# Grouped variable selection procedure for functional data

### Description

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

### Usage

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

### Arguments

`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. |

### Value

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. |

### 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

`selectGroup`

,`varImpGroup`

### Examples

1 2 3 4 5 6 |