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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 }

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