1\name{depth.space.}
2\alias{depth.space.}
3
4\title{
5Calculate Depth Space using the Given Depth
6}
7\description{
8Calculates the representation of the training classes in depth space.
9
10The detailed descriptions are found in the corresponding topics.
11}
12\usage{
13depth.space.(data, cardinalities, notion, ...)
14
15## Mahalanobis depth
16# depth.space.Mahalanobis(data, cardinalities, mah.estimate = "moment", mah.parMcd = 0.75)
17
18## projection depth
19# depth.space.projection(data, cardinalities, method = "random", num.directions = 1000)
20
21## Tukey depth
22# depth.space.halfspace(data, cardinalities, exact, alg, num.directions = 1000)
23
24## spatial depth
25# depth.space.spatial(data, cardinalities)
26
27## zonoid depth
28# depth.space.zonoid(data, cardinalities)
29
30# Potential
31# depth.space.potential(data, cardinalities, pretransform = "NMom",
32#            kernel = "GKernel", kernel.bandwidth = NULL, mah.parMcd = 0.75)
33}
34
35\arguments{
36  \item{data}{
37Matrix 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.
38}
39  \item{cardinalities}{
40Numerical vector of cardinalities of each class in \code{data}, each entry corresponds to one class.
41}
42  \item{notion}{
43The name of the depth notion (shall also work with \code{\link{Custom Methods}}).
44}
45  \item{\dots}{
46Additional parameters passed to the depth functions.
47}
48}
49
50\value{
51Matrix 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}.
52}
53
54\seealso{
55
56\code{\link{depth.space.Mahalanobis}}
57
58\code{\link{depth.space.projection}}
59
60\code{\link{depth.space.halfspace}}
61
62\code{\link{depth.space.spatial}}
63
64\code{\link{depth.space.zonoid}}
65
66}
67\examples{
68# Generate a bivariate normal location-shift classification task
69# containing 20 training objects
70class1 <- mvrnorm(10, c(0,0),
71                  matrix(c(1,1,1,4), nrow = 2, ncol = 2, byrow = TRUE))
72class2 <- mvrnorm(10, c(2,2),
73                  matrix(c(1,1,1,4), nrow = 2, ncol = 2, byrow = TRUE))
74data <- rbind(class1, class2)
75# Get depth space using zonoid depth
76depth.space.(data, c(10, 10), notion = "zonoid")
77}
78\keyword{ robust }
79\keyword{ multivariate }
80\keyword{ nonparametric }
81