waveletcoeff {BHMSMAfMRI}R Documentation

Apply discrete wavelet transform to the GLM coefficients and obtain the wavelet coefficients.

Description

Applies 2D discrete wavelet transform to the standardized GLM coefficient maps and returns the wavelet coefficients of all resolution levels.

Usage

waveletcoeff(nsubject, grid, GLMCoeffStandardized, 
wave.family="DaubLeAsymm", filter.number=6, bc="periodic")

Arguments

nsubject

Number of subjects included in the analysis.

grid

The number of voxels in one row (or, one column) of the brain slice of interest. Must be a power of 2. The total number of voxels is grid^2. The maximum grid value for this package is 512.

GLMCoeffStandardized

An array of dimension (nsubject, grid, grid), containing for each subject the standardized GLM coefficients obtained by fitting GLM to the time-series corresponding to the voxels.

wave.family

The family of wavelets to use - "DaubExPhase" or "DaubLeAsymm". Default is "DaubLeAsymm".

filter.number

The number of vanishing moments of the wavelet. By default 6.

bc

The boundary condition to use - "periodic" or "symmetric". Default is "periodic".

Details

The wavelet decomposition is performed by using R package 'wavethresh'. For details, check wavethresh package help.

Value

A list containing the following.

WaveletCoefficientMatrix

A matrix of dimension (nsubject, grid^2-1), containing for each subject the wavelet coefficients of all levels stacked together (by the increasing order of resolution level).

Author(s)

Nilotpal Sanyal <nsanyal@stanford.edu>, Marco Ferreira <marf@vt.edu>

Examples

nsubject <- 3
grid <- 8
Data <- array(dim=c(3,8,8,10),rnorm(3*8*8*10))
DesignMatrix <- cbind( c(rep(c(1,0),5)), rep(1,10) )
glm.fit <- glmcoeff(nsubject, grid, Data, DesignMatrix)
GLMCoeffStandardized <- glm.fit$GLMCoeffStandardized
wavelet.coeff <- waveletcoeff(nsubject, grid, GLMCoeffStandardized)
dim(wavelet.coeff$WaveletCoefficientMatrix)
#[1]  3 63

[Package BHMSMAfMRI version 1.3 Index]