View source: R/depth.simplicialVolume.r
depth.space.simplicialVolume | R Documentation |
Calculates the representation of the training classes in depth space using simplicial volume depth.
depth.space.simplicialVolume(data, cardinalities, exact = F, k = 0.05,
mah.estimate = "moment", mah.parMcd = 0.75, seed = 0)
data |
Matrix containing training sample where each row is a |
cardinalities |
Numerical vector of cardinalities of each class in |
exact |
|
k |
Number ( |
mah.estimate |
A character string specifying affine-invariance adjustment; can be |
mah.parMcd |
The value of the argument |
seed |
The random seed. The default value |
The depth representation is calculated in the same way as in depth.simplicialVolume
, see References below 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
.
Oja, H. (1983). Descriptive statistics for multivariate distributions. Statistics & Probability Letters 1 327–332.
Zuo, Y.J. and Serfling, R. (2000). General notions of statistical depth function. The Annals of Statistics 28 461–482.
ddalpha.train
and ddalpha.classify
for application, depth.simplicialVolume
for calculation of simplicial 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 Oja depth
depth.space.simplicialVolume(data, c(10, 10))
data <- getdata("hemophilia")
cardinalities = c(sum(data$gr == "normal"), sum(data$gr == "carrier"))
depth.space.simplicialVolume(data[,1:2], cardinalities)
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