RepeatRanking {GeneSelector} | R Documentation |
Altered data sets are typically prepared by calls to GenerateFoldMatrix or GenerateBootMatrix. The ranking procedure is then repeated for each of these new 'artificial' data sets. One major goal of this procedure is to examine the stability of the results obtained with the original dataset.
RepeatRanking(R, P, scheme=c("subsampling", "labelexchange"), iter=10, varlist = list(genewise=FALSE, factor=1/5), ...)
R |
The original ranking, represented by an object of class GeneRanking. |
P |
An object of class FoldMatrix or BootMatrix as generated
by GenerateFoldMatrix or GenerateBootMatrix, respectively. |
scheme |
Used only if |
iter |
Used only if |
varlist |
Used only if |
... |
Further arguments to be passed to the ranking method from which rankings are generated. |
An object of class RepeatedRanking
Martin Slawski
Anne-Laure Boulesteix
GeneRanking, RepeatedRanking, RankingTstat, RankingFC, RankingWelchT, RankingWilcoxon, RankingBaldiLong, RankingFoxDimmic, RankingLimma, RankingEbam, RankingWilcEbam, RankingSam, RankingShrinkageT, RankingSoftthresholdT, RankingPermutation
## Load toy gene expression data data(toydata) ### class labels yy <- toydata[1,] ### gene expression xx <- toydata[-1,] ### Get ranking for the original data set, with the ordinary t-statistic ordT <- RankingTstat(xx, yy, type="unpaired") ### Generate the leave-one-out / exchange-one-label matrix loo <- GenerateFoldMatrix(y = yy, k=1) ### Repeat the ranking with the t-statistic, using the leave-one-out scheme loor_ordT <- RepeatRanking(ordT, loo) ### .. or the label exchange scheme ex1r_ordT <- RepeatRanking(ordT, loo, scheme = "labelexchange") ### Generate the bootstrap matrix boot <- GenerateBootMatrix(y = yy, maxties=3, minclassize=5, repl=30) ### Repeat ranking with the t-statistic for bootstrap replicates boot_ordT <- RepeatRanking(ordT, boot) ### Repeat the ranking procedure for an altered data set with added noise noise_ordT <- RepeatRanking(ordT, varlist=list(genewise=TRUE, factor=1/10))