SCOUTer: Simulate Controlled Outliers
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).
| Version: |
1.0.0 |
| Depends: |
R (≥ 3.5.0), ggplot2, ggpubr, stats |
| Suggests: |
knitr, rmarkdown |
| Published: |
2020-06-30 |
| Author: |
Alba Gonzalez Cebrian [aut, cre],
Abel Folch-Fortuny [aut],
Francisco Arteaga [aut],
Alberto Ferrer [aut] |
| Maintainer: |
Alba Gonzalez Cebrian <algonceb at upv.es> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
SCOUTer results |
Documentation:
Downloads:
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