Facet reliability and separation snapshot bar plot

Compact facet-level visual of the Wright & Masters (1982) separation, strata, and reliability indices that diagnose_mfrm() computes. Helpful as a single small figure for "are persons / raters / criteria distinguishable?" review. These are Rasch/FACETS-style separation indices on the fitted logit scale, not ICCs; use compute_facet_icc() for the complementary observed-score variance-share view.

Usage

plot_reliability_snapshot(
  fit,
  diagnostics = NULL,
  metric = c("reliability", "separation", "strata"),
  preset = c("standard", "publication", "compact", "monochrome"),
  draw = TRUE
)

Arguments

fit

An mfrm_fit from fit_mfrm().

diagnostics

Optional diagnose_mfrm() output. Computed on demand when omitted.

metric

"reliability" (default), "separation", or "strata".

preset

Visual preset.

draw

If TRUE, draw with base graphics.

Value

An mfrm_plot_data whose data slot bundles a tidy Facet, Metric, Value data frame.

See also

diagnose_mfrm(), mfrmr_visual_diagnostics

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_reliability_snapshot(fit, draw = FALSE)
p$data$table
#>       Facet      Metric     Value
#> 1     Rater reliability 0.9031062
#> 2    Person reliability 0.9005787
#> 3 Criterion reliability 0.8852450
# Look for (default `metric = "reliability"`):
# - >= 0.9 strong, 0.7-0.9 adequate, < 0.7 weak (Wright & Masters 1982).
# - The Person row is the operative reliability for ability scores.
# - Non-Person rows (Rater / Criterion) report the same index but
#   should be read as "are facet elements distinguishable?"; values
#   close to 1 mean facet means differ reliably from each other.
# }