BHMSMAfMRI (version 2.3)

hyperparamest: 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)

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.

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 grid^2. The maximum value of grid for this package is 512.

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).

analysis

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

Author

Nilotpal Sanyal, Marco Ferreira

Maintainer: Nilotpal Sanyal <[e-mail]>

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