me.VVV {mclust1998}R Documentation

EM for unconstrained MVN mixture models

Description

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

Usage

me.VVV(data, z, eps, tol, itmax, equal = FALSE, noise = FALSE, 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 reciprocal condition estimate for the covariance estimate. Default : .Machine$double.eps.
tol The iteration is terminated if the relative error in the loglikelihood value falls below tol. Default : sqrt(.Machine$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 : FALSE.
noise Logical variable indicating whether or not to include a Poisson noise term in the model. Default : FALSE.
Vinv An estimate of the inverse hypervolume of the data region (needed only if noise = TRUE). 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.VVV, estep.VVV

Examples

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


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