plot.mfrm_bundle: Plot report/table bundles with base R defaults

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

Plot report/table bundles with base R defaults

Description

Plot report/table bundles with base R defaults

Usage

## S3 method for class 'mfrm_bundle'
plot(x, y = NULL, type = NULL, ...)

Arguments

x	A bundle object returned by mfrmr table/report helpers.
y	Reserved for generic compatibility.
type	Optional plot type. Available values depend on bundle class.
...	Additional arguments forwarded to class-specific plotters.

Details

plot() dispatches by bundle class:

• mfrm_unexpected -> plot_unexpected()

• mfrm_fair_average -> plot_fair_average()

• mfrm_displacement -> plot_displacement()

• mfrm_interrater -> plot_interrater_agreement()

• mfrm_facets_chisq -> plot_facets_chisq()

• mfrm_bias_interaction -> plot_bias_interaction()

• mfrm_bias_count -> bias-count plots (cell counts / low-count rates)

• mfrm_fixed_reports -> pairwise-contrast diagnostics

• mfrm_visual_summaries -> warning/summary message count plots

• mfrm_category_structure -> default base-R category plots

• mfrm_category_curves -> overview (default), ogive, CCC / category probability / conditional probability, cumulative, total-information, and category-specific-information plots

• mfrm_rating_scale -> category-counts/threshold plots

• mfrm_measurable -> measurable-data coverage/count plots

• mfrm_unexpected_after_bias -> post-bias unexpected-response plots

• mfrm_output_bundle -> graph/score output-file diagnostics, including type = "score_se" when scorefile SE columns are available

• mfrm_residual_pca -> residual PCA scree, parallel-analysis, or loadings views via plot_residual_pca()

• mfrm_specifications -> facet/anchor/convergence plots

• mfrm_data_quality -> dashboard, quality-flag, score-map, facet-pattern, and row/category/missing-row plots

• mfrm_facets_fit_review -> FACETS-style df-sensitivity plot

• mfrm_fit_measures -> fit-status counts, Infit/Outfit scatter, measure intervals, and FACETS-style df-sensitivity plots

• mfrm_iteration_report -> replayed-iteration trajectories

• mfrm_subset_connectivity -> subset-observation/connectivity plots

• mfrm_facet_statistics -> facet statistic profile plots

• mfrm_export_bundle / mfrm_summary_appendix_export -> export handoff plots (formats, artifact_groups, selection_tables, selection_handoff, selection_handoff_bundles, selection_handoff_roles, selection_handoff_role_sections, selection_bundles, selection_roles, selection_sections)

If a class is outside these families, use dedicated plotting helpers or custom base R graphics on component tables.

For mfrm_category_curves, pass preset = "monochrome" for grayscale/line-type output. Cumulative .5 boundary lines are shown only for interpretable in-range boundaries by default; use boundary_status = "all" to show every finite boundary estimate or boundary_status = "none" / show_cumulative_boundaries = FALSE to suppress those vertical boundary lines. Use plot_data(x, component = "plot_long") on a category-curve bundle when you want one ggplot2/plotly-friendly table across all curve families.

Value

A plotting-data object of class mfrm_plot_data.

Interpreting output

The returned object is plotting data (mfrm_plot_data) that captures the selected route and reusable data; set draw = TRUE for immediate base graphics.

Typical workflow

1. Create bundle output (e.g., unexpected_response_table()).

2. Inspect routing with summary(bundle) if needed.

3. Call plot(bundle, type = ..., draw = FALSE) to obtain reusable plot data.

See Also

summary(), plot_unexpected(), plot_fair_average(), plot_displacement()

Examples

toy_full <- load_mfrmr_data("example_core")
toy_people <- unique(toy_full$Person)[1:12]
toy <- toy_full[toy_full$Person %in% toy_people, , drop = FALSE]
fit <- suppressWarnings(
  fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score", method = "JML", maxit = 30)
)
t4 <- unexpected_response_table(fit, abs_z_min = 1.5, prob_max = 0.4, top_n = 5)
p <- plot(t4, draw = FALSE)
vis <- build_visual_summaries(fit, diagnose_mfrm(fit, residual_pca = "none"))
p_vis <- plot(vis, type = "comparison", draw = FALSE)
spec <- specifications_report(fit)
p_spec <- plot(spec, type = "facet_elements", draw = FALSE)
if (interactive()) {
  plot(
    t4,
    type = "severity",
    draw = TRUE,
    main = "Unexpected Response Severity (Customized)",
    palette = c(higher = "#d95f02", lower = "#1b9e77", bar = "#2b8cbe"),
    label_angle = 45
  )
  plot(
    vis,
    type = "comparison",
    draw = TRUE,
    main = "Warning vs Summary Counts (Customized)",
    palette = c(warning = "#cb181d", summary = "#3182bd"),
    label_angle = 45
  )
}

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