sigma2decomp: Convert mixture component covariances to decomposition form.

Description

Converts a set of covariance matrices from representation as a 3-D array to a parameterization by eigenvalue decomposition.

Usage

sigma2decomp(sigma, G = NULL, tol = sqrt(.Machine$double.eps), ...)

Arguments

sigma

Either a 3-D array whose [,,k]th component is the covariance matrix for the kth component in an MVN mixture model, or a single covariance matrix in the case that all components have the same covariance.

The number of components in the mixture. When sigma is a 3-D array, the number of components can be inferred from its dimensions.

tol

Tolerance for determining whether or not the covariances have equal volume, shape, and or orientation. The default is the square root of the relative machine precision, sqrt(.Machine$double.eps), which is about 1.e-8.

...

Catches unused arguments from an indirect or list call via do.call.

Value

The covariance matrices for the mixture components in decomposition form, including the following components:

References

C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631. C. Fraley, A. E. Raftery, T. B. Murphy and L. Scrucca (2012). mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.

Examples

Run this code

meEst <- meEEE(iris[,-5], unmap(iris[,5])) 
names(meEst$parameters$variance)
meEst$parameters$variance$Sigma

sigma2decomp(meEst$parameters$variance$Sigma, G = length(unique(iris[,5])))