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

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 the predictor variables an optional numeric vector of weights (by default, a vector of 1 for uniform weights) rank or name of the category of interest when y is categorical character. The type of measure of correlation measure to use between two continuous variables : "pearson", "spearman" or "kendall" (default). numeric. Number of permutations for the permutation test of independence. If NULL (default), no permutation test is performed. the null distribution of permutation test of independence can be approximated by its asymptotic distribution ("asympt", default) or via Monte Carlo resampling ("approx"). 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

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)
#>       variable category percent association perm.pvalue
#> 1 Sepal.Length               NA       0.079       0.474
#> 2  Sepal.Width               NA      -0.468       0.000
#> 3 Petal.Length               NA       0.202       0.002
#> 4  Petal.Width               NA       0.118       0.182