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.

Value

A plotting-data object of class mfrm_plot_data.

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 normal bands, and FACETS-style df-sensitivity plots. For type = "measure_ci", the caption retains the source interval interpretation (including exploratory JML bands); fixed values use open diamonds and finite estimates without intervals use crosses. main = "" omits the title and show_notes = FALSE hides the caption without removing it from saved data. Changing ci_level does not change inferential eligibility. See fit_measures_table().
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.

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

Create bundle output (e.g., unexpected_response_table()).
Inspect routing with summary(bundle) if needed.
Call plot(bundle, type = ..., draw = FALSE) to obtain reusable plot data.

Session plot defaults

Set options(mfrmr.plot_preset = "publication") to choose a session default for plotting functions that expose the common preset argument. The supported values are "standard", "publication", "compact" and "monochrome". Precedence is an explicit call argument, then the session option, then "standard". For example, preset = "standard" overrides a session set to "monochrome". Explicit preset = NULL retains the earlier package-default behavior; it does not read the session option. Invalid session values cause an error only when that option is needed.

The category-curve, data-quality, fit-review, connectivity and network routes of plot() for report bundles use the same option through .... Plots without a common preset argument, including extended-model plots with their own palette controls, keep their own settings. This option selects a preset, not a universal theme or a guarantee that all renderers implement every appearance control identically.

New plot payloads retain the resolved preset for supported saved-data rendering. Converting an existing payload with as_ggplot() uses its saved appearance, even after the session option changes. A call that creates a new plot from a fit or statistical result uses the current default. For a reproducible script, supply preset explicitly or set the option in that script. Saving only the fitted model does not save a session option. No global ggplot theme is changed.

Restore previous settings with old <- options(mfrmr.plot_preset = "monochrome") followed by options(old). Use options(mfrmr.plot_preset = NULL) to remove the option. The preset changes appearance, not estimates, confidence levels or diagnostic thresholds.

Examples

# \donttest{
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
  )
}
# }