dSVA: direct surrogate variable analysis

In dSVA: Direct Surrogate Variable Analysis

Description

Identify hidden factors in high dimensional biomedical data

Usage

dSVA(Y, X, ncomp=0)


 

Arguments

Y	n x m data matrix of n samples and m features.
X	n x p matrix of covariates without intercept.
ncomp	a number of surrogate variables to be estimated. If ncomp=0 (default), ncomp will be estimated using the be method in the num.sv function of the sva package.

Value

Bhat = Bhat.all[idx.test,], BhatSE= BhatSE[idx.test,], Pvalue=Pvalue

Bhat	n x m matrix of the estimated effect sizes of X
BhatSE	n x m matrix of the estimated standard error of Bhat
Pvalue	n x m matrix of the p-values of Bhat
Z	a matrix of the estimated surrogate variable
ncomp	a number of surrogate variables.

Author(s)

Seunggeun Lee

Examples

data(Example)
attach(Example)
out<-dSVA(Y,X, ncomp=0)

dSVA documentation built on May 2, 2019, 5:56 a.m.