Plot a diagnostic-screening simulation study — plot.mfrm_diagnostic

Builds an integrated visual summary from evaluate_mfrm_diagnostic_screening() output. The default view combines legacy residual, strict marginal, strict pairwise, strict combined, and optional report-index review rates so simulation results can be inspected in one operating-characteristic surface.

Usage

# S3 method for class 'mfrm_diagnostic_screening'
plot(
  x,
  type = c("overview", "report", "contrast", "runtime"),
  metric = NULL,
  x_var = c("n_person", "n_rater", "n_criterion", "raters_per_person"),
  group_var = NULL,
  draw = TRUE,
  ...
)

Arguments

x: Output from evaluate_mfrm_diagnostic_screening().
type: Plot family. "overview" combines screening and optional report rates or counts. "report" focuses on mfrm_report() review signals. "contrast" plots misspecification-minus-well-specified contrasts. "runtime" plots elapsed-time summaries.
metric: Metric family. Use NULL or "auto" for the default within each type. Supported values are documented by error messages and include "rate", "count", "magnitude", "elapsed", and "per_observation" depending on type.
x_var: Design variable for the horizontal axis. Public design aliases from a simulation specification are accepted.
group_var: Optional additional design variable to include in group labels. Public design aliases are accepted.
draw: Logical; if FALSE, return the plot-data bundle without drawing.
...: Reserved for generic compatibility.

Value

Invisibly, a ggplot object carrying reusable metadata, a long-form plot_long table, and interpretation handoff tables (overview, reading_order, next_actions, reporting_notes, and figure_recipes) accessible via plot_data().

Examples

# Supplementary workflow: run explicitly to keep routine checks brief.
# example("plot.mfrm_diagnostic_screening", run.dontrun = TRUE)
if (FALSE) { # \dontrun{
# One saved replication illustrates output structure, not precise rates.
# Regeneration recipe: examples/simulation-results.R in the package.
saved <- readRDS(system.file("examples", "simulation-diagnostic-screening.rds",
  package = "mfrmr", mustWork = TRUE))
# Inspect saved$generation_warnings and the result's run/status tables.
diag_eval <- saved$result
plot(diag_eval, type = "overview", draw = FALSE)
plot_data(diag_eval, type = "overview", component = "plot_long")
plot_data(diag_eval, type = "overview", component = "next_actions")
plot_data(diag_eval, type = "overview", component = "figure_recipes")
plot(diag_eval, type = "report", metric = "rate", draw = FALSE)
} # }