design_Param_BayesGLM

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

Damon Pham

David Bolin

Yu (Ryan) Yue

Daniel Spencer

Sarah Ryan

design_Param_BayesGLM function

<dl><dt>design</dt>
<dd>A numeric matrix or <code>data.frame</code>, or a
<code>"BayesfMRI_design"</code> object from <code>make_design</code>. Can also
be an array where the third dimension is the same length as the number of
data locations, to model each location with its own design.</dd></dl>

Arguments

design — design_Param_BayesGLM

<dl>

<dt>design</dt>
<dd>A numeric matrix or <code>data.frame</code>, or a
<code>"BayesfMRI_design"</code> object from <code>make_design</code>. Can also
be an array where the third dimension is the same length as the number of
data locations, to model each location with its own design.</dd>

</dl>

design

design_Param_BayesGLM: design

Description

Arguments