Complete mkin vignette, update FOCUS L vignette

The mkin vignette now describes how confidence intervals and the t-test are calculated. The FOCUS L vignette is updated with the new floating toc provided by the current rmarkdown package, and also uses current, improved mkin functionality.
author: Johannes Ranke <jranke@uni-bremen.de> 2016-06-27 19:06:42 +0200
committer: Johannes Ranke <jranke@uni-bremen.de> 2016-06-27 19:06:42 +0200
commit: 280f609d8e363bb28bee543d1fa3651762469198 (patch)
tree: d227ee7d1fffa7963d2c56ff837022fccc160f1e /vignettes/mkin.html
parent: 4b0b5346a9f026c5a19d452e4649326fe56d464c (diff)
1 files changed, 60 insertions, 13 deletions
diff --git a/vignettes/mkin.html b/vignettes/mkin.html
index 3d76697a..15f6559a 100644
--- a/vignettes/mkin.html
+++ b/vignettes/mkin.html
@@ -11,7 +11,7 @@
 
 <meta name="author" content="Johannes Ranke" />
 
-<meta name="date" content="2016-06-24" />
+<meta name="date" content="2016-06-27" />
 
 <title>mkin - Kinetic evaluation of chemical degradation data</title>
 
@@ -109,6 +109,12 @@ $(document).ready(function () {
 </script>
 
 <!-- code folding -->
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+<script>
+$(document).ready(function () {
+  window.initializeCodeFolding("hide" === "show");
+});
+</script>
 
 
 
@@ -210,11 +216,18 @@ div.tocify {
 
 <div class="fluid-row" id="header">
 
+<div class="btn-group pull-right">
+<button type="button" class="btn btn-default btn-xs dropdown-toggle" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false"><span>Code</span> <span class="caret"></span></button>
+<ul class="dropdown-menu" style="min-width: 50px;">
+<li><a id="rmd-show-all-code" href="#">Show All</a></li>
+<li><a id="rmd-hide-all-code" href="#">Hide All</a></li>
+</ul>
+</div>
 
 
 <h1 class="title toc-ignore">mkin - Kinetic evaluation of chemical degradation data</h1>
 <h4 class="author"><em>Johannes Ranke</em></h4>
-<h4 class="date"><em>2016-06-24</em></h4>
+<h4 class="date"><em>2016-06-27</em></h4>
 
