\name{Barplot} \alias{plot,genesel-method} \alias{plot,genesel,missing-method} \title{Barplot of variable importance} \description{This method can be seen as a visual pendant to \code{\link{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{\link{GeneSelection}}, s. \code{\link{genesel}}.} \arguments{ \item{x}{An object of class \code{\link{genesel}}} \item{top}{Number of top genes whose variable importance should be displayed. Defaults to 10.} \item{iter}{Iteration number (\code{learningset}) for which variable importance should be displayed.} \item{\dots}{Further graphical options passed to \code{barplot}.} } \note{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. } } \value{No return.} \references{ Slawski, M. Daumer, M. Boulesteix, A.-L. (2008) CMA - A comprehensive Bioconductor package for supervised classification with high dimensional data. \emph{BMC Bioinformatics 9: 439} } \author{Martin Slawski \email{ms@cs.uni-sb.de} Anne-Laure Boulesteix \email{boulesteix@ibe.med.uni-muenchen.de}} \seealso{\code{\link{genesel}}, \code{\link{GeneSelection}}, \code{\link{toplist}}} \keyword{multivariate}