dep_struct_several_26_100

<p>It was generated by</p><pre>
set.seed(1348879148)
N = 100
dep_struct_several_26_100 = cbind(coins(N,2),tetrahedron(N),coins(N,4),
    tetrahedron(N),tetrahedron(N),coins(N,3),coins(N,3),rnorm(N))
save(dep_struct_several_26_100,file ="dep_struct_several_26_100.rda")
</pre>

datasets

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

dep_struct_several_26_100 function

<p><code>matrix</code> 26 variables (columns), 100 independent samples (rows)</p>

Format

example dataset for <code><a rd-options='' href='dependence.structure'>dependence.structure</a></code> — dep_struct_several_26_100

example dataset for <code><a rd-options='' href='dependence.structure'>dependence.structure</a></code>

example dataset for <code><a rd-options="" href="/link/dependence.structure?package=multivariance&version=2.4.1" data-mini-rdoc="multivariance::dependence.structure">dependence.structure</a></code>

dep_struct_several_26_100: example dataset for `dependence.structure`

Description

Usage

Arguments

Format

Details