RankingWilcoxon {GeneSelector} | R Documentation

## Ranking based on the Wilcoxon statistic

### Description

The Wilcoxon statistic is rank-based and 'distribution free'. It is equivalent to the Mann-Whitney statistic and also related to the 'area under the curve' (AUC) in the two sample case. The implementation is efficient, but still far slower than that of the t-statistic.

### Usage

    RankingWilcoxon(x, y, type = c("unpaired", "paired", "onesample"), pvalues = FALSE, gene.names = NULL, ...)

### Arguments

`x` | A `matrix` of gene expression values with rows corresponding to genes and columns corresponding to observations or alternatively an object of class `ExpressionSet`. If `type = "paired"`, the first half of the columns corresponds to the first measurements and the second half to the second ones. For instance, if there are 10 observations, each measured twice, stored in an expression matrix `expr`, then `expr[,1]` is paired with `expr[,11]`, `expr[,2]` with `expr[,12]`, and so on.
`y` | If `x` is a matrix, then `y` may be a `numeric` vector or a factor with at most two levels. If `x` is an `ExpressionSet`, then `y` is a character specifying the phenotype variable in the output from `pData`. If `type = "paired"`, take care that the coding is analogously to the requirement concerning `x`
`type` | "unpaired": two-sample test, Wilcoxon Rank Sum test is performed. "paired": Wilcoxon sign rank test is performed on the differences. "onesample": `y` has only one level. The Wilcxon sign rank test for difference from zero is performed.
`pvalues` | Should p-values be computed ? Default is `FALSE`.
`gene.names` | An optional vector of gene names.
`...` | Currently unused argument.

### Value

An object of class GeneRanking.

### Author(s)

Martin Slawski
Anne-Laure Boulesteix

### See Also

RepeatRanking, RankingTstat, RankingFC, RankingWelchT, RankingBaldiLong, RankingFoxDimmic, RankingLimma, RankingEbam, RankingWilcEbam, RankingSam, RankingShrinkageT, RankingSoftthresholdT, RankingPermutation

### Examples

    ## Load toy gene expression data
    data(toydata)
    ### class labels
    yy <- toydata[1,]
    ### gene expression
    xx <- toydata[-1,]
    ### run RankingWilcoxon
    wilcox <- RankingWilcoxon(xx, yy, type="unpaired")

[Package GeneSelector version 2.30.0 Index]