summary.mfrm_fit: Summarize an 'mfrm_fit' object in a user-friendly format

In mfrmr: Estimation and Diagnostics for Many-Facet Measurement Models

Summarize an mfrm_fit object in a user-friendly format

Description

Summarize an mfrm_fit object in a user-friendly format

Usage

## S3 method for class 'mfrm_fit'
summary(object, digits = 3, top_n = 5, ...)

Arguments

object	Output from fit_mfrm().
digits	Number of digits for printed numeric values.
top_n	Number of extreme facet/person rows shown in summaries.
...	Reserved for generic compatibility.

Details

This method provides a compact, human-readable summary oriented to reporting. It returns a structured object and prints:

• model fit overview (N, LogLik, AIC/BIC, convergence)

• facet-level estimate distribution (mean/SD/range)

• person measure distribution

• step/threshold checks

• high/low person measures and extreme facet levels

Value

An object of class summary.mfrm_fit with:

• overview: global model/fit indicators

• facet_overview: per-facet estimate distribution summary

• person_overview: person-measure distribution summary

• step_overview: threshold/step diagnostics

• top_person: highest/lowest person measures

• top_facet: extreme facet-level estimates

• notes: short interpretation notes

Interpreting output

• overview: convergence and information criteria.

• facet_overview: per-facet spread and range of estimates.

• person_overview: distribution of person measures.

• step_overview: threshold spread and monotonicity checks.

• top_person / top_facet: extreme estimates for quick triage.

Typical workflow

1. Fit model with fit_mfrm().

2. Run summary(fit) for first-pass diagnostics.

3. Continue with diagnose_mfrm() for element-level fit checks.

See Also

fit_mfrm(), diagnose_mfrm()

Examples

toy <- load_mfrmr_data("example_core")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score", method = "JML", maxit = 25)
summary(fit)

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