Title: | Direct Surrogate Variable Analysis |
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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 |
Identify hidden factors in high dimensional biomedical data
dSVA(Y, X, ncomp=0)
dSVA(Y, X, ncomp=0)
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. |
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
data(Example) attach(Example) out<-dSVA(Y,X, ncomp=0)
data(Example) attach(Example) out<-dSVA(Y,X, ncomp=0)
Example data for dSVA.
Example contains the following objects:
a data matrix of 100 individuals and 5000 features
a vector of the primary variable