pikljbar {BHMSMAfMRI}R Documentation

Compute the piklj bar values of the BHMSMA model using Newton Cotes algorithm

Description

Computes the values of piklj bar of the BHMSMA model using Newton Cotes algorithm. For details, check References.

Usage

pikljbar(nsubject, grid, WaveletCoefficientMatrix, hyperparam, 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.

analysis

"MSA" or "SSA", depending on whether performing multi-subject analysis or single subject analysis.

Value

A list containing the following.

pklj.bar

A matrix of dimension (nsubject, grid^2-1), containing the piklj bar values.

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
WaveletCoefficientMatrix <- matrix(nrow=3,ncol=63)
for(i in 1:3)
 WaveletCoefficientMatrix[i,] <- rnorm(63)
hyperparam <- rep(.1,6)
analysis <- "multi"
piklj.bar <- pikljbar(nsubject, grid, WaveletCoefficientMatrix, hyperparam, analysis)
dim(piklj.bar$pklj.bar)
#[1]  3 63

[Package BHMSMAfMRI version 1.3 Index]