Maximization step in the EM algorithm for parameterized Gaussian mixture models.
mstep(data, modelName, z, prior = NULL, warn = 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 character string indicating the model. The help file for
mclustModelNames describes the available models.
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").
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.
# NOT RUN {
mstep(modelName = "VII", data = iris[,-5], z = unmap(iris[,5]))
# }
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