darma: Describes Associations as in a Regression Model Analysis.

In GDAtools: A Toolbox for Geometric Data Analysis and More

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.

Value

A data frame

Author(s)

Nicolas Robette

See Also

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.