# condesc: Measures the association between a continuous variable and... In GDAtools: A Toolbox for Geometric Data Analysis and More

 condesc R Documentation

## Measures the association between a continuous variable and some continuous and/or categorical variables

### Description

Measures the association between a continuous variable and some continuous and/or categorical variables

### Usage

```condesc(y, x, weights=rep(1,length(y)), min.cor=NULL,
robust=TRUE, nperm=NULL, distrib="asympt", dec=c(3,3,0,3))
```

### Arguments

 `y` the continuous variable to describe `x` a data frame with continuous and/or categorical variables `weights` an optional numeric vector of weights (by default, a vector of 1 for uniform weights) `min.cor` for the relationship between y and a categorical variable, only associations higher or equal to min.cor will be displayed. If NULL (default), they are all displayed. `robust` logical. If FALSE, mean and standard deviation are used instead of median and mad. Default is TRUE. `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 4 integers for number of decimals. The first value if for association measures, the second for permutation p-values, the third for medians and mads, the fourth for point biserial correlations. Default is c(3,3,0,3).

### Value

A list of the following items :

 `variables` associations between y and the variables in x `categories` a data frame with categorical variables from x and associations measured by point biserial correlation

Nicolas Robette

### References

Rakotomalala R., 'Comprendre la taille d'effet (effect size)', [http://eric.univ-lyon2.fr/~ricco/cours/slides/effect_size.pdf]

`condes`, `catdesc`, `assoc.yx`, `darma`
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