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diff --git a/docs/articles/FOCUS_D.R b/docs/articles/FOCUS_D.R index 51723496..b831e14e 100644 --- a/docs/articles/FOCUS_D.R +++ b/docs/articles/FOCUS_D.R @@ -3,7 +3,7 @@ library(knitr) opts_chunk$set(tidy = FALSE, cache = TRUE) ## ----data---------------------------------------------------------------- -library("mkin") +library("mkin", quietly = TRUE) print(FOCUS_2006_D) ## ----model--------------------------------------------------------------- diff --git a/docs/articles/FOCUS_D.html b/docs/articles/FOCUS_D.html index d6941019..e3f87eae 100644 --- a/docs/articles/FOCUS_D.html +++ b/docs/articles/FOCUS_D.html @@ -77,20 +77,15 @@ <h1>Example evaluation of FOCUS Example Dataset D</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2017-11-16</h4> + <h4 class="date">2018-01-14</h4> </div> <div class="contents"> <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>)</code></pre></div> -<pre><code>## Lade nötiges Paket: minpack.lm</code></pre> -<pre><code>## Lade nötiges Paket: rootSolve</code></pre> -<pre><code>## Lade nötiges Paket: inline</code></pre> -<pre><code>## Lade nötiges Paket: methods</code></pre> -<pre><code>## Lade nötiges Paket: parallel</code></pre> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">print</span>(FOCUS_2006_D)</code></pre></div> +<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 ## 2 parent 0 102.04 diff --git a/docs/articles/FOCUS_L.html b/docs/articles/FOCUS_L.html index 7cef7abd..bc2b9947 100644 --- a/docs/articles/FOCUS_L.html +++ b/docs/articles/FOCUS_L.html @@ -77,7 +77,7 @@ <h1>Example evaluation of FOCUS Laboratory Data L1 to L3</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2017-11-16</h4> + <h4 class="date">2018-01-14</h4> </div> @@ -98,17 +98,17 @@ FOCUS_2006_L1_mkin <-<span class="st"> </span><span class="kw"><a href="../re <p>Since mkin version 0.9-32 (July 2014), we can use shorthand notation like <code>"SFO"</code> for parent only degradation models. The following two lines fit the model and produce the summary report of the model fit. This covers the numerical analysis given in the FOCUS report.</p> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">m.L1.SFO <-<span class="st"> </span><span class="kw"><a href="../reference/mkinfit.html">mkinfit</a></span>(<span class="st">"SFO"</span>, FOCUS_2006_L1_mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>) <span class="kw">summary</span>(m.L1.SFO)</code></pre></div> -<pre><code>## mkin version: 0.9.46.2 -## R version: 3.4.2 -## Date of fit: Thu Nov 16 17:12:46 2017 -## Date of summary: Thu Nov 16 17:12:46 2017 +<pre><code>## mkin version: 0.9.47.1 +## R version: 3.4.3 +## Date of fit: Sun Jan 14 18:36:12 2018 +## Date of summary: Sun Jan 14 18:36:12 2018 ## ## Equations: ## d_parent/dt = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 37 model solutions performed in 0.253 s +## Fitted with method Port using 37 model solutions performed in 0.258 s ## ## Weighting: none ## @@ -191,10 +191,10 @@ FOCUS_2006_L1_mkin <-<span class="st"> </span><span class="kw"><a href="../re <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">plot</span>(m.L1.FOMC, <span class="dt">show_errmin =</span> <span class="ot">TRUE</span>, <span class="dt">main =</span> <span class="st">"FOCUS L1 - FOMC"</span>)</code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-6-1.png" width="576"></p> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.L1.FOMC, <span class="dt">data =</span> <span class="ot">FALSE</span>)</code></pre></div> -<pre><code>## mkin version: 0.9.46.2 -## R version: 3.4.2 -## Date of fit: Thu Nov 16 17:12:47 2017 -## Date of summary: Thu Nov 16 17:12:47 2017 +<pre><code>## mkin version: 0.9.47.1 +## R version: 3.4.3 +## Date of fit: Sun Jan 14 18:36:13 2018 +## Date of summary: Sun Jan 14 18:36:13 2018 ## ## ## Warning: Optimisation by method Port did