn+wzn8wwX8+ncEio2zGEBEUKIW0xBmeFCPk8s8nzw2jsUxfKFdS+/1/7zSVnyWOEgf+JSd70AACAASURBVG7jcaWvFIAcqaamZuvWrWwXN92srq7uoTwuw9KibHrnC2u7jkqI9P7rctw3ylnYbMr1qQDgvnAa6XmLGTrSMXE6e7k97WfF8oWdvKowMEAYGOCxaIZV3U5nFRmyCui8UlNljamyRv1TOqlwlyREUolRrrRAzNzYoj1wEgjCa+UtWjkAAI8Sezwws+0bJ90SDiGEkMNoj5w1VdYAQYCN1ez8g2aCAv9+xopq5Ybt/lGv9/HupwihvuL6dyi/rCEQDOwvv3ec9sAp5fo0v1XPcpqPM329AGRf+XXgwIF+/bptXXREREROTk5LS0snx3/77bfbtm1zc8OdfW7HqtE2vfOFpVUlDgvq99KjWP1xHu0HTplq6vm+3m6zJ/3emGvl9v0nZJPHdLXcTnq4yZLHyJLHsGYzU1huyCykswosrSrtoTPaQ2cIkZCKHk4lRkkSInv79ljKdams2XL7Fj/ye8dpD501VdZo9hz1WDTdkfEQQgg5D/2JCwAALKv+Kb2Tp5ivNpiqrnZpizGEEOqltAdP3/IdiueDc/Rns5mCS4bMAvuyg76mrxeAcnNzAcBsNnfvZbu03OzAgQMAQLjiepbuYtPTTe98aa5vFg4d2O/1J11mxocLsOkNmp/2A4DikQWE4Hf/XAQD+8uTx2oPnlalpPn+4w7L7YRAQMWFU3Hh8Phi05VaQ2YhnVlgvFxjuFhguFjQRhCioEGSEdFUYqRw8IA7fD7cofNK6ewiHiX2eOC2regJQrFyYePf/0+z46Bs4qjbT/tHCCHkqjwfWWA4nwddmW9OeriJhg7suUgIIeQkbHqDvaumYtn8G96h8GQSj0UzlOtTlSlpVEwYIehz9RCXfcIsy9psnd12wWKxWK3Wjm9JnF3iTFijqfm9r01XagX+vr5vPs2TUFwnQr9Sb91n1eqEgQHCwf6Wptv1vZJNGq07dp7Ov0RnFVIJkXd5X+GQAOGQAI9F060qjSGrkM4qpPMv2XcQU/2wh9/PS5IYSSVEiSNCekejKJtNuW4bABAiQevnG/5wOMHns0aT+oe93s8+3PPhEEIIOR1R8GCcy4MQQrdkf4cijhomGXWLaRny6RN0R86aaurb959wn5t88wDX5rIFoE8//fTll1/u5OAhQ4Zc/21X2/egHsSyrV9uZkov8709ff/xl96443uX2Bhj8wffiEICb+6m7ISsam37gVMAYKqqrX1mdSfPUv+UfvcFoA6kp7v83nHye8exJjOdX0pnFhoyCyzNbe3pJ9rTT/AoMRUXTiVGSeIjeNdtTu9srFq9ubEFAKxqLa2+1MmzjOVXejATQgghhBBCvY25tlF74DTweL/Xe5QgeZ7LFzb98z+abftl4xNv2cPUhblsAcjdvW/9Qboqze4j+rPZPAnl+/e/9IXVLprtB5iCMqawXJIYKRoWyHWcP8CjRFTUcHNDZ9tdAQAQII4J7YkwhFBg30HMi11ivFxjuJhPZxWartTpz2brz2YDjycODZIkRlIjogX9fXoiwN0g3eUDPn7domrv0lmCAb49lAchhBBCCKHeSLlhO2u1yqdNuE1TCCp6uCQh0pBVqP5xn9dTDzkyHudctgD02GOPkST53HPP6XQ6Ly+vd95555ZtnhctWgQA33zzjZeXl8Mzoj9A519S/W83EIT3c8v6wntdS1Nr+55jAAAs27Z2m/8Hrzj5TueESOj71jNcp7gJQdgnxns+OMfSojRkFtCZBUxROVNczhSXw8YdggG+khFRVGKUeFgg8JxlMxTBwP6Cgf25ToEQQgghhFBvZbhYQOcU86QSjyUzbz9SsWIRnV+qPXpONmVcn1pR67IFIABYsWLFhAkTli5dmpGRsXr16rVr186Zc+tlNbNmzRowoPd1jXVtlua2ls/Wgc3msXhmH+nQrty4gzWbpWPimEuVpsoa3ckLsqRRXIfq3fg+CrcZSW4zkmw0Q+cU05kFhuwic12Tpq5Js/Mw6SajEiIliZFUbDhuqY4QQgghhFAvxrKqjdsBgLVYGv72cWdP2bTT7+3nezaYM3HlAhAABAUFnTp1as2aNWvWrJk7d+7jjz/+6aefymQyrnOhP8CazM0ffWfT6iWJUR73z+A6jiMwBWWG83k8sUixYhFdUNr6n02qTTslI2N4lJjraK6AR4mlY+OlY+PBZmNKLhsyC+iLBebGFt2xDN2xDEIgEEcNkyRGSRIjSYUH12ERQgghhBBCXcOazNZ2HQCwRtPtd6e5nqVN1ZOhnI6LF4AAgM/nr169etq0aUuXLv3uu++OHj26cePGsWPHcp0L3U7b11tMVbUC/37ezy1z8mVQ3cNmU6akAYD7gmmkwl02YaT20BljaaVmx6Fe0Q26N+HxxBEh4ogQeGSBubbRkFlguFhgLKuis4vo7KK27whh4EDJiChJYpQwMIDrrAghhBBCCKFOIUTCgP/+06bTd+ksnqvvMnQD1y8A2Y0ZMyY3N/fZZ5/dsGHDhAkT/va3v61atUogEHCdC91Ce/px3cmLPErc79Un+sim79qDp03VdXxfb7c5kwAACMJr5aL61z5q331YNnm0wM/pmha7BkGAn3uAn/u8KdZ2HZ1VaMgsoHNLTJU1psoa9dZ9fG9PKiFKMiJKHBFCCPrKj0qEUB8RFxcHAAcOHLhlh0SEEEKoN+JJKZ60T7x/vGN96F2NXC5PSUmZOXPmk08++e677+7fv3/z5s1ch0I3MtXUqzbtBILwfmapIMCP6ziOYNMb1FvTAUCxbD7xS1FSOHSQbMII3YkLqs27+r38GKcBXR/pJpNNGi2bNJo1m5mCMsPFAkNWoaVVpT1wUnvgJI8SU7FhVGIkFR9BynEBKULIFeTm5gKA2WzmOghCCCGEHKcPFYDsFi9ePHbs2GXLlh07diwhIYHrOOg3WKu19T8bWbNFPvUeyehYruM4iHrrPqtWJ44aJhkVc/1xz4fnGS7kGzJy6bxSqme2Tkc3IAQCKj6Cio/wYllT1VXDxQJDZoGpqlZ/Lkd/Lgd4PPGwQCoxSjIiqi9sS4cQ6o1YlrXZbJ0cbLFYrFZrx7ckSfZMKIQQQgg5BWfZAtmRAgICDh8+/OGHH17/ogc5A822/aaqWr6ft+ey+VxncRBzbaP2wGng8RTLF97wEOnh5j5vCgAoU9JYa2dfzaPuQRDCoYM8Hpjl/9HfAr5+x+uxxVRsGEHymNLLqs07655/p+7Zt5UbdzDF5fhHgxByKp9++im/E+yDhwwZcvNBhBBCCLmqPvqfPY/He+WVV+bNm1dTUwMA3t7eXCdCYKyoVu84CATh/czDPLGI6zgOotywnbVa5dMmCAcPuPlRt7nJumMZ5qsNuiNn5VPvcXw8BAB8b0/59Any6RNsjJHJLTFkFtBZReaGFvPuI+27j/BkEiouQjIiiooN6yMtqxBCzszd3Z3rCAghhBByUn20AGQXEhISEhLCdQoEAMCazK1fbAKrzW1usjgsiOs4DmK4WEDnFPOkEo8lM285gBDwPZfe1/zxWtWW3dIxcTy51MEJ0fV4YpFkdKxkdCzYbExZFZ1ZYLhYYK5r0p+6qD91kSBJUXiwfS95vi/WlBFC3HjsscdIknzuued0Op2Xl9c777xzyzbPixYtAoBvvvnGy8uru25ttVo///zzpqamTo4/efIkAFgslu4KgBByDazR1PTe1wL/fl5PLOE6C0Kupk8XgJDzUG3ZY65tFAT4eT44m+ssDsJarKpNOwDA44GZt2ktLBkdS8WE0nml6rSfb14mhrjB44lDg8ShQZ5L55kbWuiL+YasQqb0MlNwiSm4pFyfKhjY314JEg0LBILgOi5CqG9ZsWLFhAkTli5dmpGRsXr16rVr186ZM+eWI2fNmjVgwC3mn96ZnJycF198satnabXa7gqAEHINml2HmcIyprBMMiKaigvnOg5CLgULQIh7THFF+75jQPK8n13WsQ2Wy2vfe8xc3ywI8JNPG3/7kYrlC+tefq99/wnZ5DHCQf6OiYc6SdDfRzA32W1usk1voLOLDZkFdG6x+WqD5mqDZsdB0k1GJURKEiLFsWF9Z2EjQohzQUFBp06dWrNmzZo1a+bOnfv4449/+umnMlnP7mOYkJDw3XfftbW1dXJ8enr6yZMn5XJ5j6ZCCPUulja1Ztdh+9fKlDT/6OEE9qdHqPtgAQhxjDWaWr/YBCzrsWC6KGgQ13EcxKanNWk/A4Clqe3qytc7cQILLKv+YW+/157o8XDojvCkEun4ROn4RNZqNRZfNmQVGjLzLY2tumMZumMZhIAvjgiRJEZRCZF8HwXXYRFCro/P569evXratGlLly797rvvjh49unHjxrFjx/bcHQmCeOyxxzo/vrW19eTJk9h8GiF0PdXG7azRJBkda66pN9c1afefdJs9ietQCLkO/E8XcUy9/YCluU0YGOC+aDrXWRyHNVuAxwMA1mxmzebOnoX71vUGBEmKo4aJo4Ypli8w1zYaMgvpzAKmrIrOLaFzS2DtT8LBA6iESElipChkCC4QQwj1qDFjxuTm5j777LMbNmyYMGHC3/72t1WrVgn6zGRbhFDvYrxUqT+bQwgFikcWmGrqm9/7Wr0tXTphBOnWsxMYEeo7sACEuGRpbG3ffRQIwuuJJX1qeifpIR+49l3W1NnSjx1uMtXrCAL83AP83Ofda9Xq6OxiOrOAzi0xVdeZqus02w+Q7nIqPkKSECmOCeVRYq7DIoRck1wuT0lJmTlz5pNPPvnuu+/u379/8+bNXIdCCKGbsKxyXSqwrPu8KXwfBd9HQcWF0znF6h/3YjdohLoLFoAQl5QbtrNms2zSaFHIEK6zOBohEPSdhkeIlMtkSSNlSSNZi5UprqCzCgxZhb8uEOOT4ogQe7cg3EEMIdQTFi9ePHbs2GXLlh07diwhIYHrOAghdCPtkbPGyzV8Lw/3++61H1EsX1iff0l7+Kx86njhkG7rWI9QX8bjOgDqu+jcEsPFfB4l9vzTXK6zIOQgBJ+koocrViwK+GL1gP97y3PpPHF4MGtj6bxS5brU2mdW1/11jWrzTqa4Amw2rsMihFxKQEDA4cOHP/zwQysuKEYIORkbzah/3AcAnssWECKh/aBggK98+niw2ZQpaZymQ8h14AwgxA3WalWuTwUA9/tnkB5uXMdBiAMdC8RsOgOdY99BrMRc26ipbdTsPMyTSajYcEliJBUbzpNJuA6LEHIFPB7vlVdemTdvXk1NDQB4e+OUQ4SQU1Bv229Vt4uGD5WOjbv+uMfimfqTmUxhmSEjVzI6lqt4CLkMLAAhbmj3HTfXNQn8fd1mTuQ6C0Ic48k6dhCzGUsv01mFhqxCc12T/nSm/nQmkDzxsEAqIZKKjxAO8uc6LEKo1wsJCQkJCeE6BUIIXWNubNHuPwEEoVi56Ib9MXhSiceSWW3fbVWmpFFx4R2TgxBCdwYLQIgDVrVWvW0/AChWLCT4faj3M0K3R5A8cUSIOCLEc9l8S2OrIauQzipgiiuYkstMyWXV5l38fl5UXIQkMVIcGYI9pBBCCCHkApTr01izRX7vOFHQoJsflU8Zpz102nSlrn3vMfeF0xwfDyFXggUgxAHV/3bZaEYyIoqKC+c6C0JOiu/n7TZrotusiTaaYfJKDVmFdE6RpblNe+Ck9sBJQiSkooZTCRFUfCTfy4PrsAghhBBCd4LOv0RnFfIosceSWbceweMpli9sXP25evsBadJIvrenYwMi5FKwAIQczVh+RXf8PCHgKx5ZyHUWhHoBHiWWjI6VjI4FljVerqGzCg1ZRaaqq4bMAkNmAQAIhwyg4iMl8RGiYUOAh639EUIIIdQ7sFabKiUN/qgrqDhymGRUrOF8rvqHvd7PPuzAgAi5GiwAIUdTbdkNLOs2ZzLfD3tPItQVBCEKHiwKHuzxwCyrSmPILqKzi5i8UtOVOtOVOs32Azy5lIoNk8RHUrFhPLmU67gIIYQQQrej3X/CVFPP