hyperparamest {BHMSMAfMRI} | R Documentation |
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.
hyperparamest(nsubject, grid, WaveletCoefficientMatrix, analysis)
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. |
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. |
Nilotpal Sanyal <nsanyal@stanford.edu>, Marco Ferreira <marf@vt.edu>
Sanyal, Nilotpal, and Ferreira, Marco A.R. (2012). Bayesian hierarchical multi-subject multiscale analysis of functional MRI data. Neuroimage, 63, 3, 1519-1531.
nsubject <- 3 grid <- 8 WaveletCoefficientMatrix <- array(dim=c(3,63),rnorm(3*63)) analysis <- "multi" hyper.est <- hyperparamest(nsubject, grid, WaveletCoefficientMatrix, analysis)