power.mhz: Power Calculation using the Henze-Zirkler Test Statistic

In mvnormalTest: Powerful Tests for Multivariate Normality

Power Calculation using the Henze-Zirkler Test Statistic

Description

Empirical power calculation using the Henze-Zirkler Test Statistic.

Usage

power.mhz(a, n, p, B = 1000, FUN, ...)

Arguments

a	significance level (\alpha).
n	number of rows (observations).
p	number of columns (variables), n>p.
B	number of Monte Carlo simulations, default is 1000 (can increase B to increase the precision).
FUN	self-defined function for generate multivariate distribution. See example.
...	optional arguments passed to FUN.

Value

Returns a numeric value of the estimated empirical power (value between 0 and 1).

References

Henze, N., & Zirkler, B. (1990). A class of invariant consistent tests for multivariate normality. Communications in statistics-Theory and Methods, 19(10), 3595-3617.

Examples

set.seed(12345)

## Power calculation against bivariate (p=2) independent Beta(1, 1) distribution ##
## at sample size n=50 at one-sided alpha = 0.05 ##

power.mhz(a = 0.05, n = 50, p = 2,  B = 100, FUN=IMMV, D1=runif)

mvnormalTest documentation built on June 8, 2025, 2 p.m.