9/N2m5F0+5GKR+bT2UW6kxdkU8aKQ4McEw8h14MFIORQTHE5U1DGk0rc503hOgtCvRjp6S5PHitPHsuaLUxxBZ1dSGcXmRta9Kcy9acygccThQyRJERQ8RHCwQNu6KeIEEIIIcQ51miydwW1NLZWP/jXTp6l3rLX75/P92QuhFwZFoCQQ6l/3AcAbnMm8yQU11kQcgWEgE/FhFIxobBikbmhhc4qpLMLmZLLxkuVxkuVqi17SIWHJD6CigsXx4TyxCKu8yKEEEIIAQCwNhshEoLe0KWzCAG+gUXozuG/H+Q4TMElpriCJ5e6zZrIdRaEXJCgv49g9iS32ZNsjJHJK6Vzig3ZRValWnv4jPbwGYJPisKCJXHhVEKkYIAv12ERQggh1KfxKPHAb9dwnQKhvgULQMhxVFv3AYD73GQeJeY6C0K9g+7IOdLbk4oJ7dJZPLFIMipGMirGi2VN1XV0dpEhu8hYdoUpuMQUXIKNO65tJx8fLo4cRoiEPRQeIYQQQggh5DywAIQchM4tMZZWkm4y+R/1eEMI2dHZRa1f/Y8QCQf8++93uOkpQQiHBAiHBLgvmGbTGei8Ejq7iM4p/nU7eaFAHBFCxUVQ8eECP5/ufgYIIYQQQgghZ4EFIOQg6q37AMBt3hTsQoJQZ7BWqzJlOwCwRpPqf7t8nl9+lxfkySTScQnScQnXtpPPLqKzi4yXa+icYjqnGNaBoL8PFRdBxYWLI0IIoaAbngNCCCGEEELIaWABCDkCnV1kLL9Cesjdpo3nOgtCvYM2/YS5vonv621VafSns9ymTxANH9o9l+7YTn7xTKtGS+eW0DnFdF6JuaHF3HC8Pf04TgtCCCGEEELI9WABCPU8lrVP/3GfN+WGbiM2xogTghC6mVWjVafuBwCvxxYby6rU2/a3rf3J/8PXun1Dd9JdLksaKUsaCTabsaKazi6mc4qMlVd/nRbk50PFh1NxEeLwYOwW1OuxbOuXm0k3meey+VxHQQghhBBCjoYFINTjDJmFxss1pKe7fOpvpv+oU/ert6b7vLhSOiaOq2wIOSf1D3tteppKiLQvyNIdP2+qqtUdPy+bNLqnbsnjiYYFioYFeiyZ9ZtpQY0t5vQT7eknCIFAHBFMxYZT8eEC/963iZj2yFnSTS4ZEcV1EC5pj5zVHT8PAOLoUCo2jOs4CCGEEELIobAAhHqcett+AHCfP/X6riKW5jZN2kFgWVVKmiQ+AmcWINTBdKVWe/QcQZKKR+YDACEUeD40t+XfKar/7ZKMiuFJqJ4OcOO0oJxiOqfYeLmGzi2hc0sgJY3fz4uKDaPiwsVRw3vFJD46t6Ttqy2EgO//2Zt9dlGbjWbUP+6zf61MSfP/5HWCJLmNhBDqdu37T9BZhT4vrOBJJVxnQQgh5HSwAIR6FlNcYaqsIT3k8iljrz+u3LiDNZuBICxtas2OQx5LZnGVECFno1yXBjabfG5yx0Qb6T0J2oOnmJLLmu0HPZfe57goHdOCHphl1eqY3FI6p4jOK7U0t2kPntYePE3wSVFoEBUbRsWGCQcP6PYVat2CtdpUG3cAAGu2qDbs6PfaE1wn4oZ6236rul0cGmTVtJtrG7UHTrnNnMh1KIRQdzI3tKg2bGctVvXWfYqV93MdByGEkNPhcR0Aubj2vUcBQD51PCH4dfoPU1hmyMglREKfF1YAQWh2H7Y0t3GXESEnoj+bzRSXk24yj4XTfz1KEIoVi4Ag2vceNdc3cxKMlMuk4xO9n3tk4Np/+X/4queDc8ShQSzLMoVlqs276l9+/+rjb7Z+sUl/OtOq1XGS8PdoD5w01dTzfb15lNhwMZ/OK+U6EQfMjS3a/SeAIDxXLPR8ZAEAqLemO9ufFELoLtmrPwDQfuCUqaae6zgIIYScDhaAUA+yNLYaMgsJgUB+/eZfNpsyJQ0APBZMk46Nl96TwJrMqs27OEuJkNPo+Lfg8eAcnvQ3S72EQwfKJo5iLVbVpp0cpfsFQQiHDnJfOM1vzQuD1n3g89KjsuQxfC8Pq7pdd/x8y/+lXF35esPfPlL/uJcpvcxabdyGtekM9lWoipWL3BdOAwDl+lTWauU2leMp16exZos8eawoaJAkMYqKC7fpDR0rwhBCLoDOKzFkFvAosXRcPFhtqg07uE6EEELI6eASMNSD2vcfB5tNOnEU6S7vOKg9dMZ0pY7v7ek2exIAKB6eZ7iQrz+bLZ8+Xhwewl1YhLin2XXY0twmHBIgTx5z86Oef7rPcD7PcDGfzi1xkg6+PCklHRNn7+Nuqqlnckvo3GKmpNJYUW2sqIbUn3kSShw13L5GjO+jcHxC1Q97bFq9OGq4JCGSjQnTHT1nrm3UHjjtNjPJ8WG4QudforMKeZS4Y7Gt4pEF9fmXtIfOyKfeIxw8gNt4CKG7x/5S8XFfNF2ePJbOv0TnlRguFvTxzvcIIYRugDOAUE+xGWjd0QwgCLdZE389+MtnzorlC+2Nn0mFh/u8KfBL3xOOwiLEPatSrdl5CAAUKxcC7xY/nEkPufuCqQCgTElzwjkswkH+bnOTff/x7KCUD3zfeMpt5kSBv6/NQBvO57Z980PtU/+oe+4d5bpUOruINZocE8l8tUF7+AyQPMWKhQBA8EnPh+cBgHrrvr6z+om12pTrUwHA4/4ZpIeb/aAgwE8+bTzYbMr1aZymQwh1D+3PJ0019Xw/b7eZE3kyicf9MwBAuSGNNVu4joYQQsiJ4Awg1FN0R87ZaEYcNfz6j5fVP6VbtTpx5DDJ6NiOg+733as7lmHf+Uh+7zguwiLEPeWmnazRJB0bf5upcG6zJ+uOnDXXNmoPnnab4aRzWAiRkIqPoOIjAMDS3EbnldK5JUzhJXN9k7m+qT39OCHgi0KHUjE93jpamZIGVpvbrInCQf72I5IR0VRsGJ1bovlpv+LRPtEhVbv/hPlqg8DPR/7bvzAeD8zUn8pkCssM53Mlo2J/73SEkPOz6Qzq1P1g/3RNwAcA+bQJusNnTTX17enH3e+7l+uACCGEnAXOAEI9w2Zr338CANxnT+o4Zq5r0v58Cng8xfKF148lhAL7xkbqH/ba9LSDkyLkDIxlVfrTWR3/Fn4PwSc9H54PAOofe8ccFn4/L/mUcf1eeWzgug/6r3nRY9EMUcgQ1mpjCjpaR7/R+vkG3YkLVnV7997acD6XzivlySQei2Zcf1yxfCFBkn2kQ6pNZ1Cn/QwAihXX3hZ24EklHg/MBADlhh2s2cxNPoRQd/h1rWvitQVfxC8zHzWpP1tVGk7TIYQQciI4Awj1CMOFfEtzm8Df1z4LwM6+bkU+9R7hkBtbTkjHxmt/PsUUl6vTflYsm+/YsAhxjWWV61KBZUkPN82Og384nBAKbHpD75rDQpA8UehQUehQjyWzbDoDnV9K55UweaWWVpXu5EXdyYtAEMLB/lR0qDgmTBwedP2+gXeAtVjt7bQ9H5zDk0uvf0gQ4Cefek/7/hPK9al+q567q2fl9FRbdtu0eip6OJUQefOj8qn3aA+dMVXXte856r5gmuPjIYTunrm28fq1rh3s9SBDZoHqx73eT/2Jq3gIIYScChaAUI/Q7D0KAG4zkzoWdxiyCumcYp6U8lgy+5anKFYurH/1Q+2+4/J7xwr8fR2XFSGuWTVa4+UaALA0t2kPnenkWYbsol5UALoeTyaRjo2Xjo0HAHNtI51XSueVMEXlpit1pit1mt1HCKFAHBYsjgmlYkKFg/zvYI1Y++7D5oYWQYCf7FarSj2WzNKfzmIKygwX8iUjo7vhKTkl89UG7ZGzQPI8fzvp8lc8nmLFwsbVn2u2H5RNHE0q3B0bECHUDZTr08Bqc5v561rXDorlC+m8Et3RDPmUe0TBgzmJhxBCyKlgAQh1P2NFtbG0kieVyCaNth9hLVb75hQe988k3WS3PEs4JEA+eYz28Bnlhh2+rz/puLgIcY30cPN962lLs7JLZ4lChvRMHIcSBPgJAvzcZk1kzRZjaSWdV0Lnl5qqaum8EjqvRAVAerhR0cPFMaFUdCjp2akKhVWt1ew4BPZ1T+QtVjrzpBL3xTOU329TbthOxYXd5Wwjp3WtBdLsSTe/LewgjhwmGRVjOJ+n2rLb+y8POzIeQujuGc7n0nklHV2fb8D383abkaTZfUS5Pq3/mhd6ruEaQgih3gILQKj7te87BgDyKePs+3wBgDb9uLm+STDAVz5jwm1O9Hhwtv5cNp1VSOcUU3HhjsiKkHOgYpxiW3cOEQK+OGqYOGqYJ9xnbdcxBZfo3BImv9TSpr62RgxAOMhfHB1KxYSKw4M7frzcTLVpp41mJKNib/O76jZtvO7QGVNNffu+4/aNCF2MIeOXFkgLp99+pGLZfDq7WHfignzaeNeoKiLUR/y61nXJ7BvWunZwv3+G7lSm8VKl/my2dFyCYwMihBByOlgAQt3MqtQYzuYQJCmffq3WY9Vo1ak/A4AsaZS5uu72p0tHx2mPnFVu2O4fPZwgyR6PixByPqSbTDouwf5exVzbSOeXMnmlTFG5qabeVFPfvvcowSdFw4dSMaHi6FDR0IHA+3Waj6myRnfyAsEnb99OG3g8xYpFjW9/rkk7IEsa2cm5Rb0Gyyo37QQA1myp/9tHnTxFtWGH35oXejYYQqj7/LrWdco9vzeGR4k9lsxq+2qLauMOSWLUbUrnCCGE+gIsAKFupjt+nrVapWPi+N6e9iPt+47bDDQAqLbsVm3Z3ZmLmGsbDWezpeNH9GBQhFBvcG2N2MyJrNVqvFTF5JfS+aXGihqmqJwpKocte3hSiThqGBUdKo4eLvDzaVu7zd5Ou3334euvw1qsBI+4vlQEADxKbKMZ1ZY93s8sdezT6lms2cIaGABgjSZLU2snz7K294J95RBCdn+41rWDfPIY3aEzxopqza7DHotnOiogQgghZ4QFINStWFZ37BwAyCaPfdSHWAAAIABJREFU6TgmDh3KBA9mbWznL0PwScHvN61ACPVBBEmKw4PF4cEeS2bb9DRTWEbnlzL5peaGFkNGriEjFwD4PgpLixIALK2qzrfTpvNKezA3FwihYMB/V9u6WNAhPdx6KA9CqNupf9xroxkgiLZvt/7hYJvOAACaXYflU8eTHvKeT4cQQshJYQEIdSemtNLc0ML38qBiQjsOUvER128GjxBCd4knpSSjYiSjYgDA0txGF1xi8kuZ/DJ79QcAAAi+l4cgwFcwwE/Q30d/NocpqQAA6Zh4cfTwG64mGh7o0PQOwaPEPErMdQqEUE+x6Q0AACzb+Vl+YLPZaAYLQAgh1JdhAQh1J93RcwAgnTjqhnUWCCHUQ/j9vOTJY+XJY4FlTVfq6LxSpuASU3rZ0qaytKnovFKC5LFWFggAFgz5pYonFpPyW+9FiBBCvYXPCys8H54HbBemV/NkEp5U0nOREEIIOb8+VADSaDSlpaWVlZU6nU6v14vFYplMFhgYGB4e7unpyXU6V2CjGcO5HCAI+aQxfzwaIYS6F0EIAwOEgQHu8+5lzRbjpUo6/xKTX2qsqAFggQUAYPWGhlc/dJs9WRw1TDiwP26KjBDqrXg8fj8vrkMghBDqZVy/AGSxWNauXbtx48aMjAz2Vp+TEAQxatSoZcuWPfroo0Ihbo5w5wxnc2yMURwewvfz5joLQqhPIwR8ceQwceQw/ZABLZ+u41Fiydh4pvCSpanN0qJUrk8FANJDLo4cTkUNF0cNw/dRCCGEEELI5bl4Aai+vn7KlCnFxcXXHyQIQiqVms1mo9EIACzLZmRkZGRkfPbZZ7t37w4NDf2di6E/oLW3f04ezXUQhBACAGBNZtXmXQDguWy+fMo4AGj96n+6I+dIdzmQPKtSoz+dqT+dCQB8X297JUgcOYx0xwYZCCGEEELIBblyAYim6Xnz5hUXFwsEghkzZsydO3fcuHHe3t6enp4kSQKAyWS6evXqmTNn9u3bt3fv3vLy8smTJ+fn53t74wSWLjPXNxlLK3mUWDo6jussCCEEAKDZddjS3CYcEiBPvrYuVbFsPn2hwKrR9nv5MUGAH1NYRhdcYgrLLU