hyperparamest {BHMSMAfMRI}R Documentation

Get the estimates of the hyperparameters of the BHMSME model along with the estimate of their covariance matrix.

Description

Computes the MLEs of the hyperparameters of the BHMSME model following an empirical Bayes approach and the estimate of the covariance matrix of the hyperparameters.

Usage

hyperparamest(nsubject, grid, WaveletCoefficientMatrix, 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).

analysis

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

Value

A list containing the following.

hyperparam

A vector containing the estimates of the six hyperparameters of the BHMSME model.

hyperparamVar

Estimated covariance matrix of the hyperparameters.

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 <- array(dim=c(3,63),rnorm(3*63))
analysis <- "multi"
hyper.est <- hyperparamest(nsubject, grid, WaveletCoefficientMatrix, analysis)

[Package BHMSMAfMRI version 1.3 Index]