R.test: Royston test for Multivariate Normality

In mvnTest: Goodness of Fit Tests for Multivariate Normality

Description

This function implements the Royston test for assessing multivariate normality.

Usage

R.test(data, qqplot = FALSE)

Arguments

data	A numeric matrix or data frame
qqplot	if TRUE creates a chi-square Q-Q plot

Details

Calculates the value of the Royston test and the approximate p-value.

Value

R	the value of the test statistic
p.value	the p-value of the test
data.name	a character string giving the name of the data

Note

The printing method and plotting are in part adapted from R package MVN (Korkmaz, S. et al., 2015, version 4.0).

Author(s)

Rashid Makarov, Vassilly Voinov, Natalya Pya

References

Royston, P. (1992). Approximating the Shapiro-Wilk W-test for non-normality. Statistics and Computing, 2, 117-119.

See Also

S2.test, DH.test, AD.test, CM.test, HZ.test

Examples

## generating n bivariate normal random variables...       
dat <- rmvnorm(n=200,mean=rep(0,2),sigma=matrix(c(4,2,2,4),2,2)) 
res <- R.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)*.8,df=10,delta=rep(0,2)) 
res1 <- R.test(dat)
res1

data(iris)
setosa = iris[1:50, 1:4] # Iris data only for setosa
res2 <- R.test(setosa, qqplot = TRUE)
res2

mvnTest documentation built on May 2, 2019, 2:44 p.m.