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.
You can install the latest released version from CRAN from within R:
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.
- 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.
- As of version 0.9-39, fitting of several models to several datasets, optionally in
parallel, is supported, see for example
- Model solution (forward modelling) in the function
mkinpredictis 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
deSolvepackage (default is
- If a C compiler is installed, the kinetic models are compiled from automatically
generated C code, see
compiled_models. The autogeneration of C code was inspired by the
ccSolvepackage. Thanks to Karline Soetaert for her work on that.
- By default, kinetic rate constants and kinetic formation fractions are
transformed internally using
transform_odeparmsso 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
mkinfitobject 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.method = "obs"to your call to
mkinfitand 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
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.
There is a graphical user interface that I consider useful for real work. Please refer to its documentation page for installation instructions and a manual.
Credits and historical remarks
It could not have been written without me being introduced to regulatory fate
modelling of pesticides by Adrian Gurney during my time at Harlan Laboratories
Ltd (formerly RCC Ltd).
mkin greatly profits from and largely follows
the work done by the
FOCUS Degradation Kinetics Workgroup,
as detailed in their guidance document from 2006, slightly updated in 2011 and
Also, it was inspired by the first version of KinGUI developed by BayerCropScience, which is based on the MatLab runtime environment.
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
Somewhat in parallel, Syngenta has sponsored the development of an
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
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.
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.