indep.etest {energy} | R Documentation |
Deprecated: use indep.test
with method = mvI
.
Computes a multivariate nonparametric E-statistic and test of independence.
indep.e(x, y) indep.etest(x, y, R=199)
x |
matrix: first sample, observations in rows |
y |
matrix: second sample, observations in rows |
R |
number of replicates |
Computes the coefficient I_n and performs a nonparametric
E-test of independence. The test decision is obtained via
bootstrap, with R
replicates.
The sample sizes (number of rows) of the two samples must agree, and
samples must not contain missing values. The statistic
E = I^2 is a ratio of V-statistics based
on interpoint distances ||x_{i}-y_{j}||.
See the reference below for details.
The sample coefficient I is returned by indep.e
.
The function indep.etest
returns a list with class
htest
containing
method |
description of test |
statistic |
observed value of the coefficient I |
p.value |
approximate p-value of the test |
data.name |
description of data |
Maria L. Rizzo mrizzo @ bgnet.bgsu.edu and Gabor J. Szekely gabors @ bgnet.bgsu.edu
Bakirov, N.K., Rizzo, M.L., and Szekely, G.J. (2006), A Multivariate
Nonparametric Test of Independence, Journal of Multivariate Analysis
93/1, 58-80,
http://dx.doi.org/10.1016/j.jmva.2005.10.005
## Not run: ## independent univariate data x <- sin(runif(30, 0, 2*pi) * 2) y <- sin(runif(30, 0, 2*pi) * 4) indep.etest(x, y, R = 99) ## dependent univariate data u <- runif(30, 0, 2*pi) x <- sin(2 * u) y <- sin(3 * u) indep.etest(x, y, R = 99) u <- runif(50, 0, 2*pi) x <- sin(2 * u) y <- sin(4 * u) indep.etest(x, y, R = 99) ## independent multivariate data x <- matrix(rnorm(60), nrow=20, ncol=3) y <- matrix(rnorm(40), nrow=20, ncol=2) indep.e(x, y) indep.etest(x, y, R = 99) ## independent bivariate data x <- matrix(rnorm(50), nrow=25, ncol=2) y <- matrix(rnorm(50), nrow=25, ncol=2) indep.e(x, y) indep.etest(x, y, R = 99) ## dependent bivariate data library(MASS) Sigma <- matrix(c(1, .5, .5, 1), 2, 2) x <- mvrnorm(30, c(0, 0), Sigma) indep.etest(x[,1], x[,2], R = 99) ## dependent multivariate data Sigma <- matrix(c(1, .1, 0, 0 , 1, 0, 0 ,.1, 1), 3, 3) x <- mvrnorm(30, c(0, 0, 0), diag(3)) y <- mvrnorm(30, c(0, 0, 0), Sigma) * x indep.etest(x, y, R = 99) ## End(Not run)