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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/multistart.R
\name{multistart}
\alias{multistart}
\alias{multistart.saem.mmkin}
\alias{print.multistart}
\alias{parms.multistart}
\title{Perform a hierarchical model fit with multiple starting values}
\usage{
multistart(
object,
n = 50,
cores = if (Sys.info()["sysname"] == "Windows") 1 else parallel::detectCores(),
cluster = NULL,
...
)
\method{multistart}{saem.mmkin}(object, n = 50, cores = 1, cluster = NULL, ...)
\method{print}{multistart}(x, ...)
\method{parms}{multistart}(object, ...)
}
\arguments{
\item{object}{The fit object to work with}
\item{n}{How many different combinations of starting parameters should be
used?}
\item{cores}{How many fits should be run in parallel (only on posix platforms)?}
\item{cluster}{A cluster as returned by \link[parallel:makeCluster]{parallel::makeCluster} to be used
for parallel execution.}
\item{\dots}{Passed to the update function.}
\item{x}{The multistart object to print}
}
\value{
A list of \link{saem.mmkin} objects, with class attributes
'multistart.saem.mmkin' and 'multistart'.
}
\description{
The purpose of this method is to check if a certain algorithm for fitting
nonlinear hierarchical models (also known as nonlinear mixed-effects models)
will reliably yield results that are sufficiently similar to each other, if
started with a certain range of reasonable starting parameters. It is
inspired by the article on practical identifiabiliy in the frame of nonlinear
mixed-effects models by Duchesne et al (2021).
}
\details{
In case the online version of this help page contains error messages
in the example code and no plots, this is due to the multistart method
not working when called by pkgdown. Please refer to the
\href{https://pkgdown.jrwb.de/mkin/dev/articles/web_only/multistart.html}{online vignette}
in this case.
}
\examples{
\dontrun{
library(mkin)
dmta_ds <- lapply(1:7, function(i) {
ds_i <- dimethenamid_2018$ds[[i]]$data
ds_i[ds_i$name == "DMTAP", "name"] <- "DMTA"
ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i]
ds_i
})
names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title)
dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]])
dmta_ds[["Elliot 1"]] <- dmta_ds[["Elliot 2"]] <- NULL
f_mmkin <- mmkin("DFOP", dmta_ds, error_model = "tc", cores = 7, quiet = TRUE)
f_saem_full <- saem(f_mmkin)
f_saem_full_multi <- multistart(f_saem_full, n = 16, cores = 16)
parhist(f_saem_full_multi, lpos = "bottomright")
f_saem_reduced <- update(f_saem_full, covariance.model = diag(c(1, 1, 0, 1)))
# On Windows, we need to create a cluster first. When working with
# such a cluster, we need to export the mmkin object to the cluster
# nodes, as it is referred to when updating the saem object on the nodes.
library(parallel)
cl <- makePSOCKcluster(12)
clusterExport(cl, "f_mmkin")
f_saem_reduced_multi <- multistart(f_saem_reduced, n = 16, cluster = cl)
parhist(f_saem_reduced_multi, lpos = "bottomright")
}
}
\references{
Duchesne R, Guillemin A, Gandrillon O, Crauste F. Practical
identifiability in the frame of nonlinear mixed effects models: the example
of the in vitro erythropoiesis. BMC Bioinformatics. 2021 Oct 4;22(1):478.
doi: 10.1186/s12859-021-04373-4.
}
\seealso{
\link{parhist}, \link{llhist}
}
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