plot_wright_unified: Plot a unified Wright map with all facets on a shared logit...

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

Plot a unified Wright map with all facets on a shared logit scale

Description

Produces a shared-logit variable map showing person ability distribution alongside measure estimates for every facet in side-by-side columns on the same scale.

Usage

plot_wright_unified(
  fit,
  diagnostics = NULL,
  bins = 20L,
  show_thresholds = TRUE,
  top_n = 30L,
  show_ci = FALSE,
  ci_level = 0.95,
  draw = TRUE,
  preset = c("standard", "publication", "compact"),
  palette = NULL,
  label_angle = 45,
  ...
)

Arguments

fit	Output from fit_mfrm().
diagnostics	Optional output from diagnose_mfrm().
bins	Integer number of bins for the person histogram. Default 20.
show_thresholds	Logical; if TRUE, display threshold/step positions on the map. Default TRUE.
top_n	Maximum number of facet/step points retained for labeling.
show_ci	Logical; if TRUE, draw approximate confidence intervals when standard errors are available.
ci_level	Confidence level used when show_ci = TRUE.
draw	If TRUE (default), draw the plot. If FALSE, return plot data invisibly.
preset	Visual preset ("standard", "publication", "compact").
palette	Optional named color overrides passed to the shared Wright-map drawer.
label_angle	Rotation angle for group labels on the facet panel.
...	Additional graphical parameters.

Details

This unified map arranges:

• Column 1: Person measure distribution (horizontal histogram)

• Shared facet/step panel: facet levels and optional threshold positions on the same vertical logit axis

• Range and interquartile overlays for each facet group to show spread

This is the package's most compact targeting view when you want one display that shows where persons, facet levels, and category thresholds sit relative to the same latent scale.

The logit scale on the y-axis is shared, allowing direct visual comparison of all facets and persons.

Value

Invisibly, a list with persons, facets, and thresholds data used for the plot.

Interpreting output

• Facet levels at the same height on the map are at similar difficulty.

• The person histogram shows where examinees cluster relative to the facet scale.

• Thresholds (if shown) indicate category boundary positions.

• Large gaps between the person distribution and facet locations can signal targeting problems.

Typical workflow

1. Fit a model with fit_mfrm().

2. Plot with plot_wright_unified(fit).

3. Compare person distribution with facet level locations.

4. Use show_thresholds = TRUE when you want the category structure in the same view.

When to use this instead of plot_information

Use plot_wright_unified() when your main question is targeting or coverage on the shared logit scale. Use plot_information() when your main question is measurement precision across theta.

Further guidance

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

See Also

fit_mfrm(), plot.mfrm_fit(), mfrmr_visual_diagnostics

Examples

toy <- load_mfrmr_data("example_core")
toy_small <- toy[toy$Person %in% unique(toy$Person)[1:12], , drop = FALSE]
fit <- fit_mfrm(toy_small, "Person", c("Rater", "Criterion"), "Score",
                 method = "JML", model = "RSM", maxit = 10)
map_data <- plot_wright_unified(fit, draw = FALSE)
names(map_data)

mfrmr documentation built on March 31, 2026, 1:06 a.m.