Computes bivariate association measures between a response and predictor variables (and, optionnaly, between every pairs of predictor variables.)
BivariateAssoc(Y, X, xx = TRUE)
A list of the following items :
: a table with the association measures between the response and predictor variables
: a table with the association measures between every couples of predictor variables
In each table :
: name of the "standard" association measure
: value of the "standard" association measure
: p-value from the permutation test
: p-value from the permutation test transformed as -log(1-p), which serves to sort rows
the response variable
the predictor variables
whether the association measures should be computed for couples of predictor variables (default) or not. With a lot of predictors, consider setting xx to FALSE (for reasons of computation time).
Nicolas Robette
For each pair of variable, a permutation test is computed, following the framework used in conditional inference trees to choose a splitting variable. This test produces a p-value, transformed as -log(1-p) for reasons of comparison stability. The function also computes a "standard" association measure : kenddal's tau correlation for pairs of numeric variables, Cramer's V for pairs of factors and eta-squared for pairs numeric-factor.
Hothorn T, Hornik K, Van De Wiel MA, Zeileis A. "A lego system for conditional inference". The American Statistician. 60:257–263, 2006.
Hothorn T, Hornik K, Zeileis A. "Unbiased Recursive Partitioning: A Conditional Inference Framework". Journal of Computational and Graphical Statistics, 15(3):651-674, 2006.
ctree
data(iris)
iris2 = iris
iris2$Species = factor(iris$Species == "versicolor")
BivariateAssoc(iris2$Species,iris2[,1:4])
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