summary.dpmeta

Generic summary function for dpmeta object in jarbes.

Provides a new class of Bayesian meta-analysis models that incorporates a model for internal and external validity bias. In this way, it is possible to combine studies of diverse quality and different types. For example, we can combine the results of randomized control trials (RCTs) with the results of observational studies (OS).

Pablo Emilio Verde

jarbes

Just a Rather Bayesian Evidence Synthesis

summary.dpmeta function

<dl><dt>object</dt>
<dd>The object generated by the dpmeta function.</dd>
<dt>digits</dt>
<dd>The number of significant digits printed. The default value is 3.</dd>
<dt>show.clustering</dt>
<dd>A logical value indicating whether to show the clustering parameters table. Defaults to `TRUE`.</dd>
<dt>show.neff</dt>
<dd>A logical value indicating whether to show the effective sample size column (n.eff) and Rhat. Defaults to `TRUE`.</dd>
<dt>max.clusters</dt>
<dd>Maximum number of clusters printed in the clustering table. Default is 10.</dd>
<dt>...</dt>
<dd>...</dd></dl>

Arguments

Generic summary function for dpmeta object in jarbes. — summary.dpmeta

<dl>

<dt>object</dt>
<dd>The object generated by the dpmeta function.</dd>


<dt>digits</dt>
<dd>The number of significant digits printed. The default value is 3.</dd>


<dt>show.clustering</dt>
<dd>A logical value indicating whether to show the clustering parameters table. Defaults to `TRUE`.</dd>


<dt>show.neff</dt>
<dd>A logical value indicating whether to show the effective sample size column (n.eff) and Rhat. Defaults to `TRUE`.</dd>


<dt>max.clusters</dt>
<dd>Maximum number of clusters printed in the clustering table. Default is 10.</dd>


<dt>...</dt>
<dd>...</dd>

</dl>

summary.dpmeta: Generic summary function for dpmeta object in jarbes.

Description

Usage

Arguments