Permutation testing for the partial distance correlation {pdcor} | R Documentation |
Permutation testing for the partial distance correlation
Description
Permutation testing for the partial distance correlation.
Usage
pdcor.test(x, y, z, type = 1, R = 500)
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. |
Details
Permuation 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.
Value
A vector with the unbiased partial distance correlation, the permutation based p-value and the asymptotic p-value.
Author(s)
Michail Tsagris and Nikolaos Kontemeniotis .
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr and Nikolaos Kontemeniotis kontemeniotisn@gmail.com.
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
See Also
Examples
x <- iris[, 1]
y <- iris[, 2]
z <- iris[, 3]
pdcor.test(x, y, z)