bspmma-package {bspmma}

bspmma: Bayesian Semiparametric Models for Meta-Analysis

Description

Two functions carry out Gibbs' sampler routines to estimate the posterior distributions from either a non-parametric Bayesian model for random effects meta-analysis, or from a semi-parametric model. A group of three functions are used to compute Bayes factors to compare the two models. Three sample datasets are included. There are routines for graphing the posteriors and computing summary statistics.

Details

Package:	bspmma
Version:	0.1-2
Date:	2019-01-19
License:	GPL-2
LazyLoad:	yes
Built:	R 2.9.2; ; 2012-07-13 19:04:37 UTC; unix

Index:

bf.c                    Compute Bayes Factors for Comparing Values of
                        the Dirichlet Precision Parameter in the
                        Conditional Dirichlet Model
bf.c.o                  Compute Bayes Factors for Conditional vs.
                        Ordinary Dirichlet Models
bf.o                    Compute Bayes Factors for Comparing Values of
                        the Dirichlet Precision Parameter in the
                        Ordinary Dirichlet Model
bf1                     Generate Chains for Computation of Bayes
                        Factors
bf2                     Compute Constants for Multi-Chain Algorithm to
                        Compute Bayes Factors
breast.17               Aspirin and Breast Cancer: 17 studies
bspmma-package          bspmma: Bayesian Semiparametric Models for
                        Meta-Analysis
caprie.3grps            CAPRIE Study: Three Risk Groups
ddtm.s                  Decontamination of the Digestive Tract
                        Mortality, Short Dataset
describe.post           Brief summary statistics of the posterior for
                        convenient comparison of several models
dirichlet.c             Mixture of Conditional Dirichlet Model
dirichlet.o             Mixture of Ordinary Dirichlet Model
draw.bf                 Plot Function for Bayes Factors
draw.post               Overlapping Plots of Posterior Distributions
                        for Several Models
print.dir.cond          printing method for objects of class dir.cond
print.dir.ord           printing method for objects of class dir.ord

The main functions are explained in Burr (2012), and are illustrated on the datasets breast.17 and ddtm.s. The function dirichlet.c carries out the Markov chain Monte Carlo (MCMC) algorithm to simulate data from the posterior distribution under the conditional Dirichlet model described in Burr and Doss (2005). The computation of Bayes factors is carried out in functions bf1, bf2, bf.c, bf.o, and bf.c.o, which implement a multi-chain algorithm described in Doss (2012).

Author(s)

Deborah Burr

Maintainer: Deborah Burr <[e-mail]>

References

Burr, Deborah (2012). “bspmma: An R package for Bayesian semi-parametric models for meta-analysis.” Journal of Statistical Software 50(4), 1–23. http://www.jstatsoft.org/v50/i04/.

Doss, Hani (2012). “Hyperparameter and model selection for nonparametric Bayes problems via Radon-Nikodym derivatives.” Statistica Sinica 22, 1–26.