BayesGLM_format_nuisance

Format nuisance 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_nuisance function

<dl><dt>nuisance</dt>
<dd>The <code>nuisance</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 nuisance — BayesGLM_format_nuisance

<dl>

<dt>nuisance</dt>
<dd>The <code>nuisance</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_nuisance: Format nuisance

Description

Usage

Value

Arguments