mardiaKurtosis: Finding Mardia's multivariate kurtosis

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/dataDiagnosis.R

Description

Finding Mardia's multivariate kurtosis of multiple variables

Usage

1
mardiaKurtosis(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 kurtosis formula (Mardia, 1970) is

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

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 kurtosis is normal, the b_{2,d} is asymptotically distributed as normal distribution with the mean of d(d + 2) and variance of 8d(d + 2)/n.

Value

A value of a Mardia's multivariate kurtosis 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

See Also

Examples

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

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