pdcor {energy} R Documentation

## Partial distance correlation and covariance

### Description

Partial distance correlation pdcor, pdcov, and tests.

### Usage

  pdcov.test(x, y, z, R)
pdcor.test(x, y, z, R)
pdcor(x, y, z)
pdcov(x, y, z)


### Arguments

 x data matrix or dist object of first sample y data matrix or dist object of second sample z data matrix or dist object of third sample R replicates for permutation test

### Details

pdcor(x, y, z) and pdcov(x, y, z) compute the partial distance correlation and partial distance covariance, respectively, of x and y removing z.

A test for zero partial distance correlation (or zero partial distance covariance) is implemented in pdcor.test, and pdcov.test.

If the argument is a matrix, it is treated as a data matrix and distances are computed (observations in rows). If the arguments are distances or dissimilarities, they must be distance (dist) objects. For symmetric, zero-diagonal dissimilarity matrices, use as.dist to convert to a dist object.

### Value

Each test returns an object of class htest.

### Author(s)

Maria L. Rizzo mrizzo@bgsu.edu and Gabor J. Szekely

### References

Szekely, G.J. and Rizzo, M.L. (2014), Partial Distance Correlation with Methods for Dissimilarities. Annals of Statistics, Vol. 42 No. 6, 2382-2412.

### Examples

  n = 30
R <- 199

## mutually independent standard normal vectors
x <- rnorm(n)
y <- rnorm(n)
z <- rnorm(n)

pdcor(x, y, z)
pdcov(x, y, z)
set.seed(1)
pdcov.test(x, y, z, R=R)
set.seed(1)
pdcor.test(x, y, z, R=R)

if (require(MASS)) {
p = 4
mu <- rep(0, p)
Sigma <- diag(p)

## linear dependence
y <- mvrnorm(n, mu, Sigma) + x
print(pdcov.test(x, y, z, R=R))

## non-linear dependence
y <- mvrnorm(n, mu, Sigma) * x
print(pdcov.test(x, y, z, R=R))
}



[Package energy version 1.7-10 Index]