Recommend a design condition from simulation results

Usage

recommend_mfrm_design(
  x,
  facets = c("Rater", "Criterion"),
  min_separation = 2,
  min_reliability = 0.8,
  max_severity_rmse = 0.5,
  max_misfit_rate = 0.1,
  min_convergence_rate = 1,
  prefer = c("n_person", "raters_per_person", "n_rater", "n_criterion")
)

Arguments

x: Output from evaluate_mfrm_design() or summary.mfrm_design_evaluation().
facets: Facets that must satisfy the planning thresholds.
min_separation: Minimum acceptable mean separation.
min_reliability: Minimum acceptable mean reliability.
max_severity_rmse: Maximum acceptable severity recovery RMSE.
max_misfit_rate: Maximum acceptable mean misfit rate.
min_convergence_rate: Minimum acceptable convergence rate.
prefer: Ranking priority among design variables. Earlier entries are optimized first when multiple designs pass. Custom public aliases from sim_spec are also accepted, as are the role keywords person, rater, criterion, and assignment.

Value

A list of class mfrm_design_recommendation with:

facet_table: facet-level threshold checks, including design-variable alias columns when applicable
design_table: design-level aggregated checks, including design-variable alias columns when applicable
recommended: the first passing design after ranking
thresholds: thresholds used in the recommendation
design_variable_aliases: accepted public aliases for design variables
design_descriptor: role-based design-variable metadata
planning_scope: explicit record of the current planning contract
planning_constraints: explicit record of mutable/locked design variables
planning_schema: combined planner-schema contract
caveats: structured warning rows for situations where the recommendation rests on weak evidence (e.g., no design met every threshold; the recommended design is at the boundary of the evaluated grid; only one rep was simulated). Empty tibble() when no caveats apply.

Details

This helper converts a design-study summary into a simple planning table.

A design is marked as recommended when all requested facets satisfy all selected thresholds simultaneously. If multiple designs pass, the helper returns the smallest one according to prefer (by default: fewer persons first, then fewer ratings per person, then fewer raters, then fewer criteria).

Typical workflow

Run evaluate_mfrm_design().
Review summary.mfrm_design_evaluation() and plot.mfrm_design_evaluation().
Use recommend_mfrm_design(...) to identify the smallest acceptable design.

Examples

if (FALSE) { # \dontrun{
sim_eval <- suppressWarnings(evaluate_mfrm_design(
  n_person = c(8, 12),
  n_rater = 2,
  n_criterion = 2,
  raters_per_person = 1,
  reps = 1,
  maxit = 30,
  seed = 123
))
rec <- recommend_mfrm_design(sim_eval)
rec$recommended
} # }