plot.bayesparobs

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

plot.bayesparobs function

<dl><dt>x</dt>
<dd>the output model from fitting a meta analysis/regression model</dd>
<dt>...</dt>
<dd>additional parameters for plot</dd></dl>

Arguments

get goodness of fit — plot.bayesparobs

<dl>

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


<dt>...</dt>
<dd>additional parameters for plot</dd>

</dl>

get goodness of fit

plot.bayesparobs: get goodness of fit

Description

Usage

Value

Arguments