Calculate Depth Space using Simplicial Depth
Calculates the representation of the training classes in depth space using simplicial depth.
depth.space.simplicial(data, cardinalities, exact = F, k = 0.05, seed = 0)
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 |
exact |
|
k |
Number (k>1) or portion (if 0<k<1) of simplices that are considered if |
seed |
The random seed. The default value |
The depth representation is calculated in the same way as in depth.simplicial
, 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
.
Chaudhuri, P. (1996). On a geometric notion of quantiles for multivariate data. Journal of the American Statistical Association 91 862–872.
Liu, R. Y. (1990). On a notion of data depth based on random simplices. The Annals of Statistics 18 405–414.
Rousseeuw, P.J. and Ruts, I. (1996). Algorithm AS 307: Bivariate location depth. Journal of the Royal Statistical Society. Seriec C (Applied Statistics) 45 516–526.
ddalpha.train
and ddalpha.classify
for application, depth.simplicial
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(1,1), matrix(c(1,1,1,4), nrow = 2, ncol = 2, byrow = TRUE)) data <- rbind(class1, class2) # Get depth space using simplicial depth depth.space.simplicial(data, c(10, 10)) data <- getdata("hemophilia") cardinalities = c(sum(data$gr == "normal"), sum(data$gr == "carrier")) depth.space.simplicial(data[,1:2], cardinalities)
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