dCov: Calculating distance covariance of two vectors or columns of...

In kzst/nda: Generalized Network-Based Dimensionality Reduction and Analysis

Calculating distance covariance of two vectors or columns of a matrix

Description

Calculating distance covariance of two vectors or columns of a matrix for Generalized Network-based Dimensionality Reduction and Analysis (GNDA).

The calculation is very slow for large matrices!

Usage

dCov(x,y=NULL)

Arguments

x	a numeric vector, matrix or data frame.
y	NULL (default) or a vector, matrix or data frame with compatible dimensions to x. The default is equivalent to y = x (but more efficient).

Details

If x is a numeric vector, y must be specified. If x is a numeric matrix or numeric data frame, y will be neglected.

Value

Either a distance covariance value of vectors x and y, or a distance covariance matrix of x if x is a matrix or a dataframe.

Author(s)

Prof. Zsolt T. Kosztyan, Department of Quantitative Methods, Institute of Management, Faculty of Business and Economics, University of Pannonia, Hungary

e-mail: [e-mail]

References

Rizzo M, Szekely G (2021). _energy: E-Statistics: Multivariate Inference via the Energy of Data_. R package version 1.7-8, <URL: https://CRAN.R-project.org/package=energy>.

Examples

# Specification of distance covariance value of vectors x and y.
x<-rnorm(36)
y<-rnorm(36)
dCov(x,y)
# Specification of distance covariance matrix.
x<-matrix(rnorm(36),nrow=6)
dCov(x)

kzst/nda documentation built on Dec. 16, 2024, 7:02 a.m.