Pre-fit Design-Support Diagnostics

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Diagnose the design before invoking Stan

Many modeling failures can be identified from the declared design itself. Version 0.2.0 adds four reporting audits that run before MCMC:

audit_missingness_structure();
audit_fixed_effect_design();
audit_random_effects_support(); and
audit_design_support().

They do not impute, exclude, drop predictors, or simplify random effects.

simulation <- simulate_hierarchical_binary_data(
  n_participants = 12,
  trials_per_participant = 10,
  n_items = 6,
  random_slope_sd = 0,
  seed = 11
)

contract <- create_model_contract(
  family = "binary",
  outcome_col = "selected",
  participant_col = "participant_id",
  item_col = "item_id",
  trial_col = "trial_id",
  condition_col = "condition",
  predictors = "trial_covariate"
)

Missingness is described, not repaired

with_missing <- simulation$data
with_missing$trial_covariate[c(3, 17, 41)] <- NA_real_

missingness <- audit_missingness_structure(
  with_missing,
  contract
)
missingness
#> <gp3bayes_missingness_audit>
#>   Status: pass
#>   Rows: 120
#>   Missing cells: 3
plot(missingness)

Fixed-effect geometry

The fixed-effects audit reports design-matrix rank, singular values, a condition-number screen, invariant columns, and leverage. These quantities are warning signals about the declared numerical design; they do not determine a scientifically preferred model.

fixed_design <- audit_fixed_effect_design(
  simulation$data,
  contract
)
fixed_design
#> <gp3bayes_fixed_effect_design_audit>
#>   Status: review
#>   Rank: 3/3
#>   Condition number: 2.6181
#>   High-leverage rows: 3
plot(fixed_design)

Repetition, crossing and random slopes

random_support <- audit_random_effects_support(
  simulation$data,
  contract
)
random_support
#> <gp3bayes_random_effects_support_audit>
#>   Status: pass
#>               component status
#>  participant_repetition   pass
#>           item_crossing   pass
#>    random_slope_support   pass
plot(random_support)

One combined preflight

design <- audit_design_support(
  simulation$data,
  contract,
  separation = FALSE,
  strict_readiness = TRUE
)
design
#> <gp3bayes_design_support_audit>
#>   Status: review
#>               component       status
#>      standard_readiness         pass
#>        strict_readiness         pass
#>             missingness         pass
#>     fixed_effect_design       review
#>  random_effects_support         pass
#>              separation not_assessed
#>   Automatic model changes: FALSE
plot(design)

For binary models, a fixed-effects separation screen can also be requested when detectseparation is installed:

audit_design_support(
  simulation$data,
  contract,
  separation = TRUE
)

A review or fail flag is a prompt for methodological inspection. It is not an automatic instruction to remove data or alter the prespecified model.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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