2t2qZW7eEzQBDCgf3FUcPEkcPFEcE8CcXtU0AIIYQQQqi7uHIBaM2aNRcvXlQoFNu3b09KSrp5gFAoDAoKCgoKWrZsWUFBwezZs2tqaj744IOPPvrI8Wl7O93RDACQjksgRLiMDiHEPatSrdl5CAAUKxd2tKXnSSUeS2a1fbdVmZI24N9/l0+fIJ8+AWw2Y1UtU1Bm7x5tqqk31dS37zsOPJ5o6EBx1HBx5DBx6FD84YYQQgghhHo1Vy4ApaamAsC77757y+rPDaKior755psZM2bs2rULC0BdxVptuhMXAEA2Gdd/IYScgnLTTtZoko6NF4eHXH9cPmWc9tBp05W69r3H3BdOAwDg8URBg0RBg9zn3ctarMayKqawjCm4ZCy/YqyoNlZUa3YcJPikaFigOHKYOGq4KGQIwSe5eVYIdUVNTc25c+cKCwv9/PySkpIiIyNvOay6unrnzp0A8Pzzzzs2IEIIIYQcypULQNXV1QAwceLETo5PSkoiSfLq1as9mMlF0TlFVpVGEOAnGhbIdRaEEAJjWZX+dBYhEHguve/Gx3g8xfKFjas/V28/IE0ayff+zS6QBJ8UhweLw4Nh8UzWaGJKLjOFl5iCMmNVLVNcwRRXwE/phEgoDg0SR4aII4cJhw4iSJ7jnhhCnWOxWN5+++333nvParXajxAEsWDBgu+//97d3f2GwaWlpX/9618BC0AIIYSQq3PlApC7u3tzc/Ply5c72de5urraarViA6A7oDuWAQCyybj7O0LICbCscl0qsKz7fffecnsvceQwyehYQ0au+oc93s8u+73LECIhFRtGxYYBgE1PM8XlTEEZU1hmutpA55XQeSUAwKPE4vBgcUSIOHKYMDAA95VHTmLJkiVpaWkAQBDEwIED29ra9Hp9WlpaRUXFkSNHvLxw2zuEEEKoL3Llzy0nTJgAAJ9//jlN03842Gq12ld+jRw5sseTuRarRktnFhIkKZuAv3UIIe7pjp83VlSTCg/3+VN+b4xi2XxCINCdvMiUXu7MNXlSSjIiWrFykf+nbwxc+y+fF1fKp94j8Pe10Ywhq1C5cUf9qx/ULH+t+YNv2/cdN1XXAct23xNCqGv2799vr/78+c9/bmtrq66uVqvV33//vVQqzcvLe/LJJ7kOiBBCCCFuuPIMoLfeeis9Pf3gwYNJSUmrV6+ePHmyWCy+eZjZbD516tT7779/6NAhgiBw/nNX6U9dZK1WyYho0gP3TkYIccxGM6otewBA8fB9t2nbzO/n5TZ3sibtgHJdmv8Hr3Rp5g7pLpeOjZeOjQcAq1JDF5YxRWX2rcQMF/MNF/MBgJTLRNdmBoUIB/bHmUHIkb766isAWLBgwddff20/wufzV65cGRwcPHny5NTU1LS0tIULF3KaESGEEEIccOUCUExMTEpKygMPPHDx4sVZs2ZJJJLExEQfHx8vLy+5XK7T6ZRKZWtra1ZWVnt7u/2Ud955Jzk5mdvYvY7+TDYAyJJw+g9CiHua7QetKg3f15vv72uqrLnNSComVLvvuKmyRnfygixp1J3djlS4yyaMkE0YAQCWVhVTWGb/ZWlVGc7nGs7nAgDpJhNHhNh/CQL8sBjk/IxG4x2fKxKJujHJnSktLQWAF1544YbjEyZMePnllz/44INXX311zpw5QmE3b2xH0/TLL7/c0tLSyfF5eXkAYDKZujcGQgghhH6PKxeAAOD+++9XKBQvvfRSXl6ewWA4efLk742Miop67733Zs2a5ch4LsDSojRWVPPEIio+gussCKG+zkYz7XuPAoClqbXhtQ87eZZ62/47LgBdj+/tKZs4SjZxlD0AU1TOFJUzhWWWNrX+XI7+XA4AkO7yaz2DsBjkxG45X7iTWCdY/VdTUwMAw4YNu/mht956a+PGjZWVlV9++eXNFaK7VFZW9t///rerZxkMhu6NgRBCCKHf4+IFIABITk7Ozc3Ny8s7cOBAYWFhVVWVTqczGAwURcnl8sDAwIiIiKlTp8bFxXGdtFfSn8sBlqUSIgmhgOssCKG+jhAKJYlR5qa2Lp0ljgju9iR8X2+Zr7e9Nb6l0V4MKqMLy63KG4tB9pViuEwMdSNPT8/Gxsa6urp+/frd8JBMJnv//fcfeeSRt99+e/HixQMGDOjG+8bExBw9erS1tbWT4zdv3rx79243N7duzIAQQgih27jDApBWqz116tSFCxdaWlr69eu3atWquro6Hx+fbp9O3F1iYmJiYmIccy+GYV555ZWmpqZOju/VU6ANZ7MBQDoWy2cIIe4RJM/npUe5TnEjvp+3zM9bljwGAMyNLUxRubGonCkq/83MIOwZ5EwyMzO5jnBXoqOjGxsbP/74482bNxM3/V1atmzZ5s2bDx06tHLlyvT0dJIku/HWkyZN6vzgCxcuAACP58obkiCEEEJO5U4KQF999dUbb7yhVqvt38bGxq5aterHH398//33X3/99RdffLFbE/Y+paWlX3zxRVfP6o1ToC3NbcbLNTyxiIrD9V8IIfTHBH4+Aj8fefJY6JgZVFzOFJVf3zOIJ5OIw4LFESHi8GDhkAGAb48dLiEhgesId2Xp0qUHDx7csmWLSCT697//LZffuEXDd999FxUVdfDgwWXLln377bechEQIIYSQ43W5APTPf/5z1apVAMDj8YYOHVpRUWE/zuPxWltbX3rppbq6uk8++aSbY/aMS5cu5eTkMAwTExMTERHRXdOXYmNjjxw50tbW2TUIvXcKtD4jF9d/IYTQnbl+ZpClqZUprmAKy5nickuLsmM3MZ6EEoUFicODxeHBwqGDCBKLQc4rPz9fKBSGhoZyHQSWLl2akpJy9OjR9evX//DDD9HR0cuXL3/qqac6BgwePHjDhg1LlizZsmXL8ePHw8LCOEyLEEIIIYfpWgEoMzPTXv1ZtmzZJ5984u3t3TG1+C9/+QtN02+++eann3760EMPOc+nZyUlJV999VVeXp5KpYqNjX3ttdciIiKsVutjjz22YcOGjmaNHh4eX3/99QMPPNAtN508eXLnB/feKdC4/gshhLrFtZ5Bk0YDgKW5jSmuYIrKmeIKS1MrnVVIZxUCAE8sEg0fam8bJAoeTAhcv4tfL2Kz2SZOnCgUChsbG7nOAgRB7N279+mnn960aRPDMBcuXIiPj79hzPz589PT0xctWlRfX19fX89JToQQQgg5WNdePv773/8GgFmzZm3YsOGGhwQCwRtvvFFfX//ll19+8sknW7Zs6baMdyElJeXpp5+madr+bUFBwc6dO0+cOLF79+6UlBT7QZIkrVarWq1esmRJWVnZ3//+d87i9iq4/gshhHoCv5+XrJ/Xtd3E2tTG4nKmuIIprjDXNdF5JXReCQAQAoEoZIg4IlgUFiweHkiInLQBnythWfbw4cMXLlzQ6XQ3P1pUVKRSqe5m+7DuRVHU+vXr33777Z07d9bU1ERE3OJ/6uTk5KtXr27YsOGHH36oqKjofO9ChBBCCPVSXSsAZWRkAMDLL7/8ewMef/zxL7/8Mj8//25zdYcrV6489dRTDMN4e3tPnz5dKpWePHmypKTkySefLC8vF4lE77333p/+9CdPT8+SkpJVq1bt3Lnz3Xfffeihh4KCgrjO3gtc2/8rMQrXfyGEUA/he3nwx4+Qjh8BAFZ1O1Ny2VhcwRSVm642MMXlTHE5ABAkKQwaKA4LFoUFicOCeFIJ16ldkMViWbp06datW28/7J577nFMnk4aNGjQc889d5sBMpnsmWeeeeaZZwBAr9c7KhdCCCGEuNG1AtDVq1cB4Dbr2wcOHAgAVVVVdxmrW/zrX/9iGCY6OvrIkSPe3t4AYLVaZ82adeDAAQD45z//+cILL9hHRkdH79ixIzk5+ejRox9//PFXX33FZe5ewnAuBwCkY3D9F0IIOQLp4SYdE2f/qWvTGZiSy0xxubG4wnil1lh2xVh2BXYdBoIQDvIXh18rBpGe7lyndhFpaWn26s+IESN8fX0PHTpkNBrnz59PkqRarT59+jTDMG+++eYrr7zCddI7J5VKuY6AEEIIoZ7VtQKQu7t7c3Pz1atX/fz8bjmguLgYAAYPHtwN0e5adnY2APzrX/+yV38AgCTJ999/314AWrFixQ3jX3nllaNHjxYUFDg4Z290bf0XJabiwrnOghBCfQ5PJpGMiJKMiAIAG80YL1UZSy8zReXGihpTdZ2pug72nwAAQX8fUWiQvR4k8PPhOnUv9v333wPA448/bt8z65NPPnn55ZdffPFF+5SfjIyMiRMnlpSUuLtjxQ0hhBBCzqtrBaCRI0fu3bt38+bNI0aMuOWAPXv2AEBMTEw3RLtr5eXlABAX95spKpGRkQRBCASCAQMG3DA+PDwcAEpKShyWsPe6tv4L9/9CCCGu8SgxFRtGxYYBAGs2GytqjMUVTOllY2mluaHF3NCiO5YBAKTCXRwWJAoNkoyI5nt7cp26l7HvedqxkZZ9q4fz58/bC0CjR49+7rnnPvroo717986ePZvDnAghhBBCt9G1naf++te/EgTxxRdffPfddzc/umfPno8//hgA5s+f3z3p7o59MvMNTQ35fL5MJpNIJB37l3UwGAwAYLFYHJaw98L1Xwgh5IQIgUAcFuS+cJrvm08P2vCh/4evKpYvlIyKJd3lVqVGfyZb+f22prc/5zpm79PQ0AAAgwYNsn8bEhICv1SF7B599FEA+Oabb7hIhxBCCN0KyypT0tr3HOU6B3IiXZsBlJyc/Oqrr37wwQdPPPHE119/fe+99wKAUqn87LPPzp49u337dpvNdv/99y9evLhn0nZNaGhoQ0PDiRMnbpgElJeXd8vxOTk5AHDzzCB0A1z/hRBCvQCPJxw6SDh0kNvsSQBgrmuyTwsSDsb/5rrM19e3urraYDB4eXkBgEwm8/LyKisr6xgQFBQkEAjse2UghBBCzkB38kL73mNAEKKwIFGwUzRpQZzrWgEIANasWUNR1EcffZSdnW1vslNTU/Piiy8CAEEQDz/88OefO8tHi0lJSceOHXvrrbcSEhLGjx/fcTwwMPDmwWq1etWqVQAwffp0x0XsnfRns3H/L4QQ6l0EA3wFA3zlyWO5DtIrhYaGVldXZ2Rk2De7AIDg4OCsrCyr1UqSJADYbDaLxWKfSowQQghxzsYYVf/bDQDAssp12/q/+xLctAIG9UFdWwIGAHw+f9WqVWVlZX//+98XLFjw/+zdeVxU9foH8OfMBjPMwDCAK2KuuaBkmntq4JKa+1op5pZlu5bVza6Zllpp+01/uYHmUqDgguGeGyhuKCqpoOygMAuzMtv5/TFmhkigMGeY+bz/gu95zsznde3W+Mx5vt/Q0NDg4ODw8PDZs2cfP348OjpaLpfXRtCHMG/evObNm+v1+vDw8H79+s2YMaPCsv379y9ZsqRt27bXrl2TSCR3J/zhQQzJ5wnzXwAA4DEce/3Mmzfv5MmTjpUuXbpoNJpff/3V8WtCQgLLsi1btuQsIgAAwD002/balBqvFiF8f7+yqzd1R09znQhcQrWfAHJo1KjRp59+WrNRapxYLN6xY8eMGTOSk5P/+OOP48ePr169+v6yYcOGmUwmR/26descg/3wIDZ1aVlGNuMlcmw4CgAA4Pbmzp27fv36jIyM7t27x8XFjRgxYvjw4T/++OOMGTPOnz/P5/N//PFHIhowYADXSQEAAMhaVFy68yAxjGLGeEtuYfGPG1Ub4yVdO/K8vbiOBhx7yAZQXdG+ffsTJ07Ex8enpKT8+eefDypTKBTDhg2bN2+e4yAwqITx7CViWXFoa8x/AQCAhxCLxVu3bn3xxRfvfpYYOHDgoEGDEhMTv/jiC8dKkyZNPvroI+4yAgAA3KGM3s5aLNJ+3bxaPebVsqk28WjZ9azSuH3yiTiq0tO5eQOIiBiGGTly5MiRIx9UkJOTExgY6MxIdZrh7CUiEj/ZnusgAAAAztO5c+dLly5lZGQoFArHyrZt2xYvXpyQkMDj8Xr27Llw4UJ/f39uQwIAAJjSrhpOpvK8vfxfGE5ExDCKaWMLPlqhiT8gDe8