Value
A tibble with stage, title, required, status (the
stage's), criterion (the bar in words), value (the number judged),
pass (TRUE, FALSE, or NA when the stage did not run or the
criterion cannot be evaluated) and note.
Details
One row per criterion, with the bar written out, so a card can print the criterion next to its verdict rather than a bare pass or fail.
The criteria are the plan's (see validation_plan(), "Criteria"):
prior predictive: the largest floor or ceiling rate under the calibration's prior (or bmm's defaults when the calibration uses them) at most
prior_bound_rate_max; not judged for a response without a known range.SBC: no ranked variable outside the
sbc_ecdf_probsimultaneous ECDF band, and no failed check (a high Rhat, a low rank ESS or missing diagnostics) in any fit. The value is the number of failed checks, so a fit that fails two counts twice.computation: the share of converged cells at least
convergence_rate_min.recovery: the coverage of every population parameter, pooled over the designs, not below
coverage_levelby a one-sided binomial test atcoverage_alphadivided by the number of parameters. The coverage of each design is reported, not judged.identifiability: at the largest design, every population parameter's mean contraction above
contraction_minand its mean z-score withinz_mean_maxof 0.individual differences: the coverage of every generating correlation held to the same pooled test, divided by the number of correlations.
data requirements: every subject-varying parameter reaches
r_low >= read_off_r_lowwithin the grid atread_off_n_subjectssubjects, the value being the largest read-off over the parameters; a parameter the grid gave no person-level correlation is not judged. Its coverage leg is the recovery criterion's pooled test, repeated as a second row: a per-design coverage bar changed or removed a calibratedezdmmodel's read-off in 24 % to 56 % of runs at 10 replications.agreement: reported, not judged.
The ECDF band is simultaneous over the ranks of one variable; it is not corrected for the number of variables, so with more ranked variables a calibrated model leaves some band more often.
