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\name{tuneRF}
\alias{tuneRF}
\title{Tune randomForest for the optimal mtry parameter}
\description{
Starting with the default value of mtry, search for the optimal value
(with respect to Out-of-Bag error estimate) of mtry for randomForest.
}
\usage{
tuneRF(x, y, mtryStart, ntreeTry=50, stepFactor=2, improve=0.05,
trace=TRUE, plot=TRUE, doBest=FALSE, ...)
}
\arguments{
\item{x}{matrix or data frame of predictor variables}
\item{y}{response vector (factor for classification, numeric for
regression)}
\item{mtryStart}{starting value of mtry; default is the same as in
\code{\link{randomForest}}}
\item{ntreeTry}{number of trees used at the tuning step}
\item{stepFactor}{at each iteration, mtry is inflated (or deflated) by
this value}
\item{improve}{the (relative) improvement in OOB error must be by this
much for the search to continue}
\item{trace}{whether to print the progress of the search}
\item{plot}{whether to plot the OOB error as function of mtry}
\item{doBest}{whether to run a forest using the optimal mtry found}
\item{...}{options to be given to \code{\link{randomForest}}}
}
\value{
If \code{doBest=FALSE} (default), it returns a matrix whose first
column contains the mtry values searched, and the second column the
corresponding OOB error.
If \code{doBest=TRUE}, it returns the \code{\link{randomForest}}
object produced with the optimal \code{mtry}.
}
%\details{
%}
%\references{
%}
\seealso{
\code{\link{randomForest}}
}
\examples{
data(fgl, package="MASS")
fgl.res <- tuneRF(fgl[,-10], fgl[,10], stepFactor=1.5)
}
%\author{}
\keyword{classif}
\keyword{tree}
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