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\name{hypvol}
\alias{hypvol}
\title{
Aproximate Hypervolume for Multivariate Data
}
\description{
Computes a simple approximation to the hypervolume of a multivariate
data set.
}
\usage{
hypvol(data, reciprocal=FALSE)
}
\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{reciprocal}{
A logical variable indicating whether or not the reciprocal
hypervolume is desired rather than the hypervolume itself. The
default is to return the hypervolume.
}
}
\value{
Returns the minimum of the hypervolume computed from simple variable bounds
and that computed from variable bounds of the principal component scores.
Used for the default hypervolume parameter for the noise
component when observations are designated as noise in \code{Mclust}
and \code{mclustBIC}.
}
\references{
A. Dasgupta and A. E. Raftery (1998).
Detecting features in spatial point processes with clutter via model-based
clustering.
\emph{Journal of the American Statistical Association 93:294-302}.
C. Fraley and A.E. Raftery (1998).
\emph{Computer Journal 41:578-588}.
C. Fraley and A. E. Raftery (2002).
Model-based clustering, discriminant analysis, and density estimation.
\emph{Journal of the American Statistical Association 97:611-631}.
}
\seealso{
\code{\link{mclustBIC}}
}
\examples{
hypvol(iris[,-5])
}
\keyword{cluster}
|
