`mardiaTest(data, cov = TRUE, qqplot = FALSE)`

data

a numeric matrix or data frame

cov

if

`TRUE`

covariance matrix is normalized by `n`

, if `FALSE`

it is normalized by `n-1`

qqplot

if

`TRUE`

it creates a chi-square Q-Q plot
- g1p
- Mardia's multivariate skewness statistic
- chi.skew
- Chi-square value of the skewness statistic
- p.value.skew
`p-value`

of the skewness statistic- g2p
- Mardia's multivariate kurtosis statistic
- z.kurtosis
- z value of the kurtosis statistic
- p.value.kurt
`p-value`

of kurtosis statistic- chi.small.skew
- Chi-square value of the small sample skewness statistic
- p.value.small
`p-value`

of small sample skew statistic

For multivariate normality, both p-values of skewness and kurtosis statistics should be greater than `0.05`

.

If sample size less than 20 then `p.value.small`

should be used as significance value of skewness instead of `p.value.skew`

.

Mardia, K. V. (1970), Measures of multivariate skewnees and kurtosis with applications. Biometrika, 57(3):519-530. Mardia, K. V. (1974), Applications of some measures of multivariate skewness and kurtosis for testing normality and robustness studies. Sankhy A, 36:115-128.

Stevens, J. (1992), Applied Multivariate Statistics for Social Sciences. 2nd. ed. New-Jersey:Lawrance Erlbaum Associates Publishers. pp. 247-248.

`roystonTest`

`hzTest`

`mvnPlot`

`mvOutlier`

`uniPlot`

`uniNorm`

setosa = iris[1:50, 1:4] # Iris data only for setosa and four variables result = mardiaTest(setosa, qqplot = TRUE) result

Run the code above in your browser using DataCamp Workspace