# power.mvnTest: Power Calculation using the Zhou-Shao's Multivariate... In mvnormalTest: Powerful Tests for Multivariate Normality

## Description

Empirical power calculation using the Zhou-Shao's multivariate normality test Statistic T_n.

## Usage

 `1` ```power.mvnTest(a, n, p, B = 1000, pct = c(0.01, 0.99), FUN, ...) ```

## Arguments

 `a` significance level (α). `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). `pct` percentiles of MK to get c1 and c2 described in the reference paper,default is (0.01, 0.99). `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

Zhou, M., & Shao, Y. (2014). A powerful test for multivariate normality. Journal of applied statistics, 41(2), 351-363.

## Examples

 ```1 2 3 4 5 6``` ```set.seed(12345) ## Power calculation against bivariate (p=2) independent Beta(1, 1) distribution ## ## at sample size n=50 for Tn at one-sided alpha = 0.05 ## power.mvnTest(a = 0.05, n = 50, p = 2, B = 100, pct = c(0.01, 0.99), FUN=IMMV, D1=runif) ```

mvnormalTest documentation built on April 28, 2020, 5:06 p.m.