hyperparamest {BHMSMAfMRI} | R Documentation |
Obtain estimates of the hyperparameters of the BHMSMA model
Description
hyperparamest
computes the MLEs (maximum likelihood estimates) of the hyperparameters of the BHMSMA model using an empirical Bayes approach for multi-subject or single subject analyses, and returns the hyperparameters estimates along with their covariance matrix estimate (see References).
Usage
hyperparamest(n, grid, waveletcoefmat, 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 |
waveletcoefmat |
A matrix of dimension |
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 BHMSMA model. |
hyperparamVar |
Estimated covariance matrix of the hyperparameters. |
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.
See Also
waveletcoef
, nlminb
, postmixprob
Examples
set.seed(1)
n <- 3
grid <- 8
waveletcoefmat <- array(dim=c(n,grid^2-1),
rnorm(n*(grid^2-1)))
analysis <- "multi"
hyperest <- hyperparamest(n,grid,waveletcoefmat,analysis)
hyperest$hyperparam
# [1] 1.00000 1.00000 1.00000 1.00000 0.00000 28.37678