Calculate Depth Space using the Given Depth
Calculates the representation of the training classes in depth space.
The detailed descriptions are found in the corresponding topics.
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)
data |
Matrix containing training sample where each row is a 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. |
cardinalities |
Numerical vector of cardinalities of each class in |
notion |
The name of the depth notion (shall also work with |
... |
Additional parameters passed to the depth functions. |
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.
# 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")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.