p.value.compute {brainwaver} | R Documentation |
Computes the p-values for all the entries in the matrix test.mat
using the asymtotic properties of the estimator of the wavelet correlation given in (Whitcher, 2000).
p.value.compute(test.mat, var.ind.mat = 0, n.ind = 0, test.method = "gaussian", proc.length, sup, num.levels, use.tanh = FALSE)
test.mat |
matrix containing the wavelet correlation to be tested |
var.ind.mat |
matrix containing the variance inter individuals 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) |
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 cor.mat |
num.levels |
specifies the number of the wavelet scale to take into account in the hypothesis test. Only used with test.method="gaussian" |
use.tanh |
logical. If FALSE take the atanh of the correlation values before applying the hypothesis test, in order to use the Fisher approximation |
sup |
indicates the correlation threshold to consider in each hypothesis test. |
Each hypothesis test is written as :
H_0 : "|correlation| <= sup"
H_1 : "|correlation| > sup"
This function is essentially an internal function called by const.adj.mat
.
Vector with the p-value for each entry of the matrix.
S. Achard
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.
code{const.adj.mat}
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 = 4, boundary = "periodic", p.corr = 0.975) # For scale 4 pvalue.cor<-p.value.compute(wave.cor.list[[4]],proc.length=dim(brain)[1], sup=0.44, num.levels=4)