Classify plausibility of the prior predictive distribution
Source:R/probit_prior_predictive.R
compute_prior_predictive_status.RdUnlike posterior predictive checking, the goal here is NOT to make the simulated distribution match the observed data closely – it is to flag obviously implausible/extreme implications of the CHOSEN priors, before conditioning on any outcomes. Computes, per prior state and averaged across states: the fraction of event-class probabilities \(\Phi(\mu_{ed})\) below 0.001 or above 0.999 (near-deterministic implied resistance), the fraction of generated events that are degenerate (all classes resistant or all susceptible), and the spread of per-"hospital" (the model's primary declared random-effect grouping) mean implied probability – a very large spread implies an implausibly extreme facility-to-facility prior.
Usage
compute_prior_predictive_status(prior_draws, thresholds = list())Arguments
- prior_draws
An
"amr_prior_predictive_draws"object fromsimulate_probit_prior_predictive.- thresholds
Named list overriding any of the defaults:
max_fraction_extreme_probability = 0.10,severe_fraction_extreme_probability = 0.50,max_fraction_degenerate_profiles = 0.50,severe_fraction_degenerate_profiles = 0.90,max_hospital_spread_sd = 0.35. All thresholds are project decisions, not universal truths – override freely and document why.