dSVA {dSVA} R Documentation

## 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.

Seunggeun Lee

### Examples



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



[Package dSVA version 1.0 Index]