FWaveD

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Description

Computes the Forward WaveD Transform.

Usage

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FWaveD(y, g = 1, L = 3, deg = 3, F = (log2(length(y)) - 1), thr = rep(0, log2(length(y))), SOFT = FALSE)

Arguments

y

Sample of f*g + (Gaussian noise), a vector of dyadic length (i.e. 2^(J-1) where J is the largest resolution level). Here f is the target function, g is the convolution kernel.

g

Sample of g or g + (Gaussian noise), same length as yobs. The default is the Dirac mass at 0.

L

Lowest resolution level; the default is 3.

deg

The degree of the Meyer wavelet, either 1, 2, or 3 (the default).

F

Finest resolution level; the default is the data-driven choice j1 (see Value below).

thr

A vector of length F-L+1, giving thresholds at each resolution levels L,L+1,...,F; default is maxiset threshold.

SOFT

if SOFT=TRUE, uses the soft thresholding policy as opposed to the hard (SOFT=FALSE, the default).

Value

Returns a vector of wavelet coefficients of length n (the same length as y), the last n/2 entries are wavelet coefficients at resolution level J-1, where J= log_2(n); the n/4 entries before that are the wavelet coefficients at resolution level J-2, and so on until level L. In addition the 2^L entries are scaling coefficients at coarse level C=L.

References

Johnstone, I., Kerkyacharian, G., Picard, D. and Raimondo, M. (2004), 'Wavelet deconvolution in a periodic setting', Journal of the Royal Statistical Society, Series B 66(3),547–573. with discussion pp.627–652.

Raimondo, M. and Stewart, M. (2006), ‘The WaveD Transform in R’, preprint, School and Mathematics and Statistics, University of Sydney.

See Also

WaveD

Examples

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library(waved)
data=waved.example(TRUE,FALSE)
lidar.w=FWaveD(data$lidar.blur,data$g)

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