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Diagnostic summary of MCMC samples

Source: R/bayes_helper.R
diagnostic.summary.Rd

Summarising diagnostics from a coda::mcmc.list, partly derived from Kruschke 2015.

Usage

diagnostic.summary(mcmc_list, HDImass = 0.95, gelman_diag = TRUE)

Arguments

mcmc_list

MCMC output from coda chains.

HDImass

numeric. Value within 0 and 1. Default = 0.95.

gelman_diag

logical. If TRUE, the Gelman-Rubin diagnostics for computing the PSRF is invoked. Default: TRUE.

Value

A data.frame of class diagnostic_summary with the row names according to the parameters to be monitored and the following numeric columns:

  • PSRF Point est. Potential scale reduction factor (= Gelman-Rubin statistic), a measure of the mixing of chains.

  • PSRF Upper C.I. The upper limit of the 0.95-confidence interval of the PSRF.

  • Mean Arithmetic mean of the estimates.

  • Median Median of the estimates.

  • Mode Mode of the estimates.

  • ESS Effective sample size, a control of autocorrelation.

  • MCSE Monte Carlo standard error.

  • HDImass Credibility level of the highest density interval.

  • HDIlow Start of the highest density interval.

  • HDIhigh End of the highest density interval.

Details

Because the first threshold is fixed, the Gelman-Rubin multivariate PSRF will always throw an error, so this is automatically set to FALSE. If the gelman diagnostics still produce an error, deactivate gelman_diag altogether by setting it to FALSE, too.

References

Kruschke JK (2015). Doing Bayesian data analysis: a tutorial with R, JAGS, and Stan. Academic Press, Amsterdam.

Examples

if (FALSE) { # interactive()
# select Sorsum data with auricular surface after Lovejoy et al. 1985
sorsum <- sorsum_as[,2]

# example with default settings, please be patient
sorsum_res <- bay.ta(method = sorsum)

# compute diagnostics of the MCMC samples
sorsum_diag <- diagnostic.summary(sorsum_res)

# show first rows
head(sorsum_diag)
}