View source: R/api-plotting-extras.R
| plot_dif_summary | R Documentation |
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.
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
)
x |
Output from |
top_n |
Maximum rows shown (default |
sort_by |
|
preset |
Visual preset. |
draw |
If |
ci_level |
Optional confidence level for approximate normal
intervals drawn from |
effect_thresholds |
Optional numeric vector of absolute effect-size
guide lines to draw at |
effect_axis_label |
Optional x-axis label override. When |
An mfrm_plot_data object whose data slot contains
columns Pair, Effect, SE, Classification, Color.
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.
analyze_dff(), analyze_dif(), plot_dif_heatmap().
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)
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