# darma: Describes Associations as in a Regression Model Analysis. In GDAtools: A Toolbox for Geometric Data Analysis and More

 darma R Documentation

## Describes Associations as in a Regression Model Analysis.

### Description

Computes bivariate association measures between a response and predictor variables, producing a summary looking like a regression analysis.

### Usage

```  darma(y, x, weights=rep(1,length(y)), target=1, twocont="kendall",
nperm=NULL, distrib="asympt", dec=c(1,3,3))
```

### 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) `target` rank or name of the category of interest when y is categorical `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 3 integers for number of decimals. The first value if for percents or medians, the second for association measures, the third for permutation p-values. Default is c(1,3,3).

### Details

The function computes association measures (phi, correlation coefficient, Kendall's correlation) between the variable of interest and the other variables. It can also compute the p-values permutation tests.

A data frame

### Author(s)

Nicolas Robette

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

### Examples

```  data(iris)
iris2 = iris
iris2\$Species = factor(iris\$Species == "versicolor")
darma(iris2\$Species, iris2[,1:4], target=2, nperm=100)
```

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