Computes a (possibly weighted) dependence measure between x and y if
these are vectors. If x and y are matrices then the measure between the
columns of x and the columns of y are computed.
wdm(x, y = NULL, method = "pearson", weights = NULL, remove_missing = TRUE)a numeric vector, matrix or data frame.
NULL (default) or a vector, matrix or data frame with compatible
dimensions to x. The default is equivalent to `y = x`` (but more
efficient).
the dependence measure; see Details for possible values.
an optional vector of weights for the observations.
if TRUE, all (pairswise) incomplete observations are
removed; if FALSE, the function throws an error if there are incomplete
observations.
Available methods:
"pearson": Pearson correlation
"spearman": Spearman's \(\rho\)
"kendall": Kendall's \(\tau\)
"blomqvist": Blomqvist's \(\beta\)
"hoeffding": Hoeffding's \(D\)
Partial matching of method names is enabled.
Spearman's \(\rho\) and Kendall's \(\tau\) are corrected for ties if there are any.
## dependence between two vectors
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] 0.02331856
wdm(x, y, method = "kendall", weights = w) # weighted
#> [1] -0.04052879
## dependence in a matrix
x <- matrix(rnorm(100 * 3), 100, 3)
wdm(x, method = "spearman") # unweighted
#> [,1] [,2] [,3]
#> [1,] 1.000000000 0.008676868 -0.04549655
#> [2,] 0.008676868 1.000000000 -0.03840384
#> [3,] -0.045496550 -0.038403840 1.00000000
wdm(x, method = "spearman", weights = w) # weighted
#> [,1] [,2] [,3]
#> [1,] 1.00000000 0.02177004 0.02048581
#> [2,] 0.02177004 1.00000000 -0.01489218
#> [3,] 0.02048581 -0.01489218 1.00000000
## dependence between columns of two matrices
y <- matrix(rnorm(100 * 2), 100, 2)
wdm(x, y, method = "hoeffding") # unweighted
#> [,1] [,2]
#> [1,] 0.002378469 0.0025429314
#> [2,] -0.001035590 0.0005252796
#> [3,] 0.005232169 -0.0035272380
wdm(x, y, method = "hoeffding", weights = w) # weighted
#> [,1] [,2]
#> [1,] 0.007900614 -0.0001424806
#> [2,] -0.006867154 -0.0039366282
#> [3,] 0.007082112 -0.0032729170