Supported by Dr. Osamu Ogasawara and  providing  . |
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Last data update: 2014.03.03 |
Factor Adjusted Discriminant Analysis 2: Decorrelation of a testing data set after running the
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faobject |
An object returned by function |
data.test |
A list containing the testing dataset, with the following component: |
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)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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> 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.test.Rd_%03d_medium.png", width=480, height=480)
> ### Name: decorrelate.test
> ### Title: Factor Adjusted Discriminant Analysis 2: Decorrelation of a
> ### testing data set after running the 'decorrelate.train' function on a
> ### training data set
> ### Aliases: decorrelate.test
>
> ### ** Examples
>
> data(data.train)
> data(data.test)
> fa = decorrelate.train(data.train)
[1] "Number of factors: 3 factors"
[1] "Objective criterion: "
[1] 0.05912524
[1] 1.603967
[1] 0.001050686
[1] 0.0004215778
> fa2 = decorrelate.test(fa,data.test)
> names(fa2)
[1] "meanclass" "fa.training" "fa.testing"
[4] "Psi" "B" "factors.training"
[7] "factors.testing" "groups" "proba.training"
[10] "proba.testing" "mod.decorrelate.test" "data.train"
[13] "maxnbfactors" "min.err" "EM"
[16] "maxiter"
>
>
>
>
>
> dev.off()
null device
1
>