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dprep (version 2.1)

robout: Outlier Detection with Robust Mahalonobis distance

Description

This function finds 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
An integer value that represents the class to detect for outliers
meth
The method used to compute the Mahalanobis distance, "mve"=minimum volume estimator, "mcd"=minimum covariance determinant
rep
Number of repetitions
plot
A boolean value to turn on and off the scatter plot of the Mahalanobis distances

Value

  • top1Index of observations identified as top outliers by frequency of selection
  • topoutIndex of observations identified as possible outliers by outlyingness measure
  • outmeIndex of observations and their outlyingness measures

Details

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

Examples

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

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