bma_posterior

The `bma_posterior` function samples posterior distributions of graph 
parameters (e.g., partial correlations or precision matrices) based on the 
graph structures sampled during a Bayesian graph search performed by 
<code>ggm_search</code>.

Fit Bayesian Gaussian graphical models. The methods are separated into
two Bayesian approaches for inference: hypothesis testing and estimation. There are
extensions for confirmatory hypothesis testing, comparing Gaussian graphical models,
and node wise predictability. These methods were recently introduced in the Gaussian
graphical model literature, including
Williams (2019) <doi:10.31234/osf.io/x8dpr>,
Williams and Mulder (2019) <doi:10.31234/osf.io/ypxd8>,
Williams, Rast, Pericchi, and Mulder (2019) <doi:10.31234/osf.io/yt386>.

Philippe Rast

BGGM

Bayesian Gaussian Graphical Models

Donald Williams

Joris Mulder

bma_posterior function

<dl><dt>object</dt>
<dd>A ggm_search object</dd>
<dt>param</dt>
<dd>Compute BMA on either partial correlations "pcor" (default) or on precision matrix "Theta".</dd>
<dt>iter</dt>
<dd>Number of samples to be drawn, defaults to 5,000</dd>
<dt>progress</dt>
<dd>Show progress bar, defaults to TRUE</dd></dl>

Arguments

Compute Posterior Distributions from Graph Search Results — bma_posterior

<dl>

<dt>object</dt>
<dd>A ggm_search object</dd>


<dt>param</dt>
<dd>Compute BMA on either partial correlations "pcor" (default) or on precision matrix "Theta".</dd>


<dt>iter</dt>
<dd>Number of samples to be drawn, defaults to 5,000</dd>


<dt>progress</dt>
<dd>Show progress bar, defaults to TRUE</dd>

</dl>

bma_posterior: Compute Posterior Distributions from Graph Search Results

Description

Usage

Value

Arguments

Details

See Also