R interface to the wdm C++ library, which provides efficient implementations of weighted dependence measures and related independence tests:
All measures are computed in O(n log n) time, where n is the number of observations.
For a detailed description of the functionality, see the API documentation.
# install.packages("devtools")
install_submodule_git <- function(x, ...) {
install_dir <- tempfile()
system(paste("git clone --recursive", shQuote(x), shQuote(install_dir)))
devtools::install(install_dir, ...)
}
install_submodule_git("https://github.com/tnagler/wdm-r")This repo contains wdm as a submodule. For a full clone use
git clone --recurse-submodules <repo-address>x <- rnorm(100)
y <- rpois(100, 1) # all but Hoeffding's D can handle ties
w <- runif(100)
wdm(x, y, method = "kendall") # unweighted
#> [1] -0.01835054
wdm(x, y, method = "kendall", weights = w) # weighted
#> [1] -0.02273855x <- matrix(rnorm(100 * 3), 100, 3)
wdm(x, method = "spearman") # unweighted
#> [,1] [,2] [,3]
#> [1,] 1.00000000 0.02384638 0.04360036
#> [2,] 0.02384638 1.00000000 0.09418542
#> [3,] 0.04360036 0.09418542 1.00000000
wdm(x, method = "spearman", weights = w) # weighted
#> [,1] [,2] [,3]
#> [1,] 1.00000000 0.09307647 0.08380492
#> [2,] 0.09307647 1.00000000 0.14823843
#> [3,] 0.08380492 0.14823843 1.00000000x <- rnorm(100)
y <- rpois(100, 1) # all but Hoeffding's D can handle ties
w <- runif(100)
indep_test(x, y, method = "kendall") # unweighted
#> estimate statistic p_value n_eff method alternative
#> 1 -0.07862879 -0.9162974 0.3595109 100 kendall two-sided
indep_test(x, y, method = "kendall", weights = w) # weighted
#> estimate statistic p_value n_eff method alternative
#> 1 -0.06030227 -0.6043739 0.5455951 76.3268 kendall two-sided