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Last data update: 2014.03.03

R: Anscombe-Glynn test of kurtosis

anscombe.test

R Documentation

Anscombe-Glynn test of kurtosis

Description

Performs Anscombe-Glynn test of kurtosis for normal samples

Usage

anscombe.test(x, alternative = c("two.sided", "less", "greater"))

Arguments

`x`	a numeric vector of data values.
`alternative`	a character string specifying the alternative hypothesis, must be one of '"two.sided"' (default), '"greater"' or '"less"'. You can specify just the initial letter.

Details

Under the hypothesis of normality, data should have kurtosis equal to 3. This test has such null hypothesis and is useful to detect a significant difference of kurtosis in normally distributed data.

Value

A list with class htest containing the following components:

`statistic`	the list containing kurtosis estimator and its transformation.
`p.value`	the p-value for the test.
`alternative`	a character string describing the alternative hypothesis.
`method`	a character string indicating what type of test was performed.
`data.name`	name of the data argument.

Author(s)

Lukasz Komsta

References

Anscombe, F.J., Glynn, W.J. (1983) Distribution of kurtosis statistic for normal statistics. Biometrika, 70, 1, 227-234

Examples

set.seed(1234)
x = rnorm(1000)
kurtosis(x)
anscombe.test(x)