Using principal component analysis as a base model, 'SCOUTer' offers a new approach to simulate outliers in a simple and precise way. The user can generate new observations defining them by a pair of well-known statistics: the Squared Prediction Error (SPE) and the Hotelling's T^2 (T^2) statistics. Just by introducing the target values of the SPE and T^2, 'SCOUTer' returns a new set of observations with the desired target properties. Authors: Alba González, Abel Folch-Fortuny, Francisco Arteaga and Alberto Ferrer (2020).
Package details |
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Author | Alba Gonzalez Cebrian [aut, cre], Abel Folch-Fortuny [aut], Francisco Arteaga [aut], Alberto Ferrer [aut] |
Maintainer | Alba Gonzalez Cebrian <algonceb@upv.es> |
License | GPL-3 |
Version | 1.0.0 |
Package repository | View on CRAN |
Installation |
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