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Type: Package
Title: Direct Surrogate Variable Analysis
Version: 1.0
Date: 2016-10-21
Author: Seunggeun (Shawn) Lee
Maintainer: Seunggeun (Shawn) Lee <leeshawn@umich.edu>
Description: Functions for direct surrogate variable analysis, which can identify hidden factors in high-dimensional biomedical data.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Depends: R (≥ 2.13.0)
Imports: sva
NeedsCompilation: no
Packaged: 2017-01-04 09:07:48 UTC; LEE7801
Repository: CRAN
Date/Publication: 2017-01-04 10:56:09

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


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)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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