# 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

 `1` ```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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```## 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.