s2m_B

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

Daniel Spencer

Damon Pham

David Bolin

Sarah Ryan

Yu (Ryan) Yue

s2m_B function

<dl><dt>B</dt>
<dd>An array of posterior samples (typically a matrix), in which the last margin corresponds to a single posterior sample</dd>
<dt>sigma</dt>
<dd>A scale parameter used to determine at what distance cluster centers are considered to be the same.</dd></dl>

Arguments

Sequential 2-means on array B — s2m_B

<dl>

<dt>B</dt>
<dd>An array of posterior samples (typically a matrix), in which the last margin corresponds to a single posterior sample</dd>


<dt>sigma</dt>
<dd>A scale parameter used to determine at what distance cluster centers are considered to be the same.</dd>

</dl>

Sequential 2-means on array B

s2m_B: Sequential 2-means on array B

Description

Usage

Value

Arguments