Aggregates multiple Receiver Operating Characteristic (ROC) curves obtained from different sources into one global ROC. Additionally, it’s also possible to calculate the aggregated precision-recall (PR) curve.
The ROC and the AUC (Area Under the Curve) can be essential metrics when evaluating a model. In situations where there is a parallelization of the model development, such as federated learning, it becomes relevant to obtain precise measures for these metrics. These approaches usually produce partial results that require aggregation methods to get the complete picture. The ROCaggregator appears in this context, allowing to compute the precise ROC curve from the partial results.
You can install the released version of ROCaggregator from CRAN with:
Check the complete example provided in the vignette.
Obtain the global ROC curve from different sources by providing: - the false positive rate (fpr) - true positive rate (tpr) - thresholds (thresh) - the total number of negative samples/control - the total number of samples from each source
library(ROCaggregator) y1 <- c(1, 0, 1, 1, 0, 0, 0, ...) # false positive rate values for each threshold fpr_1 <- c(0, 0, 0, 0, 0.002, ...) # true positive rate values for each threshold tpr_1 <- c(0, 0.004, 0.008, 0.012, 0.016, ...) # thresholds used thresh_1 <- c(0.9994038, 0.9986345, 0.99847864, 0.99575908, 0.99567612, ...) # count the number of negative labels negative_count_1 <- sum(y1 == 0) # total number of labels total_count_1 <- length(y1) ... # ROC curve roc <- roc_curve( list(fpr_1, fpr_2, ...), list(tpr_1, tpr_2, ...), list(thresh_1, thresh_2, ...), c(negative_count_1, negative_count_2, ...), c(total_count_1, total_count_2, ...) ) # Precision-recall pre_recall <- precision_recall_curve( list(fpr_1, fpr_2, ...), list(tpr_1, tpr_2, ...), list(thresh_1, thresh_2, ...), c(negative_count_1, negative_count_2, ...), c(total_count_1, total_count_2, ...) )