hqBfAdUDgUvUaQE899VQVK1NSUqofhhvo/lQda7OZLvxJRDj/CwAAPA2fz2/duvXdXyUSyeeff/75559zGAkAAOAf7Hblulgi8hs9kK/wc6x5tW4mfbqL7kiKakNc0NzpnOYDjlWvAXT6NPaO8mhllzPsBqMopBE6xwAAAOA6lOtijOeuNFj4Jt/fj+ssAACc0e49Zs7KE9QL8B0Wfu+6/6QRhlMX9EnnZJeuebfHpreeq3oNoJUrV96/yLJsUVHR6dOnd+/ezbLslClTXn/99RqKB67FcDaNMP8FAAAArqTs6o3ShD+IZVW/7Ah8fTLXcQAAuGHXG9RbE4hIMWUUI/zHhq18hdx35AD1ll0lq39t9NWHDL/ap4GDe6heA2jWrFmVXE1KSnr22WejoqI6derUpUuXRwsGrsiIDYAAAMDzuOUIvPtgWeXaGGJZItL9cUr2bB+vlk25zgQAwAH11gSbVucd2lrS7Yn7r/qNiNAdTLLkFOgOnpAN6O38eOAKanIT6B49eqxYsWLGjBkffPDBrFmzvL29a/DFgXPWWyWWvCKeROz1eDOuswAAADgPRuBdme7wybLrWYIAuaRbWGnCH8q1MQ0/m0MMw3UuAACnsuQWahOPEo+nmDqmwgJGKPSfPPL28jWqX3b49HiSJ5U4OSG4gho+BWz48OFEZDKZrl+/HhoaWrMvDtwynE4jInGntgyfz3UWAAAA58EIvMuyG02qTTuJyH/SCPFTHfXJ58uu3tAdPS3tU9WHtgAA3IMyahtrs8kGPS1q2vhBNT49OmnbtzJduqaO+V3x0mhnxgMXUcMNIIVCIRQKLRZL06Z4+NbdGM9h/gsAADwRRuBdliY20abSeLVu5tO7CzGM//PDin/cqNoYL+nakeftxXU6AAAnMZy+aDx3mecjkU8cWnmlYurY/HnLSvcclkX0EDZp6Jx44DpquAF0+vRpi8XSsGFDmUxWs68M3GLNFtOla8Qw4jAcAA8AAPA3jMBzxVpUXLr7EDGMYtpYx8yXtF83beLRsutZpXH75BOf4zogAIBT2O2q6O2OH4sW/e9fyxkBnzVbVBvj6334Si0nA5dTkw2gtLS0GTNmEFG7dugRuBvjhXTWbPFq9RhfjtYeAEBdZbp4lRF7YYvcGocReE4o129jLVbpM93//keaYRTTxhZ8tEITf0Aa3kNQL4DTgAAAzmAvM1tL1ERk1xvMmdlVvMuSX1SbocBFVa8BFBQU9KBLZWVlWq3W8fPcuXMfKRS4HuOZS0Qk7oz5LwCAusp8M7dw0Q88kbDxdx/zFXKu47gVjMA7n+niVUPKBZ7Y2/+FYfeuO8bB9EdTVBviguZO5yoeAIDT8MTewd//16YurdZdgiC0yD1R9RpAxcXFlRcIhcJly5YNHjz4ESKBKzKev0xEEmwABABQZynXxpLdbjeVqTbGB745hes4bgUj8M5mtyvXxRCR35hBfH+/chcVk0cYTqXqk87JLl3zbt+Ki3wAAE7FV8jx1Q5URfUaQL///nslV2UyWVhYmI+Pz6NFApdjzs633lby5b6iZk24zgIAAA9Dn3TOdPkaXya1m8p0R0/Lnu3j1boZ16HcBEbgna808ag5O19QP9B36DP3X+Ur5H6jBqi37Faui2n0xfvE4zk/IQAAgAuqXgNo0KBBtZQDXJnxTBo5zv9iGK6zAABAtbFmi2pDHBHJXxhmLVZqYhNLVv/WaNl7+Ld6FWEE3qXY9QbNr3uISPHSaEZY8UdZvxH9dQeTzTfztAdOyAb0dm5AAAAAF1XDp4CBm7EWq5TrYqyFxYT5LwCAOksTv996q0T0WLAsogdrser/OGXOzNb9cUrarxvX0eoGjMC7FPWW3TatzrtDa8lTHR9UwwiF/pNH3l6+Rr15l0/PzjwfsTMTAgAAuKbKGkAZGRkP/botWrR46HvBdSjXxRhOphJDDJ/vHdaG6zgAAFBtNqVaE7ePiBTTxhCPx3iJ5M8PK/4+WrUxTtItjCfGmeX/DiPwrsOSW6jde4x4PMXUsZVX+vTopG3XynT5mjpmj2LKaOfEAwAAcGWVNYBatmz50K/LsuxD3wsuwnTxquFkKhERS/wAOf6SAABQFyk3xLNlZp8enbzb3dkNV9rnKd2+46b0DM32vf4vDOc2Xp2AEXjXodq0g7XZiCh/zudVvEWb8Ifvc+GCAGyPCgAAnq6Gd8WTyWShoaGhoaE1+7LgfKztzvkawvqBRGRTamxKDdehAACgesqu3tAfO+0Yh/l7lWEU08YQw5TuOGApuM1dOoBq4/v7VXfvKr5cxvCxDzQAAEClTwCp1er7F6Oiot555x2GYcaOHfvCCy80b95coVBkZmb+/vvv33zzjdFofPvtt6dPn15rgcFJdPuOOc7XIKGAiFirVfXLjsA3JnOdCwAAqoxllWtjiGX9RvQX1Au494qoeYi0T1fdHydVG+PrvTeDq4CuCSPwrixg5oSAmRO4TgEAAFAnVdYA8vPzK7dy8uTJOXPmeHl57d+/v2fPnnfXGzVq1Lt37ylTpnTt2nXWrFmPP/547944cKEOs+sN6q0JRCQf+2zx/35hREJiSXfklHRAT+82+GgLAFA36A6fLLue5TgS+/6r/pNHGk6lGk6eN6ZeEYe1dX48l4UReAAAAHBL1Xsg9osvvrDZbIsWLbq3+3NXq1atPv/8c5vNtmzZshqKB9xQb71zvgYjEhHLerdr6TssnFhWuTaW8NEWAKAusJvKVJt3EpFi8gjGS3R/AV8uczSGlOtiWZvd2fncC0bgAQAAwPVVrwF07NgxIurfv/+DCp555hkiSk5OfsRYwCFLbqE28RjxeIqXxpjSrhKRd2hrv9ED+Qo/c2a27sgprgMCAMC/08Qm2pQar9aP+fTu8qAa3+H9hQ2DLLmFun3HnJnNxakr8u233/J4PD6fP2HChPj4+IsXL+bl5R09evSjjz7y8fFxjMBfvHiR6+wAAAAAD1S9BpBjV6BKHm82m81EpNVqHzEWcEi5Ppa12WQDe4uaNjal/UlE3qGted5e/i8OJyLVhji70cR1RgAAqIy1qLh01yFiGMW0cZXsmMsI+P6TRhCRassuu1bvxIAuze8+6enpjhH4I0eObNmyZfjw4aGhoY7598WLF587d04qlc6aNcvxPRlwovjHjTkzPrQWq7gOAgAA4Loq2wPofo0aNbp58+ahQ4eeeOKJCgsOHjxIRMHBwTUQDbhgSLloPH+F5yORTxhiU2osBbd5ErFX8yZEJO3TVfv70bJrNzVx+/yfH8Z1UgAAeCBl9HbWYhHUCzClXXU8y1kJno/ErjOof9ujmDbWOfHqnKqMwM+ePXvZsmXYA5ETprSrukPJRKSK3h40ZxrXccBzGS/8KQiUCxvV5zoIAEDFqtcAGjx48E8//fTf//53wIAB9w+6X7p06eOPP3aU1VhAcCLWalNt2E5E8glD+DKp7o9TRCRsVM9uKuNJxMQwiqljCj5aUbrjgPSZ7sIGQVznBQCACthNZYZTF4jIeqtEtTG+infpjpxCA+hBMALv0ux25bpYx4/6pHOyKxnebf9xYIXx7CViWXFn7NAEtct0JaNo0Q98f9/G337ME3tzHQcAoALVawB9+umnO3fuzM3N7dq16+uvvz527NhWrVoR0bVr12JjY7///nuj0RgcHPzJJ5/USlioZaW7DlnybwmDG8gGPU1Eji+Ny65nFX+/od77LxORV+tm0j5P6f44pdoYX+9dHBsMAOCKeN5eQW9NMd/Mq9Zd3u33j7RFAAAgAElEQVQe/ugrt4cReFem3XvMnJUnqBfg07uzZtte5ZrfGn0xj3h3djkwZ+cXLVtFRI2+/EAU0ojTpODWWFa5LoZY1qbUaGITHdO1AACupnoNoMDAwC1btjz33HNqtfrLL7/88ssvyxX4+/v/+uuvAQEBNZcQnMSm0Wq2JRKRYspohs8nItPFPx2XDCkXjKnp4rA2ROT/4nDDyVRD8vm7KwAA4Gp8enepZO9nqC6MwLssu96g3ppARIopo8Sd2uuPnTHfzNUeTJL17+UoUK6JIZudiEr+b2vDxe9wmRXcmvZgkjkzh+8rtWn1pbsPyfr3EjQI5DoUAEB51dsEmoh69eqVkZHx9ttvy2Sye9flcvn777+fmZnZo0ePmosHzqPatMNuMEq6dBB3akdE1lsljp0U+TIp3dkZ2k5EfIXcb9TAe1cAAADcm2O2/b///W9aWtr9V118BF6j0Zw8eXLz5s0///zzN998s3Llyo0bNx4/flylcof9ktVbE2xanXdoa0m3JxiR0P/FEUSk3rzLbjASkSHlgunSnT2wytIzDCdTucwK7stuNKk37yIixfTx0n7dWItVGb2N61AAABWo3hNADgqF4uuvv/7666/z8/OvXr0qEolatWoVFIQdYeow88083cFkIjJn5+fP+4KIbCoNERExPD+Z3VRmySnInb2A7ycjIrJaiciSU6A7nCyLqGA7TAAAAHdSF0fgrVbr6tWro6Ojk5OTKxxeYximW7dukZGR06dPF4lEzk/46Cy5hdrEo8TjKaaOcaz49HpSm3jEdPm6JuZ3+QvDlWt+IyKGxyOWZVm2ZM1v4ifbMUIhp6nBDal/22NTl3o93tynZyfv9q0MyecNpy4Yz18RP9GW62gAAP/wMA2guxo1atSoEaap3YG16DaxLBFZb5XQrZJ7rrCW3ALHT7YSla3kH98WWvNvOS8iAAAAR+rcCHx+fv6AAQMuX7587yLDMD4+PhaLpaysjIhYlk1OTk5OTv7666937NjRpk3dG+tWRm1jbTbZoD6ipo3vLiqmjs1//4vShMMsyzqeZZYO7sMQU7r7kE2pLt1x0G/MIO4igxuyFhZr9/xBDKOYNpYYhi+X+Y0eqPplh3J9bKPlHzr2VQAAcBGP1AACtyHp9kTjb+azZrPjV8PZS+otu4ko8M1IUXADIlKu+dX0501pryd9R/x1BgrDE2IzRQAA8AyOEfhFixatWbPm3s2e5XL5rFmzPvjgA7lczmG8exmNxpEjR16+fFkoFA4ePHj48OG9evUKDAz09/fn8/lEZDabc3Jyjh8/vnv37l27dl27di08PPzChQuBgXVpyxLD6YvGc5d5PhL5xCH3rouaBcue6a49cEK76zARMWIv/7GDScDXHz5l0+vVMb9Ln+nOV/hxExrckXJdDGuxyiJ6erUIcaz4DovQHUyy5BZq9x7zHdyX23gAAPeqbA8goVAoFAq7d+9ebqUqaj851DBhcANR8xBR8xBh4wbaxGNExBN7S59+yrEY8Ookhs/XJZ0ngcCxImoWzPCrvYcUAABAHeUYgS8tLc3Lyzt06NDx48dv3bqlUqmWLl3qOt0fIlq8eHFKSopCodi3b198fPz06dPbtGkTGBjI/+tJBJFI1KJFi8jIyK1btyYnJ4eEhBQUFCxbtozb2NXCWm2q6O1EJJ8wxLFZ4b3kLwxjBAKWtRORYtJInsyHJ/aWTxpGRKzFotoQ5/zA4K6MF/40nEnjib3lE4feXWQEfP/Jo4hIvWW3XavnLh0AQHmVPQFktVqJyGazlVsB96belujYAEj8ZHtiGMeiMLiBbGDv0j1/KNfFNFjwJqcBAQAAuOTiI/AxMTFE9Nlnn/Xt+++PHnTo0GHVqlWDBw+Oj4+/f7StWtRq9cSJE4uLi6tYn5ubS0RFRUVdupQ/sY5hmA8//HD06NH3LhYWFr744osajYaIbOpSm1LNCIXCG0n0X6Zcva1EzVqtt02Gt0/vL7uRRB/d+TBjySlkLWY6+Jto9fL/LPykktevbh7Ue2Y9a7Or1scSkd+4wbfLjC9GjL633lJwmzUaeX/ECoMUrpkf9ahHfRXr71+vw9gHO3z48OHDh8+cOXN35ViVVfKyUM67775LRI0bN+Y6CMuyrOW28uYL79wY89qNMa+V7v3Hn6NNp89+6f0bY17Tn0rlKh4AAHBIoVAQUUlJCddBoDJeXl5EdOXKlSrWGwwGPp/v7e39iO975coVfs1td/LOO++Ue/1Tp04xf30vVVm93Z7/wZc3xrwWHz7ugdWP8vqoR/1f9Zpdh26MeS3ntQV2s8UV8qAe9aivpXp3+vzDsBUdDAHO9N5773311VeNGzd2fBXGrdvL1+iTzjEiAWu2Nv5+gbDhPw53K93zh3LNb4L6gY2/mc8IsYEUAIBnCQgIUCqVJSUljk9C7soxyd65c+fk5OR7V6rCYrHUVqwqq1+//q1bt3bt2jV06NB/ryZKT09v27Zt/fr1CwsLH/Gtb968WVJS8u91RET07bffbtiwoV69egkJCeUu8Xi8Dh06CATlP2lkZGSo1Wp1zB7DqYve7VoqXhp9f73+aMrtb6OISNSq6bXzF3R8Xr33Z/AkEkelMnq7Ke0aj6FeH70jf6Z7ha9/f9TK86DeM+t5JnPuGwvtWn29D1+RdA6tsF4Tt09/4py4VdN+P3zuavlRj3rUV72+fv36bvP5p2b+Ds+ybHZ2dlBQkOSv/75CXWRKz9AnnyehgDVb+Qq/ct0fIvId9LRu33Fzdn7p7kN+IwdwEhIAAKBW1fUR+D59+sTExHz33Xfh4eFisbjyYpvN5pj86tq166O/9WOPPfbYY49Vsbh+/fpEJBQKO3fuXMVbWrRoYc7Myb9eRAH1G745U9io3t/Xysz2MjNrtijXbyMihs8LeHkCb53QdPm6LD0/YOYER5W1afO81xeyVmvphjjfHp143l7lXr+KSVCP+pLNsXat3rvD447uT4X19jZtc19faC/Wm89dFjzVsVbzoB71qHdmfd31MA2ggwcPHjlyZM6cOb6+vkSUkpIybty4rKwsoVA4aNCg6Ohof3//ms4JtY9llWtjiWXFHdoYz6aJOzxeQQ2Pp5g6tnDhd5rYRGnfrnx/HKIBAADu5vDhw0Qkk8nurhw7doyzNNU3f/78hISEvXv39u3b95NPPgkPD/f29r6/zGKxHD16dOnSpfv27WMY5q233nJ+1Ieg2rKLWJZYtuDDryopY232gve+cPys3Xfcb0R/Qb0AIhIE+vuNiFDHJtpLdaVx++QTn3NGaHA7lpwC7f7jxOcppo6ppIznI5GPG6Jc+5ty/TbxE+3w+DwAcK7a/xp6+eWXf/75ZyJ69dVXfX19DQaDo/tDRBaLZdeuXX369Dl37tz9T1WBi9PuP27OzBYE+vO8BETk1a5lhWXeHVpLnupoSLmg2rQz8LVJzs0IAABQ6+7fO7lXr16cJHk4YWFh69evnzBhQkpKytChQyUSSZcuXYKCggICAmQymU6nUyqVxcXFZ86cKS0tddyyaNGiiIgIbmNXkSikYdmfNyq8xNpZ1mQklohheJK/H33iK/yYe5708Rs9SLv/hE2j1WzfJw3v4WgMlWO9VaJcG+M7tJ93hd+HgccrWfsb2ezCxvWNZy8Zz16qrNRmZwR8a1Fx6e7DfiP7OysgAEDFqtem2b59u6P706pVK5FIREQxMTFZWVkNGzbcvHlzaWnptGnT0tLSNm/ePHny5FrJC7XDrjeqN+8iImGThsbUdCKy5BZq4vZVWMyXy4hId/ik7Nk+Xi1CnJkTAADAdbjsCPy4ceMUCsXcuXNTU1MNBsORI0ceVNmhQ4clS5ZUcbcgV+A/aaT/pJEVXrr11WpD8nkiEj/ZTtTkH8e0le44cO+vopZNjWfSWJtNuT623ryX73+pkjW/Gc+kmW/mNv72Y8ZLVHPxwR1Yb5WYLl4lIktekWpjfBXv0h1MQgMIADhXvQbQ//73PyKaOnXqmjVrHBtl79y5k4jef/99x9dln3/++csvv7xhwwY0gOoWQ8oFW6mOiIznLjtWSnce/Jd7WFZ34AQaQAAA4CHq1gh8RETE+fPnU1NTExMT09LSbty4odPpDAaDWCyWyWTNmjVr3779wIEDO3XqxHXSGsKyhlMXHT8az1wynqn0oYy/GE9fJLudeLx/LF7403gmjYisxSpN/H75+CE1HhbqNEGQQjFtnE1ZwX6xlfBu36qW8gAAVF31GkBXr14lojlz5tw9Ju3o0aNE9Oyzzzp+7d+/PxFlZmbWZEaofZIuHeRjB7NWqyWvyJByQVA/wKfHk/9yD8NI+9bAhpEAAACur46OwIeFhYWFhXGdwkkYPsPaq3kPr/y59azNplz7GxH59HpSf+KcJn6/NLyHINCFWnvAPYbxHVJ+UBQAoE6o3seUW7duEVGjRneeqs3KyioqKgoMDHz88TsD0nK5nIjy8vJqNGTN0Gg06enpmZmZOp1Or9d7e3tLpdJmzZq1a9fOpb6y4wRPKpFPHEpEyvWxRCR9prt87GCuQwEAALgEjMDXAQwjHzvYfDO3WjcJmzYq9/iPds8RS26hsEFQ4OuRRIz++BnVhrigd6bWaFYAAABuVK8B1KRJk2vXrhUWFioUCiI6dOgQEfXu3ftuwc2bN4moXr16D3gBDlit1tWrV0dHRycnJ7Mse38BwzDdunWLjIycPn2641OdJ3NsrOjVujnXQQAAAFwFRuDrBL8xgx7xFew6gzr2dyJSTB3DCAX+kaMMpy/qj5+RDXra+wGHYwAAANQhvH8vuUfbtm2JKDo6mohYll27di0RDRny92j0pk2biCgkxFX2hcnPzw8LC3v11VeTkpLudn8YhpFKpV5ed86DYFk2OTl59uzZoaGh6enp3IXlHmuxmm/mEsN4tXSVP0EAAADOYQTeQ6g27bBr9eKOj4s7hxKRIEDuN6I/ESnXxVBFXyICAADULdVrAL355ptE9MUXX4wdO3bgwIFHjx719vYeNmwYEV29evU///nPt99+S0Tjx4+vjazVZTQaR44cefnyZaFQOHz48NWrV1+5cuX27dsWi0Wr1ZpMprKysuvXr0dFRY0fP14ikVy7di08PLy4uJjr4JwxZ2SzFqsopNG9h6cCAAB4uDo9Ag9VZMkp0B44QXye/0tj7i76jRwgCFKYb+TqDiZzmA0AAKBGVK8BFBER8fLLL7MsGxsbu3//fiKaO3dugwYNiGjDhg1LliyxWCytW7eeOXNmrYStpsWLF6ekpCgUin379sXHx0+fPr1NmzaBgYF8/p0N/0QiUYsWLSIjI7du3ZqcnBwSElJQULBs2TJuY3PI9GcmEXk93ozrIAAAAC6kSZMmRFRYWOj4tU6MwEN1KdfHks3uO7ivKOTvI+QZkdD/xeHkeDjIYOQuHQAAQA2oXgOIiFatWrVp06ZJkyaNGDFi5cqVixcvvnupYcOG06ZNS0lJ8fb2rtGQDykmJoaIPvvsM8d8fuU6dOiwatUqIoqPj6/1ZK6q7OoNQgMIAADgn+rcCDxUlyH5vDE1nSeVyMc8W+6ST+8u3m1b2DRazbZETrIBADw0zY4D+qMpXKcAF/Iwh5U+//zzzz//fLnF+fPnL1q0qCYi1RjH4az9+vWrYn3fvn35fH5OTk4tZnJtZVdvEnaABgAA+Kc333xzx44dX3zxxfXr1zUaTbkR+PXr17vUCDxUF2uxqjbGE5H/88N4Mp/7CxTTxuXPW1a665A0vKewEZ7zAoC6wXDqgip6O/F5omZNhMENuI4DLqHaTwA9yN09lV2Hn58fEWVkZFSxPisry2azOe7yQNZbJTaVhi+TChsEcp0FAADAhdStEXiortIdByyFt4VNGkr796qwQNQsWPpMd9ZqU23Y7uRsAAAP5+9/ZdnsJWt+4zoOuIqHeQKIiLRa7dGjR0+dOnX79u169eotWLAgLy8vKCjIpY5R79OnT0xMzHfffRceHi4W/8uuxjab7csvvySirl27OiWdy7lzAPzjzeivI04AAADAYdWqVf369UtISNBqtYMHD541a9bdSw0bNhw8ePDXX3/tIiPwUC02dakmbh8RKV4aw/Af+M2o/4vDDcnnDSkXjecuizu1c2JAAICHUbrroKXgtjC4gU1Varr4p+H0RUmXDlyHAu49zBNAP/30U0hIyNChQxcuXPi///0vLi6OiLZs2dK4ceMVK1bUdMKHN3/+fIlEsnfv3r59+yYkJJhMpgrLLBbLwYMHBw8evHbtWoZh3nrrLSfndBF3NgBqjQ2AAAAAKvD8889v2LAhLi7u3u7P/Pnz8/Pz16xZ4+vry2E2eGiqDXF2o0nS/QlxWJtKyvh+Mr/Rg4hIGbWNtdmclQ4A4GHY1FpNbCIRKaaOkU8YQkTK9bGsxcp1LuBetZ8A+vTTTxcsWEBEPB6vefPm169fd6zzeLzi4uK5c+fm5eUtX768hmM+lLCwsPXr10+YMCElJWXo0KESiaRLly5BQUEBAQEymUyn0ymVyuLi4jNnzpSWljpuWbRoUUREBLexuWL6EztAAwAAVI8LjsBD1Zkzc3RHUojIfCM3f94X/1JttRKRJbdQdyBJNrD3vxQDAHBH9Uu83WiSdO0oDmvrHfq4bv8Jc3Z+acJhvxH9uY4GHKteA+j06dOO7k9kZOTy5csDAwOZv8aFXn/9daPR+NFHH61YseKFF17o3LlzzYetvnHjxikUirlz56amphoMhiNHjjyoskOHDkuWLBk6dKgz47kOtsxszsolPk/UAieYAAAAVKxOjMBD1VlLVMSyRGQtKq7GXbeVtZYIAOBRmTNzdIdPMgK+/+RRRMTweYqpYwoXfq+J+V3a5ym+v4fueAsO1WsAOQ65GDp0aFRUVLlLQqHwP//5T35+/o8//rh8+XLHeaiuICIi4vz586mpqYmJiWlpaTdu3NDpdAaDQSwWy2SyZs2atW/ffuDAgZ06deI6KZfKrmeRzS5q3oTnjW8yAQAAKvDTTz/95z//UavVjl+feOKJBQsWbNmyZenSpR9++OGcOXO4jQcPQfJUx8Y/LGANxmrcIxCImjSstURQh9l1hsL/fiNqFhz4RiTXWcCDsaxybQyxrO+wCGHDIMead4fHJV06GE5fVG3ZFfjqi9wGBG5VrwGUnJxMRO++++6DCmbOnPnjjz9euHDhUXPVtLCwsLCwMOe8l9VqXb58uUqlqmK947kki8VSm6H+hWMHaO/HcQA8AABABerQCDxUi7BBENcRwE2of91tzs43Z+f79HxS3DmU6zjgofTHTpvSM/hymd/ogfeuK14aY0y9ojuYLBvQ26tlU67iAeeq1wDKyckhojZtHrhJXpMmTYjoxo0bjxirTktNTf3ggw+qe5dOp6uNMFWEHaABAAAepM6NwAOAk1lyCkoTjzp+VkZtbxTWlhHwuY0EHog1W1S/7CAi/xdH8MT/OJhS0CDQd0g/Tfx+5dqYhp/NwbnPHqt6DSA/P79bt27l5OQ0aNCgwoLLly8TUdOmrtVTzM7OTkpKSktLa9CgQd++fUNDK27JZ2VlOU40e8SDwJ588smNGzfm5uZWsT4hIeHIkSNcnh7CsncaQNgBGgAA4D51cQQeAJxJGbWNbHbZwN6mKxmWnAJtwmHf4R56sAxwSLNtr7VYJWreRNqv2/1X/cY+qzuSUnb1hv7EWZ9e+LrCQ1WvAdS1a9ddu3Zt3LjxqaeeqrBg586dROS0Yat/ZbVaFy5cuGTJEttfB3YyDDN69Og1a9b4+ZXf/io9Pf3tt9+mR24AMQzz4ovVGK0sLi4+cuQIn8/ZtwSWwmJbqY4vlwnqBXCVAQAAwGXV3RF4AHACQ8oF4/krPB+J/PnnzDfyij79Xv3bHp8+T/Hl3H2/C57HWqzS7DxADKOYOrbCB3x4Ym//ic8V//SLKnq7pEsHxgvHF3giXrWq3377bYZhfvjhh59//vn+qzt37vzqq6+IaNSoUTWT7pFNnDhx8eLFNpuNYZiQkBAfHx+WZWNjY/v27VtSUsJ1OldRdjWTiLywARAAAEBFMAIPAA/CWm2qDXFEJJ8wlC+Tijs+Lu4cajea1Ft2cx0NPIsqejtbZvbp3dm7bYsH1UjDu3u1bGotUWvi9zszG7iO6jWAIiIi5s2bZ7fbX3755c6dO7///vtEpFQqv/7663Hjxo0cOdJut48bN278+PG1k7Z69uzZExsbS0SzZs0qKSnJyspSq9Vr1qzx8fFJTU195ZVXuA7oKhw7QGMDIAAAgAo5nhp2tIEq5Joj8ADgBKW7DlrybwmDG8gG9XasKKaOYYQC7YETZRnZ3GYDz1GWnqlPOseIhP4vDK+sjmEUU8cQw2ji9llvK52VDlxI9UbAiGjx4sVisfjLL788e/bs2bNniSg7O9tx9CnDMJMnT/7uu+9qPuZD+emnn4ho9OjRK1eudKwIBIJp06a1bNkyPDw8JiYmNjZ2zJgxnGZ0CWVXbxI2AAIAAHiAOjcCDwDOYVNrNdv2EpHipTHMX/s5CBsEyZ7tU7rzoHJdTMNF72C3Xah1LFuy5ldiWZ7URxW9/V/Led5edqNJ9cuOoLdfqv1w4Fqq3QASCAQLFiyYOXPmypUrL126dPXqVbVa3bp16zZt2kyaNKlHjx61kfLhpKenE9E777xTbr1Pnz7vvvvusmXL5s2bN2zYMJHIo6cfWYvFklNAfJ5XsyZcZwEAAHBFb7/99u7du3/44YfQ0NCZM2eWu+qCI/AA4ByqX+LtBqPkqY7iJ9reuy4fP0R/9LTjoQyfnk9yFQ88hN1gsmQXEJFNqdYnnaviXWXpGbUZClxUtRtADo0aNfr0009rNkqNy87OJqLWrVvff2n+/PnR0dGZmZk//vjj/R0ij2LOymdtNlFII2wDBgAAUCHHCPyyZctefvnllStX9u/fn/4agT9x4sS2bdtcagQeAJzDfCNXd/gkI+D7Tx5Z7hJP7C2fMLRk1WZV9HZJ51B8zIZaxfMRN1z6nqXwdrXuwtf/nukhG0CVs1qtAkGtvHK1+Pv7FxYW5uXl1atXr9wlqVS6dOnSKVOmLFy4cPz48Y0bN+YkoSswZ2QTkag5/v8PAADwQHVoBB4AnIFllWt/I5b1fe4ZYaPyf9cgIln/nrr9x8sysjU7DsjHDXZ+QPAoombBombBXKeAOqBKm0DbbLarV68eOXIkMzOz8kqTyRQfH39/w4UTHTt2JKKvvvqKZdn7r0ZGRg4YMECj0UybNu3uIfEeyHwjl9AAAgAAqJRjBP7q1asff/zx6NGjQ0NDg4ODw8PDZ8+effz48ejoaLlcznVGAHAe/bEzpisZfD+Z3+hBFVf8dRq3Zvte7LYLAC7iX57TMZlMH3744U8//VRWVuZY6dSp08qVK7t27UpEFy5ciIqKOnPmjEaj0Wg0er2+uLjYbrfXeuqqmTRp0t69ezdt2uTl5fXtt9/KZLJyBT///HOHDh327t0bGRn5f//3f5yE5FxZJp4AAgAAqJI6MQIPALWNNVtUm3YQkf+Lw3kS8YPKvNo09+nRSX/irGrTjqC3XnJePgCAB6isAcSybERExIkTJ+5dPHfuXERExPHjx4uKioYPH24ymWo54cObNGnS+vXrDx48uG7dus2bN3fs2PGll1569dVX7xY0bdo0Kipq4sSJmzZtOnz4cNu2bSt5NbfEWm2WnAJiGFFTzx2CAwAAqBEuMgIPALXNcYS2sElDyVMd7XpDJZV+owcaTqXqj53xfbaP1+PNnZYQAKBClX1M2bp1q6P789hjj82cObNly5aFhYW//vrr8ePHZ8yYoVKpTCaTRCIZOnRo06ZNxWKx1WqVy+WNGzfu16+fk+JXimGYXbt2zZ49e8OGDSaT6dSpU08+WX4T/lGjRiUkJIwdOzY/Pz8/P5+TnByyZOezFqswuAFP7M11FgAAANdis9kyMjIKCwuDg4ObN6/sb24mkykxMXHq1KlKJQY9ANycXWfQxO8nIktOQfbU96t4l+qXHQ0+fbs2cwEA/LvKGkBRUVFE1K5du+Tk5LvzU2+88caYMWO2b99ORM2bNz9x4kT9+vWdEPThiMXidevWLVy4MC4uLjs7u3379vfXRERE5OTkREVFbd68+fr160VFRc7PyZWyGzlEJMIO8AAAAPeo0yPwAFC7eIwgSGFTlVbrJkGQopbiAABUXWUNoIyMDCKaM2fOvbvnMAwzb948RwPoo48+cuXuz10hISFvvvlmJQVSqfS111577bXXiEiv1zsrF/fMmTlE5IUNgAAAAP5S10fgAaBW8STixt/M5zoFAMDDqKwBlJWVRUQtW7Yst966dWvHD+3ataulWBzy8fHhOoLz4Ax4AACAcur6CDwAAABAhSprAJnNZiLy8/Mrt65Q3HmCUSx+4Kb34PpYm92cnU8MI3osmOssAAAArsINRuABAAAA7vfvZ1UwDOOEHOB8ltwC1mwRNgzi+aCRBwAAcIfbjMBnZ2cnJSWlpaU1aNCgb9++oaGhFZZlZWXFxcUR0VtvveXcgAAAAOBUOKzUczk2AML8FwAAwL3cYATearUuXLhwyZIlNpvNscIwzOjRo9esWXP/k93p6elvv/02oQEEAADg7nhcBwDOmO8cARbCdRAAAAAX4gYj8BMnTly8eLHNZmMYJiQkxMfHh2XZ2NjYvn37lpSUcJ2uLrDbDacv2vVGrnMAAADUJDSAPFcZngACAAB4gLo7Ar9nz57Y2FgimjVrVklJSVZWllqtXrNmjY+PT2pq6iuvvMJ1wDpAE7f/1tJVxT9Ecx3EU9j1xvx5XyjX/sZ1EAAAN4cGkKdiWcvNPCLyaoYdoAEAANzHTz/9RESjR49euXKlv78/EQkEgmnTpiUkJPD5/JiYGEd7CB7EptRotiUSkSHlojH1CtdxPIL6tz3mzOzShD9Ml69xnQUAwKgmm28AACAASURBVJ39ewPo6aef9r9PJZfuLQCXZckrspvKBPUCeDIPOvYeAADA7aWnpxPRO++8U269T58+7777LhHNmzfPMeYGFVL9ssNuKuP7SolIuS6Wtdm5TuTmLHlF2j1/OH5Wro0hO/4HBwCoLf/eANJqter7VHLp3gJwWZj/AgAAcEvZ2dl0z5bV95o/f37Dhg0zMzN//PFHp+eqG8qu3tAdOcUIBQ0+fVvYqJ4lt1CbeITrUG5OuT6WtdmkfbsJghTmm3naA0lcJwIAcFuVnQL2wQcfOC0HOJnjCDAvNIAAAADci7+/f2FhYV5eXr169cpdkkqlS5cunTJlysKFC8ePH9+4cWNOEroullWuiyWW9R0eIQxu4D951K1lq9RbE3ye7sKXSbkO556MZ9KM5y7zxN7+k0eaLl+7vWKtevNOn55P8nxcfat1AIC6qLIG0JIlS5yWA5zszhnwzdAAAgAAqMDTTz/N5/Ore0mlUtVmqCrp2LFjYWHhV199tXHjxvu3so6MjNy4ceO+ffvu7grESUjXpDtyquzaTb5c5jdyABFJnuogfqKt8fwV9daEgBnjuU7nhlibTRm1nYjkE4bw5TKfnk9qfz9qunxNHbNHMWU01+kAANwQNoH2SCxrvplLaAABAAA8QN0dgZ80aRIRbdq0afr06Vqt9v6Cn3/+WSaT7d27NzIyUq/XOz2gi7KbylS/7CAi/8kjeWJvx6LipTEMn6/de8yclcdpOvek3X3Ykl8kbFxfNrivY0UxbQzxeNqEPyz5RdxmAwBwS5U9AQTuylJw224wCgLkfLmM6ywAAACupa6PwE+aNGn9+vUHDx5ct27d5s2bO3bs+NJLL7366qt3C5o2bRoVFTVx4sRNmzYdPny4bdu2NfK+BQUFTz/9tFKprGK9wWAgItfpQGm27bUpNaLmIdI+Xe8uCoMbyAb1Lk34Q7k+tsGCNzmM535sGq069nf6q8vmWBQ9FiwL76Hdf1wZtb3+h69wGhAAwA2hAeSJzDccO0CHcB0EAADA5dT1EXiGYXbt2jV79uwNGzaYTKZTp049+eST5WpGjRqVkJAwduzY/Pz8/Pz8Gnlfs9lcXFys0WiqdZfdNY58st4qKd15kBhGMW0M/XNuTj5hqP7oGdPFq4aTqZJuYVwldD/qzbvseqO4c6i4U7t71+XPP6dPOuvYG6jcJQAAeEQYAfNEf20AFMx1EAAAAKh5YrF43bp1mZmZ33777dy5c7t27Xp/TURERE5Ozg8//NCrV6/69es/+ps2bdq0qKhIWWVvvPEGEclkLvEwsjJqO2uxSPt09W7Totwlno9EPmEIESmjt7MWCxfp3JD5Zq72YBLD5yumjCp3ie8nk495lv46HYyLdAAAbgtPAHmiO2fAt8ATQAAAAG4rJCTkzTcrm1qSSqWvvfbaa6+9RjU0iuXl5eXl5VX14kd/xxphSrtqOHme8RLJn3+uwgLZwN7avcfM2fmluw75jRro5HhuSbk2lux22bBwYaMKmo+yof20+09Y8oq0e/7wfS7c+fEAANwVngDyPCzrGAHzwg7QAAAAQEREPj4+XEfgiN2uXB9LRPLRgwSB/hXX8HiKqWOJSBObaFNWb8YN7qc/cdZ0+RrfVyofO7jCgrtPBql/22Mr1Tk3HQCAO0MDyONYbyvtOgNfLuMr/LjOAgAAAM724YcfLlq0iOsUrkK777j5Zp4g0N/3uWcqKfPu0FrStaPdVKbavNNp2dwSa7aoNsYTkfz5YTwf8YPKHHsD2fVG9ZZdTkwHrqLs2k1LwW2uUwC4IYyAeRxzVj4RiR7DBkAAAACeaOnSpYGBgR9//DHXQbhn1xvVW3cTkbBJw9I9f1RezPfzJSLd4ZOyQU97tWzqjHzuSBO/33qrRPRYsCyiR+WVipfG5F/4U7vvuKx/Txxd4lHMmTkF81fwZdLG3/+XJ/bmOg6AW0EDyONYsvOJSBTSiOsgAAAAAFwypFxwTBgZz102nrtcpXtYVncwCQ2gh2PXGTRx+4jIfDP35vjKNqi6l2rzrvofza7NXOBKWFa5LoZsdpu6VPPbHv/I8tuEA8CjQAPI45iz84lIiAYQAAAAeDbJUx3k4wazFms17mEYad8KTlWDqmBZO0/sbSszV+sunrer7BcOTqA/cc50JYPnI7EbjKUJh6UDegsbBnEdCsB9oAHkcSw5BUQkCmnIdRAAAAAALvF8JPIJQ7lO4UH4MmmT1Z9znQJcF2u2qDbGEZEicpTpaqbuQJIqalu9D2ZxnQvAfWATaM/C2myW/CJiGGHjBlxnAQAAAAAAuEMTt896WylqFix9ppv/C8N5ErHh9MWqjmcCQBWgAeRBbKW6sj9vsFabsEEg4yXiOg4AAABwoHv37p07d+Y6BQDAP1hL1Jr4/USkmDqWeDy+n8xvzCAiUkZtY202rtMBuAmMgHkKu06f+8rHZLcRNgACAADwYElJSVxHAAAoTxW9nS0z+/Tq7N2upWPFd+gzugMnLLmF2sSjvkP6cZoOwE3gCSBPcfubdazZwlrthCPAAAAAAADAZZT9mak/cZYRCf0njbi7yAj4jlPA1FsTbFodd+kA3AcaQB6hLCPbeD7979+FePILAAAAAABcAMsq18USy/qNHCAIUtx7RdKlg7hTO7veoN6SwFU6AHeCBpBHuP31WiLi+8mIxyMi7Z6jXCcCAAAAAAAg7cGksutZggC534j+919VTBnN8PnafcfMWXnOzwbgZtAAcn/aAyeshcVEpHh5ArEsEVlVau3eY1znAgAAAAAAj2Y3mtRbdhORf+SoCo+pEQY3kA16mux25bpYp6cDcDdoALk51mpTrY8lIq8WTQUB/sSyjJeQIVJGb2MtVq7TAQAAAACA59LE/G5TabxaN/Pp+eSDauQThvBlUlPaVcPJVGdmA3A/aAC5OeXa3+zGMmKYoHenW7LziUgc1p5hGNZkLln9K9fpAAAAAADAQ1kLi0sTDhPDKKaNJYZ5UBnPRyKfMISIlNHbWYvFiQEB3A02A3ZnNpVGu/8EEUn7dhUEKczZBUTk1SqELxNrDyTpDyXLxw0WBPpzHRMAAAAAADyOMiqWtViFwQ3MN3LNN3IrL2a8RNai4tKdh/xGD3ROPAD3gwaQO7v91Rqy23kiUcCsiURkycknImFII99hEfrjZ+wm8+0Vaxt+PpfrmAAAAAAA4Fls6lJDykUisuQWlqzaXMW7tPuPowEE8NDQAHJbpivXTX9mEpH/5OGMUEhEjieARE0aMgK+/+RRJT9vLbt6w3ghXdyxDcdZAQAAAADAk/D9ZIrIUZaCW9W6S/xEu1rKA+AJ0AByW8VfrycifqC/bHA/IrLrDDaVhif2FgQpiEg26GlN/H7rrZLi76Kb/PxZJTO3AAAAAAAANYxhfIdHcB0CwLNgE2j3VLrzkFWpJqKgt6Y6VsxZeUQkbNLwbq8naM40