View source: R/depth.space.zonoid.r
depth.space.zonoid | R Documentation |
Calculates the representation of the training classes in depth space using zonoid depth.
depth.space.zonoid(data, cardinalities, seed = 0)
data |
Matrix containing training sample where each row is a |
cardinalities |
Numerical vector of cardinalities of each class in |
seed |
the random seed. The default value |
The depth representation is calculated in the same way as in depth.zonoid
, see 'References' for more information and details.
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 cardinalities
.
Dyckerhoff, R., Koshevoy, G., and Mosler, K. (1996). Zonoid data depth: theory and computation. In: Prat A. (ed), COMPSTAT 1996. Proceedings in computational statistics, Physica-Verlag (Heidelberg), 235–240.
Koshevoy, G. and Mosler, K. (1997). Zonoid trimming for multivariate distributions Annals of Statistics 25 1998–2017.
Mosler, K. (2002). Multivariate dispersion, central regions and depth: the lift zonoid approach Springer (New York).
ddalpha.train
and ddalpha.classify
for application, depth.zonoid
for calculation of zonoid depth.
# 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.zonoid(data, c(10, 10))
data <- getdata("hemophilia")
cardinalities = c(sum(data$gr == "normal"), sum(data$gr == "carrier"))
depth.space.zonoid(data[,1:2], cardinalities)
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