unmask_Mdat2In

<code>maskMdat</code> is a subset of <code>maskIn</code>. <code>x</code> is masked by
<code>maskMdat</code>; this function makes it masked by <code>maskIn</code> instead
and uses <code>fill_val</code> for the previously masked-out locations.

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

unmask_Mdat2In function

<dl><dt>x</dt>
<dd>The matrix to unmask</dd>
<dt>maskIn, maskMdat</dt>
<dd>The input mask, and the modeled locations mask, as
logical vectors. The latter should be a subset of the former. They should
have the same length.</dd>
<dt>fill_val</dt>
<dd>The fill value. Default: <code>NA</code>.</dd></dl>

Arguments

Unmask data — unmask_Mdat2In

<dl>

<dt>x</dt>
<dd>The matrix to unmask</dd>


<dt>maskIn, maskMdat</dt>
<dd>The input mask, and the modeled locations mask, as
logical vectors. The latter should be a subset of the former. They should
have the same length.</dd>


<dt>fill_val</dt>
<dd>The fill value. Default: <code>NA</code>.</dd>

</dl>

unmask_Mdat2In: Unmask data

Description

Usage

Arguments