pca_ruv2: PCA when first 'vr' rows have a variance multiplicatively...

In dcgerard/vicar: Various Ideas for Confounder Adjustment in Regression

Description

Modified truncated SVD. The variance estimates are just the column-wise mean-squares of the last n - vr rows. This form of factor analysis is mostly for variance inflation with RUV2.

Usage

pca_ruv2(Y, r, vr, mle = FALSE)

Arguments

Y	A matrix of numerics. The data.
r	the rank.
vr	The number of the first few rows whose variances differ by a multiplicative factor.
mle	A logical. Should we run an MLE on the residuals of PCA (TRUE) or just use a two-step estimator (FALSE).

Details

This doesn't work too well. I think the Z's and sigmas need to be estimated jointly.

Author(s)

David Gerard

dcgerard/vicar documentation built on July 7, 2021, 1:08 p.m.