selectGroup: Grouped variable selection procedure

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

Description

A grouped backward variable selection procedure.

Usage

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

Arguments

design	The design matrix.
ydata	The outcome data. Must be a factor for classification.
varNames	The vector of the group names.
nvarGroup	The vector of the number of variables in each group.
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 groups ;
selection	The selected groups ;
selectionIndexes	The indexes of selected groups 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 groups ;
rankingIndexes	The final ranking indexes of the groups.

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

selectLevel,selectFunctional,varImpGroup

Examples

  cat("\n\nClassification\n")
  data(toyClassif)
  attach(toyClassif)
  
  cat("Case 1\n")
  nvarGroup <- c(2,1,6); names(nvarGroup) <- paste("G", 1:length(nvarGroup), sep="")
  summary(varSel <- selectGroup(design=X, ydata=Y, nvarGroup=nvarGroup, 
                                verbose=TRUE, ntree=500, normalize=FALSE))
  plot(varSel)

  cat("Case 2\n")
  nvarGroup <- rep(1,9); names(nvarGroup) <- paste("G", 1:length(nvarGroup), sep="")
  summary(varSel <- selectGroup(design=X, ydata=Y, nvarGroup=nvarGroup, 
                                verbose=TRUE, ntree=500, normalize=FALSE))
  plot(varSel)

  detach(toyClassif)

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