copula.multivariance: copula version of distance multivariance

In multivariance: Measuring Multivariate Dependence Using Distance Multivariance

Description

Formally it is nothing but distance multivariance applied to the Monte Carlo emprical transform of the data. Hence its values vary for repeated runs.

Usage

copula.multivariance(x, vec = 1:ncol(x), type = "total", ...)

Arguments

x	either a data matrix or a list of doubly centered distance matrices
vec	if x is a matrix, then this indicates which columns are treated together as one sample; if x is a list, these are the indexes for which the multivariance is calculated. The default is all columns and all indexes, respectively.
type	default: "total.lower.upper", for details and other options see below
...	these are passed to cdms (which is only invoked if x is a matrix)

References

For the theoretic background see the reference [5] given on the main help page of this package: multivariance-package.

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