mclust (version 1.1-7)

estep.EEE: E-step for constant-variance MVN mixture models

Description

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

Usage

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

Arguments

data

matrix of observations.

mu

matrix whose columns are the Gaussian group means.

sigma

group variance matrix.

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

Lower bound on the reciprocal condition estimate for the covariance. Default : .Machine$double.eps.

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.EEE, mstep.EEE

Examples

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