E2E (version 0.1.2)

figure_dia: Plot Diagnostic Model Evaluation Figures

Description

Generates and returns a ggplot object for Receiver Operating Characteristic (ROC) curves, Precision-Recall (PRC) curves, or confusion matrices.

Usage

figure_dia(type, data, file = NULL)

Value

A ggplot object. If the file argument is provided, the plot is also saved to the specified path.

Arguments

type

String, specifies the type of plot to generate. Options are "roc", "prc", or "matrix".

data

A list object containing model evaluation results. It must include:

• sample_score: A data frame with "label" (0/1) and "score" columns.

• evaluation_metrics: A list with a "Final_Threshold" or "Final_Threshold" value.

file

Optional. A string specifying the path to save the plot (e.g., "plot.png"). If NULL (the default), the plot object is returned instead of being saved.

Examples

# Create example data for a diagnostic model
external_eval_example_dia <- list(
  sample_score = data.frame(
    ID = paste0("S", 1:100),
    label = sample(c(0, 1), 100, replace = TRUE),
    score = runif(100, 0, 1)
  ),
  evaluation_metrics = list(
    Final_Threshold = 0.53
  )
)

# Generate an ROC curve plot object
roc_plot <- figure_dia(type = "roc", data = external_eval_example_dia)
# To display the plot, simply run:
# print(roc_plot)

# Generate a PRC curve and save it to a temporary file
# tempfile() creates a safe, temporary path as required by CRAN
temp_prc_path <- tempfile(fileext = ".png")
figure_dia(type = "prc", data = external_eval_example_dia, file = temp_prc_path)

# Generate a Confusion Matrix plot
matrix_plot <- figure_dia(type = "matrix", data = external_eval_example_dia)