not converge. @@ -206,7 +206,7 @@ FOCUS_2006_L1_mkin <-<span class="st"> </span><span class="kw"><a href="../re ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 155 model solutions performed in 0.485 s +## Fitted with method Port using 155 model solutions performed in 0.452 s ## ## Weighting: none ## @@ -291,17 +291,17 @@ FOCUS_2006_L2_mkin <-<span class="st"> </span><span class="kw"><a href="../re <span class="dt">main =</span> <span class="st">"FOCUS L2 - FOMC"</span>)</code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-9-1.png" width="672"></p> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.L2.FOMC, <span class="dt">data =</span> <span class="ot">FALSE</span>)</code></pre></div> -<pre><code>## mkin version: 0.9.46.2 -## R version: 3.4.2 -## Date of fit: Thu Nov 16 17:12:48 2017 -## Date of summary: Thu Nov 16 17:12:48 2017 +<pre><code>## mkin version: 0.9.47.1 +## R version: 3.4.3 +## Date of fit: Sun Jan 14 18:36:14 2018 +## Date of summary: Sun Jan 14 18:36:14 2018 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 81 model solutions performed in 0.183 s +## Fitted with method Port using 81 model solutions performed in 0.177 s ## ## Weighting: none ## @@ -362,10 +362,10 @@ FOCUS_2006_L2_mkin <-<span class="st"> </span><span class="kw"><a href="../re <span class="dt">main =</span> <span class="st">"FOCUS L2 - DFOP"</span>)</code></pre></div> <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-10-1.png" width="672"></p> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(m.L2.DFOP, <span class="dt">data =</span> <span class="ot">FALSE</span>)</code></pre></div> -<pre><code>## mkin version: 0.9.46.2 -## R version: 3.4.2 -## Date of fit: Thu Nov 16 17:12:49 2017 -## Date of summary: Thu Nov 16 17:12:49 2017 +<pre><code>## mkin version: 0.9.47.1 +## R version: 3.4.3 +## Date of fit: Sun Jan 14 18:36:15 2018 +## Date of summary: Sun Jan 14 18:36:15 2018 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * @@ -374,7 +374,7 @@ FOCUS_2006_L2_mkin <-<span class="st"> </span><span class="kw"><a href="../re ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 336 model solutions performed in 0.84 s +## Fitted with method Port using 336 model solutions performed in 0.808 s ## ## Weighting: none ## @@ -454,10 +454,10 @@ mm.L3 <-<span class="st"> </span><span class="kw"><a href="../reference/mmkin <p>The objects returned by mmkin are arranged like a matrix, with models as a row index and datasets as a column index.</p> <p>We can extract the summary and plot for <em>e.g.</em> the DFOP fit, using square brackets for indexing which will result in the use of the summary and plot functions working on mkinfit objects.</p> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(mm.L3[[<span class="st">"DFOP"</span>, <span class="dv">1</span>]])</code></pre></div> -<pre><code>## mkin version: 0.9.46.2 -## R version: 3.4.2 -## Date of fit: Thu Nov 16 17:12:50 2017 -## Date of summary: Thu Nov 16 17:12:50 2017 +<pre><code>## mkin version: 0.9.47.1 +## R version: 3.4.3 +## Date of fit: Sun Jan 14 18:36:16 2018 +## Date of summary: Sun Jan 14 18:36:16 2018 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * @@ -466,7 +466,7 @@ mm.L3 <-<span class="st"> </span><span class="kw"><a href="../reference/mmkin ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 137 model solutions performed in 0.309 s +## Fitted with method Port using 137 model solutions performed in 0.3 s ## ## Weighting: none ## @@ -555,17 +555,17 @@ mm.L4 <-<span class="st"> </span><span class="kw"><a href="../reference/mmkin <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-15-1.png" width="672"></p> <p>The <span class="math inline">\(\chi^2\)</span> error level of 3.3% as well as the plot suggest that the SFO model fits very well. The error level at which the <span class="math inline">\(\chi^2\)</span> test passes is slightly lower for the FOMC model. However, the difference appears negligible.