plot_facet_equivalence: Plot facet-equivalence results

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

Plot facet-equivalence results

Description

Plot facet-equivalence results

Usage

plot_facet_equivalence(
  x,
  diagnostics = NULL,
  facet = NULL,
  type = c("forest", "rope"),
  draw = TRUE,
  ...
)

Arguments

x	Output from analyze_facet_equivalence() or fit_mfrm().
diagnostics	Optional output from diagnose_mfrm() when x is an mfrm_fit object.
facet	Facet to analyze when x is an mfrm_fit object.
type	Plot type: "forest" (default) or "rope".
draw	If TRUE (default), draw the plot. If FALSE, return the prepared plotting data.
...	Additional graphical arguments passed to base plotting functions.

Details

plot_facet_equivalence() is a visual companion to analyze_facet_equivalence(). It does not recompute the equivalence analysis; it only reshapes and displays the returned results.

Value

Invisibly returns the plotting data. If draw = FALSE, the plotting data are returned without drawing.

Plot types

• "forest" places each level on the logit scale with its confidence interval and shades the practical-equivalence region around the weighted grand mean.

• "rope" shows the percentage of each level's uncertainty mass that falls inside the ROPE.

Interpreting output

In the forest plot, the shaded band marks the ROPE (\pmequivalence_bound around the weighted grand mean). Levels whose entire confidence interval lies inside this band are close to the facet grand mean under this descriptive screen. Levels whose interval extends outside the band are more displaced from the facet average. Overlapping intervals between two elements suggest they are not reliably separable, but overlap alone does not establish formal equivalence—use the TOST results for that.

In the ROPE bar chart, each bar shows the proportion of the element's normal-approximation distribution that falls inside the ROPE-style grand-mean proximity. Values > 95\ the element's normal-approximation uncertainty falls near the facet average; 50–95\ meaningfully displaced from that average.

Typical workflow

1. Run analyze_facet_equivalence().

2. Start with type = "forest" to see the facet on the logit scale.

3. Switch to type = "rope" when you want a ranking of levels by grand-mean proximity.

See Also

analyze_facet_equivalence()

Examples

toy <- load_mfrmr_data("example_core")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score",
                method = "JML", maxit = 25)
eq <- analyze_facet_equivalence(fit, facet = "Rater")
pdat <- plot_facet_equivalence(eq, type = "forest", draw = FALSE)
c(pdat$facet, pdat$type)

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