Maximization step in the EM algorithm for a parameterized Gaussian mixture model.
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, …)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 for the noise component.
Specification of a conjugate prior on the means and variances. The default assumes no prior.
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").
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.
A list including the following components:
A character string identifying the model (same as the input argument).
proA vector whose kth component is the mixing proportion for the kth component of the mixture model. If the model includes a Poisson term for noise, there should be one more mixing proportion than the number of Gaussian components.
meanThe mean for each component. If there is more than one component, this is a matrix whose kth column is the mean of the kth component of the mixture model.
varianceA list of variance parameters for the model.
The components of this list depend on the model
specification. See the help file for mclustVariance
for details.
"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.
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.
# NOT RUN {
mstepVII(data = iris[,-5], z = unmap(iris[,5]))
# }
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