This function applies DeLong's test to compare the areas under two correlated ROC curves, providing a statistical approach to assess if there is a significant difference between them. It's particularly useful for comparing the performance of two diagnostic models.
Arguments
- dx1
A
dxobject containing the first ROC curve.- dx2
A
dxobject containing the second ROC curve.- detail
Character specifying the level of detail in the output: "simple" for p-value only, "full" for detailed test results.
- paired
Logical indicating if data between
dx1anddx2is paired.
Value
Depending on the detail parameter, returns a p-value or a comprehensive
summary of the test including the test statistic, p-value, and confidence interval
for the difference in AUC.
Details
The function utilizes the roc.test function from the pROC package to perform
DeLong's test, which is suitable for comparing two correlated ROC curves. This correlation
typically arises from using the same set of samples to generate both curves. A significant
p-value indicates a statistically significant difference between the AUCs of the two models.
When detail is "full", additional information about the comparison, including the estimated
difference in AUC and its confidence interval, is provided. The confidence interval gives a
range of plausible values for the difference in AUC between the two models.
See also
pROC::roc.test() for the underlying test implementation.
dx_cm() to understand how to create a dx object.
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")
simple <- dx_delong(dx_glm, dx_rf, detail = "simple")
detailed <- dx_delong(dx_glm, dx_rf)
print(simple)
#> [1] 0.0005886491
print(detailed)
#> # A tibble: 1 × 9
#> models test summary p_value estimate conf_low conf_high statistic notes
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 "" DeLong's t… 0.04 (… 5.89e-4 0.0413 0.0178 0.0649 3.44 ""