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\name{mstep}
\alias{mstep}
\title{M-step for parameterized Gaussian mixture models}
\description{
Maximization step in the EM algorithm for parameterized Gaussian
mixture models.
}
\usage{
mstep(data, modelName, z, prior = NULL, warn = NULL, \dots)
}
\arguments{
\item{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.
}
\item{modelName}{
A character string indicating the model. The help file for
\code{\link{mclustModelNames}} describes the available models.
}
\item{z}{
A matrix whose \code{[i,k]}th entry is the
conditional probability of the ith observation belonging to
the \emph{k}th component of the mixture.
In analyses involving noise, this should not include the
conditional probabilities for the noise component.
}
\item{prior}{
Specification of a conjugate prior on the means and variances.
The default assumes no prior.
}
\item{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 \code{mclust.options("warn")}.
}
\item{\dots}{
Catches unused arguments in indirect or list calls via \code{do.call}.
}
}
\value{
A list including the following components:
\item{modelName}{
A character string identifying the model (same as the input argument).
}
\item{parameters}{
\describe{
\item{\code{pro}}{
A vector whose \emph{k}th component is the mixing proportion for
the \emph{k}th 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.
}
\item{\code{mean}}{
The mean for each component. If there is more than one component,
this is a matrix whose kth column is the mean of the \emph{k}th
component of the mixture model.
}
\item{\code{variance}}{
A list of variance parameters for the model.
The components of this list depend on the model
specification. See the help file for \code{\link{mclustVariance}}
for details.
}
}
}
\item{Attributes:}{
\code{"info"} For those models with iterative M-steps
(\code{"VEI"} and \code{"VEV"}), information on the iteration.\cr
\code{"WARNING"} An appropriate warning if problems are
encountered in the computations.
}
}
\note{
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. \cr
In contrast to \code{me} for the EM algorithm, computations in \code{mstep}
are carried out unless failure due to overflow would occur. To impose
stricter tolerances on a single \code{mstep}, use \code{me} with the
\emph{itmax} component of the \code{control} argument set to 1.
}
\seealso{
\code{\link{mstepE}}, \dots,
\code{\link{mstepVVV}},
\code{\link{emControl}},
\code{\link{me}},
\code{\link{estep}},
\code{\link{mclust.options}}.
}
\examples{
\donttest{
mstep(modelName = "VII", data = iris[,-5], z = unmap(iris[,5]))}
}
\keyword{cluster}
|
