| epsilonSquared | R Documentation |
Calculates epsilon-squared as an effect size statistic, following a Kruskal-Wallis test, or for a table with one ordinal variable and one nominal variable; confidence intervals by bootstrap
epsilonSquared(
x,
g = NULL,
group = "row",
ci = FALSE,
conf = 0.95,
type = "perc",
R = 1000,
histogram = FALSE,
digits = 3,
reportIncomplete = FALSE,
...
)
x |
Either a two-way table or a two-way matrix. Can also be a vector of observations of an ordinal variable. |
g |
If |
group |
If |
ci |
If |
conf |
The level for the confidence interval. |
type |
The type of confidence interval to use.
Can be any of " |
R |
The number of replications to use for bootstrap. |
histogram |
If |
digits |
The number of significant digits in the output. |
reportIncomplete |
If |
... |
Additional arguments passed to the |
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,
if type="perc", the confidence interval will
never cross zero, and should not
be used for statistical inference.
However, if type="norm", the confidence interval
may cross zero.
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.
A single statistic, epsilon-squared. Or a small data frame consisting of epsilon-squared, and the lower and upper confidence limits.
Note that epsilon-squared as calculated by this function is equivalent to the eta-squared, or r-squared, as determined by an anova on the rank-transformed values. Epsilon-squared for Kruskal-Wallis is typically defined this way in the literature.
Salvatore Mangiafico, mangiafico@njaes.rutgers.edu
King, B.M., P.J. Rosopa, and E.W. Minium. 2018. Statistical Reasoning in the Behavioral Sciences, 7th ed. Wiley.
https://rcompanion.org/handbook/F_08.html
multiVDA,
ordinalEtaSquared
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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.