Package 'dSVA'

Title: Direct Surrogate Variable Analysis
Description: Functions for direct surrogate variable analysis, which can identify hidden factors in high-dimensional biomedical data.
Authors: Seunggeun (Shawn) Lee
Maintainer: Seunggeun (Shawn) Lee <[email protected]>
License: GPL (>= 2)
Version: 1.0
Built: 2024-11-12 06:12:30 UTC
Source: https://github.com/cran/dSVA

Help Index


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)

Example data for dSVA

Description

Example data for dSVA.

Format

Example contains the following objects:

Y

a data matrix of 100 individuals and 5000 features

X

a vector of the primary variable