Build a measurable-data summary

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

1. Run measurable_summary_table(fit).

2. Check summary(t5) for subset/connectivity warnings.

3. 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.

See also

diagnose_mfrm(), rating_scale_table(), describe_mfrm_data(), mfrmr_visual_diagnostics

Examples

if (FALSE) { # interactive()
toy <- load_mfrmr_data("example_core")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score", method = "JML", maxit = 30)
t5 <- measurable_summary_table(fit)
summary(t5)
p_t5 <- plot(t5, draw = FALSE)
p_t5$data$plot
}