FADA: Variable Selection for Supervised Classification in High
Dimension
The functions provided in the FADA (Factor Adjusted Discriminant Analysis) package aim at performing supervised classification of high-dimensional and correlated profiles. The procedure combines a decorrelation step based on a
factor modeling of the dependence among covariates and a classification method. The available methods are Lasso regularized logistic model
(see Friedman et al. (2010)), sparse linear discriminant analysis (see
Clemmensen et al. (2011)), shrinkage linear and diagonal discriminant
analysis (see M. Ahdesmaki et al. (2010)). More methods of classification can be used on the decorrelated data provided by the package FADA.
| Version: |
1.3.5 |
| Depends: |
MASS, elasticnet |
| Imports: |
sparseLDA, sda, glmnet, mnormt, crossval, corpcor, matrixStats, methods |
| Published: |
2019-12-10 |
| Author: |
Emeline Perthame (Institut Pasteur, Paris, France), Chloe Friguet
(Universite de Bretagne Sud, Vannes, France) and David Causeur (Agrocampus
Ouest, Rennes, France) |
| Maintainer: |
David Causeur <david.causeur at agrocampus-ouest.fr> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
no |
| CRAN checks: |
FADA results |
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