Summary plot of differential functioning effect sizes

Compact effect-size summary for a analyze_dff() / analyze_dif() result. Shows each contrast's signed effect size as a horizontal bar with a vertical reference at zero, coloured by the method-appropriate classification. ETS-style A / B / C colours are used only when they are actually available; residual-method screening labels otherwise use the neutral colour.

Usage

plot_dif_summary(
  x,
  top_n = 30L,
  sort_by = c("abs_effect", "effect", "classification"),
  preset = c("standard", "publication", "compact", "monochrome"),
  draw = TRUE,
  ci_level = NULL,
  effect_thresholds = NULL,
  effect_axis_label = NULL
)

Arguments

x: Output from analyze_dff() or analyze_dif().
top_n: Maximum rows shown (default 30).
sort_by: "abs_effect" (default), "effect", or "classification".
preset: Visual preset.
draw: If TRUE, draw with base graphics.
ci_level: Optional confidence level for approximate normal intervals drawn from Effect +/- z * SE when finite standard errors are available. Use NULL (default) to omit intervals.
effect_thresholds: Optional numeric vector of absolute effect-size guide lines to draw at +/- threshold. These are display aids; only use ETS-like values when the source rows support ETS interpretation.
effect_axis_label: Optional x-axis label override. When NULL, the label is chosen from the DFF method.

Value

An mfrm_plot_data object whose data slot contains columns Pair, Effect, SE, Classification, Color.

Interpreting output

Bars are anchored at zero. Width corresponds to effect size on the contrast's native scale. For method = "residual", this is the observed-minus-expected average screening contrast between groups. For method = "refit", this is the subgroup parameter difference on the fitted logit scale when linking support allows a comparable contrast. The ETS classification (A negligible, B moderate, C large) drives bar colour only when ClassificationSystem == "ETS"; otherwise the bar uses the preset's neutral.

Examples

if (FALSE) { # interactive()
toy <- load_mfrmr_data("example_bias")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score",
                method = "JML", maxit = 30)
diag <- diagnose_mfrm(fit, residual_pca = "none")
dff <- analyze_dff(fit, diagnostics = diag,
                   facet = "Rater", group = "Group", data = toy)
unique(dff$dif_table$ClassificationSystem)
p <- plot_dif_summary(dff, draw = FALSE)
head(p$data$data)
}