.findTheta

Perform the EM algorithm of the Bayesian GLM fitting

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

.findTheta function

<dl><dt>theta</dt>
<dd>the vector of initial values for theta</dd>
<dt>spde</dt>
<dd>a list containing the sparse matrix elements Cmat, Gmat, and GtCinvG</dd>
<dt>y</dt>
<dd>the vector of response values</dd>
<dt>X</dt>
<dd>the sparse matrix of the data values</dd>
<dt>QK</dt>
<dd>a sparse matrix of the prior precision found using the initial values of the hyperparameters</dd>
<dt>Psi</dt>
<dd>a sparse matrix representation of the basis function mapping the data locations to the mesh vertices</dd>
<dt>A</dt>
<dd>a precomputed matrix crossprod(X%*%Psi)</dd>
<dt>Ns</dt>
<dd>the number of columns for the random matrix used in the Hutchinson estimator</dd>
<dt>tol</dt>
<dd>a value for the tolerance used for a stopping rule (compared to
the squared norm of the differences between <code>theta(s)</code> and <code>theta(s-1)</code>)</dd>
<dt>verbose</dt>
<dd>(logical) Should intermediate output be displayed?</dd></dl>

Arguments

Perform the EM algorithm of the Bayesian GLM fitting — .findTheta

<dl>

<dt>theta</dt>
<dd>the vector of initial values for theta</dd>


<dt>spde</dt>
<dd>a list containing the sparse matrix elements Cmat, Gmat, and GtCinvG</dd>


<dt>y</dt>
<dd>the vector of response values</dd>


<dt>X</dt>
<dd>the sparse matrix of the data values</dd>


<dt>QK</dt>
<dd>a sparse matrix of the prior precision found using the initial values of the hyperparameters</dd>


<dt>Psi</dt>
<dd>a sparse matrix representation of the basis function mapping the data locations to the mesh vertices</dd>


<dt>A</dt>
<dd>a precomputed matrix crossprod(X%*%Psi)</dd>


<dt>Ns</dt>
<dd>the number of columns for the random matrix used in the Hutchinson estimator</dd>


<dt>tol</dt>
<dd>a value for the tolerance used for a stopping rule (compared to
the squared norm of the differences between <code>theta(s)</code> and <code>theta(s-1)</code>)</dd>


<dt>verbose</dt>
<dd>(logical) Should intermediate output be displayed?</dd>

</dl>

.findTheta: Perform the EM algorithm of the Bayesian GLM fitting

Description

Usage

Arguments