Description Usage Arguments Details Value Author(s) References See Also Examples
All possible filtering combinations (low- and high-pass) are performed to decompose a vector or time series. The resulting coefficients are associated with a binary tree structure corresponding to a partitioning of the frequency axis.
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x |
a vector or time series containing the data be to decomposed. This must be a dyadic length vector (power of 2). |
wf |
Name of the wavelet filter to use in the decomposition. By default
this is set to |
n.levels |
Specifies the depth of the decomposition. This must be a number less than or equal to log2[length(x)]. |
boundary |
Character string specifying the boundary condition. If
|
y |
Object of S3 class |
y.basis |
Vector of character strings that describe leaves on the DWPT basis tree. |
The code implements the one-dimensional DWPT using the pyramid algorithm (Mallat, 1989).
Basically, a list with the following components
w?.? |
Wavelet coefficient vectors. The first index is associated with the scale of the decomposition while the second is associated with the frequency partition within that level. |
wavelet |
Name of the wavelet filter used. |
boundary |
How the boundaries were handled. |
B. Whitcher
Mallat, S. G. (1989) A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, No. 7, 674-693.
Percival, D. B. and A. T. Walden (2000) Wavelet Methods for Time Series Analysis, Cambridge University Press.
Wickerhauser, M. V. (1994) Adapted Wavelet Analysis from Theory to Software, A K Peters.
dwt
, modwpt
, wave.filter
.
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