qsample

Sample from a multivariate normal with mean and precision

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

qsample function

<dl><dt>n</dt>
<dd>number of samples</dd>
<dt>mu</dt>
<dd>mean vector (length = p)</dd>
<dt>Q</dt>
<dd>sparse p x p positive definite precision matrix (class = dgCMatrix)</dd></dl>

Arguments

Sample from a multivariate normal with mean and precision — qsample

<dl>

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


<dt>mu</dt>
<dd>mean vector (length = p)</dd>


<dt>Q</dt>
<dd>sparse p x p positive definite precision matrix (class = dgCMatrix)</dd>

</dl>

qsample: Sample from a multivariate normal with mean and precision

Description

Usage

Value

Arguments