Summarize a facet-quality dashboard
Usage
# S3 method for class 'mfrm_facet_dashboard'
summary(object, digits = 3, top_n = 10, ...)Arguments
- object
Output from
facet_quality_dashboard().- digits
Number of digits for printed numeric values.
- top_n
Number of flagged levels to preview.
- ...
Reserved for generic compatibility.
Examples
# \donttest{
toy <- load_mfrmr_data("example_core")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score", method = "JML", maxit = 30)
#> Warning: Optimization convergence review did not produce an inference-ready numerical solution (code = 1, status = iteration_limit). Optimizer reached the iteration limit before the terminal gradient became small enough for review-only acceptance. Inspect the model specification, data support, and starting values. Do not interpret estimates until the review is resolved.
diag <- diagnose_mfrm(fit, residual_pca = "none")
summary(facet_quality_dashboard(fit, diagnostics = diag))
#> mfrmr Facet Quality Dashboard Summary
#>
#> Overview
#> Facet FacetSource Levels FlaggedLevels BiasSourceBundles
#> Rater inferred 4 2 0
#>
#> Summary
#> Facet Levels MeanEstimate SD MinEstimate MaxEstimate MeanInfit MeanOutfit
#> Rater 4 0 0.313 -0.329 0.333 0.994 1.019
#> SeverityFlagged MisfitFlagged CentralTendencyFlagged BiasFlagged AnyFlagged
#> 0 0 2 0 2
#> BiasRows
#> 0
#>
#> Flagged levels
#> Facet Level Estimate N.x SE ModelSE RealSE SE_Method
#> Rater R01 -0.196 192 0.097 0.097 0.100 Observation-table information
#> Rater R03 0.191 192 0.097 0.097 0.097 Observation-table information
#> Converged InferenceReady ConvergenceSeverity PrecisionTier
#> FALSE FALSE fail exploratory
#> FALSE FALSE fail exploratory
#> SupportsFormalInference SEUse
#> FALSE screening_only
#> FALSE screening_only
#> CIBasis CIUse N.y Infit
#> Normal interval from exploratory observation-table SE screening_only 192 1.051
#> Normal interval from exploratory observation-table SE screening_only 192 0.964
#> Outfit InfitZSTD OutfitZSTD DF_Infit DF_Outfit N.x.x ObservedAverage
#> 1.045 0.408 0.464 105.624 192 192 2.609
#> 0.970 -0.217 -0.263 105.751 192 192 2.396
#> ExpectedAverage Bias MeanResidual MeanStdResidual MeanAbsStdResidual ChiSq
#> 2.609 0 0 -0.009 0.841 200.557
#> 2.396 0 0 0.001 0.812 186.231
#> ChiDf ChiP SE_Residual t_Residual p_Residual SE_StdResidual t_StdResidual
#> 191 0.303 0.054 0 1 0.072 -0.122
#> 191 0.584 0.054 0 1 0.072 0.014
#> p_StdResidual DF PTMEA N.y.y CI_Lower CI_Upper CI_Level CI_Method
#> 0.903 191 0.623 192 -0.386 -0.005 0.95 Normal approximation
#> 0.989 191 0.658 192 0.000 0.382 0.95 Normal approximation
#> CIEligible CILabel N AbsEstimate SeverityFlag
#> FALSE Approximate interval; screening only 192 0.196 FALSE
#> FALSE Approximate interval; screening only 192 0.191 FALSE
#> MisfitFlag CentralTendencyFlag BiasCount BiasSources BiasFlag FlagCount
#> FALSE TRUE 0 0 FALSE 1
#> FALSE TRUE 0 0 FALSE 1
#> AnyFlag FlagLabel .AbsEstimate
#> TRUE central 0.196
#> TRUE central 0.191
#>
#> Settings
#> Setting Value
#> facet Rater
#> facet_source inferred
#> severity_warn 1
#> misfit_warn 1.5
#> central_tendency_max 0.25
#> bias_count_warn 1
#> bias_abs_t_warn 2
#> bias_abs_size_warn 0.5
#> bias_p_max 0.05
#> bias_source_bundles 0
#>
#> Notes
#> - Dashboard constructed successfully.
# }