dcor.ttest(x, y, distance=FALSE)
dcor.t(x, y, distance=FALSE)
bcdcor(x, y, distance=FALSE)dcor.t returns the t statistic, bcdcor returns the
bias corrected dcor statistic, and
dcor.ttest returns a list with class htest containing
dcor.ttest performs a nonparametric t-test of
multivariate independence in high dimension (dimension is close to
or larger than sample size). The distribution of
the test statistic is approximately Student t with $n(n-3)/2-1$
degrees of freedom and for $n \geq 10$ the statistic is approximately
distributed as standard normal.
dcor.t returns the t statistic and bcdcor returns
the bias corrected distance correlation statistic.
The sample sizes (number of rows) of the two samples must
agree, and samples must not contain missing values. Arguments
x, y can optionally be dist objects
or distance matrices (in this case set distance=TRUE).
The t statistic is a transformation of a bias corrected version of distance
correlation (see SR 2013 for details).
Large values (upper tail) of the t statistic are significant.
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. http://dx.doi.org/10.1214/009053607000000505
Szekely, G.J. and Rizzo, M.L. (2009), Brownian Distance Covariance, Annals of Applied Statistics, Vol. 3, No. 4, 1236-1265. http://dx.doi.org/10.1214/09-AOAS312
dcov.test dcor DCOR
x <- matrix(rnorm(100), 10, 10)
y <- matrix(runif(100), 10, 10)
dx <- dist(x)
dy <- dist(y)
dcor.t(x, y)
bcdcor(dx, dy, distance=TRUE)
dcor.ttest(x, y)
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