Learn R Programming
mclust (version 1.1-7)

mstep: M-step for parameterized MVN mixture models

Description

M-step for estimating parameters given conditional probabilities in a MVN mixture model with possibly one Poisson noise term.

Usage

mstep(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.
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:
  • mumatrix whose columns are the Gaussian group means.
  • sigmagroup variance matrix.
  • probprobabilities (mixing proportions) for each group (present only when equal = T). The loglikelihood is returned as an attribute.

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

me, estep

Examples

Run this code
data(iris)
cl <- mhclass(mhtree(iris[,1:4], modelid = "VVV"),3)
z <- me( iris[,1:4], modelid = "VVV", ctoz(cl))
pars <- mstep(iris[,1:4], modelid="VVV", z)

Run the code above in your browser using DataLab