# assoc.yx: Bivariate association measures between a response and... In GDAtools: A Toolbox for Geometric Data Analysis and More

 assoc.yx R Documentation

## Bivariate association measures between a response and predictor variables.

### Description

Computes bivariate association measures between a response and predictor variables (and, optionnaly, between every pairs of predictor variables.)

### Usage

```  assoc.yx(y, x, weights=rep(1,length(y)), xx = TRUE, twocont="kendall",
nperm=NULL, distrib="asympt", dec=c(3,3))
```

### Arguments

 `y` the response variable `x` the predictor variables `weights` an optional numeric vector of weights (by default, a vector of 1 for uniform weights) `xx` whether the association measures should be computed for couples of predictor variables (default) or not. With a lot of predictors, consider setting xx to FALSE (for reasons of computation time). `twocont` character. The type of measure of correlation measure to use between two continuous variables : "pearson", "spearman" or "kendall" (default). `nperm` numeric. Number of permutations for the permutation test of independence. If NULL (default), no permutation test is performed. `distrib` the null distribution of permutation test of independence can be approximated by its asymptotic distribution (`"asympt"`, default) or via Monte Carlo resampling (`"approx"`). `dec` vector of 2 integers for number of decimals. The first value if for association measures, the second for permutation p-values. Default is c(3,3).

### Details

The function computes an association measure : Pearson's, Spearman's or Kendall's correlation for pairs of numeric variables, Cramer's V for pairs of factors and eta-squared for pairs numeric-factor. It can also compute the p-value of a permutation test of association for each pair of variables.

### Value

A list of the following items :

 `YX` : a table with the association measures between the response and predictor variables `XX` : a table with the association measures between every pairs of predictor variables

In each table :

 `measure` : name of the association measure `association` : value of the association measure `permutation.pvalue` : p-value from the permutation test

### Author(s)

Nicolas Robette

`darma`, `assoc.twocat`, `assoc.twocont`, `assoc.catcont`, `condesc`, `catdesc`

### Examples

```  data(iris)
iris2 = iris
iris2\$Species = factor(iris\$Species == "versicolor")
assoc.yx(iris2\$Species,iris2[,1:4],nperm=100)
```

GDAtools documentation built on March 18, 2022, 5:13 p.m.