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\name{summary.mclustBIC}
\alias{summary.mclustBIC}
\alias{print.summary.mclustBIC}
\alias{summary.mclustBIC}
\alias{summaryMclustBIC}
\alias{summaryMclustBICn}
\alias{printSummaryMclustBIC}
\alias{printSummaryMclustBICn}
\title{Summary function for model-based clustering via BIC}
\description{
Optimal model characteristics and classification for model-based
clustering via \code{mclustBIC}.
}
\usage{
\method{summary}{mclustBIC}(object, data, G, modelNames, \dots)
}
\arguments{
\item{object}{
An \code{'mclustBIC'} object,
which is the result of applying \code{mclustBIC}
to \code{data}.
}
\item{data}{
The matrix or vector of observations used to generate `object'.
}
\item{G}{
A vector of integers giving the numbers of mixture components (clusters)
from which the best model according to BIC will be selected
(\code{as.character(G)} must be a subset of the row names of
\code{object}).
The default is to select the best model for all numbers
of mixture components used to obtain \code{object}.
}
\item{modelNames}{
A vector of integers giving the model parameterizations
from which the best model according to BIC will be selected
(\code{as.character(model)} must be a subset of the column names of
\code{object}).
The default is to select the best model for parameterizations
used to obtain \code{object}.
}
\item{\dots}{
Not used. For generic/method consistency.
}
}
\value{
A list giving the optimal (according to BIC) parameters,
conditional probabilities \code{z}, and log-likelihood,
together with the associated classification and its uncertainty.
The details of the output components are as follows:
\item{modelName}{
A character string denoting the model corresponding to the optimal BIC.
}
\item{n}{
The number of observations in the data.
}
\item{d}{
The dimension of the data.
}
\item{G}{
The number of mixture components in the model corresponding to the optimal
BIC.
}
\item{bic}{
The optimal BIC value.
}
\item{loglik}{
The log-likelihood corresponding to the optimal BIC.
}
\item{parameters}{
A list with the following components:
\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 missing, equal proportions are assumed.
}
\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{z}{
A matrix whose \emph{[i,k]}th entry is the probability that observation
\emph{i} in the data belongs to the \emph{k}th class.
}
\item{classification}{
\code{map(z)}: The classification corresponding to \code{z}.
}
\item{uncertainty}{
The uncertainty associated with the classification.
}
\item{Attributes:}{
\code{"bestBICvalues"} Some of the best bic values for the analysis.\cr
\code{"prior"} The prior as specified in the input.\cr
\code{"control"} The control parameters for EM as specified in
the input.\cr
\code{"initialization"} The parameters used to initial EM for
computing the maximum likelihood values used to obtain the BIC.
}
}
\seealso{
\code{\link{mclustBIC}}
\code{\link{mclustModel}}
}
\examples{
irisBIC <- mclustBIC(iris[,-5])
summary(irisBIC, iris[,-5])
summary(irisBIC, iris[,-5], G = 1:6, modelNames = c("VII", "VVI", "VVV"))
}
\keyword{cluster}
% docclass is function
|
