mclust (version 1.1-7)

me.EEV: EM for constant shape, constant volume MVN mixture models

Description

EM iteration (M-step followed by E-step) for estimating parameters in an MVN mixture model having constant shape, constant volume and possibly one Poisson noise term.

Usage

me.EEV(data, z, eps, tol, itmax, equal = F, noise = F, Vinv)

Arguments

data

matrix of observations.

z

matrix of conditional probabilities. z should have a row for each observation in data, and a column for each component of the mixture.

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 estimates. Default : c(.Mach

tol

The iteration is terminated if the relative error in the loglikelihood value falls below tol. Default : sqrt(.Machine$double.eps).

itmax

Upper limit on the number of iterations. Default : Inf (no upper limit).

equal

Logical variable indicating whether or not to assume equal proportions in the mixture. Default : F.

noise

Logical variable indicating whether or not to include a Poisson noise term in the model. Default : F.

Vinv

An estimate of the inverse hypervolume of the data region (needed only if noise = T). Default : determined by function hypvol

Value

• the conditional probablilities at the final iteration (information about the iteration is included as attributes).

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

me, mstep.EEV, estep.XEV

Examples

data(iris)
cl <- mhclass(mhtree(iris[,1:4], modelid = "VVV"),3)
me.EEV( iris[,1:4], ctoz(cl))