Summarize a population-level design forecast

Summarize a population-level design forecast

Usage

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

Arguments

object

Output from predict_mfrm_population().

digits

Number of digits used in numeric summaries.

...

Reserved for generic compatibility.

Value

An object of class summary.mfrm_population_prediction with:

• design: requested future design

• overview: run-level overview

• forecast: facet-level forecast table

• ademp: simulation-study metadata

• notes: interpretation notes

See also

predict_mfrm_population()

Examples

spec <- build_mfrm_sim_spec(
  n_person = 40,
  n_rater = 4,
  n_criterion = 4,
  raters_per_person = 2,
  assignment = "rotating"
)
pred <- predict_mfrm_population(
  sim_spec = spec,
  n_person = 60,
  reps = 2,
  maxit = 10,
  seed = 123
)
#> Warning: Optimizer did not fully converge (code = 1). Consider increasing maxit (current: 10) or relaxing reltol (current: 1e-06).
#> Warning: Optimizer did not fully converge (code = 1). Consider increasing maxit (current: 10) or relaxing reltol (current: 1e-06).
summary(pred)
#> $design
#> # A tibble: 1 × 4
#>   n_person n_rater n_criterion raters_per_person
#>      <dbl>   <dbl>       <dbl>             <dbl>
#> 1       60       4           4                 2
#> 
#> $overview
#> # A tibble: 1 × 5
#>   Designs Replications SuccessfulRuns ConvergedRuns MeanElapsedSec
#>     <dbl>        <dbl>          <dbl>         <dbl>          <dbl>
#> 1       1            2              2             0           1.02
#> 
#> $forecast
#> # A tibble: 3 × 34
#>   design_id Facet     n_person n_rater n_criterion raters_per_person  Reps
#>   <chr>     <chr>        <dbl>   <dbl>       <dbl>             <dbl> <dbl>
#> 1 D01       Criterion       60       4           4                 2     2
#> 2 D01       Person          60       4           4                 2     2
#> 3 D01       Rater           60       4           4                 2     2
#> # ℹ 27 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>, …
#> 
#> $ademp
#> $ademp$aims
#> [1] "Assess many-facet design conditions via repeated parametric simulation under explicit data-generating assumptions."
#> 
#> $ademp$data_generating_mechanism
#> $ademp$data_generating_mechanism$source
#> [1] "manual"
#> 
#> $ademp$data_generating_mechanism$model
#> [1] "RSM"
#> 
#> $ademp$data_generating_mechanism$step_facet
#> [1] "Criterion"
#> 
#> $ademp$data_generating_mechanism$assignment
#> [1] "rotating"
#> 
#> $ademp$data_generating_mechanism$latent_distribution
#> [1] "normal"
#> 
#> $ademp$data_generating_mechanism$threshold_mode
#> [1] "common"
#> 
#> $ademp$data_generating_mechanism$threshold_step_facet
#> [1] "Criterion"
#> 
#> $ademp$data_generating_mechanism$design_variables
#> [1] "n_person"          "n_rater"           "n_criterion"      
#> [4] "raters_per_person"
#> 
#> 
#> $ademp$estimands
#> [1] "Facet separation, reliability, and strata"     
#> [2] "Mean infit/outfit and misfit rate"             
#> [3] "Aligned facet-parameter recovery RMSE and bias"
#> [4] "Convergence rate and elapsed time"             
#> 
#> $ademp$methods
#> $ademp$methods$fit_method
#> [1] "MML"
#> 
#> $ademp$methods$fitted_model
#> [1] "RSM"
#> 
#> $ademp$methods$maxit
#> [1] 10
#> 
#> $ademp$methods$quad_points
#> [1] 7
#> 
#> $ademp$methods$residual_pca
#> [1] "none"
#> 
#> 
#> $ademp$performance_measures
#> [1] "Mean performance across replications"
#> [2] "MCSE for means and rates"            
#> [3] "Convergence rate"                    
#> [4] "Sparse-category warning rate"        
#> 
#> 
#> $notes
#> [1] "This forecast summarizes expected design-level behavior under the supplied or fit-derived simulation specification."
#> [2] "MCSE columns quantify Monte Carlo uncertainty from using a finite number of replications."                          
#> [3] "Do not interpret this output as deterministic future person/rater true values."                                     
#> 
#> attr(,"class")
#> [1] "summary.mfrm_population_prediction"