Hypothesis testing for many partial distance correlations {pdcor} | R Documentation |
Hypothesis testing for many partial distance correlations
Description
Hypothesis testing for many partial distance correlations.
Usage
mpdcor.test(y, x, z, R = 500)
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. |
Details
Hypothesis testing between y and each column of x, conditional on z is performed.
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).
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
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
Examples
y <- iris[, 1]
x <- matrix( rnorm(150 * 10), ncol = 10 )
z <- iris[, 2]
mpdcor.test(y, x, z)