postsamples: Obtain samples from the posterior distribution of a 2D GLM...
In BHMSMAfMRI: Bayesian Hierarchical Multi-Subject Multiscale Analysis of Functional MRI (fMRI) Data

postsamples

R Documentation

Obtain samples from the posterior distribution of a 2D GLM coefficient map.

Description

postsamples generates samples from the posterior distribution of a 2D GLM coefficient map (e.g., corresponding to a single brain slice) of a regressor in the BHMSMA model for each subject based on multi-subject or single subject analyses (see References).

Usage

postsamples(nsample, n, grid, glmcoefstd, waveletcoefmat, 
hyperparam, pkljbar, analysis, wave.family="DaubLeAsymm", 
filter.number=6, bc="periodic", seed)

Arguments

`nsample`	Number of samples to be generated.
`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.
`glmcoefstd`	An array of dimension `(n,grid,grid)`, containing for each subject the standardized GLM coefficients obtained by fitting GLM to the time-series corresponding to the voxels.
`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 (see References for details).
`analysis`	"MSA" or "SSA", depending on whether performing multi-subject analysis or single subject analysis.
`wave.family`	The family of wavelets to use - "DaubExPhase" or "DaubLeAsymm". Default is "DaubLeAsymm".
`filter.number`	The number of vanishing moments of the wavelet. Default is 6.
`bc`	The boundary condition to use - "periodic" or "symmetric". Default is "periodic".
`seed`	Must be a positive integer. Provide to set random number generation seed for reproducibility.

Details

The wavelet computations are performed by using the R package wavethresh.

Value

A list containing the following.

`samples`	An array of dimension `(n,grid,grid,nsample)`, containing for each subject the posterior samples of the GLM coefficients.
`postdiscovery`	An array of dimension `(n,grid,grid)`, containing for each subject the posterior discovery maps of the GLM coefficients (for details see Morris et al. (2011)).

Author(s)

Nilotpal Sanyal, Marco Ferreira

Maintainer: Nilotpal Sanyal <nilotpal.sanyal@gmail.com>

References

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

Morris, J.S. et al. (2011). Automated analysis of quantitative image data using isomorphic functional mixed models, with application to proteomic data. Ann. Appl. Stat. 5, 894-923.

Examples

set.seed(1)
n <- 3
grid <- 8
nsample <- 5
glmcoefstd <- array(rnorm(n*grid*grid),
  dim=c(n,grid,grid))
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"
postsample <- postsamples(nsample,n,grid,glmcoefstd, 
waveletcoefmat, hyperparam,pkljbar,analysis,seed=1)
dim(postsample$samples)
#[1] 3 8 8 5

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