cdms.mu.bcd: computes the doubly centered distance matrices, mus and bcds

In multivariance: Measuring Multivariate Dependence Using Distance Multivariance

Description

computes the doubly centered distance matrices, mus and bcds

Usage

cdms.mu.bcd(x, vec = 1:ncol(x), membership = NULL, cdm.normalize = TRUE, ...)

Arguments

x	matrix, each row is a sample
vec	vector which indicates which columns are treated as one sample
membership	depreciated. Now use vec.
...	these are passed to cdm

Value

A list containing the following components:

list.cdm

list of the doubly centered distance matrices - these are always normalized if 'cdm.normalize = TRUE'!!!,

mu

matrix with the limit moments in a column for each variable,

bcd

matrix with b, c, d (which are required for the computation of the finite sample moments) in columns for each variable,

mean

vector with the mean of each distance matrix.

multivariance documentation built on Oct. 6, 2021, 5:08 p.m.