brainwaver (version 1.6)

p.value.compute: Computation of the p-value for a given hypotheses test

Description

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).

Usage

p.value.compute(test.mat, var.ind.mat = 0, n.ind = 0, test.method = "gaussian", proc.length, sup, num.levels, use.tanh = FALSE)

Arguments

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.

Value

Vector with the p-value for each entry of the matrix.

Details

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.

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

codeconst.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 = 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)