Diagnostics for epidist_fit models

This function computes diagnostics to assess the quality of a fitted model. When the fitting algorithm used is "sampling" (HMC) then the output of epidist_diagnostics is a data.frame containing:

• time: the total time taken to fit all chains

• samples: the total number of samples across all chains

• max_rhat: the highest value of the Gelman-Rubin statistic

• divergent_transitions: the total number of divergent transitions

• per_divergent_transitions: the proportion of samples which had divergent transitions

• max_treedepth: the highest value of the treedepth HMC parameter

• no_at_max_treedepth: the number of samples which attained the max_treedepth

• per_at_max_treedepth: the proportion of samples which attained the max_treedepth

Usage

epidist_diagnostics(fit)

Arguments

fit

A fitted model of class epidist_fit

Details

When the fitting algorithm is not "sampling" (see brms::brm() for other possible algorithms) then diagnostics are yet to be implemented.

Examples

fit <- sierra_leone_ebola_data |>
  as_epidist_linelist_data(
    pdate_lwr = "date_of_symptom_onset",
    sdate_lwr = "date_of_sample_tested"
  ) |>
  as_epidist_aggregate_data() |>
  as_epidist_marginal_model() |>
  epidist(chains = 2, cores = 2, refresh = ifelse(interactive(), 250, 0))
#> ℹ No primary event upper bound provided, using the primary event lower bound + 1 day as the assumed upper bound.
#> ℹ No secondary event upper bound provided, using the secondary event lower bound + 1 day as the assumed upper bound.
#> ℹ No observation time column provided, using 2015-09-14 as the observation date (the maximum of the secondary event upper bound).
#> ! Setting 2394 observation times beyond 98 (=2x max delay) to Inf. This
#>   improves model efficiency by reducing unique observation times while
#>   maintaining model accuracy as these times should have negligible impact.
#> ℹ Data summarised by unique combinations of:
#> * Model variables: delay bounds, observation time, and primary censoring window
#> ! Reduced from 2453 to 272 rows.
#> ℹ This should improve model efficiency with no loss of information.
#> Compiling Stan program...
#> Start sampling
epidist_diagnostics(fit)
#> # A tibble: 1 × 8
#>    time samples max_rhat divergent_transitions per_divergent_transitions
#>   <dbl>   <dbl>    <dbl>                 <dbl>                     <dbl>
#> 1  12.3    2000     1.00                     0                         0
#> # ℹ 3 more variables: max_treedepth <dbl>, no_at_max_treedepth <int>,
#> #   per_at_max_treedepth <dbl>