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

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

- x
a numeric vector or matrix.

`x`

and`y`

can also both be factors.- y
a numeric vector; ignored if

`x`

is a matrix. If`x`

is a factor,`y`

should be a factor of the same length.- correct
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()`

.

```
# 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)
#> [1] 0.1044358
```