postmixprob | R Documentation |
postmixprob
computes the mixture probabilities (piklj.bar), which define the marginal posterior distribution of the wavelet coefficients of the BHMSMA model, using Newton Cotes algorithm for each subject based on multi-subject or single subject analyses, and returns the same (see References).
postmixprob(n, grid, waveletcoefmat, hyperparam, analysis)
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 |
waveletcoefmat |
A matrix of dimension |
hyperparam |
A vector containing the estimates of the six hyperparameters. |
analysis |
"MSA" or "SSA", depending on whether performing multi-subject analysis or single subject analysis. |
A list containing the following.
pkljbar |
A matrix of dimension |
Nilotpal Sanyal, Marco Ferreira
Maintainer: Nilotpal Sanyal <nilotpal.sanyal@gmail.com>
Sanyal, Nilotpal, and Ferreira, Marco A.R. (2012). Bayesian hierarchical multi-subject multiscale analysis of functional MRI data. Neuroimage, 63, 3, 1519-1531.
waveletcoef
, hyperparamest
, postwaveletcoef
set.seed(1)
n <- 3
grid <- 8
waveletcoefmat <- matrix(nrow=n,ncol=grid^2-1)
for(i in 1:n) waveletcoefmat[i,] <- rnorm(grid^2-1)
hyperparam <- rep(.1,6)
analysis <- "multi"
pkljbar <- postmixprob(n,grid,waveletcoefmat,hyperparam,
analysis)
dim(pkljbar$pkljbar)
#[1] 3 63
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