SIS.selection: Sure Independence Screening

Description Usage Arguments Details Value Author(s) References Examples

View source: R/SISselection.R

Description

SIS has been performed to select relevant gene expression variables. SIS ranks the importance of features according to their magnitude of marginal regression coefficients.

Usage

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SIS.selection(X,Y, pred, scale = F)

Arguments

X

a data matrix (nxp) of genes. NAs and Inf are not allowed. Each row corresponds to an observation and each column to a gene.

Y

a vector of length n giving the classes of the n observations. The classes must be coded as 1 or 0.

pred

number of relevant variable to select, pred has to be lower than p.

scale

If scale=TRUE, X will be scaled.

Details

Sure Independence Screening (SIS) has been performed to select relevant gene expression variables pred such as pred < p. SIS refers to ranking features according to marginal utility, namely, each feature is used independently as a predictor to decide its usefulness for predicting the response. Precisely SIS ranks the importance of features according to their magnitude of marginal regression coefficients.

Value

Return a matrix (nxpred) with only the pred most relevant gene and all the observations

Author(s)

Caroline Bazzoli, Thomas Bouleau, Sophie Lambert-Lacroix

References

Fan, J. and Lv, J. (2008). Sure independence screening for ultrahigh dimensional feature space. Journal of the Royal Statistical Society, 70, 849-911.

Examples

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data("BreastCancer")
X<-scale(BreastCancer$X)
Y<-BreastCancer$Y

Xsis<-SIS.selection(X,Y,50)

lsplsGlm documentation built on July 27, 2017, 5:01 p.m.