The smoothed and original data from a wavelet decomposition structure
(returned from wd) are packed into a single vector in that
structure. This function extracts the data corresponding to a
particular resolution level.
# S3 method for wd
accessC(wd, level = nlevelsWT(wd), boundary=FALSE, aspect, ...)A vector of the extracted data.
wavelet decomposition structure from which you wish to extract the smoothed or original data if the structure is from a wavelet decomposition, or the reconstructed data if the structure is from a wavelet reconstruction.
the level that you wish to extract. By default, this is the level with most detail (in the case of structures from a decomposition this is the original data, in the case of structures from a reconstruction this is the top-level reconstruction).
logical;
if TRUE then all of the boundary correction values
will be returned as well (note: the length of the returned vector
may not be a power of 2).
If boundary is false, then just the coefficients will be returned.
If the decomposition (or reconstruction) was done with periodic boundary conditions, this option has no effect.
Applies a function to the coefficients before return. Supplied as a text string which gets converted to a function. For example, "Mod" for complex-valued arguments
any other arguments
Version 3.5.3 Copyright Guy Nason 1994
G P Nason
The wd (wr.wd) function produces a wavelet decomposition (reconstruction)
structure.
For decomposition, the top level contains the original data, and subsequent lower levels contain the successively smoothed data. So if there are \(2^m\) original data points, there will be m+1 levels indexed 0,1,...,m. So
accessC.wd(wdobj, level=m)
pulls out the original data, as does
accessC.wd(wdobj)
To get hold of lower levels just specify the level that you're interested in, e.g.
accessC.wd(wdobj, level=2)
gets hold of the second level.
For reconstruction, the top level contains the ultimate step in the Mallat pyramid reconstruction algorithm, lower levels are intermediate steps.
The need for this function is a consequence of the pyramidal structure
of Mallat's algorithm and the memory efficiency gain achieved by
storing the pyramid as a linear vector. AccessC obtains information about
where the smoothed data appears from the fl.dbase component of
an wd.object, in particular the array fl.dbase$first.last.c which
gives a complete specification of index numbers and offsets for
wd.object$C.
Note that this function is method for the generic function accessC.
When the wd.object is definitely a wd class object then you only need use the generic version of this function.
Note that this function only gets information from wd class
objects. To insert coefficients etc. into wd structures you have to
use the putC function (or more precisely, the putC.wd method).
Mallat, S. G. (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 674--693.
Nason, G. P. and Silverman, B. W. (1994). The discrete wavelet transform in S. Journal of Computational and Graphical Statistics, 3, 163--191.
wr, wd, accessD, accessD.wd,
filter.select, threshold, putC.wd, putD.wd.
## Get the 3rd level of smoothed data from a decomposition
dat <- rnorm(64)
accessC(wd(dat), level=3)
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