Title: | Fast and Light-Weight Partial Distance Correlation |
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Description: | Fast and memory-less computation of the partial distance correlation for vectors and matrices. Permutation-based and asymptotic hypothesis testing for zero partial distance correlation are also performed. References include: Szekely G. J. and Rizzo M. L. (2014). "Partial distance correlation with methods for dissimilarities". The Annals Statistics, 42(6): 2382--2412. <doi:10.1214/14-AOS1255>. 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. <doi:10.1080/10618600.2021.1938585>. Szekely G. J. and Rizzo M. L. (2023). "The Energy of Data and Distance Correlation". Chapman and Hall/CRC. <ISBN:9781482242744>. |
Authors: | Michail Tsagris [aut, cre], Nikolaos Kontemeniotis [aut] |
Maintainer: | Michail Tsagris <[email protected]> |
License: | GPL (>= 2) |
Version: | 1.0 |
Built: | 2025-02-25 15:25:03 UTC |
Source: | CRAN |
Fast and memory-less computation of the partial distance correlation for vectors and matrices. Permutation-based and asymptotic hypothesis testing for zero partial distance correlation are also performed.
Package: | pdcor |
Type: | Package |
Version: | 1.0 |
Date: | 2025-02-23 |
License: | GPL-2 |
Michail Tsagris [email protected].
Michail Tsagris [email protected] and Nikolaos Kontemeniotis [email protected].
Partial distance correlation.
pdcor(x, y, z)
pdcor(x, y, z)
x |
A numerical vector or matrix. |
y |
A numerical vector or matrix. |
z |
A numerical vector or matrix. |
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.
The unbiased partial distance correlation.
Michail Tsagris.
R implementation and documentation: Michail Tsagris [email protected].
Szekely G. J. and Rizzo M. L. (2014). Partial Distance Correlation with Methods for Dissimilarities. The Annals of Statistics, 42(6): 2382–2412.
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
x <- iris[, 1] y <- iris[, 2] z <- iris[, 3] pdcor(x, y, z)
x <- iris[, 1] y <- iris[, 2] z <- iris[, 3] pdcor(x, y, z)
Permutation testing for the partial distance correlation.
pdcor.test(x, y, z, type = 1, R = 500)
pdcor.test(x, y, z, type = 1, R = 500)
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. |
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.
A vector with the unbiased partial distance correlation, the permutation based p-value and the asymptotic p-value.
Michail Tsagris and Nikolaos Kontemeniotis .
R implementation and documentation: Michail Tsagris [email protected] and Nikolaos Kontemeniotis [email protected].
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
x <- iris[, 1] y <- iris[, 2] z <- iris[, 3] pdcor.test(x, y, z)
x <- iris[, 1] y <- iris[, 2] z <- iris[, 3] pdcor.test(x, y, z)