</p> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(mm.L4[[<span class="st">"SFO"</span>, <span class="dv">1</span>]], <span class="dt">data =</span> <span class="ot">FALSE</span>)</code></pre></div> -<pre><code>## mkin version: 0.9.46.2 -## R version: 3.4.2 -## Date of fit: Thu Nov 16 17:12:50 2017 -## Date of summary: Thu Nov 16 17:12:51 2017 +<pre><code>## mkin version: 0.9.47.1 +## R version: 3.4.3 +## Date of fit: Sun Jan 14 18:36:16 2018 +## Date of summary: Sun Jan 14 18:36:17 2018 ## ## Equations: ## d_parent/dt = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 46 model solutions performed in 0.098 s +## Fitted with method Port using 46 model solutions performed in 0.097 s ## ## Weighting: none ## @@ -615,17 +615,17 @@ mm.L4 <-<span class="st"> </span><span class="kw"><a href="../reference/mmkin ## DT50 DT90 ## parent 106 352</code></pre> <div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">summary</span>(mm.L4[[<span class="st">"FOMC"</span>, <span class="dv">1</span>]], <span class="dt">data =</span> <span class="ot">FALSE</span>)</code></pre></div> -<pre><code>## mkin version: 0.9.46.2 -## R version: 3.4.2 -## Date of fit: Thu Nov 16 17:12:51 2017 -## Date of summary: Thu Nov 16 17:12:51 2017 +<pre><code>## mkin version: 0.9.47.1 +## R version: 3.4.3 +## Date of fit: Sun Jan 14 18:36:17 2018 +## Date of summary: Sun Jan 14 18:36:17 2018 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 66 model solutions performed in 0.136 s +## Fitted with method Port using 66 model solutions performed in 0.138 s ## ## Weighting: none ## diff --git a/docs/articles/FOCUS_Z.R b/docs/articles/FOCUS_Z.R index a77441cf..65ddbeba 100644 --- a/docs/articles/FOCUS_Z.R +++ b/docs/articles/FOCUS_Z.R @@ -3,7 +3,7 @@ require(knitr) opts_chunk$set(engine='R', tidy = FALSE) ## ---- echo = TRUE, fig = TRUE, fig.width = 8, fig.height = 7------------- -library(mkin, quiet = TRUE) +library(mkin, quietly = TRUE) LOD = 0.5 FOCUS_2006_Z = data.frame( t = c(0, 0.04, 0.125, 0.29, 0.54, 1, 2, 3, 4, 7, 10, 14, 21, diff --git a/docs/articles/FOCUS_Z.html b/docs/articles/FOCUS_Z.html index d16299ca..7a37c66d 100644 --- a/docs/articles/FOCUS_Z.html +++ b/docs/articles/FOCUS_Z.html @@ -77,7 +77,7 @@ <h1>Example evaluation of FOCUS dataset Z</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2017-11-16</h4> + <h4 class="date">2018-01-14</h4> </div> @@ -88,7 +88,7 @@ <h1 class="hasAnchor"> <a href="#the-data" class="anchor"></a>The data</h1> <p>The following code defines the example dataset from Appendix 7 to the FOCUS kinetics report <span class="citation">(FOCUS Work Group on Degradation Kinetics 2014, 354)</span>.</p> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(mkin, <span class="dt">quiet =</span> <span class="ot">TRUE</span>) +<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(mkin, <span class="dt">quietly =</span> <span class="ot">TRUE</span>) LOD =<span class="st"> </span><span class="fl">0.5</span> FOCUS_2006_Z =<span class="st"> </span><span class="kw">data.frame</span>( <span class="dt">t =</span> <span class="kw">c</span>(<span class="dv">0</span>, <span class="fl">0.04</span>, <span class="fl">0.125</span>, <span class="fl">0.29</span>, <span class="fl">0.54</span>, <span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">3</span>, <span class="dv">4</span>, <span class="dv">7</span>, <span class="dv">10</span>, <span class="dv">14</span>, <span class="dv">21</span>, diff --git a/docs/articles/compiled_models.R b/docs/articles/compiled_models.R index 25c39dac..c5b06c11 100644 --- a/docs/articles/compiled_models.R +++ b/docs/articles/compiled_models.R @@ -6,7 +6,7 @@ opts_chunk$set(tidy = FALSE, cache = FALSE) Sys.which("gcc") ## ----create_SFO_SFO------------------------------------------------------ -library("mkin") +library("mkin", quietly = TRUE) SFO_SFO <- mkinmod( parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO")) diff --git a/docs/articles/compiled_models.html b/docs/articles/compiled_models.html index 68aa1bac..4c850d14 100644 --- a/docs/articles/compiled_models.html +++ b/docs/articles/compiled_models.html @@ -77,7 +77,7 @@ <h1>Performance benefit by using compiled model definitions in mkin</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2017-11-16</h4> + <h4 class="date">2018-01-14</h4> </div> @@ -91,7 +91,7 @@ <pre><code>## gcc ## "/usr/bin/gcc"</code></pre> <p>First, we build a simple degradation model for a parent compound with one metabolite.</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>) SFO_SFO <-<span class="st"> </span><span class="kw"><a href="../reference/mkinmod.html">mkinmod</a></span>( <span class="dt">parent =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"m1"</span>), <span class="dt">m1 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>))</code></pre></div> @@ -115,9 +115,9 @@ SFO_SFO <-<span class="st"> </span><span class="kw"><a href="../reference/mki }</code></pre></div> <pre><code>## Lade nötiges Paket: rbenchmark</code></pre> <pre><code>## test replications elapsed relative user.self sys.self -## 3 deSolve, compiled 3 2.029 1.000 2.028 0 -## 1 deSolve, not compiled 3 14.237 7.017 14.236 0 -## 2 Eigenvalue based 3 2.700 1.331 2.700 0 +## 3 deSolve, compiled 3 2.083 1.000 2.084 0.000 +## 1 deSolve, not compiled 3 14.501 6.962 14.472 0.016 +## 2 Eigenvalue based 3 2.566 1.232 2.564 0.000 ## user.child sys.child ## 3 0 0 ## 1 0 0 @@ -146,14 +146,14 @@ SFO_SFO <-<span class="st"> </span><span class="kw"><a href="../reference/mki }</code></pre></div> <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> <pre><code>## test replications elapsed relative user.self sys.self -## 2 deSolve, compiled 3 3.495 1.000 3.496 0 -## 1 deSolve, not compiled 3 29.264 8.373 29.260 0 +## 2 deSolve, compiled 3 3.534 1.000 3.532 0.000 +## 1 deSolve, not compiled 3 29.973 8.481 29.960 0.004 ## user.child sys.child ## 2 0 0 ## 1 0 0</code></pre> <p>Here we get a performance benefit of a factor of 8 using the version of the differential equation model compiled from C code!</p> -<p>This vignette was built with mkin 0.9.46.2 on</p> -<pre><code>## R version 3.4.2 (2017-09-28) +<p>This vignette was built with mkin 0.9.47.1 on</p> +<pre><code>## R version 3.4.3 (2017-11-30) ## Platform: x86_64-pc-linux-gnu (64-bit) ## Running under: Debian GNU/Linux 9 (stretch)</code></pre> <pre><code>## CPU model: Intel(R) Core(TM) i7-4710MQ CPU @ 2.50GHz</code></pre> diff --git a/docs/articles/index.html b/docs/articles/index.html index 0a355e17..c1dc0b64 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -96,7 +96,7 @@ </header> <div class="page-header"> - <h1>Articles <small>version 0.9.46.3</small></h1> + <h1>Articles <small>version 0.9.47.1</small></h1> </div> <div class="row"> diff --git a/docs/articles/mkin.R b/docs/articles/mkin.R index 67dc3623..19e80322 100644 --- a/docs/articles/mkin.R +++ b/docs/articles/mkin.R @@ -3,7 +3,7 @@ require(knitr) opts_chunk$set(engine='R', tidy=FALSE) ## ---- echo = TRUE, cache = TRUE, fig = TRUE, fig.width = 8, fig.height = 7---- -library(mkin) +library("mkin", quietly = TRUE) # Define the kinetic model m_SFO_SFO_SFO <- mkinmod(parent = mkinsub("SFO", "M1"), M1 = mkinsub("SFO", "M2"), diff --git a/docs/articles/mkin.html b/docs/articles/mkin.html index 14349e53..13df8a5d 100644 --- a/docs/articles/mkin.html +++ b/docs/articles/mkin.html @@ -77,7 +77,7 @@ <h1>Introduction to mkin</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2017-11-16</h4> + <h4 class="date">2018-01-14</h4> </div> @@ -88,13 +88,8 @@ <h1 class="hasAnchor"> <a href="#abstract" class="anchor"></a>Abstract</h1> <p>In the regulatory evaluation of chemical substances like plant protection products (pesticides), biocides and other chemicals, degradation data play an important role. For the evaluation of pesticide degradation experiments, detailed guidance has been developed, based on nonlinear optimisation. The <code>R</code> add-on package <code>mkin</code> <span class="citation">(Ranke 2016)</span> implements fitting some of the models recommended in this guidance from within R and calculates some statistical measures for data series within one or more compartments, for parent and metabolites.