mult.norm {QuantPsyc}

Tests for Multivariate Normality

Description

Returns tests for multivariate Skewness and kurtosis as well as Mahalanobis' D-squared.

Usage

mult.norm(x, s = var(x), chicrit = 0.005)

Arguments

x

A multivariate data object as in columns from a data.frame

s

Covariance matrix of x (not necessary to specify)

chicrit

p-value corresponding to critical value of chi-square distribution for detecting multivariate outliers

Details

Tests for multivariate skewness and kurtosis were adapted from SAS macros in Khatree & Naik (1999). They attribute the formula to Mardia (1970; 1974). Mahalanobis' Dsq is based on Mahalanobis (1936). Dsq is multivariate analogue to z scores, but based on the chi-sq distribution rather than normal distribution. Once can specify at what level one wishes to define multivariate outliers (e.g., .005, .001)

Value

A list containing the following:

mult.test

Values for multivariate skeweness and kurtosis and their significance

Dsq

Mahalanobis' distances

CriticalDsq

Critical value of chi-sq distribution based on df and specified critical level

Note

Mahalanobis is returned without regard to NAs (missing observations) and is useful only in detecting IF multivariate outliers are present. If one wishes to determine which cases are multivariate outliers and if one has missing observations, mahalanobis is perhaps a better choice.

These statistics are known to be susceptible to sample size (as in their univariate counterparts). One should always use graphical methods such as qqplot in addition to statistical.

Author(s)

Thomas D. Fletcher [e-mail]

References

Khattree, R. & Naik, D. N. (1999). Applied multivariate statistics with SAS software (2nd ed.). Cary, NC: SAS Institute Inc.

See Also

mahalanobis, qqplot

Examples

# assess the multivariate normality of variables 4,5,6 in USJudgeRatings
data(USJudgeRatings)	
mn <- mult.norm(USJudgeRatings[,4:6],chicrit=.001)
mn

mn$Dsq > mn$CriticalDsq