selectLevel: Wavelet levels selection procedure

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

Description

A grouped backward variable selection procedure for selecting the most significant wavelet levels of a functional variable. The groups are the wavelet coefficients belonging to the same frequency level.

Usage

selectLevel(design, ydata, typeRF = ifelse(is.factor(ydata), "classif", "reg"), 
            verbose = TRUE, ntree = 500, ...)

Arguments

design	The design matrix of a functional variable.
ydata	The outcome data. Must be a factor for classification.
typeRF	The type of forest we want to construct, ‘classif’ for classification or ‘reg’ for regression.
verbose	Should the details be printed.
ntree	The number of trees in the forests (default: 500).
...	optional parameters to be passed to the ‘varImpGroup’ function.

Value

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

nselected	The number of selected wavelet levels.
selection	The selected wavelet levels.
selectionIndexes	The indexes of selected wavelet levels in the input matrix ‘design’.
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 wavelet levels.
rankingIndexes	The final ranking indexes of the wavelet levels.

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,selectFunctional,varImpGroup

Examples

  data(toyRegFD)
  x <- toyRegFD$FDlist[[1]]
  y <- toyRegFD$Y

  design <- projectWavelet(xdata=x)
  summary(levSel <- selectLevel(design, y, ntree=100, verbose=TRUE))
  plot(levSel)

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