1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
|
\name{depth.}
\alias{depth.}
\title{
Calculate Depth
}
\description{
Calculates the depth of points w.r.t. a multivariate data set.
The detailed descriptions are found in the corresponding topics.
}
\usage{
depth.(x, data, notion, ...)
## beta-skeleton depth
# depth.betaSkeleton(x, data, beta = 2, distance = "Lp", Lp.p = 2,
# mah.estimate = "moment", mah.parMcd = 0.75)
## Tukey depth
# depth.halfspace(x, data, exact, method, num.directions = 1000, seed = 0)
## L2-depth
# depth.L2(x, data, mah.estimate = "moment", mah.parMcd = 0.75)
## Mahalanobis depth
# depth.Mahalanobis(x, data, mah.estimate = "moment", mah.parMcd = 0.75)
## projection depth
# depth.projection(x, data, method = "random", num.directions = 1000)
## simplicial depth
# depth.simplicial(x, data, exact = F, k = 0.05, seed = 0)
## simplicial volume depth
# depth.simplicialVolume(x, data, exact = F, k = 0.05, seed = 0)
## spatial depth
# depth.spatial(x, data)
## zonoid depth
# depth.zonoid(x, data)
## potential
# depth.potential (x, data, pretransform = "1Mom",
# kernel = "GKernel", kernel.bandwidth = NULL, mah.parMcd = 0.75)
## convex hull peeling depth
# depth.qhpeeling(x, data)
}
\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{notion}{
The name of the depth notion (shall also work with a user-defined depth function named \code{"depth.<name>"}).
}
\item{\dots}{
Additional parameters passed to the depth functions.
}
}
\seealso{
\code{\link{depth.betaSkeleton}}
\code{\link{depth.halfspace}}
\code{\link{depth.L2}}
\code{\link{depth.Mahalanobis}}
\code{\link{depth.projection}}
\code{\link{depth.simplicial}}
\code{\link{depth.simplicialVolume}}
\code{\link{depth.spatial}}
\code{\link{depth.zonoid}}
\code{\link{depth.potential}}
\code{\link{depth.qhpeeling}}
\code{\link{depth.graph}} for building the depth surfaces of the two dimensional data.
}
\value{
Numerical vector of depths, one for each row in \code{x}; or one depth value if \code{x} is a numerical vector.
}
\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.(x, data, notion = "zonoid")
cat("Depths: ", depths, "\n")
}
\keyword{ robust }
\keyword{ multivariate }
\keyword{ nonparametric }
|
