Compute Cramer's V, which measures the strength of the association between categorical variables.
cramer_v(x, y = NULL, correct = TRUE, ...)
a numeric vector or matrix.
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
# 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