mclust (version 1.1-7)

mstep.EI: M-step for spherical, constant-volume MVN mixture models

Description

M-step for estimating parameters given conditional probabilities in an MVN mixture model having equal, spherical variances and possibly one Poisson noise term.

Usage

mstep.EI(data, z, eps, 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

Lower bound on the estimated value of sigma-squared. Default : .Machine$double.eps

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

• A list whose components are the parameter estimates corresponding to z:
• mu
  matrix whose columns are the Gaussian group means.
• sigma
  group variance matrix.
• prob
  probabilities (mixing proportions) for each group (present only when equal = T). The loglikelihood and reciprocal condition estimate are returned 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). mstep, me.EI, estep.EI

Examples

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