MCMC-diagnostics: Compute MCMC diagnostic measures
In mcmcsae: Markov Chain Monte Carlo Small Area Estimation

MCMC-diagnostics

R Documentation

Compute MCMC diagnostic measures

Description

R_hat computes Gelman-Rubin convergence diagnostics based on the MCMC output in a model component, and n_eff computes the effective sample sizes, .i.e. estimates for the number of independent samples from the posterior distribution.

Usage

R_hat(dc)

n_eff(dc, useFFT = TRUE, lag.max, cl = NULL)

Arguments

`dc`	a draws component (dc) object corresponding to a model parameter.
`useFFT`	whether to use the Fast Fourier Transform algorithm. Default is `TRUE` as this is typically faster.
`lag.max`	the lag up to which autocorrelations are computed in case `useFFT=FALSE`.
`cl`	a cluster for parallel computation.

Value

In case of R_hat the split-R-hat convergence diagnostic for each component of the vector parameter, and in case of n_eff the effective number of independent samples for each component of the vector parameter.

References

A. Gelman and D. B. Rubin (1992). Inference from Iterative Simulation Using Multiple Sequences. Statistical Science 7, 457-511.

A. Gelman, J.B. Carlin, H.S. Stern, D.B. Dunson, A. Vehtari and D.B. Rubin (2013). Bayesian Data Analysis, 3rd edition. Chapman & Hall/CRC.

Examples


ex <- mcmcsae_example()
sampler <- create_sampler(ex$model, data=ex$dat)
sim <- MCMCsim(sampler, burnin=100, n.iter=300, thin=2, n.chain=4, store.all=TRUE)
n_eff(sim$beta)
n_eff(sim$v_sigma)
n_eff(sim$v_rho)
R_hat(sim$beta)
R_hat(sim$llh_)
R_hat(sim$v_sigma)

mcmcsae documentation built on Oct. 11, 2023, 1:06 a.m.