brainwaver (version 1.6)

correlations.to.adjacencies: Produce adjencency matrices for a given number of edges

Description

Given a correlations thingy as produced by const.cor.list, produce a list of adjacency matrices fiddled to have a preferred number of edges Actually this is not quite possible, but come as close as choose.thresh.nbedges will allow us. A functional parameter allows us to say things like produce the graphs with n log n edges where n is the number of nodes

Usage

correlations.to.adjacencies(correlations, edge.func)
ideal.wavelet.levels(brain)
distance(x,y,z)

Arguments

correlations

a list of correlation matrices produced by const.cor.list

edge.func

a function to mention the way to choose the number of edges given the number of nodes in the graph. In the companion scripts files, the small-limit is used and by default edge.func=(function(x){x*log(x)})

brain

matrix containing the data time series. Each column of the matrix represents one time series.

x

x coordinate

y

y coordinate

z

z coordinate

Value

correlations.to.adjacencies

Description of 'comp1'

ideal.wavelets.levels

number indicating up to each wavelet scale it is possible to go given the length of the time series

disctance

the euclidean distance in 3D

Details

Functions produced to manipulate better nice outputs of the package

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.cor.list

Examples

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

n.levels<-4
wave.cor.list<-const.cor.list(brain,n.levels=n.levels)
adj.mat<-correlations.to.adjacencies(wave.cor.list,edge.func=(function(x){x*log(x)}))