A dataset for demonstrating Bayesian Decision Curve Analysis (DCA). It typically includes an outcome variable and predictions from one or more models or tests.
Usage
data(bayesdca_test_data)Format
A data frame with 500 rows and 4 variables:
- outcome
Character. The true outcome status (e.g., presence or absence of a condition).
- model_prediction
Numeric. Predicted probability or risk score from a statistical model.
- binary_test
Integer. Results of a binary diagnostic test, likely coded as 0 or 1.
- weak_test
Integer. Results of another binary diagnostic test, potentially with lower accuracy, likely coded as 0 or 1.
Examples
data(bayesdca_test_data)
str(bayesdca_test_data)
#> 'data.frame': 500 obs. of 4 variables:
#> $ outcome : chr "No Disease" "No Disease" "No Disease" "Disease" ...
#> $ model_prediction: num 0.407 0.363 0.415 0.748 0.931 ...
#> $ binary_test : int 0 0 0 0 0 0 0 1 0 0 ...
#> $ weak_test : int 0 1 1 1 1 0 0 1 0 0 ...
head(bayesdca_test_data)
#> outcome model_prediction binary_test weak_test
#> 1 No Disease 0.4071879 0 0
#> 2 No Disease 0.3631134 0 1
#> 3 No Disease 0.4148582 0 1
#> 4 Disease 0.7484752 0 1
#> 5 Disease 0.9307655 0 1
#> 6 No Disease 0.4778732 0 0
summary(bayesdca_test_data$model_prediction)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.0568 0.3730 0.5494 0.5425 0.7172 0.9563