This function finds the outliers of a dataset using robust versions of the Mahalanobis distance.
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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 |
It requires the use of the cov.rob function from the MASS library.
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 |
Edgar Acuna
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.
robout
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