measurable_summary_table: Build a measurable-data summary
In mfrmr: Estimation and Diagnostics for Many-Facet Measurement Models

measurable_summary_table

R Documentation

Build a measurable-data summary

Description

Build a measurable-data summary

Usage

measurable_summary_table(fit, diagnostics = NULL)

Arguments

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

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").

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)

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

toy <- load_mfrmr_data("example_core")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score", method = "JML", maxit = 25)
t5 <- measurable_summary_table(fit)
summary(t5)
p_t5 <- plot(t5, draw = FALSE)
class(p_t5)

mfrmr documentation built on March 31, 2026, 1:06 a.m.