| plot_bias_interaction | R Documentation |
Plot bias interaction diagnostics (preferred alias)
plot_bias_interaction(
x,
plot = c("scatter", "ranked", "abs_t_hist", "facet_profile"),
diagnostics = NULL,
facet_a = NULL,
facet_b = NULL,
interaction_facets = NULL,
top_n = 40,
abs_t_warn = 2,
abs_bias_warn = 0.5,
p_max = 0.05,
sort_by = c("abs_t", "abs_bias", "prob"),
main = NULL,
palette = NULL,
label_angle = 45,
preset = c("standard", "publication", "compact"),
draw = TRUE
)
x |
Output from |
plot |
Plot type: |
diagnostics |
Optional output from |
facet_a |
First facet name (required when |
facet_b |
Second facet name (required when |
interaction_facets |
Character vector of two or more facets. |
top_n |
Maximum number of ranked rows to keep. |
abs_t_warn |
Warning cutoff for absolute t statistics. |
abs_bias_warn |
Warning cutoff for absolute bias size. |
p_max |
Warning cutoff for p-values. |
sort_by |
Ranking key: |
main |
Optional plot title override. |
palette |
Optional named color overrides ( |
label_angle |
Label angle hint for ranked/profile labels. |
preset |
Visual preset ( |
draw |
If |
Visualization front-end for bias_interaction_report() with multiple views.
A plotting-data object of class mfrm_plot_data.
"scatter" (default)Scatter plot of bias size (x) vs
screening t-statistic (y). Points colored by flag status. Dashed reference
lines at abs_bias_warn and abs_t_warn. Use for overall triage
of interaction effects.
"ranked"Ranked bar chart of top top_n interactions sorted
by sort_by criterion (absolute t, absolute bias, or probability).
Bars colored red for flagged cells.
"abs_t_hist"Histogram of absolute screening t-statistics across all
interaction cells. Dashed reference line at abs_t_warn. Use for
assessing the overall distribution of interaction effect sizes.
"facet_profile"Per-facet-level aggregation showing mean absolute bias and flag rate. Useful for identifying which individual facet levels drive systematic interaction patterns.
Start with "scatter" or "ranked" for triage, then confirm pattern shape
using "abs_t_hist" and "facet_profile".
Consistent flags across multiple views are stronger screening signals of systematic interaction bias than a single extreme row, but they do not by themselves establish formal inferential evidence.
Estimate bias with estimate_bias() or pass mfrm_fit directly.
Plot with plot = "ranked" for top interactions.
Cross-check using plot = "scatter" and plot = "facet_profile".
bias_interaction_report(), estimate_bias(), plot_displacement()
toy <- load_mfrmr_data("example_bias")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score", method = "JML", maxit = 25)
p <- plot_bias_interaction(
fit,
diagnostics = diagnose_mfrm(fit, residual_pca = "none"),
facet_a = "Rater",
facet_b = "Criterion",
preset = "publication",
draw = FALSE
)
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