mstep.EI(mclust)R Documentation

M-step for spherical, constant-volume MVN mixture models

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.

DESCRIPTION

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

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)


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