% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/api-tables.R
\name{measurable_summary_table}
\alias{measurable_summary_table}
\title{Build a measurable-data summary}
\usage{
measurable_summary_table(fit, diagnostics = NULL)
}
\arguments{
\item{fit}{Output from \code{\link[=fit_mfrm]{fit_mfrm()}}.}

\item{diagnostics}{Optional output from \code{\link[=diagnose_mfrm]{diagnose_mfrm()}}.}
}
\value{
A named list with:
\itemize{
\item \code{summary}: one-row measurable-data summary
\item \code{facet_coverage}: per-facet coverage summary
\item \code{category_stats}: category-level usage/fit summary
\item \code{subsets}: subset summary table (when available)
}
}
\description{
Build a measurable-data summary
}
\details{
This helper consolidates measurable-data diagnostics into a dedicated
report bundle: run-level summary, facet coverage, category usage, and
subset (connected-component) information.

\code{summary(t5)} is supported through \code{summary()}.
\code{plot(t5)} is dispatched through \code{plot()} for class
\code{mfrm_measurable} (\code{type = "facet_coverage"}, \code{"category_counts"},
\code{"subset_observations"}).
}
\section{Interpreting output}{

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

\section{Typical workflow}{

\enumerate{
\item Run \code{measurable_summary_table(fit)}.
\item Check \code{summary(t5)} for subset/connectivity warnings.
\item Use \code{plot(t5, ...)} to inspect facet/category/subset views.
}
}

\section{Further guidance}{

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

\section{Output columns}{

The \code{summary} data.frame (one row) contains:
\describe{
\item{Observations, TotalWeight}{Total observations and summed weight.}
\item{Persons, Facets, Categories}{Design dimensions.}
\item{ConnectedSubsets}{Number of connected subsets.}
\item{LargestSubsetObs, LargestSubsetPct}{Largest subset coverage.}
}

The \code{facet_coverage} data.frame contains:
\describe{
\item{Facet}{Facet name.}
\item{Levels}{Number of estimated levels.}
\item{MeanSE}{Mean standard error across levels.}
\item{MeanInfit, MeanOutfit}{Mean fit statistics across levels.}
\item{MinEstimate, MaxEstimate}{Measure range for this facet.}
}

The \code{category_stats} data.frame contains:
\describe{
\item{Category}{Score category value.}
\item{Count, Percent}{Observed count and percentage.}
\item{Infit, Outfit, InfitZSTD, OutfitZSTD}{Category-level fit.}
\item{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 = 30)
t5 <- measurable_summary_table(fit)
summary(t5)
p_t5 <- plot(t5, draw = FALSE)
p_t5$data$plot
}
\seealso{
\code{\link[=diagnose_mfrm]{diagnose_mfrm()}}, \code{\link[=rating_scale_table]{rating_scale_table()}}, \code{\link[=describe_mfrm_data]{describe_mfrm_data()}},
\link{mfrmr_visual_diagnostics}
}
