const.adj.list: Computation of the list of adjacency matrices

In brainwaver: Basic wavelet analysis of multivariate time series with a visualisation and parametrisation using graph theory.

Description

Computes the list of the adjacency matrices in terms of the scale of the wavelet decomposition.

Usage

const.adj.list(wave.cor.list, wave.var.ind = 0, n.ind = 0, thresh = 0.05, 
                 sup = 0, test.method = "gaussian", proc.length, 
                 use.tanh = FALSE)

Arguments

wave.cor.list	object of class "Wave Correlation" containing the correlation matrices to be analysed
wave.var.ind	object of class "Wave Correlation" containing the inter individuals variance of the correlation. Only used with test.method="t.test". (default not used)
n.ind	number of individuals to take into account in the test. Only used with test.method="t.test". (default not used)
thresh	indicates the rate at which the FDR procedure is controlled. (default 0.05)
sup	indicates the correlation threshold to consider in each hypothesis test
test.method	name of the method to be applied. "gaussian" assumes a gaussian law for the estimator. "t.test" implements a t.test for computing the p-value. (default "gaussian") .
proc.length	specifies the length of the original processes using to construct the wave.cor.list
use.tanh	logical. If FALSE take the atanh of the correlation values before applying the hypothesis test, in order to use the Fisher approximation

Details

Each hypothesis test is written as :

H_0 : "|correlation| <= sup"

H_1 : "|correlation| > sup"

Value

Object of class "Wave Adjacency matrix", basically, a list with the following components

d?	Adjacency matrix for each scale of the wavelet decomposition

Author(s)

S. Achard

References

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.

See Also

const.adj.mat

Examples

data(brain)
brain<-as.matrix(brain)
# WARNING : To process only the first five regions
brain<-brain[,1:5]


# Construction of the correlation matrices for each level of the wavelet decomposition
wave.cor.list<-const.cor.list(brain, method = "modwt" ,wf = "la8", n.levels = 6, 
                               boundary = "periodic", p.corr = 0.975)

# Construction of the adjacency matrices associated to each level of the 
# wavelet decomposition
wave.adj.list<-const.adj.list(wave.cor.list, sup = 0.44, proc.length=dim(brain)[1])

par(mfrow=c(3,2))

for(i in 1:4)
{
name.txt<-paste("Level ",i,sep="")
image(wave.adj.list[[i]],col=gray((0:20)/20),main=name.txt)
}

brainwaver documentation built on May 2, 2019, 10:23 a.m.