# mardiaSkew: Finding Mardia's multivariate skewness In semTools: Useful Tools for Structural Equation Modeling

## Description

Finding Mardia's multivariate skewness of multiple variables

## Usage

 1 mardiaSkew(dat, use = "everything") 

## Arguments

 dat The target matrix or data frame with multiple variables use Missing data handling method from the cov function.

## Details

The Mardia's multivariate skewness formula (Mardia, 1970) is

b_{1, d} = \frac{1}{n^2}∑^n_{i=1}∑^n_{j=1}≤ft[ ≤ft(\bold{X}_i - \bold{\bar{X}} \right)^{'} \bold{S}^{-1} ≤ft(\bold{X}_j - \bold{\bar{X}} \right) \right]^3,

where d is the number of variables, X is the target dataset with multiple variables, n is the sample size, \bold{S} is the sample covariance matrix of the target dataset, and \bold{\bar{X}} is the mean vectors of the target dataset binded in n rows. When the population multivariate skewness is normal, the \frac{n}{6}b_{1,d} is asymptotically distributed as χ^2 distribution with d(d + 1)(d + 2)/6 degrees of freedom.

## Value

A value of a Mardia's multivariate skewness with a test statistic

## Author(s)

Sunthud Pornprasertmanit (psunthud@gmail.com)

## References

Mardia, K. V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57(3), 519–530. doi: 10.2307/2334770

• skew Find the univariate skewness of a variable

• kurtosis Find the univariate excessive kurtosis of a variable

• mardiaKurtosis Find the Mardia's multivariate kurtosis of a set of variables

## Examples

 1 2 library(lavaan) mardiaSkew(HolzingerSwineford1939[ , paste0("x", 1:9)]) 

semTools documentation built on Jan. 13, 2021, 8:09 p.m.