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\name{densityMclust}
\alias{densityMclust}
\title{Density Estimation via Model-Based Clustering}
\description{
Produces a density estimate for each data point using a Gaussian finite
mixture model from \code{Mclust}.
}
\usage{
densityMclust(data, \dots, plot = TRUE)
}
\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{\dots }{
Additional arguments for the \code{\link{Mclust}} function.
In particular, setting the arguments \code{G} and \code{modelNames}
allow to specify the number of mixture components and the type of
model to be fitted. By default an "optimal" model is selected based
on the BIC criterion.
}
\item{plot}{
A logical value specifying if the estimated density should be
plotted. For more contols on the resulting graph see the associated
\code{\link{plot.densityMclust}} method.
}
}
\value{
An object of class \code{densityMclust}, which inherits from
\code{Mclust}. This contains all the components described in
\code{\link{Mclust}} and the additional element:
\item{density}{The density evaluated at the input \code{data}
computed from the estimated model.}
}
%\details{}
\references{
Scrucca L., Fraley C., Murphy T. B. and Raftery A. E. (2023) \emph{Model-Based Clustering, Classification, and Density Estimation Using mclust in R}. Chapman & Hall/CRC, ISBN: 978-1032234953, https://mclust-org.github.io/book/
Scrucca L., Fop M., Murphy T. B. and Raftery A. E. (2016) mclust 5: clustering, classification and density estimation using Gaussian finite mixture models, \emph{The R Journal}, 8/1, pp. 289-317.
Fraley C. and Raftery A. E. (2002) Model-based clustering, discriminant analysis and density estimation, \emph{Journal of the American Statistical Association}, 97/458, pp. 611-631.
}
\author{Revised version by Luca Scrucca based on
the original code by C. Fraley and A.E. Raftery.}
\seealso{
\code{\link{plot.densityMclust}},
\code{\link{Mclust}},
\code{\link{summary.Mclust}},
\code{\link{predict.densityMclust}}.
}
\examples{
dens <- densityMclust(faithful$waiting)
summary(dens)
summary(dens, parameters = TRUE)
plot(dens, what = "BIC", legendArgs = list(x = "topright"))
plot(dens, what = "density", data = faithful$waiting)
dens <- densityMclust(faithful, modelNames = "EEE", G = 3, plot = FALSE)
summary(dens)
summary(dens, parameters = TRUE)
plot(dens, what = "density", data = faithful,
drawlabels = FALSE, points.pch = 20)
plot(dens, what = "density", type = "hdr")
plot(dens, what = "density", type = "hdr", prob = c(0.1, 0.9))
plot(dens, what = "density", type = "hdr", data = faithful)
plot(dens, what = "density", type = "persp")
\donttest{
dens <- densityMclust(iris[,1:4], G = 2)
summary(dens, parameters = TRUE)
plot(dens, what = "density", data = iris[,1:4],
col = "slategrey", drawlabels = FALSE, nlevels = 7)
plot(dens, what = "density", type = "hdr", data = iris[,1:4])
plot(dens, what = "density", type = "persp", col = grey(0.9))
}
}
\keyword{cluster}
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