MCMC-diagnostics {mcmcsae}

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)