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Last data update: 2014.03.03

R: Grouped variable selection procedure for functional data

selectFunctional

R Documentation

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

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.

Examples

  data(toyRegFD)
  varSel <- selectFunctional( toyRegFD$FDlist, toyRegFD$Y, normalize=FALSE, 
                              dimensionReductionMethod="fpca", nbasisInit=16, 
                              verbose=FALSE, ntree=10)
  summary(varSel)
  plot(varSel)

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(RFgroove)
Loading required package: randomForest
randomForest 4.6-12
Type rfNews() to see new features/changes/bug fixes.
Loading required package: wmtsa
Loading required package: fda
Loading required package: splines
Loading required package: Matrix

Attaching package: 'fda'

The following object is masked from 'package:graphics':

    matplot

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/RFgroove/selectFunctional.Rd_%03d_medium.png", width=480, height=480)
> ### Name: selectFunctional
> ### Title: Grouped variable selection procedure for functional data
> ### Aliases: selectFunctional
> 
> ### ** Examples
> 
>   data(toyRegFD)
>   varSel <- selectFunctional( toyRegFD$FDlist, toyRegFD$Y, normalize=FALSE, 
+                               dimensionReductionMethod="fpca", nbasisInit=16, 
+                               verbose=FALSE, ntree=10)
>   summary(varSel)


 --- 				 ---
 --- 	Summary functional RFE	 ---
 --- 				 ---


Number of selected variables using a validation set: 10 

Selected variables:
V1 V5 V4 V2 V7 V3 V6 V19 V17 V12 

Validation error for the best model: 0.8328 

>   plot(varSel)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>