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\name{mmkin}
\alias{mmkin}
\title{
Fit one or more kinetic models with one or more state variables to one or more datasets
}
\description{
This function calls \code{\link{mkinfit}} on all combinations of models and datasets
specified in its first two arguments.
}
\usage{
mmkin(models, datasets,
cores = round(detectCores()/2), cluster = NULL, ...)
}
\arguments{
\item{models}{
Either a character vector of shorthand names ("SFO", "FOMC", "DFOP",
"HS", "SFORB"), or an optionally named list of \code{\link{mkinmod}}
objects.
}
\item{datasets}{
An optionally named list of datasets suitable as observed data for
\code{\link{mkinfit}}.
}
\item{cores}{
The number of cores to be used for multicore processing. This is only
used when the \code{cluster} argument is \code{NULL}.
}
\item{cluster}{
A cluster as returned by \code{\link{makeCluster}} to be used for parallel
execution.
}
\item{\dots}{
Further arguments that will be passed to \code{\link{mkinfit}}.
}
}
\value{
A matrix of \code{\link{mkinfit}} objects that can be indexed using the model
and dataset names as row and column indices.
}
\seealso{
\code{\link{[.mmkin}} for subsetting, \code{\link{plot.mmkin}} for plotting.
}
\author{
Johannes Ranke
}
\examples{
\dontrun{
m_synth_SFO_lin <- mkinmod(parent = mkinsub("SFO", "M1"),
M1 = mkinsub("SFO", "M2"),
M2 = mkinsub("SFO"), use_of_ff = "max")
m_synth_FOMC_lin <- mkinmod(parent = mkinsub("FOMC", "M1"),
M1 = mkinsub("SFO", "M2"),
M2 = mkinsub("SFO"), use_of_ff = "max")
models <- list(SFO_lin = m_synth_SFO_lin, FOMC_lin = m_synth_FOMC_lin)
datasets <- lapply(synthetic_data_for_UBA_2014[1:3], function(x) x$data)
names(datasets) <- paste("Dataset", 1:3)
time_default <- system.time(fits.0 <- mmkin(models, datasets, quiet = TRUE))
time_1 <- system.time(fits.4 <- mmkin(models, datasets, cores = 1, quiet = TRUE))
time_default
time_1
endpoints(fits.0[["SFO_lin", 2]])
# plot.mkinfit handles rows or columns of mmkin result objects
plot(fits.0[1, ])
plot(fits.0[1, ], obs_var = c("M1", "M2"))
plot(fits.0[, 1])
# Use double brackets to extract a single mkinfit object, which will be plotted
# by plot.mkinfit and can be plotted using plot_sep
plot(fits.0[[1, 1]], sep_obs = TRUE, show_residuals = TRUE, show_errmin = TRUE)
plot_sep(fits.0[[1, 1]])
# Plotting with mmkin (single brackets, extracting an mmkin object) does not
# allow to plot the observed variables separately
plot(fits.0[1, 1])
}
}
\keyword{ optimize }
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