dSVA {dSVA} | R Documentation |
Identify hidden factors in high dimensional biomedical data
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)