wt.filter {wavelets} | R Documentation |
Generates a wavelet transform filter.
wt.filter(filter="la8", modwt=FALSE)
filter |
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. |
modwt |
A logical value indicating whether to compute the maximal overlap discrete wavelet transform filter. |
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:
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
.
Returns an object of class wt.filter
, which is an S4 object
with slots
L |
An integer representing the length of the wavelet and scaling filters. |
h |
A numeric vector of wavelet filter coefficients. |
g |
A numeric vector of scaling filter coefficients. |
wt.class |
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" . |
wt.name |
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" . |
transform |
A character string indicating whether the resulting
wavelet transform object contains DWT or MODWT coefficients. Possible
values are "dwt" and "modwt" . |
The notation h
and g
for wavelet and scaling
coefficients, respectively, follows Percival and Walden (2000). In
other texts and articles the reverse notation is often adopted.
Eric Aldrich. ealdrich@gmail.com.
Percival, D. B. and A. T. Walden (2000) Wavelet Methods for Time Series Analysis, Cambridge University Press.
wt.filter("la14") wt.filter(1:10, modwt=TRUE)