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diff --git a/vignettes/FOCUS_D.html b/vignettes/FOCUS_D.html index abd7d129..84e3748c 100644 --- a/vignettes/FOCUS_D.html +++ b/vignettes/FOCUS_D.html @@ -12,7 +12,7 @@ <meta name="author" content="Johannes Ranke" /> -<meta name="date" content="2017-11-16" /> +<meta name="date" content="2018-01-14" /> <title>Example evaluation of FOCUS Example Dataset D</title> @@ -70,12 +70,12 @@ code > span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Inf <h1 class="title toc-ignore">Example evaluation of FOCUS Example Dataset D</h1> <h4 class="author"><em>Johannes Ranke</em></h4> -<h4 class="date"><em>2017-11-16</em></h4> +<h4 class="date"><em>2018-01-14</em></h4> <p>This is just a very simple vignette showing how to fit a degradation model for a parent compound with one transformation product using <code>mkin</code>. After loading the library we look a the data. We have observed concentrations in the column named <code>value</code> at the times specified in column <code>time</code> for the two observed variables named <code>parent</code> and <code>m1</code>.</p> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(<span class="st">"mkin"</span>) +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(<span class="st">"mkin"</span>, <span class="dt">quietly =</span> <span class="ot">TRUE</span>) <span class="kw">print</span>(FOCUS_2006_D)</code></pre></div> <pre><code>## name time value ## 1 parent 0 99.46 @@ -141,10 +141,10 @@ code > span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Inf <p><img src="data:image/png;base64,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" /><!-- --></p> <p>A comprehensive report of the results is obtained using the <code>summary</code> method for <code>mkinfit</code> objects.</p> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(fit)</code></pre></div> -<pre><code>## mkin version: 0.9.46.2 -## R version: 3.4.2 -## Date of fit: Thu Nov 16 17:07:26 2017 -## Date of summary: Thu Nov 16 17:07:27 2017 +<pre><code>## mkin version: 0.9.47.1 +## R version: 3.4.3 +## Date of fit: Sun Jan 14 17:50:03 2018 +## Date of summary: Sun Jan 14 17:50:03 2018 ## ## Equations: ## d_parent/dt = - k_parent_sink * parent - k_parent_m1 * parent @@ -152,7 +152,7 @@ code > span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Inf ## ## Model predictions using solution type deSolve ## -## Fitted with method Port using 153 model solutions performed in 1.031 s +## Fitted with method Port using 153 model solutions performed in 1.072 s ## ## Weighting: none ## |