indep_test: Independence Tests for Weighted Dependence Measures In wdm: 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

 1 2 3 4 5 6 7 8 indep_test( x, y, method = "pearson", weights = NULL, remove_missing = TRUE, alternative = "two-sided" )

Arguments

 x, y numeric vectors of data values. x and y must have the same length. 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. alternative indicates the alternative hypothesis and must be one of "two-sided", "greater" or "less". You can specify just the initial letter. "greater" corresponds to positive association, "less" to negative association.

Details

Available methods:

• "pearson": Pearson correlation

• "spearman": Spearman's ρ

• "kendall": Kendall's τ

• "blomqvist": Blomqvist's β

• "hoeffding": Hoeffding's D

Partial matching of method names is enabled. All methods except "hoeffding" work with discrete variables.

Examples

 1 2 3 4 5 6 x <- rnorm(100) y <- rpois(100, 1) # all but Hoeffding's D can handle ties w <- runif(100) indep_test(x, y, method = "kendall") # unweighted indep_test(x, y, method = "kendall", weights = w) # weighted

wdm documentation built on Aug. 2, 2020, 5:06 p.m.