# Grouped variable selection procedure for functional data

### Description

Backward variable selection procedure for multivariate functional data which is a set of p functional covariates and n observations of dimension N.

### Usage

 1 2 3 selectFunctional( FDlist, ydata, normalize=TRUE, dimensionReductionMethod=c("fpca", "wave"), nbasisInit, verbose=TRUE, ...) 

### Arguments

 FDlist A p-dimensional list containing the set of functional variables which are matrices of size n \times N. ydata The outcome data. Must be a factor for classification. normalize Should the functions be normalized ? dimensionReductionMethod The dimension reduction method, ‘fpca’ for Functional Principal Component Analysis or ‘wave’ for the multiple wavelet thresholding. nbasisInit The number of initial spline coefficients. verbose Should the details be printed. ... further arguments passed to or from other methods.

### Value

An object of class fRFE which is a list with the following components:

 nselected The number of selected functional variables ; selection The selected functional variables ; selectionIndexes The indexes of selected functional variables in the input data ‘FDlist’ ; error The prediction error computed in each iteration of the backward procedure ; typeRF The type of the forests, classification or regression ; ranking The final ranking of the functional variables ; rankingIndexes The final ranking indexes of the functional variables.

### Author(s)

Baptiste Gregorutti

### References

Gregorutti, B., Michel, B. and Saint Pierre, P. (2015). Grouped variable importance with random forests and application to multiple functional data analysis, Computational Statistics and Data Analysis 90, 15-35.

selectGroup,varImpGroup
 1 2 3 4 5 6  data(toyRegFD) varSel <- selectFunctional( toyRegFD$FDlist, toyRegFD$Y, normalize=FALSE, dimensionReductionMethod="fpca", nbasisInit=16, verbose=FALSE, ntree=10) summary(varSel) plot(varSel)