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Plot per-rater severity ranking with confidence interval whiskers

Source: R/api-plotting-extras.R
plot_rater_severity_profile.Rd

Ranks the levels of a chosen rater facet by estimated severity and draws each level as a horizontal CI whisker around the point estimate. Optional gentle / strict guidance bands at +/-0.5 and +/-1.0 logit relative to the centred mean make rater calibration easy to read for training feedback.

Usage

plot_rater_severity_profile(
  fit,
  diagnostics = NULL,
  facet = "Rater",
  ci_level = 0.95,
  show_bands = TRUE,
  preset = c("standard", "publication", "compact", "monochrome"),
  draw = TRUE
)

Arguments

fit

An mfrm_fit from fit_mfrm().

diagnostics

Optional diagnose_mfrm() output. When omitted, diagnose_mfrm(fit, residual_pca = "none") is run internally.

facet

Facet name to plot (default "Rater"). Any non-Person facet name is accepted.

ci_level

Confidence level used for the whiskers (default 0.95). Bounds use +/- z * ModelSE.

show_bands

Logical. When TRUE (default) draw shaded +/-0.5 (gentle) and +/-1.0 (strict) logit guidance bands.

preset

Visual preset.

draw

If TRUE, draw with base graphics.

Value

An mfrm_plot_data object whose data slot contains columns Level, Estimate, SE, CI_Lower, CI_Upper, Band.

Interpreting output

The vertical reference line at zero is the sum-to-zero centring point. Levels well within +/- 0.5 logit (gentle band) are typically interchangeable in operational scoring; levels outside +/- 1.0 logit (strict band) deserve targeted training or anchoring.

See also

diagnose_mfrm(), analyze_facet_equivalence(), plot_facet_equivalence().

Examples

# \donttest{
toy <- load_mfrmr_data("example_core")
fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score",
                method = "JML", maxit = 30)
#> Warning: Optimization convergence review did not produce an inference-ready numerical solution (code = 1, status = iteration_limit). Optimizer reached the iteration limit before the terminal gradient became small enough for review-only acceptance. Inspect the model specification, data support, and starting values. Do not interpret estimates until the review is resolved.
p <- plot_rater_severity_profile(fit, draw = FALSE)
head(p$data$data)
#>   Level   Estimate         SE      CI_Lower     CI_Upper   Band
#> 1   R02 -0.3287963 0.09769808 -0.5202810670 -0.137311629 gentle
#> 2   R01 -0.1957561 0.09730123 -0.3864630271 -0.005049222 gentle
#> 3   R03  0.1910876 0.09724282  0.0004951599  0.381680000 gentle
#> 4   R04  0.3334649 0.09763161  0.1421104554  0.524819330 gentle
# }