rcompanion (version 2.2.2)

epsilonSquared: Epsilon-squared

Description

Calculates epsilon-squared for a table with one ordinal variable and one nominal variable; confidence intervals by bootstrap.

Usage

epsilonSquared(x, g = NULL, group = "row", ci = FALSE, conf = 0.95,
  type = "perc", R = 1000, histogram = FALSE, digits = 3, ...)

Arguments

x

Either a two-way table or a two-way matrix. Can also be a vector of observations of an ordinal variable.

g

If x is a vector, g is the vector of observations for the grouping, nominal variable.

group

If x is a table or matrix, group indicates whether the "row" or the "column" variable is the nominal, grouping variable.

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.

...

Additional arguments passed to the kruskal.test function.

Value

A single statistic, epsilon-squared. Or a small data frame consisting of epsilon-squared, and the lower and upper confidence limits.

Details

Epsilon-squared is used as a measure of association for the Kruskal-Wallis test or for a two-way table with one ordinal and one nominal variable.

Currently, the function makes no provisions for NA values in the data. It is recommended that NAs be removed beforehand.

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

When epsilon-squared is close to 0 or very large, or with small counts in some cells, the confidence intervals determined by this method may not be reliable, or the procedure may fail.

References

http://rcompanion.org/handbook/H_11.html

See Also

freemanTheta

Examples

# NOT RUN {
data(Breakfast)
library(coin)
chisq_test(Breakfast, scores = list("Breakfast" = c(-2, -1, 0, 1, 2)))
epsilonSquared(Breakfast)

data(PoohPiglet)
kruskal.test(Likert ~ Speaker, data = PoohPiglet)
epsilonSquared(x = PoohPiglet$Likert, g = PoohPiglet$Speaker)

### Same data, as matrix of counts
data(PoohPiglet)
XT = xtabs( ~ Speaker + Likert , data = PoohPiglet)
epsilonSquared(XT)

# }