squeezeMVar: Smooth sample covariance matrices

In betr: Identify differentially expressed genes in microarray time-course data

Description

An internal function to smooth a set of sample covariance matrices by computing empirical Bayes posterior means.

Usage

squeezeMVar(S, df, Lambda = NULL, nu = NULL)

Arguments

S	a list of covariance matrices
df	numeric vector of degrees of freedom for covariance matrices
Lambda	use this target covariance matrix instead of calculating it from the data
nu	use this nu instead of calculating it from the data

Details

Calculate shrinkage estimates for covariance matrices using the procedure of Tai and Speed (2006) and Smyth (2004)

Value

varPost	list of posterior covariance matrices
varPrior	target covariance matrix
dfPrior	prior degrees of freedom

Author(s)

Martin Aryee

References

Smyth, G. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Statistical applications in genetics and molecular biology (2004) vol. 3

Tai, Y and Speed, T. A multivariate empirical Bayes statistic for replicated microarray time course data. Annals of Statistics (2006) vol. 34 (5) pp. 2387-2412

See Also

betr

betr documentation built on April 14, 2017, 5:16 a.m.