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Plot ROC Curves for Multiple Models

Source: R/dx_plots.R
dx_plot_rocs.Rd

Generates Receiver Operating Characteristic (ROC) curves for multiple models and overlays them for comparison. Optionally, it adds text annotations for DeLong's test results to indicate statistical differences between the models' Area Under the ROC Curve (AUC).

Usage

dx_plot_rocs(
  dx_comp,
  add_text = TRUE,
  axis_color = "#333333",
  text_color = "black"
)

Arguments

dx_comp

A dx_compare object containing the results of pairwise model comparisons and the list of dx objects with ROC data.

add_text

Logical, whether to add DeLong's test results as text annotations on the plot. Defaults to TRUE.

axis_color

Color of the axes lines, specified as a color name or hex code. Defaults to "#333333".

text_color

Color of the text annotations, specified as a color name or hex code. Defaults to "black".

Value

A ggplot object representing the ROC curves for the models included in the dx_comp object. Each model's ROC curve is color-coded, and the plot includes annotations for DeLong's test p-values if add_text is TRUE.

Details

This function is a visualization tool that plots ROC curves for multiple models to facilitate comparison. It uses DeLong's test to statistically compare AUC values and, if desired, annotates the plot with the results. The function expects a dx_compare object as input, which should contain the necessary ROC and test comparison data. Ensure that the ROC data and DeLong's test results are appropriately generated and stored in the dx_compare object before using this function.

See also

dx_compare() to generate the required input object. dx_delong() for details on DeLong's test used in comparisons.

Examples

dx_glm <- dx(data = dx_heart_failure, true_varname = "truth", pred_varname = "predicted")
dx_rf <- dx(data = dx_heart_failure, true_varname = "truth", pred_varname = "predicted_rf")
dx_list <- list(dx_glm, dx_rf)
dx_comp <- dx_compare(dx_list, paired = TRUE)
dx_plot_rocs(dx_comp)