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R: Factor Adjusted Discriminant Analysis 1: Decorrelation of the...

decorrelate.train

R Documentation

Factor Adjusted Discriminant Analysis 1: Decorrelation of the training data

Description

This function decorrelates the training dataset by adjusting data for the effects of latent factors of dependence.

Usage

decorrelate.train(data.train, nbf = NULL, maxnbfactors=12, diagnostic.plot = FALSE, 
min.err = 0.001, verbose = TRUE,EM = TRUE, maxiter = 15,...)

Arguments

`data.train`	A list containing the training dataset with the following components: `x` is the n x p matrix of explanatory variables, where n stands for the training sample size and p for the number of explanatory variables ; `y` is a numeric vector giving the group of each individual numbered from 1 to K.
`nbf`	Number of factors. If `nbf = NULL`, the number of factors is estimated. `nbf` can also be set to a positive integer value. If `nbf = 0`, the data are not factor-adjusted.
`maxnbfactors`	The maximum number of factors. Default is `maxnbfactors=12`.
`diagnostic.plot`	If `diagnostic.plot =TRUE`, the values of the variance inflation criterion are plotted for each number of factors. Default is `diagnostic.plot =FALSE`. This option might be helpful to manually determine the optimal number of factors.
`min.err`	Threshold of convergence of the algorithm criterion. Default is min.err=0.001.
`verbose`	Print out number of factors and values of the objective criterion along the iterations. Default is `TRUE`.
`EM`	The method used to estimate the parameters of the factor model. If `EM=TRUE`, parameters are estimated by an EM algorithm. Setting `EM=TRUE` is recommended when the number of covariates exceeds the number of observations. If `EM=FALSE`, the parameters are estimated by maximum-likelihood using `factanal`. Default is `EM=TRUE`
`maxiter`	Maximum number of iterations for estimation of the factor model.
`...`	Other arguments that can be passed in the `cv.glmnet` and `glmnet` functions from glmnet package. These functions are used to estimate individual group probabilities. Modifying these parameters should not affect the decorrelation procedure. However, the argument `nfolds` in `cv.glmnet` is set to 10 by default and should be reduced (minimum 3) for large datasets, in order to decrease the computation time of `decorrelation.train`.

Value

Returns a list with the following elements:

`meanclass`	Group means estimated after iterative decorrelation
`fa.training`	Decorrelated training 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
`groups`	Recall of group variable of training data
`proba.training`	Internal value (estimation of individual probabilities for the training dataset)

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.

Examples

data(data.train)

res0 = decorrelate.train(data.train,nbf=3) #  when the number of factors is forced

res1 = decorrelate.train(data.train) #  when the optimal number of factors is unknown

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(FADA)
Loading required package: MASS
Loading required package: elasticnet
Loading required package: lars
Loaded lars 1.2

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/FADA/decorrelate.train.Rd_%03d_medium.png", width=480, height=480)
> ### Name: decorrelate.train
> ### Title: Factor Adjusted Discriminant Analysis 1: Decorrelation of the
> ###   training data
> ### Aliases: decorrelate.train
> 
> ### ** Examples
> 
> data(data.train)
> 
> res0 = decorrelate.train(data.train,nbf=3) #  when the number of factors is forced
[1] "Number of factors: 3 factors"
[1] "Objective criterion: "
[1] 0.05912524
[1] 1.603967
[1] 0.001050686
[1] 0.0004215778
> 
> res1 = decorrelate.train(data.train) #  when the optimal number of factors is unknown
[1] "Number of factors: 3 factors"
[1] "Objective criterion: "
[1] 0.05912524
[1] 1.603967
[1] 0.001050686
[1] 0.0004215778
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>