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+\name{mkinfit}
+\alias{mkinfit}
+\title{
+  Fit a kinetic model to data with one or more state variables.
+}
+\description{
+  This function uses the Flexible Modelling Environment package
+  \code{\link{FME}} to create a function calculating the model cost, which is
+  then minimised, using the specified initial or fixed parameters and starting
+  values.
+}
+\usage{
+mkinfit(mkinmod, observed, parms.ini = rep(0.1, length(mkinmod$parms)), state.ini = c(100, rep(0, length(mkinmod$diffs) - 1)), fixed_parms = rep(FALSE, length(mkinmod$parms)), fixed_initials = c(FALSE, rep(TRUE, length(mkinmod$diffs) - 1)), plot = NULL, err = NULL, weight = "none", scaleVar = FALSE, ...)
+}
+\arguments{
+  \item{mkinmod}{
+    A list of class \code{\link{mkinmod}}, containing the kinetic model to be fitted to the data.
+  }
+  \item{observed}{
+    The observed data. It has to be in the long format as described in
+    \code{\link{modFit}}, i.e. the first column must contain the name of the
+    observed variable for each data point. The second column must contain the
+    times of observation, named "time".  The third column must be named "value"
+    and contain the observed values. Optionally, a further column can contain
+    weights for each data point. If it is not named "err", its name must be
+    passed as a further argument named \code{err} which is then passed on to
+    \code{\link{modFit}}.
+  }
+  \item{parms.ini}{
+    A named vector if initial values for the parameters, including both parameters to
+    be optimised and potentially also fixed parameters as indicated by \code{fixed_parms}.
+}
+  \item{state.ini}{
+    A named vector of initial values for the state variables of the model. In case the 
+    observed variables are represented by more than one model variable, the names will
+    differ from the names of the observed variables (see \code{map} component of 
+    \code{\link{mkinmod}}). The default is to set the initial value of the first model
+    variable to 100 and all others to 0.
+}
+  \item{fixed_parms}{
+    A vector of booleans specifying which parameters are not to be optimised. The default
+    is to include all model parameters in the optimisation.
+}
+  \item{fixed_initials}{
+    A vector of booleans specifying which initial values to include in the optimisation.
+    The default is to optimise the initial value of the first model variable and to
+    keep all other initial values fixed.
+}
+  \item{plot}{
+    Should the observed values and the numerical solutions be plotted at each stage
+    of the optimisation?
+}
+  \item{err }{either \code{NULL}, or the name of the column with the
+    \emph{error} estimates, used to weigh the residuals (see details of
+    \code{\link{modCost}}); if \code{NULL}, then the residuals are not weighed.
+}
+  \item{weight}{only if \code{err}=\code{NULL}: how to weigh the
+    residuals, one of "none", "std", "mean", see details of \code{\link{modCost}}.
+}
+  \item{scaleVar}{
+    Will be passed to \code{\link{modCost}}. Default is not to scale Variables according
+    to the number of observations.
+}
+  \item{\dots}{
+    Further arguments that will be passed to \code{\link{modFit}}. 
+}
+}
+\value{
+    A list of class \code{\link{modFit}}. Thus, at present, a summary can be obtained
+    by \code{\link{summary.modFit}}. 
+}
+\author{
+  Johannes Ranke <jranke@{harlan.com,uni-bremen.de}>
+}
+\examples{
+# One parent compound, one metabolite, both single first order.
+SFO_SFO <- mkinmod(spec = list(
+  parent = list(type = "SFO", to = "m1", sink = TRUE),
+  m1 = list(type = "SFO", to = NA, sink = TRUE)))
+# Fit the model to the FOCUS example dataset D using defaults
+fit <- mkinfit(SFO_SFO, FOCUS_2006_D)
+summary(fit)
+}
+\keyword{ models }
+\keyword{ optimize }