pdcor | R Documentation |
Partial distance correlation pdcor, pdcov, and tests.
pdcov.test(x, y, z, R) pdcor.test(x, y, z, R) pdcor(x, y, z) pdcov(x, y, z)
x |
data or dist object of first sample |
y |
data or dist object of second sample |
z |
data or dist object of third sample |
R |
replicates for permutation test |
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
.
Argument types supported are numeric data matrix, data.frame, tibble, numeric vector, class "dist" object, or factor. For unordered factors a 0-1 distance matrix is computed.
Each test returns an object of class htest
.
Maria L. Rizzo mrizzo@bgsu.edu and Gabor J. Szekely
Szekely, G.J. and Rizzo, M.L. (2014), Partial Distance Correlation with Methods for Dissimilarities. Annals of Statistics, Vol. 42 No. 6, 2382-2412.
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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