 </div>
 
@@ -223,22 +236,44 @@ 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>require(mkin)
+m_SFO_SFO_SFO &lt;- mkinmod(parent = mkinsub(&quot;SFO&quot;, &quot;M1&quot;),
+                         M1 = mkinsub(&quot;SFO&quot;, &quot;M2&quot;),
+                         M2 = mkinsub(&quot;SFO&quot;), 
+                         use_of_ff = &quot;max&quot;, quiet = TRUE)
+
+sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
+
+d_SFO_SFO_SFO &lt;- mkinpredict(m_SFO_SFO_SFO,
+  c(k_parent = 0.03, 
+    f_parent_to_M1 = 0.5, k_M1 = log(2)/100, 
+    f_M1_to_M2 = 0.9, k_M2 = log(2)/50),
+  c(parent = 100, M1 = 0, M2 = 0),
+  sampling_times)
+
+d_SFO_SFO_SFO_err &lt;- add_err(d_SFO_SFO_SFO, function(x) 3, n = 1, seed = 123456789 )
+
+f_SFO_SFO_SFO &lt;- mkinfit(m_SFO_SFO_SFO, d_SFO_SFO_SFO_err[[1]], quiet = TRUE)
+
+plot.mkinfit(f_SFO_SFO_SFO, sep_obs = TRUE, show_residuals = TRUE, show_errmin = TRUE,
+             lpos = c(&quot;topright&quot;, &quot;bottomright&quot;, &quot;bottomright&quot;))</code></pre>
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title alt width="768" /></p>
 </div>
 <div id="background" class="section level1">
 <h1>Background</h1>
 <p>Many approaches are possible regarding the evaluation of chemical degradation data.</p>
-<p>The now deprecated <code>kinfit</code> package <span class="citation">(Ranke 2015)</span> in <code>R</code> <span class="citation">(R Development Core Team 2016)</span> implements the approach recommended in the kinetics report provided by the FOrum for Co-ordination of pesticide fate models and their USe [<span class="citation">FOCUS Work Group on Degradation Kinetics (2006)</span>; FOCUSkinetics2014] for simple data series for one parent compound in one compartment.</p>
+<p>The now deprecated <code>kinfit</code> package <span class="citation">(Ranke 2015)</span> in <code>R</code> <span class="citation">(R Development Core Team 2016)</span> implements the approach recommended in the kinetics report provided by the FOrum for Co-ordination of pesticide fate models and their USe <span class="citation">(FOCUS Work Group on Degradation Kinetics 2006; 2014)</span> for simple data series for one parent compound in one compartment.</p>
 <p>The <code>mkin</code> package <span class="citation">(Ranke 2016)</span> extends this approach to data series with transformation products, commonly termed metabolites, and to more than one compartment. It is also possible to include back reactions, so equilibrium reactions and equilibrium partitioning can be specified, although this oftentimes leads to an overparameterisation of the model.</p>
-<p>When mkin was first published, the most commonly used tools for fitting more complex kinetic degradation models to experimental data were KinGUI <span class="citation">(Schäfer et al. 2007)</span>, a MATLAB based tool with a graphical user interface that was specifically tailored to the task and included some output as proposed by the FOCUS Kinetics Workgroup, and ModelMaker, a general purpose compartment based tool providing infrastructure for fitting dynamic simulation models based on differential equations to data.</p>
-<p>The code was first uploaded to the BerliOS platform. When this was taken down, the version control history was imported into the R-Forge site, where the code is still mirrored today (see <em>e.g.</em> <a href="http://cgit.jrwb.de/mkin/commit/?id=30cbb4092f6d2d3beff5800603374a0d009ad770">the initial commit on 11 May 2010</a>.</p>
+<p>When the first <code>mkin</code> code was published in 2010, the most commonly used tools for fitting more complex kinetic degradation models to experimental data were KinGUI <span class="citation">(Schäfer et al. 2007)</span>, a MATLAB based tool with a graphical user interface that was specifically tailored to the task and included some output as proposed by the FOCUS Kinetics Workgroup, and ModelMaker, a general purpose compartment based tool providing infrastructure for fitting dynamic simulation models based on differential equations to data.</p>
+<p>The code was first uploaded to the BerliOS platform. When this was taken down, the version control history was imported into the R-Forge site, where the code is still mirrored today (see <em>e.g.</em> <a href="http://cgit.jrwb.de/mkin/commit/?id=30cbb4092f6d2d3beff5800603374a0d009ad770">the initial commit on 11 May 2010</a>).</p>
 <p>At that time, the R package <code>FME</code> (Flexible Modelling Environment) <span class="citation">(Soetaert and Petzoldt 2010)</span> was already available, and provided a good basis for developing a package specifically tailored to the task. The remaining challenge was to make it as easy as possible for the users (including the author of this vignette) to specify the system of differential equations and to include the output requested by the FOCUS guidance, such as the relative standard deviation that has to be assumed for the residuals, such that the <span class="math inline">\(\chi^2\)</span> goodness-of-fit test as defined by the FOCUS kinetics workgroup would pass using an significance level <span class="math inline">\(\alpha\)</span> of 0.05.</p>
-<p>Also, mkin introduced using analytical solutions for parent only kinetics for improved optimization speed. Later, Eigenvalue based solutions were introduced to mkin for the case of linear differential equations (<em>i.e.</em> where the FOMC or DFOP models were not used for the parent compound), greatly improving the optimization speed for these cases.</p>
-<p>The possibility to specify back-reactions and a biphasic model (SFORB) for metabolites were present in mkin from the very beginning.</p>
