distmat_HDLSS: Distance matrix calculation using Mean Absolute Deviation of...

In DawnTrisha/hclustHDCP: High Dimensional Change-point detection based on Hierarchical clustering

Description

Distance matrix calculation using Mean Absolute Deviation of Distances (genMADD or MADD distance)

Usage

distmat_HDLSS(X, option = c("genMADD", "MADD"))

Arguments

X	Data matrix (n X p) where n denotes number of observations. Each row is a p dimensional observation vector.
option	Distance function to use. Should be one of "genMADD" or "MADD". The default is "genMADD". For the exact mathematical form of the distance functions, refer to the vignette or https://ieeexplore.ieee.org/document/8695805

Value

Returns a (n X n) matrix containing the distance between the observations.

Examples

set.seed(1)
# Generating n = 10 observations from t distribution with dimension p = 5
X = matrix(stats::rt((10*5), ncp = 0, df = 4), nrow = 10, ncol = 5)

# Distance matrix
D_mat1 = distmat_HDLSS(X = X) # using default generalized MADD distance ("genMADD")
D_mat2 = distmat_HDLSS(X = X, option = "MADD") # using MADD distance ("MADD")

DawnTrisha/hclustHDCP documentation built on Dec. 17, 2021, 4:10 p.m.