estimateSigmaMV {plw} | R Documentation

## Fit zero mean multivariate t-distribution

### 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

[Package plw version 1.4.0 Index]