Description Usage Arguments Value References Examples

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

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

`a` |
significance level ( |

`n` |
number of rows (observations). |

`p` |
number of columns (variables), |

`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 |

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

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

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)
``` |

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