Repeatedly simulates from the approved hierarchical Bernoulli-logit generator, fits the restricted model, and compares posterior intervals with known generating values.
Usage
run_binary_recovery(
repetitions = 20L,
n_participants = 30L,
trials_per_participant = 16L,
n_items = 12L,
include_items = TRUE,
random_slope = TRUE,
seed = 1001L,
chains = 4L,
iter = 1500L,
warmup = 750L,
cores = min(chains, .gp3b_default_cores(chains)),
adapt_delta = 0.95,
max_treedepth = 12L,
refresh = 0L,
interval_probability = 0.95,
minimum_repetitions = 20L,
maximum_standardized_bias = 0.25,
minimum_coverage = 0.8,
minimum_diagnostic_pass_fraction = 0.8,
continue_on_error = TRUE
)Arguments
- repetitions
Number of simulation-fit repetitions.
- n_participants, trials_per_participant, n_items
Synthetic design sizes.
- include_items
Whether crossed item effects are included.
- random_slope
Whether a participant condition slope is generated and fitted.
- seed
First simulation seed.
- chains, iter, warmup, cores, adapt_delta, max_treedepth, refresh
Restricted sampling controls.
- interval_probability
Central posterior interval probability.
- minimum_repetitions
Repetitions required before an overall pass is possible.
- maximum_standardized_bias
Maximum absolute bias divided by the empirical standard deviation of estimates for a pass.
- minimum_coverage
Minimum empirical interval coverage for a pass.
- minimum_diagnostic_pass_fraction
Minimum fraction of fits with a diagnostic pass.
- continue_on_error
Whether failed repetitions are recorded instead of stopping.