</p> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(mkin)</code></pre></div> -<pre><code>## Lade nötiges Paket: minpack.lm</code></pre> -<pre><code>## Lade nötiges Paket: rootSolve</code></pre> -<pre><code>## Lade nötiges Paket: inline</code></pre> -<pre><code>## Lade nötiges Paket: methods</code></pre> -<pre><code>## Lade nötiges Paket: parallel</code></pre> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># Define the kinetic model</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="co"># Define the kinetic model</span> m_SFO_SFO_SFO <-<span class="st"> </span><span class="kw"><a href="../reference/mkinmod.html">mkinmod</a></span>(<span class="dt">parent =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"M1"</span>), <span class="dt">M1 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"M2"</span>), <span class="dt">M2 =</span> <span class="kw"><a href="../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>), diff --git a/docs/articles/twa.html b/docs/articles/twa.html index 0a4df0ff..f141ce63 100644 --- a/docs/articles/twa.html +++ b/docs/articles/twa.html @@ -77,7 +77,7 @@ <h1>Calculation of time weighted average concentrations with mkin</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2017-11-16</h4> + <h4 class="date">2018-01-14</h4> </div> diff --git a/vignettes/FOCUS_D.Rmd b/vignettes/FOCUS_D.Rmd index 9cb9c529..037dd854 100644 --- a/vignettes/FOCUS_D.Rmd +++ b/vignettes/FOCUS_D.Rmd @@ -24,7 +24,7 @@ named `parent` and `m1`. ```{r data}
-library("mkin")
+library("mkin", quietly = TRUE)
print(FOCUS_2006_D)
```
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 ## diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html index 180c0323..ccde0c82 100644 --- a/vignettes/FOCUS_L.html +++ b/vignettes/FOCUS_L.html @@ -11,7 +11,7 @@ <meta name="author" content="Johannes Ranke" /> -<meta name="date" content="2017-07-21" /> +<meta name="date" content="2018-01-14" /> <title>Example evaluation of FOCUS Laboratory Data L1 to L3</title> @@ -223,7 +223,7 @@ div.tocify { <h1 class="title toc-ignore">Example evaluation of FOCUS Laboratory Data L1 to L3</h1> <h4 class="author"><em>Johannes Ranke</em></h4> -<h4 class="date"><em>2017-07-21</em></h4> +<h4 class="date"><em>2018-01-14</em></h4> </div> @@ -242,17 +242,17 @@ 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: 0.9.45.2 -## R version: 3.4.1 -## Date of fit: Fri Jul 21 18:02:22 2017 -## Date of summary: Fri Jul 21 18:02:22 2017 +<pre><code>## mkin version: 0.9.47.1 +## R version: 3.4.3 +## Date of fit: Sun Jan 14 17:50:05 2018 +## Date of summary: Sun Jan 14 17:50:05 2018 ## ## Equations: ## d_parent/dt = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 37 model solutions performed in 0.258 s +## Fitted with method Port using 37 model solutions performed in 0.242 s ## ## Weighting: none ## @@ -324,7 +324,7 @@ summary(m.L1.SFO)</code></pre> ## 30 parent 4.0 5.251 -1.2513</code></pre> <p>A plot of the fit is obtained with the plot function for mkinfit objects.</p> <pre class="r"><code>plot(m.L1.SFO, show_errmin = TRUE, main = "FOCUS L1 - SFO")</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <p>The residual plot can be easily obtained by</p> <pre class="r"><code>mkinresplot(m.L1.SFO, ylab = "Observed", xlab = "Time")</code></pre> <p><img src="data:image/png;base64,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" /><!