+<p>Also, <code>mkin</code> introduced using analytical solutions for parent only kinetics for improved optimization speed. Later, Eigenvalue based solutions were introduced to <code>mkin</code> for the case of linear differential equations (<em>i.e.</em> where the FOMC or DFOP models were not used for the parent compound), greatly improving the optimization speed for these cases.</p>
+<p>The possibility to specify back-reactions and a biphasic model (SFORB) for metabolites were present in <code>mkin</code> from the very beginning.</p>
 <div id="derived-software-tools" class="section level2">
 <h2>Derived software tools</h2>
-<p>Soon after the publication of mkin, two derived tools were published, namely KinGUII (available from Bayer Crop Science) and CAKE (commissioned to Tessella by Syngenta), which added a graphical user interface (GUI), and added fitting by iteratively reweighted least squares (IRLS) and characterisation of likely parameter distributions by Markov Chain Monte Carlo (MCMC) sampling.</p>
-<p>CAKE focuses on a smooth use experience, sacrificing some flexibility in the model definition, originally allowing only two primary metabolites in parallel. The current version 3.2 of CAKE release in March 2016 uses a basic scheme for up to six metabolites in a flexible arrangement.</p>
-<p>KinGUI offers quite an even more flexible widget for specifying complex kinetic models. Back-reactions (non-instanteneous equilibria) were not present in the first version of KinGUII, and only simple first-order models could be specified for transformation products. Later, starting from KinGUII version 2.1 published in ), back-reactions and biphasic modelling of metabolites were also available in KinGUII.</p>
+<p>Soon after the publication of <code>mkin</code>, two derived tools were published, namely KinGUII (available from Bayer Crop Science) and CAKE (commissioned to Tessella by Syngenta), which added a graphical user interface (GUI), and added fitting by iteratively reweighted least squares (IRLS) and characterisation of likely parameter distributions by Markov Chain Monte Carlo (MCMC) sampling.</p>
+<p>CAKE focuses on a smooth use experience, sacrificing some flexibility in the model definition, originally allowing only two primary metabolites in parallel. The current version 3.2 of CAKE release in March 2016 uses a basic scheme for up to six metabolites in a flexible arrangement, but does not support back-reactions (non-instantaneous equilibria) or biphasic kinetics for metabolites.</p>
+<p>KinGUI offers an even more flexible widget for specifying complex kinetic models. Back-reactions (non-instanteneous equilibria) were supported early on, but until 2014, only simple first-order models could be specified for transformation products. Starting with KinGUII version 2.1, biphasic modelling of metabolites was also available in KinGUII.</p>
 <p>A further graphical user interface (GUI) that has recently been brought to a decent degree of maturity is the browser based GUI named <code>gmkin</code>. Please see its <a href="http://kinfit.r-forge.r-project.org/gmkin_static">documentation page</a> and <a href="http://kinfit.r-forge.r-project.org/gmkin_static/vignettes/gmkin_manual.html">manual</a> for further information.</p>
 </div>
 <div id="recent-developments" class="section level2">
@@ -251,21 +286,33 @@ div.tocify {
 <p>For rate constants, the log transformation is used, as proposed by Bates and Watts <span class="citation">(1988, 77, 149)</span>. Approximate intervals are constructed for the transformed rate constants <span class="citation">(compare Bates and Watts 1988, 135)</span>, <em>i.e.</em> for their logarithms. Confidence intervals for the rate constants are then obtained using the appropriate backtransformation using the exponential function.</p>
 <p>In the first version of <code>mkin</code> allowing for specifying models using formation fractions, a home-made reparameterisation was used in order to ensure that the sum of formation fractions would not exceed unity.</p>
 <p>This method is still used in the current version of KinGUII (v2.1 from April 2014), with a modification that allows for fixing the pathway to sink to zero. CAKE uses penalties in the objective function in order to enforce this constraint.</p>
-<p>In 2012, an alternative reparameterisation of the formation fractions was proposed together with René Lehmann , based on isometric logratio transformation (ILR). The aim was to improve the validity of the linear approximation of the objective function during the parameter estimation procedure as well as in the subsequent calculation of parameter confidence intervals.</p>
-<p>In the first attempt at providing improved parameter confidence intervals introduced to  in 2013, confidence intervals obtained from FME on the transformed parameters were simply all backtransformed one by one to yield asymetric confidence intervals for the backtransformed parameters.</p>
+<p>In 2012, an alternative reparameterisation of the formation fractions was proposed together with René Lehmann <span class="citation">(Ranke and Lehmann 2012)</span>, based on isometric logratio transformation (ILR). The aim was to improve the validity of the linear approximation of the objective function during the parameter estimation procedure as well as in the subsequent calculation of parameter confidence intervals.</p>
+<div id="confidence-intervals-based-on-transformed-parameters" class="section level2">
+<h2>Confidence intervals based on transformed parameters</h2>
+<p>In the first attempt at providing improved parameter confidence intervals introduced to <code>mkin</code> in 2013, confidence intervals obtained from FME on the transformed parameters were simply all backtransformed one by one to yield asymetric confidence intervals for the backtransformed parameters.</p>
