plot.pcor_sum

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

plot.pcor_sum function

<dl><dt>x</dt>
<dd>An object of class <code>posterior_sum</code></dd>
<dt>fill</dt>
<dd>Character string. What fill for the histogram
(defaults to colorblind "pink")?</dd>
<dt>...</dt>
<dd>Currently ignored.</dd></dl>

Arguments

Plot pcor_sum Object — plot.pcor_sum

<dl>

<dt>x</dt>
<dd>An object of class <code>posterior_sum</code></dd>


<dt>fill</dt>
<dd>Character string. What fill for the histogram
(defaults to colorblind "pink")?</dd>


<dt>...</dt>
<dd>Currently ignored.</dd>

</dl>

plot.pcor_sum: Plot `pcor_sum` Object

Description

Usage

Value

Arguments

See Also