postgroupcoeff {BHMSMAfMRI} | R Documentation |
Computes posterior group coefficients using the BHMSMA methodology.
postgroupcoeff( nsubject, grid, GLMCoeffStandardized, PostMeanWaveletCoeff, wave.family="DaubLeAsymm", filter.number=6, bc="periodic" )
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. |
PostMeanWaveletCoeff |
A matrix of size (nsubject, 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). |
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". |
The wavelet computations are performed by using R package 'wavethresh'. For details, check wavethresh package help.
A list containing the following.
groupcoeff |
A matrix of dimension (grid, grid), containing the posterior group coefficients obtained by BHMSMA methodology. |
Nilotpal Sanyal <nsanyal@stanford.edu>, Marco Ferreira <marf@vt.edu>
Sanyal, Nilotpal, and Ferreira, Marco A.R. (2012). Bayesian hierarchical multi-subject multiscale analysis of functional MRI data. Neuroimage, 63, 3, 1519-1531.
nsubject <- 3 grid <- 8 GLMCoeffStandardized <- array(rnorm(3*8*8),dim=c(3,8,8)) PostMeanWaveletCoeff <- array(rnorm(3*63),dim=c(3,63)) post.groupcoeff <- postgroupcoeff( nsubject, grid, GLMCoeffStandardized, PostMeanWaveletCoeff) dim(post.groupcoeff$groupcoeff) #[1] 8 8