extract_estimates

Obtains the posterior mean or other summary statistic for each latent field

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

extract_estimates function

<dl><dt>INLA_model_obj</dt>
<dd>An object of class <code>"inla"</code>, a result of a call to
<code>inla</code>.</dd>
<dt>session_names</dt>
<dd>Vector of fMRI session names</dd>
<dt>stat</dt>
<dd>A string representing the posterior summary statistic to be returned</dd></dl>

Arguments

Extract Estimates of Activation — extract_estimates

<dl>

<dt>INLA_model_obj</dt>
<dd>An object of class <code>"inla"</code>, a result of a call to
<code>inla</code>.</dd>


<dt>session_names</dt>
<dd>Vector of fMRI session names</dd>


<dt>stat</dt>
<dd>A string representing the posterior summary statistic to be returned</dd>

</dl>

extract_estimates: Extract Estimates of Activation

Description

Usage

Value

Arguments