postwaveletcoef: Obtain posterior estimates of the BHMSMA wavelet coefficients

In BHMSMAfMRI: Bayesian Hierarchical Multi-Subject Multiscale Analysis of Functional MRI (fMRI) Data

Obtain posterior estimates of the BHMSMA wavelet coefficients

Description

postwaveletcoef computes posterior mean and posterior median of the wavelet coefficients of the BHMSMA model for each subject based on multi-subject or single subject analyses (see References).

Usage

postwaveletcoef(n, grid, waveletcoefmat, hyperparam, 
pkljbar, analysis)

Arguments

n	Number of subjects.
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 value of grid for this package is 512.
waveletcoefmat	A matrix of dimension (n,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.
pkljbar	A matrix of dimension (n,grid^2-1), containing the piklj bar values.
analysis	"MSA" or "SSA", depending on whether performing multi-subject analysis or single subject analysis.

Value

A list containing the following.

PostMeanWaveletCoef	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).
PostMedianWaveletCoef	A matrix of size (n,grid^2-1), containing for each subject the posterior median of the wavelet coefficients of all levels stacked together.

Author(s)

Nilotpal Sanyal, Marco Ferreira

Maintainer: Nilotpal Sanyal <[e-mail]>

References

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

See Also

waveletcoef, hyperparamest, postmixprob, postglmcoef

Examples

set.seed(1)
n <- 3
grid <- 8
nsample <- 5
waveletcoefmat <- array(rnorm(n*(grid^2-1)),
  dim=c(n,grid^2-1))
hyperparam <- rep(.2,6)
pkljbar <- array(runif(n*(grid^2-1)),
  dim=c(n,grid^2-1))
analysis <- "multi"
postwavecoef <- postwaveletcoef(n,grid,waveletcoefmat, 
hyperparam,pkljbar,analysis)
dim(postwavecoef$PostMeanWaveletCoef)
#[1]  3 63

BHMSMAfMRI documentation built on Oct. 2, 2022, 9:05 a.m.