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Summarize a DIF/bias screening simulation

Source: R/api-simulation.R
summary.mfrm_signal_detection.Rd

Summarize a DIF/bias screening simulation

Usage

# S3 method for class 'mfrm_signal_detection'
summary(object, digits = 3, ...)

Arguments

object

Output from evaluate_mfrm_signal_detection().

digits

Number of digits used in numeric summaries.

...

Reserved for generic compatibility.

Value

An object of class summary.mfrm_signal_detection with:

  • overview: run-level overview

  • detection_summary: aggregated detection rates by design, with design-variable alias columns when applicable

  • ademp: simulation-study metadata carried forward from the original object

  • facet_names: public facet labels carried from the simulation specification

  • 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: structured planning metadata

  • structural_design_review: deterministic structural review of the named-facet design grid; it reports design bookkeeping rather than signal-detection performance

  • gpcm_boundary: bounded-GPCM caveat row when present

  • notes: short interpretation notes, including the bias-side screening caveat

See also

evaluate_mfrm_signal_detection(), plot.mfrm_signal_detection

Examples

# \donttest{
sig_eval <- suppressWarnings(evaluate_mfrm_signal_detection(
  n_person = 8,
  n_rater = 2,
  n_criterion = 2,
  raters_per_person = 1,
  reps = 1,
  maxit = 30,
  bias_max_iter = 1,
  seed = 123
))
summary(sig_eval)
#> mfrmr Signal Detection Summary
#> 
#> Overview
#>  Designs Replications SuccessfulRuns ConvergedRuns MeanElapsedSec
#>        1            1              1             0          0.849
#> 
#> Detection summary (preview)
#>  design_id n_person n_rater n_criterion raters_per_person DIFTargetLevel
#>        S01        8       2           2                 1            C02
#>  BiasTargetRater BiasTargetCriterion Reps ConvergenceRate McseConvergenceRate
#>              R02                 C02    1               0                  NA
#>  DIFPower McseDIFPower DIFClassificationPower McseDIFClassificationPower
#>         0           NA                      0                         NA
#>  MeanTargetContrast McseTargetContrast MeanTargetContrastAbs
#>                 NaN                 NA                   NaN
#>  McseTargetContrastAbs DIFFalsePositiveRate McseDIFFalsePositiveRate
#>                     NA                    0                       NA
#>  BiasScreenRate McseBiasScreenRate MeanTargetBias McseTargetBias
#>               0                 NA         -6.756             NA
#>  MeanAbsTargetBias McseAbsTargetBias MeanTargetBiasT McseTargetBiasT
#>              6.756                NA          -0.326              NA
#>  BiasScreenMetricAvailabilityRate McseBiasScreenMetricAvailabilityRate
#>                                 1                                   NA
#>  BiasScreenFalsePositiveRate McseBiasScreenFalsePositiveRate MeanElapsedSec
#>                            0                              NA          0.849
#>  McseElapsedSec
#>              NA
#> 
#> Structural design review
#>  review_available n_designs recommended_design_id   view  mode surface
#>             FALSE         0                  <NA> public brief  digest
#>  table_component RecommendedAppendixTables CompactAppendixTables NumericTables
#>             grid                         4                     3             6
#>  AnyNumericTable
#>             TRUE
#> 
#> ADEMP metadata
#>  - aims
#>  - data_generating_mechanism
#>  - estimands
#>  - methods
#>  - performance_measures
#> 
#> Notes
#>  - Some design conditions did not converge in every replication.
#>  - Some design conditions showed DIF power below 0.80.
#>  - Some design conditions showed bias-screen hit rates below 0.80.
#>  - Bias-side rates are screening summaries derived from `estimate_bias()` output and should not be interpreted as formal power or alpha-calibrated false-positive rates.
#>  - MCSE columns summarize finite-replication uncertainty around the reported means and rates.
#>  - Planning helpers vary one person count and two named non-person facet roles (Rater and Criterion). Estimation may contain additional facets, but planning and forecasting are limited to this role-based design.
#>  - Current scalar-argument planning paths allow `n_person`, `n_rater`, `n_criterion`, and `raters_per_person` to vary subject to `raters_per_person <= n_rater`.
#>  - Named-facet structural design metadata for person count, non-person facet counts, and assignments per person. These deterministic design summaries do not establish arbitrary-facet simulation support or parameter-recovery performance.
#>  - The structural design review reports deterministic bookkeeping and conservative design guidance, not DIF/bias detection power.
# }