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.

1 2 | ```
selectLevel(design, ydata, typeRF = ifelse(is.factor(ydata), "classif", "reg"),
verbose = TRUE, ntree = 500, ...)
``` |

`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. |

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. |

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`

,`selectFunctional`

,`varImpGroup`

1 2 3 4 5 6 7 | ```
data(toyRegFD)
x <- toyRegFD$FDlist[[1]]
y <- toyRegFD$Y
design <- projectWavelet(xdata=x)
summary(levSel <- selectLevel(design, y, ntree=100, verbose=TRUE))
plot(levSel)
``` |

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