Calculates the Area Under the Receiver Operating Characteristic (ROC) Curve from prediction probabilities and true binary outcomes. AUC is a measure of the ability of a classifier to distinguish between classes and is used as a summary of the ROC curve.
Arguments
- truth
Vector of true binary class outcomes (0 and 1).
- predprob
Vector of prediction probabilities corresponding to the true outcomes.
- detail
Character string specifying the level of detail in the output: "simple" for just the AUC value, "full" for the AUC value along with confidence intervals.
Value
Depending on the detail parameter, returns a single numeric value of AUC or
a data frame with the AUC and its confidence intervals.
Examples
# Assuming you have a vector of true class labels and predicted probabilities
true_classes <- c(1, 0, 1, 1, 0, 0, 1)
predicted_probs <- c(0.9, 0.1, 0.8, 0.75, 0.33, 0.25, 0.67)
simple_auc <- dx_auc(true_classes, predicted_probs, detail = "simple")
#> Warning: ci.auc() of a ROC curve with AUC == 1 is always 1-1 and can be misleading.
#> Warning: ci.auc() of a ROC curve with AUC == 1 is always 1-1 and can be misleading.
detailed_auc <- dx_auc(true_classes, predicted_probs)
#> Warning: ci.auc() of a ROC curve with AUC == 1 is always 1-1 and can be misleading.
#> Warning: ci.auc() of a ROC curve with AUC == 1 is always 1-1 and can be misleading.
print(simple_auc)
#> [1] 1
print(detailed_auc)
#> # A tibble: 1 × 8
#> measure summary estimate conf_low conf_high fraction conf_type notes
#> <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> <chr>
#> 1 AUC ROC 1.000 (1.000, 1.… 1 1 1 "" DeLong ""