-- --></p> @@ -333,12 +333,12 @@ summary(m.L1.SFO)</code></pre> <pre><code>## Warning in mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet = TRUE): Optimisation by method Port did not converge. ## Convergence code is 1</code></pre> <pre class="r"><code>plot(m.L1.FOMC, show_errmin = TRUE, main = "FOCUS L1 - FOMC")</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>summary(m.L1.FOMC, data = FALSE)</code></pre> -<pre><code>## mkin version: 0.9.45.2 -## R version: 3.4.1 -## Date of fit: Fri Jul 21 18:02:23 2017 -## Date of summary: Fri Jul 21 18:02:23 2017 +<pre><code>## mkin version: 0.9.47.1 +## R version: 3.4.3 +## Date of fit: Sun Jan 14 17:50:06 2018 +## Date of summary: Sun Jan 14 17:50:06 2018 ## ## ## Warning: Optimisation by method Port did not converge. @@ -350,7 +350,7 @@ summary(m.L1.SFO)</code></pre> ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 155 model solutions performed in 0.445 s +## Fitted with method Port using 155 model solutions performed in 0.432 s ## ## Weighting: none ## @@ -432,17 +432,17 @@ plot(m.L2.FOMC, show_residuals = TRUE, main = "FOCUS L2 - FOMC")</code></pre> <p><img src="data:image/png;base64,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" /><!-- --></p> <pre class="r"><code>summary(m.L2.FOMC, data = FALSE)</code></pre> -<pre><code>## mkin version: 0.9.45.2 -## R version: 3.4.1 -## Date of fit: Fri Jul 21 18:02:24 2017 -## Date of summary: Fri Jul 21 18:02:24 2017 +<pre><code>## mkin version: 0.9.47.1 +## R version: 3.4.3 +## Date of fit: Sun Jan 14 17:50:07 2018 +## Date of summary: Sun Jan 14 17:50:07 2018 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 81 model solutions performed in 0.246 s +## Fitted with method Port using 81 model solutions performed in 0.166 s ## ## Weighting: none ## @@ -502,10 +502,10 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, main = "FOCUS L2 - DFOP")</code></pre> <p><img src="data:image/png;base64,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" /><!-- --></p> <pre class="r"><code>summary(m.L2.DFOP, data = FALSE)</code></pre> -<pre><code>## mkin version: 0.9.45.2 -## R version: 3.4.1 -## Date of fit: Fri Jul 21 18:02:24 2017 -## Date of summary: Fri Jul 21 18:02:24 2017 +<pre><code>## mkin version: 0.9.47.1 +## R version: 3.4.3 +## Date of fit: Sun Jan 14 17:50:08 2018 +## Date of summary: Sun Jan 14 17:50:08 2018 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * @@ -514,7 +514,7 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 336 model solutions performed in 0.742 s +## Fitted with method Port using 336 model solutions performed in 0.712 s ## ## Weighting: none ## @@ -591,10 +591,10 @@ 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: 0.9.45.2 -## R version: 3.4.1 -## Date of fit: Fri Jul 21 18:02:25 2017 -## Date of summary: Fri Jul 21 18:02:25 2017 +<pre><code>## mkin version: 0.9.47.1 +## R version: 3.4.3 +## Date of fit: Sun Jan 14 17:50:08 2018 +## Date of summary: Sun Jan 14 17:50:08 2018 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * @@ -603,7 +603,7 @@ plot(mm.L3)</code></pre> ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 137 model solutions performed in 0.371 s +## Fitted with method Port using 137 model solutions performed in 0.291 s ## ## Weighting: none ## @@ -688,20 +688,20 @@ mm.L4 <- mmkin(c("SFO", "FOMC"), cores = 1, list("FOCUS L4" = FOCUS_2006_L4_mkin), quiet = TRUE) plot(mm.L4)</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 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: 0.9.45.2 -## R version: 3.4.1 -## Date of fit: Fri Jul 21 18:02:26 2017 -## Date of summary: Fri Jul 21 18:02:26 2017 +<pre><code>## mkin version: 0.9.47.1 +## R version: 3.4.3 +## Date of fit: Sun Jan 14 17:50:09 2018 +## Date of summary: Sun Jan 14 17:50:09 2018 ## ## Equations: ## d_parent/dt = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 46 model solutions performed in 0.098 s +## Fitted with method