cholQsample

Sample from the multivariate normal distribution with Cholesky(Q)

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

cholQsample function

<dl><dt>n</dt>
<dd>number of samples</dd>
<dt>mu</dt>
<dd>mean vector</dd>
<dt>cholQ</dt>
<dd>Cholesky decomposition of the precision (found via <code>Matrix::Cholesky(Q)</code>)</dd></dl>

Arguments

Sample from the multivariate normal distribution with Cholesky(Q) — cholQsample

<dl>

<dt>n</dt>
<dd>number of samples</dd>


<dt>mu</dt>
<dd>mean vector</dd>


<dt>cholQ</dt>
<dd>Cholesky decomposition of the precision (found via <code>Matrix::Cholesky(Q)</code>)</dd>

</dl>

cholQsample: Sample from the multivariate normal distribution with Cholesky(Q)

Description

Usage

Value

Arguments