dprep (version 3.0.2)

mahaout: Multivariate outlier detection through the boxplot of the Mahalanobis distance

Description

This function finds multivariate outliers by constructing a boxplot of the Mahalanobis distance of all the instances.

Usage

mahaout(data, nclass=0, plot = TRUE)

Arguments

data

Name of the dataset

nclass

Number of the class to check for outliers. By default nclass=0 meaning the column of classes it is not used.

plot

Logical value. If plot=T a plot of the mahalanobis distance is drawn

Value

Returns a list of top outliers according to their Mahalanobis distance and a list of all the instances ordered according to their Mahalanobis distance.If Plot=T, a plot of the instances ranked by their Mahalanobis distance is provided.

Details

uses cov.rob function from the MASS library

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)