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

1 2 3 | ```
varImpGroup(object, xdata, ngroups = length(nvarGroup), nvarGroup,
idxGroup, groupsNames = names(nvarGroup),
normalize = (length(unique(nvarGroup)) != 1))
``` |

`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. |

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

Baptiste Gregorutti

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.

`selectGroup`

,`selectLevel`

,`selectFunctional`

,`plot.importance`

1 2 3 4 5 6 7 8 9 10 11 | ```
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)
``` |

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