mvnTest (version 1.1-0)

AD.test: Anderson-Darling test for multivariate normality

Description

This function implements the Anderson-Darling test for assessing multivariate normality. It calculates the value of the test and its approximate p-value.

Usage

AD.test(data, qqplot = FALSE)

Arguments

data

A numeric matrix or data frame.

qqplot

If TRUE produces a chi-squared QQ plot.

Value

AD

the value of the test statistic.

p.value

the p-value of the test.

References

Paulson, A., Roohan, P., and Sullo, P. (1987). Some empirical distribution function tests for multivariate normality. Journal of Statistical Computation and Simulation, 28, 15-30

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

Selcuk Korkmaz, Dincer Goksuluk, and Gokmen Zararsiz. MVN: Multivariate Normality Tests, 2015. R package version 4.0

See Also

S2.test, CM.test, DH.test, R.test, HZ.test

Examples

## Not run: 
# ## generating n bivariate normal random variables...       
# dat <- rmvnorm(n=100,mean=rep(0,2),sigma=matrix(c(4,2,2,4),2,2)) 
# res <- AD.test(dat)
# res
# 
# ## generating n bivariate t distributed with 10df random variables...   
# dat <- rmvt(n=200,sigma=matrix(c(4,2,2,4),2,2),df=10,delta=rep(0,2)) 
# res1 <- AD.test(dat)
# res1
# 
# data(iris)
# setosa <- iris[1:50, 1:4] # Iris data only for setosa
# res2 <- AD.test(setosa, qqplot = TRUE)
# res2
# 
#     ## End(Not run)