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Plot Cut-point Optimisation Stability Surface and Help Catalog Page

Source: R/plotting_functions.R, R/validate_cutpoint_methods.R
plot_validation.Rd

Generates a premium, continuous 2D contour surface density topology map tracking the statistical stability of paired discovered cut-points across bootstrap resampling profiles. Automatically handles 1, 2, or multi-cut architectures dynamically.

Usage

plot_validation(
  validation_result,
  main = "Resampling Convergence & Stability Landscape",
  focus_cuts = c(1, 2),
  ...
)

# S3 method for class 'validate_cutpoint_result'
print(x, ...)

# S3 method for class 'validate_cutpoint_result'
plot(x, ...)

# S3 method for class 'validate_cutpoint_result'
summary(
  object,
  show_descriptives = TRUE,
  show_ci = TRUE,
  show_params = TRUE,
  plot.it = FALSE,
  ...
)

Arguments

validation_result

A validation object generated by OptSurvCutR containing the validation log history dataset.

main

Main title of the chart canvas. Defaults to "Resampling Convergence & Stability Landscape".

focus_cuts

A numeric vector of length 2 specifying which two cut-points to map if the model contains more than 2 cuts. Defaults to c(1, 2).

...

Additional arguments passed down to downstream rendering pipelines.

x

A validation result object for generic printing or plotting methods dispatch.

object

A validation result object passed to down-stream summary tracking.

show_descriptives

Logical. Show complete bootstrap distribution descriptives? Defaults to TRUE.

show_ci

Logical. Print 95% Empirical Confidence Interval boundaries? Defaults to TRUE.

show_params

Logical. Display original metadata loop execution parameters? Defaults to TRUE.

plot.it

Logical. If TRUE, automatically outputs the visual sampling line charts. Defaults to FALSE.

Value

A publication-ready ggplot canvas object displaying stability density bounds.

srrstats compliance

.

.

.

Examples

mock_val <- list(
  bootstrap_distribution = data.frame(Cut_point_1 = rnorm(30, 10, 1)),
  original_cuts = 10.2,
  parameters = list(predictor = "Biomarker", num_replicates = 30, successful_reps = 30)
)
p <- plot_validation(mock_val)