IW_var

Compute theoretical Inverse-Wishart variance of covariance matrix elements

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

IW_var function

<dl><dt>nu</dt>
<dd>Inverse Wishart degrees of freedom parameter</dd>
<dt>p</dt>
<dd>Matrix dimension for IW distribution</dd>
<dt>xbar_ij</dt>
<dd>Empirical mean of covariance matrices at element (i,j)</dd>
<dt>xbar_ii</dt>
<dd>Empirical mean of covariance matrices at the ith diagonal element</dd>
<dt>xbar_jj</dt>
<dd>Empirical mean of covariance matrices at the jth diagonal element</dd></dl>

Arguments

Compute theoretical Inverse-Wishart variance of covariance matrix elements — IW_var

<dl>

<dt>nu</dt>
<dd>Inverse Wishart degrees of freedom parameter</dd>


<dt>p</dt>
<dd>Matrix dimension for IW distribution</dd>


<dt>xbar_ij</dt>
<dd>Empirical mean of covariance matrices at element (i,j)</dd>


<dt>xbar_ii</dt>
<dd>Empirical mean of covariance matrices at the ith diagonal element</dd>


<dt>xbar_jj</dt>
<dd>Empirical mean of covariance matrices at the jth diagonal element</dd>

</dl>

IW_var: Compute theoretical Inverse-Wishart variance of covariance matrix elements

Description

Usage

Value

Arguments