dprep (version 3.0.2)

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=0, 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. By default nclass=0 meaning the column of classes it is not used.

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

top1

Index of observations identified as top outliers by frequency of selection

topout

Index of observations identified as possible outliers by outlyingness measure

outme

Index of observations and their outlyingness measures

Details

It requires the use of the 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.

See Also

robout

Examples

## Not run: #---- Outlier Detection in bupa-class 1 using MCD
# data(bupa)
# robout(bupa,1,"mcd")
# ## End(Not run)