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, ...)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.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.
y
is correct (s. above).
y has only one level.
Test whether the true mean is different
from zero.
lambda, s. details.GeneRanking.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.
### 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")
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