BayesGLM_format_design

Format design 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_design function

<dl><dt>design</dt>
<dd>The <code>design</code> argument input. Will be formatted to a
<code>nS</code>-length list.</dd>
<dt>scale_design</dt>
<dd>Scale the design matrix by dividing each column by its
maximum and then subtracting the mean? Default: <code>TRUE</code>. If
<code>FALSE</code>, the design matrix is centered but not scaled.</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>
<dt>nD_expect</dt>
<dd>The expected number of designs, if known. For per-location
modeling this is equal to <code>nVd0</code>, the initial number of data locations.
For multi-session data this is a session-length vector.</dd>
<dt>per_location_design</dt>
<dd><code>FALSE</code> if per-location modeling is not
being performed (i.e. for multiGLM); <code>TRUE</code> if it is; or, <code>NULL</code>
to infer based on the dimensions of <code>design</code> (<code>TRUE</code> if the
design has three dimensions.)</dd></dl>

Arguments

Format design — BayesGLM_format_design

<dl>

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


<dt>scale_design</dt>
<dd>Scale the design matrix by dividing each column by its
maximum and then subtracting the mean? Default: <code>TRUE</code>. If
<code>FALSE</code>, the design matrix is centered but not scaled.</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>


<dt>nD_expect</dt>
<dd>The expected number of designs, if known. For per-location
modeling this is equal to <code>nVd0</code>, the initial number of data locations.
For multi-session data this is a session-length vector.</dd>


<dt>per_location_design</dt>
<dd><code>FALSE</code> if per-location modeling is not
being performed (i.e. for multiGLM); <code>TRUE</code> if it is; or, <code>NULL</code>
to infer based on the dimensions of <code>design</code> (<code>TRUE</code> if the
design has three dimensions.)</dd>

</dl>

BayesGLM_format_design: Format design

Description

Usage

Value

Arguments