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Last data update: 2014.03.03

R: Wavelets hard-thresholding rule for independents processes

hardThresholding

R Documentation

Wavelets hard-thresholding rule for independents processes

Description

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.

Usage

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

Arguments

`xdata`	The matrix of n independent curves of dimension N=2^J, where J is the number of maximum wavelet level.
`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’.

Value

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.

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.

Examples

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

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(RFgroove)
Loading required package: randomForest
randomForest 4.6-12
Type rfNews() to see new features/changes/bug fixes.
Loading required package: wmtsa
Loading required package: fda
Loading required package: splines
Loading required package: Matrix

Attaching package: 'fda'

The following object is masked from 'package:graphics':

    matplot

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/RFgroove/hardThresholding.Rd_%03d_medium.png", width=480, height=480)
> ### Name: hardThresholding
> ### Title: Wavelets hard-thresholding rule for independents processes
> ### Aliases: hardThresholding
> 
> ### ** Examples
> 
>   data(toyRegFD)
>   x <- toyRegFD$FDlist[[1]]
>   newDesignMatrix <- hardThresholding(xdata=x, verbose=TRUE)
Automatic threshold  0.0485696 
59 selected coefficients using multiple hard-thresholding.	Filter:  s8 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>