GDAtools (version 1.7)

assoc.twocat: Cross-tabulation and measures of association between two categorical variables

Description

Cross-tabulation and measures of association between two categorical variables

Usage

assoc.twocat(x,y,weights=rep.int(1,length(x)),na_value=NULL,nperm=NULL,distrib="asympt")

Arguments

x

the first categorical variable (must be a factor)

y

the second categorical variable (must be a factor)

weights

an optional numeric vector of weights (by default, a vector of 1 for uniform weights)

na_value

character. Name of the level for NA category. If NULL (default), NA values are ignored.

nperm

numeric. Number of permutations for the permutation test of independence. If NULL (default), no permutation test is performed. Default is 1000.

distrib

the null distribution of permutation test of independence can be approximated by its asymptotic distribution ("asympt", default) or via Monte Carlo resampling ("approx".

Value

A list with the following elements :

freq

cross-tabulation

prop

percentages

rprop

row percentages

cprop

column percentages

expected

expected values

chi.squared

chi-squared value

cramer.v

Cramer's V between the two variables

permutation.pvalue

p-value from a permutation (so non-parametric) test of independence

pearson.residuals

the table of Pearson residuals, i.e. (observed - expected) / sqrt(expected).

phi

the table of the phi coefficients for each pair of levels

phi.perm.pval

the table of permutation p-values for each pair of levels

local.pem

the table of local Percentages of Maximum Deviation from Independences

global.pem

value of the global Percentage of Maximum Deviation from Independence

gather

a data frame gathering informations, with one row per cell of the cross-tabulation

References

Rakotomalala R., 'Comprendre la taille d'effet (effect size)', http://eric.univ-lyon2.fr/~ricco/cours/slides/effect_size.pdf

See Also

assoc.catcont, assoc.twocont, assoc.yx, condesc, catdesc, darma, ggassoc_crosstab, ggassoc_phiplot

Examples

# NOT RUN {
data(Music)
assoc.twocat(Music$Jazz,Music$Age,nperm=100)
# }