R: Independence Tests for Weighted Dependence Measures

indep_test {wdm} | R Documentation

## Independence Tests for 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

    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

    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

[Package wdm version 0.2.3 Index]