Estimates the precision of transdimensional Markov chain Monte Carlo (MCMC) output, which is often used for Bayesian analysis of models with different dimensionality (e.g., model selection). Transdimensional MCMC (e.g., reversible jump MCMC) relies on sampling a discrete model-indicator variable to estimate the posterior model probabilities. If only few switches occur between the models, precision may be low and assessment based on the assumption of independent samples misleading. Based on the observed transition matrix of the indicator variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2017) <https://arxiv.org/abs/1703.10364> draws posterior samples of the stationary distribution to (a) assess the uncertainty in the estimated posterior model probabilities and (b) estimate the effective sample size of the MCMC output.
install.packages("devtools") library(devtools) install_github("danheck/MCMCprecision")
|Author||Daniel W. Heck [aut, cre]|
|Date of publication||None|
|Maintainer||Daniel W. Heck <email@example.com>|
|License||GPL (>= 2)|
best.k: Precision for the k Best-Performing Models
dirichlet.mle: Estimate Parameters of Dirichlet Distribution
MCMCprecision-package: MCMCprecision: Precision of discrete parameters in...
rdirichlet: Random Sample from Dirichlet Distribution
sim.mc: Generate a sample of a discrete-state Markov chain
stationary: Posterior of stationary distribution for discrete MCMC...
stationary.mle: MLE for stationary distribution of discrete MCMC variables
summary.stationary: Summary for Posterior Model Probabilities
table.mc: Get matrix of observed transition frequencies
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