Plot a diagnostic-screening validation study

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 future extensions.

Value

An mfrm_plot_data object with reusable metadata, a long-form plot_long table, and interpretation handoff tables (overview, reading_order, next_actions, reporting_notes, and figure_recipes). When draw = TRUE, the object is returned invisibly after drawing.

Examples

if (FALSE) { # \dontrun{
diag_eval <- evaluate_mfrm_diagnostic_screening(
  design = list(person = 10, rater = 2, criterion = 2, assignment = 2),
  reps = 1,
  maxit = 30,
  include_report = TRUE,
  seed = 123
)
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)
} # }