BayesGLM_format_scrub

Format scrub for <code>BayesGLM</code>, <code>fit_bayesglm</code>,
<code>multiGLM</code>, and <code>multiGLM_fun</code>.

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

Damon Pham

David Bolin

Yu (Ryan) Yue

Daniel Spencer

Sarah Ryan

BayesGLM_format_scrub function

<dl><dt>scrub</dt>
<dd>The <code>scrub</code> argument input. Will be formatted to a
<code>nS</code>-length list.</dd>
<dt>nS_expect</dt>
<dd>The expected number of sessions, if known.</dd>
<dt>nT_expect</dt>
<dd>The expected number of timepoints, if known. For
multi-session data this is a session-length vector.</dd></dl>

Arguments

Format scrub — BayesGLM_format_scrub

<dl>

<dt>scrub</dt>
<dd>The <code>scrub</code> argument input. Will be formatted to a
<code>nS</code>-length list.</dd>


<dt>nS_expect</dt>
<dd>The expected number of sessions, if known.</dd>


<dt>nT_expect</dt>
<dd>The expected number of timepoints, if known. For
multi-session data this is a session-length vector.</dd>

</dl>

BayesGLM_format_scrub: Format scrub

Description

Usage

Value

Arguments