wdm: Weighted Dependence Measures

In tnagler/wdm-r: Weighted Dependence Measures

Weighted Dependence Measures

Description

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.

Usage

wdm(x, y = NULL, method = "pearson", weights = NULL, remove_missing = TRUE)

Arguments

x	a numeric vector, matrix or data frame.
y	NULL (default) or a vector, matrix or data frame with compatible dimensions to x. The default is equivalent to 'y = x“ (but more efficient).
method	the dependence measure; see Details for possible values.
weights	an optional vector of weights for the observations.
remove_missing	if TRUE, all (pairswise) incomplete observations are removed; if FALSE, the function throws an error if there are incomplete observations.

Details

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.

Examples

##  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
wdm(x, y, method = "kendall", weights = w)  # weighted

##  dependence in a matrix
x <- matrix(rnorm(100 * 3), 100, 3)
wdm(x, method = "spearman")               # unweighted
wdm(x, method = "spearman", weights = w)  # weighted

##  dependence between columns of two matrices
y <- matrix(rnorm(100 * 2), 100, 2)
wdm(x, y, method = "hoeffding")               # unweighted
wdm(x, y, method = "hoeffding", weights = w)  # weighted

tnagler/wdm-r documentation built on Aug. 25, 2023, 11:11 a.m.