Compute Cramer's V, which measures the strength of the association between categorical variables.

cramer_v(x, y = NULL, correct = TRUE, ...)

## Arguments

x a numeric vector or matrix. x and y can also both be factors. a numeric vector; ignored if x is a matrix. If x is a factor, y should be a factor of the same length. a logical indicating whether to apply continuity correction when computing the test statistic for 2 by 2 tables: one half is subtracted from all $$|O - E|$$ differences; however, the correction will not be bigger than the differences themselves. No correction is done if simulate.p.value = TRUE. other arguments passed to the function chisq.test().

## Examples


# Data preparation
df <- as.table(rbind(c(762, 327, 468), c(484, 239, 477)))
dimnames(df) <- list(
gender = c("F", "M"),
party = c("Democrat","Independent", "Republican")
)
df#>       party
#> gender Democrat Independent Republican
#>      F      762         327        468
#>      M      484         239        477# Compute cramer's V
cramer_v(df)#>  0.1044358