estimateSigmaMV: Fit zero mean multivariate t-distribution

In plw: Probe level Locally moderated Weighted t-tests.

Description

estimate the parameters Sigma, m and v of the multivariate t-distribution with zero expectation.

Usage

estimateSigmaMV(y,maxIter=100,epsilon=0.000001,verbose=FALSE)

Arguments

y	data matrix
maxIter	maximum number of iterations
epsilon	convergence criteria
verbose	print computation info or not

Details

The multivariate t-distribution is parametrized as:

y|c ~ N(mu,c*Sigma)

c ~ InvGamma(m/2,m*v/2)

Here N denotes a multivariate normal distribution, Sigma is a covariance matrix and InvGamma(a,b) is the inverse-gamma distribution with density function

f(x)=b^a exp{-b/x} x^{-a-1} /Gamma(a)

In this application mu equals zero, and m is the degrees of freedom.

Value

Sigma	Estimated covariance matrix for y
m	Estimated shape parameter for inverse-gamma prior for gene variances
v	Estimated scale parameter for inverse-gamma prior for gene variances
converged	T if the EM algorithms converged
iter	Number of iterations
modS2	Moderated estimator of gene-specific variances
histLogS2	Histogram of log(s2) where s2 is the ordinary variance estimator
fittedDensityLogS2	The fitted density for log(s2)

Author(s)

Magnus Astrand

References

Hastie, T., Tibshirani, R., and Friedman, J. (2001). The Elements of Statistical Learning, volume 1. Springer, first edition.

Kristiansson, E., Sjogren, A., Rudemo, M., Nerman, O. (2005). Weighted Analysis of Paired Microarray Experiments. Statistical Applications in Genetics and Molecular Biology 4(1)

Astrand, M. et al. (2007a). Improved covariance matrix estimators for weighted analysis of microarray data. Journal of Computational Biology, Accepted.

Astrand, M. et al. (2007b). Empirical Bayes models for multiple-probe type arrays at the probe level. Bioinformatics, Submitted 1 October 2007.

See Also

estimateSigma

plw documentation built on April 28, 2020, 6:38 p.m.