Plot strict marginal-fit follow-up cells with ggplot2

Usage

plot_marginal_fit(
  x,
  diagnostics = NULL,
  plot_type = c("std_residual", "prop_diff"),
  top_n = 20,
  facet = NULL,
  main = NULL,
  palette = NULL,
  label_angle = 45,
  preset = c("standard", "publication", "compact", "monochrome"),
  draw = TRUE,
  title = NULL
)

Arguments

x: Output from fit_mfrm() or diagnose_mfrm().
diagnostics: Optional output from diagnose_mfrm() when x is mfrm_fit.
plot_type: "std_residual" or "prop_diff".
top_n: Maximum cells shown.
facet: Optional facet name used to keep only matching facet-level rows. When NULL, the plot uses the mixed top-cell table returned by the strict marginal screen.
main: Compatibility title argument. Omitted or NULL keeps the default title. Existing calls remain supported without a deprecation warning. For new code, prefer title; do not supply both arguments.
palette: Optional named color overrides. Recognized names: positive, negative, flag.
label_angle: X-axis label angle.
preset: Visual preset ("standard", "publication", "compact", or "monochrome").
draw: If TRUE, print the ggplot object.
title: Plot title. Omit it to keep the default, supply one character string to replace it, or use NULL (or "") to suppress it. This changes only the heading; numerical results, reference lines, subtitles and interpretation notes remain. Both main and title explicitly supplied is an error, even if equal or NULL. Positional legacy arguments retain their order; use the exact name title.

Value

Invisibly, a ggplot object. Use plot_data() to retrieve its reusable plotting data and notes.

Details

This helper visualizes the largest first-order strict marginal-fit cells from diagnose_mfrm(..., diagnostic_mode = "both") or diagnostic_mode = "marginal_fit".

The "std_residual" view ranks cells by the absolute standardized residual from posterior-integrated expected category counts. The "prop_diff" view ranks cells by the absolute observed-minus-expected proportion gap and plots their signed gaps. Both views apply the facet filter before ranking all cells. The returned full_table retains all candidate rows; retention records available/unavailable values for the selected metric. Undefined values are not zero residuals, and grey bars/labels indicate unavailable values or flags.

Use this plot after summary(diagnostics) indicates strict marginal flags. The display is exploratory: it highlights which facet/category cells deserve follow-up, but it is not a standalone inferential test.

Interpreting output

Positive bars mean the observed category usage exceeded the posterior- expected marginal usage for that cell.
Negative bars mean the observed usage fell below the posterior-expected marginal usage.
Red bars indicate the current strict marginal warning rule was triggered by |StdResidual| >= abs_z_warn.

Typical workflow

Fit with fit_mfrm() using method = "MML" for RSM / PCM.
Run diagnose_mfrm() with diagnostic_mode = "both".
Use plot() to inspect the largest strict marginal cells.
Follow up with rating_scale_table() or substantive design review.

Further guidance

For a plot-selection guide and a longer walkthrough, see mfrmr_visual_diagnostics and vignette("mfrmr-visual-diagnostics", package = "mfrmr").

Session plot defaults

See mfrmr_visual_diagnostics for options(mfrmr.plot_preset), argument precedence, and appearance when saving or reusing a plot.

Migration in 0.2.4

This renderer is internal. Use the corresponding plot() route in mfrmr_plot_migration; the former exported name is no longer available.

Examples

library(mfrmr)

# Saved synthetic MML/RSM analysis of example_operational.
# For your own data, fit a model first; see ?fit_mfrm.
example_fit <- readRDS(system.file("examples", "operational-mml.rds", package = "mfrmr"))
fit <- example_fit$fit

# Use the diagnostics saved with this fit
diagnostics <- example_fit$diagnostics

# Which score categories occur more or less often than the model expects?
plot(diagnostics, type = "marginal_fit")

# Positive bars: more frequent than expected; negative bars: less frequent

# Optional: show observed-minus-expected proportions instead
# Run this command separately to inspect the second figure
plot(diagnostics, type = "marginal_fit", view = "prop_diff")