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\name{depth.zonoid}
\alias{depth.zonoid}
\title{
Calculate Zonoid Depth
}
\description{
Calculates the zonoid depth of points w.r.t. a multivariate data set.
}
\usage{
depth.zonoid(x, data, seed = 0)
}
\arguments{
\item{x}{
Matrix of objects (numerical vector as one object) whose depth is to be calculated; each row contains a \eqn{d}-variate point. Should have the same dimension as \code{data}.
}
\item{data}{
Matrix of data where each row contains a \eqn{d}-variate point, w.r.t. which the depth is to be calculated.
}
\item{seed}{
the random seed. The default value \code{seed=0} makes no changes.
}
}
\details{
Calculates zonoid depth (Koshevoy and Mosler, 1997; Mosler, 2002) exactly based on the algorithm of Dyckerhoff, Koshevoy and Mosler (1996), implemented in C++ (and provided) by Rainer Dyckerhoff.
}
\value{
Numerical vector of depths, one for each row in \code{x}; or one depth value if \code{x} is a numerical vector.
}
\references{
Dyckerhoff, R., Koshevoy, G., and Mosler, K. (1996). Zonoid data depth: theory and computation. In: Prat A. (ed), \emph{COMPSTAT 1996. Proceedings in computational statistics}, Physica-Verlag (Heidelberg), 235--240.
Koshevoy, G. and Mosler, K. (1997). Zonoid trimming for multivariate distributions \emph{Annals of Statistics} \bold{25} 1998--2017.
Mosler, K. (2002). \emph{Multivariate dispersion, central regions and depth: the lift zonoid approach} Springer (New York).
}
\seealso{
\code{\link{depth.halfspace}} for calculation of the Tukey depth.
\code{\link{depth.Mahalanobis}} for calculation of Mahalanobis depth.
\code{\link{depth.projection}} for calculation of projection depth.
\code{\link{depth.simplicial}} for calculation of simplicial depth.
\code{\link{depth.simplicialVolume}} for calculation of simplicial volume depth.
\code{\link{depth.spatial}} for calculation of spatial depth.
\code{\link{depth.potential}} for calculation of data potential.
}
\examples{
# 5-dimensional normal distribution
data <- mvrnorm(1000, rep(0, 5),
matrix(c(1, 0, 0, 0, 0,
0, 2, 0, 0, 0,
0, 0, 3, 0, 0,
0, 0, 0, 2, 0,
0, 0, 0, 0, 1),
nrow = 5))
x <- mvrnorm(10, rep(1, 5),
matrix(c(1, 0, 0, 0, 0,
0, 1, 0, 0, 0,
0, 0, 1, 0, 0,
0, 0, 0, 1, 0,
0, 0, 0, 0, 1),
nrow = 5))
depths <- depth.zonoid(x, data)
cat("Depths: ", depths, "\n")
}
\keyword{ robust }
\keyword{ multivariate }
\keyword{ nonparametric }
|
