selectFunctional: Grouped variable selection procedure for functional data

In RFgroove: Importance Measure and Selection for Groups of Variables with Random Forests

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.

See Also

selectGroup,varImpGroup

Examples

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

Example output

Loading required package: randomForest
randomForest 4.6-14
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



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


Number of selected variables using a validation set: 7 

Selected variables:
V1 V3 V2 V5 V4 V7 V12 

Validation error for the best model: 0.5948 

RFgroove documentation built on May 2, 2019, 8:31 a.m.