sva.id {eigenR2} | R Documentation |
Estimate the number of significant eigenvectors from the residuals of a high-dimensional data set given the model fit.
sva.id(dat, mod, B=20, eigen.sig=0.10, seed=NULL)
dat |
A m response variables by n samples matrix of data |
mod |
A n by k model matrix corresponding to the primary model fit, if mod is NULL, we estimate the number of significant eigenvectors in the expression data. (see model.matrix) |
B |
The number of null iterations to perform. |
eigen.sig |
The significance cutoff for eigenvectors. |
seed |
A numeric seed for reproducible results. Optional. |
Note that this function is a modified function from the package
sva
by Leek JT and Storey JD.
The model matrix should include a column for an intercept. sva.id
estimates the number of surrogate variables to include in the analysis
as described in Leek and Storey (2007).
A list containing:
n.sv |
The number of significant surrogate variables |
p.sv |
The p-values for each eigenvector |
Jeff T. Leek jtleek@gmail.com and John D. Storey jstorey@princeton.edu
Leek JT and Storey JD (2007) Capturing heterogeneity in gene expression studies by "surrogate variable analysis." PLoS Genetics, 3:e161.