wave.trans {brainwaver} | R Documentation |
Uses the wavelet decomposition implemented by Whitcher in the library waveslim
. See all the details there.
wave.trans(x, method = "modwt", wf = "la8", n.levels = 4, boundary = "periodic")
x |
original vector to be decomposed |
method |
wavelet decomposition to be used, algorithm implemented in the waveslim package (Whitcher, 2000). By default, the Maximal Overlap Discrete Wavelet Transform is used "modwt" . It is also possible to use the classical Discrete Wavelet Transform "dwt" . |
wf |
name of the wavelet filter to use in the decomposition. By default
this is set to "la8" , the Daubechies orthonormal compactly
supported wavelet of length L=8 (Daubechies, 1992), least
asymmetric family. |
n.levels |
specifies the depth of the decomposition. This must be a number less than or equal to log(length(x),2). |
boundary |
Character string specifying the boundary condition. If
boundary=="periodic" the default, then the vector you
decompose is assumed to be periodic on its defined interval,if boundary=="reflection" , the vector beyond its boundaries
is assumed to be a symmetric reflection of itself. |
See the library package waveslim
(Whitcher, 2000).
Object of class "modwt"
, basically, a list with the following
components
d? |
Wavelet coefficient vectors. |
s? |
Scaling coefficient vector. |
wavelet |
Name of the wavelet filter used. |
boundary |
How the boundaries were handled. |
S. Achard
R. Gencay, F. Selcuk and B. Whitcher (2001) An Introduction to Wavelets and Other Filtering Methods in Finance and Economics, Academic Press.
D. B. Percival and A. T. Walden (2000) Wavelet Methods for Time Series Analysis, Cambridge University Press.
data(brain) # the result brain is a matrix brain<-as.matrix(brain) # WARNING : To process only the first five regions brain<-brain[,1:5] PreCG.R<-brain[,1] # LA(8) PreCG.R.la8 <- wave.trans(PreCG.R, wf="la8") names(PreCG.R.la8) <- c("w1", "w2", "w3", "w4", "v4") ## plot partial MODWT for PreCG.R data par(mfcol=c(6,1), pty="m", mar=c(5-2,4,4-2,2)) plot.ts(PreCG.R, axes=FALSE, ylab="", main="(a)") for(i in 1:5) plot.ts(PreCG.R.la8[[i]], axes=FALSE, ylab=names(PreCG.R.la8)[i]) axis(side=1, at=seq(0,518,by=50), labels=c(0,"",100,"",200,"",300,"",400,"",500))