Bayesian Multivariate Meta-Analysis
Summary statistics from a posterior distribution
Interface for the BayesMultMeta class
Duplication matrix
Computes the ranks within the pooled draws of Markov chains
Plot a BayesMultMeta object
Metropolis-Hastings algorithm for the normal distribution and the Jeff...
Metropolis-Hastings algorithm for the normal distribution and the Jeff...
Metropolis-Hastings algorithm for the normal distribution and the Berg...
Metropolis-Hastings algorithm for the normal distribution and the Berg...
Metropolis-Hastings algorithm for the t-distribution and the Jeffreys ...
Metropolis-Hastings algorithm for the t-distribution and the Jeffreys ...
Metropolis-Hastings algorithm for the t-distribution and Berger and Be...
Metropolis-Hastings algorithm for the t-distribution and Berger and Be...
Computes the split- estimate based on the rank normalization
Summary statistics from the posterior of a BayesMultMeta class
Objective Bayesian inference procedures for the parameters of the multivariate random effects model with application to multivariate meta-analysis. The posterior for the model parameters, namely the overall mean vector and the between-study covariance matrix, are assessed by constructing Markov chains based on the Metropolis-Hastings algorithms as developed in Bodnar and Bodnar (2021) (<arXiv:2104.02105>). The Metropolis-Hastings algorithm is designed under the assumption of the normal distribution and the t-distribution when the Berger and Bernardo reference prior and the Jeffreys prior are assigned to the model parameters. Convergence properties of the generated Markov chains are investigated by the rank plots and the split hat-R estimate based on the rank normalization, which are proposed in Vehtari et al. (2021) (<DOI:10.1214/20-BA1221>).