pca_shrinkvar: Use PCA to estimate confounders, then shrink variances using...

In dcgerard/succotashr: Surrogate and Confounder Correction Occuring Together with Adaptive Shrinkage

Description

Factors are estimated using PCA. Variances are shrunk using an empirical Bayesian approach assuming a hierarchical model as described in Smyth (2004).

Usage

pca_shrinkvar(Y, k, df = "rank_based")

Arguments

Y	A matrix of numerics. n by p.
k	A positive integer. The known rank of the mean.
df	A string. Should we subtract off the rank ("rank_based") or just 1 ("minus_one")?

References

Smyth, G. K. (2004). Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology Volume 3, Issue 1, Article 3.

dcgerard/succotashr documentation built on May 15, 2019, 1:25 a.m.