plot_marginal_pairwise: Plot strict pairwise local-dependence follow-up using base R

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

Plot strict pairwise local-dependence follow-up using base R

Description

Plot strict pairwise local-dependence follow-up using base R

Usage

plot_marginal_pairwise(
  x,
  diagnostics = NULL,
  metric = c("exact", "adjacent"),
  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.
metric	"exact" or "adjacent".
top_n	Maximum level pairs shown.
facet	Optional facet name used to keep only matching pairwise rows.
main	Optional custom plot title.
palette	Optional named color overrides. Recognized names: ok, 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 strict pairwise local-dependence follow-up derived from posterior-integrated expected exact and adjacent agreement.

The "exact" view ranks level pairs by the absolute exact-agreement standardized residual. The "adjacent" view uses the adjacent-agreement standardized residual instead. Both are exploratory corroboration screens for strict marginal-fit flags.

Value

A plotting-data object of class mfrm_plot_data.

Interpreting output

• Positive bars mean the observed agreement exceeded the posterior-expected agreement for that level pair.

• Negative bars mean the observed agreement fell below the posterior-expected agreement.

• Red bars indicate the pair exceeded the current strict-warning threshold.

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_pairwise() to inspect level pairs behind pairwise local-dependence flags.

4. Corroborate with legacy diagnostics, design review, and substantive interpretation before making claims.

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(), plot_marginal_fit(), 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_pairwise(diag, draw = FALSE, preset = "publication")
p$data$preset
if (interactive()) {
  plot_marginal_pairwise(
    diag,
    metric = "adjacent",
    draw = TRUE,
    preset = "publication"
  )
}

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