pdcor (version 1.2)

Hypothesis testing for many partial distance correlations: Hypothesis testing for many partial distance correlations

Description

Hypothesis testing for many partial distance correlations.

Usage

mpdcor.test(y, x, z, R = 500)

Value

A matrix with three columns: the unbiased partial distance correlation, the permutation based p-value and the asymptotic p-value as proposed by Shen, Panda and Vogelstein (2022).

Arguments

y

A numerical vector.

x

A numerical matrix.

z

A numerical vector.

R

The number of permutations to implement. If R = 1, the the asymptotic p-value is returned only.

Author

Michail Tsagris.

R implementation and documentation: Michail Tsagris .

Details

Hypothesis testing between y and each column of x, conditional on z is performed.

References

Szekely G. J. and Rizzo M. L. (2014). Partial Distance Correlation with Methods for Dissimilarities. The Annals of Statistics, 42(6): 2382--2412.

Shen C., Panda S. and Vogelstein J. T. (2022). The Chi-Square Test of Distance Correlation. Journal of Computational and Graphical Statistics, 31(1): 254--262.

Szekely G. J. and Rizzo M. L. (2023). The Energy of Data and Distance Correlation. Chapman and Hall/CRC.

Tsagris M. and Papadakis M. (2025). Fast and light-weight energy statistics using the R package Rfast. https://arxiv.org/abs/2501.02849

Kontemeniotis N., Vargiakakis R. and Tsagris M. (2025). On independence testing using the (partial) distance correlation. https://arxiv.org/abs/2506.15659v1

See Also

mpdcor, pdcor.test

Examples

y <- iris[, 1]
x <- matrix( rnorm(150 * 10), ncol = 10 )
z <- iris[, 2]
mpdcor.test(y, x, z)