postglmcoef {BHMSMAfMRI}

Obtain posterior estimate of a 2D GLM coefficients map of a regressor

Description

postglmcoef computes posterior mean (or median) of a 2D GLM coefficients map (e.g., corresponding to a single brain slice) of a regressor using the posterior mean (or median) of the corresponding wavelet coefficients in the inverse discrete wavelet transform for each subject based on multi-subject or single subject analyses (see References).

Usage

postglmcoef(n, grid, glmcoefstd, postmeanwaveletcoef, 
wave.family="DaubLeAsymm", filter.number=6, bc="periodic")

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.

glmcoefstd

An array of dimension (n,grid,grid), containing for each subject the standardized GLM coefficients obtained by fitting GLM to the time-series corresponding to the voxels.

postmeanwaveletcoef

A matrix of size (n,grid^2-1), containing for each subject the posterior mean of the wavelet coefficients of all levels stacked together (by the increasing order of resolution level).

wave.family

The family of wavelets to use - "DaubExPhase" or "DaubLeAsymm". Default is "DaubLeAsymm".

filter.number

The number of vanishing moments of the wavelet. Default is 6.

bc

The boundary condition to use - "periodic" or "symmetric". Default is "periodic".

Details

The wavelet transformation and reconstruction are performed by using the functions imwd and imwr, respectively.

Value

A list containing the following.

GLMcoefposterior

An array of dimension (n,grid,grid), containing for each subject the posterior means of the standardized GLM coefficients.

Author(s)

Nilotpal Sanyal, Marco Ferreira

Maintainer: Nilotpal Sanyal <[e-mail]>

See Also

glmcoef, postwaveletcoef, substituteWaveletCoef, imwr, postgroupglmcoef, postsamples

Examples

set.seed(1)
n <- 3
grid <- 8
glmcoefstd <- array(rnorm(n*grid*grid),
  dim=c(n,grid,grid))
postmeanwaveletcoef <- array(rnorm(n*(grid^2-1)),
  dim=c(n,(grid^2-1)))
postmeanglmcoef <- postglmcoef(n,grid,glmcoefstd,
  postmeanwaveletcoef)
dim(postmeanglmcoef$GLMcoefposterior)
#[1] 3 8 8