plot_marginal_fit: Plot strict marginal-fit follow-up cells using base R

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

Plot strict marginal-fit follow-up cells using base R

Description

Plot strict marginal-fit follow-up cells using base R

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
)

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	Optional custom plot title.
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, draw with base graphics.

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 the same cells by the signed observed-minus-expected proportion gap.

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.

Value

A plotting-data object of class mfrm_plot_data.

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

1. Fit with fit_mfrm() using method = "MML" for RSM / PCM.

2. Run diagnose_mfrm() with diagnostic_mode = "both".

3. Use plot_marginal_fit() to inspect the largest strict marginal cells.

4. 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").

See Also

diagnose_mfrm(), rating_scale_table(), plot_marginal_pairwise(), mfrmr_visual_diagnostics

Examples

toy <- load_mfrmr_data("example_core")
fit <- fit_mfrm(
  toy,
  "Person",
  c("Rater", "Criterion"),
  "Score",
  method = "MML",
  quad_points = 7,
  maxit = 30
)
diag <- diagnose_mfrm(fit, residual_pca = "none", diagnostic_mode = "both")
p <- plot_marginal_fit(diag, draw = FALSE, preset = "publication")
p$data$preset
if (interactive()) {
  plot_marginal_fit(
    diag,
    plot_type = "prop_diff",
    draw = TRUE,
    preset = "publication"
  )
}

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