This function projects *n* indepedent processes on a common wavelet basis and shrinks to zero the *n* coefficients whose *\ell_2*-norm is lower than a threshold.

1 | ```
hardThresholding(xdata, delta, verbose = FALSE, varName = NULL, wavFilter="s8")
``` |

`xdata` |
The matrix of |

`delta` |
The desired threshold. If missing, an automatic threshold is computed. |

`verbose` |
Should the details be printed. |

`varName` |
The name of the current functional variable. |

`wavFilter` |
A character string denoting the filter type. Supported types include: EXTREMAL PHASE (daublet): ‘haar’, ‘d2’, ‘d4’, ‘d6’, ‘d8’, ‘d10’, ‘d12’, ‘d14’, ‘d16’, ‘d18’, ‘d20’ LEAST ASYMMETRIC (symmlet): ‘s2’, ‘s4’, ‘s6’, ‘s8’, ‘s10’, ‘s12’, ‘s14’, ‘s16’, ‘s18’, ‘s20’ BEST LOCALIZED: ‘l2’, ‘l4’, ‘l6’, ‘l14’, ‘l18’, ‘l20’ COIFLET: ‘c6’, ‘c12’, ‘c18’, ‘c24’, ‘c30’ Default: ‘s8’. |

A list with two components

`mht.names` |
The names of the common wavelet basis after thresholding the coefficients. |

`estimatedDesign` |
The new design matrix after thresholding. |

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.

1 2 3 | ```
data(toyRegFD)
x <- toyRegFD$FDlist[[1]]
newDesignMatrix <- hardThresholding(xdata=x, verbose=TRUE)
``` |

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