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Summarize a design-simulation study

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

Summarize a design-simulation study

Usage

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

Arguments

object

Output from evaluate_mfrm_design().

digits

Number of digits used in the returned numeric summaries.

...

Reserved for generic compatibility.

Value

An object of class summary.mfrm_design_evaluation with components:

  • overview: run-level overview

  • design_summary: aggregated design-by-facet metrics, with design-variable alias columns when applicable

  • sparse_review: compact planned-missingness and rater-link review counts when sparse linked designs are active

  • 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 simulation performance

  • notes: short interpretation notes

Details

The summary emphasizes condition-level averages that are useful for practical design planning, especially:

  • convergence rate

  • separation and reliability by facet

  • severity recovery RMSE

  • mean misfit rate

See also

evaluate_mfrm_design(), plot.mfrm_design_evaluation

Examples

# \donttest{
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
))
s <- summary(sim_eval)
s$overview
#> # A tibble: 1 × 5
#>   Designs Replications SuccessfulRuns ConvergedRuns MeanElapsedSec
#>     <dbl>        <dbl>          <dbl>         <dbl>          <dbl>
#> 1       2            2              2             1          0.768
head(s$design_summary)
#> # A tibble: 6 × 44
#>   design_id Facet     n_person n_rater n_criterion raters_per_person  Reps
#>   <chr>     <chr>        <dbl>   <dbl>       <dbl>             <dbl> <dbl>
#> 1 D01       Criterion        8       2           2                 1     1
#> 2 D02       Criterion       12       2           2                 1     1
#> 3 D01       Person           8       2           2                 1     1
#> 4 D02       Person          12       2           2                 1     1
#> 5 D01       Rater            8       2           2                 1     1
#> 6 D02       Rater           12       2           2                 1     1
#> # ℹ 37 more variables: ConvergenceRate <dbl>, McseConvergenceRate <dbl>,
#> #   MeanSeparation <dbl>, SdSeparation <dbl>, McseSeparation <dbl>,
#> #   MeanReliability <dbl>, McseReliability <dbl>, MeanInfit <dbl>,
#> #   McseInfit <dbl>, MeanOutfit <dbl>, McseOutfit <dbl>, MeanMisfitRate <dbl>,
#> #   McseMisfitRate <dbl>, MeanSeverityRMSE <dbl>, McseSeverityRMSE <dbl>,
#> #   MeanSeverityBias <dbl>, McseSeverityBias <dbl>, MeanSeverityRMSERaw <dbl>,
#> #   McseSeverityRMSERaw <dbl>, MeanSeverityBiasRaw <dbl>, …
# }