Description Usage Arguments Details Value Author(s) References See Also Examples

Score distances and orthogonal distances are computed and plotted.

1 |

`X` |
numeric data frame or matrix |

`X.pca` |
PCA object resulting e.g. from |

`a` |
number of principal components |

`quantile` |
quantile for the critical cut-off values |

`scale` |
if TRUE then X will be scaled - and X.pca should be from scaled data too |

`plot` |
if TRUE a plot is generated |

`...` |
additional graphics parameters, see |

The score distance measures the outlyingness of the onjects within the PCA space using Mahalanobis distances. The orthogonal distance measures the distance of the objects orthogonal to the PCA space. Cut-off values for both distance measures help to distinguish between outliers and regular observations.

`SDist ` |
Score distances |

`ODist ` |
Orthogonal distances |

`critSD ` |
critical cut-off value for the score distances |

`critOD ` |
critical cut-off value for the orthogonal distances |

Peter Filzmoser <P.Filzmoser@tuwien.ac.at>

K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, 2009.

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```
Loading required package: rpart
Loading required package: robustbase
```

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