const.var.list {brainwaver} | R Documentation |
Computes the list of the variance vectors in terms of the scale of the wavelet decomposition.
const.var.list(data.mat, names.data = 0, method = "modwt", wf = "la8", n.levels = 4, boundary = "periodic", save.wave = FALSE, export.data = FALSE)
data.mat |
matrix containing the data time series. Each column of the matrix represents one time series. |
names.data |
optional character vector containing the name associated to the column of the matrix data.mat . |
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. |
save.wave |
logical. If TRUE all the wavelet coefficient are saved. |
export.data |
logical. If TRUE the variance vectors with the upper and lower bound are exported to text file. |
This function uses the wavelet decomposition implemented in the R package waveslim
, (whitcher, 2000).
Object of class "Wave Variance"
, basically, a list with the following
components
d? |
Variance vectors for each scale of the wavelet decomposition. |
lowerd? |
vector containing the lower bound of the variance for each scale of the wavelet decomposition. |
upperd? |
vector containing the upper bound of the variance for each scale of the wavelet decomposition. |
S. Achard
Gencay, R., F. Selcuk and B. Whitcher (2001) An Introduction to Wavelets and Other Filtering Methods in Finance and Economics, Academic Press.
Percival, D. B. and A. T. Walden (2000) Wavelet Methods for Time Series Analysis, Cambridge University Press.
S. Achard, R. Salvador, B. Whitcher, J. Suckling, Ed Bullmore (2006) A Resilient, Low-Frequency, Small-World Human Brain Functional Network with Highly Connected Association Cortical Hubs. Journal of Neuroscience, Vol. 26, N. 1, pages 63-72.
const.cor.list
, read.var.txt
, save.var.txt
data(brain) brain<-as.matrix(brain) # WARNING : To process only the first five regions brain<-brain[,1:5] n.levels<-4 wave.var.list<-const.var.list(brain,n.levels=n.levels) tot.regions <- dim(brain)[2] n.series <- dim(brain)[1] nb.num.regions <- 9 num.regions <- round(runif(nb.num.regions,2,tot.regions)) par(mfrow=c(3,3)) for(k in 1:(nb.num.regions)){ reg <- num.regions[k] var.vector<-matrix(0,4,3) for(i in 1:n.levels){ var.vector[i,1]<-(wave.var.list[[i]])[reg] var.vector[i,2]<-(wave.var.list[[i+n.levels]])[reg] var.vector[i,3]<-(wave.var.list[[i+2*n.levels]])[reg] } title <- num.regions[k] matplot(2^(0:(n.levels-1)),var.vector,main=title,type="b", log="x", pch="*LU", xaxt="n", lty=1, col=c(1,4,4), xlab="Wavelet Scale",ylab="Wavelet Variance") axis(side=1, at=2^(0:7)) abline(h=0) }