IsamLtXE7+cqJwAAAAAAAAA4ARpAbog1W1RbdhKRd7uW3m2bOxbN2flEJAppdLfMq2VT7/atiEi9dTdbZuYiKQAAAAAAAAA4A0bA3FDxyk1smZkYRvZsH+OFPx2LxnNXiIj4/LsrROQ7uI/pyjXWYi3+aVPQ2y9xERYAAAAAAAAAah0aQG6HZQ3Hzjh+uL1ibbmL2sQj2sQj99+kP34m6I3JxOc7ISAAAAAAAAAAOBlGwNwQI/au7i08iTfx8A8DAAAAQO1iy8zGs5fIbuc6CAAAeBw8AeR2GKbJ2iWsqezeNWuJOn/O53ypT+MfF1R8k7cXDgIDAAAAqG3FKzfpj572f36Y35hBXGcBAADP4kENII1Gk56enpmZqdPp9Hq9t7e3VCpt1qxZu3bt/P39uU5Xkxg+n/GR3Ltiu3qTiITNGvP+uQ4AAAAATlOWnqk/doaI1NsSffp1EwTIuU4EAAAexP0bQFardfXq1dHR0cnJySzL3l/AMEy3bt0iIyOnT58uEomcn9AJzFnljwADAAAAAKdi2ZK1McSyPKnErjOoNsYHvTWF60wAAOBB3Hzbl/z8/LCwsFdffTUpKelu94dhGKlU6uXl5fiVZdnk5OTZs2eHhoamp6dzF7YWWXIKiEjYpCHXQQAAAAA8lO5gsjkzWxAgb/jZXMZLpD922pSewXUoAAD3V5aRbckt5DqFS3DnBpDRaBw5cuTly5eFQuHw4cNXr1595cqV27dvWywWrVZrMpnKysquX78eFRU1fvx4iURy7dq18PDw4uJiroPXPHN2HuEJIAAAAACO2I0m1ZZdROQ/eaSwcX2/YeHEssq1MVTR8+kAAFBTLLmFhR8tL/hoha1Ux3UW7rlzA2jx4sUpKSkKhWLfvn3x8fHTp09v06ZNYGAg/6/DzkUiUYsWLSIjI7du3ZqcnBwSElJQULBs2TJuY9c8u92SV0QMgyeAAAAAADihifndptJ4tW7m06szEfmNGigI9Ddn5ugOn+Q6GgCAO1NGbWOtNrveoN68k+ss3HPnBlBMTAwRffbZZ3379v3X4g4dOqxatYqI4uPjaz2Zc1kKb7NmiyBIwav+8fAAAAAA8IishcWlCYeJYRTTxjrOXWW8RPIXhhOR6pd4u9HEdUAAAPdkPJNmPHeZJxEzfL72QJL5Zi7XiTjmzg2grKwsIurXr18V6/v27cvn83NycmoxExcs2QVEJMLjPwAAAABcUK6PZS1WaXh3r5ZN7y5Kn+7i3aaFTa3VbNvLYTYAAHfFWm3KqO1EJB8/RDa4L9ntJat/9fDBW3duAPn5+RFRRkZVd9fLysqy2WyOu9yJGTtAAwAAAHDEdPFPw+mLPLG3/8Tn/nHhrweCSncesBTc5igdAIDbKk04bMkvEjauLxvcRz5hCF/uW5aeqU8+z3UuLrlzA6hPnz5E9N133xmNxn8tttlsX375JRF17dq11pM5l2PDc2GTBlwHAQAAAPAsrM2uXBdLRH5jnuX7l/+WUdS8ibRvV9ZqU22M4yIdAIDbsmm0mpjfiUjx0hiGz+eJveUThhCRKmobW2bmOh1n3LkBNH/+fIlEsnfv3r59+yYkJJhMFc9XWyyWgwcPDh48eO3atQzDvPXWW07OWdss+UVEJGxUn+sgAAAAAJ5Fm3jEnJ0vaBDoO7RfhQX+k0byxN6Gk6nG1CvOjQYA4M7Um3baDUZJ51Bxp3aOFVn/XqLmIdZilWbnAW6zcUjAdYBaFBYWtn79+gkTJqSkpAwdOlQikXTp0iUoKCggIEAmk+l0OqVSWVxcfObMmdLSUsctixYtioiI4DZ2DWNZa8FtIhI2rMd1FAAAAAAPYtcZ1L/tISLFlDGMsOJP3Xy5zG/UQNWmHcp1sY2W/4fhu/O3swAAzmG+mas9lMzw+f5TRv29yjAB08YWfPy1Ztteab/ugkB/7gJyxp0bQEQ0btw4hUIxd+7c1NRUg8Fw5MiRB1V26NBhyZIlQ4cOdWY8J7AqNXZTGd9PxpNKuM4CAAAALqFTp05ElJiYWK8evh+qRarNu+xavXeHxyVPdaikz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width="768" /></p> <p>Confidence intervals for the parameter estimates are obtained using the <code>mkinparplot</code> function.</p> <pre class="r"><code>mkinparplot(fit)</code></pre> <p><img src="data:image/png;base64,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width="768" /></p> <p>A comprehensive report of the results is obtained using the <code>summary</code> method for <code>mkinfit</code> objects.</p> <pre class="r"><code>summary(fit)</code></pre> -<pre><code>## mkin version used for fitting: 1.0.3 -## R version used for fitting: 4.0.3 -## Date of fit: Mon Feb 15 14:11:17 2021 -## Date of summary: Mon Feb 15 14:11:17 2021 +<pre><code>## mkin version used for fitting: 1.0.4 +## R version used for fitting: 4.0.4 +## Date of fit: Wed Mar 31 19:01:35 2021 +## Date of summary: Wed Mar 31 19:01:35 2021 ## ## Equations: ## d_parent/dt = - k_parent * parent @@ -472,7 +448,7 @@ print(FOCUS_2006_D)</code></pre> ## ## Model predictions using solution type analytical ## -## Fitted using 401 model solutions performed in 0.143 s +## Fitted using 401 model solutions performed in 0.146 s ## ## Error model: Constant variance ## @@ -616,7 +592,7 @@ $(document).ready(function () { $(document).ready(function () { $('.tabset-dropdown > .nav-tabs > 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$(window).load(function() { + $(window).on("load", function() { // Sets the active TOC item self._setActiveElement(true); @@ -1306,11 +1315,6 @@ color: #d14; </style> <style type="text/css">code{white-space: pre;}</style> -<style type="text/css"> - pre:not([class]) { - background-color: white; - } -</style> <script type="text/javascript"> if (window.hljs) { hljs.configure({languages: []}); @@ -1323,32 +1327,6 @@ if (window.hljs) { -<style type="text/css"> -h1 { - font-size: 34px; -} -h1.title { - font-size: 38px; -} -h2 { - font-size: 30px; -} -h3 { - font-size: 24px; -} -h4 { - font-size: 18px; -} -h5 { - font-size: 16px; -} -h6 { - font-size: 12px; -} -.table th:not([align]) { - text-align: left; -} -</style> @@ -1360,10 +1338,6 @@ h6 { margin-left: auto; margin-right: auto; } -code { - color: inherit; - background-color: rgba(0, 0, 0, 0.04); -} img { max-width:100%; } @@ -1379,6 +1353,9 @@ button.code-folding-btn:focus { summary { display: list-item; } +pre code { + padding: 0; +} </style> @@ -1391,7 +1368,6 @@ summary { max-height: 500px; min-height: 44px; overflow-y: auto; - background: white; border: 1px solid #ddd; border-radius: 4px; } @@ -1523,24 +1499,24 @@ div.tocify { <!-- setup 3col/9col grid for toc_float and main content --> -<div class="row-fluid"> -<div class="col-xs-12 col-sm-4 col-md-3"> +<div class="row"> +<div class="col-sm-12 col-md-4 col-lg-3"> <div id="TOC" class="tocify"> </div> </div> -<div class="toc-content col-xs-12 col-sm-8 col-md-9"> +<div class="toc-content col-sm-12 col-md-8 col-lg-9"> -<div class="fluid-row" id="header"> +<div id="header"> <h1 class="title toc-ignore">Example evaluation of FOCUS Laboratory Data L1 to L3</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">Last change 17 November 2016 (rebuilt 2021-02-15)</h4> +<h4 class="date">Last change 17 November 2016 (rebuilt 2021-03-31)</h4> </div> @@ -1559,10 +1535,10 @@ FOCUS_2006_L1_mkin <- mkin_wide_to_long(FOCUS_2006_L1)</code></pre> <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> <pre class="r"><code>m.L1.SFO <- mkinfit("SFO", FOCUS_2006_L1_mkin, quiet = TRUE) summary(m.L1.SFO)</code></pre> -<pre><code>## mkin version used for fitting: 1.0.3 -## R version used for fitting: 4.0.3 -## Date of fit: Mon Feb 15 14:11:19 2021 -## Date of summary: Mon Feb 15 14:11:19 2021 +<pre><code>## mkin version used for fitting: 1.0.4 +## R version used for fitting: 4.0.4 +## Date of fit: Wed Mar 31 19:01:37 2021 +## Date of summary: Wed Mar 31 19:01:37 2021 ## ## Equations: ## d_parent/dt = - k_parent * parent @@ -1660,17 +1636,17 @@ summary(m.L1.SFO)</code></pre> <pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> <pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is ## doubtful</code></pre> -<pre><code>## mkin version used for fitting: 1.0.3 -## R version used for fitting: 4.0.3 -## Date of fit: Mon Feb 15 14:11:19 2021 -## Date of summary: Mon Feb 15 14:11:19 2021 +<pre><code>## mkin version used for fitting: 1.0.4 +## R version used for fitting: 4.0.4 +## Date of fit: Wed Mar 31 19:01:37 2021 +## Date of summary: Wed Mar 31 19:01:37 2021 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 369 model solutions performed in 0.082 s +## Fitted using 369 model solutions performed in 0.083 s ## ## Error model: Constant variance ## @@ -1752,7 +1728,7 @@ FOCUS_2006_L2_mkin <- mkin_wide_to_long(FOCUS_2006_L2)</code></pre> <pre class="r"><code>m.L2.SFO <- mkinfit("SFO", FOCUS_2006_L2_mkin, quiet=TRUE) plot(m.L2.SFO, show_residuals = TRUE, show_errmin = TRUE, main = "FOCUS L2 - SFO")</code></pre> -<p><img 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" 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" /><!-- --></p> <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 14% suggests that the model does not fit very well. This is also obvious from the plots of the fit, in which we have included the 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> @@ -1763,19 +1739,19 @@ plot(m.L2.SFO, show_residuals = TRUE, show_errmin = TRUE, <pre class="r"><code>m.L2.FOMC <- mkinfit("FOMC", FOCUS_2006_L2_mkin, quiet = TRUE) plot(m.L2.FOMC, show_residuals = TRUE, main = "FOCUS L2 - FOMC")</code></pre> -<p><img 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" 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" /><!-- --></p> <pre class="r"><code>summary(m.L2.FOMC, data = FALSE)</code></pre> -<pre><code>## mkin version used for fitting: 1.0.3 -## R version used for fitting: 4.0.3 -## Date of fit: Mon Feb 15 14:11:19 2021 -## Date of summary: Mon Feb 15 14:11:19 2021 +<pre><code>## mkin version used for fitting: 1.0.4 +## R version used for fitting: 4.0.4 +## Date of fit: Wed Mar 31 19:01:37 2021 +## Date of summary: Wed Mar 31 19:01:37 2021 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 239 model solutions performed in 0.05 s +## Fitted using 239 model solutions performed in 0.049 s ## ## Error model: Constant variance ## @@ -1841,12 +1817,12 @@ plot(m.L2.FOMC, show_residuals = TRUE, <pre class="r"><code>m.L2.DFOP <- mkinfit("DFOP", FOCUS_2006_L2_mkin, quiet = TRUE) plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, main = "FOCUS L2 - DFOP")</code></pre> -<p><img 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" 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" /><!-- --></p> <pre class="r"><code>summary(m.L2.DFOP, data = FALSE)</code></pre> -<pre><code>## mkin version used for fitting: 1.0.3 -## R version used for fitting: 4.0.3 -## Date of fit: Mon Feb 15 14:11:20 2021 -## Date of summary: Mon Feb 15 14:11:20 2021 +<pre><code>## mkin version used for fitting: 1.0.4 +## R version used for fitting: 4.0.4 +## Date of fit: Wed Mar 31 19:01:38 2021 +## Date of summary: Wed Mar 31 19:01:38 2021 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -1943,10 +1919,10 @@ plot(mm.L3)</code></pre> <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> <pre class="r"><code>summary(mm.L3[["DFOP", 1]])</code></pre> -<pre><code>## mkin version used for fitting: 1.0.3 -## R version used for fitting: 4.0.3 -## Date of fit: Mon Feb 15 14:11:20 2021 -## Date of summary: Mon Feb 15 14:11:20 2021 +<pre><code>## mkin version used for fitting: 1.0.4 +## R version used for fitting: 4.0.4 +## Date of fit: Wed Mar 31 19:01:38 2021 +## Date of summary: Wed Mar 31 19:01:38 2021 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -2051,17 +2027,17 @@ plot(mm.L4)</code></pre> <p><img 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" /><!-- --></p> <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 3.3% as well as the plot suggest that the SFO model fits very well. The error level at which the <span class="math inline"><em>χ</em><sup>2</sup></span> test passes is slightly lower for the FOMC model. However, the difference appears negligible.</p> <pre class="r"><code>summary(mm.L4[["SFO", 1]], data = FALSE)</code></pre> -<pre><code>## mkin version used for fitting: 1.0.3 -## R version used for fitting: 4.0.3 -## Date of fit: Mon Feb 15 14:11:20 2021 -## Date of summary: Mon Feb 15 14:11:20 2021 +<pre><code>## mkin version used for fitting: 1.0.4 +## R version used for fitting: 4.0.4 +## Date of fit: Wed Mar 31 19:01:38 2021 +## Date of summary: Wed Mar 31 19:01:38 2021 ## ## Equations: ## d_parent/dt = - k_parent * parent ## ## Model predictions using solution type analytical ## -## Fitted using 142 model solutions performed in 0.03 s +## Fitted using 142 model solutions performed in 0.031 s ## ## Error model: Constant variance ## @@ -2115,17 +2091,17 @@ plot(mm.L4)</code></pre> ## DT50 DT90 ## parent 106 352</code></pre> <pre class="r"><code>summary(mm.L4[["FOMC", 1]], data = FALSE)</code></pre> -<pre><code>## mkin version used for fitting: 1.0.3 -## R version used for fitting: 4.0.3 -## Date of fit: Mon Feb 15 14:11:20 2021 -## Date of summary: Mon Feb 15 14:11:20 2021 +<pre><code>## mkin version used for fitting: 1.0.4 +## R version used for fitting: 4.0.4 +## Date of fit: Wed Mar 31 19:01:38 2021 +## Date of summary: Wed Mar 31 19:01:38 2021 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 224 model solutions performed in 0.046 s +## Fitted using 224 model solutions performed in 0.045 s ## ## Error model: Constant variance ## @@ -2222,7 +2198,7 @@ $(document).ready(function () { $(document).ready(function () { $('.tabset-dropdown > .nav-tabs > li').click(function () { - $(this).parent().toggleClass('nav-tabs-open') + $(this).parent().toggleClass('nav-tabs-open'); }); }); </script> diff --git a/vignettes/web_only/mkin_benchmarks.rda b/vignettes/web_only/mkin_benchmarks.rda Binary files differindex 4421cf5b..fe4ab843 100644 --- a/vignettes/web_only/mkin_benchmarks.rda +++ b/vignettes/web_only/mkin_benchmarks.rda |
