Build a measurable-data summary

Usage

measurable_summary_table(fit, diagnostics = NULL)

Arguments

fit: Output from fit_mfrm().
diagnostics: Optional output from diagnose_mfrm().

Value

A named list with:

summary: one-row measurable-data summary
facet_coverage: per-facet coverage summary
category_stats: category-level usage/fit summary
subsets: subset summary table (when available)

Details

This helper consolidates measurable-data diagnostics into a dedicated report bundle: run-level summary, facet coverage, category usage, and subset (connected-component) information.

summary(t5) is supported through summary(). plot(t5) is dispatched through plot() for class mfrm_measurable (type = "facet_coverage", "category_counts", "subset_observations").

Interpreting output

summary: overall measurable design status.
facet_coverage: spread/precision by facet.
category_stats: category usage and fit context.
subsets: connectivity diagnostics (fragmented subsets reduce comparability).

Typical workflow

Run measurable_summary_table(fit).
Check summary(t5) for subset/connectivity warnings.
Use plot(t5, ...) to inspect facet/category/subset views.

Further guidance

For a plot-selection guide and a longer walkthrough, see mfrmr_visual_diagnostics and vignette("mfrmr-visual-diagnostics", package = "mfrmr").

Output columns

The summary data.frame (one row) contains:

Observations, TotalWeight: Total observations and summed weight.
Persons, Facets, Categories: Design dimensions.
ConnectedSubsets: Number of connected subsets.
LargestSubsetObs, LargestSubsetPct: Largest subset coverage.

The facet_coverage data.frame contains:

Facet: Facet name.
Levels: Number of estimated levels.
MeanSE: Mean standard error across levels.
MeanInfit, MeanOutfit: Mean fit statistics across levels.
MinEstimate, MaxEstimate: Measure range for this facet.

The category_stats data.frame contains:

Category: Score category value.
Count, Percent: Observed count and percentage.
Infit, Outfit, InfitZSTD, OutfitZSTD: Category-level fit.
ExpectedCount, DiffCount, LowCount: Expected-observed comparison and low-count flag.

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.
t5 <- measurable_summary_table(fit)
summary(t5)
#> mfrmr Measurable Summary 
#>   Class: mfrm_measurable
#>   Components: 4
#> 
#> Run overview
#>  Observations TotalWeight Persons Facets Categories ConnectedSubsets
#>           768         768      48      2          4                1
#>  LargestSubsetObs LargestSubsetPct
#>               768              100
#> 
#> Facet/category rows: facet_coverage
#>      Facet Levels MeanSE MeanInfit MeanOutfit MinEstimate MaxEstimate
#>  Criterion      4  0.097     0.994      1.019      -0.415       0.249
#>     Person     48  0.344     1.000      1.019      -2.178       2.684
#>      Rater      4  0.097     0.994      1.019      -0.329       0.333
#> 
#> Notes
#>  - Measurable-data summary with facet coverage, category diagnostics, and
#>    subset/connectivity checks.
p_t5 <- plot(t5, draw = FALSE)
p_t5$data$plot
#> [1] "facet_coverage"
# }