.BG
.FN meEEE
.TL
EM for constant-variance MVN mixture models
.SH DESCRIPTION
EM iteration (M-step followed by E-step) for estimating parameters in an MVN 
mixture model having constant variance and possibly one Poisson noise term.
.CS
meEEE(data, z, eps, tol, itmax, equal = F, noise = F, Vinv)
.PP
.RA
.AG data
matrix of observations.
.AG z
matrix of conditional probabilities. `z' should have a row for each observation
in `data', and a column for each component of the mixture.
.OA
.AG eps
Lower bound on the reciprocal condition estimate for the cholesky factor
of the covariance estimate.
Default : `sqrt(.Machine$double.eps)'.
.AG tol
The iteration is terminated if the relative error in the loglikelihood value
falls below `tol'. Default : `sqrt(.Machine$double.eps)'.
.AG itmax
Upper limit on the number of iterations. Default : `Inf' (no upper limit).
.AG equal
Logical variable indicating whether or not to assume equal proportions in the
mixture. Default : `F'.
.AG noise
Logical variable indicating whether or not to include a Poisson noise term in
the model. Default : `F'.
.AG Vinv
An estimate of the inverse hypervolume of the data region (needed only if
`noise = T'). Default : determined by function `hypvol'
.RT
the conditional probablilities at the final iteration (information about the
iteration is included as attributes).
.SH REFERENCES
G. Celeux and G. Govaert, Gaussian parsimonious clustering models,
\fIPattern Recognition, \fR28:781-793 (1995).

A. P. Dempster, N. M. Laird and D. B. Rubin, Maximum Likelihood from
Incomplete Data via the EM Algorithm, \fIJournal of the Royal Statistical
Society, Series B, \fR39:1-22 (1977).

C. Fraley and A. E. Raftery, How many clusters? Which clustering method?
Answers via model-based cluster analysis. \fIComputer Journal,
\fR41:578-588 (1998).

C. Fraley and A. E. Raftery, \fIMCLUST:Software for model-based cluster
and discriminant analysis. \fRTechnical Report No. 342, Department of
Statistics, University of Washington (1998).

G. J. MacLachlan and T. Krishnan, The EM Algorithm and Extensions, Wiley
(1997).
.SA
`me', `mstepEEE', `estepEEE'
.EX
> data <- matrix(aperm(iris, c(1,3,2)), 150, 4)
> cl <- mhclass(mhtree(data, modelid = 4),3)
> meEEE( data, ctoz(cl))

.KW clustering
.WR

