mahaout {dprep}R Documentation

Multivariate outlier detection through the boxplot of the Mahalanobis distance

Description

This function finds out multivariate outliers through the boxplot of the Mahalanobis distance of all the instances.

Usage

mahaout(data, nclass, plot = TRUE)

Arguments

data name of the dataset
nclass number of the class to check for outliers
plot logical value. If plot=T a plot of the mahalanobis distance is drawn

Details

uses cov.rob function from the MASS library

Value

Returns a list of top outliers according to their Mahalanobis distance. Furthermore returns a list of all the instances ordered according to their Mahalanobis distance.
If Plot=T returns a plot of the instances ranked by their Mahalanobis distance.

Author(s)

Edgar Acuna

References

Rousseeuw, P, and Leroy, A. (1987). Robust Regression and outlier detection. John Wiley & Sons. New York.

See Also

robout

Examples

#---- Detecting outliers using the Mahalanobis distance----
data(bupa)
mahaout(bupa,1)

[Package dprep version 1.0 Index]