condesc | R Documentation |
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))
y |
the continuous variable to describe |
x |
a data frame with continuous and/or categorical variables |
weights |
an optional numeric vector of weights (by default, a vector of 1 for uniform weights) |
min.cor |
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. |
robust |
logical. If FALSE, mean and standard deviation are used instead of median and mad. Default is TRUE. |
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 ( |
dec |
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 :
variables |
associations between y and the variables in x |
categories |
a data frame with categorical variables from x and associations measured by point biserial correlation |
Nicolas Robette
Rakotomalala R., 'Comprendre la taille d'effet (effect size)', [http://eric.univ-lyon2.fr/~ricco/cours/slides/effect_size.pdf]
condes
, catdesc
, assoc.yx
, darma
data(Movies) condesc(Movies$BoxOffice, Movies[,c("Budget","Genre","Country")])
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