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Last data update: 2014.03.03

R: A grouped variable importance with Random Forests

varImpGroup

R Documentation

A grouped variable importance with Random Forests

Description

A permutation variable importance for groups of variables with Random Forests.

Usage

varImpGroup(object, xdata, ngroups = length(nvarGroup), nvarGroup,
            idxGroup, groupsNames = names(nvarGroup), 
            normalize = (length(unique(nvarGroup)) != 1))

Arguments

`object`	A randomForest object.
`xdata`	The input data.
`ngroups`	The number of groups.
`nvarGroup`	The vector of the number of variables in each group.
`idxGroup`	A list of size ‘ngroups’ containing the indexes of each group starting from 0.
`groupsNames`	The group names.
`normalize`	Should the normalized grouped importance measure be computed.

Value

An object of class ‘importance’ which is a vector of the importance for each group.

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(toyClassif)
  attach(toyClassif)

  rf <- randomForest(x=X,y=Y,keep.forest=TRUE, keep.inbag=TRUE, ntree=500)
  ngroups <- 3
  nvarGroup <- c(4,3,6)
  idxGroup <- list(c(0,1,2,5), c(2,4,5), c(0,1,5,6,7,8))
  grImp <- varImpGroup(rf, X, ngroups, nvarGroup, idxGroup, NULL, normalize=FALSE )
  cat("Group importance\n", grImp, "\n")

  detach(toyClassif)

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)

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> 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/varImpGroup.Rd_%03d_medium.png", width=480, height=480)
> ### Name: varImpGroup
> ### Title: A grouped variable importance with Random Forests
> ### Aliases: varImpGroup
> 
> ### ** Examples
> 
>   data(toyClassif)
>   attach(toyClassif)
> 
>   rf <- randomForest(x=X,y=Y,keep.forest=TRUE, keep.inbag=TRUE, ntree=500)
>   ngroups <- 3
>   nvarGroup <- c(4,3,6)
>   idxGroup <- list(c(0,1,2,5), c(2,4,5), c(0,1,5,6,7,8))
>   grImp <- varImpGroup(rf, X, ngroups, nvarGroup, idxGroup, NULL, normalize=FALSE )
>   cat("Group importance\n", grImp, "\n")
Group importance
 0.2678949 0.1235128 0.1726564 
> 
>   detach(toyClassif)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>