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))
    }
  

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