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pdftitle = {mkin - Routines for fitting kinetic models with one or more state variables to chemical degradation data},
pdfsubject = {Manuscript},
pdfauthor = {Johannes Ranke},
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\title{mkin -\\
Routines for fitting kinetic models with one or more state variables to
chemical degradation data}
\author{\textbf{Johannes Ranke} \\[0.5cm]
%EndAName
Wissenschaftlicher Berater\\
Kronacher Str. 8, 79639 Grenzach-Wyhlen, Germany\\[0.5cm]
and\\[0.5cm]
Privatdozent at the University of Bremen\\
}
\maketitle
\begin{abstract}
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 \RR{} add-on package \Rpackage{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.
\end{abstract}
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\tableofcontents
\textbf{Key words}: Kinetics, FOCUS, nonlinear optimisation
\section{Introduction}
\label{intro}
Many approaches are possible regarding the evaluation of chemical degradation
data. The \Rpackage{kinfit} package \citep{pkg:kinfit} in \RR{}
\citep{rcore2014} implements the approach recommended in the kinetics report
provided by the FOrum for Co-ordination of pesticide fate models and their
USe \citep{FOCUS2006, FOCUSkinetics2011} for simple data series for one parent
compound in one compartment.
The \Rpackage{mkin} package \citep{pkg:mkin} 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.
When mkin was first published in May 2010, the most commonly used tools
for fitting more complex kinetic degradation models to experimental data were KinGUI
\citep{schaefer2007}, a MATLAB$^\circledR$ 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.
At that time, the R package \Rpackage{FME} (Flexible Modelling Environment)
\citep{soetaert2010} 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 $\chi^2$
goodness-of-fit test as defined by the FOCUS kinetics workgroup would pass
using an significance level $\alpha$ of 0.05.
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 (\textit{i.e.}
where the FOMC or DFOP models were not used for the parent compound), greatly
improving the optimization speed for these cases.
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.
CAKE focuses on a smooth use experience, sacrificing some flexibility in the model
definition, allowing only two primary metabolites in parallel. KinGUI offers
quite a 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. As of May 2014 (KinGUII version 2.1), back-reactions
and biphasic modelling of metabolites are also available in KinGUII.
Currently (May 2014), the main feature available in \Rpackage{mkin} which is
not present in KinGUII or CAKE, is the estimation of parameter confidence
intervals based on transformed parameters. For rate constants, the log
transformation is used, as proposed by Bates and Watts \citep[p. 77, p.
149]{bates1988}. Approximate intervals are constructed for the transformed rate
constants \citep[compare][p. 153]{bates1988}, \textit{i.e.} for their logarithms.
Confidence intervals for the rate constants are then obtained using the
appropriate backtransformation using the exponential function.
In the first version of \Rpackage{mkin} 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.
This method is still used in the current version of KinGUII (v2.1), 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.
In 2012, an alternative reparameterisation of the formation fractions was
proposed together with René Lehmann \citep{ranke2012}, 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.
In the first attempt at providing improved parameter confidence intervals
introduced to \Rpackage{mkin} 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.
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.
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.
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.
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