Distance covariance test of multivariate independence.
Distance covariance and distance correlation are
multivariate measures of dependence.
Usage
dcov.test(x, y, index = 1.0, R = 199)
Arguments
x
data or distances of first sample
y
data or distances of second sample
R
number of replicates
index
exponent on Euclidean distance, in (0,2]
Details
dcov.test performs a nonparametric
test of multivariate independence. The test decision is
obtained via permutation bootstrap, with R replicates.
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;
otherwise these arguments are treated as data.
The statistic is
nV_n^2 where
V_n(x,y) = dcov(x,y),
which is based on interpoint Euclidean distances
||x_{i}-x_{j}||. The index
is an optional exponent on Euclidean distance.
Distance correlation is a new measure of dependence between random
vectors introduced by Szekely, Rizzo, and Bakirov (2007).
For all distributions with finite first moments, distance
correlation R generalizes the idea of correlation in two
fundamental ways:
(1) R(X,Y) is defined for X and Y in arbitrary dimension.
(2) R(X,Y)=0 characterizes independence of X and
Y.
Characterization (2) also holds for powers of Euclidean distance |x_i-x_j|^s, where 0<s<2, but (2) does not hold when s=2.
Distance correlation satisfies 0 ≤ R ≤ 1, and
R = 0 only if X and Y are independent. Distance
covariance V provides a new approach to the problem of
testing the joint independence of random vectors. The formal
definitions of the population coefficients V and
R are given in (SRB 2007). The definitions of the
empirical coefficients are given in the energy
dcov topic.
For all values of the index in (0,2), under independence
the asymptotic distribution of nV_n^2
is a quadratic form of centered Gaussian random variables,
with coefficients that depend on the distributions of X and Y. For the general problem of testing independence when the distributions of X and Y are unknown, the test based on n V_n^2 can be implemented as a permutation test. See (SRB 2007) for
theoretical properties of the test, including statistical consistency.
Value
dcov.test returns a list with class htest containing
method
description of test
statistic
observed value of the test statistic
estimate
dCov(x,y)
estimates
a vector: [dCov(x,y), dCor(x,y), dVar(x), dVar(y)]
replicates
replicates of the test statistic
p.value
approximate p-value of the test
data.name
description of data
Note
For the test of independence,
the distance covariance test statistic is the V-statistic
n V_n^2 (not dCov).
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
Szekely, G.J. and Rizzo, M.L. (2009),
Rejoinder: Brownian Distance Covariance,
Annals of Applied Statistics, Vol. 3, No. 4, 1303-1308.
See Also
dcov dcor DCORdcor.ttest
Examples
x <- iris[1:50, 1:4]
y <- iris[51:100, 1:4]
set.seed(1)
dcov.test(x, y)
set.seed(1)
dcov.test(dist(x), dist(y)) #same thing
set.seed(1)
dcov.test(x, y, index=.5)
set.seed(1)
dcov.test(dist(x), dist(y), index=.5) #same thing
## Example with dvar=0 (so dcov=0 and pval=1)
x <- rep.int(1, 10)
y <- 1:10
dcov.test(x, y, R=199)