mclust (version 1.1-7)

me: EM for parameterized MVN mixture models

Description

EM iteration (M-step followed by E-step) for estimating parameters in an MVN mixture model with possibly one Poisson noise term.

Usage

me(data, modelid, z, ...)

Arguments

data

matrix of observations.

modelid

An integer specifying a parameterization of the MVN covariance matrix defined by volume, shape and orientation charactertistics of the underlying clusters. The allowed values for modelid and their interpretation are as follows: "EI"

z

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

...

additional arguments, as follows:

eps

Tolerance for determining singularity in the covariance matrix. The precise definition of eps varies the parameterization, each of which has a default.

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).

NOTE

The reciprocal condition estimate returned as an attribute ranges in value between 0 and 1. The closer this estimate is to zero, the more likely it is that the corresponding EM result (and BIC) are contaminated by roundoff error.

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

mstep, estep

Examples

Run this code

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

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