\HeaderA{rfCMA-methods}{Classification based on Random Forests}{rfCMA.Rdash.methods}
\aliasA{rfCMA,data.frame,missing,formula-method}{rfCMA-methods}{rfCMA,data.frame,missing,formula.Rdash.method}
\aliasA{rfCMA,ExpressionSet,character,missing-method}{rfCMA-methods}{rfCMA,ExpressionSet,character,missing.Rdash.method}
\aliasA{rfCMA,matrix,factor,missing-method}{rfCMA-methods}{rfCMA,matrix,factor,missing.Rdash.method}
\aliasA{rfCMA,matrix,numeric,missing-method}{rfCMA-methods}{rfCMA,matrix,numeric,missing.Rdash.method}
\keyword{multivariate}{rfCMA-methods}
\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}.
\end{Description}
\begin{Section}{Methods}
\describe{

\item[X = "matrix", y = "numeric", f = "missing"] signature 1

\item[X = "matrix", y = "factor", f = "missing"] signature 2

\item[X = "data.frame", y = "missing", f = "formula"] signature 3

\item[X = "ExpressionSet", y = "character", f = "missing"] signature 4
}
For references, further argument and output information, consult
\code{\LinkA{rfCMA}{rfCMA}}
\end{Section}

