Measures the association between a continuous variable and some continuous and/or categorical variables
condesc(y, x, weights=rep(1,length(y)), min.cor=NULL,
robust=TRUE, nperm=NULL, distrib="asympt", dec=c(3,3,0,3))the continuous variable to describe
a data frame with continuous and/or categorical variables
an optional numeric vector of weights (by default, a vector of 1 for uniform weights)
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.
logical. If FALSE, mean and standard deviation are used instead of median and mad. Default is TRUE.
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 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).
A list of the following items :
associations between y and the variables in x
a data frame with categorical variables from x and associations measured by point biserial correlation
Rakotomalala R., 'Comprendre la taille d'effet (effect size)', [http://eric.univ-lyon2.fr/~ricco/cours/slides/effect_size.pdf]
# NOT RUN {
data(Movies)
condesc(Movies$BoxOffice, Movies[,c("Budget","Genre","Country")])
# }
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