pdcor (version 1.2)

Hypothesis testing for the partial distance correlation: Hypothesis testing for the partial distance correlation

Description

Hypothesis testing for the partial distance correlation.

Usage

pdcor.test(x, y, z, type = 1, R = 500)

Value

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

Arguments

x

A numerical vector or matrix.

y

A numerical vector or matrix.

z

A numerical vector or matrix.

type

In case that all x, y, and z are vectors the user may select the type = 2 which is even faster, but at the expense of requiring more memory.

R

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

Author

Michail Tsagris and Nikolaos Kontemeniotis .

R implementation and documentation: Michail Tsagris and Nikolaos Kontemeniotis .

Details

Hypothesis testing using the unbiased partial distance correlation between x and y conditioning on z is computed. Note: currently, ony two cases are supported, all x, y, and z are vectors or they are all matrices with the same dimensions.

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

pdcor

Examples

x <- iris[, 1]
y <- iris[, 2]
z <- iris[, 3]
pdcor.test(x, y, z)