 <p>However, while there is a 1:1 relation between the rate constants in the model and the transformed parameters fitted in the model, the parameters obtained by the isometric logratio transformation are calculated from the set of formation fractions that quantify the paths to each of the compounds formed from a specific parent compound, and no such 1:1 relation exists.</p>
 <p>Therefore, parameter confidence intervals for formation fractions obtained with this method only appear valid for the case of a single transformation product, where only one formation fraction is to be estimated, directly corresponding to one component of the ilr transformed parameter.</p>
 <p>The confidence intervals obtained by backtransformation for the cases where a 1:1 relation between transformed and original parameter exist are considered by the author of this vignette to be more accurate than those obtained using a re-estimation of the Hessian matrix after backtransformation, as implemented in the FME package.</p>
 </div>
+<div id="parameter-t-test-based-on-untransformed-parameters" class="section level2">
+<h2>Parameter t-test based on untransformed parameters</h2>
+<p>The standard output of many nonlinear regression software packages includes the results from a test for significant difference from zero for all parameters. Such a test is also recommended to check the validity of rate constants in the FOCUS guidance <span class="citation">(FOCUS Work Group on Degradation Kinetics 2014, 96ff)</span>.</p>
+<p>It has been argued that the precondition for this test, <em>i.e.</em> normal distribution of the estimator for the parameters, is not fulfilled in the case of nonlinear regression <span class="citation">(Ranke and Lehmann 2015)</span>. However, this test is commonly used by industry, consultants and national authorities in order to decide on the reliability of parameter estimates, based on the FOCUS guidance mentioned above. Therefore, the results of this one-sided t-test are included in the summary output from <code>mkin</code>.</p>
+<p>As it is not reasonable to test for significant difference of the transformed parameters (<em>e.g.</em> <span class="math inline">\(log(k)\)</span>) from zero, the t-test is calculated based on the model definition before parameter transformation, <em>i.e.</em> in a similar way as in packages that do not apply such an internal parameter transformation. A note is included in the <code>mkin</code> output, pointing to the fact that the t-test is based on the unjustified assumption of normal distribution of the parameter estimators.</p>
+</div>
+</div>
 <div id="references" class="section level1">
 <h1>References</h1>
 <!-- vim: set foldmethod=syntax: -->
 <div class="references">
 <p>Bates, D., and D. Watts. 1988. <em>Nonlinear Regression and Its Applications</em>. Wiley-Interscience.</p>
 <p>FOCUS Work Group on Degradation Kinetics. 2006. <em>Guidance Document on Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in EU Registration. Report of the FOCUS Work Group on Degradation Kinetics</em>. <a href="http://focus.jrc.ec.europa.eu/dk" class="uri">http://focus.jrc.ec.europa.eu/dk</a>.</p>
+<p>———. 2014. <em>Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in EU Registration</em>. 1.1. <a href="http://focus.jrc.ec.europa.eu/dk" class="uri">http://focus.jrc.ec.europa.eu/dk</a>.</p>
 <p>R Development Core Team. 2016. <em>R: A Language and Environment for Statistical Computing</em>. Vienna, Austria: R Foundation for Statistical Computing. <a href="http://www.R-project.org" class="uri">http://www.R-project.org</a>.</p>
-<p>Ranke, Johannes. 2015. <em>Kinfit: Routines for Fitting Simple Kinetic Models to Chemical Degradation Data</em>. <a href="http://CRAN.R-project.org/package=kinfit" class="uri">http://CRAN.R-project.org/package=kinfit</a>.</p>
+<p>Ranke, J. 2015. <em>`kinfit`: Routines for Fitting Simple Kinetic Models to Chemical Degradation Data</em>. <a href="http://CRAN.R-project.org/package=kinfit" class="uri">http://CRAN.R-project.org/package=kinfit</a>.</p>
 <p>———. 2016. <em>`mkin`: Kinetic Evaluation of Chemical Degradation Data</em>. <a href="http://CRAN.R-project.org/package=mkin" class="uri">http://CRAN.R-project.org/package=mkin</a>.</p>
+<p>Ranke, J., and R. Lehmann. 2012. “Parameter Reliability in Kinetic Evaluation of Environmental Metabolism Data - Assessment and the Influence of Model Specification.” In <em>SETAC World 20-24 May</em>. Berlin.</p>
+<p>———. 2015. “To T-Test or Not to T-Test, That Is the Question.” In <em>XV Symposium on Pesticide Chemistry 2-4 September 2015</em>. Piacenza. <a href="http://chem.uft.uni-bremen.de/ranke/posters/piacenza_2015.pdf" class="uri">http://chem.uft.uni-bremen.de/ranke/posters/piacenza_2015.pdf</a>.</p>
 <p>Schäfer, D., B. Mikolasch, P. Rainbird, and B. Harvey. 2007. “KinGUI: a New Kinetic Software Tool for Evaluations According to FOCUS Degradation Kinetics.” In <em>Proceedings of the XIII Symposium Pesticide Chemistry</em>, edited by Del Re A. A. M., Capri E., Fragoulis G., and Trevisan M., 916–23. Piacenza.</p>
 <p>Soetaert, Karline, and Thomas Petzoldt. 2010. “Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME.” <em>Journal of Statistical Software</em> 33 (3): 1–28. <a href="http://www.jstatsoft.org/v33/i03/" class="uri">http://www.jstatsoft.org/v33/i03/</a>.</p>
 </div>
author	Johannes Ranke <jranke@uni-bremen.de>	2016-06-27 19:06:42 +0200
committer	Johannes Ranke <jranke@uni-bremen.de>	2016-06-27 19:06:42 +0200
commit	280f609d8e363bb28bee543d1fa3651762469198 (patch)
tree	d227ee7d1fffa7963d2c56ff837022fccc160f1e /vignettes/mkin.html
parent	4b0b5346a9f026c5a19d452e4649326fe56d464c (diff)