Two-fold wavelet shrinkage cross-validation (in C)
CWCV(ynoise, ll, x = 1:length(ynoise), filter.number = 10, family =
"DaubLeAsymm", thresh.type = "soft", tol = 0.01,
maxits=500, verbose = 0,
plot.it = TRUE, interptype = "noise")A list with the following components
This is just the x that was input. It gets passed through more or less for convenience for the user.
A copy of the input ynoise noisy data.
The cross-validated wavelet shrunk estimate.
The universal thresholded version (note this is merely a starting point for the cross-validation algorithm. It should not be ta ken seriously as an estimate. In particular its estimate of variance is likely to be inflated.)
The cross-validated threshold
The number of non-zero coefficients in the cross-validated shrunk wavelet object (which is not returned).
The number of non-zero coefficients in the universal threshold shrunk wavelet object (which also is not returned)
always returns NULL!
always returns NULL!
A vector of dyadic (power of two) length that contains the noisy data that you wish to apply wavelet shrinkage by cross-validation to.
The primary resolution that you wish to assume. No wavelet coefficients that are on coarser scales than ll will be thresholded.
This function is capable of producing informative plots. It can be useful to supply the x values corresponding to the ynoise values. Further this argument is returned by this function which can be useful for later processors.
This selects the smoothness of wavelet that you want to perform wavelet shrinkage by cross-validation.
specifies the family of wavelets that you want to use. The options are "DaubExPhase" and "DaubLeAsymm".
this option specifies the thresholding type which can be "hard" or "soft".
this specifies the convergence tolerance for the cross-validation optimization routine (a golden section search).
maximum number of iterations for the cross-validation optimization routine (a golden section search).
Controls the printing of "informative" messages whilst the computations progress. Such messages are generally annoying so it is turned off by default
If this is TRUE then plots of the universal threshold (used to obtain an upper bound on the cross-validation threshold) reconstruction and the resulting cross-validation estimate are produced.
Can take two values noise or normal. This option controls how cross-validation compares the estimate formed by leaving out the data with the "left-out" data. If interptype="noise" then two noisy values are averaged to compare with the estimated curve in between, otherwise if interptype="normal" then the curve estimate is averaged either side of a noisy left-out point.
Version 3.0 Copyright Guy Nason 1994
G P Nason
Compute the two-fold cross-validated wavelet shrunk estimate given the noisy data ynoise according to the description given in Nason, 1996.
You must specify a primary resolution given by ll. This must be specified individually on each data set and can itself be estimated using cross-validation (although I haven't written the code to do this).
Note. The two-fold cross-validation method performs very badly if the input data is correlated. In this case I would advise using the methods proposed in Donoho and Johnstone, 1995 or Johnstone and Silverman, 1997 which can be carried out in WaveThresh using the threshold function using the policy="sure" option.
threshold. threshold.wd.
#
# This function is best used via the policy="cv" option in
# the threshold.wd function.
# See examples there.
#
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