Computes bias-corrected Cramer's V (extension of the chi-squared test), a measure of association between two categorical variables. Results are in the range 0-1, where 0 indicates no association, and 1 indicates a perfect association.
In essence, Cramer's V assesses the co-occurrence of the categories of two variables to quantify how strongly these variables are related.
Even when its range is between 0 and 1, Cramer's V values are not directly comparable to R-squared values, and as such, a multicollinearity analysis containing both types of values must be assessed with care. It is probably preferable to convert non-numeric variables to numeric using target encoding rather before a multicollinearity analysis.
Arguments
- x
(required; character vector) character vector representing a categorical variable. Default: NULL
- y
(required; character vector) character vector representing a categorical variable. Must have the same length as 'x'. Default: NULL
- check_input
(required; logical) If FALSE, disables data checking for a slightly faster execution. Default: TRUE
References
Cramér, H. (1946). Mathematical Methods of Statistics. Princeton: Princeton University Press, page 282 (Chapter 21. The two-dimensional case). ISBN 0-691-08004-6
See also
Other pairwise_correlation:
cor_clusters(),
cor_df(),
cor_matrix(),
cor_select()
Examples
#loading example data
data(vi)
#subset to limit example run time
vi <- vi[1:1000, ]
#computing Cramer's V for two categorical predictors
v <- cor_cramer_v(
x = vi$soil_type,
y = vi$koppen_zone
)
v
#> [1] 0.3354274