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Diffstat (limited to 'vignettes/mkin.html')
| -rw-r--r-- | vignettes/mkin.html | 43 |
1 files changed, 26 insertions, 17 deletions
diff --git a/vignettes/mkin.html b/vignettes/mkin.html index 34c1d1fa..e15775a4 100644 --- a/vignettes/mkin.html +++ b/vignettes/mkin.html @@ -11,7 +11,7 @@ <meta name="author" content="Johannes Ranke" /> -<meta name="date" content="2019-05-02" /> +<meta name="date" content="2019-07-04" /> <title>Introduction to mkin</title> @@ -69,8 +69,6 @@ overflow: auto; margin-left: 2%; position: fixed; border: 1px solid #ccc; -webkit-border-radius: 6px; -moz-border-radius: 6px; border-radius: 6px; } @@ -98,10 +96,15 @@ font-size: 12px; .tocify-subheader .tocify-subheader { text-indent: 30px; } - .tocify-subheader .tocify-subheader .tocify-subheader { text-indent: 40px; } +.tocify-subheader .tocify-subheader .tocify-subheader .tocify-subheader { +text-indent: 50px; +} +.tocify-subheader .tocify-subheader .tocify-subheader .tocify-subheader .tocify-subheader { +text-indent: 60px; +} .tocify .tocify-item > a, .tocify .nav-list .nav-header { margin: 0px; @@ -504,13 +507,13 @@ float: none; item.append($("<a/>", { - "text": self.text() + "html": self.html() })); } else { - item.text(self.text()); + item.html(self.html()); } @@ -1573,8 +1576,6 @@ div.tocify { .tocify-subheader .tocify-item { font-size: 0.90em; - padding-left: 25px; - text-indent: 0; } .tocify .list-group-item { @@ -1620,7 +1621,7 @@ div.tocify { <h1 class="title toc-ignore">Introduction to mkin</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">2019-05-02</h4> +<h4 class="date">2019-07-04</h4> </div> @@ -1628,7 +1629,7 @@ div.tocify { <p><a href="http://www.jrwb.de">Wissenschaftlicher Berater, Kronacher Str. 12, 79639 Grenzach-Wyhlen, Germany</a><br /> <a href="http://chem.uft.uni-bremen.de/ranke">Privatdozent at the University of Bremen</a></p> <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> +<p>In the regulatory evaluation of chemical substances like plant protection products (pesticides), biocides and other chemicals, degradation data play an important role. For the evaluation of pesticide degradation experiments, detailed guidance has been developed, based on nonlinear optimisation. The <code>R</code> add-on package <code>mkin</code> implements fitting some of the models recommended in this guidance from within R and calculates some statistical measures for data series within one or more compartments, for parent and metabolites.</p> <pre class="r"><code>library("mkin", quietly = TRUE) # Define the kinetic model m_SFO_SFO_SFO <- mkinmod(parent = mkinsub("SFO", "M1"), @@ -1657,27 +1658,29 @@ f_SFO_SFO_SFO <- mkinfit(m_SFO_SFO_SFO, d_SFO_SFO_SFO_err[[1]], quiet = TRUE) # Plot the results separately for parent and metabolites plot_sep(f_SFO_SFO_SFO, lpos = c("topright", "bottomright", "bottomright"))</code></pre> -<p><img 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" 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" /><!-- --></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 <code>mkin</code> package <span class="citation">(Ranke 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> implements this approach for simple decline data series, data series with transformation products, commonly termed metabolites, data series for 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>The <code>mkin</code> package <span class="citation">(Ranke 2019)</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 decline data series, data series with transformation products, commonly termed metabolites, and for data series for 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 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 (see <em>e.g.</em> <a href="http://cgit.jrwb.de/mkin/commit/?id=30cbb4092f6d2d3beff5800603374a0d009ad770">the initial commit on 11 May 2010</a>), where the code is still occasionally updated.</p> -<p>At that time, the R package <code>FME</code> (Flexible Modelling Environment) <span class="citation">(Soetaert and Petzoldt 2010)</span> was already available, and provided a good basis for developing a package specifically tailored to the task. The remaining challenge was to make it as easy as possible for the users (including the author of this vignette) to specify the system of differential equations and to include the output requested by the FOCUS guidance, such as the 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, <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>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. This relative error, expressed as a percentage, is often termed <span class="math inline">\(\chi^2\)</span> error level or similar.</p> +<p>Also, <code>mkin</code> introduced using analytical solutions for parent only kinetics for improved optimization speed. Later, Eigenvalue based solutions were introduced to <code>mkin</code> for the case of linear differential equations (<em>i.e.