FADA (version 1.1)

decorrelate: Factor Adjusted Discriminant Analysis 1: Decorrelation of a testing data set after running the FA function on a training data set

Description

This function decorrelates the test dataset by adjusting data for the effects of latent factors of dependence, after running the FA function on a training data set.

Usage

decorrelate(faobject,test)

Arguments

faobject

An object returned by function FA.

test

A list containing the testing dataset, with the following component: x is a n x p matrix of explanatory variables, where n stands for the testing sample size and p for the number of explanatory variables.

Value

• Returns a list with the following elements:
• meanclass
  Group means estimated after iterative decorrelation
• fadta
  Decorrelated training data
• fatest
  Decorrelated testing data
• Psi
  Estimation of the factor model parameters: specific variance
• B
  Estimation of the factor model parameters: loadings
• groups
  Recall of group variable of training data

References

Friedman, J., Hastie, T. and Tibshirani, R. (2010), Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33, 1-22.

Friguet, C., Kloareg, M. and Causeur, D. (2009), A factor model approach to multiple testing under dependence. Journal of the American Statistical Association, 104:488, 1406-1415.

Perthame, E., Friguet, C. and Causeur, D. (2014), Stability of feature selection in classification issues for high-dimensional correlated data, Submitted.

See Also

FADA-package FADA glmnet-package

Examples

data(data.train)
data(data.test)
fa = FA(data.train)
fa2 = decorrelate(fa,data.test)
names(fa2)