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)
x  a numeric vector, matrix or data frame. 

y 

method  the dependence measure; see Details for possible values. 
weights  an optional vector of weights for the observations. 
remove_missing  if 
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] 0.007244855wdm(x, y, method = "kendall", weights = w) # weighted#> [1] 0.006367486## dependence in a matrix x < matrix(rnorm(100 * 3), 100, 3) wdm(x, method = "spearman") # unweighted#> [,1] [,2] [,3] #> [1,] 1.0000000 0.1084668 0.1078428 #> [2,] 0.1084668 1.0000000 0.0780318 #> [3,] 0.1078428 0.0780318 1.0000000wdm(x, method = "spearman", weights = w) # weighted#> [,1] [,2] [,3] #> [1,] 1.00000000 0.09497972 0.1067206 #> [2,] 0.09497972 1.00000000 0.1292720 #> [3,] 0.10672057 0.12927201 1.0000000## dependence between columns of two matrices y < matrix(rnorm(100 * 2), 100, 2) wdm(x, y, method = "hoeffding") # unweighted#> [,1] [,2] #> [1,] 0.002148337 0.006288094 #> [2,] 0.002800238 0.001640325 #> [3,] 0.007099477 0.003868609wdm(x, y, method = "hoeffding", weights = w) # weighted#> [,1] [,2] #> [1,] 0.002481089 0.006531391 #> [2,] 0.004807590 0.001435961 #> [3,] 0.003446762 0.001962314