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.


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install.packages("FADA")

1.3.3 by David Causeur, a year ago


Browse source code at https://github.com/cran/FADA


Authors: Emeline Perthame (INRIA , Grenoble , France) , Chloe Friguet (Universite de Bretagne Sud , Vannes , France) and David Causeur (Agrocampus Ouest , Rennes , France)


Documentation:   PDF Manual  


GPL (>= 2) license


Imports sparseLDA, sda, glmnet, mnormt, crossval, corpcor, matrixStats, methods

Depends on MASS, elasticnet


See at CRAN