RankingSoftthresholdT {GeneSelector} | R Documentation |
The 'soft-threshold' statistic (Wu, 2005) is constructed
using a linear regression model with an L1
penalty (also referred to as LASSO penalty). In special
cases (like here) the LASSO estimator can be
calculated analytically and is then known as 'soft threshold
estimator'.
RankingSoftthresholdT(x, y, type = c("unpaired", "paired", "onesample"), lambda = c("lowess", "cor", "user"), userlambda = NULL, gene.names = NULL, ...)
x |
A |
y |
If |
type |
|
lambda |
s. details |
userlambda |
A user-specified value for |
gene.names |
An optional vector of gene names. |
... |
Currently unused argument. |
There are currently three ways of specifying the shrinkage intensity
lambda
. Both "lowess"
and "cor"
are relatively
slow, especially if rankings are calculated repeatedly (RepeatRanking).
Therefore, a 'reasonable' value can be set by the user.
An object of class GeneRanking
.
The code is a modified version of an implementation available in the st
package of Opgen-Rhein and Strimmer (2007).
Martin Slawski
Anne-Laure Boulesteix
Wu, B. (2005). Differential gene expression using penalized linear regression models: The improved SAM statistic. Bioinformatics, 21, 1565-1571
RepeatRanking, RankingTstat, RankingFC, RankingWelchT, RankingWilcoxon, RankingBaldiLong, RankingFoxDimmic, RankingLimma, RankingEbam, RankingWilcEbam, RankingSam, RankingShrinkageT, RankingPermutation
### Load toy gene expression data data(toydata) ### class labels yy <- toydata[1,] ### gene expression xx <- toydata[-1,] ### run RankingSoftthresholdT softt <- RankingSoftthresholdT(xx, yy, type="unpaired")