Compare Multiple Value Functions

Creates a multi-panel plot comparing value functions for multiple criteria from MCDA clinical scales. This function takes the clinical_scales list structure used in MCDA functions and creates visualizations for all criteria.

Usage

plot_multiple_value_functions(
  clinical_scales = NULL,
  criteria = NULL,
  ncol = 2,
  show_titles = TRUE,
  show_reference_lines = TRUE,
  base_font_size = 9
)

Arguments

clinical_scales

List defining clinical reference levels for each criterion. Each element should be a list with: min (lower threshold), max (upper threshold), and direction ("increasing" for higher is better, "decreasing" for lower is better). Required.

criteria

Character vector of criterion names to plot. If NULL, plots all criteria in clinical_scales. Default is NULL.

ncol

Integer specifying number of columns in the grid layout. Default is 2.

show_titles

Logical indicating whether to show individual plot titles. Default is TRUE.

show_reference_lines

Logical indicating whether to show horizontal reference lines at value = 50. Default is TRUE.

base_font_size

Numeric; base font size in points for all text elements in the plot (default: 9).

Value

A combined plot (using patchwork) showing all value functions in a grid layout.

Examples

# Define clinical scales
clinical_scales <- list(
  `Benefit 1` = list(min = 0, max = 1, direction = "increasing"),
  `Benefit 2` = list(min = 0, max = 100, direction = "decreasing"),
  `Risk 1` = list(min = 0, max = 0.5, direction = "decreasing"),
  `Risk 2` = list(min = 0, max = 0.3, direction = "decreasing")
)

# Plot all criteria
all_plots <- plot_multiple_value_functions(
  clinical_scales = clinical_scales
)

# Plot specific criteria only
if (FALSE) { # \dontrun{
selected_plots <- plot_multiple_value_functions(
  clinical_scales = clinical_scales,
  criteria = c("Benefit 1", "Risk 1"),
  ncol = 2
)
} # }