copula.multivariance

either a data matrix or a list of doubly centered distance matrices

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.

default: "total.lower.upper", for details and other options see below

type

these are passed to <code><a rd-options="" href="/link/cdms?package=multivariance&version=2.4.1" data-mini-rdoc="multivariance::cdms">cdms</a></code> (which is only invoked if <code>x</code> is a matrix)

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

Distance multivariance is a measure of dependence which can be used to detect
and quantify dependence of arbitrarily many random vectors. The necessary functions are
implemented in this packages and examples are given. It includes: distance multivariance,
distance multicorrelation, dependence structure detection, tests of independence and
copula versions of distance multivariance based on the Monte Carlo empirical transform.
Detailed references are given in the package description, as starting point for the
theoretic background we refer to:
B. B<c3><b6>ttcher, Dependence and Dependence Structures: Estimation and Visualization Using
the Unifying Concept of Distance Multivariance. Open Statistics, Vol. 1, No. 1 (2020),
<doi:10.1515/stat-2020-0001>.

Bj<c3><b6>rn B<c3><b6>ttcher

multivariance

Measuring Multivariate Dependence Using Distance Multivariance

Martin Keller-Ressel

copula.multivariance function

these are passed to <code><a rd-options='' href='cdms'>cdms</a></code> (which is only invoked if <code>x</code> is a matrix)

copula.multivariance: copula version of distance multivariance

Description

Usage

Arguments

References