Backward variable selection procedure for multivariate functional data which is a set of *p* functional covariates and *n* observations of dimension *N*.

1 2 3 | ```
selectFunctional( FDlist, ydata, normalize=TRUE,
dimensionReductionMethod=c("fpca", "wave"),
nbasisInit, verbose=TRUE, ...)
``` |

`FDlist` |
A p-dimensional list containing the set of functional variables which are matrices of size |

`ydata` |
The outcome data. Must be a factor for classification. |

`normalize` |
Should the functions be normalized ? |

`dimensionReductionMethod` |
The dimension reduction method, ‘fpca’ for Functional Principal Component Analysis or ‘wave’ for the multiple wavelet thresholding. |

`nbasisInit` |
The number of initial spline coefficients. |

`verbose` |
Should the details be printed. |

`...` |
further arguments passed to or from other methods. |

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

`nselected` |
The number of selected functional variables ; |

`selection` |
The selected functional variables ; |

`selectionIndexes` |
The indexes of selected functional variables in the input data ‘FDlist’ ; |

`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 functional variables ; |

`rankingIndexes` |
The final ranking indexes of the functional variables. |

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.

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