Port using 46 model solutions performed in 0.094 s ## ## Weighting: none ## @@ -751,17 +751,17 @@ 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: 0.9.45.2 -## R version: 3.4.1 -## Date of fit: Fri Jul 21 18:02:26 2017 -## Date of summary: Fri Jul 21 18:02:26 2017 +<pre><code>## mkin version: 0.9.47.1 +## R version: 3.4.3 +## Date of fit: Sun Jan 14 17:50:09 2018 +## Date of summary: Sun Jan 14 17:50:09 2018 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 66 model solutions performed in 0.141 s +## Fitted with method Port using 66 model solutions performed in 0.139 s ## ## Weighting: none ## diff --git a/vignettes/FOCUS_Z.Rmd b/vignettes/FOCUS_Z.Rmd index d61f7387..b1ac7d42 100644 --- a/vignettes/FOCUS_Z.Rmd +++ b/vignettes/FOCUS_Z.Rmd @@ -29,7 +29,7 @@ The following code defines the example dataset from Appendix 7 to the FOCUS kine report [@FOCUSkinetics2014, p. 354]. ```{r, echo = TRUE, fig = TRUE, fig.width = 8, fig.height = 7} -library(mkin, quiet = TRUE) +library(mkin, quietly = TRUE) LOD = 0.5 FOCUS_2006_Z = data.frame( t = c(0, 0.04, 0.125, 0.29, 0.54, 1, 2, 3, 4, 7, 10, 14, 21, diff --git a/vignettes/FOCUS_Z.html b/vignettes/FOCUS_Z.html index 369e913d..1428ea85 100644 --- a/vignettes/FOCUS_Z.html +++ b/vignettes/FOCUS_Z.html @@ -11,7 +11,7 @@ <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 dataset Z</title> @@ -234,7 +234,7 @@ div.tocify { <h1 class="title toc-ignore">Example evaluation of FOCUS dataset Z</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> </div> @@ -243,7 +243,7 @@ div.tocify { <div id="the-data" class="section level1"> <h1>The data</h1> <p>The following code defines the example dataset from Appendix 7 to the FOCUS kinetics report <span class="citation">(FOCUS Work Group on Degradation Kinetics 2014, 354)</span>.</p> -<pre class="r"><code>library(mkin, quiet = TRUE) +<pre class="r"><code>library(mkin, quietly = TRUE) LOD = 0.5 FOCUS_2006_Z = data.frame( t = c(0, 0.04, 0.125, 0.29, 0.54, 1, 2, 3, 4, 7, 10, 14, 21, diff --git a/vignettes/compiled_models.Rmd b/vignettes/compiled_models.Rmd index 21b6b0d5..e97876da 100644 --- a/vignettes/compiled_models.Rmd +++ b/vignettes/compiled_models.Rmd @@ -28,7 +28,7 @@ Sys.which("gcc") First, we build a simple degradation model for a parent compound with one metabolite.
```{r create_SFO_SFO}
-library("mkin")
+library("mkin", quietly = TRUE)
SFO_SFO <- mkinmod(
parent = mkinsub("SFO", "m1"),
m1 = mkinsub("SFO"))
diff --git a/vignettes/compiled_models.html b/vignettes/compiled_models.html index 30f29699..8aaa70d6 100644 --- a/vignettes/compiled_models.html +++ b/vignettes/compiled_models.html @@ -12,7 +12,7 @@ <meta name="author" content="Johannes Ranke" /> -<meta name="date" content="2017-07-21" /> +<meta name="date" content="2018-01-14" /> <title>Performance benefit by using compiled model definitions in mkin</title> @@ -70,7 +70,7 @@ code > span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Inf <h1 class="title toc-ignore">Performance benefit by using compiled model definitions in mkin</h1> <h4 class="author"><em>Johannes Ranke</em></h4> -<h4 class="date"><em>2017-07-21</em></h4> +<h4 class="date"><em>2018-01-14</em></h4> @@ -81,13 +81,8 @@ code > span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Inf <pre><code>## gcc ## "/usr/bin/gcc"</code></pre> <p>First, we build a simple degradation model for a parent compound with one metabolite.