FWaveD | R Documentation |
Computes the Forward WaveD Transform.
FWaveD(y,g=1,L=3,deg=3,F=(log2(length(y))-1),thr=rep(0,log2(length(y))),SOFT=FALSE)
y |
Sample of |
g |
Sample of |
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 |
SOFT |
if SOFT=TRUE, uses the soft thresholding policy as opposed to the hard (SOFT=FALSE, the default). |
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
.
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.
WaveD
library(waved)
data=waved.example(TRUE,FALSE)
lidar.w=FWaveD(data$lidar.blur,data$g)
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