Calculates the area under the ROC curve (AUC) for all pairs of columns
between a prediction matrix B and binary targets in target.
Each column of B is ranked, and AUC is computed based on how well
the ranks separate positive vs. negative samples in each target column.
allAgainstAllAUCs(B, target)
Arguments
- B
A numeric matrix of predictions (samples × features).
- target
A binary matrix of the same number of rows as B
(samples × targets), where 1 indicates positive and 0 indicates negative.
Value
A numeric matrix of AUC values (features × targets).
Examples
set.seed(1)
B <- matrix(rnorm(100), nrow = 20)
target <- matrix(sample(0:1, 40, replace = TRUE), nrow = 20)
allAgainstAllAUCs(B, target)
#> [,1] [,2]
#> [1,] 0.4848485 0.4895833
#> [2,] 0.5959596 0.6354167
#> [3,] 0.1919192 0.5520833
#> [4,] 0.4545455 0.5000000
#> [5,] 0.2929293 0.2500000