postgroupglmcoef.Rd
postgroupglmcoef
computes posterior group mean (or group median) of a 2D GLM coefficients map (e.g., corresponding to a single brain slice) of a regressor using the posterior means (or medians) of the corresponding wavelet coefficients from all subjects in the inverse discrete wavelet transform based on multi-subject or single subject analyses (see References).
postgroupglmcoef( n, grid, glmcoefstd, postmeanwaveletcoef,
wave.family="DaubLeAsymm", filter.number=6, bc="periodic" )
Number of subjects.
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 value of grid
for this package is 512.
An array of dimension (n,grid,grid)
, containing for each subject the standardized GLM coefficients obtained by fitting GLM to the time-series corresponding to the voxels.
A matrix of size (n,grid^2-1)
, containing for each subject the posterior mean of the wavelet coefficients of all levels stacked together (by the increasing order of resolution level).
The family of wavelets to use - "DaubExPhase" or "DaubLeAsymm". Default is "DaubLeAsymm".
The number of vanishing moments of the wavelet. Default is 6.
The boundary condition to use - "periodic" or "symmetric". Default is "periodic".
A list containing the following.
A matrix of dimension (grid, grid), containing the posterior group coefficients obtained by BHMSMA methodology.
The wavelet transformation and reconstruction are performed by using the functions imwd
and imwr
, respectively.
Sanyal, Nilotpal, and Ferreira, Marco A.R. (2012). Bayesian hierarchical multi-subject multiscale analysis of functional MRI data. Neuroimage, 63, 3, 1519-1531.