summary.bayesparobs

<code>summary</code> method for class "<code>bayesparobs</code>"

Contains functions performing Bayesian inference for meta-analytic and network meta-analytic models through Markov chain Monte Carlo algorithm. Currently, the package implements Hui Yao, Sungduk Kim, Ming-Hui Chen, Joseph G. Ibrahim, Arvind K. Shah, and Jianxin Lin (2015) <doi:10.1080/01621459.2015.1006065> and Hao Li, Daeyoung Lim, Ming-Hui Chen, Joseph G. Ibrahim, Sungduk Kim, Arvind K. Shah, Jianxin Lin (2021) <doi:10.1002/sim.8983>. For maximal computational efficiency, the Markov chain Monte Carlo samplers for each model, written in C++, are fine-tuned. This software has been developed under the auspices of the National Institutes of Health and Merck & Co., Inc., Kenilworth, NJ, USA.

Daeyoung Lim

metapack

Bayesian Meta-Analysis and Network Meta-Analysis

Ming-Hui Chen

Sungduk Kim

Joseph Ibrahim

Arvind Shah

Jianxin Lin

summary.bayesparobs function

<dl><dt>object</dt>
<dd>the output model from fitting a meta analysis/regression model</dd>
<dt>level</dt>
<dd>credible level for interval estimation; set to 0.95 by default</dd>
<dt>HPD</dt>
<dd>a logical argument indicating whether HPD intervals should be computed; if FALSE, equal-tail credible intervals are computed</dd>
<dt>...</dt>
<dd>additional arguments for summary</dd></dl>

Arguments

summary method for class "bayesparobs" — summary.bayesparobs

<dl>

<dt>object</dt>
<dd>the output model from fitting a meta analysis/regression model</dd>


<dt>level</dt>
<dd>credible level for interval estimation; set to 0.95 by default</dd>


<dt>HPD</dt>
<dd>a logical argument indicating whether HPD intervals should be computed; if FALSE, equal-tail credible intervals are computed</dd>


<dt>...</dt>
<dd>additional arguments for summary</dd>

</dl>

summary.bayesparobs: `summary` method for class "`bayesparobs`"

Description

Usage

Value

Arguments