mclust (version 1.1-7)

estep.XEV: E-step for constant shape MVN mixture models

Description

E-step for estimating conditional probabilities from parameter estimates in an MVN mixture model having constant shape and possibly one Poisson noise term.

Usage

estep.XEV(data, mu, sigma, prob, eps, Vinv)

Arguments

data

matrix of observations.

mu

matrix whose columns are the Gaussian group means.

sigma

group variance matrices.

prob

mixing proportions (probabilities) for each group. If prob is missing, the number of groups is assumed to be the number of columns in mu (no noise). A Poisson noise term will appear in the conditional probabilities if leng

eps

A 2-vector specifying lower bounds on the pth root of the volume of the ellipsoids defining the clusters, where p is the data dimension, and on the reciprocal condition number for the estimated shape of the covariance estimate. Default : c(.Machine$

Vinv

An estimate of the inverse hypervolume of the data region (needed only if prob indicates a noise term). Default : determined by function hypvol

Value

• the conditional probablilities corresponding to the parameter estimates. The loglikelihood is returned as an attribute.

References

G. Celeux and G. Govaert, Gaussian parsimonious clustering models, Pattern Recognition,28:781-793 (1995).

A. P. Dempster, N. M. Laird and D. B. Rubin, Maximum Likelihood from Incomplete Data via the EM Algorithm, Journal of the Royal Statistical Society, Series B,39:1-22 (1977).

G. J. MacLachlan and K. E. Basford, The EM Algorithm and Extensions, Wiley (1997).

See Also

estep, me.EEV, me.VEV, mstep.EEV, mstep.VEV

Examples

data(iris)
cl <- mhclass(mhtree(iris[,1:4], modelid = "VVV"),3)
z <- me.EEV( iris[,1:4], ctoz(cl))
Mstep <- mstep.EEV(iris[,1:4], z)
estep.XEV( iris[,1:4], Mstep$mu, Mstep$sigma, Mstep$prob)