\HeaderA{plot}{Barplot of variable importance}{plot}
\aliasA{plot,genesel,missing-method}{plot}{plot,genesel,missing.Rdash.method}
\aliasA{plot,genesel-method}{plot}{plot,genesel.Rdash.method}
\keyword{multivariate}{plot}
\begin{Description}\relax
This method can be seen as a visual pendant to
\code{\LinkA{toplist}{toplist}}. The plot visualizes
variable importance by a barplot. The height
of the barplots correspond to variable importance.
What variable importance exactly means depends
on the method chosen when calling \code{\LinkA{GeneSelection}{GeneSelection}},
s. \code{\LinkA{genesel}{genesel}}.
\end{Description}
\begin{Arguments}
\begin{ldescription}
\item[\code{x}] An object of class \code{\LinkA{genesel}{genesel}}
\item[\code{top}] Number of top genes whose variable importance should be displayed.
Defaults to 10.
\item[\code{iter}] Iteration number (\code{learningset}) for which variable
importance should be displayed.
\item[\code{...}] Further graphical options passed to \code{barplot}.
\end{ldescription}
\end{Arguments}
\begin{Value}
No return.
\end{Value}
\begin{Note}\relax
Note the following
\Itemize{
\item If \code{scheme = "multiclass"}, only one plot will be made.
Otherwise, one plot will be made for each binary scenario
(depending on whether \code{"scheme"} is \code{"one-vs-all"}
or \code{"pairwise"}).
\item Variable importance do not make sense for variable selection
(ranking) methods that are essentially discrete, such as
the Wilcoxon-Rank sum statistic or the Kruskal-Wallis statistic.
\item For the methods \code{"lasso", "elasticnet", "boosting"}
the number of nonzero coefficients can be very small, resulting
in bars of height zero if \code{top} has been chosen too
large.
}
\end{Note}
\begin{Author}\relax
Martin Slawski \email{martin.slawski@campus.lmu.de}

Anne-Laure Boulesteix \url{http://www.slcmsr.net/boulesteix}
\end{Author}
\begin{SeeAlso}\relax
\code{\LinkA{genesel}{genesel}}, \code{\LinkA{GeneSelection}{GeneSelection}}, \code{\LinkA{toplist}{toplist}}
\end{SeeAlso}

