GetRepeatRanking {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' altered data sets. One major goal of this procedure is to examine the stability of the results obtained with the original dataset.
GetRepeatRanking(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. Can also be missing . In this case, the original dataset
is perturbed by adding gaussian noise, s. argument
varlist . |
scheme |
Used only if P is a Foldmatrix .
Can be "Subsampling" or "Labelexchange" .
'Subsampling' means that observations are removed
as determined by the slot foldmatrix .
'Labelexchange' means that those observations
which would be removed are instead kept in the sample,
but are assigned to the opposite class. |
iter |
Used only if P is missing, specifying the
number of different noise-perturbed datasets to be created.
Per default, the number of iterations is 10. |
varlist |
Used only if P is missing. A list with two components
(genewise , a logical and frac , a positive real number),
both controlling the variance of the added noise. If
genewise=FALSE (default) then the noise has the same variance for
all genes: it is estimated by pooled variance estimation from the
original data set. Otherwise, the variance of the noise is different
for each gene and estimated genewise from the original data set. frac
is the fraction of the variance of the estimated variance(s)
to be used as the variance of the added noise. The default value is 1/5
and is usually clearly smaller than 1. |
... |
Further arguments to be passed to the Ranking method from which rankings are generated. |
An object of class RepeatRanking
Martin Slawski martin.slawski@campus.lmu.de
Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix
GeneRanking, RepeatRanking, RankingTstat, RankingFC, RankingWelchT, RankingWilcoxon, RankingBaldiLong, RankingFoxDimmic, RankingLimma, RankingEbam, RankingWilcEbam, RankingSam, RankingBstat, RankingShrinkageT, RankingSoftthresholdT, RankingPermutation, RankingGap
## 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(xx, yy, k=1) ### Repeat the ranking with the t-statistic, using the leave-one-out scheme loor_ordT <- GetRepeatRanking(ordT, loo) ### .. or the label exchange scheme ex1r_ordT <- GetRepeatRanking(ordT, loo, scheme = "Labelexchange") ### Generate the bootstrap matrix boot <- GenerateBootMatrix(xx, yy, maxties=3, minclassize=5, repl=30) ### Repeat ranking with the t-statistic for bootstrap replicates boot_ordT <- GetRepeatRanking(ordT, boot) ### Repeat the ranking procedure for an altered data set with added noise noise_ordT <- GetRepeatRanking(ordT, varlist=list(genewise=TRUE, factor=1/10))