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| -rw-r--r-- | README.html | 18 | ||||
| -rw-r--r-- | README.md | 20 | ||||
| -rw-r--r-- | vignettes/mkin.Rmd | 38 | ||||
| -rw-r--r-- | vignettes/mkin.html | 43 | ||||
| -rw-r--r-- | vignettes/references.bib | 32 |
5 files changed, 106 insertions, 45 deletions
diff --git a/README.html b/README.html index 5aaf5822..3578674b 100644 --- a/README.html +++ b/README.html @@ -254,9 +254,7 @@ h6 { </style> -</head> -<body> <style type="text/css"> .main-container { @@ -288,8 +286,6 @@ summary { -<div class="container-fluid main-container"> - <!-- tabsets --> <style type="text/css"> @@ -363,6 +359,15 @@ $(document).ready(function () { +</head> + +<body> + + +<div class="container-fluid main-container"> + + + <div class="fluid-row" id="header"> @@ -405,9 +410,10 @@ $(document).ready(function () { <li>The usual one-sided t-test for significant difference from zero is nevertheless shown based on estimators for the untransformed parameters.</li> <li>Summary and plotting functions. The <code>summary</code> of an <code>mkinfit</code> object is in fact a full report that should give enough information to be able to approximately reproduce the fit with other tools.</li> <li>The chi-squared error level as defined in the FOCUS kinetics guidance (see below) is calculated for each observed variable.</li> -<li>Iteratively reweighted least squares fitting is implemented in a similar way as in KinGUII and CAKE (see below). Simply add the argument <code>reweight.method = "obs"</code> to your call to <code>mkinfit</code> and a separate variance componenent for each of the observed variables will be optimised in a second stage after the primary optimisation algorithm has converged.</li> -<li>Iterative reweighting is also possible using a two-component error model for analytical data similar to the one proposed by <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">Rocke and Lorenzato</a> using the argument <code>reweight.method = "tc"</code>.</li> <li>When a metabolite decline phase is not described well by SFO kinetics, SFORB kinetics can be used for the metabolite.</li> +<li>Three different error models can be selected using the argument <code>error_model</code> to the <a href="https://pkgdown.jrwb.de/mkin/reference/mkinfit.html"><code>mkinfit</code></a> function.</li> +<li>Iteratively reweighted least squares fitting is now obsolete, and the variance by variable error model should now be specified as <code>error_model = "obs"</code>.</li> +<li>A two-component error model similar to the one proposed by <a href="https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html">Rocke and Lorenzato</a> can be selected using the argument <code>error_model = "tc"</code>.</li> </ul> </div> <div id="gui" class="section level2"> @@ -2,7 +2,7 @@ [](https://cran.r-project.org/package=mkin) [](https://travis-ci.com/jranke/mkin) -[](https://codecov.io/github/jranke/mkin) +[](https://codecov.io/github/jranke/mkin) The R package **mkin** provides calculation routines for the analysis of chemical degradation data, including <b>m</b>ulticompartment <b>kin</b>etics as @@ -86,17 +86,17 @@ and at [R-Forge](http://kinfit.r-forge.r-project.org/mkin_static/index.html). approximately reproduce the fit with other tools. * The chi-squared error level as defined in the FOCUS kinetics guidance (see below) is calculated for each observed variable. -* Iteratively reweighted least squares fitting is implemented in a similar way - as in KinGUII and CAKE (see below). Simply add the argument - `reweight.method = "obs"` to your call to `mkinfit` and a separate variance - componenent for each of the observed variables will be optimised - in a second stage after the primary optimisation algorithm has converged. -* Iterative reweighting is also possible using a two-component error model - for analytical data similar to the one proposed by - [Rocke and Lorenzato](https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html) - using the argument `reweight.method = "tc"`. * When a metabolite decline phase is not described well by SFO kinetics, SFORB kinetics can be used for the metabolite. +* Three different error models can be selected using the argument `error_model` + to the [`mkinfit`](https://pkgdown.jrwb.de/mkin/reference/mkinfit.html) + function. +* Iteratively reweighted least squares fitting is now obsolete, and the + variance by variable error model should now be specified as `error_model + = "obs"`. +* A two-component error model similar to the one proposed by + [Rocke and Lorenzato](https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html) + can be selected using the argument `error_model = "tc"`. ## GUI diff --git a/vignettes/mkin.Rmd b/vignettes/mkin.Rmd index b0d97f7e..78fd098f 100644 --- a/vignettes/mkin.Rmd +++ b/vignettes/mkin.Rmd @@ -28,7 +28,7 @@ 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 `R` add-on package `mkin` [@pkg:mkin] implements fitting some of the models +The `R` add-on package `mkin` 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. @@ -71,10 +71,10 @@ data. The `mkin` package [@pkg:mkin] implements the approach recommended in the kinetics report provided by the FOrum for Co-ordination of pesticide fate -models and their USe [@FOCUS2006; -@FOCUSkinetics2014] implements this approach +models and their USe [@FOCUS2006; -@FOCUSkinetics2014] 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 +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. @@ -99,13 +99,16 @@ 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 $\chi^2$ goodness-of-fit test as defined by the FOCUS kinetics workgroup would pass -using an significance level $\alpha$ of 0.05. +using an significance level $\alpha$ of 0.05. This relative error, expressed +as a percentage, is often termed $\chi^2$ error level or similar. 