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]