| 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)