From 3010c9dcbc7f90f15b55c14919f6688d61d14414 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Sat, 4 Mar 2017 09:58:00 +0100 Subject: Mention pkgdown.jrwb.de/mkin in README Add very small twa vignette, update pkgdown docs --- docs/index.html | 97 ++++++++++++++++++++++++++++++++++++++++----------------- 1 file changed, 68 insertions(+), 29 deletions(-) (limited to 'docs/index.html') diff --git a/docs/index.html b/docs/index.html index a8d8521d..4ec9fd79 100644 --- a/docs/index.html +++ b/docs/index.html @@ -1,8 +1,20 @@ -Kinetic Evaluation of Chemical Degradation Data • mkin + + + + + +Kinetic Evaluation of Chemical Degradation Data • mkin + + + + + + +
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mkin

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The R package mkin provides calculation routines for the analysis of chemical degradation data, including multicompartment kinetics as needed for modelling the formation and decline of transformation products, or if several compartments are involved.

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Background

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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 and helpful tools have been developed as detailed in ‘Credits and historical remarks’ below.

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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 and helpful tools have been developed as detailed in ‘Credits and historical remarks’ below.

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Documentation

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The HTML documentation is available at github and at R-Forge.

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The HTML documentation is available at jrwb.de, at github and at R-Forge.

Features

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  • Highly flexible model specification using mkinmod, including equilibrium reactions and using the single first-order reversible binding (SFORB) model, which will automatically create two latent state variables for the observed variable.
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  • As of version 0.9-39, fitting of several models to several datasets, optionally in parallel, is supported, see for example plot.mmkin.
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  • Model solution (forward modelling) in the function mkinpredict is performed either using the analytical solution for the case of parent only degradation, an eigenvalue based solution if only simple first-order (SFO) or SFORB kinetics are used in the model, or using a numeric solver from the deSolve package (default is lsoda).
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    • Highly flexible model specification using mkinmod, including equilibrium reactions and using the single first-order reversible binding (SFORB) model, which will automatically create two latent state variables for the observed variable.
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    • As of version 0.9-39, fitting of several models to several datasets, optionally in parallel, is supported, see for example plot.mmkin.
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    • Model solution (forward modelling) in the function mkinpredict is performed either using the analytical solution for the case of parent only degradation, an eigenvalue based solution if only simple first-order (SFO) or SFORB kinetics are used in the model, or using a numeric solver from the deSolve package (default is lsoda).
    • If a C compiler is installed, the kinetic models are compiled from automatically generated C code, see
      vignette compiled_models. The autogeneration of C code was inspired by the ccSolve package. Thanks to Karline Soetaert for her work on that.
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    • By default, kinetic rate constants and kinetic formation fractions are transformed internally using transform_odeparms so their estimators can more reasonably be expected to follow a normal distribution. This has the side effect that no constraints are needed in the optimisation. Thanks to René Lehmann for the nice cooperation on this, especially the isometric logration transformation that is now used for the formation fractions.
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    • By default, kinetic rate constants and kinetic formation fractions are transformed internally using transform_odeparms so their estimators can more reasonably be expected to follow a normal distribution. This has the side effect that no constraints are needed in the optimisation. Thanks to René Lehmann for the nice cooperation on this, especially the isometric logration transformation that is now used for the formation fractions.
    • A side effect of this is that when parameter estimates are backtransformed to match the model definition, confidence intervals calculated from standard errors are also backtransformed to the correct scale, and will not include meaningless values like negative rate constants or formation fractions adding up to more than 1, which can not occur in a single experiment with a single defined radiolabel position.
    • The usual one-sided t-test for significant difference from zero is nevertheless shown based on estimators for the untransformed parameters.
    • Summary and plotting functions. The summary of an mkinfit object is in fact a full report that should give enough information to be able to 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 = "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.
    • When a metabolite decline phase is not described well by SFO kinetics, SFORB kinetics can be used for the metabolite.
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GUI

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Also, it was inspired by the first version of KinGUI developed by BayerCropScience, which is based on the MatLab runtime environment.

The companion package kinfit (now deprecated) was started in 2008 and first published on CRAN on 01 May 2010.

The first mkin code was published on 11 May 2010 and the first CRAN version on 18 May 2010.

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In 2011, Bayer Crop Science started to distribute an R based successor to KinGUI named KinGUII whose R code is based on mkin, but which added, amongst other refinements, a closed source graphical user interface (GUI), iteratively reweighted least squares (IRLS) optimisation of the variance for each of the observed variables, and Markov Chain Monte Carlo (MCMC) simulation functionality, similar to what is available e.g. in the FME package.

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In 2011, Bayer Crop Science started to distribute an R based successor to KinGUI named KinGUII whose R code is based on mkin, but which added, amongst other refinements, a closed source graphical user interface (GUI), iteratively reweighted least squares (IRLS) optimisation of the variance for each of the observed variables, and Markov Chain Monte Carlo (MCMC) simulation functionality, similar to what is available e.g. in the FME package.

Somewhat in parallel, Syngenta has sponsored the development of an mkin and KinGUII based GUI application called CAKE, which also adds IRLS and MCMC, is more limited in the model formulation, but puts more weight on usability. CAKE is available for download from the CAKE website, where you can also find a zip archive of the R scripts derived from mkin, published under the GPL license.

Finally, there is KineticEval, which contains a further development of the scripts used for KinGUII, so the different tools will hopefully be able to learn from each other in the future as well.

Development

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Contributions are welcome! Your mkin fork is just a mouse click away… The master branch on github should always be in good shape, I implement new features in separate branches now. If you prefer subversion, project members for the r-forge project are welcome as well. Generally, the source code of the latest CRAN version should be available there. You can also browse the source code at cgit.jrwb.de/mkin.

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Contributions are welcome! Your mkin fork is just a mouse click away… The master branch on github should always be in good shape, I implement new features in separate branches now. If you prefer subversion, project members for the r-forge project are welcome as well. Generally, the source code of the latest CRAN version should be available there. You can also browse the source code at cgit.jrwb.de/mkin.

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Dev status

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Site built with pkgdown.

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