FADA: Variable Selection for Supervised Classification in High Dimension

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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.

Author
Emeline Perthame (INRIA, Grenoble, France), Chloe Friguet (Universite de Bretagne Sud, Vannes, France) and David Causeur (Agrocampus Ouest, Rennes, France)
Date of publication
2016-05-20 22:36:50
Maintainer
David Causeur <david.causeur@agrocampus-ouest.fr>
License
GPL (>= 2)
Version
1.3.2

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Man pages

data.test
Test dataset simulated with the same distribution as the...
data.train
Training data
decorrelate.test
Factor Adjusted Discriminant Analysis 2: Decorrelation of a...
decorrelate.train
Factor Adjusted Discriminant Analysis 1: Decorrelation of the...
FADA
Factor Adjusted Discriminant Analysis 3-4 : Supervised...
FADA-package
Variable selection for supervised classification in high...

Files in this package

FADA
FADA/NAMESPACE
FADA/data
FADA/data/datalist
FADA/data/data.test.rda
FADA/data/data.train.rda
FADA/R
FADA/R/func.R
FADA/R/decorrelate.test.R
FADA/R/decorrelate.train.R
FADA/R/FADA.R
FADA/MD5
FADA/DESCRIPTION
FADA/man
FADA/man/FADA-package.Rd
FADA/man/decorrelate.test.Rd
FADA/man/data.test.Rd
FADA/man/FADA.Rd
FADA/man/data.train.Rd
FADA/man/decorrelate.train.Rd