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\name{predict.MclustDR}
\alias{predict.MclustDR}
\alias{predict2D.MclustDR}
\title{Classify multivariate observations on a dimension reduced subspace by Gaussian finite mixture modeling}
\description{Classify multivariate observations on a dimension reduced subspace estimated from a Gaussian finite mixture model.}
\usage{
\method{predict}{MclustDR}(object, dim = 1:object$numdir, newdata, eval.points, \dots)
}
\arguments{
\item{object}{an object of class \code{'MclustDR'} resulting from a call to \code{\link{MclustDR}.}}
\item{dim}{the dimensions of the reduced subspace used for prediction.}
\item{newdata}{a data frame or matrix giving the data. If missing the data obtained from the call to \code{\link{MclustDR}} are used.}
\item{eval.points}{a data frame or matrix giving the data projected on the reduced subspace. If provided \code{newdata} is not used.}
\item{\dots}{further arguments passed to or from other methods.}
}
% \details{}
\value{
Returns a list of with the following components:
\item{dir}{a matrix containing the data projected onto the \code{dim} dimensions of the reduced subspace.}
\item{density}{densities from mixture model for each data point.}
\item{z}{a matrix whose \emph{[i,k]}th entry is the probability that
observation \emph{i} in \code{newdata} belongs to the \emph{k}th class.}
\item{uncertainty}{The uncertainty associated with the classification.}
\item{classification}{A vector of values giving the MAP classification.}
}
\references{
Scrucca, L. (2010) Dimension reduction for model-based clustering.
\emph{Statistics and Computing}, 20(4), pp. 471-484.
}
\author{Luca Scrucca}
% \note{}
\seealso{\code{\link{MclustDR}}.}
\examples{
mod = Mclust(iris[,1:4])
dr = MclustDR(mod)
pred = predict(dr)
str(pred)
data(banknote)
mod = MclustDA(banknote[,2:7], banknote$Status)
dr = MclustDR(mod)
pred = predict(dr)
str(pred)
}
\keyword{multivariate}
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