These functions print summaries of important HMC diagnostics or extract
those diagnostics from a stanfit object. See the Details
section, below.
check_hmc_diagnostics(object)
check_divergences(object)
check_treedepth(object)
check_energy(object)get_divergent_iterations(object)
get_max_treedepth_iterations(object)
get_num_leapfrog_per_iteration(object)
get_num_divergent(object)
get_num_max_treedepth(object)
get_bfmi(object)
get_low_bfmi_chains(object)
A stanfit object.
The check_hmc_diagnostics function calls the other check_*
functions internally and prints an overall summary, but the other
functions can also be called directly:
check_divergences prints the number (and percentage) of
iterations that ended with a divergence,
check_treedepth prints the number (and percentage) of iterations
that saturated the max treedepth,
check_energy prints E-BFMI values for each chain for which E-BFMI
is less than 0.2.
The get_* functions are for programmatic access to the diagnostics.
get_divergent_iterations and get_max_treedepth_iterations
return a logical vector indicating problems for individual iterations,
get_num_divergent and get_num_max_treedepth return the
number of offending interations,
get_num_leapfrog_per_iteration returns an integer vector with the
number of leapfrog evalutions for each iteration,
get_bfmi returns per-chain E-BFMI values and get_low_bfmi_chains
returns the indices of chains with low E-BFMI.
The following are several of many resources that provide more information on these diagnostics:
Brief explanations of some of the problems detected by these diagnostics can be found in the Brief Guide to Stan's Warnings.
Betancourt (2017) provides much more depth on these diagnostics as well as a conceptual introduction to Hamiltonian Monte Carlo in general.
Gabry et al. (2018) and the bayesplot package vignettes demonstrate various visualizations of these diagnostics that can be made in R.
The Stan Development Team Stan Modeling Language User's Guide and Reference Manual. https://mc-stan.org/.
Betancourt, M. (2017). A conceptual introduction to Hamiltonian Monte Carlo. https://arxiv.org/abs/1701.02434.
Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., and Gelman, A. (2018). Visualization in Bayesian workflow. Journal of the Royal Statistical Society Series A, accepted for publication. arXiv preprint: https://arxiv.org/abs/1709.01449.
if (FALSE) {
schools <- stan_demo("eight_schools")
check_hmc_diagnostics(schools)
check_divergences(schools)
check_treedepth(schools)
check_energy(schools)
}
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