R: Barplot of variable importance

Barplot {CMA}

R Documentation

Barplot of variable importance

Description

This method can be seen as a visual pendant to 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 GeneSelection, s. genesel.

Arguments

`x`	An object of class `genesel`
`top`	Number of top genes whose variable importance should be displayed. Defaults to 10.
`iter`	Iteration number (`learningset`) for which variable importance should be displayed.
`...`	Further graphical options passed to `barplot`.

Value

No return.

Note

Note the following

If scheme = "multiclass", only one plot will be made. Otherwise, one plot will be made for each binary scenario (depending on whether "scheme" is "one-vs-all" or "pairwise").
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.
For the methods "lasso", "elasticnet", "boosting" the number of nonzero coefficients can be very small, resulting in bars of height zero if top has been chosen too large.

Author(s)

Martin Slawski ms@cs.uni-sb.de

Anne-Laure Boulesteix boulesteix@ibe.med.uni-muenchen.de

References

Slawski, M. Daumer, M. Boulesteix, A.-L. (2008) CMA - A comprehensive Bioconductor package for supervised classification with high dimensional data. BMC Bioinformatics 9: 439