postwaveletcoeff {BHMSMAfMRI}R Documentation

Obtain posterior mean and posterior median of the wavelet coefficients using BHMSMA methodology.

Description

Computes posterior mean and posterior median of the wavelet coefficients using BHMSMA methodology.

Usage

postwaveletcoeff(nsubject, grid, WaveletCoefficientMatrix, hyperparam, 
pklj.bar, analysis)

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.

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.

Value

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.

Author(s)

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

References

Sanyal, Nilotpal, and Ferreira, Marco A.R. (2012). Bayesian hierarchical multi-subject multiscale analysis of functional MRI data. Neuroimage, 63, 3, 1519-1531.

Examples

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

[Package BHMSMAfMRI version 1.3 Index]