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 (*i.e.* where the FOMC or DFOP models were not used for the parent compound), greatly -improving the optimization speed for these cases. +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. The possibility to specify back-reactions and a biphasic model (SFORB) for metabolites were present in `mkin` from the very beginning. @@ -120,7 +123,7 @@ parameter distributions by Markov Chain Monte Carlo (MCMC) sampling. 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 +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. @@ -136,13 +139,28 @@ degree of maturity is the browser based GUI named `gmkin`. Please see its [manual](https://pkgdown.jrwb.de/gmkin/articles/gmkin_manual.html) for further information. +A comparison of scope, usability and numerical results obtained with these +tools has been recently been published by @ranke2018. + ## Recent developments -Currently (June 2016), the main features available in `mkin` which are -not present in KinGUII or CAKE, are the speed increase by using compiled code when +Currently (July 2019), the main features available in `mkin` 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 `mmkin` function, and the estimation of parameter confidence intervals based on -transformed parameters. These are explained in more detail below. +transformed parameters. + +In addition, the possibility to use two alternative error models to constant +variance have been integrated. The variance by variable error model introduced +by @gao11 has been available via an iteratively reweighted least squares (IRLS) +procedure since mkin +[version 0.9-22](https://pkgdown.jrwb.de/mkin/news/index.html#mkin-0-9-22-2013-10-26). +With [release 0.9.49.5](https://pkgdown.jrwb.de/mkin/news/index.html#mkin-0-9-49-5-2019-07-04), +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 +[two-component error model](https://pkgdown.jrwb.de/mkin/reference/sigma_twocomp.html) +inspired by error models developed in analytical chemistry. # Internal parameter transformations 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> diff --git a/vignettes/references.bib b/vignettes/references.bib index 44c66f13..a18922c9 100644 --- a/vignettes/references.bib +++ b/vignettes/references.bib @@ -51,9 +51,9 @@ } @MANUAL{pkg:mkin, - title = {`mkin`: {K}inetic evaluation of chemical degradation data}, + title = {`{mkin}`: {K}inetic evaluation of chemical degradation data}, author = {J. Ranke}, - year = {2016}, + year = {2019}, url = {https://CRAN.R-project.org/package=mkin} } @@ -102,3 +102,31 @@ institution = {Umweltbundesamt}, volume = {Projektnummer 27452} } + +@Article{ranke2018, + author="Ranke, Johannes + and W{\"o}ltjen, Janina + and Meinecke, Stefan", + title="Comparison of software tools for kinetic evaluation of chemical degradation data", + journal="Environmental Sciences Europe", + year="2018", + month="May", + day="18", + volume="30", + number="1", + pages="17", + abstract="For evaluating the fate of xenobiotics in the environment, a variety of degradation or environmental metabolism experiments are routinely conducted. The data generated in such experiments are evaluated by optimizing the parameters of kinetic models in a way that the model simulation fits the data. No comparison of the main software tools currently in use has been published to date. This article shows a comparison of numerical results as well as an overall, somewhat subjective comparison based on a scoring system using a set of criteria. The scoring was separately performed for two types of uses. Uses of type I are routine evaluations involving standard kinetic models and up to three metabolites in a single compartment. Evaluations involving non-standard model components, more than three metabolites or more than a single compartment belong to use type II. For use type I, usability is most important, while the flexibility of the model definition is most important for use type II.", + issn="2190-4715", + doi="10.1186/s12302-018-0145-1", + url="https://doi.org/10.1186/s12302-018-0145-1" +} + +@Article{gao11, + Title = {Improving uncertainty analysis in kinetic evaluations using iteratively reweighted least squares}, + Author = {Gao, Z. and Green, J.W. and Vanderborght, J. and Schmitt, W.}, + Journal = {Environmental Science and Technology}, + Year = {2011}, + Pages = {4429-4437}, + Volume = {45}, + Type = {Journal} +} |