View source: R/dCov.R View source: R/nda.R
dCov | R Documentation |
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!
dCov(x,y=NULL)
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). |
If x
is a numeric vector, y
must be specified. If x
is a numeric matrix or numeric data frame, y will be neglected.
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.
Prof. Zsolt T. Kosztyan, Department of Quantitative Methods, Institute of Management, Faculty of Business and Economics, University of Pannonia, Hungary
e-mail: kosztyan.zsolt@gtk.uni-pannon.hu
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>.
# 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)
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