postwaveletcoeff {BHMSMAfMRI} | R Documentation |
Computes posterior mean and posterior median of the wavelet coefficients using BHMSMA methodology.
postwaveletcoeff(nsubject, grid, WaveletCoefficientMatrix, hyperparam, pklj.bar, analysis)
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. |
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). |
hyperparam |
A vector containing the estimates of the six hyperparameters. |
pklj.bar |
A matrix of dimension (nsubject, grid^2-1), containing the piklj bar values (see Reference for details). |
analysis |
"MSA" or "SSA", depending on whether performing multi-subject analysis or single subject analysis. |
A list containing the following.
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). |
PostMedianWaveletCoeff |
A matrix of size (nsubject, grid^2-1), containing for each subject the posterior median of the wavelet coefficients of all levels stacked together. |
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 nsample <- 5 GLMCoeffStandardized <- array(rnorm(3*8*8),dim=c(3,8,8)) WaveletCoefficientMatrix <- array(rnorm(3*63),dim=c(3,63)) hyperparam <- rep(.2,6) pklj.bar <- array(runif(3*63),dim=c(3,63)) analysis <- "multi" post.waveletcoeff <- postwaveletcoeff(nsubject, grid, WaveletCoefficientMatrix, hyperparam, pklj.bar, analysis) dim(post.waveletcoeff$PostMeanWaveletCoeff) #[1] 3 63