robout {dprep} | R Documentation

## Outlier Detection with Robust Mahalonobis distance

### Description

This function finds out the outliers of a dataset using robust versions of the Mahalanobis distance.

### Usage

    robout(data, nclass, meth = c("mve", "mcd"), rep = 10,
    plot = TRUE)

### Arguments

`data` | the dataset for which outlier detection will be carried out.
`nclass` | integer value that represents the class to detect for outliers
`meth` | method used to compute the Mahalanobis distance, "mve"=minimum volume estimator, "mcd"=minimum covariance determinant
`rep` | number of repetitions
`plot` | boolean value to turn on or off the scatter plot of the Mahalanobis distances

### Details

Requires uses cov.rob function from the MASS library.

### Value

`top1` |
`topout` | index of observations identified as possible outliers by outlyingness measure
`outme` | index of observations and their outlyingness measures

### Author(s)

Edgar Acuna

### References

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

Atkinson, A. (1994). Fast very robust methods for the detection of multiple outliers. Journal of the American Statistical Association, 89:1329-1339.

### Examples

    #---- Outlier Detection in bupa-class 1 using MCD
    data(bupa)
    robout(bupa,1,"mcd")

[Package dprep version 1.0 Index]