dx_plot_rocs | R Documentation |
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).
dx_plot_rocs(
dx_comp,
add_text = TRUE,
axis_color = "#333333",
text_color = "black"
)
dx_comp |
A |
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". |
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.
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.
dx_compare()
to generate the required input object.
dx_delong()
for details on DeLong's test used in comparisons.
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)
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