mstepE: M-step for a parameterized Gaussian mixture model.

Description

Maximization step in the EM algorithm for a parameterized Gaussian mixture model.

Usage

mstepE( data, z, prior=NULL, warn=NULL, ...)
mstepV( data, z, prior=NULL,  warn=NULL, ...)
mstepEII( data, z, prior=NULL, warn=NULL, ...)
mstepVII( data, z, prior=NULL, warn=NULL, ...)
mstepEEI( data, z, prior=NULL,  warn=NULL, ...)
mstepVEI( data, z, prior=NULL, warn=NULL, control=NULL, ...)
mstepEVI( data, z, prior=NULL, warn=NULL, ...)
mstepVVI( data, z, prior=NULL, warn=NULL, ...)
mstepEEE( data, z, prior=NULL, warn=NULL, ...)
mstepEEV( data, z, prior=NULL, warn=NULL, ...)
mstepVEV( data, z, prior=NULL, warn=NULL, control=NULL,...)
mstepVVV( data, z, prior=NULL, warn=NULL, ...)

Arguments

data

A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables.

A matrix whose [i,k]th entry is the conditional probability of the ith observation belonging to the kth component of the mixture. In analyses involving noise, this should not include the conditional probabilities fo

prior

Specification of a conjugate prior on the means and variances. The default assumes no prior.

warn

A logical value indicating whether or not certain warnings (usually related to singularity) should be issued when the estimation fails. The default is set in .Mclust$warn.

control

Values controling termination for models "VEI" and "VEV" that have an iterative M-step. This should be a list with components named itmax and tol. These components can be of length 1 or 2; in the l

...

Catches unused arguments in indirect or list calls via do.call.

Value

A list including the following components:
modelNameA character string identifying the model (same as the input argument).
parameters[object Object],[object Object],[object Object]
Attributes:"info" For those models with iterative M-steps ("VEI" and "VEV"), information on the iteration. "WARNING" An appropriate warning if problems are encountered in the computations. This function computes the M-step only for MVN mixtures, so in analyses involving noise, the conditional probabilities input should exclude those for the noise component. In contrast to me for the EM algorithm, computations in mstep are carried out unless failure due to overflow would occur. To impose stricter tolerances on a single mstep, use me with the itmax component of the control argument set to 1.
References
{ C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631. C. Fraley and A. E. Raftery (2006). MCLUST Version 3 for R: Normal Mixture Modeling and Model-Based Clustering, Technical Report no. 504, Department of Statistics, University of Washington. } mstep, me, estep, priorControl emControl mstepVII(data = iris[,-5], z = unmap(iris[,5])) cluster