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\name{plot.MclustBootstrap}
\alias{plot.MclustBootstrap}
\title{Plot of bootstrap distributions for mixture model parameters}
\description{
Plots the bootstrap distribution of parameters as returned by the \code{\link{MclustBootstrap}} function.
}
\usage{
\method{plot}{MclustBootstrap}(x, what = c("pro", "mean", "var"),
show.parest = TRUE, show.confint = TRUE,
hist.col = "grey", hist.border = "lightgrey", breaks = NA,
col = "forestgreen", lwd = 2, lty = 3,
xlab = NULL, xlim = NULL, ylim = NULL, \dots)
}
\arguments{
\item{x}{Object returned by \code{MclustBootstrap}.}
\item{what}{Character string specifying if mixing proportions (\code{"pro"}),
component means (\code{"mean"}) or component variances (\code{"var"})
should be drawn.}
\item{show.parest}{A logical specifying if the parameter estimate should be drawn as vertical line.}
\item{show.confint}{A logical specifying if the resampling-based confidence interval should be drawn at the bottom of the graph. Confidence level can be provided as further argument \code{conf.level}; see \code{\link{summary.MclustBootstrap}}.}
\item{hist.col}{The color to be used to fill the bars of the histograms.}
\item{hist.border}{The color of the border around the bars of the histograms.}
\item{breaks}{The number of breaks used in histogram to visualize the bootstrap distribution. When \code{NA} (default), it is computed as the maximum between the "Sturges" and "Freedman-Diaconis" methods; see \code{\link{nclass.Sturges}} and \code{\link{nclass.FD}}.}
\item{col, lwd, lty}{The color, line width and line type to be used to represent the estimated parameters and confidence intervals.}
\item{xlab}{Optional label for the horizontal axis.}
\item{xlim, ylim}{A two-values vector of axis range for, respectively, horizontal and
vertical axis.}
\item{\dots}{Other graphics parameters.}
}
\value{
A plot for each variable/component of the selected parameters.
}
\seealso{
\code{\link{MclustBootstrap}}
}
\examples{
\donttest{
data(diabetes)
X <- diabetes[,-1]
modClust <- Mclust(X, G = 3, modelNames = "VVV")
bootClust <- MclustBootstrap(modClust, nboot = 99)
par(mfrow = c(1,3), mar = c(4,2,2,0.5))
plot(bootClust, what = "pro")
par(mfrow = c(3,3), mar = c(4,2,2,0.5))
plot(bootClust, what = "mean")
}
}
\keyword{cluster}
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