postwaveletcoef {BHMSMAfMRI}

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