dim_reduce

Performs dimension reduction and prewhitening based on probabilistic PCA using
SVD. If dimensionality is not specified, it is estimated using the method
described in Minka (2008).

internal

Implements Bayesian brain mapping models, including the prior
ICA (independent components analysis) model proposed in Mejia et al. (2020)
<doi:10.1080/01621459.2019.1679638> and the spatial prior ICA model
proposed in proposed in Mejia et al. (2022)
<doi:10.1080/10618600.2022.2104289>. Both models estimate subject-level
brain as deviations from known population-level networks, which are
estimated using standard ICA algorithms. Both models employ an
expectation-maximization algorithm for estimation of the latent brain
networks and unknown model parameters. Includes direct support for 'CIFTI',
'GIFTI', and 'NIFTI' neuroimaging file formats.

Amanda Mejia

BayesBrainMap

Estimate Brain Networks and Connectivity with Population-Derived
Priors

Damon Pham

Daniel Spencer

Mary Beth Nebel

dim_reduce function

<dl><dt>X</dt>
<dd>A numeric matrix, with each column being a centered timeseries.
For fMRI data, <code>X</code> should be <code>T</code> timepoints by <code>V</code> brain
locations.</dd>
<dt>Q</dt>
<dd>Number of latent dimensions to estimate. If <code>NULL</code> (default),
estimated using PESEL (Sobczyka et al. 2020).</dd>
<dt>Q_max</dt>
<dd>Maximal number of principal components for automatic
dimensionality selection with PESEL. Default: <code>100</code>.</dd></dl>

Arguments

PCA-based Dimension Reduction and Prewhitening — dim_reduce

<dl>

<dt>X</dt>
<dd>A numeric matrix, with each column being a centered timeseries.
For fMRI data, <code>X</code> should be <code>T</code> timepoints by <code>V</code> brain
locations.</dd>


<dt>Q</dt>
<dd>Number of latent dimensions to estimate. If <code>NULL</code> (default),
estimated using PESEL (Sobczyka et al. 2020).</dd>


<dt>Q_max</dt>
<dd>Maximal number of principal components for automatic
dimensionality selection with PESEL. Default: <code>100</code>.</dd>

</dl>

dim_reduce: PCA-based Dimension Reduction and Prewhitening

Description

Usage

Value

Arguments