summary.coef

Summarize regression parameters with the posterior mean,
standard deviation, and credible interval.

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

summary.coef function

<dl><dt>object</dt>
<dd>An object of class <code>coef</code>.</dd>
<dt>cred</dt>
<dd>Numeric. The credible interval width for summarizing the posterior
distributions (defaults to 0.95; must be between 0 and 1).</dd>
<dt>...</dt>
<dd>Currently ignored</dd></dl>

Arguments

Summarize coef Objects — summary.coef

<dl>

<dt>object</dt>
<dd>An object of class <code>coef</code>.</dd>


<dt>cred</dt>
<dd>Numeric. The credible interval width for summarizing the posterior
distributions (defaults to 0.95; must be between 0 and 1).</dd>


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

</dl>

summary.coef: Summarize `coef` Objects

Description

Usage

Value

Arguments