Moutlier: Plots classical and robust Mahalanobis distances

In chemometrics: Multivariate Statistical Analysis in Chemometrics

Plots classical and robust Mahalanobis distances

Description

For multivariate outlier detection the Mahalanobis distance can be used. Here a plot of the classical and the robust (based on the MCD) Mahalanobis distance is drawn.

Usage

Moutlier(X, quantile = 0.975, plot = TRUE, ...)

Arguments

X	numeric data frame or matrix
quantile	cut-off value (quantile) for the Mahalanobis distance
plot	if TRUE a plot is generated
...	additional graphics parameters, see par

Details

For multivariate normally distributed data, a fraction of 1-quantile of data can be declared as potential multivariate outliers. These would be identified with the Mahalanobis distance based on classical mean and covariance. For deviations from multivariate normality center and covariance have to be estimated in a robust way, e.g. by the MCD estimator. The resulting robust Mahalanobis distance is suitable for outlier detection. Two plots are generated, showing classical and robust Mahalanobis distance versus the observation numbers.

Value

md	Values of the classical Mahalanobis distance
rd	Values of the robust Mahalanobis distance
cutoff	Value with the outlier cut-off

...

Author(s)

Peter Filzmoser <[e-mail]>

References

K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, 2009.

See Also

covMcd

Examples

data(glass)
data(glass.grp)
x=glass[,c(2,7)]
require(robustbase)
res <- Moutlier(glass,quantile=0.975,pch=glass.grp)

chemometrics documentation built on Aug. 25, 2023, 5:18 p.m.