\HeaderA{rfCMA}{Classification based on Random Forests}{rfCMA}
\keyword{multivariate}{rfCMA}
\begin{Description}\relax
Random Forests were proposed by Breiman (2001)
and are implemented in the package \code{randomForest}.

In this package, they can as well be used to rank variables
according to their importance, s. \code{GeneSelection}.

For \code{S4} method information, see \LinkA{rfCMA-methods}{rfCMA.Rdash.methods}
\end{Description}
\begin{Usage}
\begin{verbatim}
rfCMA(X, y, f, learnind, varimp = TRUE, seed = 111, ...)\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{X}] Gene expression data. Can be one of the following:
\Itemize{
\item A \code{matrix}. Rows correspond to observations, columns to variables.
\item A \code{data.frame}, when \code{f} is \emph{not} missing (s. below).
\item An object of class \code{ExpressionSet}.
}

\item[\code{y}] Class labels. Can be one of the following:
\Itemize{
\item A \code{numeric} vector.
\item A \code{factor}.
\item A \code{character} if \code{X} is an \code{ExpressionSet} that
specifies the phenotype variable.
\item \code{missing}, if \code{X} is a \code{data.frame} and a
proper formula \code{f} is provided.
}
\bold{WARNING}: The class labels will be re-coded to
range from \code{0} to \code{K-1}, where \code{K} is the
total number of different classes in the learning set.

\item[\code{f}] A two-sided formula, if \code{X} is a \code{data.frame}. The
left part correspond to class labels, the right to variables.
\item[\code{learnind}] An index vector specifying the observations that
belong to the learning set. May be \code{missing};
in that case, the learning set consists of all
observations and predictions are made on the
learning set.
\item[\code{varimp}] Should variable importance measures be computed ? Defauls to \code{TRUE}.
\item[\code{seed}] Fix Random number generator seed to \code{seed}. This is
useful to guarantee reproducibility of the results.
\item[\code{...}] Further arguments to be passed to \code{randomForest} from the
package of the same name.
\end{ldescription}
\end{Arguments}
\begin{Value}
If \code{varimp}, then an object of class \code{\LinkA{clvarseloutput}{clvarseloutput}} is returned,
otherwise an object of class \code{\LinkA{cloutput}{cloutput}}
\end{Value}
\begin{Author}\relax
Martin Slawski \email{martin.slawski@campus.lmu.de}

Anne-Laure Boulesteix \url{http://www.slcmsr.net/boulesteix}
\end{Author}
\begin{References}\relax
Breiman, L. (2001)

Random Forest.

\emph{Machine Learning, 45:5-32.}
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{compBoostCMA}{compBoostCMA}}, \code{\LinkA{dldaCMA}{dldaCMA}}, \code{\LinkA{ElasticNetCMA}{ElasticNetCMA}},
\code{\LinkA{fdaCMA}{fdaCMA}}, \code{\LinkA{flexdaCMA}{flexdaCMA}}, \code{\LinkA{gbmCMA}{gbmCMA}},
\code{\LinkA{knnCMA}{knnCMA}}, \code{\LinkA{ldaCMA}{ldaCMA}}, \code{\LinkA{LassoCMA}{LassoCMA}},
\code{\LinkA{nnetCMA}{nnetCMA}}, \code{\LinkA{pknnCMA}{pknnCMA}}, \code{\LinkA{plrCMA}{plrCMA}},
\code{\LinkA{pls\_ldaCMA}{pls.Rul.ldaCMA}}, \code{\LinkA{pls\_lrCMA}{pls.Rul.lrCMA}}, \code{\LinkA{pls\_rfCMA}{pls.Rul.rfCMA}},
\code{\LinkA{pnnCMA}{pnnCMA}}, \code{\LinkA{qdaCMA}{qdaCMA}},
\code{\LinkA{scdaCMA}{scdaCMA}}, \code{\LinkA{shrinkldaCMA}{shrinkldaCMA}}, \code{\LinkA{svmCMA}{svmCMA}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
 ### load Khan data
data(khan)
### extract class labels
khanY <- khan[,1]
### extract gene expression
khanX <- as.matrix(khan[,-1])
### select learningset
set.seed(111)
learnind <- sample(length(khanY), size=floor(2/3*length(khanY)))
### run random Forest
rfresult <- rfCMA(X=khanX, y=khanY, learnind=learnind, varimp = FALSE)
### show results
show(rfresult)
ftable(rfresult)
plot(rfresult)\end{ExampleCode}
\end{Examples}

