% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/api-estimation.R
\name{summary.mfrm_data_description}
\alias{summary.mfrm_data_description}
\title{Summarize a data-description object}
\usage{
\method{summary}{mfrm_data_description}(object, digits = 3, top_n = 10, ...)
}
\arguments{
\item{object}{Output from \code{\link[=describe_mfrm_data]{describe_mfrm_data()}}.}

\item{digits}{Number of digits for numeric rounding.}

\item{top_n}{Maximum rows shown in preview blocks.}

\item{...}{Reserved for generic compatibility.}
}
\value{
An object of class \code{summary.mfrm_data_description}.
\itemize{
\item \code{overview}: design/sample counts
\item \code{missing}: top columns by missingness
\item \code{score_distribution}: compact score-usage table, including zero-count
categories retained by the prepared score support
\item \code{facet_overview}: facet-level coverage summary
\item \code{agreement}: inter-rater agreement summary when available
\item \code{row_retention}: row counts before and after preparation filters
\item \code{preparation_notes}: structured preparation notes retained from
\code{\link[=describe_mfrm_data]{describe_mfrm_data()}}
\item \code{reporting_map}: manuscript-oriented guide to what is covered here versus
which companion outputs should be consulted
\item \code{caveats}: structured warning/review rows for score-support issues;
\code{print(summary(ds))} shows a compact \code{Caveats} block when rows are present
}
}
\description{
Summarize a data-description object
}
\details{
This summary is intended as a compact pre-fit quality snapshot for
manuscripts and analysis logs.
}
\section{Interpreting output}{

Recommended read order:
\itemize{
\item \code{overview}: sample size, persons/facets/categories.
\item \code{missing}: missingness hotspots by selected input columns.
\item \code{score_distribution}: category usage balance.
\item \code{notes} / printed \code{Caveats}: retained zero-count score categories and
related score-support caveats; intermediate unused categories should be
treated as threshold-functioning warnings before model fitting.
\item \code{facet_overview}: coverage per facet (minimum/maximum weighted counts).
\item \code{agreement}: observed-score inter-rater agreement (when available).
}

Very low \code{MinWeightedN} in \code{facet_overview} is a practical warning for
unstable downstream facet estimates.
}

\section{Typical workflow}{

\enumerate{
\item Run \code{\link[=describe_mfrm_data]{describe_mfrm_data()}} on raw long-format data.
\item Inspect \code{summary(ds)} before model fitting.
\item Resolve sparse/missing issues, then run \code{\link[=fit_mfrm]{fit_mfrm()}}.
}
}

\examples{
toy <- load_mfrmr_data("example_core")
ds <- describe_mfrm_data(toy, "Person", c("Rater", "Criterion"), "Score")
summary(ds)
}
\seealso{
\code{\link[=describe_mfrm_data]{describe_mfrm_data()}}, \code{\link[=summary.mfrm_fit]{summary.mfrm_fit()}}
}
