Sampler diagnostic summaries and warnings

Warnings and summaries of sampler diagnostics. To instead get the underlying values of the sampler diagnostics for each iteration and chain use the $sampler_diagnostics() method.

Currently parameter-specific diagnostics like R-hat and effective sample size are not handled by this method. Those diagnostics are provided via the $summary() method (using posterior::summarise_draws()).

Usage

diagnostic_summary(
  diagnostics = c("divergences", "treedepth", "ebfmi"),
  quiet = FALSE
)

Arguments

diagnostics

(character vector) One or more diagnostics to check. The currently supported diagnostics are "divergences", "treedepth", and "ebfmi". The default is to check all of them.

quiet

(logical) Should warning messages about the diagnostics be suppressed? The default is FALSE, in which case warning messages are printed in addition to returning the values of the diagnostics.

Value

A list with as many named elements as diagnostics selected. The possible elements and their values are:

• "num_divergent": A vector of the number of divergences per chain.

• "num_max_treedepth": A vector of the number of times max_treedepth was hit per chain.

• "ebfmi": A vector of E-BFMI values per chain, NA for a chain whose energy never changes (a model with no parameters).

See also

CmdStanMCMC and the $sampler_diagnostics() method

Examples

# \dontrun{
fit <- cmdstanr_example("schools")
#> Warning: 121 of 4000 (3.0%) transitions ended with a divergence.
#> See https://mc-stan.org/misc/warnings for details.
#> Warning: 1 of 4 chains had an E-BFMI less than 0.3.
#> See https://mc-stan.org/misc/warnings for details.
fit$diagnostic_summary()
#> Warning: 121 of 4000 (3.0%) transitions ended with a divergence.
#> See https://mc-stan.org/misc/warnings for details.
#> Warning: 1 of 4 chains had an E-BFMI less than 0.3.
#> See https://mc-stan.org/misc/warnings for details.
#> $num_divergent
#> [1] 23 29 31 38
#> 
#> $num_max_treedepth
#> [1] 0 0 0 0
#> 
#> $ebfmi
#> [1] 0.3927448 0.2994857 0.3067239 0.3595112
#> 
fit$diagnostic_summary(quiet = TRUE)
#> $num_divergent
#> [1] 23 29 31 38
#> 
#> $num_max_treedepth
#> [1] 0 0 0 0
#> 
#> $ebfmi
#> [1] 0.3927448 0.2994857 0.3067239 0.3595112
#> 
# }