pw_estimate

Estimate residual autocorrelation for prewhitening

Implements Bayesian brain mapping with population-derived priors,
including the original model described in Mejia et al. (2020)
<doi:10.1080/01621459.2019.1679638>, the model with spatial priors described
in Mejia et al. (2022) <doi:10.1080/10618600.2022.2104289>, and the model
with population-derived priors on functional connectivity described in Mejia
et al. (2025) <doi:10.1093/biostatistics/kxaf022>. Population-derived priors
are based on templates representing established brain network maps, for
example derived from independent component analysis (ICA), parcellations,
or other methods.  Model estimation is based on expectation-maximization or
variational Bayes algorithms. 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

Nohelia Da Silva

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