pw_estimate

Estimate residual autocorrelation for prewhitening

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

pw_estimate function

<dl><dt>A</dt>
<dd>Estimated A matrix (T x Q)</dd>
<dt>ar_order, aic</dt>
<dd>Order of the AR model used to prewhiten the data at each location.
If <code>!aic</code> (default), the order will be exactly <code>ar_order</code>. If <code>aic</code>,
the order will be between zero and <code>ar_order</code>, as determined by the AIC.</dd></dl>

Arguments

Estimate residual autocorrelation for prewhitening — pw_estimate

<dl>

<dt>A</dt>
<dd>Estimated A matrix (T x Q)</dd>


<dt>ar_order, aic</dt>
<dd>Order of the AR model used to prewhiten the data at each location.
If <code>!aic</code> (default), the order will be exactly <code>ar_order</code>. If <code>aic</code>,
the order will be between zero and <code>ar_order</code>, as determined by the AIC.</dd>

</dl>

pw_estimate: Estimate residual autocorrelation for prewhitening

Description

Usage

Value

Arguments