</em> where the FOMC or DFOP models were not used for the parent compound), greatly improving the optimization speed for these cases. This, however, has become somehow obsolete, as the use of compiled code described below gives even smaller execution times.</p> <p>The possibility to specify back-reactions and a biphasic model (SFORB) for metabolites were present in <code>mkin</code> from the very beginning.</p> <div id="derived-software-tools" class="section level2"> <h2>Derived software tools</h2> <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>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 3.3 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="https://pkgdown.jrwb.de/gmkin">documentation page</a> and <a href="https://pkgdown.jrwb.de/gmkin/articles/gmkin_manual.html">manual</a> for further information.</p> +<p>A comparison of scope, usability and numerical results obtained with these tools has been recently been published by <span class="citation">Ranke, Wöltjen, and Meinecke (2018)</span>.</p> </div> <div id="recent-developments" class="section level2"> <h2>Recent developments</h2> -<p>Currently (June 2016), the main features available in <code>mkin</code> which are not present in KinGUII or CAKE, are the speed increase by using compiled code when a compiler is present, parallel model fitting on multicore machines using the <code>mmkin</code> function, and the estimation of parameter confidence intervals based on transformed parameters. These are explained in more detail below.</p> +<p>Currently (July 2019), the main features available in <code>mkin</code> which are not present in KinGUII or CAKE, are the speed increase by using compiled code when a compiler is present, parallel model fitting on multicore machines using the <code>mmkin</code> function, and the estimation of parameter confidence intervals based on transformed parameters.</p> +<p>In addition, the possibility to use two alternative error models to constant variance have been integrated. The variance by variable error model introduced by <span class="citation">Gao et al. (2011)</span> has been available via an iteratively reweighted least squares (IRLS) procedure since mkin <a href="https://pkgdown.jrwb.de/mkin/news/index.html#mkin-0-9-22-2013-10-26">version 0.9-22</a>. With <a href="https://pkgdown.jrwb.de/mkin/news/index.html#mkin-0-9-49-5-2019-07-04">release 0.9.49.5</a>, the IRLS algorithm has been replaced by direct or step-wise maximisation of the likelihood function, which makes it possible not only to fit the variance by variable error model but also a <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">two-component error model</a> inspired by error models developed in analytical chemistry.</p> </div> </div> <div id="internal-parameter-transformations" class="section level1"> @@ -1713,8 +1716,11 @@ plot_sep(f_SFO_SFO_SFO, lpos = c("topright", "bottomright", <div id="ref-FOCUSkinetics2014"> <p>———. 2014. <em>Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="uri">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> </div> +<div id="ref-gao11"> +<p>Gao, Z., J.W. Green, J. Vanderborght, and W. Schmitt. 2011. “Improving Uncertainty Analysis in Kinetic Evaluations Using Iteratively Reweighted Least Squares.” Journal. <em>Environmental Science and Technology</em> 45: 4429–37.</p> +</div> <div id="ref-pkg:mkin"> -<p>Ranke, J. 2016. <em>‘Mkin‘: Kinetic Evaluation of Chemical Degradation Data</em>. <a href="https://CRAN.R-project.org/package=mkin" class="uri">https://CRAN.R-project.org/package=mkin</a>.</p> +<p>Ranke, J. 2019. <em>‘mkin‘: Kinetic Evaluation of Chemical Degradation Data</em>. <a href="https://CRAN.R-project.org/package=mkin" class="uri">https://CRAN.R-project.org/package=mkin</a>.</p> </div> <div id="ref-ranke2012"> <p>Ranke, J., and R. Lehmann. 2012. “Parameter Reliability in Kinetic Evaluation of Environmental Metabolism Data - Assessment and the Influence of Model Specification.” In <em>SETAC World 20-24 May</em>. Berlin.</p> @@ -1722,6 +1728,9 @@ plot_sep(f_SFO_SFO_SFO, lpos = c("topright", "bottomright", <div id="ref-ranke2015"> <p>———. 2015. “To T-Test or Not to T-Test, That Is the Question.” In <em>XV Symposium on Pesticide Chemistry 2-4 September 2015</em>. Piacenza. <a href="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> </div> +<div id="ref-ranke2018"> +<p>Ranke, Johannes, Janina Wöltjen, and Stefan Meinecke. 2018. “Comparison of Software Tools for Kinetic Evaluation of Chemical Degradation Data.” <em>Environmental Sciences Europe</em> 30 (1): 17. <a href="https://doi.org/10.1186/s12302-018-0145-1" class="uri">https://doi.org/10.1186/s12302-018-0145-1</a>.</p> +</div> <div id="ref-schaefer2007"> <p>Schäfer, D., B. Mikolasch, P. Rainbird, and B. Harvey. 2007. “KinGUI: A New Kinetic Software Tool for Evaluations According to FOCUS Degradation Kinetics.” In <em>Proceedings of the Xiii Symposium Pesticide Chemistry</em>, edited by Del Re A. A. M., Capri E., Fragoulis G., and Trevisan M., 916–23. Piacenza.</p> </div> |