dcovu.RdThese functions compute unbiased estimators of squared distance covariance and a bias-corrected estimator of (squared) distance correlation.
bcdcor(x, y)
dcovU(x, y)The unbiased (squared) dcov is inner product definition of dCov, in the Hilbert space of U-centered distance matrices.
The sample sizes (number of rows) of the two samples must agree, and samples must not contain missing values.
Argument types supported are numeric data matrix, data.frame, or tibble, with observations in rows; numeric vector; ordered or unordered factors. In case of unordered factors a 0-1 distance matrix is computed.
dcovU returns the unbiased estimator of squared dcov.
bcdcor returns a bias-corrected estimator of squared dcor.
Unbiased distance covariance (SR2014) corresponds to the biased
(original) \(\mathrm{dCov^2}\). Since dcovU is an
unbiased statistic, it is signed and we do not take the square root.
For the original distance covariance test of independence (SRB2007,
SR2009), the distance covariance test statistic is the V-statistic
\(\mathrm{n\, dCov^2} = n \mathcal{V}_n^2\) (not dCov).
Similarly, bcdcor is bias-corrected, so we do not take the
square root as with dCor.
Szekely, G.J. and Rizzo, M.L. (2014), Partial Distance Correlation with Methods for Dissimilarities. Annals of Statistics, Vol. 42 No. 6, 2382-2412.
Szekely, G.J., Rizzo, M.L., and Bakirov, N.K. (2007),
Measuring and Testing Dependence by Correlation of Distances,
Annals of Statistics, Vol. 35 No. 6, pp. 2769-2794.
doi:10.1214/009053607000000505
Szekely, G.J. and Rizzo, M.L. (2009),
Brownian Distance Covariance,
Annals of Applied Statistics,
Vol. 3, No. 4, 1236-1265.
doi:10.1214/09-AOAS312
x <- iris[1:50, 1:4]
y <- iris[51:100, 1:4]
dcovU(x, y)
#> dCovU
#> -0.002748351
bcdcor(x, y)
#> bcdcor
#> -0.0271709