Package 'pdcor'

Title: Fast and Light-Weight Partial Distance Correlation
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

Help Index


Fast and Light-Weight Partial Distance Correlation

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.

Details

Package: pdcor
Type: Package
Version: 1.0
Date: 2025-02-23
License: GPL-2

Maintainers

Michail Tsagris [email protected].

Author(s)

Michail Tsagris [email protected] and Nikolaos Kontemeniotis [email protected].


Partial distance correlation

Description

Partial distance correlation.

Usage

pdcor(x, y, z)

Arguments

x

A numerical vector or matrix.

y

A numerical vector or matrix.

z

A numerical vector or matrix.

Details

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

The unbiased partial distance correlation.

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris [email protected].

References

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

See Also

pdcor.test

Examples

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

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 [email protected] and Nikolaos Kontemeniotis [email protected].

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

pdcor

Examples

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