decorrelate.test {FADA}

Factor Adjusted Discriminant Analysis 2: Decorrelation of a testing data set after running the decorrelate.train 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 decorrelate.train function on a training data set.

Usage

decorrelate.test(faobject,data.test)

Arguments

faobject

An object returned by function decorrelate.train.

data.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

fa.training

Decorrelated training data

fa.testing

Decorrelated testing data

Psi

Estimation of the factor model parameters: specific variance

B

Estimation of the factor model parameters: loadings

factors.training

Scores of the trainings individuals on the factors

factors.testing

Scores of the testing individuals on the factors

groups

Recall of group variable of training data

proba.training

Internal value (estimation of individual probabilities for the training dataset)

proba.testing

Internal value (estimation of individual probabilities for the testing dataset)

mod.decorrelate.test

Internal value (classification model)

Author(s)

Emeline Perthame, Chloe Friguet and David Causeur

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. (2015), Stability of feature selection in classification issues for high-dimensional correlated data, Statistics and Computing.

See Also

FADA-package FADA glmnet-package

Examples

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