make_Q

Make the full SPDE precision based on theta, the spde, and the number of sessions

internal

Performs a spatial Bayesian general linear model (GLM) for task
functional magnetic resonance imaging (fMRI) data on the cortical surface.
Additional models include group analysis and inference to detect thresholded
areas of activation. Includes direct support for the 'CIFTI' neuroimaging
file format. For more information see A. F. Mejia, Y. R. Yue, D. Bolin, F.
Lindgren, M. A. Lindquist (2020) <doi:10.1080/01621459.2019.1611582> and D.
Spencer, Y. R. Yue, D. Bolin, S. Ryan, A. F. Mejia (2022)
<doi:10.1016/j.neuroimage.2022.118908>.

Amanda Mejia

BayesfMRI

Spatial Bayesian Methods for Task Functional MRI Studies

Daniel Spencer

Damon Pham

David Bolin

Sarah Ryan

Yu (Ryan) Yue

make_Q function

<dl><dt>theta</dt>
<dd>the hyperparameter theta vector of length K * 2 + 1, where the
first K elements are the kappas, the next K elements are the phis, and the
last element (unused here) corresponds to sigma2</dd>
<dt>spde</dt>
<dd>a list containing three spde elements: Cmat, Gmat, and GtCinvG</dd>
<dt>n_sess</dt>
<dd>The integer number of sessions</dd></dl>

Arguments

Make the full SPDE precision based on theta, the spde, and the number of sessions — make_Q

<dl>

<dt>theta</dt>
<dd>the hyperparameter theta vector of length K * 2 + 1, where the
first K elements are the kappas, the next K elements are the phis, and the
last element (unused here) corresponds to sigma2</dd>


<dt>spde</dt>
<dd>a list containing three spde elements: Cmat, Gmat, and GtCinvG</dd>


<dt>n_sess</dt>
<dd>The integer number of sessions</dd>

</dl>

make_Q: Make the full SPDE precision based on theta, the spde, and the number of sessions

Description

Usage

Value

Arguments