# Outlier Detection with Robust Mahalonobis distance

### Description

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

### Usage

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### 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 |

### Details

It requires the use of the cov.rob function from the MASS library.

### 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 |

### 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.

### See Also

`robout`

### Examples

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