mfrm_results: Build comprehensive first-screen MFRM results

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

Build comprehensive first-screen MFRM results

Description

Build comprehensive first-screen MFRM results

Usage

mfrm_results(
  fit,
  include = "standard",
  response_time = NULL,
  response_time_data = NULL,
  response_time_facets = NULL,
  response_time_score = NULL,
  output = c("object", "summary", "tables", "html")
)

Arguments

fit	Output from fit_mfrm() or run_mfrm_facets(). A standard long-format data.frame is also accepted when person and score columns can be inferred unambiguously from common names such as Person and Score; all remaining columns are treated as facets.
include	Result sections or purpose presets to include. Purpose presets are "standard", "publication", "validation", "facets", "bias", "misfit_review", "linking", "network", "gpcm_review", and "all". Section names include "fit", "diagnostics", "tables", "precision", "reporting", "categories", "plots", "facets_fit", "bias", "misfit", "linking", "network", and "apa".
response_time	Optional response-time column name. When NULL and include contains "response_time", conservative column names such as ResponseTime, response_time, or RT are detected when available.
response_time_data	Optional original long-format data containing the timing column. Required for already fitted objects unless the timing column is still present in fit$prep$data.
response_time_facets	Optional facet columns for response-time summaries. Defaults to the fitted model's source facet columns when available.
response_time_score	Optional score column for response-time summaries. Defaults to the fitted model's source score column when available.
output	Return format: "object" for an mfrm_results object, "summary" for its compact summary, "tables" for a named list of available data frames, or "html" for a temporary HTML report.

Details

mfrm_results() is a high-level result object. It does not introduce a new estimator or a new validity rule. It fits only when fit is a data frame, computes diagnostics automatically when needed, and collects output from existing helpers such as diagnose_mfrm(), fit_measures_table(), precision_review_report(), and reporting_checklist(). Sections that are unsupported for a particular fit are retained in the status table as not_available rather than stopping the whole results workflow. The returned object also carries next_actions and input$reproducible_code so users can move from the comprehensive first screen to explicit reporting or replay code.

Value

Depending on output, an mfrm_results object, a summary.mfrm_results object, a named table list, or an mfrm_results_html object.

Include presets

• "standard": fit, diagnostics, tables, precision, reporting, categories, and plot routes

• "publication": standard sections plus APA output assembly

• "validation": standard sections plus FACETS-fit/df-sensitivity review

• "facets": fit, diagnostics, tables, categories, plots, and FACETS-fit review for FACETS-facing migration work

• "bias" / "bias_review": standard sections plus facet-level bias-screen guidance; interaction bias still requires explicit facet-pair selection

• "misfit" / "misfit_review": standard sections plus unexpected-response, displacement, and pathway-map case-review surfaces

• "linking" / "anchors": standard sections plus anchor-readiness and operational linking-review surfaces from the fitted object's stored anchor review; drift and screened-chain review still require multiple fitted forms or waves

• "network": standard sections plus network/connectivity review

• "response_time": descriptive response-time QC review when timing metadata are supplied through response_time / response_time_data

• "gpcm_review": standard sections with bounded-GPCM caveats retained in the collected summaries and reports

• "all": standard sections plus FACETS-fit, network, APA, and response-time sections

Response-time metadata

Response-time review is opt-in and descriptive. It does not change fitted MFRM estimates, fit a joint speed-accuracy model, or create automatic exclusion rules. Use include = "response_time" together with response_time = "ResponseTime". When fit is an already fitted object, also supply response_time_data = original_data because fitted objects keep only the measurement columns needed for estimation.

What to inspect first

Start with summary(res). The most useful fields are:

• overview: input mode, model, method, table count, and plot-route count

• triage: first-screen signals ordered by unavailable/review/info/ok

• status: which sections were available, skipped, or unsupported

• plot_map: the supported plot(res, type = ...) routes for this object

• next_actions: recommended follow-up calls

• reproducible_code: replay scaffold for the first-screen route

Data-frame input

Direct data-frame input is intentionally conservative. It is intended for standard columns such as Person, Score, Rater, and Criterion. For research scripts, use fit_mfrm() or run_mfrm_facets() explicitly when column roles, model, method, anchors, or missing-data rules need to be documented. Use mfrm_results_interactive() only when you want an opt-in column-selection wizard in an interactive session.

Visualization and HTML

plot(res) routes to a FACETS-style model-level visual bundle by default. Other routes include plot(res, type = "wright"), "pathway", "qc", "category", "anchors", and "tables". output = "html" writes a lightweight temporary HTML file; use launch_mfrmr_viewer() when you want an optional local Shiny reader for an already-created mfrm_results object. Use export_mfrm_results() for a lightweight download of the comprehensive results object, or export_mfrm_bundle() when a fit-centered durable analysis archive is needed.

Typical workflow

1. Fit explicitly with fit_mfrm() in scripts and manuscripts.

2. Call res <- mfrm_results(fit).

3. Read summary(res)$triage, summary(res)$status, summary(res)$plot_map, and summary(res)$next_actions.

4. Use plot(res, type = "qc") for the first visual screen.

5. Optionally inspect the same result with launch_mfrmr_viewer() in an interactive session.

6. Use build_summary_table_bundle() or the helper named in summary(res)$next_actions for report-specific follow-up.

See Also

fit_mfrm(), run_mfrm_facets(), diagnose_mfrm(), reporting_checklist(), build_summary_table_bundle(), export_mfrm_results(), launch_mfrmr_viewer(), mfrmr_output_guide()

Examples

toy <- load_mfrmr_data("example_core")
toy_small <- toy[toy$Person %in% unique(toy$Person)[1:8], , drop = FALSE]

# JML keeps the help example fast; use the recommended workflow settings
# for final analyses.
fit <- fit_mfrm(toy_small, "Person", c("Rater", "Criterion"), "Score",
                method = "JML", maxit = 30)
res <- mfrm_results(fit)

sx <- summary(res)
sx$overview
sx$triage
sx$plot_map
sx$next_actions
mfrm_results(fit, include = "validation", output = "summary")$status

plot(res, type = "qc", draw = FALSE)

# Direct data-frame input is available for conservative exploratory use
# when Person and Score columns are unambiguous.
mfrm_results(
  toy_small,
  include = c("fit", "diagnostics"),
  output = "summary"
)$mapping

mfrmr documentation built on June 13, 2026, 1:07 a.m.