Sequential 2-means variable selection

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 function

<dl><dt>x</dt>
<dd>A vector consisting of all variables of interest for a single draw
from a posterior distribution</dd>
<dt>b</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 variable selection — s2m

<dl>

<dt>x</dt>
<dd>A vector consisting of all variables of interest for a single draw
from a posterior distribution</dd>


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

</dl>

s2m: Sequential 2-means variable selection

Description

Usage

Value

Arguments