R: Weighted Dependence Measures

wdm {wdm} | R Documentation

## 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 ρ

- `"kendall"`: Kendall's τ

- `"blomqvist"`: Blomqvist's β

- `"hoeffding"`: Hoeffding's D Partial matching of method names is enabled.

Spearman's ρ and Kendall's τ 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

[Package wdm version 0.2.3 Index]