copulas: Random Generation for the Copula Generated Distributions

In mvnormalTest: Powerful Tests for Multivariate Normality

Description

Generate univariate or multivariate random sample for the Copula Generated Distributions.

Usage

copulas(n, p, c = "clayton", param, invF, ...)

Arguments

n	number of rows (observations).
p	total number of columns (variables).
c	name of an Archimedean copula, choosing from "clayton" (default), "frank", or "gumbel".
param	number (numeric) specifying the copula parameter.
invF	inverse function (quantile function, e.g. qnorm).
...	optional arguments passed to invF.

Value

univariate (p=1) or multivariate (p>1) random sample.

References

Yan, J. (2007). Enjoy the joy of copulas: with a package copula. Journal of Statistical Software, 21(4), 1-21.

Examples

set.seed(12345)

## Generate 5X2 random sample matrix from Clayton(0.5, qnorm) ##
copulas(n=50, p=2, c="clayton", param=0.5, invF=qnorm)


## Power calculation against bivariate (p=2) Clayton(0.5, qnorm) distribution ##
## at sample size n=50 at one-sided alpha = 0.05 ##

# Zhou-Shao's test #
power.mvnTest(a=0.05, n=50, p=2, B=100, FUN=copulas, c="clayton", param=0.5, invF=qnorm)

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