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\name{depth.space.}
\alias{depth.space.}
\title{
Calculate Depth Space using the Given Depth
}
\description{
Calculates the representation of the training classes in depth space.
The detailed descriptions are found in the corresponding topics.
}
\usage{
depth.space.(data, cardinalities, notion, ...)
## Mahalanobis depth
# depth.space.Mahalanobis(data, cardinalities, mah.estimate = "moment", mah.parMcd = 0.75)
## projection depth
# depth.space.projection(data, cardinalities, method = "random", num.directions = 1000)
## Tukey depth
# depth.space.halfspace(data, cardinalities, exact, alg, num.directions = 1000)
## spatial depth
# depth.space.spatial(data, cardinalities)
## zonoid depth
# depth.space.zonoid(data, cardinalities)
# Potential
# depth.space.potential(data, cardinalities, pretransform = "NMom",
# kernel = "GKernel", kernel.bandwidth = NULL, mah.parMcd = 0.75)
}
\arguments{
\item{data}{
Matrix containing training sample where each row is a \eqn{d}-dimensional object, and objects of each class are kept together so that the matrix can be thought of as containing blocks of objects representing classes.
}
\item{cardinalities}{
Numerical vector of cardinalities of each class in \code{data}, each entry corresponds to one class.
}
\item{notion}{
The name of the depth notion (shall also work with \code{\link{Custom Methods}}).
}
\item{\dots}{
Additional parameters passed to the depth functions.
}
}
\value{
Matrix of objects, each object (row) is represented via its depths (columns) w.r.t. each of the classes of the training sample; order of the classes in columns corresponds to the one in the argument \code{cardinalities}.
}
\seealso{
\code{\link{depth.space.Mahalanobis}}
\code{\link{depth.space.projection}}
\code{\link{depth.space.halfspace}}
\code{\link{depth.space.spatial}}
\code{\link{depth.space.zonoid}}
}
\examples{
# Generate a bivariate normal location-shift classification task
# containing 20 training objects
class1 <- mvrnorm(10, c(0,0),
matrix(c(1,1,1,4), nrow = 2, ncol = 2, byrow = TRUE))
class2 <- mvrnorm(10, c(2,2),
matrix(c(1,1,1,4), nrow = 2, ncol = 2, byrow = TRUE))
data <- rbind(class1, class2)
# Get depth space using zonoid depth
depth.space.(data, c(10, 10), notion = "zonoid")
}
\keyword{ robust }
\keyword{ multivariate }
\keyword{ nonparametric }
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