# assoc.twocont: Measures the association between two continuous variables In GDAtools: A Toolbox for Geometric Data Analysis and More

 assoc.twocont R Documentation

## Measures the association between two continuous variables

### Description

Measures the association between two continuous variables with Pearson, Spearman and Kendall correlations.

### Usage

```assoc.twocont(x,y,weights=rep(1,length(x)),
nperm=NULL,distrib="asympt")
```

### Arguments

 `x` a continuous variable (must be a numeric vector) `y` a continuous variable (must be a numeric vector) `weights` an optional numeric vector of weights (by default, a vector of 1 for uniform weights) `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"`.

### Value

A data frame with Pearson, Spearman and Kendall correlations. The correlation value is in the first row and a p-value from a permutation (so non parametric) test of independence is in the second row.

### Author(s)

Nicolas Robette

`assoc.twocat`, `assoc.catcont`, `assoc.yx`, `condesc`, `catdesc`, `darma`, `ggassoc_scatter`

### Examples

```## Hollander & Wolfe (1973), p. 187f.
## Assessment of tuna quality.  We compare the Hunter L measure of
##  lightness to the averages of consumer panel scores (recoded as
##  integer values from 1 to 6 and averaged over 80 such values) in
##  9 lots of canned tuna.
x <- c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
y <- c( 2.6,  3.1,  2.5,  5.0,  3.6,  4.0,  5.2,  2.8,  3.8)
assoc.twocont(x,y,nperm=100)
```

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