spde_Q_phi

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

spde_Q_phi function

<dl><dt>kappa2</dt>
<dd>A scalar</dd>
<dt>phi</dt>
<dd>A scalar</dd>
<dt>spde</dt>
<dd>An object containing the information about the
mesh structure for the SPDE prior</dd></dl>

Arguments

Calculate the SPDE covariance — spde_Q_phi

<dl>

<dt>kappa2</dt>
<dd>A scalar</dd>


<dt>phi</dt>
<dd>A scalar</dd>


<dt>spde</dt>
<dd>An object containing the information about the
mesh structure for the SPDE prior</dd>

</dl>

Calculate the SPDE covariance

spde_Q_phi: Calculate the SPDE covariance

Description

Usage

Value

Arguments