</p> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(<span class="st">"mkin"</span>)</code></pre></div> -<pre><code>## Loading required package: minpack.lm</code></pre> -<pre><code>## Loading required package: rootSolve</code></pre> -<pre><code>## Loading required package: inline</code></pre> -<pre><code>## Loading required package: methods</code></pre> -<pre><code>## Loading required package: parallel</code></pre> -<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">SFO_SFO <-<span class="st"> </span><span class="kw">mkinmod</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>) +SFO_SFO <-<span class="st"> </span><span class="kw">mkinmod</span>( <span class="dt">parent =</span> <span class="kw">mkinsub</span>(<span class="st">"SFO"</span>, <span class="st">"m1"</span>), <span class="dt">m1 =</span> <span class="kw">mkinsub</span>(<span class="st">"SFO"</span>))</code></pre></div> <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> @@ -110,9 +105,9 @@ code > span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Inf }</code></pre></div> <pre><code>## Loading required package: rbenchmark</code></pre> <pre><code>## test replications elapsed relative user.self sys.self -## 3 deSolve, compiled 3 2.140 1.000 2.140 0 -## 1 deSolve, not compiled 3 15.070 7.042 15.068 0 -## 2 Eigenvalue based 3 2.577 1.204 2.576 0 +## 3 deSolve, compiled 3 2.005 1.000 2.000 0.004 +## 1 deSolve, not compiled 3 14.202 7.083 14.196 0.000 +## 2 Eigenvalue based 3 2.427 1.210 2.428 0.000 ## user.child sys.child ## 3 0 0 ## 1 0 0 @@ -140,14 +135,14 @@ code > span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Inf }</code></pre></div> <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> <pre><code>## test replications elapsed relative user.self sys.self -## 2 deSolve, compiled 3 3.646 1.000 3.644 0 -## 1 deSolve, not compiled 3 30.752 8.434 30.752 0 +## 2 deSolve, compiled 3 3.489 1.000 3.488 0 +## 1 deSolve, not compiled 3 28.906 8.285 28.904 0 ## user.child sys.child ## 2 0 0 ## 1 0 0</code></pre> <p>Here we get a performance benefit of a factor of 8 using the version of the differential equation model compiled from C code!</p> -<p>This vignette was built with mkin 0.9.45.2 on</p> -<pre><code>## R version 3.4.1 (2017-06-30) +<p>This vignette was built with mkin 0.9.47.1 on</p> +<pre><code>## R version 3.4.3 (2017-11-30) ## Platform: x86_64-pc-linux-gnu (64-bit) ## Running under: Debian GNU/Linux 9 (stretch)</code></pre> <pre><code>## CPU model: Intel(R) Core(TM) i7-4710MQ CPU @ 2.50GHz</code></pre> diff --git a/vignettes/mkin.Rmd b/vignettes/mkin.Rmd index 84bc5adb..4f3ac7fc 100644 --- a/vignettes/mkin.Rmd +++ b/vignettes/mkin.Rmd @@ -34,7 +34,7 @@ measures for data series within one or more compartments, for parent and metabolites. ```{r, echo = TRUE, cache = TRUE, fig = TRUE, fig.width = 8, fig.height = 7} -library(mkin) +library("mkin", quietly = TRUE) # Define the kinetic model m_SFO_SFO_SFO <- mkinmod(parent = mkinsub("SFO", "M1"), M1 = mkinsub("SFO", "M2"), diff --git a/vignettes/mkin.html b/vignettes/mkin.html index f87c967f..635dd79e 100644 --- a/vignettes/mkin.html +++ b/vignettes/mkin.html @@ -11,7 +11,7 @@ <meta name="author" content="Johannes Ranke" /> -<meta name="date" content="2017-11-16" /> +<meta name="date" content="2018-01-14" /> <title>Introduction to mkin</title> @@ -234,7 +234,7 @@ div.tocify { <h1 class="title toc-ignore">Introduction to mkin</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> </div> @@ -243,7 +243,7 @@ div.tocify { <div id="abstract" class="section level1"> <h1>Abstract</h1> <p>In the regulatory evaluation of chemical substances like plant protection products (pesticides), biocides and other chemicals, degradation data play an important role. For the evaluation of pesticide degradation experiments, detailed guidance has been developed, based on nonlinear optimisation. The <code>R</code> add-on package <code>mkin</code> <span class="citation">(Ranke 2016)</span> implements fitting some of the models recommended in this guidance from within R and calculates some statistical measures for data series within one or more compartments, for parent and metabolites.</p> -<pre class="r"><code>library(mkin) +<pre class="r"><code>library("mkin", quietly = TRUE) # Define the kinetic model m_SFO_SFO_SFO <- mkinmod(parent = mkinsub("SFO", "M1"), M1 = mkinsub("SFO", "M2"), |