R: Partial distance correlation and covariance

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)
      pdcov.test(x, y, z, R=R)
      print(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 1.7-6 Index]