Generates a wavelet transform filter.
wt.filter(filter="la8", modwt=FALSE, level=1)
A character string indicating which wavelet transform filter to compute or a numeric vector of wavelet (high pass) filter coefficients (not scaling (low pass) coefficients). If a numeric vector is supplied, the length must be even.
A logical value indicating whether to compute the maximal overlap discrete wavelet transform filter.
An integer value indicating the level of the wavelet filter to compute.
Returns an object of class wt.filter
, which is an S4 object
with slots
An integer representing the length of the wavelet and scaling filters.
A numeric vector of wavelet filter coefficients.
A numeric vector of scaling filter coefficients.
A character string indicating the class of the wavelet
transform filter. Possible values are "Daubechies"
,
"Least Asymetric"
, "Best Localized"
, and
"Coiflet"
. If the wt.filter
object is generated from a
numeric vector of wavelet coefficients, wt.class
is
"none"
.
A character string indicating the name of the wavlet
filter as listed in the Details section, above. If the
wt.filter
object is generated from a numeric vector of
wavelet coefficients, wt.name
is "none"
.
A character string indicating whether the resulting
wavelet transform object contains DWT or MODWT coefficients. Possible
values are "dwt"
and "modwt"
.
The character strings currently supported are derived from one of four
classes of wavelet transform filters: Daubechies, Least Asymetric,
Best Localized and Coiflet. The prefixes for filters of these classes
are d
, la
, bl
and c
,
respectively. Following the prefix, the filter name consists of an
integer indicating length. Supported lengths are as follows:
2,4,6,8,10,12,14,16,18,20.
8,10,12,14,16,18,20.
14,18,20.
6,12,18,24,30.
Thus, to obtain the Daubechies wavelet transform filter of length 4,
the character string "d4"
can be passed to
wt.filter
.
This naming convention has one exception: the Daubechies wavelet
transform filter of length 2 is denoted by haar
instead of
d2
.
Percival, D. B. and A. T. Walden (2000) Wavelet Methods for Time Series Analysis, Cambridge University Press.
# NOT RUN {
wt.filter("la14")
wt.filter(1:10, modwt=TRUE)
# }
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