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.

Value

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

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.

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 (\(\pm\)equivalence_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

Run analyze_facet_equivalence().
Start with type = "forest" to see the facet on the logit scale.
Switch to type = "rope" when you want a ranking of levels by grand-mean proximity.

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)
#> [1] "Rater"  "forest"