mcmc_diagnostics: Markov Chain Monte Carlo diagnostics

In rnmamod: Bayesian Network Meta-Analysis with Missing Participants

Markov Chain Monte Carlo diagnostics

Description

Evaluates whether convergence has been achieved for the monitored parameters of the Bayesian models. The Gelman-Rubin convergence diagnostic, the Markov Chain Monte Carl (MCMC) error and relevant diagnostic plots are applied.

Usage

mcmc_diagnostics(net, par = NULL)

Arguments

net	An object of S3 class run_metareg, run_model, run_nodesplit, run_sensitivity, run_series_meta, and run_ume. See 'Value' in the functions above.
par	A vector of at least one character string that refers to the monitored parameters in jagsfit which is an object of S3 class run_metareg, run_model, and run_ume. The selected parameters will be considered in the diagnostic plots (see 'Value'). This argument will be ignored for objects of S3 class run_nodesplit, run_sensitivity, and run_series_meta.

Details

For each monitored parameter, mcmc_diagnostics considers the R-hat and MCMC error and compares them with the thresholds 1.1 and 5% of the posterior standard deviation (the rule of thumb), respectively. Convergence is achieved for the monitored parameter, when the R-hat is below the corresponding threshold. Visual inspection of the trace plots and posterior density of the monitored parameters should also be considered when drawing conclusions about convergence.

Value

mcmc_diagnostics considers the following monitored parameters:

EM	The estimated summary effect measure.
EM_pred	The predicted summary effect measure.
delta	The estimated trial-specific effect measure.
tau	The between-trial standard deviation.
direct	The direct estimate of the split node (see 'Value' in run_nodesplit).
indirect	The indirect estimate of the split node (see 'Value' in run_nodesplit).
diff	The inconsistency factor of the split node (see 'Value' in run_nodesplit).
phi	The informative missingness parameter.
beta	The regression coefficient.

For each monitored parameter mentioned above, mcmc_diagnostics also returns a barplot on the ratio of MCMC error to the posterior standard deviation and a barplot on the Gelman-Rubin R diagnostic. Bars that correspond to a ratio less than 5% are indicated in green (the corresponding parameters have been estimated accurately); otherwise, the bars are indicated in red (inaccurate estimation). Furthermore, bars that correspond to an R value less than 1.10 are indicated in green (the corresponding parameters have been converged); otherwise, the bars are indicated in red (convergence is not achieved). mcmc_diagnostics returns histograms than barplots for EM when run_sensitivity is considered.

mcmc_diagnostics also uses the mcmcplot function of the R-package mcmcplots to create an HTML file with a panel of diagnostic plots (trace, density, and autocorrelation) for each monitored parameter.

Author(s)

Loukia M. Spineli

References

Gelman, A, Rubin, DB. Inference from iterative simulation using multiple sequences. Stat Sci 1992;7(4):457–72. doi: 10.1214/ss/1177011136

See Also

mcmcplot, run_metareg, run_model, run_nodesplit, run_sensitivity, run_series_meta, run_ume

Examples

data("nma.baker2009")

# Read results from 'run_nodesplit' (using the default arguments)
res <- readRDS(system.file('extdata/node_baker.rds', package = 'rnmamod'))

# Check convergence based on R-hat
mcmc_diagnostics(net = res,
                 par = c("tau", "EM[2,1]", "EM.pred[2,1]"))

rnmamod documentation built on May 29, 2024, 2:44 a.m.