mclust (version 5.2.2)

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, ...)
mstepEVE( data, z, prior = NULL, warn = NULL, control = NULL, ...)
mstepEVV( data, z, prior = NULL, warn = NULL, ...)
mstepVEE( data, z, prior = NULL, warn = NULL, control = NULL, ...)
mstepVVE( data, z, prior = NULL, warn = NULL, control = 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.

z

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 for the noise component.

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 given by mclust.options("warn").

control

Values controlling 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 latter case, mstep will use the second value, under the assumption that the first applies to an outer iteration (as in the function me). The default uses the default values from the function emControl, which sets no limit on the number of iterations, and a relative tolerance of sqrt(.Machine$double.eps) on successive iterates.

...

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

Value

A list including the following components:

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, A. E. Raftery, T. B. Murphy and L. Scrucca (2012). mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.

See Also

mstep, me, estep, mclustVariance, priorControl, emControl.

Examples

mstepVII(data = iris[,-5], z = unmap(iris[,5]))