rcompanion (version 2.2.2)

cramerV: Cramer's V (phi)

Description

Calculates Cramer's V for a table of nominal variables; confidence intervals by bootstrap.

Usage

cramerV(x, y = NULL, ci = FALSE, conf = 0.95, type = "perc",
  R = 1000, histogram = FALSE, digits = 4, bias.correct = FALSE,
  ...)

Arguments

x

Either a two-way table or a two-way matrix. Can also be a vector of observations for one dimension of a two-way table.

y

If x is a vector, y is the vector of observations for the second dimension of a two-way table.

ci

If TRUE, returns confidence intervals by bootstrap. May be slow.

conf

The level for the confidence interval.

type

The type of confidence interval to use. Can be any of "norm", "basic", "perc", or "bca". Passed to boot.ci.

R

The number of replications to use for bootstrap.

histogram

If TRUE, produces a histogram of bootstrapped values.

digits

The number of significant digits in the output.

bias.correct

If TRUE, a bias correction is applied.

...

Additional arguments passed to chisq.test.

Value

A single statistic, Cramer's V. Or a small data frame consisting of Cramer's V, and the lower and upper confidence limits. In the data frame, r is just a duplicate of the Cramer's V value.

Details

Cramer's V is used as a measure of association between two nominal variables, or as an effect size for a chi-square test of association. For a 2 x 2 table, the absolute value of the phi statistic is the same as Cramer's V.

Because V is always positive, the confidence interval will never cross zero. The confidence interval range should not be used for statistical inference.

When V is close to 0 or very large, or with small counts, the confidence intervals determined by this method may not be reliable, or the procedure may fail.

References

http://rcompanion.org/handbook/H_10.html

See Also

cohenW

Examples

# NOT RUN {
### Example with table
data(Anderson)
fisher.test(Anderson)
cramerV(Anderson)

### Example with two vectors
Species = c(rep("Species1", 16), rep("Species2", 16))
Color   = c(rep(c("blue", "blue", "blue", "green"),4),
            rep(c("green", "green", "green", "blue"),4))
fisher.test(Species, Color)
cramerV(Species, Color)

# }